Analysis of Smart Energy System approach in local Alpine ... · Introduction: Valle d’Aosta case study. 5th International Conference on Smart Energy Systems Copenhagen, 10-11 September

5th International Conference on Smart Energy SystemsCopenhagen, 10-11 September 2019

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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


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*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


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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


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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


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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


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• 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

https://www.ctc-n.org/resources/heavy-duty-trucks-development-business-cases-fuel-cells-and-hydrogen-applications-regions

Results: techo-economic analysis


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-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


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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


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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)



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-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



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-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%



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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


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• 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


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• 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.


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Thank you for your kind attention!

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Supporting information


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• Valle d’Aosta 2015• Low-carbon scenarios assumptions• Surplus of RES electricity• Annual electricity importation

Valle d’Aosta 2015


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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


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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


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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



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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)



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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



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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.



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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


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-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


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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

Documents

Analysis of Smart Energy System approach in local Alpine ... · Introduction: Valle d’Aosta case study. 5th International Conference on Smart Energy Systems Copenhagen, 10-11 September