2.1.pdf · FP7‐SMARTCITIES‐2013 | ICT‐2013.6.4 | GA 608774 Deliverable 2.1 Page 2 / 182 2 of 182 Document technical details Document Number D2.1b Document Title Final Report

Deliverable D2.1

Final Report about Energy, ICT and Physical Systems of

the CIVIS Pilot Sites

Work Package 2

Responsible Unit: KIT

Authors: FBK, KTH, IST

FP7‐SMARTCITIES‐2013 | ICT‐2013.6.4 | GA 608774

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Document technical details

Document Number D2.1b

Document Title Final Report about Energy, ICT and Physical Systems

of the CIVIS Pilot Sites

Version V2.0

Status Reviewed final draft

Work Package 2

Deliverable Type Report

Contractual Date of delivery 1st Oct 2014

Actual Date of Delivery 31st Aug 2015

Responsible Unit KIT

Authors KIT: Russell McKenna, Erik Merkel, Jan Müller

FBK: Diego Viesi, Shahriar Mahbub, Luigi Crema

KTH: Omar Shafqat, Björn Palm

IST: Raquel Segurado, Sandrina Pereira

Keywords List CIVIS, Community energy system, ICT system

Dissemination Level PU


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CIVIS Consortium

CIVIS (Grant Agreement contract No. 608774) is a Collaborative Project within the 7th Framework

Programme, theme FP7‐SMARTCITIES‐2013, ICT‐2013.6.4. As defined in the Consortium Agreement,

members of the Consortium are:

No. Beneficiaries

1 UNIVERSITA DEGLI STUDI DI TRENTO, established in VIA CALEPINA, 14 38122 TRENTO ‐ ITALY, represented by Mr. Paolo Collini, Rector, or his authorised representative, the beneficiary acting as coordinator of the consortium (the "coordinator" ).

2 AALTO‐KORKEAKOULUSAATIO established in OTAKAARI 1, 00076 AALTO ‐ FINLAND, represented by Mr Ilkka NIEMELÄ, Deputy President and/or Mr Martti RAEVAARA, Vice President, or their authorised representative.

3 FONDAZIONE CENTRO STUDI ENEL established in VIALE REGINA MARGHERITA 137, 00198 ROMA ‐ ITALY, represented byMr Francesco STARACE, President, or his authorised representative.

4

IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE established in Exhibition Road, South Kensington Campus, SW7 2AZ LONDON ‐ UNITED KINGDOM, represented by Ms Carole MEADS, Senior Negotiator, European Policy and/or Mr James LLOYD, Contracts Administrator (Europe), or their authorised representative.

5 INSTITUTO SUPERIOR TECNICO established in Avenida Rovisco Pais 1, 1049‐001 LISBOA ‐ PORTUGAL, represented by Mr Arlindo OLIVEIRA, President, or his authorised representative.

6

Karlsruher Institut fuer Technologie established in Kaiserstrasse 12, 76131 Karlsruhe ‐ GERMANY, represented by Mr Bernhard DASSELAAR, Head of Cost and Fund Management and/or Mr Wolf FICHTNER, Head of IIP, or their authorised representative.

7 KUNGLIGA TEKNISKA HOEGSKOLAN established in BRINELLVAGEN 8, 100 44 STOCKHOLM ‐ SWEDEN, represented by Mr Peter GUDMUNDSON, President and/or


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Mr Kenneth BILLQVIST, Head of Research Office, or their authorised representative.

8 SANTER REPLY SPA established in VIA ROBERT KOCH 1/4, 20152 MILANO ‐ ITALY, represented by Mr Luigi CICCHESE, Partner, or his authorised representative.

9

NEDERLANDSE ORGANISATIE VOOR TOEGEPAST NATUURWETENSCHAPPELIJK ONDERZOEK ‐ TNO established in Schoemakerstraat 97, 2600 JA DELFT ‐ THE NETHERLANDS, represented by Ms Suzanne VAN KOOTEN, Director of Innovation, or her authorised representative.

10 TECHNISCHE UNIVERSITEIT DELFT established in Stevinweg 1, 2628 CN DELFT ‐ THE NETHERLANDS, represented by Mr Theo TOONEN, Dean and/or Mr Jeroen VAN DEN HOVEN, Vice‐Dean, or their authorised representative.

11

CREATE‐NET (CENTER FOR RESEARCH AND TELECOMMUNICATION EXPERIMENTATION FOR NETWORKED COMMUNITIES) established in VIA ALLA CASCATA 56/D, 38123 TRENTO ‐ ITALY, represented by Mr Imrich CHLAMTAC, President, or his authorised representative.

12 FONDAZIONE BRUNO KESSLER established in VIA SANTA CROCE 77, 38122 TRENTO ‐ ITALY, represented by Mr Andrea SIMONI, General Secretary and/or Mr Massimo EGIDI, President, or their authorised representative.


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Table of Contents

Preface ............................................................................................................................................... 16

Executive Summary ............................................................................................................................ 17

1 Introduction and overview ....................................................................................................... 21

1.1 Objectives of Work Package 2 ............................................................................................. 21

1.2 Objectives and methodology of this report ......................................................................... 22

1.3 Target audience ................................................................................................................... 24

2 Updated overview of the pilot sites ......................................................................................... 25

2.1 Italian pilot sites ................................................................................................................... 25

2.1.1 CEIS consortium .......................................................................................................... 25

2.1.1.1 Location ............................................................................................................. 25

2.1.1.2 Electrical energy production and consumption ................................................ 26

2.1.1.3 Thermal energy demand ................................................................................... 37

2.1.1.4 Energy demand for transport ........................................................................... 42

2.1.1.5 General overview of the CEIS Energy System (“Current Scenario”) ................. 44

2.1.2 CEDIS Consortium ....................................................................................................... 47

2.1.2.1 Location ............................................................................................................. 47

2.1.2.2 Electrical energy production and consumption ................................................ 48

2.1.2.3 Thermal energy demand ................................................................................... 57

2.1.2.4 Energy demand for transport ........................................................................... 61


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2.1.2.5 General overview of the CEDIS Energy System “Current Scenario” ................. 61

2.2 Swedish pilot sites ................................................................................................................ 64

2.2.1 Hammarby Sjöstad ..................................................................................................... 64

2.2.1.1 Data availability ................................................................................................. 65

2.2.1.2 Additional sensors ............................................................................................. 67

2.2.1.3 Actions planned ................................................................................................ 69

2.2.2 Fårdala ........................................................................................................................ 69

2.2.2.1 Data availability ................................................................................................. 69

2.2.2.2 Additional sensors ............................................................................................. 70

2.2.2.3 Actions planned ................................................................................................ 70

3 Energy system modelling addressing optimisation and storage in the test sites .................... 71

3.1 Italian test sites .................................................................................................................... 71

3.1.1 Use of EnergyPLAN for modelling “Current Scenario” and “Future Optimised

Scenarios” in CIVIS pilot sites energy systems ......................................................................... 71

3.1.2 Modelling “Current Scenario” .................................................................................... 77

3.1.3 Modelling “Future Optimised Scenarios” ................................................................... 79

3.1.3.1 Introduction ...................................................................................................... 79

3.1.3.2 Assessment of additional local sustainable energy resources: wood and solar

79

3.1.3.3 Decision variables, constraints and objectives ................................................. 81

3.1.3.4 Results for CEIS Consortium .............................................................................. 85


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3.1.3.5 Results for CEDIS Consortium ........................................................................... 97

3.1.4 Summary and conclusions ........................................................................................ 107

3.2 Swedish test sites ............................................................................................................... 110

3.2.1 Methodology ............................................................................................................ 110

3.2.2 Definition of use cases.............................................................................................. 116

3.2.3 Results ...................................................................................................................... 119

3.2.4 Summary and conclusions ........................................................................................ 131

4 Individual appliances analysis ................................................................................................. 134

4.1 Discussion of relevant literature ........................................................................................ 134

4.2 Electricity profile of the domestic sector for the test sites ............................................... 140

4.2.1 Italy ........................................................................................................................... 140

4.2.2 Sweden ..................................................................................................................... 141

4.3 Energy reduction potential by demand side measure ...................................................... 143

4.3.1 Storo, Italy ................................................................................................................ 145

4.3.1.1 Electricity consumption by appliance in Storo ............................................... 145

4.3.1.2 Thermal energy consumption in Storo ........................................................... 148

4.3.2 San Lorenzo, Italy ..................................................................................................... 149

4.3.2.1 Electricity consumption by appliance in San Lorenzo .................................... 149

4.3.2.2 Thermal energy in San Lorenzo ...................................................................... 151

4.3.3 Fårdala and Hammarby Sjöstad, Sweden ................................................................. 153


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4.3.3.1 Electricity consumption by appliance in the Swedish test sites ..................... 153

4.3.3.2 Thermal energy consumption in the Swedish test sites ................................. 155

4.4 Energy demand – potential for load shifting ..................................................................... 156

4.5 Baseload energy demand and stand‐by ............................................................................ 157

4.6 Summary and conclusions ................................................................................................. 162

5 Summary and conclusions ...................................................................................................... 165

6 Appendix ................................................................................................................................. 171

6.1 Pilot site description .......................................................................................................... 171

6.2 Investment cost, Lifetime, Fixed O&M cost ...................................................................... 172

6.3 Variable O&M cost ............................................................................................................. 172

6.4 Generation efficiency ......................................................................................................... 172

6.5 Fuel price and additional cost ............................................................................................ 173

6.6 Modelling CEIS “Future Optimised Scenarios” .................................................................. 175

6.7 Modelling CEDIS “Future Optimised Scenarios” ................................................................ 176

7 References .............................................................................................................................. 177

LIST OF FIGURES

Figure 1: The Province of Trento and the area of Giudicarie Esteriori served by CEIS ...................... 26

Figure 2: CEIS electrical production plants (hydro and PV) ............................................................... 27

Figure 3: CEIS electric grid: production vs consumption (monthly, 2013) ........................................ 29

Figure 4: CEIS electric grid: production vs consumption (Mon 04/02/2013 – Wed 06/02/2013) .... 30


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Figure 5: CEIS electric grid: production vs consumption (Mon 13/05/2013 – Wed 15/05/2013) .... 30

Figure 6: CEIS electric grid: power purchased from the national Grid (hourly, 2013) ...................... 32

Figure 7: CEIS electric grid: power sold to the national Grid (hourly, 2013) ..................................... 32

Figure 8: Thermal energy demand in CEIS area (monthly, 2013) ...................................................... 40

Figure 9: Thermal energy demand in CEIS area (Mon 04/02/2013 – Wed 06/02/2013) .................. 41

Figure 10: Primary Energy Demand in the CEIS area (2013) ............................................................. 45

Figure 11: Overview of the CEIS Energy System – “Current Scenario” (2013) .................................. 46

Figure 12: The Province of Trento and the area of Chiese Valley served by CEDIS ........................... 48

Figure 13: CEDIS electrical production plants (Hydro and PV) .......................................................... 49

Figure 14: CEDIS electric grid: production vs consumption (monthly, 2013) .................................... 51

Figure 15: CEDIS hydro storage reservoir .......................................................................................... 52

Figure 16: CEDIS electric grid: production vs consumption (Mon 04/02/2013 – Wed 06/02/2013) 52

Figure 17: CEDIS electric grid: production vs consumption (Mon 13/05/2013 – Wed 15/05/2013) 53

Figure 18: CEDIS electric grid: power purchased from the national Grid (hourly, 2013) .................. 54

Figure 19: CEDIS electric grid: power sold to the national Grid (hourly, 2013) ................................. 55

Figure 20: Thermal energy demand in the CEDIS area (monthly, 2013) ........................................... 59

Figure 21: Thermal energy demand in the CEDIS area (Mon 04/02/2013 – Wed 06/02/2013) ....... 59

Figure 22: Primary Energy Demand in the CEDIS area (2013) ........................................................... 62

Figure 23: Overview of the CEDIS Energy System – “Current Scenario” (2013) ................................ 63

Figure 24: Hourly heating data for 2014 & 2013 (green line: energy, grey line: outdoor temperature)

............................................................................................................................................................ 66


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Figure 25: Daily electricity load profile from installed energy monitor with 5 min resolution ......... 68

Figure 26: Detected appliances from household ............................................................................... 68

Figure 27: Heating control system for Fårdala test site. .................................................................... 70

Figure 28: Structure of the EnergyPLAN ............................................................................................ 73

Figure 29: Energy System Optimisation Model ................................................................................. 74

Figure 30: Night‐charging (NC) profile for electric car batteries ....................................................... 83

Figure 31: Overview of the CEIS and CEDIS Energy System – “Future Optimised Scenarios” ........... 84

Figure 32: CEIS: Pareto‐front and comparison “Current Scenario”/”Future Optimised Scenarios ... 86

Figure 33: CEIS: best 15 scenarios in terms of AC (2‐16), comparison with “Current Scenario” (1) . 89

Figure 34: CEIS: “Future Optimised Scenarios” (blue) and correlation between annual cost and CO2‐

emission reduction (%). In red “Current Scenario” ........................................................................... 90

Figure 35: CEIS: best scenarios in terms of CO2‐emission, comparison with “Current Scenario” ..... 92

Figure 36: CEIS: “Future Optimised Scenarios” (blue) and correlation between annual cost and ESD

reduction. In red “Current Scenario” ................................................................................................. 93

Figure 37: CEIS: best scenarios in terms of ESD, comparison with “Current Scenario” .................... 95

Figure 38: CEDIS: Pareto‐front and comparison “Current Scenario”/”Future Optimised Scenarios”

............................................................................................................................................................ 97

Figure 39: CEDIS: best 15 scenarios in terms of AC (2‐16), comparison with “Current Scenario” (1)

.......................................................................................................................................................... 100

Figure 40: CEDIS: “Future Optimised Scenarios” (blue) and correlation between annual cost and CO2‐

emission reduction (%). In red “Current Scenario” ......................................................................... 101


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Figure 41: CEDIS: best scenarios in terms of CO2‐emission, comparison with “Current Scenario” 103

Figure 42: CEDIS: “Future Optimised Scenarios” (blue) and correlation between annual cost and ESD

reduction. In red “Current Scenario” ............................................................................................... 104

Figure 43: CEDIS: best scenarios in terms of ESD, comparison with “Current Scenario” ................ 106

Figure 44: Overview of the methodology of the optimisation of the energy system of the pilot sites

(source: own illustration based on [39]) .......................................................................................... 111

Figure 45: Overview of the consumption for space heat and hot water in the buildings in Fårdala in

the period from September 2012 to September 2013 .................................................................... 117

Figure 46: Technology selection and capacity for the three use cases of decentralised heat supply

and the (average) case of centralised supply .................................................................................. 121

Figure 47: Comparison of the total annual system cost per household for the three cases of

decentralised heat supply and the case of centralised heat supply ................................................ 122

Figure 48: Comparison of the total annual primary energy consumption per household for the three

cases of decentralised heat supply and the case of centralised heat supply .................................. 123

Figure 49: Comparison of the total annual emission of CO2 per household for the three cases of

decentralised heat supply and the case of centralised heat supply ................................................ 125

Figure 50: Comparison of the degree of self‐sufficiency with respect to energy and electricity supply

for the three cases of decentralised heat supply and the case of centralised heat supply ............ 126

Figure 51: Comparison of the self‐consumption rate with respect to energy and electricity supply for

the three cases of decentralised heat supply and the case of centralised heat supply .................. 127

Figure 52: Thermal demand and energy dispatch of technologies for the minimum case for an

example week in winter ................................................................................................................... 129


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Figure 53: Distribution of electricity demand in the domestic sector by appliance in Italy, in 2008

.......................................................................................................................................................... 141

Figure 54: Distribution of electricity demand in the domestic sector by appliance in Sweden, in 2008

.......................................................................................................................................................... 142

Figure 55: Average hourly energy demand for families between 26‐64 years old [40] .................. 158

Figure 56: Average summer baseload electricity demand .............................................................. 159

Figure 57: Daily load profile of the domestic sector members of CEDIS and CEIS in 2013 ............. 160

Figure 58: Typical values of standby power of some appliances [49] ............................................. 161

Figure 59: Trend of the Italian electricity market in 2013 [59] ........................................................ 174

Figure 60: CEIS: best 15 scenarios in terms of AC, comparison between “Current Scenario” (0) and

“Future AC Optimised Scenarios” (1‐15) in terms of AC, CO2‐emission, LFC, ESD .......................... 175

Figure 61: CEDIS: best 15 scenarios in terms of AC, comparison between “Current Scenario” (0) and

“Future AC Optimised Scenarios” (1‐15) in terms of AC, CO2‐emission, LFC, ESD .......................... 176

LIST OF TABLES

Table 1: CIVIS Deliverables and Test Sites Information ..................................................................... 24

Table 2: Number of plants, installed power (kW) and peak power on CEIS grid, divided by renewable

energy source (2013) ......................................................................................................................... 27

Table 3: CEIS electric grid: production vs consumption (monthly, 2013) ......................................... 29

Table 4: CEIS grid LFC (monthly, 2013) .............................................................................................. 31

Table 5: Primary energy source (PES) mix for Italian, Trenta and CEIS/CEDIS grid (2013) ................ 36

Table 6: CEIS electric grid: conversion FEC to PEC (2013) ................................................................. 36


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Table 7: Space heating demand in CEIS area (2013).......................................................................... 38

Table 8: Thermal energy demand in CEIS area, (monthly, 2013) ...................................................... 40

Table 9: Energy source mix for thermal energy in the CEIS area (Final Energy Demand FED and

Primary Energy Demand PED) (2013) ................................................................................................ 42

Table 10: Energy and environmental characteristics of the Italian fleet (2013) ............................... 43

Table 11: Energy demand and CO2‐emission for transport in the CEIS area (2013) ......................... 43

Table 12: Number of plants and installed power (kW) and peak power on the CEDIS grid, divided by

renewable energy source (2013) ....................................................................................................... 49

Table 13: CEDIS electric grid: production vs consumption (monthly, 2013) ..................................... 51

Table 14: CEDIS grid LFC (monthly, 2013) .......................................................................................... 54

Table 15: CEDIS electric grid: conversion FEC to PEC (2013) ............................................................. 56

Table 16: Space heating demand in CEDIS area (2013) ..................................................................... 57

Table 17: Thermal energy demand in the CEDIS area, (monthly, 2013) ........................................... 58

Table 18: Energy source mix for thermal energy in the CEDIS area (Final Energy Demand FED and

Primary Energy Demand PED) (2013) ................................................................................................ 60

Table 19: Energy demand and CO2‐emission for transport in CEDIS area (2013) ............................. 61

Table 20: Housing association in Hammarby Sjöstad part of CIVIS ................................................... 65

Table 21: CEIS “Current Scenario”: Economical, environmental, technical and political parameters

............................................................................................................................................................ 78

Table 22: CEDIS “Current Scenario”: Economical, environmental, technical and political parameters

............................................................................................................................................................ 78


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Table 23: Potential for a sustainable use of the wood resource in CEIS and CEDIS areas ................ 80

Table 24: CEIS: Best 15 scenarios in terms of AC, comparison with “Current Scenario” .................. 87

Table 25: CEIS: best scenarios in terms of CO2‐emission, comparison with “Current Scenario” ...... 91

Table 26: CEIS: best scenarios in terms of ESD, comparison with “Current Scenario” ..................... 94

Table 27: CEDIS: Best 15 scenarios in terms of AC, comparison with “Current Scenario” ................ 99

Table 28: CEDIS: best scenarios in terms of CO2‐emission, comparison with “Current Scenario” . 102

Table 29: CEDIS: best scenarios in terms of ESD, comparison with “Current Scenario” ................. 105

Table 30: Overview of the technologies to be chosen in the optimisation approach .................... 111

Table 31: Energy use in subdivisions for three cases of decentralised heat supply and the case of

centralised heat supply in Fårdala ................................................................................................... 118

Table 32: Key parameters as assumptions for the optimisation runs (Source: own calculations based

on [42]) ............................................................................................................................................. 119

Table 33: Technology selection and capacity for the use cases of centralised heat supply ........... 122

Table 34: Energy reduction by demand side measure [51,52,53,54] .............................................. 144

Table 35: Electricity consumption by appliance in Storo in 2013 .................................................... 145

Table 36: Electricity reduction in Storo for the conservative and optimistic scenarios .................. 146

Table 37: Electricity reduction for the 150 families directly involved in CIVIS in Storo, ................. 147

Table 38: Thermal energy reduction in Storo .................................................................................. 148

Table 39: Thermal energy reduction for the 150 families directly involved in CIVIS in Storo ........ 148

Table 40: Electricity consumption by appliance in San Lorenzo, in 2013 ........................................ 149

Table 41: Electricity reduction in San Lorenzo for the conservative and optimistic scenarios ....... 150


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Table 42: Electricity reduction for the 150 families directly involved in CIVIS in San Lorenzo ....... 151

Table 43: Thermal energy reduction in San Lorenzo ....................................................................... 152

Table 44: Thermal energy reduction for the 150 families ............................................................... 152

Table 45: Electricity consumption by appliance for the 300 families that will be involved in the

Swedish test sites ............................................................................................................................. 154

Table 46: Electricity reduction for the families directly involved in CIVIS in the Swedish test sites for

the conservative and optimistic scenarios ...................................................................................... 155

Table 47: Thermal energy reduction for the families ...................................................................... 156

Table 48: Potential of demand shifting by appliance/activity [54] .................................................. 156

Table 49: Ecodesign requirements related to standby and off mode ............................................. 162

Table 50: Possible total energy reduction envisaged in CIVIS project by the direct involvement of 600

families ............................................................................................................................................. 164

Table 51: CEIS area: Number of dwellings for age of construction and dwelling renovations with

energy upgrading in the periods 1982‐1991 and >1991 ................................................................. 171

Table 52: CEDIS area: Number of dwellings for age of construction and dwelling renovations with

energy upgrading in the periods 1982‐1991 and >1991 ................................................................. 171

Table 53: Investment cost, lifetime, fixed O&M cost ...................................................................... 172

Table 54: Variable O&M cost ........................................................................................................... 172

Table 55: Generation efficiency ....................................................................................................... 172

Table 56: Fuel price and additional cost .......................................................................................... 173


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Preface

The review process of the CIVIS deliverable D2.1a highlighted a lack of attention regarding storage

and supply‐side flexibilities within the CIVIS test beds. As shown in the presentation of the pilot sites

in the proposal and the Description of Work (DoW), there are few distributed renewable energy

resources in the Stockholm test site, as well as little or no energy storage within any of the test sites.

There are no investments or financial means in energy infrastructure foreseen in the project apart

from metering devices. Therefore, empirical questions of the optimal superstructure of the energy

supply of the test site are out of the project scope. The authors understand the limitation of

excluding storage and supply‐side options from the projects completely, even if it is beyond the

scope to fully implement them. Hence modelling work was carried out in the scope of this

deliverable (D2.1b), in order to identify the optimum energy supply system and investigate the use

of energy storage for each of the test sites, in the context of WPs 2 and 7. In particularly, the

following work was carried out:

1. For the Trento test sites:

a. Energy system modelling with the Hybrid Optimisation of Multiple Energy Resources

(HOMER) in order to investigate the scope for load balancing, optimal location and

charging strategies for electric vehicles. This work was led by FBK.

b. Energy system modelling in EnergyPLAN to investigate the potential for using solid

oxide and PEM fuel cells within the test sites for cogeneration of heat and electricity.

This work was led by FBK.

2. For the Stockholm test sites: Optimisation of capacities and operation of multi‐generation

systems consisting of combined heat power (CHP), boiler, PV as well as thermal and battery

storage to be carried out for individual buildings and district within the Stockholm test sites.

This work was led by KIT and KTH.


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In addition, appliance‐level data was acquired from literature reviews and from data availability

from the test sites in order to assess energy reduction potential by demand‐side measures. This

work was led by IST.

A timeframe to complete this work has been set at six months from the end of February 2015, i.e.

by the end of August 2015. The work will be documented in joint publications in academic journals.

This approach has been explicitly endorsed by the Project Officer during February 2015.

Executive Summary

It is the main objective of CIVIS project work package 2 to assess the potential for distributed energy

management and energy efficiency measures in the pilot sites in order to reach CIVIS main goals:

the increase in energy efficiency and flexibility and the reduction of CO2‐emissions. The present

report therefore provides a framework for the energy and ICT system analysis for the CIVIS project.

This deliverable has significantly updated previous results for the pilot sites as well as applied the

methods previously developed in D2.1a. Due to severe constraints on data availability for the

Stockholm pilot sites, the updated overview is much less detailed for these than the Italian test sites.

From the updated analysis of the pilot sites based on the latest available date several new insights

relating to the potential for energy and CO2‐saving measures through the application of energy

storage, as well as through specific measures on a household and appliance level, are gained as

summarised in this section.

The energy systems in the Italian and Swedish test sites were analysed using energy system models

and their latest available data. Thereby a particular emphasis was placed on the potential for

optimising the energy systems by employing thermal and electrical storage devices. To this end,

energy system analysis tools including optimisation and simulation‐based approaches were

employed to three of the four test sites.


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Starting from the “Current Scenario” additional local sustainable energy resources are investigated,

in particular wood and solar PV. Both in CEIS and in CEDIS the local forest is managed in a sustainable

way. There is an additional margin (+ 30.4% in CEIS and + 35.5% in CEDIS) to increase the use of local

wood. Local PV availability is considerable both in CEIS and in CEDIS areas.

Using EnergyPLAN + a multi‐objective evolutionary algorithm, several “Future Optimised Scenarios”

are analysed, in order to identify possible solutions able to:

Reduce annual energy cost (AC);

Reduce the environmental impact (CO2‐emission);

Allow a satisfactory technical regulation for the electric grid (Load Following Capacity, LFC);

Increase local security through a greater energy independency (Energy System Dependency,

ESD)

As decision variables both the additional implementation of existing technologies (PV, individual

wood boiler) and the introduction of new ones (in CEIS&CEDIS: GSHP, electric cars; in CEIS: wood

ORC CHP; in CEDIS: gas SOFC mCHP,) are addressed, as well as the possibility to involve local

renewable resources (reducing fossil fuels).

In terms of annual cost this study suggests consistent economical savings both in CEIS (from ‐4.8 %

to ‐11.9%) and in CEDIS (from ‐4.4% to ‐8.6%). It is also possible to both minimise energy cost while

reducing CO2‐emission (from ‐28.1% to ‐70.9% in CEIS, from ‐24.3% to ‐29.9% in CEDIS). In other

words, several energy scenarios that are greener and cheaper than the current one are possible.

Also ESD is highly improved, these scenarios are able to import from ‐14% to ‐20% in CEIS and from

‐16% to ‐19% in CEDIS fewer energy resources from outside. LFC in most cases is very close to the

value of the "Current Scenario", ensuring electrical grid stability. In CEIS and CEDIS the electrical

resource mix of “Current Scenario” (local production + import) maintains its competitiveness also in

“Future AC Optimised Scenarios”. While the introduction of wood CHP in CEIS and gas mCHP in


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CEDIS appears often negligible, increasing PV capacity is suggested in several of the proposed

scenarios.

