A method for holistic energy system design

A method for holistic energy

system design

World Sustainable Energy Days (WSED) 2018, Wels / Austria S. Thiem, V. Danov, M. Kautz, V. Chapotard, A. Zillich, J. Schaefer | CT REE ENS DEH-DE | March 02, 2018

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under

grant agreement No 680447

Unrestricted © Siemens AG 2018

March 2018 Page 2 S. Thiem | CT REE ENS DEH-DE

Mobilization of innovative design tools for refurbishing of

buildings at district level – Motivation for energy system design

Picture source: https://www.siemens.com/content/dam/internet/siemens-com/innovation/pictures-of-the-future/infrastructure-and-finance/other-

assets/aspern-luftbild-a5-cschreinerkastler.jpg.adapt.916.high.jpg/1480604675037.jpg

(1) How should the optimal energy system design

concept look like?

(2) How much total expenditures can be saved?

(3) How can carbon dioxide emissions cost-efficiently

be reduced?

(4) What synergies can be achieved from considering

electricity, thermal energy and water holistically?

[…]

Method for holistic energy system design



Agenda

• Method for holistic energy system design 1

• Case study introduction: Suonenjoki, Finland 2

• Key results 3

• Conclusion & discussion 4



Agenda


• Key results 3





Holistic energy system design – Novel approach for the holistic

optimization of the on-site energy supply system

• Optimization

objective

Results (output data)

• Climate data

• Commodity prices

• Load profiles

Energy system design Mandatory input data

• Technology

selection

• Optimal capacities

• Optimal operation

schedule

• Economical

analysis

Optional input data

• Technology

pre-selection

• Technology models

and parameters

• Technology

cost models

• Renewable

generation profiles

$ CO2 PE • Mathematical optimization problem

• Find optimal combination within solution space





Energy system design

Solution space (“superstructure”)

Inputs Energy conversion & storage Demands

Case-specific optimal system

Inputs Energy conversion & storage Demands

Existing

technologies

• What is the economic

advantage?

• What is the impact on the carbon

footprint and how can it be reduced?

• How future-proof / robust is

the optimal design?

Further questions to be answered:





Energy system design

“MM-ESD optimizes the design of an energy system

with respect to a certain objective under given constraints.”

• Optimization: Solving a mathematical optimization problem

• Design: Selection and capacities of energy conversion units, energy storages and energy transfer

• Energy system: Combination of energy conversion units, energy storages, energy transfer lines, loads,

renewables and others at multiple energy forms (multi-modal energy system)

• Objective: Achieve minimum total expenditures, CO2 emissions or primary energy consumption, or

combinations of these

• Constraints: Constraints to the optimization problem (e.g., power balances, part-load efficiencies, …)



Agenda


• Key results 3





© 2018 DigitalGlobe, Kartendaten, © 2018 Google

Modeling the energy system – Partition of the city into districts for

considering district heating losses (multi-node approach)

North District

(Sairaalapolku 4)

South District

(Olavi Leskisen katu 10)

West District

(Koulukatu 21)

LK14

(Rautalammintie 8)

Kuo

(Kuopiontie 2)

LK25

(Kimpankatu 5)

LK15

(Koulukatu 23)

PN_LK25

PN_ND

PN_Kuo

PN_SD

Center District

(Väinönkatu 7)

Suenonjoki, Finland

• Population: 7366 [1]

• Area: 713.54 km2 [2]

Investigated area

• Population: approximately 1500

• Area: 0.56 km2 [4]

[1] Population density by area 1.1.2016. Statistics Finland. Retrieved 12 February 2017..

[2] Population according to language and the number of foreigners and land area km2 by area as of 31 December 2008". Statistics Finland's PX-

Web databases. Statistics Finland. Retrieved 29 March 2009.

[4] Measured with Google Maps

[5] Project communication with VTT and Sweco, 2017.

• LK14: LFO

• LK15: HFO

• LK25: Wood, peat (main); LFO (peak)

• Kuo: LPG

LFO: Light fuel oil; LPG: Liquid petroleum gas; HFO: Heavy fuel oil

[5]



Required input data for the energy system design study

Climate data

• Temperature

• Global horizontal irradiance

Load profiles

• Electricity consumption

• Heat consumption

• District heat

• Other heat sources

Energy technologies

• Existing heat plants and boilers with different conventional fuels

• Installable energy conversion units:

LPG-CHP (Residential + Utility), WP-CHP, GSHP, AWHP, EB

• Installable storage units: HWS, LIB (Utility + Residential), RFB

• Installable renewable: PV (Utility + Residential), ST

Commodity prices

• Electricity

• CO2 emission price (carbon tax)

• Heat generation fuels:

LPG, Wood, Peat, Light fuel oil (LFO), Heavy fuel oil (HFO),

Heat oil

AWHP: Air-water heat pump, EB: Electric boiler, LIB: Lithium-ion battery, LPG: Liquid petroleum gas,

LPG-CHP: Combined heat and power fired with liquid petroleum gas, GSHP: Ground-source heat pump,

HWS: Hot water storage, PV: Photovoltaic, RFB . Redox-flow battery, ST: Solar thermal heat,

WP-CHP: Combined heat and power fired with wood and peat

Multi-objective optimization considering total expenditures and carbon footprint

(!) Social welfare optimum for entire city (residents + utility supplying district heat); assuming “on-site generation” is OK



Energy system design at different levels of detail


Suonenjoki

One-node model


North District

(Sairaalapolku 4)

South District

(Olavi Leskisen katu 10)

West District

(Koulukatu 21)

LK14

(Rautalammintie 8)

Kuo

(Kuopiontie 2)

LK25

(Kimpankatu 5)

