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5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
Analysis of Smart Energy System approach in local Alpine regions -
a case study in Northern Italy
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S. Bellocchia, G. Guidib, R. De Iuliob, M. Mannoa, B. Nastasic,M. Noussand, M.G. Prinae, R. Robertob
a University of Rome Tor Vergatab ENEA - Italian National Agency for New Technologies, Energy and Sustainable Economic Development c Sapienza University of Romed FEEM - Fondazione Eni Enrico Matteie EURAC Research – European Academy of Bozen-Bolzano
Summary
• Introduction: Valle d’Aosta case study*• Low-carbon future scenarios• Results
– Techno-economic analysis– Optimization analysis (CO2 vs Cost)
• Conclusions • Future developments
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
*The analysis is performed in the framework of IMEAS Project, co-financed by the European Union via Interreg Alpine Space
Introduction: Valle d’Aosta case study
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
Hydro power production exceeds more than 3 times the electricity demand
Electrification
0,02%
47,18%
22,77%
26,00%
4,03%
Heating for residential and services -2015
Coal Oil Natural gas Biomass DH
99,99%
0,01%Transport sector - 2015
Oil Natural gas
Account together for about 80% of total emissions in 2015
Introduction: Valle d’Aosta case study
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
Research Question:• Investigation of low-carbon scenarios with the use of local
resources, optimizing the total cost of the energy system and CO2emissions reduction.
Methodology• Techno-economic analysis using EnergyPLAN.• Optimization analysis using genetic algorithm.
Low-carbon future scenarios
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
Increase of Heat Pumps up to 60%, phase-out of oil boilers
Shift from oil to biofuels consumption up to 50%
Shift from oil to natural gas consumption up to 65%
Exploitation of local dung wastes to produce biogas
Penetration of electric vehicles up to 80% of light-duty fleet
Substitution of diesel heavy trucks with fuel cell ones up to 30%
HP-refHP-LowHP-MidHP-High
0%EV20%EV50%EV80%EV
Technical assumptions
Low-carbon future scenarios
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
• Reference year: 2050• Cost of all the technologies (excl. transport):
Cost database, output of HRE4 (HeatRoadmap Europe 4) project
• Conventional cars and electric vehicles pricesare derived from UNRAE database
• Diesel trucks and H2 trucks prices areextracted from a publication* of ClimateTechnology Centre and Network
Economic assumptions:
Vehicles Cost [k€]
Conventional car 21.64
Electric vehicle 32.75
Diesel heavy truck 78.00
Fuel Cell heavy truck 115.00
*Heavy-duty trucks- Development of Business Cases for Fuel Cells and Hydrogen Applications for Regions and Cities, https://www.ctc-n.org/resources/heavy-duty-trucks-development-business-cases-fuel-cells-and-hydrogen-applications-regions
Results: techo-economic analysis
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
-25%
-40%
-65%
0%
20%
40%
60%
80%
100%
0
100
200
300
400
500
600
700
0%EV 20%EV 50%EV 80%EV
CO2/
CO2,
2015
CO2
emiss
ions
[kt]
CO2 emissions
Reference HP-Low HP-Mid HP-High
Results: techo-economic analysis
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
- €
200 €
400 €
600 €
800 €
1.000 €
1.200 €
1.400 €
1.600 €
CO2
emiss
ion
redu
ctio
n
Tota
l ann
ual c
ost [
M€]
Total annual cost and CO2 emission reduction
Fixed operation costs Variable costs Investment costs CO2 emission variation
-40%
-25%
-65%
Results: optimization analysis
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
Optimization performed using genetic algorithm (NSGA), a MOEAMulti-Objective Evolutionary Algorithm [MATLAB]
Objective: minimize annual costs and CO2 emissions Decision variables:
petrol LDV consumption; diesel LDV consumption; number of diesel HDV; oil boilers consumption; NG boilers consumption; HP heat demand.
Cases: Only Transport (all the other sectors as reference scenario) Transport+Heating - A (industry and agriculture sectors as reference scenario) Transport+Heating - B (industry and agriculture as HP-High scenario)
Results: optimization analysis
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
-10%
-5%
0%
5%
10%
15%
-70% -60% -50% -40% -30% -20% -10% 0%
Annu
al c
osts
var
iatio
n
CO2 emissions variation
Pareto frontsOnly Transport Transport+Heating - A Transport+Heating - B
Results: optimization analysis
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
-70% -65% -60% -55% -50% -45% -40%
Annu
al c
osts
var
iatio
n
CO2 emissions variation
Pareto Front - Transport+Heating - B
All the points are closeto HP-High share
scenarios (59.5%-60%)
EV share = 0.0%H2 trucks share = 0.0%HP share = 60.0%
EV share = 76.8%H2 trucks share = 2.0%HP share = 59.9%
Scenario characterized by massive electrificationEV share = 80.0%H2 trucks share = 30.0%HP share = 60.0%
Results: optimization analysis
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
0
100
200
300
400
500
600
816 822 828 834 840
Pow
er [M
W]
Hour (3rd February)
Hourly distribution of electricity production and demand
Production + import Demand
Conclusions
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
• The technical analysis illustrates:45% CO2 reduction acting on the heating, industry and
agriculture sectors;25% CO2 reduction acting only on the transport sector;65% CO2 reduction in HP-High/80%EV scenario;
• Optimized scenarios are characterized by high-share of heatpumps because it does not affect the total annual cost and itallows to reduce CO2 emission
• The electricity importation in high-electrified scenarios hasbeen identified.
Future developments
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
• Evaluation for integration of electricity storages in order to improve the Sector Coupling (Power-to-X) and the Smart Energy System Approach.
• Further analysis about CO2 and other emissions • Optimization analysis may be enhanced introducing further
decision variables in other sectors.