What instead is deeply transformed is the thermal sector, first of all individual oil and LPG boiler

reach approximately zero capacity. In other words it is suggested to dismiss the actual oil and LPG

individual boilers because fuels cost are too high compared to other alternative energy carriers.

What gains importance is instead the use of local wood (individual wood boiler capacity is

maximised) and the electrification of the thermal sector (wide introduction of GSHP guarantee very

cost effective scenarios). In the proposed “CEIS Future AC Optimised Scenarios” individual wood

boiler covers 56‐73% of heat demand (from 53% of “Current Scenario”) while GSHP 20‐41%; in the

proposed “CEDIS Future AC Optimised Scenarios” individual wood boiler cover 44‐49% of heat

demand (from 32% of “Current Scenario”) while GSHP 37‐50%.

For the Swedish test sites, the focus was on Fårdala because for Hammarby Sjöstad no data was

available at the time of producing this deliverable. An existing mixed integer linear program (MILP)

for the optimisation of the capacity and dispatch of multi‐energy systems consisting of CHP, PV,

boilers, thermal and electrical storage, was employed for this task. The main input parameters for

the model, especially the electricity and gas prices as well as the demand profiles for heat and

electricity in households, were adapted to the Swedish case as far as possible. Furthermore, three

typical residential buildings in Fårdala were differentiated based on their annual heat demand for

space heating and hot water, a minimum, average and maximum case respectively. These three

cases were optimised with the model with respect to the total annual costs for energy, including

heat and electricity, per household. In addition, a centralised case of heat supply was investigated,

corresponding to one large centralised CHP unit, which provides all of the heat and electricity for

the whole site (178 buildings). This roughly corresponds to the actual situation, the main difference

being that the capacity and dispatch of the CHP unit is optimised by the model in this case and is


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not predetermined. In addition, the methodology developed in D2.1 for energy, CO2 and flexibility

potential determination was applied to Fårdala.

These results of the optimisation for Fårdala indicate that, based on the employed data and

methodology, the current system is the most optimal in terms of energy supply costs, primary

energy consumption and CO2‐emissions. However, several limitations of the methodology should

be borne in mind, which could strongly affect this conclusion. Most importantly, it is recommended

to devote further attention to the households within the buildings and their behaviour, which has a

strong but varied impact on energy consumption. Further work should also focus on modelling the

demand side within the households, including the occupants and measures such as building

insulation that may reduce the overall demand of the building fabric.

A brief overview of some of the literature in the field of energy saving interventions and dynamic

pricing tariffs highlighted the difficulties in generalising results from different studies. Some

common themes include the importance of targeted, as opposed to general, information, as well as

its combination with other measures in order to be effective. This especially applies to the

installation of smart meters and the required supervision of the affected households in using them.

Appliance ownership and use within households varies largely, especially but not only due to

socioeconomic factors within the household such as income, household structure and age, and

tenancy type (owner occupier, rented or other). In addition, a significant proportion of the energy

demand in households is often unknown, i.e. cannot be allocated to specific appliances or energy

service demands, which makes influencing it very difficult. Dynamic pricing trials have also found

partly diverging results, with indications that a reduction in peak demand of 5‐10 % might be

possible with the right incentives.

To overcome the lack of data regarding energy consumption by appliance in each test site,

estimations were carried out based on the energy profiles within Italian and Swedish households.

Two scenarios were considered: a conservative and an optimistic scenario. The optimistic scenario

considers the adoption of more energy efficiency measures than the conservative scenario. For the


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150 families of each test site that will be directly involved in CIVIS project, the implementation of

the measures could result in an electricity consumption reduction of about 12% in the conservative

scenario and about 18% in the optimistic scenario. At thermal energy level, the implementation of

the energy efficiency measures could result in a decrease of about 14%. An analysis was also carried

out on the energy consuming activities that have more potential to be shifted in order to assess the

potential for load shifting, an important measure for the Italian test sites. Stand‐by and off‐mode of

appliances were also analysed and they can have an important weight in the energy demand of a

household, namely in the base load period.

Furthermore, the estimations based on the energy profiles within the Italian and the Swedish

households in two scenarios ‐ considering that 150 families will be directly involved in the CIVIS

project in each test site ‐ show that the implementation of the measures could result in an electricity

consumption reduction of about 12 % in the conservative scenario and about 18 % in the optimistic

scenario. At thermal energy level, the implementation of the energy efficiency measures could

result in a decrease of about 14 %.

1 Introduction and overview

1.1 Objectives of Work Package 2

The main objectives of Work Package 2 are the analysis of energy and ICT systems within the pilot

sites. This involves particular focus on the potential for distributed energy management and energy

efficiency measures in the pilot sites, in order to increase energy efficiency and flexibility, and

reduce CO2‐emissions. Furthermore, the goal is to analyse the utilisation of information and

communication technology (ICT) systems and standards for integration of devices to enable

distributed energy management at the individual peer and community levels. Moreover, this Work


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Package aims at developing recommendations for future enhancements concerning energy and ICT

systems and changes in the use of existing systems, relying also on the impact and dynamics

triggered at the social level. Finally, the objective is to analyse the utilisation and the social adoption

of software tools used to connect consuming, producing and storing devices as distributed energy

system in the pilot sites.

1.2 Objectives and methodology of this report

This report is the second version of the deliverable D2.1, hence referred to as D2.1b. Whilst it builds

on and extends the work reported in the first version, D2.1a, both reports can be read and

understood independently. However, as being necessary for independent readability of the report

and for setting the context a certain degree of repetition of D2.1a is featured in this report. In the

current report the focus is on updating previous results for the pilot sites as well as applying the

methods previously developed. The objectives are therefore to give an update relating to the status

quo in the pilot sites, to analyse the energy systems in the pilot sites and, according to the data

availability allowing, to determine the potential for energy efficiency improvement and CO2‐

reduction through the application of energy storage, as well as through specific measures on a

household and appliance level. Several methods are employed to meet these objectives. Firstly, in

chapter two an overview of the current state of the art relating to the physical energy and ICT

systems in the pilot sites, based on the latest data (July 2015), is given, serving as an update to

chapter 2 in D2.1a. Then, in chapter three, simulation and optimisation methodologies for energy

system analysis of the pilot sites in Sweden and Italy are presented and applied to three of the four

pilot sites (due to lacking data for the fourth). In addition, the methodologies previously reported in

chapter 3 of D2.1a for analysing primary energy, CO2 and flexibility aspects, are applied to one pilot

site. In chapter 4, the results of the previous analysis of suitable measures for the CIVIS project in

D2.1a are taken as a basis for analysing specific energy and power saving measures on the household


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and appliance level. The report then closes with a summary and conclusions.

For an overview of the contents of this report in relation to other work packages and deliverables,

the reader is directed to Table 1 below.

Deliverable Test Sites Related Information In particular, see

D 2.1a ‐ Final Report about Energy, ICT

and Physical Systems of the CIVIS Pilot

Sites

The report provides an extended analysis

of the energy and ICT systems within the

pilot sites.

Section 2 and

Section 4

D 4.1 ‐ Energy ICT Platform: system

requirements, architecture and

interfaces

This report includes the architecture

specifications and system requirements for

CIVIS Decision Support System (DSS)

Platform. In particular, it includes the state

of the art for the sensors in the pilot sites.

Section 3

D 5.1 – Current Context and State of

the Art

With regards to the pilot sites, the report

provides national and local level

descriptions of their current regulatory and

institutional context.

Section 3.2 and

Section 3.8

D 6.1 – Description of New Style

Business Models for an Emerging

Social Energy System

With regards to the pilot sites, the report

provides national and local level

descriptions of key market stakeholders

and their ‘position’ in the energy value

chain.

Section 3 and

Section 4

D 7.1 – CIVIS Test Sites report

It is the main reference document with

regards to CIVIS test sites descriptions. It

provides an overview of the test sites with

Whole document


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regards to all CIVIS dimensions (Energy,

ICT, Social)

Table 1: CIVIS Deliverables and Test Sites Information

1.3 Target audience

The dissemination level of this report is public. This report is aimed at providing decision support

for various stakeholders. Local authorities and public utilities might have a deeper interest in the

report’s outcomes as they can draw implications from the implementation of a project which

considers both the field of smart metering and community involvement. Also stakeholders in the

private sector such as private investors and energy service companies are likely to benefit from this

report as inter alia issues of flexibility potential are investigated.


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2 Updated overview of the pilot sites

In this chapter, we present an updated is overview of the four pilot sites and available data since

the previous WP2 deliverable (D2.1a) as of 31st July 2015. Due to severe constraints on data

availability for the Stockholm pilot sites, the updated overview is much less detailed for these than

the Italian test sites. For more detailed test site specific information extending beyond this section

the reader is referred to CIVIS Deliverable D5.1 where the national and local regulatory frame is

analysed, CIVIS Deliverable D6.1 in which the main market actors (national and local) from the

perspective of the energy value chain are investigated, CIVIS Deliverable D7.1 that provides an

overall, condensed and comprehensive description of the pilot sites and to CIVIS Deliverable D2.1a.

2.1 Italian pilot sites

2.1.1 CEIS consortium

2.1.1.1 Location

CEIS (Consorzio Elettrico Industriale di Stenico) is a cooperative established in 1905 with the aim of

contributing, through the production and distribution of electricity, to the economic and social

improvement of the people living in the area of activity of the company.

CEIS experience comes from the beginning of Italian electrification, with a specific action in the

Alpine Region. It is an institutional enterprise focused on the mutual cooperation with a social and

economic embedded relationship within local territory [1].

CEIS is one of the 77 Italian Electric Historical Cooperatives. Thanks to the ownership of electrical

production plants, transport and distribution grid it is able to ensure to their members a

considerable economic saving in the bill (up to 40 % comparing to the national average).

The Consortium is located in the Province of Trento in the area named Giudicarie Esteriori (Figure


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1). It delivers its services in the territory of six municipalities: Bleggio Superiore, Comano Terme,

Dorsino, Fiavè, San Lorenzo in Banale and Stenico. The supplied area has a surface area of 248 km2

and includes 8,426 citizens and 3,536 families. On the 31st December 2013, the shareholding

structure was made up of 3,425 members who represented about 80 % of households served [2].

Figure 1: The Province of Trento and the area of Giudicarie Esteriori served by CEIS

2.1.1.2 Electrical energy production and consumption

CEIS produces electricity using only renewable sources, it has the ownership of 1 hydropower plant

and 5 centralised PV plants (Figure 2). In addition, in these last 7 years, the diffused electricity

production on CEIS grid from PV panels has greatly increased (442 PV plants at 31st December 2013),

driven by high national grants for PV. CEIS has directly promoted the local spread of PV installations

both from the technical and legislative points of view. The third type of renewable electricity source

is biogas (from cow waste), owned by two CEIS members with an installed power of 500 kW. Part of

the electricity produced by diffused PV panels and biogas is used for self‐consumption and the

remaining is injected into the CEIS grid and sells to "Gestore dei Servizi Energetici (GSE)". GSE is the

state‐owned company which promotes and supports renewable energy sources (RES) in Italy; GSE

purchase electricity generated by renewable‐energy plants connected to the national electric grid,

reselling it in the electricity market [3]. Table 2 reports the number of plants, the installed power


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(kW) and the peak power (kW) on CEIS grid for the reference year 2013 [2].

Figure 2: CEIS electrical production plants (hydro and PV)

Renewable energy

source

Number of

plants

Installed

Power (kW)

Peak Power

2013 (kW)

Biogas 2 500 452

PV 447 7,515 4,998

Hydropower 1 4,000 3,959

Total 450 12,015 9,409

Table 2: Number of plants, installed power (kW) and peak power on CEIS grid, divided by

renewable energy source (2013)

The electrical distribution grid of medium voltage (MV) and low voltage (LV) is owned by CEIS. Two

MV interconnection points with the national grid ensure the continuous, bidirectional, exchange of

electrical energy between CEIS local grid and national grid. CEIS sells excess production and buys

lacking electricity from the regional company called Trenta S.p.A.

At the end of 2013 the injection points have reached 450 units with a total installed power of about

12 MW while the withdrawal points have reached 6,475 units with a peak demand power of about

5.3 MW [2].

During the reference year 2013 the electrical consumption on CEIS grid was equal to 26,177,984

kWh. This consumption includes all sectors: domestic sector, industry, services sector, agriculture


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etc. [2]. As a stakeholder of CIVIS project, CEIS has made available to FBK the hourly data of electrical

injection and electrical consumption.

Table 3 and Figure 3 compare, at monthly level, electrical injection and consumption on the CEIS

grid. Electrical consumption shows small variations at a monthly level:

In winter season a slight increasing occurs due to less hours of light;

The busiest holiday periods (like August) generate also an increase in consumption.

Electrical injection shows considerable fluctuations because of hydro and PV variation in

productivity:

Hydro production dominates the overall total production in the CEIS area;

Hydro productivity is characterised by a major peak in spring and a secondary peak in

autumn while PV productivity is characterised by one major peak in summer.

During the reference year 2013 total monthly injection exceeds the electrical demand essentially

between April and November while between December and March a considerable import of

electricity from national grid is required.

Month Hydro PV Biogas Consumption

Peak Power (kW)

Injection (kWh)

Peak Power (kW)

Injection (kWh)

Peak Power (kW)

Injection (kWh)

Peak Power (kW)

Consumption (kWh)

January 1,986 1,082,275 2,771 173,355 452 275,348 4,903 2,320,374

February 1,503 678,331 3,457 226,957 452 252,924 4,281 2,017,376

March 1,556 933,574 4,998 370,019 453 252,569 4,508 2,130,821

April 3,815 1,777,968 4,650 587,674 379 247,346 4,121 2,060,892

May 3,959 2,780,877 4,871 724,221 433 235,011 4,236 2,045,048

June 3,823 2,542,839 4,775 867,003 354 212,273 4,060 1,975,838

July 3,658 2,386,335 4,494 931,874 328 212,233 4,271 2,262,435

August 3,071 1,792,746 4,534 840,269 328 214,867 4,434 2,372,587

September 2,738 1,243,648 4,145 624,491 423 200,112 4,454 2,126,558

October 3,786 1,892,442 3,442 319,648 433 250,450 4,432 2,272,973


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November 3,556 1,989,212 2,892 219,326 421 255,011 4,469 2,139,946

December 2,680 1,348,186 2,508 226,543 432 229,142 5,307 2,453,139

TOTAL 3,959 20,448,432 4,998 6,111,381 453 2,837,286 5,307 26,177,984

Table 3: CEIS electric grid: production vs consumption (monthly, 2013)

Figure 3: CEIS electric grid: production vs consumption (monthly, 2013)

Problems of energy balancing or load following capacity are also visible at the daily level. In Figure

4 and Figure 5 two examples in February (period of lower production) and May (period of greatest

production) are shown. Indeed production profiles do not match the two peaks of consumption

(morning and evening). While hydro production is only moderately regulated during low‐production

months (using a very small hydro storage reservoir), in order to have two major peaks (morning and

evening, same as peaks of consumption), such activity is not possible for PV production.

Considering February (Figure 4) most hours see a deficit of production (particularly during the two

peaks of consumption) except midday hours (peak of PV production). On the other hand, during

May (Figure 5), a large amount of excess production is present on the CEIS grid and exported to the

national grid. Also in this case the maximum production is concentrated during the midday hours.

0

500.000

1.000.000

1.500.000

2.000.000

2.500.000

3.000.000

3.500.000

4.000.000

1 2 3 4 5 6 7 8 9 10 11 12

CEIS electric grid: production vs consumption (kWh)

Hydro PV Biogas Consumption TOTAL injection


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Figure 4: CEIS electric grid: production vs consumption (Mon 04/02/2013 – Wed 06/02/2013)

Figure 5: CEIS electric grid: production vs consumption (Mon 13/05/2013 – Wed 15/05/2013)

Load following capacity (LFC) is a technical parameter that measure how electricity production can

follow electricity demand in monthly or yearly basis:

where:

0

500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

4.500

5.000

0:00 6:00 12:00 18:00 0:00 6:00 12:00 18:00 0:00 6:00 12:00 18:00 0:00

CEIS electric grid: production vs consumption (kW, Mon 04/02 ‐Wed 06/02)


0

1.000

2.000

3.000

4.000

5.000

6.000

7.000

8.000

9.000

10.000

0:00 6:00 12:00 18:00 0:00 6:00 12:00 18:00 0:00 6:00 12:00 18:00 0:00

CEIS electric grid: production vs consumption (kW, Mon 13/05 ‐Wed 15/05)



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: Electrical import from national grid

: Electrical export to national grid

: Electrical demand

Using HOMER Microgrid Software [4], electricity exchange with the national Grid (import and export)

and LFC are analysed and illustrated in Table 4, Figure 6 and Figure 7. During the reference year

2013 CEIS imports about 4.6 GWh and export about 7.8 GWh, the maximum peak of purchasing is

about 3.2 MW while the maximum peak of selling is about 7 MW. LFC is on average 0.48, with best

value in November (0.29) and worst in May and June (0.83). Monthly LC values can provide

information about which month the energy system is more stable than other months. However,

yearly LFC values provide overall load following capability of the system.

CEIS GRID 2013

Month Energy Purchased (kWh)

EnergySold (kWh)

NetPurchased (kWh)

EnergyDemand (kWh)

LFC

January 829,861 40,465 789,396 2,320,374 0.38

February 921,920 62,755 859,165 2,017,376 0.49

March 730,170 155,511 574,659 2,130,821 0.42

April 230,628 782,724 ‐552,096 2,060,892 0.49

May 0 1,695,062 ‐1,695,062 2,045,048 0.83

June 667 1,646,945 ‐1,646,278 1,975,838 0.83

July 27,119 1,295,126 ‐1,268,006 2,262,435 0.58

August 224,299 699,594 ‐475,295 2,372,587 0.39

September 467,723 409,417 58,306 2,126,558 0.41

October 296,912 486,478 ‐189,566 2,272,973 0.34

November 149,297 472,901 ‐323,604 2,139,946 0.29

December 751,381 102,114 649,267 2,453,139 0.35

TOTAL 4,629,977 7,849,092 ‐3,219,115 26,177,984 0.48

Table 4: CEIS grid LFC (monthly, 2013)


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Figure 6: CEIS electric grid: power purchased from the national Grid (hourly, 2013)

Figure 7: CEIS electric grid: power sold to the national Grid (hourly, 2013)

It should be noted that when import and export are low, LFC is also low. In addition, import and


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export are low when an energy system can produce electricity as required. This parameter is

particularly relevant in energy systems with a high fraction of renewable energy sources (RES). In

these cases the production profiles depend on natural factors (solar radiation, wind speed, rainfall)

and not on human planning (as in conventional thermal power plants).

Solar photovoltaics (PV) production is subject to both seasonal as well as hourly weather variability.

Significant amounts of excess renewable energy emerge, with surpluses characterised by periods of

high power output far in excess of demand. These periods alternate with times when solar PV only

generates at a fraction of its capacity. In addition, the large intermittent power flows put strain on

the transmission and distribution network and make it more challenging to ensure that the

electricity supply matches demand at all times.

In order to better match electrical production and electrical demand, in a CEIS grid that has seen a

recent considerable rise in the share of PV variable renewable energy production (VRE), three

different types of interventions are possible/suggested:

1) A different regulation of hydro production, using the existing small hydro storage reservoir

or enlarging it. The hydro production should be flexible in order to follow the gap between

electrical demand and PV production: when this gap is maximum (morning and evening) also

hydro power should reach its maximum daily power. Such management interventions are

really interesting and have no additional cost for CEIS.

2) A different regulation of the demand profile (Demand Side Management), promoting a load

shifting from morning and evening peaks to midday hours. Such load shifting need to be

economically encouraged by an hourly tariff that currently does not exist but that can be

introduced and tested by the CIVIS project (TOU tariff). Nowadays at the domestic level,

programmable electrical loads are not so prevalent, examples are the washing machine and

dishwasher. In future scenarios, where the use of heat pumps for space heating (space

cooling) and hot sanitary water (HSW) and charging of electric car batteries could become


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significant, an efficient and smart regulation of domestic demand profiles could really have

a considerable role in the overall CEIS grid balancing (and correlated cost of electricity).

3) Introduction of energy storage solutions [5]. Energy storage fulfils three functions: to charge,

to hold and to discharge energy. It is possible to subdivide three types of storage

technologies:

P2P: where the energy carrier that is charged and discharged is electricity (e.g.

pumped hydro, compressed and liquid air, Li‐ion, flow and lead‐acid batteries,

electrolytic hydrogen production and re‐electrification);

Conversion of power to heat and storage of heat for final consumption;

Conversion of power to hydrogen for use outside the power sector (e.g. power to gas,

use as fuel for mobility or in the industry).

Storage can provide a range of services to the energy sector, in order to mitigate the effects

of VRE:

a. Electricity time shift: charging electric power at times of high supply and low demand,

storing it and discharging as electricity at times of low supply and high demand.

Electricity time shift contributes to a better use of local production, reducing export

and costly import to and from the national grid.

b. Conversion to other energy carriers: transforming electric power (preferably at times

of high supply and/or low demand) into a different energy carrier, storing this carrier

and using it outside the electric power sector. The alternative energy carriers are heat

and hydrogen. Heat is used for space and water heating, in residential and

commercial buildings as well as in many industrial processes. The use of hydrogen

outside the electric power sector includes mobility applications (e.g. cars, buses),

industrial applications (e.g. refineries, chemical industry) or injecting into the natural

gas grid.


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c. Provision of frequency reserve and grid services: ensuring the necessary continuous

balance between power supply and demand in the electric power grid. To provide

the reserve, storage commits capacity to charging when there is an unexpected

excess in power supply and to discharging when there is unexpected excess in power

demand.

d. Transmission and distribution infrastructure investment deferral: using storage to

absorb power that exceeds the capacity of a T&D line or another component and

releasing it at a later time when sufficient T&D capacity is available. T&D upgrades

usually have high fixed costs (permitting, construction work, etc.), which have to be

incurred even when the required additional capacity is limited.

Promoting balancing between electrical production and demand by CEIS can also have an

environmental impact. While CEIS electrical production comes completely from renewable sources

(zero emission), the electricity imported from the national grid implies primary energy source and

correlated emissions indicated by the vendor Trenta S.p.A. [6] (Table 5). The energy mix used for

the production of electricity sold by Trenta S.p.A. in 2013 is characterised by a considerable

percentage of fossil fuels (natural gas 38.4 %, coal 20.8 % and petroleum products 1.2 %).

En. mix used for the

production of el. sold

by Trenta S.p.A. in

2013


production of el.

injected in the Italian

electrical grid in 2013


production of el.

injected in

CEIS/CEDIS electrical

grid in 2013

PES SEF

(tCO2/MWh)

% % %

Renewable

sources

0 29.5 37.5 100

Coal 0.354 20.8 18.5


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Natural gas 0.202 38.4 33.7

Petroleum

products

0.267 1.2 1

Nuclear 0 4.9 4.7

Other

sources

0 5.2 4.6

Table 5: Primary energy source (PES) mix for Italian, Trenta and CEIS/CEDIS grid (2013)

In order to convert Final Energy Consumption (FEC) to Primary Energy Consumption (PEC) for the

CEIS electrical sector (Table 6) the following formula is considered:

∗

Primary energy factors (PEF) are defined in this way:

Physical energy content method (as applied by Eurostat and IEA) for local production mix

(hydro, PV and biogas);

EEN 3/08 for imported electricity.

Type of consumption/losses

FEC (kWh) PEF PEC (kWh)

Local RE production (hydro, PV, biogas)

21,548,007 1.27 27,406,162

Imported electricity 4,629,977 2.17 10,047,050

Total 26,177,984 37,453,212

Table 6: CEIS electric grid: conversion FEC to PEC (2013)

Summarising, the sustainability and environmental performances of the CEIS “Current Scenario”

could be improved by:

Reducing the import of electricity from Trenta trough a better match between electrical

production and electrical demand (regulation of hydro production, demand side

management, energy storage);


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Increasing local RE production (e.g. PV, biomass cogeneration, mini‐hydro).

2.1.1.3 Thermal energy demand

The production of thermal energy for space heating and hot sanitary water is guaranteed by

individual plants at building level (wood boilers, oil boilers, liquid petroleum gas (LPG) boilers). CEIS

area is not provided with a gas grid. It has not been possible to obtain real consumption data.

Process heat for the industrial sector is not evaluated.

The yearly space heating demand (kWh/year) (Table 7) is evaluated at municipality level by the

following formula:

∗ ∗ ∗

where:

:Number of dwellings within a period in time (FBK elaboration on data from ISTAT [7]). The

number of dwelling renovations with energy upgrading in the periods 1982‐1991 and >1991 (FBK

elaboration on data from Servizio Statistica PAT [8]) (Table 51 in the Appendix) are also considered;

∶Average dwelling space heating demand for age of construction (in the city of Trento, FBK

elaboration considering law 373/76 [9], law 10/91 [10], DPR 59/09 [11]) (Table 7);

: Local degree days (considering D.P.R. 412/93 [12]) (Table 7);

∶City of Trento degree days (equal to 2567, considering D.P.R. 412/93 [12]) (Table 7);

: Average living area of dwellings (FBK elaboration on data from ISTAT [7]) (m2) (Table 7).


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Table 7: Space heating demand in CEIS area (2013)

The demand for space heating is considered variable throughout the year, according to the outdoor

temperature. For the CEIS area, the weather station “Meteotrentino T0414 San Lorenzo in Banale

(Pergoletti)” [14] and the conventional ambient temperature 20°C are considered. The hourly space

heating demand (kW) is evaluated at municipality level by the following formula:

∗ 1

∑∗

where:

: Yearly space heating demand (kWh/year);

: Conventional ambient temperature (20°C);

: Outdoor temperature (°C).

