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17-18 May 2018 ETSAP Meeting 1
An interdisciplinary approach to improve heat
decarbonization assessments at the urban scal...
2
COMPANIES
UNIVERSITY
Cross-
fertilizat
ion
Multi-
disciplin
arity
PUBLIC
ADMINISTRATION
ENERGY CENTRE - EC
The framework
3
The framework
DECISION SUPPORT SYSTEM for
PUBLIC ADMINISTRATION
4
?
Current research activity – Bando «Metti in Rete la
tua idea di ricerca»:
Research Question:
“how different energy car...
5
The framework
ENERGY
PLANNING
Optimization
models
EXPERT
UTILITYDM
DATA COLLECTION AND DATA PROTOCOL:
meeting with stake...
6
?
STATISTICAL LAYER
Socio-economic data: population
BUILDING LAYER
Heated volume, building types
DISTRICT HEATING,
ENERG...
7
?
Model structure
28/05/2018
• Building sector
• Space heating, water heating,
space cooling, lighting, other
electric
•...
?Variables and scenarios
9
?
0%
20%
40%
60%
80%
100%
0
2
4
6
8
10
12
14
16
18
2015
Fossil(direct)
Non-fossil
2050
2015
Fossil(direct)
Non-fossil
20...
0
50
0
10
Equipment efficiency improvement and fuel switching
Stock replacement
Building Retrofit
Thermal consumption
Heat...
0%
20%
40%
60%
80%
100%
012345678
2015
Fossil(direct)
Non-fossil
2050
2015
Fossil(direct)
Non-fossil
2050
2015
Fossil(dire...
12
?
Baseline
40% decarbonization
Decarbon
80% decarbonization
49.6 %
Total consumption
52.6%
Residential final
consumptio...
13
?
49.6 %
Total consumption
52.6%
Residential final
consumption
60%
Reduction of space
heating demand
16.6%
Residential
...
0%
20%
40%
60%
80%
100%
0
2
4
6
8
10
12
14
16
18
2015 Fossil (direct) Non-fossil 2050
Decarbon
CO2consumptionreduction(%)
...
?
0.0%
0.2%
0.4%
0.6%
0.8%
1.0%
1.2%
0% 10% 20% 30% 40% 50% 60%
Yearlyhydrogenpenetration(%)*
Variable RES penetration (%)...
– Backward-Forward
Sweep method
– Steady state
– AC Power Flow
16
?
Co-simulation of electricity and gas networks
Electric...
17
?
Case Study 1: Penetration of PV
+
Demand
Profiles
PV
Progressive Penetration
+ 1kWp each user
Electrical
Network
Simu...
18
?
Case Study: Electrical Network Simulation
Results:
19
?
Case Study: Electrical Network Simulation
July case focus: “Duck Curve” formation and generation of Hydrogen producti...
20
?
Case Study: from Electrical to Gas Network
July case , PPV installed = 4 kWp : rationale of the case study
+
Demand
P...
21
?
Results: on the H2 presence in the natural gas stream [ % Vol ]
Case Study: Gas Network Simulation
22
?
Results: from the thermo-fluid-dynamic perspective
Case Study: Gas Network Simulation
Gas distribution network can provide only limited
flexibility to an over-producing electricity distribution
grid because o...
?Conclusions
Occupant behaviour
Willingness to invest in new
technologies
Multi-criteria methods
High level of expertise
T...
25
Thanks
chiara.delmastro@polito.it
?
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An interdisciplinary approach to improve heat decarbonization assessments at the urban scale

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An interdisciplinary approach to improve heat decarbonization assessments at the urban scale

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An interdisciplinary approach to improve heat decarbonization assessments at the urban scale

  1. 1. 17-18 May 2018 ETSAP Meeting 1 An interdisciplinary approach to improve heat decarbonization assessments at the urban scale Chiara Delmastro, Maurizio Gargiulo Research group: Stefano Corgnati, Andrea Lanzini, Pierluigi Leone, Marco Cavana Department of Energy, Politecnico di Torino & Energy Center Initiative E4SMA s.r.l ?
