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5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency
Modeling the flexibility offered by coupling the heating sector and the power sector: an assessment at the EU level
Faculty of Engineering TechnologyJoint Research Centre – European Commission
Matija Pavičević, Juan-Pablo Jimenez, Konstantinos Kavvadias, Sylvain Quoilin
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency
• Main questions: • How much flexibility can we obtain from district heating, CHPs and thermal
storage in the EU power system?• How does that compare to other flexibility options (hydro, EVs)?• How can this be modeled in a long-term planning context?
2
Introduction
Dispa-SET Model
JRC EU-TIMES
JRC models:
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency
• Model horizon: 2005-2050 (2075)
• Technology rich (300+) bottom-up energy system optimisation (partial equilibrium) model based on the TIMES model generator of the IEA
• Designed for analysing the role of energy technologies and their innovation for meeting Europe's energy and climate related policy objectives
• Electricity multi-grid model (high, medium and low voltage grid), tracking demand-supply via 12 time slices (4 seasons, 3 diurnal periods), and gas across 4 seasons
• 70 exogenous demands for energy services
3
JRC-EU-TIMES in a nutshell
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency
Dispa-SET in a nutshell
• Unit commitment and dispatch model of theEuropean power system
• Optimises short-term scheduling of powerstations in large-scale power systems
• Assess system adequacy and flexibility needsof power systems, with growing share ofrenewable energy generation
• Assess feasibility of power sector solutionsgenerated by the JRC-EU-TIMES model
• Technology mix from ProRES 2050 scenarioused as inputs for Dispa-SET power plant portfolio
4
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency
Dispa-SET 2.3: unit commitment and dispatch
Objective• Minimise variable system costsConstraints• Hourly demand balances
(power and reserve)• Ramping constraints, minimum up and
down times• Storage balances (PHS,CAES)• NTC based market coupling• Curtailment of wind, PV and load
shedding (optional)
Wind, PV Generation(MWh/h)
CommodityPrices
(EUR/t)
Power Demand(MWh/h)
Variable costs/prices(EUR/MWh)
Plant output (MWh/h)
Emissions (t CO2)
Plant data(MW, eff,…)
• Formulated as a tight and compact mixed integer program (MILP)• Implemented in Python and GAMS, solved with CPLEX
Plant on/off status (binary)
5
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency 6
Dispa-SET 2.3: System structure & technology overview for a single node
Heat bus
Transport bus
Electric busImport
Dis
char
ge
Cha
rge
BEVS
Non-CHPGenerators
Exports
CHPGenerators
Electric demand
E-Mobility demand
Heatingdemand
LIGHRD PEA GASOTHNUC
WIN
HDAM
HPHS
TES
BATS
Ren
ewab
leG
ener
ator
s
ICEN COMCGTURSTUR
WAT
HROR
PHOT
WTON
WTOF
SUN
GEOBIOOIL WST
• Sector coupling options: P2H, P2V, V2G…
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency 7
JRC-EU-TIMES ProRES Scenario
0
1.000
2.000
3.000
4.000
5.000
6.000
2016 2030 2050
Cap
acity
[GW
]
Scenario
Used in this case study
• Ambitious scenario in terms of additions of RES-E technologies
• Significant reduction of fossil fuel use, in parallel with nuclear phase out
• CCS doesn’t become commercial• Deep emission reduction is achieved
with high deployment of RES, electrification of transport and heat and high efficiency gains
• Primary energy is about 430 Ej, renewables supply 93% of electricity demand in 2050
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency
Evaluating the “suitable” heating demandHeating and cooling needs are responsible for half of the EU28's energy consumptionIn this analysis, we consider only space heating and DHW for the residential and tertiary sectors:
0 500 1000 1500 2000 2500 3000 3500
Residential
Tertiary
Industrial
Final Energy consumption for 2015 (TWh)
Space Cooling Process Cooling Space Heating Hot Water Process Heating Cooking Non H&C
Data source: JRC IDEES Database
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency 9
• We consider only the heating demand that fulfills the following conditions:• Medium heat demand density areas: > 120 TJ/km²• Maximum distance from a Power plant: 100 km
Evaluating the “suitable” heating demand
Pan-European Thermal Atlas Peta v4.3:
JRC Power plant database:
Considered heat demand: 3520 TWh 690 TWh (630 TWh in 2050)
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
NOFLEX THFLEX ALFLEX
% o
ftot
al h
eatc
apac
ity
CHP’s and TES
Back-pressure
Extraction + TES
10
Modeling the flexibility resources linked to DH• Flexibility of CHP + thermal storage:
• Back-pressure• no flexibility, based on P2H ratio,
installed heat capacity = 100% ofmaximum hourly heat demand
• Extraction + TES• dispatch flexibility, based on P2H
ratio and Power Loss Factor• additional flexibility, provided by
thermal storage unit (24H)
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency 11
Alternative flexibility options: Hydro
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