The demand for HSW energy (kWh/year) is considered constant throughout the year, with an

average HSW usage of 65 l/(day*person) and the necessity to increase the temperature of the

volume identified from tap water temperature (10°C) up to 40°C. The yearly amount

(kWh/year) is evaluated at the municipality level by the following formula:

Degree

days (DD)

< 1982 1982 ‐ 1992 > 1992 (loca l ) av. S (m2) < 1982 < 1992 > 1992 TOTAL

Bleggio

Superiore165 105 60 3,349 92.03 5,283,037 1,583,531 1,431,384 8,297,952

Comano

Terme165 105 60 3,180 99.36 10,726,966 3,032,939 2,814,875 16,574,780

Dors ino 165 105 60 3,381 86.61 1,425,887 512,736 399,962 2,338,586

Fiavè 165 105 60 3,434 97.68 3,534,944 928,367 943,761 5,407,072

S.Lorenzo in

Banale165 105 60 3,573 89.73 5,810,589 1,465,097 1,396,229 8,671,915

Stenico 165 105 60 3,411 97.55 4,815,909 1,433,009 1,318,571 7,567,489

TOT CEDIS

municipal i ties165 105 60 31,597,333 8,955,679 8,304,782 48,857,793

Municipal i ty

av. space heating demand

(Trento) (kWh/m2y)

Space heating demand (kWh/year)


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∗ ∗ ∗ ∗ ∗

where :

: Volumetric mass of water, 1000 kg/m3;

: Specific heat of water, 1.162*10‐3 kWh/(kg*K);

: Volume of daily HSW required, 65 l/(m3*day) – estimate;

: Temperature HSW, 40°C;

: Temperature tap water, 10°C – average value;

: Number of days in a year (days/year);

: Number of inhabitants.

Table 8 and Figure 8 show, at monthly level, space heating and HSW demand in the CEIS area

(reference year 2013). HSW is considered constant throughout the year while space heating is

considered variable throughout the year, according to the outdoor temperature. Space heating

demand dominates the overall thermal energy demand in the CEIS area, reaching its peak in the

middle of winter (December – March). During summer (July – August) almost only HSW demand is

remaining.

Month Space heating HSW TOT thermal energy

Peak Power (kW)

Demand (kWh)

Peak Power (kW)

Demand (kWh)

Peak Power (kW)

Demand (kWh)

January 14,918 8,489,290 796 591,867 15,713 9,081,157

February 17,222 8,104,216 796 534,589 18,018 8,638,805

March 15,100 7,434,549 796 591,867 15,895 8,026,416

April 11,946 3,955,657 796 572,774 12,742 4,528,431

May 10,430 2,896,914 796 591,867 11,226 3,488,781

June 7,459 766,450 796 572,774 8,254 1,339,224

July 5,033 9,945 796 591,867 5,829 601,812

August 5,155 25,348 796 591,867 5,950 617,215

September 8,429 742,133 796 572,774 9,225 1,314,907

October 11,037 2,702,072 796 591,867 11,832 3,293,939


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November 14,433 6,159,619 796 572,774 15,228 6,732,393

December 13,887 7,571,600 796 591,867 14,682 8,163,466

TOTAL 17,222 48,857,793 796 6,968,753 18,018 55,826,546

Table 8: Thermal energy demand in CEIS area, (monthly, 2013)

Figure 8: Thermal energy demand in CEIS area (monthly, 2013)

With a daily level focus (Figure 9) the interconnection between space heating demand and outdoor

air temperature is well illustrated. Actually space heating consumption could be decoupled from

outdoor temperature as well as HSW consumption could be differently modulated, simply using a

buffer as thermal storage. Also the building itself has a certain thermal inertia that decouples space

heating consumption and outdoor temperature and can have a role of thermal storage (increasing

the conventional indoor ambient temperature).


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Figure 9: Thermal energy demand in CEIS area (Mon 04/02/2013 – Wed 06/02/2013)

Energy source mix and generation technologies for thermal energy in the CEIS area (Table 9) are

outlined considering the municipality level:

Number of dwellings that use solid, liquid and gas fuel [15];

Interviews and questionnaires elaborated in the context of CIVIS project.

Different types of building level system produce the necessary thermal energy for heating and HSW,

the main are: wood boilers (56 % of PED), oil boilers (33 % of PED) and LPG boilers (11 % of PED).

The use of solar thermal panels and electric heaters for HSW production are considered negligible.

Use of wood individual boilers is highly widespread, thanks to the large local surface covered by

forest and the low cost of the wood in the market.

Compared to the electrical part, there is a lower, but still considerable share of RES (wood 56 %) and

a large use of imported fossil fuels (oil and LPG).


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Thermal energy source

Thermal energy demand (FED,

kWh)

Peak Power 2013 (kW)

Generation eff. (%)

Thermal energy demand (PED,

kWh)

SEF (t CO2/MWh)

Individual wood boiler

29,694,971 9,549 75 39,593,295 0

Individual oil boiler

19,004,782 6,126 80 23,755,977 0.267

Individual LPG boiler

7,126,793 2,342 90 7,918,659 0.202

TOT 55,826,546 18,018 71,267,931

Table 9: Energy source mix for thermal energy in the CEIS area (Final Energy Demand FED and

Primary Energy Demand PED) (2013)

The sustainability and environmental performances of the CEIS Current Scenario could be improved

reducing the use of fossil fuels individual boilers, which requires:

Reduction of local consumption (higher efficiency of the building envelope and heat

generation, behavioural change etc.);

Increasing of local RE production through wood individual boilers and cogeneration, solar

thermal panels, heat pumps.

2.1.1.4 Energy demand for transport

The analysis of transport energy demand in the CEIS area, at municipality level, is based on data

collected from ACI [16] and from Unione Petrolifera Italiana [17] for the reference year 2013 (Table

10 and Table 11).

On the basis of the local fleet characteristics, the following things are considered negligible:

Energy demand from other types of vehicles with respect to car (e.g. motorcycles, tractors,

special vehicles);

Demand for other fuels with respect to diesel and petrol (in Italy in 2013 LPG cars were 5 %,


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natural gas cars 2 %, electric cars 0.1 % [16]).

The transport energy demand (kWh/year) is calculated by the following formula:

∗ ∗ ∗ ∗ ∗ ∗

where:

, : number of cars fuelled by petrol and diesel [16] (Table 11);

, : average km/year for petrol car and diesel car [17] (Table 10);

, : average km/l for petrol car and diesel car [17] (Table 10);

, : lower calorific value for petrol and diesel (kWh/l) [17] (Table 10);

Type of fuel

Km/year km/l LCV (kWh/l)

SEF(tCO2/MWh)

Petrol 7,250 15.5 8.86 0.249

Diesel 13,400 18.2 10.12 0.267

Table 10: Energy and environmental characteristics of the Italian fleet (2013)

Municipality n. inhabitants petrol (kWh)

diesel (kWh)

(PED, kWh)

Bleggio Superiore

1,576 898 511 387 2,117,683 2,883,533 5,001,216

Comano Terme 2,963 1,647 937 710 3,883,109 5,290,202 9,173,312

Dorsino 428 237 135 102 559,466 760,001 1,319,467

Fiavè 1,127 676 385 291 1,595,515 2,168,238 3,763,752

San Lorenzo in Banale

1,168 677 385 292 1,595,515 2,175,689 3,771,203

Stenico 1,164 721 410 311 1,699,119 2,317,258 4,016,377

TOT CEIS area 8,426 4,856 2,762 2,094 11,446,263 15,602,371 27,048,634

Table 11: Energy demand and CO2‐emission for transport in the CEIS area (2013)


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The transport sector has a huge potential to improve its energy and environmental performance.

EU Commission [18] underline that decarbonising transport is likely to be challenging given that

transport‘s greenhouse gas (GHG) emissions have continued to increase in recent years in spite of

reductions in most other major sectors of the economy. The review identified that there are

technical options that could reduce the GHG emissions of all modes of transport. These included:

options to reduce the GHG intensity of existing conventional fuels (through increasing the

use of biofuels);

improvements to the energy efficiency of existing vehicles;

increased use of alternative energy carriers, such as electricity and hydrogen.

2.1.1.5 General overview of the CEIS Energy System (“Current Scenario”)

In previous chapters the “Current Scenario” of the CEIS Energy System has been analysed in detail

in terms of local production and local demand (electricity, thermal, transport).

Figure 11 gives a general overview in terms of Primary Energy:

A consistent renewable electricity production from hydro, PV and Biogas characterises the

CEIS study area, part of this electricity is used locally, part is in excess and exported;

Thermal energy for space heating and HSW is the main source of demand (52.49 %) followed

by electricity (27.59 %) and transport (19.92 %) (Figure 10);


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Figure 10: Primary Energy Demand in the CEIS area (2013)

In order to cover missing electricity import from the national grid is necessary;

Use of fossil fuels dominate both the thermal sector and in particular the transport sector;

Local wood is an important resource for thermal energy.


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Figure 11: Overview of the CEIS Energy System – “Current Scenario” (2013)


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2.1.2 CEDIS Consortium

2.1.2.1 Location

The Consorzio Elettrico di Storo (CEDIS) is a cooperative founded in 1904 in the lower Chiese Valley

(Province of Trento, Italy) (Figure 12) with the aim to produce and distribute electricity in the

municipalities of Storo (villages: Storo, Darzo, Lodrone, Riccomassimo), Ledro (villages: Tiarno di

Sopra, Tiarno di Sotto, Bezzecca) and Bondone (Bondone, Baitoni) [19].

Nowadays CEDIS activities follow three main business areas:

Energy: production, transport and distribution of electricity;

Telecommunications: fast Internet, telephony and IPTV thorough property fiber‐optic and

Hiperlan network;

Gas: collaboration with Trenta S.p.A. for the supply of gas (local gas grid is owned by Italgas).

CEDIS is one of the 77 Italian Electric Historical Cooperatives: thanks to the ownership of electrical

production plants, transport and distribution grid it is able to ensure to its members a considerable

economic saving in the bill (up to 40 % comparing to the national average).

The supplied area (electric energy) has a surface area of 148 km2, includes 7,805 citizens and 3,136

families.

On the 31st of December 2013, the shareholding structure is made up of 3,259 members who

represent about 76 % of households served [20].


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Figure 12: The Province of Trento and the area of Chiese Valley served by CEDIS

2.1.2.2 Electrical energy production and consumption

CEDIS produces electricity using only renewable sources, it has the ownership of 3 hydropower

plants and 3 centralised PV plants (Figure 13). In addition, in these last 7 years, the diffused

electricity production on the CEDIS grid from PV panels has greatly increased (374 PV plants at 31st

December 2013), driven by high national grants for PV. CEDIS has directly promoted the local spread

of PV installations both from the technical and legislative points of view. Part of the electricity

produced by diffused PV panels is used for self‐consumption and the remaining is injected into the

CEDIS grid and sold to "Gestore dei Servizi Energetici (GSE)".


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Figure 13: CEDIS electrical production plants (Hydro and PV)

Table 12 reports the number of plants and the installed power (kW) and the peak power (kW) on

the CEDIS grid for the reference year 2013 [20].

Renewable energy

source

Number of

plants

Installed

Power (kW)

Peak Power

2013 (kW)

PV 377 8,563 5,567

Hydropower 3 4,679 4,592

Total 380 13,242 10,159

Table 12: Number of plants and installed power (kW) and peak power on the CEDIS grid, divided by

renewable energy source (2013)

The electrical distribution grid of medium voltage (MV) and low voltage (LV) is owned by CEDIS. One

MV interconnection point with the national grid ensures the continuous, bidirectional, exchange of

electrical energy between the CEDIS local grid and national grid. CEDIS sells excess production and

buys lacking electricity from the regional company called Trenta S.p.A.

At the end of 2013 the injection points have reached 380 units with a total installed power of about


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13.2 MW while the withdrawal points have reached 4,601 units with a peak demand power of about

10.4 MW [20].

During the reference year 2013 electrical consumption on the CEDIS grid is equal to 42,521,789 kWh.

This consumption includes all sectors of the society, domestic sector, industry, services sector,

agriculture etc. [20]. As a stakeholder of CIVIS project, CEDIS has made available to FBK the hourly

data of electrical injection and electrical consumption.

Table 13 and Figure 14 compare, at a monthly level, electrical injection and consumption on the

CEDIS grid. Electrical consumption shows small variations at monthly level:

In general, the winter season a slight increasing is stated due to less hours of light;

In August a fall reflects the stop of the majority of industries.

Electrical injection shows considerable fluctuations because of hydro and PV variation in

productivity:

Hydro production dominates the overall total production in the CEDIS area.

Hydro productivity is characterised by a major peak in spring and a secondary peak in

autumn while PV productivity is characterised by one major peak in summer.

During the reference year 2013 total monthly injection exceeded the electrical demand only in April

and May, all other months required a considerable import of electricity from the national grid,

particularly winter months (from December to March) and early autumn September ‐ October.

Month Hydro PV Consumption

Peak Power (kW)

Injection (kWh) Peak Power (kW)

Injection (kWh) Peak Power (kW)

Consumption (kWh)

January 3,532 1,117,900 3,021 190,771 9,726 4,273,684

February 2,181 714,992 3,798 253,729 9,791 3,815,370

March 4,258 1,444,000 5,026 439,093 10,422 4,089,832

April 4,592 2,799,202 5,148 550,049 8,201 3,288,408

May 4,588 2,849,248 5,567 668,771 8,730 3,504,152


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June 4,276 2,041,763 5,419 857,412 7,123 2,977,837

July 3,815 1,053,391 4,993 933,189 8,359 3,544,852

August 1,983 668,841 4,991 849,736 4,040 1,763,923

September 1,786 508,326 4,507 602,081 8,066 3,477,212

October 4,551 1,398,877 4,099 267,340 9,685 3,901,127

November 4,493 1,930,978 3,143 181,789 8,765 3,758,966

December 4,527 1,408,524 2,426 163,943 8,704 4,126,426

TOTAL 4,592 17,936,042 5,567 5,957,902 10,422 42,521,789

Table 13: CEDIS electric grid: production vs consumption (monthly, 2013)

Figure 14: CEDIS electric grid: production vs consumption (monthly, 2013)

Problems of energy balancing or load following capacity are also visible at the daily level. In Figure

16 and Figure 17 two examples in February (period of lower production) and May (period of greatest

production) are shown. Indeed production profiles do not match the two peaks of consumption

(morning and evening). While Hydro production is partially regulated (using a small hydro storage

reservoir, Figure 15), in order to have a major peak between 8 pm and 20 pm (period in which the

electricity consumption and cost in the market are higher), such activity is not possible for PV

production.

0

500.000

1.000.000

1.500.000

2.000.000

2.500.000

3.000.000

3.500.000

4.000.000

4.500.000

1 2 3 4 5 6 7 8 9 10 11 12

CEDIS electric grid: production vs consumption (kWh)

Hydro PV Consumption TOTAL injection


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Figure 15: CEDIS hydro storage reservoir

Considering February (Figure 16) almost each hour sees a deficit of production, particularly during

the two peaks of consumption. Instead during the midday hours the increase of PV production

matches local supply and local demand. On the other hand, during May (Figure 17), a large amount

of excess production is present on the CEDIS grid and exported to the national grid. Also in this case

the maximum production is concentrated during the midday hours.

Figure 16: CEDIS electric grid: production vs consumption (Mon 04/02/2013 – Wed 06/02/2013)

0

1.000

2.000

3.000

4.000

5.000

6.000

7.000

8.000

9.000

10.000

0:00 6:00 12:00 18:00 0:00 6:00 12:00 18:00 0:00 6:00 12:00 18:00 0:00

CEDIS electric grid: production vs consumption (kW, Mon 04/02 ‐Wed 06/02)



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Figure 17: CEDIS electric grid: production vs consumption (Mon 13/05/2013 – Wed 15/05/2013)

Using HOMER Microgrid Software [4], electricity exchange with the national Grid (import and export)

and LFC are analysed and illustrated in Figure 14, Figure 18 and Figure 19. During the reference year

2013 CEDIS imported about 21.1 GWh and exported about 2.5 GWh, the maximum peak of

purchasing is about 9 MW while the maximum peak of selling is about 7 MW. LFC is on average 0.55,

with the best value in May (0.29) and the worst in February (0.75).

CEDIS GRID 2013

Month Energy Purchased (kWh)

EnergySold (kWh)

NetPurchased (kWh)

EnergyDemand (kWh)

LFC

January 2,965,013 0 2,965,013 4,273,684 0.69

February 2,847,764 1,115 2,846,649 3,815,370 0.75

March 2,266,202 59,463 2,206,739 4,089,832 0.57

April 484,282 545,125 ‐60,843 3,288,408 0.31

May 502,948 516,815 ‐13,867 3,504,152 0.29

June 624,202 545,539 78,663 2,977,837 0.39

July 1,735,507 177,236 1,558,271 3,544,852 0.54

August 691,567 446,221 245,346 1,763,923 0.65

September 2,391,331 24,525 2,366,806 3,477,212 0.69

October 2,311,880 76,970 2,234,910 3,901,127 0.61

November 1,702,918 56,718 1,646,199 3,758,966 0.47

0

1.000

2.000

3.000

4.000

5.000

6.000

7.000

8.000

9.000

10.000

0:00 6:00 12:00 18:00 0:00 6:00 12:00 18:00 0:00 6:00 12:00 18:00 0:00

CEDIS electric grid: production vs consumption (kW, Mon 13/05 ‐Wed 15/05)



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December 2,566,989 13,030 2,553,960 4,126,426 0.63

TOTAL 21,090,604 2,462,758 18,627,846 42,521,789 0.55

Table 14: CEDIS grid LFC (monthly, 2013)

Figure 18: CEDIS electric grid: power purchased from the national Grid (hourly, 2013)


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Figure 19: CEDIS electric grid: power sold to the national Grid (hourly, 2013)

In order to better match electrical production and electrical demand, in a CEDIS grid that have seen

a recent considerable rise in the share of PV variable renewable energy production (VRE), similarly

as for CEIS, three different types of interventions are possible/suggested (for more details see

chapter 2.1.1.2):

1) A different regulation of hydro production, using the existing small hydro storage reservoir

or enlarging it. The one major peak between 8 pm and 20 pm should be modified with a

more flexible profile that better follows the gap between electrical demand and PV

production: when this gap is maximum (morning and evening) also hydro power should

reach its maximum daily power. Indeed it does not make sense to maintain the peak of hydro

production also during midday hours. Such management interventions are really interesting

and have no additional costs for CEDIS.

2) A different regulation of demand profile (Demand Side Management), promoting a load

shifting from morning and evening peaks to midday hours. Such load shifting needs to be


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economically encouraged by an hourly tariff that currently does not exist but that can be

introduced and tested by the CIVIS project (TOU tariff).

3) Introduction of energy storage solutions [21].

Promoting CEDIS balancing between electrical production and demand can also have an

environmental impact. While CEDIS electrical production comes completely from renewable

sources (zero emission), the electricity imported from the national grid implies primary energy

source and correlated emissions indicated by the vendor Trenta S.p.A. [6] (Table 5). Energy mix used

for the production of electricity sold by Trenta S.p.A. in 2013 sees a considerable percentage of fossil

fuels (natural gas 38.4 %, coal 20.8 % and petroleum products 1.2 %).

In Table 15 the conversion from Final Energy Consumption (FEC) to Primary Energy Consumption

(PEC) is reported for CEDIS electrical sector.

Type of consumption/losses

FEC (kWh) PEF PEC (kWh)

Local RE production (hydro, PV)

21,431,185 1.00 21,431,185

Imported electricity 21,090,604 2.17 45,766,611

Total 42,521,789 67,197,796

Table 15: CEDIS electric grid: conversion FEC to PEC (2013)

Summarising, the sustainability and environmental performances of the CEDIS “Current Scenario”

could be improved by:

Reducing the import of electricity from Trenta trough a better match between electrical

production and electrical demand (regulation of hydro production, demand side

management, energy storage);

Increasing local RE production (e.g. PV, biomass cogeneration, mini‐hydro).


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2.1.2.3 Thermal energy demand

The production of thermal energy for space heating and hot sanitary water is guaranteed by

individual plants at the building level (gas boilers, wood boilers, oil boilers). Two (Storo and Bondone)

of the three municipalities within the CEDIS area are provided with a gas grid (Italgas is the company

owner of the local gas grid). It has not been possible to obtain real consumption data.

Process heat for the industrial sector is not evaluated.

The yearly space heating demand (kWh/year) is evaluated at municipality level in Table 52

(Appendix) and Table 16 following the same methodology of CEIS (chapter 2.1.1.3).

Table 16: Space heating demand in CEDIS area (2013)

The demand of space heating is considered variable throughout the year, according to the outdoor

temperature. For the CEDIS area, the weather station “Meteotrentino T0393 Storo” [14] and the

conventional ambient temperature 20°C are considered. The hourly space heating demand (kW)

is evaluated at municipality level following the same methodology of CEIS (chapter 2.1.1.3).

The demand for HSW (kWh/year) is evaluated at municipality level following the same

Degree

days (DD)

< 1982 1982 ‐ 1992 > 1992 (loca l ) av. S (m2) < 1982 < 1992 > 1992 TOTAL

Storo 165 105 60 3029 101.03 14,071,152 5,045,743 3,959,970 23,076,865

Bondone 165 105 60 3514 84.15 2,647,049 1,249,084 798,108 4,694,241

Ledro (Tiarno

di Sotto)165 105 60 3526 92.87 3,238,063 1,110,746 1,053,184 5,401,993

Ledro (Tiarno

di Sopra)165 105 60 3551 92.87 3,946,382 2,125,373 1,472,867 7,544,622

Ledro

(Bezzecca)165 105 60 3478 92.87 2,317,724 1,002,886 804,830 4,125,440

TOT CEDIS

municipa l i ties165 105 60 26,220,370 10,533,833 8,088,959 44,843,163

Municipa l i ty

av. space heating demand

(Trento) (kWh/m2y)

Space heating demand (kWh/year)


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methodology of CEIS (chapter 2.1.1.3).

Table 17 and Figure 20 show, at a monthly level, space heating and HSW demand in CEDIS area

(reference year 2013). HSW is considered constant throughout the year while space heating is

considered variable throughout the year, according to the outdoor temperature. Space heating

demand dominates the overall thermal energy demand in the CEDIS area, reaching its peak in the

middle of winter (December – February). During summer (June – August) almost only HSW demand

remains.

Month Space heating HSW TOT thermal energy

Peak Power (kW)

Demand (kWh)

Peak Power (kW)

Demand (kWh)

Peak Power (kW)

Demand (kWh)

January 15,649 8,570,986 737 548,246 16,385 9,119,232

February 16,449 7,652,032 737 495,190 17,186 8,147,222

March 14,232 6,441,978 737 548,246 14,968 6,990,224

April 11,274 3,112,527 737 530,560 12,011 3,643,088

May 9,734 2,107,137 737 548,246 10,471 2,655,383

June 7,578 477,651 737 530,560 8,315 1,008,212

July 5,606 10,658 737 548,246 6,343 558,904

August 6,222 81,693 737 548,246 6,959 629,939

September 9,118 775,590 737 530,560 9,855 1,306,151

October 11,274 1,517,111 737 548,246 12,011 2,065,357

November 15,402 5,360,871 737 530,560 16,139 5,891,431

December 15,895 8,734,927 737 548,246 16,632 9,283,172

TOTAL 16,449 44,843,163 737 6,455,153 17,186 51,298,315

Table 17: Thermal energy demand in the CEDIS area, (monthly, 2013)


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Figure 20: Thermal energy demand in the CEDIS area (monthly, 2013)

With a daily level focus (Figure 21) the interconnection between space heating demand and outdoor

air temperature is well illustrated, therefore the same consideration as for Figure 9 remains valid.

Figure 21: Thermal energy demand in the CEDIS area (Mon 04/02/2013 – Wed 06/02/2013)

Energy source mix and generation technologies for thermal energy in CEDIS area (Table 18) are

outlined considering at municipality level:


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Number of dwellings that use solid, liquid and gas fuel [15];

Interviews and questionnaires elaborated in the context of CIVIS project.

Different types of building level systems produce the necessary thermal energy for heating and HSW,

the main types are: gas boilers (49 % of PED), wood boilers (35 % of PED) and oil boilers (15 % of

PED). The use of solar thermal panels and electric heaters for HSW production are considered

negligible.

Use of biomass individual boilers is highly widespread, thanks to the large local surface covered by

forest and low cost of the wood in the market.

Compared to the electrical part, there is a lower share of RES (wood 35 %) and a large use of

imported fossil fuels (gas, oil).

Thermal energy source

Thermal energy demand (FED,

kWh)

Peak Power 2013 (kW)

Generation eff. (%)

Thermal energy demand (PED,

kWh)

SEF (t CO2/MWh)

Individual gas boiler

27,286,338 9,109 90 30,318,154 0.202

Individual wood boiler

16,371,803 5,500 75 21,829,071 0

Individual oil boiler

7,640,175 2,578 80 9,550,218 0.267

TOT 51,298,316 17,186 61,697,443

Table 18: Energy source mix for thermal energy in the CEDIS area (Final Energy Demand FED and

Primary Energy Demand PED) (2013)

The sustainability and environmental performances of CEDIS “Current Scenario” could be improved

by reducing the use of fossil fuels individual boilers, this requires:

Reduction of local consumption (higher efficiency of the building envelope and heat

generation, behavioural change etc.);


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Increasing of local RE production through wood individual boilers and cogeneration, solar

thermal panels, heat pumps.

2.1.2.4 Energy demand for transport

The analysis of transport energy demand (kWh/year) in CEDIS area, at municipality level (Table

19), follows the same methodology of CEIS (chapter 2.1.1.4).

Municipality n. inhabitants petrol (kWh)

diesel (kWh)

(PED, kWh)

Storo 4,700 2,690 1,530 1,160 6,340,616 8,643,147 14,983,763

Ledro (Tiarno di Sotto)

757 433 246 187 1,019,472 1,393,335 2,412,807

Ledro (Tiarno di Sopra)

1,074 619 352 267 1,458,756 1,989,414 3,448,170

Ledro (Bezzecca)

595 340 194 147 803,974 1,095,295 1,899,269

Bondone 679 377 214 163 886,857 1,214,511 2,101,369

TOT CEDS area 7,805 4,460 2,537 1,923 10,513,819 14,328,252 24,842,071

Table 19: Energy demand and CO2‐emission for transport in CEDIS area (2013)

The demand for transport (kWh/year) is considered constant throughout the year at hourly level.

The transport sector has a huge potential to improve its energy and environmental performance

(see chapter 2.1.1.4).

2.1.2.5 General overview of the CEDIS Energy System “Current Scenario”

In previous chapters the “Current Scenario” of the CEDIS Energy System has been analysed in detail


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in terms of local production and local demand (electricity, thermal, transport).

Figure 23 gives a general overview in terms of Primary Energy:

A consistent renewable electricity production from hydro and PV characterises the CEDIS

study area, part of this electricity is used locally, part is in excess and exported;

Electricity is the main source of consumption (43.71 %) followed by thermal energy for space

heating and HSW (40.13 %) and transport (16.16 %) (Figure 22);

Figure 22: Primary Energy Demand in the CEDIS area (2013)

In order to cover missing electricity import from the national grid is necessary;

Use of fossil fuels dominates both the thermal sector and in particular the transport sector;

Local wood is an important resource for thermal energy.


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Figure 23: Overview of the CEDIS Energy System – “Current Scenario” (2013)


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2.2 Swedish pilot sites

2.2.1 Hammarby Sjöstad

In Hammarby Sjöstad test site, two use cases are being implemented. The first one involves building

level energy efficiency and creation of social network around that, while the second one focuses on

apartment level energy efficiency. For the building level use case 13 housing associations have been

recruited and their data is being collected. For the apartment level use case, 5 housing associations

are being considered and deployment of additional sensors is underway. Table 20 lists the housing

associations being included.

