Modeling the flexibility offered by coupling the heating sector and … · 2019-09-26 · 5th...

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


• 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:


• 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


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


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…


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


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


• 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)


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)


Alternative flexibility options: Hydro

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


% 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


• 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%


% o

ftot

al E

V ca

paci

ty

EV’s and V2G units

EV

P2G

Alternative flexibility options: electric vehicles


Example simulation results (Summer) – NOFLEX


Example simulation results (Summer) – THFLEX


Example simulation results (Summer) – ALFLEX


Flexibility - load shifting (Fuel / Technology)

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6


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


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


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


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


• 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

https://github.com/energy-modelling-toolkit/Dispa-SET


Thank [email protected]

http://www.dispaset.eu


• 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)

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Time horizon


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

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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.


Total system costs (Fuel / Technology)

0

15

30

45

60

75

90

105

120

135

150


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

Modeling the flexibility offered by coupling the heating sector and … · 2019-09-26 · 5th...

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