NOFLEX THFLEX ALFLEX
% o
ftot
al h
ydro
capa
city
Hydro units
HPHS
HDAM
HROR
• Flexibility of hydro units:• HROR units
• no flexibility, based on availability factors
• HDAM units• dispatch flexibility, based on
inflows and accumulation capacity• HPHS units
• load shifting flexibility, pumped storage units based on inflows from upper and lower streams and accumulation capacity
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency 12
• We assume that EVs constitute 75% of the whole vehicle fleet by 2050
• Flexibility by EVs:• Base case:
• no flexibility, based on charging patterns, charging demand integrated into the electricity demand
• V2G• Possibility for the system to use the
connected batteries. Restricted by the charging paterns and the shareof the fleet that is connected to the grid and available for providing flexibility
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
NOFLEX THFLEX ALFLEX
% o
ftot
al E
V ca
paci
ty
EV’s and V2G units
EV
P2G
Alternative flexibility options: electric vehicles
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency 13
Example simulation results (Summer) – NOFLEX
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency 14
Example simulation results (Summer) – THFLEX
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency 15
Example simulation results (Summer) – ALFLEX
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency 16
Flexibility - load shifting (Fuel / Technology)
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1,6
NOFLEX THFLEX ALFLEX
Ener
gy [P
Wh]
Scenario
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
NOFLEX THFLEX ALFLEXScenario
BIO_STUR
GAS_COMC
GAS_GTUR
GAS_STUR
OTH_BEVS
WAT_HPHS
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency 17
CO2 Emissions and share of renewables
0,00
100,00
200,00
300,00
400,00
500,00
600,00
700,00
800,00
900,00
1000,00
NOFLEX THFLEX ALFLEX
CO
2 [m
ilion
t]
Scenario
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
NOFLEX THFLEX ALLFLEX
Gen
erat
ion
byfu
elty
pe
RES Non-RES NUC
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency 18
Effect of flexible technologies on curtailment and load shedding
1,25
1,3
1,35
1,4
1,45
1,5
0
200
400
600
800
1000
1200
1400
1600
1800
NOFLEX HYFLEX EVFLEX THFLEX ALFLEX
Max
Cou
rtai
lmen
t [TW
]
Cour
tailm
ent [
TWh]
Scenario
Curtailment
Curtailment Max Curtailment
0
0,02
0,04
0,06
0,08
0,1
0,12
0,14
0,16
0,18
0
5
10
15
20
25
NOFLEX HYFLEX EVFLEX THFLEX ALFLEX
Max
Loa
d Sh
eddi
ng[T
W]
Load
She
ddin
g [T
Wh]
Scenario
Load Shedding
Load Shedding Max Load Shedding
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency
• Soft-linking long-term planning models and power dispatch models allows to evaluate the adequacy and flexibility of the system, even over long time horizons.
• District heating with thermal storage does provide flexibility, but less than those provided by EVs or hydro power plants
• This is partly explained by the low share of the thermal demand covered by DH in our simulations. Considering heat pump with thermal storage would increase the benefits of heat-power sector coupling.
• All methods and models are released as open-source (Dispa-SET side):
https://github.com/energy-modelling-toolkit/Dispa-SET
Conclusions
19
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency
Thank [email protected]
http://www.dispaset.eu
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency
• Simulation is performed for a whole year with a time step of one hour, • Problem dimensions (not computationally tractable for the whole time-horizon)• Problem is split into smaller optimization problems that are run recursively
throughout the year. • Optimization horizon is three days, with a look-ahead period of one day. • The initial values of the optimization for day j are the final values of the
optimization of the previous day. • Avoid issues linked to the end of the optimization period (emptying the hydro)
21
Time horizon
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency
Dispa-SET 2.3 InputsInput database:• RES generation
profiles• Power plants• Demand curves• Outages• Fuel prices• Lines capacities• Minimum reservoir
levels
From the same database different levels of model complexity are available:• MILP• LP with all power
plants• LP one cluster per
technology• LP presolve +
MILP
22
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency 23
Dispa-SET validation for 2016
Validation of the Dispa-SET model (red lines) on the ENTSOE dataset (black/grey lines). The annotated factorscorrespond to the capacity factor of each technology/year.
5th International Conference on Smart Energy Systems4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency 24
Total system costs (Fuel / Technology)
0
15
30
45
60
75
90
105
120
135
150
NOFLEX THFLEX ALFLEX
Bilio
n EU
R
Scenario
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
NOFLEX THFLEX ALFLEXScenario
Spillage
Heat Slack
Lost Load
RampUp
StartUp
Load Shedding
GAS_STUR_CHP
GAS_GTUR_CHP
GAS_COMC_CHP
BIO_STUR_CHP
OIL_STUR
NUC_STUR
LIG_STUR
HRD_STUR
GEO_STUR
GAS_STUR
GAS_ICEN
GAS_GTUR
GAS_COMC
BIO_STUR