Housing association Building level heating

data

Apartment level data (electricity &

hot water)

Sickla Kanal X X

Älven X X

Båtbyggaren X X

Sjöstaden 1 X X1

Seglatsen X X

Grynnan X

Holmen X

Slusstornet X

Hammarby Ekbacke X

Strandkanten X

1 System upgrade is currently underway by the housing association.


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Sjöstadsviken X

Sjöportalen X

Redaren X

Table 20: Housing association in Hammarby Sjöstad part of CIVIS

2.2.1.1 Data availability

Heating: Hourly data at building level is available for 13 housing associations at building level.

Historical (up to 2010) and normalised data (for each year) is also available. The data from the 13

housing associations is currently being connected to the CIVIS servers using the Energimolnet cloud

service. Figure 24 shows the hourly energy usage for heating for Housing associations Strandkanten

and Älven for 2014 and 2013. Outdoor temperature is also provided for reference. In addition, more

detailed data such as flow rates, supply and return temperatures are also available for each

association. Heating and hot water are provided through district heating which has a very high share

of renewables as discussed in D2.1. Heating demand also has the highest share in terms of overall

energy use with significant possibilities for savings.


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Figure 24: Hourly heating data for 2014 & 2013 (green line: energy, grey line: outdoor

temperature)

Domestic Hot water: For domestic hot water consumption data, apartment level data is available

in at least 3 of the five housing associations and is being connected to the CIVIS servers.

Building services electricity: Hourly and historical data for the building services electricity is

available through the DSO from the energy accounts. It is being connected to the CIVIS servers using

Energimolnet.

Household electricity: The five selected housing associations in Table 20 have household electricity


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data available at hourly resolution. Installation of additional sensors is in progress to get higher

resolution (5 min) data for household electricity.

2.2.1.2 Additional sensors

In Hammarby Sjöstad 50 households are being equipped with smart energy monitors (SMAPPEE2)

to provide high resolution (5 min) electricity usage data. The device recognises the main appliances

in the household and allows the users to label them and keep track of their consumption.

Additionally each household is being provided with 7 smart plugs that allow control of appliances

remotely. Figure 25 shows the load profile for one day from one of the Smappee units installed in

the test site and Figure 26 shows some of the appliances labelled by the user.

2 www.smappee.com


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Figure 25: Daily electricity load profile from installed energy monitor with 5 min resolution

Figure 26: Detected appliances from household


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2.2.1.3 Actions planned

In Hammarby Sjöstad test site the key actions planned include:

Completion of sensor deployment in 50 households (10 per housing association).

Connection of data streams from housing associations and DSO to CIVIS servers.

Deployment of CIVIS apps for both household level and building level.

Implementation of feasible measures suggested in Chapter 4 in test sites.

2.2.2 Fårdala

In Fårdala test site the focus is at the household level. There is only one housing association and the

area is divided into 3 sub‐areas with a total of 178 houses.

2.2.2.1 Data availability

Heating: At present historical data until 2008 is available for monthly heating energy use. New

master units have been installed in the area with two out of three online and the last one expected

to be online in September 2015. With this hourly data for heating along with hot and cold water

consumption is now available. The data streams are being connected to the CIVIS servers.

Hot water: Similar to the heating data, hot water data on runtime hourly basis will be available after

the installation of the new Master units. Historical data is also available.

Electricity: Since each household has a contract with the DSO and is individually metered, electricity

data is available through the DSOs. This will be managed through the Energimolnet service and the

work is ongoing in this regard.


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2.2.2.2 Additional sensors

In Fårdala 10 households have been equipped with Smappee smart energy monitors and smart plugs

(as discussed in Chapter 2.2.1.2). Additionally, in 45 households a smart heating control system is

being deployed enabling users to control the indoor temperature by offering control of radiator

thermostat valves (MAX eq3) through a smart phone or web portal. The installation of heating

controls will be completed before the start of heating season by the end of September. The heating

control system can be used for thermal load shifting as well as reduction of energy use. Figure 27

shows the heating control system being installed in Fårdala.

Figure 27: Heating control system for Fårdala test site.

2.2.2.3 Actions planned

In Fårdala test site the key actions planned include:

Completion of sensor deployment in 45 households.

Connection of data streams for hot water and heating sensors to CIVIS servers.

Deployment of CIVIS apps for both households.

Implementation of feasible measures suggested in Chapter 4 in test sites.


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3 Energy system modelling addressing optimisation and

storage in the test sites

3.1 Italian test sites

3.1.1 Use of EnergyPLAN for modelling “Current Scenario” and “Future

Optimised Scenarios” in CIVIS pilot sites energy systems

This section we focus on the Italian test sites energy network layer, i.e. the energy system of CEIS

and CEDIS Consortium and on the modelling and optimisation of this system. The process can be

divided into two parts. In the first part, the current scenario of the consortiums are modelled and

analysed and in the second part, future optimised scenarios are identified.

The modelling and analysis of the current energy system is done using EnergyPLAN. EnergyPLAN is

developed and maintained by the Sustainable Energy Planning Research Group at Aalborg

University, Denmark. The model is used by many researchers, consultancies, and policymakers

worldwide. The main purpose of EnergyPLAN is to analyse the energy, environmental and economic

impact of various energy systems. The model has been used in a number of studies ranging in size

from national systems [22], [23], [24] to small villages or in studies focusing on the performance of

a single technology [25], [26], [27] or complex energy systems [28], [29]. The model is described in

[30], [31] and is freely available at www.energyplan.eu.

The model is a deterministic input/output model (Figure 28). General inputs are demands,

renewable energy sources, energy station capacities, costs and a number of optional different

regulation strategies emphasising import/export and excess electricity production. Outputs are

energy balances and resulting annual productions, fuel consumption, import/export of electricity,

CO2‐emission and total costs including income from the exchange of electricity.


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It is an hour‐simulation model as opposed to a model based on aggregated annual demands and

production. Consequently, the model can analyse the influence of fluctuating RES on the system as

well as weekly and seasonal differences in electricity and heat demands and water inputs to large

hydropower systems. For each hour, the model ensures a balance between demand of electricity,

thermal and transport and production plus import.

Identification of future scenarios will be described in the second part of this section. A completed

framework that integrates a multi‐objective evolutionary algorithm and EnergyPLAN is used to find

out future scenarios.

As opposed to a single‐objective optimisation problem, most the real‐world problems are complex

enough to be considered as multi‐objective optimisation problems. Most real‐world problems have

more than one objective that needs to be optimised simultaneously. Generally, these objectives are

contradictory to each other. Therefore, researchers are interested to develop algorithms for solving

multi‐objective optimisation problems. Evolutionary algorithms mimic the idea of evolution and

natural selection for solving problems. As evolutionary algorithms deal with multiple solutions at

the same time, researchers are interested to develop multi‐objective versions of evolutionary

algorithms. As a result, multiple multi‐objective evolutionary algorithms are developed such as

NSGA‐II [32], SPEA2 [33], MOEA/D [34].


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Figure 28: Structure of the EnergyPLAN

In this work, the optimisation framework is based on the integration of SPEA2 with EnergyPLAN. The

choice of SPEA2 among several available algorithms is made because SPEA2 is preferable than

NSGA‐II when more than three objectives are optimised (here, we are considering to optimise four

objectives). SPEA2 is also preferable over MOEA/D because of less algorithmic parameters settings

and more used in solving practical problems. Figure 29 illustrates the basic steps of the framework.

The first step of the process is initialisation. In this phase, a number of individuals (i.e. energy

scenarios in terms of decision variables) are randomly initialised. EnergyPLAN model is integrated

within the “evaluate individuals” step of the algorithm. Therefore, each scenario is evaluated using

EneryPLAN. The evaluated metrics (values for different objectives) are fed back to the algorithm. In

the ranking stage, individuals are ranked according to the dominance relation. Afterwards, some

natural evolutionary processes (i.e. parent selection, crossover and mutation) are applied.

Crossover is an operator that generates new individuals by somehow combining the information

contained within a pair of parents. Mutation is instead a random change that happens in an


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individual. In the next step, the concept of ‘survival of the fittest’ is implemented to ensure that only

the best individuals have the chance to reproduce to the next generation. The loop continues until

stopping criteria are met. When the loop ends, the algorithm provides a Pareto‐optimal front3.

Figure 29: Energy System Optimisation Model

In this report, starting from the “Current Scenario” and using EnergyPLAN + SPEA2, several “Future

Optimised Scenarios” are analysed, in order to identify possible solutions able to:

Reduce annual energy cost (AC, €/year);

Reduce the environmental impact (CO2‐emission, t/year);


3 The Pareto‐optimal front of a multi‐objective optimisation problem consists of the function values representing the

different trade‐off with respect to the given objective functions.


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ESD)

Four parameters describe the economic behaviour of the CEIS/CEDIS systems, their sum defines the

annual cost (AC) (see also the Appendix):

1) Investment cost: cost needed for the introduction of a new technology in the energy system or

the increasing of an existing one. Annual investment cost (AIC, €/year) for a particular

technology can be formulated as follows:

∗1 1

where:

: is the total investment cost (multiplying the number of units by the unitary cost);

n : is the lifetime (in year) of a given technology;

: is the interest rate (4 % for CEIS/CEDIS case).

Annual investment cost is the summation of all the investment costs for all technologies. Please

note that for the “Current Scenario” no annual investment cost is considered.

2) Variable O&M cost: cost associated with production of plants (such as fuel cost) and electricity

exchange cost. Electricity exchange cost is the cost related to import and export with the

national grid. To calculate the cost, an hourly cost data for electricity market is included into the

model, as provided by CEIS/CEDIS.

3) Fixed O&M cost: cost related to a plant regardless of how often the plant operates such as

service charge in each year.

4) Additional cost: all the electricity consumed inside the consortium has to have extra cost

(general system charges, grid and metering cost, taxes). The average additional electricity cost

is 106.27 €/MWh.


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CO2‐emission is the most important parameter in terms of environmental aspect. Total CO2‐

emission in tons is defined through the specific standard emission factor (SEF) of the different

energy sources. In line with the IPCC principles [35] SEF covers all the CO2‐emissions that occur due

to energy consumption within a territory, either directly due to fuel combustion within the territory

or indirectly via fuel combustion associated with electricity and heat/cold usage within the area. The

standard emission factors are based on the carbon content of each fuel. In this approach, CO2 is the

most important greenhouse gas. Furthermore, the CO2‐emissions from the sustainable use of

biomass/biofuels, as well as emissions of certified green electricity, are considered to be zero.

The 2nd Objective we consider is Load Following Capacity (LFC). LFC is described before, however,

in the optimisation phase, electricity import and export and the demand are considered on a yearly

basis.

where:

: Electrical import from national grid in a year

: Electrical export to national grid in a year

: Electrical demand in a year

It should be noted that when import and export are low, LFC is also low. In addition, import and

export are low when an energy system can produce electricity as required. This parameter is

particularly relevant in energy systems with a high fraction of RES. In these cases the production

profiles depend on natural factors (solar radiation, wind speed, rainfall) and not on human planning

(as in conventional thermal power plants).

The last considered parameter, Energy System Dependency (ESD), is a ratio between primary

energy supply from outside of the system (i.e., import) and total primary demand of the system. In

another word ESD reveals how much an energy system depends on foreign energy import.


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Minimizing ESD implies that less energy is imported in terms of local demand. Being energy

independent is an important target for a community like CEIS and CEDIS (CEIS and CEDIS are the

owner of the grid) and its policy makers, it could indeed reduce energy costs, increase local

employees, create independence from external sources and markets not directly controllable. The

following is the formulation of ESD:

where:

: total primary energy imported in a year

: total primary energy demand in a year

For CEIS, the following formulation is used:

. ∗ _

. . ∗ _ . ∗ _

For CEDIS, the following formulation is used:

. ∗ _

. . ∗ _ . ∗ _

is the local primary energy factor, is the primary energy factor for importing

electricity.

3.1.2 Modelling “Current Scenario”

After collecting all the data about CEIS and CEDIS, as described in Chapter 2.1.1 and in Chapter 2.1.2,

it is now possible to model the “Current Scenario” using EnergyPLAN, in order to evaluate the four

identified parameters characterising the energy system. It is mentioned earlier that no investment

is considered for current scenario. All other related costs including fuel, fixed operational and

maintenance, variable operational and maintenance cost can be found in appendix (Table 53, Table

54 and Table 56).


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Table 21 and Table 22 summarise economical, environmental, technical and political parameters

identified for the “Current Scenario”. With this baseline characterisation it is therefore possible to

propose and compare “Future Optimised Scenarios” in which the EnergyPLAN + Evolutionary Multi‐

objective allow to optimise for each one of the selected parameter and to quantify the resulting

benefits.

Economic parameter

Variable cost 11,782 kEuro

Fixed operational cost 713 kEuro

Additional cost 2,780 kEuro

Investment cost 0 kEuro

Total annual cost 15,275 kEuro

Environmental parameter

CO2 emission 13.092 kt

Technical parameter (electric grid)

Import 4.63 GWh

Export 7.87 GWh

Load Following Capacity (LFC) 0.48

Political parameter

Energy System Dependency (ESD) 0.51

Table 21: CEIS “Current Scenario”: Economical, environmental, technical and political parameters

Economic parameter

Variable cost 10,101 KEuro

Fixed operational cost 653 KEuro

Additional cost 4,518 KEuro

Investment cost 0 KEuro

Total annual cost 15,272 KEuro

Environmental parameter

CO2 emission 21.63 kt

Technical parameter (electric grid)

Import 21.08 GWh

Export 2.48 GWh

Load Following Capacity (LFC) 0.55

Political parameter

Energy System Dependency (ESD) 0.72

Table 22: CEDIS “Current Scenario”: Economical, environmental, technical and political parameters


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3.1.3 Modelling “Future Optimised Scenarios”

3.1.3.1 Introduction

The Power System of the Future will be radically different, with high penetration of unpredictable

variable output of renewables [36]. There are a lot of potential new technologies and control

mechanisms to utilise. We need to determine what combination of generation, storage, distributed

resources management, interconnection trading, ancillary services provision and

transmission/distribution management will deliver the goods as regards safe, secure, efficient, and

economic operation with reduced fossil fuel burn. The three sectors (Power, Heat, Transport) will

interact to a far greater extent than currently.

In order to plan possible future interventions to improve the sustainability and efficiency of an

energy system it is important for policy makers to have access to specific studies that identify future

scenarios, which optimise in terms of economic, environmental, technical and political perspectives.

However, these perspectives are often contradictory to each other’s. If someone wants to minimise

CO2‐emission, the annual cost could increase because of additional investment cost (i.e. investment

on new technologies). At the same time, intermittent behaviour of renewable sources are difficult

to handle and there are constraints regarding electric grid capacity.

A detailed analysis has been developed and here illustrated based on EnergyPLAN + Evolutionary

Multi‐objective to identify “Future Optimised Scenarios” for decision makers. The framework will

identify a set of scenarios, comparable with “Current Scenario”, some may be good in one objective,

others may be better in another objective.

3.1.3.2 Assessment of additional local sustainable energy resources: wood

and solar

In order to verify the potential for a sustainable use of the wood resource in the CEIS and CEDIS


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areas (Table 23) a specific calculation is performed. It follows the indications of the recent “Energetic

and Environmental Plan of the Province of Trento (PEAP 2013‐2020)” [37] considering the local

surface covered by forest and the potential for sustainable wood use (compatible with the annual

forest growing):

∗

where:

: Sustainable wood resource (GWh/year);

: Surface covered by forest (ha);

: Potentiality for a sustainable use of the biomass resource (MWh/(ha*year)).

Area (ha)

(%) (ha) (MWh/(ha*year))

(GWh/year)

CEIS 24,857 55 13,671.35 4.16 56.873

CEDIS 14,785 55 8,131.75 4.16 33.828

Table 23: Potential for a sustainable use of the wood resource in CEIS and CEDIS areas

In terms of wood sustainability it can be considered that in the CEIS area the local forest is managed

in a sustainable way, meaning that on average forest growth (56.873 GWh/year) is higher than

harvesting (39.593 GWh/year). In other word it is possible to harvest 56.873 GWh/year of wood in

the CEIS area without harming the environment, there is an additional margin (+ 30.4 %) to increase

the use of local wood.

The same considerations can be done in the CEDIS area: the local forest is managed in a sustainable

way, forest growth (33.828 GWh/year) is higher than harvesting (21.829 GWh/year). There is an

additional margin (+ 35.5 %) to increase the use of local wood.

Moving to the solar resource, considering the data of PV peak power (CEIS 2013, CEDIS 2013)

and PV injection (CEIS 2013, CEDIS 2013), the following local PV producibility (PVLP) indicating


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the full load hours or the capacity factor is defined:

6,111,3814,998

1,223 /

5,957,9025,567

1,070 /

3.1.3.3 Decision variables, constraints and objectives

In the following different technologies describing the possibility to be a decision variable in the CEIS

and CEDIS energy system are addressed:

There are still possibilities to increase the capacity of PV both in CEIS and in CEDIS. It is

decided to limit the maximum capacity dividing the annual electrical consumption

(assuming that all the energy system is electrified in this way: existing electrical demand,

existing thermal demand covered by GSHP, existing transport demand covered by electric

car) for the local PV producibility . :

26,177,984 55,826,546

3.2 48,084,100 ∗ 0.168

1,223 /

51,701,9081,223 /

42.275


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42,521,789 51,298,315

3.2 44,161,450 ∗ 0.168

1,070 /

65,971,6361,070 /

61.656

Please note that is around 42 MW and is 61 MW. These are

the maximum possible PV capacities for the communities. Specifying maximum capacity is

necessary because the optimiser will find the optimal scenario within the specified maximum

limit. We specify a large maximum limit to let the optimiser find optimal scenarios within the

range.

Since the study in Chapter 3.1.3.2 shows the potentiality to increase the use of wood, it is

interesting to investigate a further diffusion of individual wood boiler in CEIS and in CEDIS

and the introduction of wood ORC combined heat and power (wood ORC CHP) in CEIS. It is

necessary to respect the limit of 56.873 GWh/year of wood consumption in CEIS area and

33.828 GWh/year in CEDIS area;

Diffused gas SOFC mCHP could be a viable option in CEDIS thanks to the availability of a gas

grid and low gas price;

An interesting solution for the thermal sector (space heating and HSW) of CEIS and CEDIS

could be the use of ground source heat pump (GSHP). Heat pump is a well consolidated

technology that can use ground, water or air as sources of heat or cold, the need of an

electric compressor for running the thermodynamic process implies an electrical

consumption. If local produced electricity (from hydro, PV, biogas) is used the GSHP could

operate with zero emission (SEF methodology). In CEIS and CEDIS it is decided to investigate

the use of ground because it guarantees high performance (high coefficient of performance

(COP)) even in cold climate;


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Transport sector of CEIS and CEDIS could be radically transformed increasing the use of an

alternative energy carrier such as electricity. This transition, from fossil fuel cars to electric

cars could lead to very high benefits in terms of CO2‐emission and ESD. In this report a night

charging modality is considered: it is assumed that from 21 pm to 4 am all electric cars will

be charged (Figure 30). It also means that in these hours new electricity demand will be

introduced to charge cars.

Figure 30: Night‐charging (NC) profile for electric car batteries

Other existing technologies as hydro and biogas are not considered as decision variables, their

capacity will be maintained the same as in “Current Scenarios”.

In Figure 31 the above identified technologies outlining an overview of the CEIS and CEDIS “Future

Optimised Scenarios” are introduced.


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Figure 31: Overview of the CEIS and CEDIS Energy System – “Future Optimised Scenarios”


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3.1.3.4 Results for CEIS Consortium

3.1.3.4.1 Introduction

After completion of the simulation, 401 “Future Optimised Scenarios” are found. Having four

objectives to optimise is a difficult task to visualise the Pareto‐front. However, using a three‐

dimensional view with different colours (4th objective is presented as colour) it is possible to

visualise the front (Figure 32). In Figure 32 x, y and z‐axis correspond respectively to CO2‐emission

(kt/year), annual cost (KEuro) and LFC, the different colours represent different values of ESD. Figure

32 also visualises the comparison between “Current Scenario” (in grey cube) and “Future Optimised

Scenarios”. It is very difficult to study all the scenarios individually from the Pareto‐front. Z‐axis of a

point can be seen by the height of a point and x, y –axis value can be identified by the projection of

the point in xy plain. The colour is of a point present ESD value. However, it may not require to study

all the scenario from the Pareto‐front. For this purpose, we have identified some interesting

scenarios which is discussed below.

Please note that some of the “Future Optimised Scenarios” have negative emissions because not

only do we consider emission for electricity generation within but also outside the local system.

When electricity that has less emission factor than outside is exported the difference is subtracted

from internal emission.


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Figure 32: CEIS: Pareto‐front and comparison “Current Scenario”/”Future Optimised Scenarios

It is not practical to discuss all of the 401 “Future Optimised Scenarios”. Therefore, we want to

present some significant scenarios and their trends in three different categories. In the first category,

some best scenarios in terms of annual cost are presented. Afterwards, we present some target

scenarios in term of CO2‐emission and ESD. Target scenarios in terms of CO2‐emission or EDS involve

a specific range of percentage reduction of CO2‐emission or ESD (e.g. ‐40 % to ‐45 % reduction); we

present the 3 least costly scenarios for each target range. Finally, we present a general discussion

about the implementation possibility for the different addressed technologies (decision variables).

3.1.3.4.2 Best scenarios in terms of Annual Cost

The most interesting parameter is definitely annual cost (AC), cheaper scenarios are the most

attractive for policy makers (and their communities). In Table 24 and in Figure 33 the best 15


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scenarios in terms of AC are identified. Figure 60Figure 60 in appendix also illustrates the

comparison of different objective values for these best 15 scenarios with current scenario. This

study suggests consistent economical savings (from ‐4.8 % to ‐11.9 %). It is also possible to both

minimise energy cost while reducing CO2‐emission (from ‐28.1 % to ‐70.9 %). In other words several

energy scenarios greener and cheaper than the current one are possible. Also ESD is highly improved,

these scenarios are able to import from ‐14% to ‐20% less energy resources from outside, mainly

thanks to the increase in the use of local wood and partial electrification of the thermal sector

(through heat pumps powered by local hydro, PV and biogas electricity). LFC in most cases is very

close to the value of the "Current Scenario", ensuring electrical grid stability. Electrical import

increases from 4.63 GWh of “Current Scenario” to a range of 6.76 – 9.68 GWh, electrical export

moves from 7.87 GWh to a range of 6.48 – 14.87 GWh.

Table 24: CEIS: Best 15 scenarios in terms of AC, comparison with “Current Scenario”

The electrical resource mix of “Current Scenario” (local production + import) maintains its

Scn.AC

(KEuro)

Imp

(%)

CO2

Emission

(Kt)

LFC ESD PV (kWe)

Wood

CHP

(kWe)

GSHP

(kWth)

Oil boiler

(kWth)

LPG

boiler

(kWth)

Wood

boiler

(kWth)

Petrol

Cars

Diesel

Cars

Electr

Cars

Curr 15,275 0.0 13.092 0.48 0.51 5,000 0 0 9,155 3,431 14,306 2,762 2,094 0

AC1 13,456 ‐11.9 9.162 0.49 0.36 5,261 151 7,008 97 406 15,657 2,759 2,091 6

AC2 13,800 ‐9.7 9.409 0.48 0.37 5,224 110 5,558 1,579 412 16,404 2,761 2,093 2

AC3 13,829 ‐9.5 8.480 0.49 0.34 5,363 320 6,195 139 280 16,706 2,693 2,042 121

AC4 13,836 ‐9.4 8.509 0.48 0.34 5,015 44 5,216 597 132 18,278 2,669 2,023 164

AC5 14,041 ‐8.1 6.351 0.62 0.36 9,765 82 7,334 67 732 14,973 2,759 2,092 5

AC6 14,047 ‐8.0 7.991 0.47 0.33 5,057 204 3,523 621 1,020 19,665 2,760 2,092 4

AC7 14,079 ‐7.8 5.388 0.60 0.32 8,786 30 3,949 860 48 20,021 2,755 2,089 12

AC8 14,145 ‐7.4 7.420 0.47 0.32 5,204 773 3,661 559 441 19,244 2,761 2,093 2

AC9 14,196 ‐7.1 5.198 0.60 0.32 8,786 211 3,738 860 48 20,066 2,755 2,089 12

AC10 14,243 ‐6.8 7.819 0.47 0.33 5,023 687 3,654 646 831 18,908 2,757 2,090 9

AC11 14,296 ‐6.4 3.813 0.72 0.35 12,819 87 7,072 97 161 15,903 2,759 2,091 6

AC12 14,372 ‐5.9 7.274 0.47 0.31 5,170 592 3,738 257 464 19,679 2,657 2,014 185

AC13 14,398 ‐5.7 7.674 0.47 0.32 5,158 1,269 4,970 101 522 16,918 2,721 2,063 72

AC14 14,458 ‐5.3 8.262 0.47 0.34 5,092 1,114 4,739 590 1,037 16,492 2,757 2,090 9

AC15 14,546 ‐4.8 7.401 0.46 0.31 5,015 1,175 4,042 597 132 18,345 2,669 2,023 164


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competitiveness also in “Future AC Optimised Scenarios”. While the introduction of wood CHP

appears often negligible (30 – 1,269 kWel for a yearly production of 0.02 – 0.89 GWhel), the

increasing of PV capacity is suggested in several of the proposed scenarios from 5,015 kWel (+0.3 %,

yearly production of 6.13 GWh) to 12,819 kWel (+156.38 %, yearly production of 15.68 GWh).

What instead is deeply transformed is the thermal sector, first of all individual oil and LPG boilers


individual boiler capacity because fuel costs are too high compared to other alternative energy

carriers. What gains importance is instead the use of local wood and the electrification of the

thermal sector. Individual wood boiler capacity is maximised in almost all of the 15 proposed

scenarios, wood consumption move from 39.59 GWh of “Current Scenario” to a range of 41.75 –

56.76 GWh (close to the constrain of 56.873 GWh). While wood CHP appears economically not so

attractive it is observed that to have a very cost effective scenario a wide introduction of GSHP is

interesting. GSHP thermal capacity ranges between 3,523 kW and 7,334 kW. In the proposed

“Future AC Optimised Scenarios” individual wood boilers cover 56 – 73 % of the heat demand (from

53% of “Current Scenario”) while GSHP 20 – 41 %.

Concerning the transport sector, the number of introduced electric cars is almost negligible (from 2

to 185). Indeed, the investment necessary to replace oil cars with electric cars is economically

unattractive in the current market condition.