  2. 2. 2 COMPANIES UNIVERSITY Cross- fertilizat ion Multi- disciplin arity PUBLIC ADMINISTRATION ENERGY CENTRE - EC The framework
  3. 3. 3 The framework DECISION SUPPORT SYSTEM for PUBLIC ADMINISTRATION
  4. 4. 4 ? Current research activity – Bando «Metti in Rete la tua idea di ricerca»: Research Question: “how different energy carriers such as electricity, hydrogen, biological and synthetic fuels can be combined together for a more prosperous, inclusive society, aware of resources and environment?” Different scenarios will be considered in the study that will focus in particular on the evolution and role of the gas network in the energy transition. Heat in The Pipe(2017-2019) Partners: Finantial support:Collaboration
  5. 5. 5 The framework ENERGY PLANNING Optimization models EXPERT UTILITYDM DATA COLLECTION AND DATA PROTOCOL: meeting with stakeholder LONG-TERM CAPACITY PLANNING: urban TIMES model STATISTICS TECHNOLOGY ENERGY URBAN GIS DATABASE BUILDINGS INFRASTRUCTURE CO-SIMULATION: Electricity, gas, DH MANAGEMENTANDVISUALIZATION PLATFORM: Decisiontheatre,soft-linkwithothermodels
  6. 6. 6 ? STATISTICAL LAYER Socio-economic data: population BUILDING LAYER Heated volume, building types DISTRICT HEATING, ENERGY MIX, SOLAR MAP Generation mix, technical data, connected buildings ENERGY CONSUMPTION DATA Energy map Data collection TORINO, ITALY i. ≈ 900 kpeole, 2617 HDD ii. 62% residential, 38% non-residential buildings iii. 57 Mm3 (35%V) connected volume iv. 100% natural gas v. ≈ 740 MW CHP Public buildings Residential buildings DH generation mix and profiles ENERGY BALANCE 2005
  7. 7. 7 ? Model structure 28/05/2018 • Building sector • Space heating, water heating, space cooling, lighting, other electric • High detail level on retrofit building types • Retrofit options • End-use technologies • DH technologies • Latest version: P2G and bio- methane • 2015-2050 • 40 timeslices • Evolution of building demand together with building evolution • Potential role of bio- methane and P2G options
  8. 8. ?Variables and scenarios
  9. 9. 9 ? 0% 20% 40% 60% 80% 100% 0 2 4 6 8 10 12 14 16 18 2015 Fossil(direct) Non-fossil 2050 2015 Fossil(direct) Non-fossil 2050 2015 Fossil(direct) Non-fossil 2050 Baseline S1 S2 CO2emissionreduction(%) UrbanConsumptionMix(TWh) Oil derivates Natural gas Biomass Municipal Solid Waste Electricity import -17.4% -36.4% -46.7% Baseline 40% decarbonization Low carbon 60% decarbonization Decarbon 80% decarbonization 49.6 % Total consumption 52.6% Residential final consumption 60% Reduction of space heating demand 16.6% Residential consumption 12.5% Electricity supplied by solar energy < 130 g/kWh CO2 emissions of the electricity mix (-64% compared to 2015) Reaching targets without renewable gas and P2G
  10. 10. 0 50 0 10 Equipment efficiency improvement and fuel switching Stock replacement Building Retrofit Thermal consumption Heat intensity 10 ? Decarbon 80% decarbonization 49.6 % Total consumption 52.6% Residential final consumption 60% Reduction of space heating demand 16.6% Residential consumption 12.5% Electricity supplied by solar energy < 130 g/kWh CO2 emissions of the electricity mix (-64% compared to 2015) Reaching targets without renewable gas and P2G 0 5 10 15 20 25 30 35 40 45 0 1 2 3 4 5 6 7 2005 2015 2020 2025 2030 2035 2040 2045 2050 Heatintensity(kWh/m3) Spaceheating (TWh)
  11. 11. 0% 20% 40% 60% 80% 100% 012345678 2015 Fossil(direct) Non-fossil 2050 2015 Fossil(direct) Non-fossil 2050 2015 Fossil(direct) Non-fossil 2050 Baseline S1 S2 Oil derivates Natural gas Biomass Heat Electricity Solar Renewable share 0% 20% 40% 60% 80% 100% 0 1 2 3 4 5 6 7 8 2015 Fossil(direct) Non-fossil 2050 2015 Fossil(direct) Non-fossil 2050 2015 Fossil(direct) Non-fossil 2050 Baseline S1 S2 Renewableshare(%) Residentialfinalenergyconsumption(TWh) 11 ? Baseline 40% decarbonization Low carbon 60% decarbonization Decarbon 80% decarbonization 49.6 % Total consumption 52.6% Residential final consumption 60% Reduction of space heating demand 16.6% Residential consumption 12.5% Electricity supplied by solar energy < 130 g/kWh CO2 emissions of the electricity mix (-64% compared to 2015) Reaching targets without renewable gas and P2G
  12. 12. 12 ? Baseline 40% decarbonization Decarbon 80% decarbonization 49.6 % Total consumption 52.6% Residential final consumption 60% Reduction of space heating demand 16.6% Residential consumption 12.5% Electricity supplied by solar energy < 130 g/kWh CO2 emissions of the electricity mix (-64% compared to 2015) Reaching targets without renewable gas and P2G Land Roof Water heating production Electricity Production 0 100 200 300 400 500 600 0 50 100 150 200 250 300 Thermalpanels SolarPV Thermalpanels SolarPV Thermalpanels SolarPV Thermalpanels SolarPV Thermalpanels SolarPV Thermalpanels SolarPV Thermalpanels SolarPV 2020 2025 2030 2035 2040 2045 2050 Energyproduction(GWh) Newcapacity(MW) 0 100 200 300 400 500 600 0 50 100 150 200 250 300 Thermalpanels SolarPV Thermalpanels SolarPV Thermalpanels SolarPV Thermalpanels SolarPV Thermalpanels SolarPV Thermalpanels SolarPV Thermalpanels SolarPV 2020 2025 2030 2035 2040 2045 2050 Energyproduction(GWh) Newcapacity(MW)