LK15

(Koulukatu 23)

PN_LK25

PN_ND

PN_Kuo

PN_SD

Center District

(Väinönkatu 7)

Multi-node model (district scale)

• Simplified model of the city with no transport processes

• Faster to solve

• Results easier to analyze and to communicate

• District heating system (and, e.g., losses) between districts can be

considered; electric grid considered as “copper plate” here

• Computationally much more intense to solve

• Results are difficult to analyze and quite complex to communicate



Energy system design for Suonenjoki – Scenario overview

One-node model Multi-node model (at district scale)

Op

tim

izati

on

su

pers

tru

ctu

re

Exis

tin

g s

yste

m

“Reference”

(optimized

operation of the

existing system)

“Optimized”

(optimized

operation of an

optimally

designed

system)

CHP: Combined heat and power unit, EB: Electric boiler, GSHP: Ground-source heat pump, HFO: Heavy fuel oil, HGP: Heat

generation plant, HWS: hot water storage, LFO: Light fuel oil, LPG: Liquid petroleum gas, OB: Oil boiler, P: Peat,

PG: Power grid, W: Wood, WB: Wood boiler, WP: Wood and peat



Agenda




• Key results 3



Aiming for lowest costs – How should Suonenjoki’s energy system

look like?

CHP: Combined heat and power unit, DH: District heating pipeline, EB: Electric boiler, GSHP: Ground-source heat pump,

HFO: Heavy fuel oil, HGP: Heat generation plant, HWS: hot water storage, LFO: Light fuel oil, LPG: Liquid petroleum gas,

OB: Oil boiler, P: Peat, PG: Power grid, W: Wood, WB: Wood boiler, WP: Wood and peat

(1) Which technologies should be installed?

Peat-fired combined heat and power unit at LK14 and LK15, hot water storages




look like?



OB: Oil boiler, P: Peat, PG: Power grid, W: Wood, WB: Wood boiler, WP: Wood and peat

(2) How large is the initial investment?

Roughly 1.5 Mio. € needed (district heating utility)

(3) What is the economic advantage of such system?

25.8% of total expenditures could be saved

✓ Significant cost saving possible




look like?

HFO: Heavy fuel oil, LFO: Light fuel oil, P: Peat, PG: Power grid, W: Wood

(4) But what about the carbon footprint?

Carbon footprint increased by 39.9%

X Significant increase of carbon footprint

Multi-objective energy system design

aiming for both low costs and low carbon

footprint necessary

? Is wood carbon neutral?

(No carbon footprint?)



Multi-objective optimization of total expenditures and the carbon

footprint – Wood carbon-neutral (one-node case)

Carbon footprint

reduction without

any significant

cost increase…

Cost-optimized

case

Cost and CO2

improvement

…by firing wood

instead of peat



OB: Oil boiler, P: Peat, PG: Power grid, RFB: Redox-flow battery, W: Wood, WB: Wood boiler, WP: Wood and peat

✓




footprint – Wood not carbon-neutral (one-node case)



OB: Oil boiler, P: Peat, PG: Power grid, RFB: Redox-flow battery, W: Wood, WB: Wood boiler, WP: Wood and peat

[…]

?

Carbon footprint

reduction much

more costly…

Cost-optimized

case

Cost and CO2

improvement

…due to increased

power grid usage and

heat pumps

Note that peat replacement

by wood cannot

lower the carbon footprint

to ~0 anymore

-43.9%




footprint – Wood not carbon-neutral (one-node case)

AWHP: Air-water heat pump, CHP: Combined heat and power unit, DH: District heating pipeline, EB: Electric boiler,

GSHP: Ground-source heat pump, HFO: Heavy fuel oil, HGP: Heat generation plant, HWS: hot water storage,

LFO: Light fuel oil, LIB: Lithium-ion battery, LPG: Liquid petroleum gas, OB: Oil boiler, P: Peat, PG: Power grid,

PV: Photovoltaic, RFB: Redox-flow battery, ST: Solar thermal heating, W: Wood, WB: Wood boiler, WP: Wood and peat

✓

(0) Cost-

optimized case

(1) Replace

peat by wood

(2) Increase

utilization of power

grid and decrease

use of CHP

(3) Use electricity-driven

heating technologies (in

particular heat pumps)

and hot water storages

(4) Install renewables

(in particular photovoltaic) (5) Install batteries

(redox-flow and

lithium-ion batteries)

-43.9%



Agenda



• Key results 3




Conclusion – Concluding remarks concerning the energy system

design study for Suonenjoki

• Holistic multi-modal energy system design methods developed within EU H2020 MODER project (WP4)

• Testing and validation by energy system design study for Suonenjoki, Finland (WP6)

• Assumption of “on-site energy system” use of combined heat and power attractive

• Implications from one-node vs. multi-node model

• Multi-objective optimization Both costs and carbon footprint can be reduced simultaneously

• Is the thermal use of wood carbon-neutral?

• If not, follow this guideline to reduce carbon footprint most cost-efficiently:

(1) Replace peat by wood

(2) Increase utilization of power grid and decrease use of CHP

(3) Use electricity-driven heating technologies (in particular heat pumps) and hot water storages

(4) Install renewables (in particular photovoltaic)

(5) Install batteries (redox-flow and lithium-ion batteries)



Acknowledgements – We’d like to thank…

• … the European Commission for funding this Horizon 2020 project MODER:

• … MODER project partners for their support for and contribution to this case study:

• … other sources for making this case study possible:

Thank you for your attention! Do you have any questions?

This project has received funding from the European Union’s Horizon

2020 research and innovation programme under grant agreement No

680447

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A method for holistic energy system design