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Figure 33: CEIS: best 15 scenarios in terms of AC (2‐16), comparison with “Current Scenario” (1)

3.1.3.4.3 Target scenarios in terms of CO2‐emission reduction

However annual cost is not always the only parameter considered. Indeed, many communities

commit themselves to reach environmental targets (see for example the Convenant of Majors [38]):

this study suggests several scenarios with ambitious CO2‐emission reduction (Table 25 and Figure

35). In particular three less costly scenarios for targets between ‐30 % to ‐35 % and ‐100% to ‐105%

are proposed. In order to reach the highest values it is necessary to progressively increase the use

of greener technologies, which leads to AC growth (Figure 34).


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Figure 34: CEIS: “Future Optimised Scenarios” (blue) and correlation between annual cost and CO2‐

emission reduction (%). In red “Current Scenario”

ESD is deeply improved in all proposed scenarios through the reduction of fossil fuel imports, the

increasing in the use of local wood and local PV production. LFC is close to the value of the "Current

Scenario" only for lower “CO2‐emission reduction targets”. In all other cases the high fraction of

non‐programmable RES production (PV) leads to poor load following capacity, electrical export

increases and may require an expensive adaption of the grid transmission capacity. Electrical import

moves from 4.63 GWh of “Current Scenario” to a range of 2.58 – 9.68 GWh, electrical export from

7.87 to a range of 6.51 – 20.44 GWh.


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Table 25: CEIS: best scenarios in terms of CO2‐emission, comparison with “Current Scenario”

The electrical resource mix sees the gradual PV capacity increasing, in particular the highest targets

make widely use of this technology. Wood CHP is an alternative but appears more expensive.

As expected in the thermal sector individual oil and LPG boilers reach approximately zero capacity.

Individual wood boiler capacity is maximised from the lower targets. GSHP is widely introduced, it

is not only a cheap technology, as seen previously, but also a green technology.

Target scenarios from ‐30.0 % to ‐103.8 % do not introduce a significant number of electric cars

(from 2 to 371): it is more cost effective to invest in other technologies such as wood individual

boiler, GSHP, PV, wood CHP.

Scn.

CO2

Emission

(Kt)

Imp

(%)

AC

(KEuro)LFC ESD

PV

(kWe)

Wood

CHP

(kWe)

GSHP

(kWth)

Oil

boiler

(kWth)

LPG

boiler

(kWth)

Wood

boiler

(kWth)

Petrol

Cars

Diesel

Cars

Electr

Cars

Curr 13.092 0.0 15,275 0.48 0.51 5,000 0 0 9,155 3,431 14,306 2,762 2,094 0

EM30A 9.162 ‐30.0 13,456 0.49 0.36 5,261 151 7,008 97 406 15,657 2,759 2,091 6

EM40A 7.819 ‐40.3 14,243 0.47 0.33 5,023 687 3,654 646 831 18,908 2,757 2,090 9

EM40B 7.420 ‐43.3 14,145 0.47 0.32 5,204 773 3,661 559 441 19,244 2,761 2,093 2

EM40C 7.274 ‐44.4 14,372 0.47 0.31 5,170 592 3,738 257 464 19,679 2,657 2,014 185

EM50A 6.454 ‐50.7 15,114 0.52 0.32 7,380 2,618 6,902 91 76 12,449 2,738 2,076 42

EM50B 6.360 ‐51.4 15,827 0.44 0.28 5,123 3,323 4,166 364 172 15,128 2,632 1,995 229

EM50C 6.351 ‐51.5 14,041 0.62 0.36 9,765 82 7,334 67 732 14,973 2,759 2,092 5

EM60A 5.198 ‐60.3 14,196 0.60 0.32 8,786 211 3,738 860 48 20,066 2,755 2,089 12

EM60B 4.727 ‐63.9 15,400 0.57 0.30 8,625 2,414 4,678 56 737 15,467 2,755 2,089 12

EM60C 4.620 ‐64.7 16,426 0.51 0.28 7,612 4,671 5,094 276 123 11,851 2,754 2,088 14

EM70A 3.813 ‐70.9 14,296 0.72 0.35 12,819 87 7,072 97 161 15,903 2,759 2,091 6

EM70B 3.654 ‐72.1 15,078 0.61 0.29 9,765 1,865 4,282 67 146 17,463 2,754 2,088 14

EM70C 3.501 ‐73.3 15,588 0.64 0.31 10,768 2,201 5,053 427 253 15,337 2,711 2,055 90

EM80A 2.414 ‐81.6 16,918 0.61 0.27 10,039 3,628 3,626 44 566 15,407 2,577 1,954 325

EM80B 2.149 ‐83.6 17,058 0.60 0.27 10,053 4,806 3,843 44 506 13,374 2,717 2,060 79

EM80C 2.128 ‐83.7 18,225 0.53 0.24 8,612 6,582 3,683 147 75 11,276 2,609 1,978 269

EM90A 0.989 ‐92.4 16,235 0.74 0.29 13,095 2,632 3,533 773 28 16,847 2,718 2,060 78

EM90B 0.796 ‐93.9 18,792 0.62 0.25 11,545 7,429 5,411 20 270 7,349 2,718 2,060 78

EM90C 0.735 ‐94.4 18,224 0.70 0.28 12,754 3,893 3,872 315 902 14,032 2,407 1,825 624

EM100A ‐0.022 ‐100.2 19,813 0.59 0.22 10,067 9,488 3,670 42 110 7,009 2,719 2,062 75

EM100B ‐0.231 ‐101.8 15,875 0.86 0.32 17,414 1,090 6,602 83 19 15,259 2,663 2,019 174

EM100C ‐0.500 ‐103.8 15,966 0.88 0.31 16,709 52 4,064 392 283 20,046 2,551 1,934 371


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Figure 35: CEIS: best scenarios in terms of CO2‐emission, comparison with “Current Scenario”

3.1.3.4.4 Target scenarios in terms of ESD reduction

ESD suggests which technologies can optimise the use of local energy resources and in which entity

we can rely on them. This study suggests several scenarios with ambitious ESD reduction (Table 26

and Figure 37). In absolute terms the best identified scenario has an ESD of 0.11, this means that it

needs only 11% of external energy resources to cover all the local energy demand (electricity,

thermal, transport). Afterwards, the three less costly scenarios for targets between ‐0.15 to ‐0.17

and ‐0.35 to ‐0.37 are proposed in detail. As for CO2‐emission reduction, in order to reach the

highest values it is necessary to progressively increase the use of costly technologies, this leads to

AC growth (Figure 36).


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Figure 36: CEIS: “Future Optimised Scenarios” (blue) and correlation between annual cost and ESD

reduction. In red “Current Scenario”

CO2‐emission is highly improved in all proposed scenarios (from ‐30 % to ‐155.9 %): reducing import

of fossil fuels (and electricity from the national grid) means reducing CO2‐emission in parallel. LFC in

most cases is very close to the value of the "Current Scenario", ensuring electrical grid stability.

Electrical import moves from 4.63 GWh of “Current Scenario” to a range of 1.92 – 9.68 GWh,

electrical export from 7.87 to a range of 6.48 – 22.65 GWh.


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Table 26: CEIS: best scenarios in terms of ESD, comparison with “Current Scenario”

The electrical resource mix sees a moderate PV capacity increase for lower targets (‐0.15 to ‐0.26)

while most of the highest targets (‐0.30 to ‐0.36) make widely use of this technology. Wood CHP is

negligible only for ‐0.15 to ‐0.17 target instead for ‐0.20 to ‐0.37 and is preferred to individual wood

boiler in the use of local limited wood resources (reaching a significant maximum capacity of 13,125

kWel).

In the thermal sector individual oil and LPG boilers move to zero capacity. Individual wood boiler is

maximised for ‐0.15 to ‐0.22 targets, leaving space to wood CHP for the highest values. GSHP is

always widely introduced, it is not only cheap and green but also allows for an optimal use of local

resources.

Target scenarios ‐0.15 to ‐0.27 do not introduce a significant number of electric cars (from 3 to 314):

it is more cost effective to invest in other technologies such as wood individual boiler, GSHP, PV,

wood CHP. Instead in order to reach the highest targets ‐0.30 to ‐0.32 and in particular ‐0.35 to ‐

Scn. ESD ΔAC

(KEuro)

CO2

Emission

(Kt)

LFC PV (kWe)

Wood

CHP

(kWe)

GSHP

(kWth)

Oil

boiler

(kWth)

LPG

boiler

(kWth)

Wood

boiler

(kWth)

Petrol

Cars

Diesel

Cars

Electr

Cars

Curr 0.51 0.000 15,275 13.092 0.48 5,000 0 0 9,155 3,431 14,306 2,762 2,094 0

ESD15A 0.36 ‐0.150 13,456 9.162 0.49 5,261 151 7,008 97 406 15,657 2,759 2,091 6

ESD15B 0.34 ‐0.166 13,829 8.480 0.49 5,363 320 6,195 139 280 16,706 2,693 2,042 121

ESD15C 0.34 ‐0.169 13,836 8.509 0.48 5,015 44 5,216 597 132 18,278 2,669 2,023 164

ESD20A 0.30 ‐0.202 15,400 4.727 0.57 8,625 2,414 4,678 56 737 15,467 2,755 2,089 12

ESD20B 0.30 ‐0.210 15,429 6.665 0.46 5,462 2,789 4,554 278 434 15,171 2,684 2,035 137

ESD20C 0.29 ‐0.212 15,078 3.654 0.61 9,765 1,865 4,282 67 146 17,463 2,754 2,088 14

ESD25A 0.25 ‐0.253 17,112 4.572 0.45 6,007 6,066 4,013 277 109 11,394 2,752 2,086 18

ESD25B 0.25 ‐0.258 16,812 4.945 0.42 5,182 5,923 3,843 90 4 12,156 2,760 2,093 3

ESD25C 0.25 ‐0.261 17,793 4.326 0.44 6,007 6,168 3,910 277 109 11,394 2,583 1,959 314

ESD30A 0.20 ‐0.304 23,238 ‐2.958 0.68 12,868 12,489 4,010 34 693 1,425 2,467 1,870 519

ESD30B 0.20 ‐0.306 23,533 ‐5.477 0.81 17,254 9,937 4,128 202 47 5,550 2,075 1,573 1,208

ESD30C 0.19 ‐0.320 23,521 ‐3.216 0.66 12,303 13,125 3,859 105 25 1,292 2,407 1,825 624

ESD35A 0.15 ‐0.352 30,718 ‐7.312 0.82 17,663 9,036 3,875 78 397 7,058 10 8 4,838

ESD35B 0.15 ‐0.353 30,556 ‐6.601 0.79 16,648 9,036 3,875 78 397 7,058 10 8 4,838

ESD35C 0.15 ‐0.359 26,934 0.304 0.43 6,504 10,485 3,482 63 133 5,753 830 629 3,397


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0.37 it is necessary to involve a deep transformation also in the transport sector moving from fossil

fuels engines to electric engines.

Figure 37: CEIS: best scenarios in terms of ESD, comparison with “Current Scenario”

3.1.3.4.5 Final overview of addressed technologies

We are now ready to provide an overview of the implementation possibility for the different

addressed technologies:

There are still possibilities to increase the capacity of PV (it is decided to limit the maximum

capacity to 42.275 MW): this technology is suggested to be increased in several of the

proposed “best AC scenarios” and “best ESD scenarios”, allows considerable benefits in

terms of CO2‐emission reduction (in particular for the highest targets);


interesting investigate a further diffusion of individual wood boilers and the introduction of

wood ORC combined heat and power (wood ORC CHP) (it is necessary to respect the limit of


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56.873 GWh/year of wood consumption): wood resources appear economically very

attractive for individual boilers but not for CHP. Maximum exploitation of wood induces also

benefits in terms of CO2‐emission reduction and ESD. Wood mCHP is a more expensive

alternative of PV for the “highest CO2‐emission targets” while the “highest ESD reduction

targets” make widely use of this technology;

An interesting solution for the thermal sector (space heating and HSW) could be the use of

ground source heat pump (GSHP): this technology is always widely introduced, it is cheap,

green and also allowed an optimal use of local resources;

The transport sector could be radically transformed increasing the use of an alternative

energy carrier such as electricity: the investment necessary to replace oil cars with electric

cars is economically unattractive in the current market condition. Target CO2‐emission

scenarios from ‐30.0 % to ‐103.8 % and ESD from ‐0.15 to ‐0.27 do not introduce a significant

number of electric cars: it is more cost effective to invest in other technologies such as wood

individual boilers, GSHP, PV, wood CHP. Instead in order to reach the highest ESD targets ‐

0.30 to ‐0.32 and in particular ‐0.35 to ‐0.37 it is necessary to involve a deep transformation

also in the transport sector moving from fossil fuels engines to electric engines.


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3.1.3.5 Results for CEDIS Consortium

3.1.3.5.1 Introduction

In the simulation 385 “Future Optimised Scenarios” are found. In Figure 38 the Pareto‐front with

the four optimised objectives (AC, CO2 emission, ESD, LFC) is visualised. Figure 38 also visualises the

comparison between “Current Scenario” (in grey cube) and “Future Optimised Scenarios”.

Figure 38: CEDIS: Pareto‐front and comparison “Current Scenario”/”Future Optimised Scenarios”

It is not possible to discuss all of the 385 “Future Optimised Scenarios”. As for CEIS also for CEDIS

some significant scenarios and their trends will be presented in three different categories: “best AC

scenarios”, “target CO2‐emission reduction scenarios” and “target ESD reduction scenarios” in terms


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of CO2‐emission and ESD. Finally, we present a general discussion about the implementation

possibility for the different addressed technologies (decision variables).

3.1.3.5.2 Best scenarios in terms of Annual Cost

The most interesting parameter is definitely annual cost (AC), cheaper scenarios are the most

attractive for policy makers (and their communities). In Table 28 and in Figure 39 the best 15

scenarios in terms of AC are identified. Figure 61 in appendix also illustrates the comparison of

different objective values for these best 15 scenarios with current scenario. This study suggests

consistent economical savings (from ‐4.4 % to ‐8.6 %). It is also possible to both minimise energy

cost while reducing CO2‐emission (from ‐24.3 % to ‐29.9 %). In other words several energy scenarios

greener and cheaper than the current one are possible. Also ESD is highly improved, these scenarios

are able to import from ‐16% to ‐19% less energy resources from outside, mainly thanks to the

increase in the use of local wood and partial electrification of the thermal sector (through heat

pumps powered by local hydro, PV and biogas electricity). LFC in most cases is very close to the

value of the "Current Scenario", ensuring electrical grid stability. Electrical import increases from

21.08 GWh of “Current Scenario” to a range of 24.17 – 27.41 GWh, electrical export moves from

2.48 GWh to a range of 2.10 – 4.75 GWh.


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Table 27: CEDIS: Best 15 scenarios in terms of AC, comparison with “Current Scenario”

The electrical resource mix of “Current Scenario” (local production + import) maintains its

competitiveness also in “Future AC Optimised Scenarios”. While the introduction of gas mCHP

appears often limited (1 – 787 kWel for a yearly production of 0.01 – 3.49 GWhel), the increase of PV

capacity is suggested in several of the proposed scenarios from 5,631 kWel (+1.2 %, yearly

production of 6.03 GWh) to 9,380 kWel (+68.55 %, yearly production of 10.05 GWh).

Instead, the thermal sector is deeply transformed. While individual oil boiler reach approximately

zero capacity, individual gas boilers are greatly reduced but maintain a not negligible capacity, this

is due to less expensive fuel cost of gas than oil (86 €/MWh vs 145 €/MWh). In other words it is

suggested to dismiss almost all actual oil and most gas individual boiler capacity, because fuel costs

are too high compared to other alternative energy carriers. What gains importance is instead the

use of local wood and the electrification of the thermal sector. Individual wood boiler capacity is

maximised in almost all 15 proposed scenarios, wood consumption moves from 21.83 GWh of

Scn.AC

(KEuro)

Imp

(%)

CO2

Emission

(Kt)

LFC ESD PV (kWe)

Gas

mCHP

(kWe)

GSHP

(kWth)

Oil boiler

(kWth)

Gas

boiler

(kWth)

Wood

boiler

(kWth)

Petrol

Cars

Diesel

Cars

Electr

Cars

Curr 15,272 0.0 21.630 0.55 0.72 5,565 0 0 3,688 13,175 7,903 2,537 1,923 0

AC1 13,963 ‐8.6 16.363 0.60 0.56 5,631 1 6,912 289 1,993 12,117 2,536 1,922 2

AC2 14,030 ‐8.1 16.373 0.59 0.56 5,793 46 6,410 3 2,917 12,165 2,526 1,915 19

AC3 14,138 ‐7.4 16.262 0.58 0.55 5,803 194 6,669 289 1,993 12,191 2,530 1,918 12

AC4 14,159 ‐7.3 16.092 0.61 0.56 6,929 24 7,555 585 1,533 11,279 2,537 1,923 0

AC5 14,195 ‐7.0 16.362 0.58 0.56 5,987 230 6,176 3 3,110 12,045 2,525 1,914 21

AC6 14,261 ‐6.6 16.257 0.58 0.55 5,655 304 8,240 123 1,047 10,782 2,491 1,888 81

AC7 14,311 ‐6.3 16.052 0.59 0.56 7,182 183 6,618 128 2,923 11,516 2,525 1,914 21

AC8 14,351 ‐6.0 15.902 0.57 0.54 6,574 439 7,872 520 471 11,310 2,537 1,923 0

AC9 14,369 ‐5.9 16.306 0.55 0.55 5,633 543 6,320 289 1,993 12,191 2,530 1,918 12

AC10 14,407 ‐5.7 15.283 0.60 0.55 8,596 195 6,342 42 2,675 12,243 2,536 1,922 2

AC11 14,460 ‐5.3 15.639 0.59 0.55 7,322 329 8,214 122 1,047 10,782 2,491 1,888 81

AC12 14,462 ‐5.3 16.162 0.55 0.54 5,926 694 7,699 520 484 11,172 2,537 1,923 0

AC13 14,515 ‐5.0 15.175 0.61 0.55 9,380 154 6,419 0 3,130 11,779 2,525 1,914 21

AC14 14,588 ‐4.5 15.152 0.59 0.54 8,729 327 6,669 48 2,260 11,964 2,511 1,903 46

AC15 14,601 ‐4.4 15.889 0.54 0.53 6,574 787 7,523 520 484 11,298 2,537 1,923 0


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“Current Scenario” to a range of 29.78 – 33.81 GWh (close to the constraint of 33.828 GWh). While

gas mCHP appears economically not so attractive it is observed that to have a very cost effective

scenario a wide introduction of GSHP is interesting. GSHP thermal capacity ranges between 6,176

kW and 8,240 kW. In the proposed “Future AC Optimised Scenarios” individual wood boiler covers

44 – 49 % of the heat demand (from 32% of “Current Scenario”) while GSHP 37 – 50 %.

Concerning the transport sector, the number of introduced electric cars is almost negligible (from 0

to 81). Indeed, the investment necessary to replace oil cars with electric cars is economically

unattractive in the current market condition.

Figure 39: CEDIS: best 15 scenarios in terms of AC (2‐16), comparison with “Current Scenario” (1)

3.1.3.5.3 Target scenarios in terms of CO2‐emission reduction

However annual cost is not always the only parameter considered. Indeed, many communities

commit themselves to reach environmental targets: this study suggests several scenarios with


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ambitious CO2‐emission reduction (Table 28 and Figure 41). In particular three less costly scenarios

for targets between ‐30 % to ‐35 % and ‐100 % to ‐105 % are proposed. In order to reach the highest

values it is necessary to progressively increase the use of greener technologies, this leads to AC

growth (Figure 40).

Figure 40: CEDIS: “Future Optimised Scenarios” (blue) and correlation between annual cost and

CO2‐emission reduction (%). In red “Current Scenario”

ESD is deeply improved in all proposed scenarios through the reduction of fossil fuels import, the

increasing in the use of local wood and local PV production. LFC is close to the value of the "Current

Scenario" only for lower “CO2‐emission reduction targets”. In all other cases the high fraction of

non‐programmable RES production (PV) leads to poor load following capacity, electrical export

increases and may require an expensive adaption of the grid transmission capacity. Electrical import

moves from 21.08 GWh of “Current Scenario” to a range of 6.57 – 24.90 GWh, electrical export from

2.48 to a range of 4.60 – 45.49 GWh.


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Table 28: CEDIS: best scenarios in terms of CO2‐emission, comparison with “Current Scenario”

The electrical resource mix sees the gradual PV capacity increasing, in particular the highest targets

make widely use of this technology. Gas mCHP is an alternative but appears more expensive.

As expected in the thermal sector individual oil and (to a less extent) gas boilers reach marginal

capacity. Individual wood boiler capacity is maximised from the lower targets. GSHP is widely

introduced, it is not only a cheap technology, as seen previously, but also a green technology.

Most target scenarios from ‐30.1 % to ‐104.4 % do not introduce a significant number of electric

Scn.

CO2

Emission

(Kt)

Imp

(%)

AC

(KEuro)LFC ESD

PV

(kWe)

Gas

mCHP

(kWe)

GSHP

(kWth)

Oil

boiler

(kWth)

Gas

boiler

(kWth)

Wood

boiler

(kWth)

Petrol

Cars

Diesel

Cars

Electr

Cars

Curr 21.630 0.0 15,272 0.55 0.72 5,565 0 0 3,688 13,175 7,903 2,537 1,923 0

EM30A 15.110 ‐30.1 15,298 0.53 0.52 8,729 1,070 5,926 305 2,003 11,964 2,475 1,876 109

EM30B 14.489 ‐33.0 15,109 0.57 0.52 9,367 743 7,062 76 975 12,006 2,453 1,859 148

EM30C 14.214 ‐34.3 14,853 0.60 0.52 10,291 465 8,077 120 370 11,463 2,491 1,888 81

EM40A 12.698 ‐41.3 15,369 0.66 0.52 15,145 495 8,314 563 28 10,963 2,524 1,913 23

EM40B 12.581 ‐41.8 15,431 0.68 0.53 15,920 331 8,310 197 1,128 10,482 2,491 1,888 81

EM40C 12.488 ‐42.3 15,488 0.69 0.54 16,465 198 6,378 0 3,130 11,773 2,481 1,881 98

EM50A 10.813 ‐50.0 19,333 0.57 0.47 18,166 4,532 3,139 62 1,118 12,080 2,307 1,749 404

EM50B 10.732 ‐50.4 21,600 0.55 0.48 16,026 2,690 6,147 126 1,555 9,830 1,274 965 2,221

EM50C 10.542 ‐51.3 19,874 0.65 0.50 22,456 5,257 4,966 105 1,671 7,654 2,442 1,851 167

EM60A 8.473 ‐60.8 18,687 0.68 0.47 25,613 3,308 5,514 52 542 10,941 2,459 1,864 137

EM60B 8.315 ‐61.6 18,038 0.81 0.51 28,858 2,015 4,598 1 4,042 10,805 2,525 1,914 21

EM60C 8.030 ‐62.9 18,075 0.79 0.50 27,648 1,576 7,594 6 1,165 9,840 2,354 1,784 322

EM70A 6.221 ‐71.2 18,959 0.85 0.48 32,718 2,611 4,925 16 2,151 11,298 2,482 1,882 96

EM70B 5.552 ‐74.3 19,494 0.88 0.48 34,450 3,081 4,454 16 2,151 11,298 2,480 1,880 100

EM70C 5.521 ‐74.5 19,411 0.88 0.48 34,434 2,967 4,454 16 2,141 11,479 2,480 1,880 100

EM80A 4.241 ‐80.4 17,884 1.05 0.50 38,432 416 8,218 32 1,288 10,497 2,507 1,900 53

EM80B 3.797 ‐82.4 18,643 1.04 0.49 39,126 1,073 6,586 174 1,611 11,496 2,462 1,866 132

EM80C 3.655 ‐83.1 18,567 1.05 0.49 38,432 501 8,320 61 475 10,996 2,327 1,764 369

EM90A 1.948 ‐91.0 18,911 1.17 0.50 44,023 389 7,165 294 1,520 11,622 2,409 1,826 225

EM90B 1.892 ‐91.3 19,881 1.07 0.47 44,070 2,515 6,691 141 311 10,504 2,535 1,921 4

EM90C 1.708 ‐92.1 18,766 1.18 0.50 45,495 457 8,240 89 1,386 10,247 2,491 1,888 81

EM100A ‐0.313 ‐101.4 19,367 1.29 0.50 50,960 457 8,240 89 1,386 10,247 2,491 1,888 81

EM100B ‐0.760 ‐103.5 19,410 1.29 0.49 51,833 634 9,149 33 338 9,722 2,523 1,912 25

EM100C ‐0.952 ‐104.4 19,632 1.30 0.49 51,725 463 8,109 591 107 11,213 2,457 1,862 141


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cars: it is more cost effective to invest in other technologies such as wood individual boilers, GSHP,

PV, gas mCHP.

Figure 41: CEDIS: best scenarios in terms of CO2‐emission, comparison with “Current Scenario”

3.1.3.5.4 Target scenarios in terms of ESD reduction

ESD suggests which technologies can optimise the use of local energy resources and in which entity

we can rely on them. This study suggests several scenarios with ambitious ESD reduction (Table 29

and Figure 43). In absolute terms the best identified scenario has an ESD of 0.42, this means that it

needs 42 % of external energy resources to cover all the local energy demand (electricity, thermal,

transport). Afterwards, three less costly scenarios for targets between ‐0.15 to ‐0.17 and ‐0.30 to ‐

0.32 (for last target ‐0.30 to ‐0.32 there is only one scenario) are proposed in detail. As for CO2‐

emission reduction, in order to reach the highest values it is necessary to progressively increase the


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use of costly technologies, this leads to AC growth (Figure 42).

Figure 42: CEDIS: “Future Optimised Scenarios” (blue) and correlation between annual cost and

ESD reduction. In red “Current Scenario”

CO2‐emissions are highly improved in all proposed scenarios (from ‐24.3 % to ‐135.6 %): reducing

import of fossil fuels (and electricity from national grid) means reducing CO2‐emission in parallel.

LFC in most cases (except target ‐0.30 to ‐0.32) is very close to the value of the "Current Scenario",

ensuring electrical grid stability. Electrical import moves from 21.08 GWh of “Current Scenario” to

a range of 8.09 – 27.35 GWh, electrical export from 2.48 to a range of 2.10 – 55.98 GWh.


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Table 29: CEDIS: best scenarios in terms of ESD, comparison with “Current Scenario”

The electrical resource mix sees a negligible PV capacity increasing for the lower target (‐0.15 to ‐

0.17) while most of the highest targets (‐0.20 to ‐0.32) make widely use of this technology. Gas

mCHP is negligible only for ‐0.15 to ‐0.17 target instead in the interval ‐0.20 to ‐0.32 is increasingly

introduced.

