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Modeling the flexibility offered by coupling the heating sector and - - PowerPoint PPT Presentation

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Modeling the flexibility offered by coupling the heating sector and the power sector: an assessment at the EU level Matija Pavievi , Juan-Pablo Jimenez, Konstantinos Kavvadias, Sylvain Quoilin Faculty of Engineering Technology Joint Research

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5th International Conference on Smart Energy Systems 4th 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 Technology Joint Research Centre – European Commission Matija Pavičević, Juan-Pablo Jimenez, Konstantinos Kavvadias, Sylvain Quoilin

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5th International Conference on Smart Energy Systems 4th 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?

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Introduction

Dispa-SET Model JRC EU-TIMES JRC models:

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5th International Conference on Smart Energy Systems 4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency

  • Model horizon: 2005-2050 (2075)
  • Technology rich (300+) bottom-up energy system
  • ptimisation (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

  • bjectives
  • 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

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JRC-EU-TIMES in a nutshell

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5th International Conference on Smart Energy Systems 4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency

Dispa-SET in a nutshell

  • Unit commitment and dispatch model of the

European power system

  • Optimises

short-term scheduling

  • f

power stations in large-scale power systems

  • Assess system adequacy and flexibility needs
  • f

power systems, with growing share

  • f

renewable energy generation

  • Assess

feasibility

  • f

power sector solutions generated by the JRC-EU-TIMES model

  • Technology mix from ProRES 2050 scenario

used as inputs for Dispa-SET power plant portfolio

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5th International Conference on Smart Energy Systems 4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency

Dispa-SET 2.3: unit commitment and dispatch

Objective

  • Minimise variable system costs

Constraints

  • 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) Commodity Prices (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)

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5th International Conference on Smart Energy Systems 4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency

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Dispa-SET 2.3: System structure & technology

  • verview for a single node

Heat bus Transport bus Electric bus Import Discharge Charge BEVS Non-CHP Generators Exports CHP Generators Electric demand E-Mobility demand Heating demand LIG HRD PEA GAS OTH NUC WIN HDAM HPHS TES BATS Renewable Generators ICEN COMC GTUR STUR WAT HROR PHOT WTON WTOF SUN GEO BIO OIL WST

  • Sector coupling options: P2H, P2V, V2G…
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5th International Conference on Smart Energy Systems 4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency

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JRC-EU-TIMES ProRES Scenario

1,000 2,000 3,000 4,000 5,000 6,000 2016 2030 2050 Capacity [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

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5th International Conference on Smart Energy Systems 4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency

Evaluating the “suitable” heating demand

Heating and cooling needs are responsible for half of the EU28's energy consumption In this analysis, we consider only space heating and DHW for the residential and tertiary sectors:

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

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5th International Conference on Smart Energy Systems 4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency

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  • 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)

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5th International Conference on Smart Energy Systems 4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% NOFLEX THFLEX ALFLEX

% of total heat capacity

CHP’s and TES

Back-pressure Extraction + TES

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Modeling the flexibility resources linked to DH

  • Flexibility of CHP + thermal storage:
  • Back-pressure
  • no flexibility, based on P2H ratio,

installed heat capacity = 100% of maximum hourly heat demand

  • Extraction + TES
  • dispatch flexibility, based on P2H

ratio and Power Loss Factor

  • additional flexibility, provided by

thermal storage unit (24H)

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Alternative flexibility options: Hydro

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% NOFLEX THFLEX ALFLEX

% of total hydro capacity

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

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  • 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 share

  • f 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

% of total EV capacity

EV’s and V2G units

EV P2G

Alternative flexibility options: electric vehicles

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Example simulation results (Summer) – NOFLEX

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Example simulation results (Summer) – THFLEX

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Example simulation results (Summer) – ALFLEX

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5th International Conference on Smart Energy Systems 4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency

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Flexibility - load shifting (Fuel / Technology)

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 NOFLEX THFLEX ALFLEX

Energy [PWh] Scenario

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% NOFLEX THFLEX ALFLEX

Scenario

BIO_STUR GAS_COMC GAS_GTUR GAS_STUR OTH_BEVS WAT_HPHS

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5th International Conference on Smart Energy Systems 4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency

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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

CO2 [milion t] Scenario

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% NOFLEX THFLEX ALLFLEX

Generation by fuel type

RES Non-RES NUC

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5th International Conference on Smart Energy Systems 4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency

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Effect of flexible technologies on curtailment and load shedding

1.25 1.3 1.35 1.4 1.45 1.5 200 400 600 800 1000 1200 1400 1600 1800 NOFLEX HYFLEX EVFLEX THFLEX ALFLEX Max Courtailment [TW] Courtailment [TWh] Scenario

Curtailment

Curtailment Max Curtailment 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 5 10 15 20 25 NOFLEX HYFLEX EVFLEX THFLEX ALFLEX Max Load Shedding[TW] Load Shedding [TWh] Scenario

Load Shedding

Load Shedding Max Load Shedding

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5th International Conference on Smart Energy Systems 4th 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

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5th International Conference on Smart Energy Systems 4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency

Thank You!

sylvain.quoilin@kuleuven.be

http://www.dispaset.eu

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5th International Conference on Smart Energy Systems 4th 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
  • ptimization of the previous day.
  • Avoid issues linked to the end of the optimization period (emptying the hydro)

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Time horizon

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5th International Conference on Smart Energy Systems 4th Generation District Heating, Electrification, Electrofuels and Energy Efficiency

Dispa-SET 2.3 Inputs

Input 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 factors correspond to the capacity factor of each technology/year.

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Total system costs (Fuel / Technology)

15 30 45 60 75 90 105 120 135 150 NOFLEX THFLEX ALFLEX

Bilion EUR Scenario

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% NOFLEX THFLEX ALFLEX

Scenario

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