  13. 13. 13 ? 49.6 % Total consumption 52.6% Residential final consumption 60% Reduction of space heating demand 16.6% Residential consumption 12.5% Electricity supplied by solar energy < 130 g/kWh CO2 emissions of the electricity mix (-64% compared to 2015) Reaching targets without renewable gas and P2G 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 5 000 1990 2000 2010 2020 2030 2040 2050 CO2emissions(kt/y) Historic Baseline Scenario 1 Scenario 2
  14. 14. 0% 20% 40% 60% 80% 100% 0 2 4 6 8 10 12 14 16 18 2015 Fossil (direct) Non-fossil 2050 Decarbon CO2consumptionreduction(%) Urbanconsumptionmix(TWh) Oil derivates Natural gas Biogas Municipal Solid Waste Net Electricity import Solar CO2 emission reduction (1990 levels) ? -5% 0% 5% 10% 15% Delta BIOGAS80 - Baseline Delta BIOGAS80PS - No Biogas PS TotalSystemCostDifference(%) Inv Cost Fix O&M Var O&M + 2.9% - 0.06% 52.7 % Gas has a dominant role Biogas penetrate in the grid for 37% Important role of biogas for decarbonizing future energy systems at competitive costs, even when the power sector is 37% decarbonized compared to 2015. P2G not selected in these conditions. < 227 g/kWh CO2 emissions of the electricity mix (- 36.23% compared to 2015) -59.92% Introducing renewable gas 14
  15. 15. ? 0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 0% 10% 20% 30% 40% 50% 60% Yearlyhydrogenpenetration(%)* Variable RES penetration (%) 80% 2050 Decarbonization, limits of the grid: 17% in terms of volume (6.3% in terms of energy) * Yearly energy contribution To reach the decarbonization goal (investments postponed), at roughly 20% electricity produced by solar RES, P2G starts to penetrate in the energy mix. It contributes for roughly the 1% of the total yearly energy produced by gas. P2G option without methanation: delivering H2 from renewable If methanation is allowed, it is easier to exploit the whole electricity excess, gas maintain the higher share of total consumption. Considering technical limits of the gas grid, P2G option without methanation is attractive when it is needed to avoid curtailment and to improve the flexibility of the electricity grid when high decarbonization targets should be meet.
  16. 16. – Backward-Forward Sweep method – Steady state – AC Power Flow 16 ? Co-simulation of electricity and gas networks Electric Network Model Gas Network Model 1D analytical method – Steady state – Non-isothermal – Multi-component –Parameters Parameters Inputs Inputs Outputs Physical Model – Gas Demand Profiles – P, T, Composition at injection node – Network Topology – Pipelines Features Physical Model Outputs Network Topology – Cables Features – Power Demand Profiles – Reference Y, V – at slack node P, T, Composition – at each node Gas Flow – for each branch – Voltage at each node – Current for each branch
  17. 17. 17 ? Case Study 1: Penetration of PV + Demand Profiles PV Progressive Penetration + 1kWp each user Electrical Network Simulation PV daily generation profile each month Abnormal Electrical Network operations ? YesNo P2G H2 Injection Gas Network Simulation Electricity Network Simulation: rationale of the case study
  18. 18. 18 ? Case Study: Electrical Network Simulation Results:
  19. 19. 19 ? Case Study: Electrical Network Simulation July case focus: “Duck Curve” formation and generation of Hydrogen production pattern Hydrogen production from PEM (efficiency 0.7)
  20. 20. 20 ? Case Study: from Electrical to Gas Network July case , PPV installed = 4 kWp : rationale of the case study + Demand Profiles PV Overproduction Gas Network Simulation H2 daily generation profile to avoid reverse flow Abnormal Gas Network operations ? P2G H2 Injection in the Gas Network Assumptions Gas Network in summer operation H2 injection node choice: proximity criteria Gas Consumption is 15% wrt Winter Mode H2 injection node is the nearest to the HV-MV station
  21. 21. 21 ? Results: on the H2 presence in the natural gas stream [ % Vol ] Case Study: Gas Network Simulation
  22. 22. 22 ? Results: from the thermo-fluid-dynamic perspective Case Study: Gas Network Simulation
  23. 23. Gas distribution network can provide only limited flexibility to an over-producing electricity distribution grid because of a negative overlapping between the seasonal fluctuation of the respective operational mode and limited penetration of hydrogen conversion technologies (e.g. heating, transport) in the short term.28/05/2018 23 ?Case Study: Gas Network Simulation Results: on the gas quality parameters 0,55 47,3 34,9
  24. 24. ?Conclusions Occupant behaviour Willingness to invest in new technologies Multi-criteria methods High level of expertise Time consuming Low understanding for decision makers Low data availability New DB requires new instruments and resources Adding sectors (transport, industry) Enlarging the study at the regional level Adding models PLATFORM CREATION INTEROPERABIL ITY DATA NEW DATA PROTOCOL HUMAN BEHAVIOUR STOCASTIC INPUTS & SOCIO- ECONOMIC ANALYSES TRADITIONAL THINKING HIGHER TRANSPARENCY
  25. 25. 25 Thanks chiara.delmastro@polito.it ?

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