In the thermal sector individual oil and gas boiler move to zero capacity, in particular for the highest

targets. Individual wood boiler is always maximised. GSHP is always widely introduced, it is not only

cheap and green but also allows for an optimal use of local resources.

Target scenarios ‐0.15 to ‐0.27 do not introduce a significant number of electric cars: it is more cost

effective to invest in other technologies such as wood individual boiler, GSHP, PV, gas mCHP. Instead

in order to reach the highest target ‐0.30 to ‐0.32 it is necessary to involve a deep transformation

also in the transport sector moving from fossil fuels engines to electric engines.

Scn. ESD ΔAC

(KEuro)

CO2

Emission

(Kt)

LFC PV (kWe)

Gas

mCHP

(kWe)

GSHP

(kWth)

Oil

boiler

(kWth)

Gas

boiler

(kWth)

Wood

boiler

(kWth)

Petrol

Cars

Diesel

Cars

Electr

Cars

Curr 0.72 0.000 15,272 21.630 0.55 5,565 0 0 3,688 13,175 7,903 2,537 1,923 0

ESD15A 0.56 ‐0.156 14,030 16.373 0.59 5,793 46 6,410 3 2,917 12,165 2,526 1,915 19

ESD15B 0.56 ‐0.158 13,963 16.363 0.60 5,631 1 6,912 289 1,993 12,117 2,536 1,922 2

ESD15C 0.55 ‐0.165 14,138 16.262 0.58 5,803 194 6,669 289 1,993 12,191 2,530 1,918 12

ESD20A 0.52 ‐0.200 15,109 14.489 0.57 9,367 743 7,062 76 975 12,006 2,453 1,859 148

ESD20B 0.52 ‐0.200 15,369 12.698 0.66 15,145 495 8,314 563 28 10,963 2,524 1,913 23

ESD20C 0.51 ‐0.206 15,256 15.336 0.49 7,719 1,457 5,686 196 1,645 12,213 2,520 1,910 30

ESD25A 0.47 ‐0.253 18,687 8.473 0.68 25,613 3,308 5,514 52 542 10,941 2,459 1,864 137

ESD25B 0.47 ‐0.253 18,514 12.835 0.45 12,585 4,661 4,102 4 135 11,484 2,371 1,797 292

ESD25C 0.46 ‐0.262 19,011 12.464 0.44 12,670 3,733 5,466 21 19 10,928 2,056 1,558 846

ESD30A 0.42 ‐0.302 31,097 ‐7.690 1.29 58,717 3,430 5,360 72 78 11,432 87 66 4,307


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Figure 43: CEDIS: best scenarios in terms of ESD, comparison with “Current Scenario”

3.1.3.5.5 Final overview on addressed technologies

We are now ready to provide an overview of the implementation possibility for the different

addressed technologies:

There are still possibilities to increase the capacity of PV (it is decided to limit the maximum

capacity to 61.656 MW): this technology is suggested to be increased in several of the

proposed “best AC scenarios” and “best ESD scenarios”, allows considerable benefits in

terms of CO2‐emission reduction (in particular for the highest targets);


interesting to investigate a further diffusion of individual wood boiler (it is necessary to

respect the limit of 33.828 GWh/year of wood consumption): wood resources appear

economically very attractive for individual boilers. Maximum exploitation of wood induces

also benefits in terms of CO2‐emission reduction and ESD;

Diffused gas SOFC mCHP could be a viable option in CEDIS thanks to the availability of gas


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grid and a low gas price: gas mCHP appears limited in “best AC scenarios”, it is a more

expensive alternative of PV for the “highest CO2‐emission targets” while the “highest ESD

reduction targets” make widely use of this technology;

An interesting solution for the thermal sector (space heating and HSW) could be the use of

ground source heat pump (GSHP): this technology is always widely introduced, it is cheap,

green and also allowed an optimal use of local resources;

The transport sector could be radically transformed increasing the use of an alternative

energy carrier such as electricity: the investment necessary to replace oil cars with electric

cars is economically unattractive in current market conditions. Target CO2‐emission

scenarios from ‐30.1 % to ‐104.4 % and ESD from ‐0.15 to ‐0.27 do not introduce a significant

number of electric cars: it is more cost effective to invest in other technologies such as wood

individual boiler, GSHP, PV, gas mCHP. Instead in order to reach the highest ESD target ‐0.30

to ‐0.32 it is necessary to involve a deep transformation also in the transport sector moving

from fossil fuels engines to electric engines.

3.1.4 Summary and conclusions

The purpose of the analysis in Trento was to determine the optimisation potential in the Italian

pilot sites, starting from the existing energy infrastructure, including generation, distribution and

demand aspects for electrical, thermal and transport sectors. A detailed energy flow model for the

“Current Scenario” is set up, based on hourly data received from local stakeholder or specifically

calculated for the CIVIS project.



way. There is an additional margin (+ 30.4 % in CEIS and + 35.5 % in CEDIS) to increase the use of

local wood. Local PV availability is considerable both in CEIS and in CEDIS areas.


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ESD)






to ‐11.9 %) and in CEDIS (from ‐4.4 % to ‐8.6 %). It is also possible to both minimise energy cost while

reducing CO2‐emission (from ‐28.1 % to ‐70.9 % in CEIS, from ‐24.3 % to ‐29.9 % in CEDIS). In other


Also ESD is highly improved, these scenarios are able to import from ‐14 % to ‐20 % in CEIS and from

‐16 % to ‐19 % in CEDIS fewer energy resources from outside. LFC in most cases is very close to the





scenarios.




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boiler covers 56‐73 % of heat demand (from 53 % of “Current Scenario”) while GSHP 20‐41 %; in the

proposed “CEDIS Future AC Optimised Scenarios” individual wood boiler cover 44‐49 % of heat

demand (from 32 % of “Current Scenario”) while GSHP 37‐50 %.

Concerning the transport sector, the number of introduced electric cars is almost negligible both in

CEIS and CEDIS. Indeed, the investment necessary to replace oil cars with electric cars is

economically unattractive in the current market conditions.

However, annual cost is not always the only parameter considered. Indeed, many communities

commit themselves to reach environmental targets (see for example the Covenant of Mayors): this

study suggests several target scenarios with ambitious CO2‐emission reductions. In order to reach

the highest values it is necessary to progressively increase the use of greener technologies (mainly

PV, individual wood boiler, GSHP, electric cars), this leads to AC growth.

Finally, ESD suggests which technologies can optimise the use of local energy resources and in which

entity we can rely on them. This study suggests several target scenarios with ambitious ESD

reduction. As for CO2‐emission reduction, in order to reach the highest values it is necessary to

progressively increase the use of costly technologies (mainly PV, wood CHP, GSHP, electric cars),

which leads to AC growth.

The results could be further studied by the policy makers of the specific communities and finally an

optimum scenario could be chosen.


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3.2 Swedish test sites

One of the objectives of work package 2 is the determination of the optimal energy system of the

pilot sites. This issue was touched upon in D2.1a but not addressed in detail. Thus, in this section a

model‐based optimisation approach is presented which can be applied to the pilot sites in the CIVIS

project. It is the overarching aim of the model approach to determine the optimal energy system of

the pilot sites in a way that the aspects of optimisation and storage are addressed in an appropriate

way.

Complementary to the methodological approach set out in section 3.1 the underlying reasoning in

this section focusing on the Swedish test sites investigates in‐depth the aspects of optimisation and

storage. The analysis of the Italian test sites in section 3.1 is carried out in EnergyPLAN and includes

hydro, PV and individual boilers in the current scenario, and gas‐driven micro‐CHP units as well as

ground source heat pumps in the future scenarios. The analysis in this section further comprises

thermal and battery storage. It can thus be considered as an extension and an enlargement of detail

of the approach in the previous section. At the same time, the optimisation objective of the total

annual cost coincides for both approaches. However, other than for CO2‐emission the approach

presented in this section is not subject to the remaining objectives investigated in section 3.1, i.e.

load following capacity and energy system dependency.

3.2.1 Methodology

In the energy modelling in this section, the energy system of the pilot sites is modelled in an abstract

way focusing on its key components, i.e. the supply and demand structure. For this, a set of

technologies is defined that serves as potential energy conversion units for the supply of electricity

and heat in the optimal energy superstructure. Table 30 gives an overview of the technologies that

can be chosen in the optimisation.


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Gas boiler Micro‐CHP PV Thermal storage Battery storage

Capacity unit kWth kWel kWel l kWhel

Table 30: Overview of the technologies to be chosen in the optimisation approach

In addition, electricity can be sourced from the electricity grid which is model‐endogenous but

neither represents a technology nor an investment decision. Likewise, the building objects which

constitute the consumption side in the energy system under investigation are integrated in the

model using their total annual levels of electricity and heat consumption as well as their distribution

over time implemented as load profiles. In the modelling approach, investment as well as dispatch

decision are taken, i.e. the optimal energy supply technologies are selected as well as their operation

determined. Figure 44 gives an overview of the methodology of the optimisation of the energy

system of the pilot sites.

Figure 44: Overview of the methodology of the optimisation of the energy system of the pilot sites

(source: own illustration based on [39])

Data from pilot sites

Building

Monitoring data

Technology

Design data

Optimisation model of the capacity and dispatch (MILP)

Total annual cost

Data processing

Output

Annual emission of CO2

Capacity

Dispatch

Building


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It can be seen that the model relies on data input which is gained from the pilot sites. The data from

the field trial is further differentiated between design data and monitoring data. This is to say that

metered data is taken as well as data which is known beforehand, e.g. techno‐economic

characteristics of energy plants or fuel prices also based on external sources. The data is processed

to be appropriate for the data format the model requires. The technologies are described in their

techno‐economic properties. At the core of the methodological approach the optimisation model

of the capacity and dispatch is situated in Figure 44. It is based on Mixed‐Integer Linear

Programming (MILP). As for the model output several model results are obtained. Among them the

capacity and dispatch of technologies can be found as well as the total annual cost and annual

emission of CO2. For a detailed qualitative description as well as energetic characterisation of the

building stock in the pilot sites the reader is referred to section 2 in D2.1a.

The model horizon is 20 years with the base year being 2015. The temporal resolution amounts to

15 minutes. The year is further not modelled in full chronology based on 52 weeks but partitioned

into representative segments meaning that 3 weeks from the summer, winter and spring/autumn

season constitute the time base in the model respectively. This amounts to 9 weeks considered in

the model which are extended to the full year scale applying a weight factor. Thus results refer to a

full year.

The objective function f of the optimisation model of the capacity and dispatch is defined according

to equation 3‐1. It can be seen that the total system cost summed over every energy supply unit

over the planning horizon is incurred.

min ∑ , , ∝∙ ∑ , , , , (3‐1)

where

, … Capital‐related annual cost of energy supply unit p

, … Fixed annual cost of energy supply unit p


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, , … Variable operation cost of p in time step t

, , … Revenue from p in time step t

∝ … Weight factor for aggregation to a full calendrical year

As for the constraints of the optimisation model, the description is limited to a selection of only the

most relevant ones. Therefore, the fulfilment of the demand for electricity is formulated in equation

3‐2. There it can be seen, that the demand can be met by either by the generation of electricity‐

producing units, i.e. a CHP and a PV unit, the battery storage or by the sourcing from the electricity

grid.

, , 1 ∙ , ∀ ∈ (3‐2)

where

, … Electricity output of the CHP unit for internal use (self‐consumption) in time step t

, … Electricity output of the PV unit for internal use (self‐consumption) in time step t

, … Electricity output of the battery storage in time step t

… Electricity input from the grid in time step t

… Battery storage loss

To meet the thermal demand induced by space heat and domestic hot water use at every point in

time the equation 3‐3 ensures that the thermal demand is covered by the heat output of the thermal

(water) storage in time step t. According to the assumption, every unit of heat generated by the CHP

unit and the gas boiler is passed through the thermal storage in the first place.

, ∀ ∈ (3‐3)


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where

, … Heat output of the thermal storage in time step t

… Demand for space heat and domestic hot water in time step t

The balance equations for the thermal storage are described in the equations 3‐4 and 3‐5. Thus the

storage level of the successive time step equals the one of the preceding time step diminished by a

loss and augmented by the storage inflow and reduced by the storage outflow.

, 1 ∙ , 1 , , ∀ ∈ (3‐4)

where

, … Storage level of the thermal storage in time step t

, … Heat input of the thermal storage in time step t

… Thermal loss of the storage in time interval

The inflow of the thermal storage is therefore comprised of the heat output of the gas boiler and

the CHP unit as can be seen from the equation 3‐5.

, ∀ ∈ (3‐5)

where

… Heat output of the gas boiler in time step t

… Heat output of the CHP unit in time step t

The balance equations for the battery storage are described in the equations 3‐6 and 3‐7. Again, the


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storage level of the successive time step equals the one of the preceding time step augmented by

the storage inflow and reduced by the storage outflow.

, , 1 , , ∀ ∈ (3‐6)

where

, … Storage level of the battery storage in time step t

, … Electricity input of the battery storage in time step t

The inflow of the battery storage is therefore comprised of the electricity output of the CHP and the

PV unit for internal use as can be seen from the equation 3‐7.

, 1 ∙ , , ∀ ∈ (3‐7)

For a more detailed mathematical description of the presented optimisation model with regard to

e.g. the operation characteristics of the CHP unit the reader is referred to [39].

It is important to highlight that the approach set out is a “greenfield” approach, i.e. it does not take

into account the energy supply structure prevalent at the time of investigation. It thus refers to a

situation where either investment decisions for the energy infrastructure for the pilot sites are firstly

taken which originally occurred in the past or in which investment for substitution has to be made.

It should thus be borne in mind that the model logic does not fully represent the real case in the

first place. However, to address the question of the optimal energy system one has to consider the

case of the highest degree of freedom which is given in the “greenfield” case.

The methodological approach is rooted in the theory of mathematical optimisation. In order to

describe the energy system of the pilot sites, an objective function has to be declared as well as


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constraints defined. Therefore, a criterion has to be found which has to be cardinal scaled and can

thus be maximised or minimised. Second, restrictions have to be defined that account for

constraints of the energy system such as the availability of fuels or demand for energy services and

operation constraints.

The criterion deemed most appropriate for the optimisation problem in the present context is found

to be the total system cost incurred for energy supply. Thus a least‐cost approach is chosen that

takes into account all relevant cost that are incurred throughout the period under consideration. In

particular that means that capital‐related cost as well as cost that are demand‐ and operation‐

related are part of the objective function to be minimised.

3.2.2 Definition of use cases

For the application of the methodology a data framework has to be defined. Use cases prove to be

a means to validate the methodology using a bandwidth of parameter variation. Therefore, several

use cases are defined in this section. The pilot site of Fårdala provides historic data for the

consumption of space heat and domestic hot water in the period from September 2012 to

September 2013 for a total of 178 buildings (cf. Figure 45). For Hammarby Sjöstad still no data could

be retrieved at the time of compiling the deliverable D2.1b. Thus, the use cases are limited to the

pilot site of Fårdala. Figure 45 gives an overview of the consumption for space heat and hot water

in the 178 buildings in Fårdala. It can be seen that there exists a great bandwidth of consumption

values.


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Figure 45: Overview of the consumption for space heat and hot water in the buildings in Fårdala in

the period from September 2012 to September 2013

It would be a cumbersome task to perform model runs for all existent buildings in Fårdala. Therefore,

the runs are limited to a selection of cases. It is deemed appropriate to focus on extreme cases, i.e.

minimum and maximum as well as the average performers in terms of heat consumption. Therefore,

altogether three cases for decentralised heat supply are defined that reflect the minimum, average

and maximum consumption of space heat and hot water in the metering period. In addition, the

community of Fårdala is investigated considering the option of centralised heat supply, i.e. energy

supply units that are dimensioned and dispatched in a way to cover the electricity and heat demand

of the entire community.

Contrary to heat data, for electricity a lack of metered data for the Swedish test sites has to be

stated also as for the date of 31st July 2015 which is the reference date for data collection. Every

0

5.000

10.000

15.000

20.000

25.000

30.000

1 7

13

19

25

31

37

43

49

55

61

67

73

79

85

91

97

103

109

115

121

127

133

139

145

151

157

163

169

175

Consumption for space heating an

d domestic hot

water [kWh/a]

Building

Space heat Domestic hot water


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data that is retrieved thereafter cannot be integrated in the analysis anymore. To be able to

integrate mandatory electricity data nevertheless, a literature study was conducted to identify

appropriate data comparable to the present use case. [40] We performed a study among 400

households in Sweden examining houses and apartments which differ amongst other things in

occupancy and electricity‐based heating systems. As a result, the therein cited situation of a house

which is occupied by a family with members aged between 26 and 64 years and no electricity‐based

heating is found to be representative also for the situation in Fårdala. The cited total amount of

annual electricity consumption is thus also assumed for the three cases of decentralised supply and

as a multiple in the case of centralised supply investigated in Fårdala. Due to the lack of also a

temporal pattern of electricity consumption a load curve is derived from hourly load values from

[41]. Table 31 summarises the annual energy consumption for the respective energy forms and the

use cases.

heat supply [kWh/(year HH)]

Decentralised Centralised

Minimum Average Maximum

Space heat and domestic hot water 8,574 14,657 26,274 2,609,027

Electricity 4,143 4,143 4,143 737,454

Table 31: Energy use in subdivisions for three cases of decentralised heat supply and the case of

centralised heat supply in Fårdala

In addition, the relevant key parameters for the results of the optimisation runs to be presented in

section 3.2.3 are set out in Table 32.

Parameter Unit Value

Electricity price (for households) EUR2015/kWh 0.187


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Gas price (for households) EUR2015/kWh 0.114

Interest rate % p.a. 5

Time horizon a 20

Table 32: Key parameters as assumptions for the optimisation runs (Source: own calculations

based on [42])

3.2.3 Results

Figure 46 puts into context the technology selection and capacity dimension for the three use cases

of decentralised heat supply the (average) case of centralised supply. Several observations can be

made from this graph. All generating technologies as well as the storage options are solely chosen

in the minimum case whereas in the average and maximum case PV is not part of the optimal

solution in comparison to the minimum case. On the contrary, thermal and battery storages are part

of the optimised energy system in every case. In conclusion, cogeneration is a reasonable choice for

every case from an economic point of view.

Photovoltaic is only an option for the minimum case where the lowest thermal demand is accounted

for. Its capacity is 0.39 kWel. Yet, according to the definition in Table 31 the minimum case has the

same electricity consumption as the other cases. Likewise, the capacities of the CHP unit, the

thermal and the battery storage are the lowest in this case, amounting to 0.42 kWel, 26.3 l and 0.41

kWhel respectively. It can thus be concluded that PV as electricity‐only generating technology is a

reasonable choice in an energy system with a relatively high electricity consumption compared to

heat consumption and substitutes capacity of CHP, the other on‐site electricity generating

technology considered. This result is also due to the relative low power‐to‐heat‐ratio that amounts

to 0.4 and which in this case favours a comparatively high thermal demand in order to achieve a

high number of operating hours and therefore be profitable. By contrast, as can be seen from the


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technology selection and capacity dimension in the maximum case, PV is no longer part of the

optimised energy system. Instead, the storage options are prevalent with the greatest dimensioning

among the cases (120 l and 0.54 kWhel). This means that in this case a high number of operating

hours is targeted for the CHP unit with an excess production of electricity and heat in times of lower

demand being shifted to points in time of higher demand by electricity and heat storage. It can also

be derived that the gas boiler has its greatest capacity in the maximum case (6.41 kWth). For the

average case of electricity and heat consumption the optimal set of technologies consists of a CHP

unit and a gas boiler as well as a thermal and battery storage. This is to say that PV is not part of the

optimised system. The electricity demand is thus fully covered by the on‐site generation of the CHP

unit with a capacity of 0.58 kWel and the sourcing from the electricity grid. Likewise, the thermal

demand is met by the heat generated from the cogeneration unit and the gas boiler. As in the

maximum case, from an economic point of view this system requires storage options as the CHP

unit is not intended to be operating in a continuously flexible manner, according to the current

demand. Therefore, it is complemented by a thermal storage (44.9 l) and a battery storage (0.54

kWhel). Whereas the capacity of the CHP unit is the highest in this case the storage capacities rank

average among the three cases.

The average case of centralised heat supply relates the determined capacity of energy supply units

for the entire community to the average housing unit. It can thus be seen that the optimal system

is comprised of a CHP unit, a gas boiler and a thermal storage. PV and battery storage are not an

option anymore. This is due to mainly two reasons. First, with increasing heat demand of the

building objects PV is outperformed by CHP with the capacity of PV being substituted by the capacity

of CHP. This observation is also made for the case of decentralised supply where PV is not selected

for the average and maximum case anymore. Second, because of economies of scale CHP has a

higher share in the total thermal capacity in the case of centralised supply as in the average case of

decentralised heat supply. As a result, the electric capacity is also greater. This is why the battery


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storage is phased out of the optimal system in this case as a unit of electricity which is generated in

times of non‐existing electricity demand is no longer a scarce commodity that is shifted to points in

time of electricity demand. In summary, the overall electricity generation level is significantly higher

in the centralised case.

Figure 46: Technology selection and capacity for the three use cases of decentralised heat supply

and the (average) case of centralised supply

Table 33 further quantifies the selection and capacity dimension for the case of centralised heat

supply.

CHP Gas boiler Thermal storage

Unit kWel kWth l

Value 114 501 10,000

0 20 40 60 80 100 120 140

0 1 2 3 4 5 6 7

Min.

Avg.

Max.

Cen

tralised

supply

(avg.)

Decentralised supply

Thermal storage volume [l]

Capacity [kWel, kWth, kWhel]

Battery storage PV Gas boiler CHP Thermal storage


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Table 33: Technology selection and capacity for the use cases of centralised heat supply

In Figure 47 the total annual system cost are compared for the three cases of decentralised heat

supply and the case of centralised heat supply. The cost includes to the cost incurred for electricity

and heat supply and refers to the individual household unit. It can be observed that for centralised

heat supply the cost per household (HH) total up to 2,653 €/(a HH) whereas for decentralised supply

they sum up to 2,267 €/(a HH), 3,262 €/(a HH) and 5,162 €/(a HH) in the minimum, average and

maximum case respectively. It can thus be concluded that on average centralised heat supply leads

to total system costs that are lower by 18.7 %.

Figure 47: Comparison of the total annual system cost per household for the three cases of

decentralised heat supply and the case of centralised heat supply

It is also possible to assess the energy system not only with respect to economic criteria but also

with regard to different key indicators. In D2.1a the primary energy consumption and the emission

0

1,000

2,000

3,000

4,000

5,000

6,000

Min. Avg. Max.

decentralised supply centralised supply

Total annual system cost [€/(a HH)]


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of CO2 are defined as the key indicators for the ecological assessment of the test sites. Likewise, the

evaluation is complemented by figures indicating the flexibility potential, i.e. the degree of self‐

sufficiency and the rate of self‐consumption (cf. D2.1a). Therefore Figure 48 reports the total annual

primary energy consumption per household for the three cases of decentralised heat supply and

the case of centralised heat supply. It can be derived that it amounts to 10.1 MWh/(a HH), 17.9

MWh/(a HH) and 33.4 MWh/(a HH) for the minimum, the average and the maximum case

respectively. In comparison for the centralised supply option the total annual primary energy

consumption per household is in the range of 16.2 MWh/(a HH). As a result, comparing the obtained

figure of centralised heat supply to the one of the average case it is found that primary energy

consumption can be reduced by 9.6 % if the centralised supply is selected over the average

decentralised one.

Figure 48: Comparison of the total annual primary energy consumption per household for the three

cases of decentralised heat supply and the case of centralised heat supply

0

5

10

15

20

25

30

35

Min. Avg. Max.


Primary en

ergy consumption [MWh/(a HH)]


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Furthermore Figure 49 sets out the total annual emission of CO2 per household for the three cases

of decentralised heat supply and the case of centralised heat supply. The tendency follows the one

for the primary energy consumption. The emission of CO2 increases by ascending consumption

object. In more concrete terms, the CO2‐emission adds to 2.39 tCO2/(a HH), 4.01 tCO2/(a HH) and 7.02

tCO2/(a HH) for the three use cases of decentralised heat supply respectively. However, the emission

of CO2 per household is only 3.98 tCO2/(a HH) in the case of centralised heat supply. Again, this

implies a reduction and thus preference for the centralised solution. However, the reduction

amounts to only 0.6 %. The reduction can be attributed to the operation of the CHP unit which is

greater in the centralised case compared to the decentralised case in relative terms. Thus heat

production from the gas boiler which is assumed be less energy‐efficient, given the lower net

efficiency, is substituted by the heat production from the high‐efficiency CHP. However, this effect

is dampened by the low emission factor of electricity in Sweden, which causes the CHP unit to

substitute only a comparatively low amount of CO2 by generating electricity which it is credited for.

For electricity the emission factor is only about 105.8 gCO2/kWhel which ranks in the lower range

compared to the other European Member States.


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Figure 49: Comparison of the total annual emission of CO2 per household for the three cases of

decentralised heat supply and the case of centralised heat supply

With reference to the flexibility potential Figure 50 illustrates the degree of self‐sufficiency for three

cases of decentralised heat supply and the case of centralised heat supply. It is differentiated for

energy, which integrates heat and electricity, and for electricity. It can be observed that the

indicators follow an ascending order with increasing energy demand. Therefore, if energy is

considered the degree of self‐sufficiency is 94.1 % for the minimum case, 97.9 % in the average case

and 99.2 % in the maximum case of decentralised heat supply. This figure can even be augmented

by a central provision of heat as is derivable by the indicator being 99.5 % in the central case. For

electricity only the degree of self‐sufficiency is considerably lower and ranges between 81.4 % in

the minimum case and 96.6 % in the case of centralised heat supply. In the average and maximum

case of decentralised heat supply the indicator is 90.2 % and 93.4 %. The overall lower performance

can be explained by the heat demand making up the largest portion of energy demand and being

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

Min. Avg. Max.


Emission of CO2[tCO2/(a HH)]


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generated on‐site in any case by the CHP unit and the gas boiler.

Figure 50: Comparison of the degree of self‐sufficiency with respect to energy and electricity supply

for the three cases of decentralised heat supply and the case of centralised heat supply

Finally, Figure 51 illustrates the self‐consumption rate with respect to energy and electricity supply

for the three cases of decentralised heat supply and the case of centralised heat supply. For the

minimum case of decentralised heat supply it can be inferred that almost all self‐generated energy

is also self‐consumed and thus remains within the system boundaries of the building object.

Quantitatively, the rate of self‐consumption amounts to 99.8 % with respect to total energy and to

99.3 % referred to electricity. In the average case the indicators considerably decrease to 97.7 %

and 89.8 % meaning that a significant amount of electricity is generated for external use. As opposed

the maximum case reveals a high level of self‐consumption as the figures total up to 99.5 % and

96.1 % based on total energy and electricity. The obtained results reflect the differences in the

80

82

84

86

88

90

92

94

96

98

100

Min. Avg. Max.