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
Thank you for your kind attention!
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5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
Supporting information
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
• Valle d’Aosta 2015• Low-carbon scenarios assumptions• Surplus of RES electricity• Annual electricity importation
Valle d’Aosta 2015
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
3465,098,6%
3,30,1%16,4
0,5%
3,80,1%
24,10,7%47,6
1,4%
Hydro Natural gas Biofuel Wind Solar
2,3 2,4
3,1
2,52,3
2,7
3,1 3,1 3,2
0,0
0,5
1,0
1,5
2,0
2,5
3,0
3,5
0
500
1000
1500
2000
2500
3000
3500
4000
2007 2008 2009 2010 2011 2012 2013 2014 2015
Elec
tric
ity*
[GW
h]
Total gross production Gross demand
Production/Demand
*Includes electricity production and distribution losses
Electricity production
* Source: “Exploring potential synergies among energy sectors in alpine regions: the case of Valle d’Aosta” presentation in Proceedings of ECOS 2019 - The 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental impact of Energy Systems – June 23-28, 2019 – Wroclaw, Poland
Valle d’Aosta 2015
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
7,191%
0,060%
304,028%
317,129%
462,942%
78071%
Transport sector fuel demand [GWh] - 2015
Jet fuel NG Petrol Diesel LDV Diesel HDV
Low-carbon scenarios assumptions
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
TechnologyShare of total heating demand
Assumptions2015-ref HP-Low HP-Mid HP-High
Heat pump 2.0% 10.0% 30.0% 60.0% increase up to 60%Oil boiler 46.4% 15.0% 10.0% 0.0% gradual phase-outBiomass boiler 24.0% 24.0% 24.0% 24.0% constantDistrict heating 3.9% 8.6% 7.8% 6.5% slight decrease of fossil DHNatural gas boiler 23.7% 42.3% 28.2% 9.5% remaining part
Other assumptions:• Energy efficiency in buildings: heat demand -10%;• Heat pumps COP +25% from 2.5 to 3.1;• DH demand initially grow (LOW scenario) due to a new CHP plant in the area of
Cervinia that is currently in exercise
Low-carbon scenarios assumptions
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
Sector FuelFuel consumption
[GWh] Assumptions2015-ref HP-Low HP-Mid HP-High
Industry Oil 145.9 114.3 82.7 51.1 Reduction up to 65%Industry Natural Gas 423.7 455.3 486.9 518.5 Shift from oilIndustry Biomass 13.9 13.9 13.9 13.9 Constant
Agriculture Oil 19.9 16.6 13.3 10.0 Reduction up to 50%Agriculture Biofuel 0.1 3.4 6.8 10.1 Shift from oil
Other assumptions:• Biogas production, exploiting dung waste and other farming wastes, increases
gradually until 46.76 GWh/year in the high scenario (upgraded to methane – to the grid)
Low-carbon scenarios assumptions
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
Vehicle typeShare of total Light duty fleet Electricity and fuel consumption
[GWh]0%EV 20%EV 50%EV 80%EV 0%EV 20%EV 50%EV 80%EV
Electric LDV 0% 20% 50% 80% 0.0 36.5 91.1 145.8
Petrol LDV 50% 65% 40% 15% 304.0 387.6 238.8 90.2
Diesel LDV 50% 15% 10% 5% 317.1 79.7 53.1 26.6
Vehicle typeShare of total Heavy duty fleet Fuel consumption
[GWh]0%EV 20%EV 50%EV 80%EV 0%EV 20%EV 50%EV 80%EV
Diesel HDV 100% 90% 80% 70% 462.9 328.6 292.1 255.6
H2 HDV 0% 10% 20% 30% 0.0 35.2 70.5 105.7
Low-carbon scenarios assumptions
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
Transport vehicle consumption [kWh/100km]
Vehicles Scenarios0%EV 20%EV 50%EV 80%EV
Diesel heavy trucks1 251.0 198.0 198.0 198.0Fuel cell heavy trucks1 211.0 191.0 191.0 191.0Petrol cars2 57.5 51.3 51.3 51.3Diesel cars2 50.0 45.7 45.7 45.7Electric Vehicles3 18.4 15.7 15.7 15.7
1 Climate Centre and Network, Development of Business Cases for Fuel Cells and Hydrogen Applications for Regions and Cities. 2 Fuel economy for conventional cars derived from Unione Petrolifera current and projected estimations.3 EV technical specifications derived from a weighted average from the actual national fleet composition.
Low-carbon scenarios assumptions
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
0%EV 20%EV 50%EV 80%EV
HP-ref HP-ref/0%EV 2015-ref/20%EV 2015-ref/50%EV 2015-ref/80%EV
HP-Low HP-Low/0%EV HP-Low/20%EV HP-Low/50%EV HP-Low/80%EV
HP-Mid HP-Mid/0%EV HP-Mid/20%EV HP-Mid/50%EV HP-Mid/80%EV
HP-High HP-High/0%EV HP-High/20%EV HP-High/50%EV HP-High/80%EV
Low-carbon scenarios: 16 combinations
RES surplus
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
-32%-33%
-54%
0%
20%
40%
60%
80%
100%
0,0
0,5
1,0
1,5
2,0
0%EV 20%EV 50%EV 80%EV
RES
surp
lus/
RES
surp
lus2
015
RES
surp
lus
Local use of renewable electricity surplus
Reference HP-Low HP-Mid HP-High Hp-High/80%EVExport=1624 GWh
Annual importation
5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019
#SESAAU2019
0
100
200
300
400
500
600El
ectr
icity
pro
duct
ion
[GW
h]How the added demand is covered
RES CHP Import
HP-High/80%EVElectricity
import: 86 GWh