Degree of self‐sufficiency [%]

energy electricity


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capacity of the CHP unit in such a way that an increasing capacity brings about a reduced self‐

consumption rate. This is why this indicator is most elevated in the minimum case and dips in the

average case. The results of the case of centralised heat supply are reminiscent of the ones of the

average case, that is to say that the rate of self‐consumption is significantly less compared to the

other cases. In total the rate of self‐consumption is 90.4 % referred to total energy and 60.4 % in

terms of electricity. Thus a large amount of electricity is fed back to the grid which is in line with the

above findings as the CHP unit is relatively over‐dimensioned compared to the decentralised

solution with energy demand being greater yet in a proportionate way. Thus the excess electricity

being cogenerated cannot be consumed on‐site but has to be fed‐back to the electricity grid in an

economic operation mode.

Figure 51: Comparison of the self‐consumption rate with respect to energy and electricity supply

for the three cases of decentralised heat supply and the case of centralised heat supply

50

55

60

65

70

75

80

85

90

95

100

Min. Avg. Max.


Self‐consumption rate [%

]

energy electricity


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Figure 52 showcases the energy dispatch of the selected technologies and the thermal and

electricity demand for the minimum case for an example week in winter. According to the chart the

CHP unit and the gas boiler contribute to the provision of heat. The heat demand profile is

observable for the week. While it follows a pattern of an approximately constant mean throughout

the weekdays it decreases significantly on the weekend. It is derivable from the chart that the CHP

unit follows the thermal load, most notably on the weekend. Most of the time during weekdays in

winter it operates at full capacity. The CHP unit is shut down only once in the course of the week.

This happens on Sunday for two hours. During peak times it can be seen that the gas boiler provides

additional capacity. This occurs in the morning three times and once in the evening. To complement

the energy supply units, the energy conversion of the PV unit and the electricity generation of the

CHP unit for direct use are also depicted in the picture. It can be seen that electricity from PV is

produced following a similar pattern every day, peaking at noon time. However, the patterns differ

in absolute levels, which is due to varying diurnal irradiation levels. The CHP unit dispatches

electricity for direct use, i.e. on‐site consumption, diametrically. This means that the higher the

generation level by the PV unit is the more the electricity generation from the CHP unit is shifted

towards external use, i.e. injection to the electricity grid. During the time interval of two hours on

Sunday when the CHP unit is not operated at all the PV unit is able to fully cover the electricity

demand. It can also be derived that whereas the CHP unit generates electricity for external use in

the course of the week there is no electricity fed‐back on the weekend. The thermal pattern and the

temporal structure of the electricity for internal use perfectly correlate in this time interval.


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Figure 52: Thermal demand and energy dispatch of technologies for the minimum case for an

example week in winter

It is also important to critically appraise the methodological approach and to point to the limitations

of the meaningfulness of the results. Several aspects have to be mentioned in this respect. Firstly,

the model is so far only validated by domestic buildings taking into account the residential buildings

in Fårdala. The inclusion of buildings from the tertiary and/or the industrial sector would have a

significant impact on the results as the demand both in level and structure would considerably

change. For instance, the thermal and electricity demand curve would be smoothed if an industrial

or tertiary site which might show a uniform energy demand was part of the energy system under

investigation. In this context the analysis of the pilot site in Hammarby Sjöstad would be very

beneficial and insightful once the data availability is sufficient. Secondly, it has to be stressed that

the approach represents a “greenfield” approach which is apt to analyse the energy system under

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Energy output [kW]

Thermal demand Electricity demand

Gas boiler CHP (thermal)

PV (internal use) CHP (electricity ‐ internal use)


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the premise that no infrastructure already exists. On the contrary, both Hammarby Sjöstad and

Fårdala are already built‐up sites. As a consequence, the determined costs are likely to be

overestimated as capital‐related costs for installation have to be incurred. In this regard it has to be

stressed that the model determines building‐specific (decentralised) and community‐specific

(centralised) solutions. Integrating also other consumers (e.g. industrial and tertiary sites) that are

locally dispersed could bring about a more centralised optimal solution, e.g. a large‐sized CHP plant

connected to a district heating network. This could bring about an improved solution with respect

to total system cost due to economies of scale. On the other hand, by applying a “greenfield”

approach the model results give a clear implication of what the idealised energy system of the

buildings would consist of. This provides decision support in case a full replacement of the energy

infrastructure (e.g. due to aging reasons) or efforts towards energy autonomy are considered.

Furthermore, the reliance of the model validation on 4 use cases represents a large approximation.

However, as can be seen from Figure 45, the bandwidth of consumption data is relatively low,

indicating that the heat consumption of the buildings in Fårdala is in a similar range. Yet, the

definition of use cases based on the minimum, average and maximum values represents an attempt

to exploit the variance in data to the best extent possible. Lastly, various aspects which are out of

scope of the modelling context have to be highlighted. Thus, energy efficiency measures other than

relating to the energy supply systems like thermal insulation measures, are not considered. Also,

DSM measures as presented in the other sections are not included in the analysis. This is primarily

due to two reasons. Firstly, DSM measures are analysed and assessed in the section 4 and can thus

be seen as a complementary element to the work in this section. Secondly, the presented

optimisation model has a focus on the decentralised energy conversion technologies for electricity

and heat supply and storage which makes it challenging to include and elaborate other foci like DSM

measures in an adequate level of detail in view of the already high model complexity and the

required model solvability.


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3.2.4 Summary and conclusions
















The following conclusions can be drawn from the optimisation of these four cases:

PV is only employed in the “minimum” case, which is thought to be due to the less favourable

ratio of heat to power in this case

Thermal and electrical storage devices are employed in all decentralised cases, with a

capacity broadly corresponding to the annual specific heat demand

A CHP unit with approximately the same capacity is employed in all three centralised cases,

the extra thermal demand in the “average” and “maximum” cases being met by a

combination of a (larger) gas boiler and thermal storage


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In the centralised energy supply case, a large CHP unit with thermal storage and gas boiler

are employed, whereby the heat to power ratio and higher capacity of the CHP unit in this

case leads to an absence of PV and battery storage capacities

The resulting capacities are realistic for these sort of decentralised and centralised energy

systems

The total annual system costs for energy supply are 19 % cheaper in the centralised than the

decentralised (average) case

A similar trend is observed in terms of primary energy consumption and CO2‐emissions

The degree of self‐sufficiency, defined as the fraction of onsite demand met by onsite

generation, is:

o Between 94 % and 99 % for all energy;

o Between 81 % and 97 % for electricity; and

o Increases across the cases minimum, average, maximum and centralised.

The self‐consumption rate, defined as the fraction of onsite generation that is used onsite,

is:

o Between 90 % and 100 % for all energy;

o Between 60 % and 100 % for electricity;

o Highest in the minimum and maximum cases, due to generally smaller CHP capacities;

and

o Is significantly lower in the cases average and centralised due to the significantly

larger CHP units employed in these cases.

These results indicate that, based on the employed data and methodology, the current system is

the most optimal in terms of energy supply costs, primary energy consumption and CO2‐emissions.

However, several limitations of the methodology should be noted which could strongly affect this

conclusion. The model takes a “greenfield” perspective, i.e. it does not consider the existing


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infrastructure such as boilers and/or district heating network. In addition, the approach focuses on

the domestic sector and does not consider other, surrounding buildings and sectors, which could

affect the demand structure. Furthermore, the diversity between the households could not be

considered because of a lack of data, but a large and growing literature on the social dimensions of

energy use shows that the people living in the buildings can strongly influence the overall energy

demand. Finally, this work focused only on the supply side options, again due to a lack of data, but

any approach towards reducing energy consumption and related emissions should start with the

demand side, according to the energy hierarchy. It is on the demand side that large energy and

emission savings can be made, often with no or only small capital investments. Hence further work

should focus on modelling the demand side as well as in attempting to achieve a more detailed

differentiation between the analysed households.


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4 Individual appliances analysis

This chapter is concerned with the potential for specific interventions within households in order to

reduce energy consumption and/or shift the demand, e.g. away from peak times. These

interventions include providing consumers with more or better information on their energy use,

subjecting them to dynamic electricity tariffs and specific measures aimed at saving energy, as will

be implemented within the CIVIS project. It begins with an over of recent literature on interventions,

especially focussing on information provision and dynamic pricing.

In the second part of the chapter, the potential of some measures on an appliance level is examined.

An analysis of individual appliances used in the domestic sector is carried out in order to assess their

influence on the overall energy consumption of each household in each test site. The objective is to

determine the impact of that each energy efficiency measure can have in the decrease of the overall

energy consumption.

4.1 Discussion of relevant literature

There is a large and growing literature on the potential impacts that qualitative interventions can

have on household energy consumption. These measures include but are not limited to advice,

feedback, benchmarking, smart meters, real time displays (RTDs) and incentives to reduce

consumption [43]. Many studies have found that advice alone has little or no effect, instead it must

be combined with other measures in order to be effective. Savings for advice, when combined with

other measures are typically in the range 0‐5 %. But these figures need to be interpreted with

caution because often studies have small sample sizes and/or do not quote a level of statistical

significance, and in some cases the requirement to “opt‐in” to such schemes can bias the results in

favour of participants who are more inclined to do so. Targeted advice seems to have the potential

to have a higher impact, but this is also associated with a higher effort and thus cost, because for

example a more extensive intervention in the form of an energy audit is required. Feedback, for


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example in the form of enhanced billing, can contribute 2‐3 % savings on the monthly bill. It is

thought to be more cost‐effective because it can be provided on an “opt‐out” basis. But a wide

variability in the findings, partly related to a wide variability in the forms and nature of feedback

itself, mean that also these figures are not necessarily applicable in all situations. For a more detailed

discussion of the potential of qualitative measures for energy saving in households the reader is

referred to the literature review in the context of the EDRP project (ibid.). The remainder of this

section focusses on the scope of dynamic prices to invoke energy savings and load shifting.

Two of the most recent trials to examine the potential impacts of dynamic pricing on consumer

behavior and potential peak time reductions are SmartCurrents [44] and Low Carbon London &

UKPN [45]. SmartCurrents was a study carried out in 2012 and 2013, with approximately 1,915

treatment and control groups, and 11 successful “event days” that occurred in 2013. The study

applied a three‐tiered TOU tariff on weekdays overlaid by a Critical Peak Price (CPP) on a maximum

of 20 days per year. There were four main treatment groups depending upon whether they have in

home displays (IHDs) and programmable communicating thermostats (PCTs): 1. Education and DPP,

2. as 1. but with IHD, 3. as 1 but with PCT, 4. as 1. but with IHD and PCT. There were varied responses

according to the group: groups 1 and 2 achieved between 12.6 % (1) and 17.5 % (2) load shifting,

but no overall energy consumption reduction. Group 3 performed best with 44.5% reduction in peak

hours and 14.3 % reduction on average during all hours and days. Group 3 also reduced overall

energy consumption by 11.8 % and 9.1 % on hot and cold weather days respectively. Group 4 results

were not much different to 3, despite the fact that they had a combination of IHD and PCT.

Similarities between 3 and 4 results show that the additional display (IHD) does not appear to bring

many additional benefits. Overall it was estimated that customers saved 10‐15 % during the 11

event days.

Low Carbon London & UKPN involved a trial of 5,533 households in London with smart meters,

including 1,119 with three‐tier TOU tariffs. Valid data are available for 2013 for 922 households on


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the TOUT and 3,437 on the non‐TOUT. The mid‐price was used as a baseline and “events” were

created to incur the lower and higher prices. The events were “constraint management” (CM), to

target peak demand and thus ease network constraints, and “supply following”, to investigate the

demand response to low or high price signals of varying duration. The TOUT contained three price

banks, low, medium and peak, and the non‐TOUT involved one standard flat‐rate tariff. Overall, 95 %

of households saved money relative to the flat tariff, although this cannot be generalized to all

households. In order to account and correct for the possibility that households respond “by chance”,

they are ranked according to their responsiveness to the TOUTs, and grouped into four quartile

groups according to this ranking. CM events consistently reduced demand levels: households

reduced their demand by around 10 % in the peak price periods, with the most engaged ones

showing a significantly larger reduction. Robust load reduction of about 0.05 kW/household was

demonstrated, obviously higher than this for the most engaged households. The ability of

households to increase consumption was only weakly affected by the time of year, but the

reductions were strongest during the colder and darker winter months. A strong correlation

between demand reduction potential and absolute demand levels was also found. Hence the

demand response ability of households is biased towards increases in demand, and during the day,

rather than decreases and/or at night. Interestingly, socio‐economic factors were found to hardly

affect response magnitude. The load shift potential is in general directly proportional to the number

of occupants in the household, with the exception in the “adversity” Acorn class, which has a more

uniform distribution of DSR across different household sizes from 1‐3.

The EDRP Project [43] involved 60,000 UK households from 2007 to 2010, and as such was one of

the largest trials of its kind. Of these, about 18,000 had smart meters for electricity and gas installed.

Four electric utilities investigated the load reduction/shifting and energy saving potential of a

combination of measures including Real Time Displays (RTDs), TOUTs, increased information with

bills, tips on energy efficiency etc. Results show no significant energy saving without a smart meter,


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the exception here being RTDs with benchmarking, which SSE found led to 1 % savings. RTDs in

combination with smart meters increased the achieved savings. For gas the smart meter itself,

without additional measures, resulted in savings of around 3%, though this may require support

from other interventions to be sustained over time. One important finding was that more

engagement and supervision during the installation and operation of the smart meters would have

been desirable. Advice was generic and not tailored to the customer. In particular customers need

to know what to do with the additional information provided and this information needs to clear,

attractive and distinct from other generic material sent from the supplier (quality). The information

should preferably be tailored to the household rather than generic, and the quantity is also

important. Finally, the dynamic nature of using smart meters is also stressed: rather than being a

one‐off occurrence, households require guidance along the “journey”.

The overall findings of the EDRP were that EDF found about 2.3 % savings in the first year, through

a combination of smart meters and historical feedback and advice. Overall the savings of around or

below 5 % confirmed results from the literature from other non‐UK concepts that this level could

be expected through a combination of generic advice and historical feedback. Load shifting varied

by trial (i.e. utility and group) but was up to 10 %, and the effect was stronger with smaller

households; neither of the trials involved automatic load shifting of appliances, but instead relied

on customer intervention and hence it is not known what appliances were responsible for the load

shifts (an insight that seems relevant to several studies); no overall reduction in energy consumption

was found by EDRP. Some segmentation effects were observed but these are difficult to generalize.

In the context of evaluating the potential for households reduce and shift their demand, the

behavior and ownership of appliances in the household are crucial aspects to consider. The

Household Electricity Usage Survey (HEUS) [46] was the largest survey of electricity use ever in the

UK and one of largest in Europe, carried out in 251 owner‐occupied households selected on the

basis of the life‐stage of the occupants in 2010/2011. Of these 251, 26 were monitored for 1 year,


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others for one month at intervals throughout the year. Dwellings were given Energy Performance

Certificates (EPCs) and the occupants were surveyed about their environmental attitudes. During

the survey period the occupants also completed diaries of use for some of the products they used.

Annual consumption per dwelling, m2 and person were provided for all dwelling types, as well as

average maximum power demand. Within the daily load curve it was possible to allocate 90 % of

the daily energy consumption to particular products. The average contribution of different

appliances to the electric load are shown for households without and with electric heating, broken

down by household type. The average annual energy consumption as well as the standby power

consumption for appliances were also determined. Finally, energy and power savings potentials

were also calculated, based on assumptions about replacing appliances, lighting, etc. Cold

appliances account for the largest determined average annual saving of 310 kWh. The total in

England ranges from 491 to 677 kWh, depending on the type of household. Priority measures are

cold appliances, lighting, audiovisual devices and computers. A later report which evaluated the

potential energy efficiency measures within the investigated households by focusing on the

differentiation between households drew the following crucial insights [46]:

1. There is an enormous range in the age of appliances owned, with some appliances being

more likely in specific social demographic groups.

2. Annual purchase and replacement rates: 2.7 % of households purchased a new fridge each

year (the lowest mean purchase rate of all appliances), 21.4 % of households bought a new

television (the highest mean purchase rate, partly driven by the Digital Switchover)

3. Energy ratings and socio‐demographic indicators: no significant trend linking energy ratings

to socio‐demographic groups, also no significant link between the environmental attitudes

of the households and the appliances they own

4. HEUS energy ratings and national sales data: most appliances are getting more efficient

over time, but the exception here is tumble dryers, e.g. between 2008 and 2010, and the

penetration of fridges and freezers of A+ and above is very low – more information required

etc.


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5. Electricity use in single‐person households: these HHs own significantly fewer cold

appliances and TVs and watch fewer hours of TV, they use the washing machine less than

others, and could save energy by using a half‐size washing machine and, as with all

households, running at lower temps

6. Appliance use associations: strong link between cooking and watching TV, as well as

between TV, ICT, audiovisual and washing appliances, strong correlations between the use

of related ICT and audiovisual appliances, lower social grades more likely to have TV on in

the background whilst performing other tasks

7. Seasonality trends in non‐heating appliances: large seasonal variation in tumble dryer use

but not for dishwashers or washing machines, seasonal trend for cold appliances was

reversed: greater use in summer than winter, hardly any seasonal variation in the use of

most electric cooking equipment

8. Electricity demand for products with high agency: strong links between demographic

factors like number of people in the household and energy use for appliances, significant

savings potentials, e.g. by persuading high‐use households to reduce to the average etc.

9. New washing machines and powders for low temperature washes have been successful in

improving energy efficiency, but newer machines are not demonstrably more efficient than

older models – on average newer (2010‐11) machines use about 35% more electricity per

cycle than 1997‐98

10. Appliances left on when not in use: 80/251 households left lights on overnight, at least 18

HHs left appliances on in empty rooms for more than 1 h/day

In summary, whilst the projects discussed here provide some very useful insights, it is difficult to

generalise across and beyond existing studies on demand response because they show a large range

in the size of demand response (in % of the peak load) achievable. Furthermore, there seems to be

little consistency between the size of the difference between peak and off‐peak power demand and

the scope of demand response (Low Carbon London & UKPN 2015). This is illustrated, for example,

by the fact that the EDRP study [43] concluded a potential 4 % reduction in weekday peak demand,

whereas the Ireland Electricity Smart Metering Trials concluded a 7‐12 % peak demand reduction

potential [47].


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4.2 Electricity profile of the domestic sector for the test sites

The distribution of electricity demand in the domestic sector by appliance was estimated for Italy

and Sweden based on literature review, namely the results of the European Project ODYSSEE [48]

and the reports of the European Commission’s Joint Research Centre: Electricity Consumption and

Efficiency Trends ‐ the Energy Efficiency Status Report for 2009 [49] and 2012 [50]. It is important

to mention that ODYSSEE project has data from each country, provided regularly from the respective

national authority. The data from [49] and [50] is more global (European level) and was only used

to complete data missing in ODYSSEE project. These results were applied to the test sites of each

country.

4.2.1 Italy

Figure 53 shows the results of the calculation of the distribution of electricity demand in the

domestic sector by appliance in Italy, in 2008. It is important to highlight that this distribution not

being constant for all years does not change much, as can be confirmed in the reports of Energy

Efficiency Status Report for 2009 [48] and 2012 [49] where a distribution of electricity consumption

in the domestic sector in Europe is done, for two different years and, is very similar. In 2008, the

total electricity consumption in the domestic sector in Italy was about 5.88 Mtoe.


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Figure 53: Distribution of electricity demand in the domestic sector by appliance in Italy, in 2008

ICT that includes entertainment appliances, set‐top boxes and office equipment is responsible for

the highest electricity consumption (about 19 %) in the domestic sector of Italy. The cold appliances

(refrigerators and freezers) are responsible for 16 % and lighting, air conditioning and water heating

have a weight of 11 % each.

The category “others” includes equipment like coffee machine, vacuum cleaner, dehumidifier, hair

dryer, fan, etc. in other words equipment that could not be sorted into one of the other categories.

This category is responsible for about 10 % of the total electricity consumed, but keeping in mind

that it includes a huge number of equipment, their weight individually is not so significant. On the

other hand, the appliances included in the category “others” cannot be found in all households as

they are less fundamental on the citizen’s day to day activities than the remaining appliances. Their

use and purchase depends much more on the culture, habits and purchasing power.

4.2.2 Sweden

Figure 54 shows the results of the calculation of the distribution of electricity demand in the

domestic sector by appliance in Sweden in 2008. In this year, the total electricity consumption in


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the domestic sector in the country was about 3.58 Mtoe.

Figure 54: Distribution of electricity demand in the domestic sector by appliance in Sweden, in

20084

About 29 % of the consumption of electricity in the domestic sector is for space heating, 18 % is due

to ICT and 16 % results from cold appliances. Lighting is responsible for about 9 % of the total

electricity consumption.

In Sweden, the category “others” represents about 10 % of the total electricity consumed in the

domestic sector and, as for Italy, includes all the equipment not considered in the remaining

categories – fan, dehumidifier, coffee machine, aquarium, etc.

4 Space and water heating are electric heating.


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4.3 Energy reduction potential by demand side measure

There is a lot of information available on the actions that can be taken by the end‐users (demand

side measures) in the domestic sector to reduce their energy consumption, namely actions

regarding the use of individual appliances. However, information regarding the actual impact of

these measures in the overall energy consumption of the household is scarce. This is because this

quantification depends on many factors namely consumer behaviour and technology in use. A real

quantification would need the installation of measurement instruments in each appliance and a

daily monitoring of the end‐use actions, which can be a problem at economic level, but also can

raise issues regarding privacy.

However, a few reports were found that quantify some of these types of measures [51,52,53,54].

Based on the data from these reports, the energy reduction was estimated for each measure, the

results obtained are in the Table 34.

Energy Service

Demand

Appliance/Activity Measure Energy

reduction

(%)

Space heating Space heating Turn down thermostat from 22°C

to 20°C during the day and to

18°C during the night.

15

Hot water Water Heating Turn down water heater

thermostat from 60°C to 49°C.

11

Space cooling A/C Turn up thermostat from 23°C to

26°C.

10

Cold food/drink Cold appliances Turn up the refrigerator

thermostat from 0.6°C to 3°C and

12


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the freezer thermostat from ‐

20°C to ‐18°C.

Cleaning (clothes

and dishes)

Washing machine Change washer temperature

settings from hot wash, warm

rinse to warm wash, cold rinse

19

Drying machine Line‐dry clothing (do not use

dryer) 5 months of the year

44

Dishwasher

machines

Reduce dishwashers cycles

temperature from 70°C to 55°C

22

ICT/electronics Stand‐by Reduce stand‐by 18

Lighting Lamps Exchange of lamps* 16

Table 34: Energy reduction by demand side measure [51,52,53,54]

*Approximately 85 % of lamps currently in EU homes are energy inefficient. With CFL the energy consumption can be reduced

between 70 to 80 %. The measure considers the exchange of 25 % of lamps and a medium reduction of 75 %.

Two of the four reports used are from the United States. The measures selected from these reports

are the ones that are suitable for Europe in the scope of CIVIS, and it is considered that the

percentage of reduction is close to the situation in Europe.

The present analysis considers two different scenarios – a conservative and an optimistic scenario.

The first one considers that all the measures included in Table 34 will be adopted. The second

scenario considers the adoption of additional measures in cooking and other activities that will

result in a reduction of 15 % of the energy consumption in each activity; and the measure of line‐

dry clothing, i.e. not using the dryer for 5 months per year, resulting in a 44 % energy reduction.


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4.3.1 Storo, Italy

4.3.1.1 Electricity consumption by appliance in Storo

The electricity consumption in the domestic sector in 2013 in Storo test site was about 2,596,674

kWh [55]. The electricity consumption by appliance was calculated based on this value of electricity

consumption and on the distribution of electricity demand in the domestic sector by appliance in

Italy (Figure 53). This data is considered representative of the region.

The results obtained are reported in the Table 35.

Appliances Electricity consumption (kWh)

Cold Appliances 421,935

Washing & Drying 209,513

Dishwashers 87,297

Lighting 290,990

ICT 500,502

Space heating 140,441

Air Conditioning 269,093

Water heating 285,517

Cooking 131,920

Others 259,466

Table 35: Electricity consumption by appliance in Storo in 2013

Considering the adoption of the measures mentioned in Table 34 in all households of Storo and the

electricity consumption by appliance (Table 35), the resulting energy reduction for both scenarios –

conservative and optimistic, are set out in the table below.


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Appliances Electricity reduction (kWh)

Conservative

scenario

Optimistic scenario


Washing & Drying 38,679 130,865

Dishwashers 19,205 19,205

Ligthing 23,852

ICT 88,088


Air Condicioning 26,041


Cooking ‐ 19,788

Others ‐ 38,920

Total 296,593 447,486

Table 36: Electricity reduction in Storo for the conservative and optimistic scenarios

In the conservative scenario, with the implementation of the efficiency measures, the community

could save about 11 % of electricity. In turn, in the optimistic scenario a 17 % reduction in the

electricity consumption is possible to reach.

These results are based on the implementation of energy reduction measures in all domestic sector

of Storo which is important since CIVIS intends to have direct and indirect impact in all the

community. On the other hand, CIVIS project plans to involve 150 families directly, in each test site.

Considering that each family consumes about 1,969 MWh of electricity per year [55], Table 37 shows


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the possible reduction of the electricity consumption resulting from the application of the measures

mentioned above in 150 households in both scenarios.


Conservative

scenario

Optimistic scenario




Lighting 2,713

ICT 10,019

Space heating 2,379


Water heating 3,497

Cooking ‐ 2,251

Others ‐ 4,427

Total 33,735 50,898

Table 37: Electricity reduction for the 150 families directly involved in CIVIS in Storo,

for the conservative and optimistic scenarios

CIVIS could contribute to a total reduction of electricity consumption in Storo of about 33,735 kWh,

in a conservative scenario and of about 50,898 kWh in an optimistic scenario.


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4.3.1.2 Thermal energy consumption in Storo

The thermal energy consumption in Storo was estimated in 29,964,016 kWh (3,887,151 kWh for

HSW and 23,076,865 kWh for space heating) [55]. Based in the measures regarding space and water

heating in Table 34 and the respective energy consumption reduction forecasted, the possible

reduction in the consumption of thermal energy in all households of Storo was estimated and the

results are illustrated in Table 38.

Scope Energy reduction (kWh)

Space heating 3,436,980


Total 3,855,596

Table 38: Thermal energy reduction in Storo

The implementation of the energy efficiency measures in the scope of space and water heating can

result in a reduction of 3,855,596 kWh which represents about 14 % of the thermal energy

consumption in Storo in 2013.

For the 150 families that are planned to be involved directly in the project, the total thermal energy

reduction by activity is illustrated in Table 39. For this estimation it was considered that each family

consumes 13,802 kWh for space heating and 1,969 kWh for HSW [55].




Total 342,233

Table 39: Thermal energy reduction for the 150 families

directly involved in CIVIS in Storo


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Considering the 150 families of Storo that will be involved in the project, the adoption of the energy

efficiency measures in the scope of space and water heating could result in a reduction of 342,233

kWh of thermal energy consumption.

4.3.2 San Lorenzo, Italy

4.3.2.1 Electricity consumption by appliance in San Lorenzo

The electricity consumption in the domestic sector in 2013 in this test site was about 1,265,713 kWh

[55]. As for Storo test site, the electricity consumption by appliance was calculated based on the

value of electricity consumption in 2013 and on the distribution of electricity demand in the

domestic sector by appliance in Italy (Figure 53). This data is considered representative of the region.

The results obtained are outlined in the Table 40.




Dishwashers 42,552

Lighting 141,839

ICT 243,963




Cooking 64,302

Others 126,237

Table 40: Electricity consumption by appliance in San Lorenzo, in 2013


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Considering the adoption of the measures mentioned above in all households of San Lorenzo, the

resulting energy reduction for both scenarios considered can be found in the table below.


Conservative

scenario

Optimistic scenario




Lighting 11,626

ICT 42,937




Cooking ‐ 9,645

Others ‐ 18,936

Total 144,570 218,086

Table 41: Electricity reduction in San Lorenzo for the conservative and optimistic scenarios

The adoption of the efficiency measures considered could contribute to a reduction of 144,570 kWh

of electricity consumption in a conservative scenario and a reduction of 218,086 kWh in an

optimistic scenario.

Considering that each family in San Lorenzo consumes about 1,655 kWh of electricity per year [55],

the reduction of electricity consumption for the 150 households that will be directly involved in

CIVIS was calculated and it is shown in Table 42 for both scenarios considered.


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Conservative

scenario

Optimistic scenario




Lighting 2,280

ICT 8,422

Space heating 2,000


Water heating 2,940

Cooking ‐ 1,892

Others ‐ 3,714

Total 28,355 42,774

Table 42: Electricity reduction for the 150 families directly involved in CIVIS in San Lorenzo

for the conservative and optimistic scenarios

Considering the 150 families of San Lorenzo that will be involved in CIVIS, the adoption of the energy

efficiency measures could result in a reduction of 28,355 kWh of electricity consumption in a

conservative scenario and of 42,774 kWh in an optimistic scenario.

4.3.2.2 Thermal energy in San Lorenzo

The thermal energy consumption in San Lorenzo was estimated to 9,637,913 kWh (965,998 kWh for


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HSW and 8,671,915 kWh for space heating) [50]. Based on the measures regarding space and water

heating of Table 34 and the respective energy consumption reduction forecasted, the reduction in

the consumption of thermal energy in all dwellings of San Lorenzo was estimated. The results

obtained are reported in Table 43.


Space heating 1,291,562


Total 1,395,592

Table 43: Thermal energy reduction in San Lorenzo

The adoption of the measures proposed for space and water heating, by all the citizens of San

Lorenzo, could contribute to a reduction of 1,395,592 kWh in the consumption of thermal energy.

For the 150 families that are planned to be involved directly in CIVIS, the total thermal energy

reduction by activity is illustrated in Table 44. For this calculation it was considered that each family

consumes 14,952 kWh for space heating and 1,948 kWh for HSW [50].




Total 365,502

Table 44: Thermal energy reduction for the 150 families

directly involved in CIVIS in San Lorenzo


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Regarding the 150 families envisaged in the project, the adoption of the measures in space and

water heating could result in a decrease of 365,502 kWh of thermal energy consumption.

4.3.3 Fårdala and Hammarby Sjöstad, Sweden

Currently there is no real data available on Fårdala and Hammarby Sjöstad electricity consumption.

In this sense, in order to carry out the individual appliances analysis in the Swedish test sites and

estimate the possible electricity consumption reductions, the average value of electricity

consumption per households referred to in the report of the Swedish Energy Agency [40] on this

issue was used. This value is 4,143 kWh per household per year [40]. Regarding the consumption of

thermal energy, only data for Fårdala is available and this data was used also for Hammarby Sjöstad.

Due to the unavailability of data for these two test sites, the estimation of electricity and thermal

energy consumption reduction was carried out only for the 300 families (150 in each test site) that

are planned to be involved directly in the project.

4.3.3.1 Electricity consumption by appliance in the Swedish test sites

The electricity consumption by appliance for the 300 families that will be involved in CIVIS, was

calculated based in the distribution of electricity demand in the domestic sector by appliance in

Sweden (Figure 54) and on the electricity consumption by family referred above (4,143 kWh). The

results obtained are in Table 45.


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Dishwashers 39,964

Lighting 110,664

ICT 229,129



Cooking 46,370

Others 118,561

Table 45: Electricity consumption by appliance for the 300 families

that will be involved in the Swedish test sites

For these test sites the conservative and optimistic scenarios were also considered in order to

estimate the electricity consumption reduction for the 300 families.


Conservative

scenario

Optimistic scenario




Lighting 9,071


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ICT 40,327


Water heating 4,665

Cooking ‐ 6,956

Others ‐ 17,784

Total 157,511 224,454

Table 46: Electricity reduction for the families directly involved in CIVIS in the Swedish test sites for

the conservative and optimistic scenarios

In the conservative scenario the community can save up to 13 % of electricity (157,511 kWh). In the

optimistic scenario a 18 % reduction in the electricity consumption is possible (224,454 kWh).

4.3.3.2 Thermal energy consumption in the Swedish test sites

The thermal energy consumption per household per year for Fårdala was estimated as 14,660 kWh

(12,860 for space heating and 1,800 kWh for HSW) [55], due to the lack of information the same

data as for Hammarby Sjöstad was assumed. Keeping in mind the measures and respective

reduction forecasted for space and water heating in Table 34, the thermal energy consumption

reductions that can be reached by the implementation of these measures in the 300 families were

estimated and the results are set out in Table 47.


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Total 632,750

Table 47: Thermal energy reduction for the families

directly involved in CIVIS in the Swedish test sites

The implementation of energy efficiency measures in the scope of space and hot water heating can

result in a 14 % decrease in the thermal energy (632,750 kWh).

4.4 Energy demand – potential for load shifting

An important measure for the Italian test sites is the load shifting, in order to match the production

of energy by PV and the energy consumption if possible and to reduce the needs of energy

importation. This sub‐chapter will perform an analysis on which activities are more or less suitable

to be shifted, i.e. activities that have more or less potential to be shifted, from high to none (Table

48).

High Medium None

Washing and drying machines Space heating Cold appliances

Dishwasher machines A/C Lights

Water heating Ovens ICT

Microwaves

Hobs

Table 48: Potential of demand shifting by appliance/activity [54]


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Washing, drying and dishwasher activities have a high potential to be shifted and matched with

periods of higher PV production and less energy consumption. Also the activities related to water

heating, namely bathing, are switchable. Activities like space heating, use of A/C and of ovens can

be partially switchable, since it is possible, for instance, to start it before energy peak consumption,

but it is not possible to switch them completely. On the other hand, some activities are completely

not switchable because the end‐users need to perform it when they are at home.

In Italy, the activities with higher shifting potential (washing, drying, dishwasher and water heating)

represent about 22 % of the total electricity consumed in the domestic sector. Regarding the 300

families that will be directly involved in CIVIS in the Italian test sites, this represents about 121,907

kWh that can be shifted to periods of the day with lower consumption and higher endogenous

electricity production. It is important to emphasise that this represents the maximum technical

potential that can be shifted wherein in the reality this value will be difficult to reach. A suitable

awareness campaign and an effective involvement of the citizens in CIVIS project will contribute to

reach a significant amount of energy consumption shifted for periods with lower demand.

This type of measure is important to improve the use of intermittent renewable energy resources

and reduce the importation of electricity, reducing in this way the energy invoice of the communities

and contributing to the reduction of CO2‐emissions.

4.5 Baseload energy demand and stand-by

The baseload is the minimum energy consumed in a household along the day. It is important to

assess which appliance contributes to the baseload in order to reduce the consumption of energy

not used to produce any work/service or not necessary in this period.

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installation of individual meters in the several appliances. At this moment, no data is available from

the test sites to carry out this assessment. But, in order to help in future analysis in the test sites

and in the selection of in which appliances it is important to install individual meters, an analysis of

studies carried out on this matter is conducted. In [40] an analysis of energy consumption by

appliance is performed for different types of houses and of families across Sweden from August

2005 to December 2008. Figure 55 presents the average hourly energy demand (calculated by

averaging the individual load curves for each household) for families between 26‐64 years old,

leaving in a house. Space and water heating are not considered [40].

Figure 55: Average hourly energy demand for families between 26‐64 years old [40]

The baseload energy demand is between 3 am and 5 am, and the cold appliances, as expected, have

an important weight in the total consumption in this period. It is also possible to verify that


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computer site and lighting have a significant contribution in the electricity consumed in the base

load period. This information is important to the end‐user since it can analyse what is happening in

this period, the lights are on and some appliances are on or in stand‐by, giving the possibility to end‐

users to turn‐off all the equipment that are not needed. The category “miscellaneous” that

represents all the other appliances has also a high contribution, it is necessary to understand if this

consumption is necessary or if it is a waste.

Similar conclusions were taken from the study done in the UK [54], as showed in Figure 56, where

the average summer base load electricity demand for the households monitored is illustrated.

Figure 56: Average summer baseload electricity demand

This information can be used in the awareness campaigns in order to alert the citizens to the energy

consumed that it is not necessary. It needs to be highlighted that part of the consumption in the

baseload period is due to stand‐by, namely the one related to audiovisual and ICT. This consumption


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is easy to reduce by switching off all the equipment that are not necessary.

Figure 57 shows an average of the daily load profile of the domestic sector members of CEDIS and

CEIS in 2013.

Figure 57: Daily load profile of the domestic sector members of CEDIS and CEIS in 2013

The minimum power demand in CEDIS members is about 388 kW and it is reached at 3 am and the

minimum value for CEIS members is about 614 kW and is reached at 2 am. The evolution of the load

profiles is similar and the base load is between 2 and 4 am for CEDIS and 2 and 3 am for CEIS. With

the CIVIS project, it would be important to analyse which appliances are responsible for the

baseload energy demand and if it is possible to reduce it.

As referred above, part of the energy consumed by some appliances like TVs, computers, printers,

in the baseload energy demand are due to stand‐by consumption and not due to the equipment

use. Figure 58 illustrates some typical values of standby power of some appliances [54].

0

200

400

600

800

1000

1200

0 4 8 12 16 20 24

Power (kW)

Hours

CEDIS members

CEIS members


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Figure 58: Typical values of standby power of some appliances [49]

The audiovisual and ICT categories have the appliances with higher standby power. The sky and set‐

top box are the equipment with the highest standby power, about 18.9 W and 14.1 W, respectively.

Hi‐Fi, modem and routers are also appliances with a significant standby power namely, 6.8 W, 7.3

W and 7.1 W. In the kitchen, the equipment with higher standby power is the microwave with 2.2

W.

If it is true that many of the householders do not have all of these appliances, it is also true that they

have many of them. It is estimated that more than 5 % of the electricity consumed in a household

is due to standby consumption [49,54]. This problem can be solved by the end‐user switching off all

the appliances that are not in use or by the manufacturer, by the reduction of the consumption of

energy of the appliances in standby or off‐mode.

In order to face this problem and reduce standby and off‐mode energy consumption in December

0

2

4

6

8

10

12

14

16

18

20

Sky box

Set‐top box

Hi‐Fi

VCR

Home cinema

Wii

DVD

PS3 TV

CD Player

PS2

Modem

Router

Multifunction printer

Scanner

Fax/printer

Laptop

Desktop

Printer

Monitor

Microwave

Tumble dryer

Washing machines

Washing/drying machines

Oven

Cooker

Dishwasher

Hob

Audiovisual ICT Kitchen

Pow

er (W

)


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2008, the EU Commission adopted the Commission Regulation (EC) nº 1275/2008 that establishes

ecodesign requirements related to standby and off‐mode power consumption for a broad range of

products and equipment. According to this regulation, household appliances, household IT

equipment, consumer electronics, electric toys, leisure and sports equipment have to fulfil the

requirements presented in Table 49 from January 2010 and January 2013 [56,57].

January 2010 January 2013

Maximum power consumption in off‐mode 1 W 0.5 W

Maximum power consumption in a passive standby

mode without information display

1 W 0.5 W

Maximum power consumption in a passive standby

mode with information or status display

2 W 1 W

Table 49: Ecodesign requirements related to standby and off mode

Additionally, the regulation requires that the equipment will have an auto‐power down feature that

will put them automatically into passive standby or off mode when they are not providing their main

function (unless inappropriate) [56].

The application of these requirements by the manufacturers will reduce significantly the energy

consumption of standby and off‐mode.

4.6 Summary and conclusions

Energy efficiency demand side measures can have an important role in the reduction of the energy

import of a given community. A real reduction on the demand side faces several barriers namely,

the lack of data regarding real energy consumption by appliance. Without this data it is impossible


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to understand where and when citizens are consuming energy, which complicates the delineation

of measures and a real quantification of their impact. Additionally, citizens’ behaviour also plays an

important role in the energy consumption and it is a parameter that is difficult to quantify. Experts

in energy demand side field propose several measures to reduce the consumption of energy, but

the quantification of these measures is not simple to assess, since it depends not only on the

technology used but also on how the end‐user uses the technology.

The first obstacle to the development of this study is the lack of data about energy consumption by

appliance in each test site. To overcome this, some estimations were carried out based on the

energy profile Italy and Sweden. Based on the measures of energy reduction selected, two different

scenarios were considered – conservative and optimistic scenarios. In the conservative scenario, the

consumption of electricity could be reduced by about 11 % for the Italian test sites and by about

13 % for the Swedish test sites, while in the optimistic scenario the reductions will be about 17 %

and 18 % respectively. Regarding the thermal energy, with the implementation of the measures

identified, the reduction will reach about 14 %.

Considering the 150 families for each test site that will be directly involved in CIVIS project, the

implementation of the measures could result in the energy reduction illustrated in Table 50.


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Test site Electricity reduction (kWh) Thermal Energy

reduction (kWh) Conservative

scenario

Optimistic scenario

Storo 33,735 50,898 342,233

San Lorenzo 28,355 42,774 365,502

Fårdala and

Hammarby Sjöstad

157,511 224,454 632,750

Total 219,601 318,126 1,340,485

Table 50: Possible total energy reduction envisaged in CIVIS project by the direct involvement of

600 families

CIVIS can contribute to a total electricity reduction of 219,601 kWh (12 %) in a conservative scenario

and of 318,126 kWh (18 %) in an optimistic scenario. At thermal energy level, the implementation

of the measures can decrease the consumption in about 1,340,485 kWh (14 %).

The baseload energy demand period needs to be analysed in order to understand if the energy

consumed in this period is necessary and, if not, to show the citizens how they can reduce

consumption in this period. Standby and off‐mode can have an important weight in the energy

demand of a household, namely in the baseload period. Keeping in mind this issue the EC has

developed legislation that aims at the reduction of the energy consumption of the equipment in

standby and off‐mode. Additionally, it is necessary to warn citizens about this hidden consumption.


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5 Summary and conclusions

This deliverable has significantly updated previous results for the pilot sites as well as applied the

methods previously developed in D2.1a. Due to severe constraints on data availability for the

Stockholm pilot sites, the updated overview is much less detailed for these than the Italian test sites.

From the updated analysis of the pilot sites based on the latest available date several new insights

relating to the potential for energy and CO2‐saving measures through the application of energy

storage, as well as through specific measures on a household and appliance level, are gained as

summarised in this section.

Chapter 2 presented an update of the energy systems in the test sites. For the Italian test sites,

describes the electrical, thermal and transportation energy demands and productions for each site.

For each sector, the demand and production is fully analyzed. Consumption and production for the

electricity sector is gathered from real data. Some recommendations for the electricity sector are

suggested to enhance the stability of the system. As it is not possible to gather real data for the

thermal sector because of a lack of monitoring facilities, the demands are estimated using the

dwelling properties and local climate. The thermal energy generation mix is also estimated by using

interviews and questionnaires. Transportation demand is calculated based on the available

transportation data for the local community. Finally, an overview for each pilot site is given.

Regarding the Swedish test sites, the Swedish test site report presents an overview of the data

availability, hence this is why only a short summary is given here. The focus of measures in the

Swedish test sites is on energy efficiency. In Hammarby Sjöstad both building level and individual

household data is being collected. Additional sensors to obtain high‐granularity electricity usage

data and appliances usage profiles are being installed, while also offering users control of some

appliances through smart plugs. In Fårdala test site sensors and actuators to control the hydronic

heating system are being installed and data streams for household electricity, domestic hot water


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and heating are being utilised. This will provide high quality data and assist with the implementation

of the measures feasible for each test site, as outlined in Chapter 4 of this report.

In Chapter 3, the energy systems in the Italian and Swedish test sites were analysed using energy

system models and their latest available data. Thereby a particular emphasis was placed on the

potential for optimising the energy systems by employing thermal and electrical storage devices. To

this end, energy system analysis tools including optimisation and simulation‐based approaches were

employed to three of the four test sites.

The purpose of the analysis in Trento was to determine the optimisation potential in the Italian pilot

sites, starting from the existing energy infrastructure, including generation, distribution and

demand aspects for electrical, thermal and transport sectors. A detailed energy flow model for the

“Current Scenario” is set up, based on hourly data received from local stakeholder or specifically

calculated for the CIVIS project.



way. There is an additional margin (+ 30.4 % in CEIS and + 35.5 % in CEDIS) to increase the use of

local wood. Local PV availability is considerable both in CEIS and in CEDIS areas.







ESD)



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to ‐11.9 %) and in CEDIS (from ‐4.4 % to ‐8.6 %). It is also possible to both minimise energy cost while

reducing CO2‐emission (from ‐28.1 % to ‐70.9 % in CEIS, from ‐24.3 % to ‐29.9 % in CEDIS). In other


Also ESD is highly improved, these scenarios are able to import from ‐14 % to ‐20 % in CEIS and from

‐16 % to ‐19 % in CEDIS fewer energy resources from outside. LFC in most cases is very close to the





scenarios.







boiler covers 56‐73 % of heat demand (from 53 % of “Current Scenario”) while GSHP 20‐41 %; in the

proposed “CEDIS Future AC Optimised Scenarios” individual wood boiler cover 44‐49 % of heat

demand (from 32 % of “Current Scenario”) while GSHP 37‐50 %.

Concerning the transport sector, the number of introduced electric cars is almost negligible both in

CEIS and CEDIS. Indeed, the investment necessary to replace oil cars with electric cars is


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economically unattractive in the current market conditions.

However, annual cost is not always the only parameter considered. Indeed, many communities

commit themselves to reach environmental targets (see for example the Covenant of Mayors): this

study suggests several target scenarios with ambitious CO2‐emission reductions. In order to reach

the highest values it is necessary to progressively increase the use of greener technologies (mainly

PV, individual wood boiler, GSHP, electric cars), this leads to AC growth.

Finally, ESD suggests which technologies can optimise the use of local energy resources and in which

entity we can rely on them. This study suggests several target scenarios with ambitious ESD

reduction. As for CO2‐emission reduction, in order to reach the highest values it is necessary to

progressively increase the use of costly technologies (mainly PV, wood CHP, GSHP, electric cars),

which leads to AC growth.

The results could be further studied by the policy makers of the specific communities and finally an

optimum scenario could be chosen.













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These results of the optimisation for Fårdala indicate that, based on the employed data and

methodology, the current system is the most optimal in terms of energy supply costs, primary

energy consumption and CO2‐emissions. However, several limitations of the methodology should

be borne in mind, as discussed in detail in Chapter 3, which could strongly affect this conclusion.

Most importantly, it is recommended to devote further attention to the households within the

buildings and their behaviour, which has a strong but varied impact on energy consumption. Further

work should also focus on modelling the demand side within the households, including the

occupants and measures such as building insulation that may reduce the overall demand of the

building fabric.

Chapter 4 examined the impact that individual energy efficiency and load shifting measures, on an

appliance level, could have in reducing the overall energy, power and CO2‐consumption of the

households. A brief overview of some of the literature in the field of energy saving interventions

and dynamic pricing tariffs highlighted the difficulties in generalising results from different studies.

Some common themes include the importance of targeted, as opposed to general, information, as

well as its combination with other measures in order to be effective. This especially applies to the

installation of smart meters and the required supervision of the affected households in using them.

Appliance ownership and use within households varies largely, especially but not only due to

socioeconomic factors within the household such as income, household structure and age, and

tenancy type (owner occupier, rented or other). In addition, a significant proportion of the energy

demand in households is often unknown, i.e. cannot be allocated to specific appliances or energy

service demands, which makes influencing it very difficult. Dynamic pricing trials have also found


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partly diverging results, with indications that a reduction in peak demand of 5‐10 % might be

possible with the right incentives.

To overcome the lack of data regarding energy consumption by appliance in each test site,

estimations were carried out based on the energy profiles within Italian and Swedish households.

Two scenarios were considered: a conservative and an optimistic scenario. The optimistic scenario

considers the adoption of more energy efficiency measures than the conservative scenario. For the

150 families of each test site that will be directly involved in CIVIS project, the implementation of

the measures could result in an electricity consumption reduction of about 12 % in the conservative

scenario and about 18 % in the optimistic scenario. At thermal energy level, the implementation of

the energy efficiency measures could result in a decrease of about 14 %. An analysis was also carried

out on the energy consuming activities that have more potential to be shifted in order to assess the

potential for load shifting, an important measure for the Italian test sites. Stand‐by and off‐mode of

appliances were also analysed and they can have an important weight in the energy demand of a

household, namely in the base load period.


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6 Appendix

6.1 Pilot site description

Table 51: CEIS area: Number of dwellings for age of construction and dwelling renovations with

energy upgrading in the periods 1982‐1991 and >1991

Table 52: CEDIS area: Number of dwellings for age of construction and dwelling renovations with

energy upgrading in the periods 1982‐1991 and >1991

< 19191919 ‐

1945

1946 ‐

1961

1962 ‐

1971

1972 ‐

1981

1982 ‐

1991> 1991 TOTAL

1982 ‐

1991> 1991

Bleggio

Superiore58 175 84 88 92 54 39 591 72 159

Comano Terme 423 178 126 132 127 93 66 1,144 142 315

Dorsino 61 16 15 25 25 22 13 177 20 45

Fiavè 170 17 43 45 31 24 22 352 44 98

S.Lorenzo in

Banale293 71 57 58 46 36 18 580 76 168

Stenico 209 74 45 39 53 45 35 500 60 134

TOT CEIS

municipalities1,214 530 370 387 375 274 194 3,344 414 920

Municipality

Number of dwellings for age of constructionNumber of dwelling

renovations

< 19191919 ‐

1945

1946 ‐

1961

1962 ‐

1971

1972 ‐

1981

1982 ‐

1991> 1991 TOTAL

1982 ‐

1991> 1991

Storo 412 125 117 274 405 211 127 1,672 192 427

Bondone 29 1 26 86 117 66 32 358 37 83

Ledro (Tiarno

di Sotto)89 54 14 64 67 42 46 374 41 92

Ledro (Tiarno

di Sopra)67 86 45 55 94 108 80 535 50 111

Ledro

(Bezzecca)53 45 18 40 52 46 40 294 30 67

TOT CEDIS

municipalities650 311 220 520 736 472 325 3,233 351 780

Municipality

Number of dwellings for age of constructionNumber of dwelling

renovations


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6.2 Investment cost, Lifetime, Fixed O&M cost

Technology Unit Investment cost (KEuro)

Lifetime % Fixed O&M Reference

Hydro KWel 1.9 50 2.7 EnergyPLAN Database

PV KWel 2.6 30 0.77 EnergyPLAN Database

Biogas KWel 4 20 3.8 www.enama.it

Individual wood, gas and oil boilers

KWth 0.588 15 2.10 EnergyPLAN Database

Individual GSHP KWel 1.188 15 0.60 EnergyPLAN Database

Borehole for GSHP

KWel 3.2 100 0 www.anighp.it; www.rehau.com

Individual gas SOFC mCHP

KWel 4.0 15 2.8 www.solidpower.com

Wood ORC CHP KWel 6.7 15 1.45 [58]

Fossil fuel car car 9.450 15 0 www.fiat.it/punto

Electric car car 18.690 15 5.5 www.nissan.it/Leaf

Table 53: Investment cost, lifetime, fixed O&M cost

6.3 Variable O&M cost

Technology Unit Variable O&M Reference

Hydro €/MWh 1.19 EnergyPLAN Database

Wood ORC CHP €/MWh 2.7 [58]

Table 54: Variable O&M cost

6.4 Generation efficiency

Technology Efficiency Reference

Individual wood boiler Th = 0.75 EnergyPLAN Database

Individual gas boiler Th = 0.9 EnergyPLAN Database

Individual oil boiler Th = 0.8 EnergyPLAN Database

Individual GSHP COP = 3.2 www.anighp.it

Individual gas SOFC mCHP El = 0.5, Th = 0.35 www.solidpower.com

Wood ORC CHP El = 0.18, Th = 0.8 [58]

Electric car 0.168 kWh/km www.nissan.it/Leaf

Table 55: Generation efficiency


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6.5 Fuel price and additional cost

Fuel Price (€/MWh) Reference

Oil & Diesel 145 dgerm.sviluppoeconomico.gov.it/dgerm

Petrol 181.29 dgerm.sviluppoeconomico.gov.it/dgerm

Gas 86 dgerm.sviluppoeconomico.gov.it/dgerm

Wood 35 www.aiel.cia.it

Electricity import Hourly price (average 61.58) Figure 59

www.mercatoelettrico.org/it

Electricity export Hourly price (average 61.58) Figure 59

www.mercatoelettrico.org/it

Electricity internal use additional cost

(Supply and sale + general system charges

+ grid and metering cost + taxes – CEIS and

CEdiS discount (15%))

106.27 www.autorita.energia.it, www.cedis.info, www.ceis‐stenico.it

Table 56: Fuel price and additional cost


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Figure 59: Trend of the Italian electricity market in 2013 [59]


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6.6 Modelling CEIS “Future Optimised Scenarios”

Figure 60: CEIS: best 15 scenarios in terms of AC, comparison between “Current Scenario” (0) and

“Future AC Optimised Scenarios” (1‐15) in terms of AC, CO2‐emission, LFC, ESD


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6.7 Modelling CEDIS “Future Optimised Scenarios”

Figure 61: CEDIS: best 15 scenarios in terms of AC, comparison between “Current Scenario” (0) and

“Future AC Optimised Scenarios” (1‐15) in terms of AC, CO2‐emission, LFC, ESD


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