Evaluating flexibility and adequacy in future EU power systems: model coupling and long-term forecasting Sylvain Quoilin, Wouter Nijs and Andreas Zucker Joint Research Center (JRC)

Flexibility assessment in long-term planning models Main challenge: integrate flexibility contraints into low time-resolution models Should be expressed linear constraints between model variables (no integers!) Information about the chronology of timesteps is lost Focus on high shares of renewables Dispa-SET Model ? JRC EU-TIMES JRC models:

JRC-EU-TIMES in a nutshell 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

The JRC-EU-TIMES model optimises the energy system over long time periods Aligned to latest EU Energy Reference Scenarios Energy services demands (pkm, tkm, PJ, Mt) Supply and demand technologies Objective Minimise total energy system costs Constraints Demand and supply balances: Transport, industry, buildings, agriculture - primary energy (RES, fossils), refineries and electricity Impacts of high variable RES-e Flexible use possible excess RES-e: curtailment, Power2gas and storage Reduced operation dispatch. power Material and energy flows Resources and costs Technology costs (O&M, investments) Technologies ETRI Policies (GHG and energy target, subs.) Emissions (t CO2) and prices

JRC EU-TIMES - illustrative results

Flexibility constraints in JRC-EU-TIMES Water Heating Space Heating DSM Space Cooling Excess (20 GWh) Batteries PtX Diesel/Kero Demand (100 GWh) Methanol Curtailment Methane Deal with the surplus Power (GW) VRE (60 GWh) Other (40 GWh)

Dispa-SET in a nutshell Unit commitment and dispatch model of the European power system Optimises short-term scheduling of power stations in large-scale power systems Assess system adequacy and flexibility needs of power systems, with growing share of renewable energy generation Assess feasibility of power sector solutions generated by the JRC-EU- TIMES model

Dispa-SET 2.1: unit commitment and dispatch model Wind, PV Generation (MWh/h) 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) Plant output (MWh/h) Power Demand (MWh/h) Plant on/off status (binary) Commodity Prices (EUR/t) Variable costs/prices (EUR/MWh) Plant data (MW, eff,…) Emissions (t CO2) Formulated as a tight and compact mixed integer program (MILP) Implemented in Python and GAMS, solved with CPLEX

Dispa-SET 2.1 Current coverage

Dispa-SET 2.1: typical outputs

Example Dispa-SET simulation: the Belgian power system Nuclear plants: Doel 1 Doel 2 Doel 3 Doel 4 Tihange 1 Tihange 2 Tihange 3 CCGT plants: + Pumped hydro, wind, solar

Increasing VRE capacity Peak load: 11 GW VRE: 2.6 GW 7% of demand VRE: 23 GW 49% of demand

Increasing power plant flexibility 6 GW Nuclear 5 GW CCGT

Increasing power plant flexibility 0 GW Nuclear 11 GW CCGT

Effect of storage power and capacity Power: 1.3 GW Capacity: 4.5h

Effect of storage power and capacity Power: 4 GW Capacity: 4.5h

Effect of storage power and capacity Power: 6.3 GW Capacity: 4.5h

Effect of Wind/Solar share 0% 100% Total: 20GW

Effect of Wind/Solar share 25% 75% Total: 20GW 20 September 201820 September 2018

Effect of Wind/Solar share 50% 50% Total: 20GW 20 September 201820 September 2018

Effect of Wind/Solar share 75% 25% Total: 20GW 20 September 201820 September 2018

Effect of Wind/Solar share 100% 0% Total: 20GW 20 September 201820 September 2018

Simplified evaluation of curtailment and storage Focus on high shares of renewables! Approach: Simulation of the power system Identification of the curtailed energy Express the curtailed amount as a function of the power system characteristics

Methodology Input parameters of the simulations: Thermal capacity margin Share of flexible capacity (CCGT vs Nuclear) Storage Power [MW] Storage Capacity [MWh] Wind penetration PV penetration

Simulation in Dispa-SET Methodology Input parameters of the simulations: Thermal capacity margin Share of flexible capacity (CCGT vs Nuclear) Storage Power [MW] Storage Capacity [MWh] Wind penetration PV penetration Latin hypercube: Definition of 245 Runs Simulation in Dispa-SET

Simulation in Dispa-SET Methodology Input parameters of the simulations: Thermal capacity margin Share of flexible capacity (CCGT vs Nuclear) Storage Power [MW] Storage Capacity [MWh] Wind penetration PV penetration Latin hypercube: Definition of 245 Runs Simulation in Dispa-SET

Simulation in Dispa-SET Methodology Input parameters of the simulations: Thermal capacity margin Share of flexible capacity (CCGT vs Nuclear) Storage Power [MW] Storage Capacity [MWh] Wind penetration PV penetration Latin hypercube: Definition of 245 Runs Simulation in Dispa-SET

Simulation in Dispa-SET Methodology Input parameters of the simulations: Thermal capacity margin Share of flexible capacity (CCGT vs Nuclear) Storage Power [MW] Storage Capacity [MWh] Wind penetration PV penetration Latin hypercube: Definition of 245 Runs Simulation in Dispa-SET

Simulation in Dispa-SET Methodology Input parameters of the simulations: Thermal capacity margin Share of flexible capacity (CCGT vs Nuclear) Storage Power [MW] Storage Capacity [MWh] Wind penetration PV penetration Latin hypercube: Definition of 245 Runs Simulation in Dispa-SET

Simulation in Dispa-SET Methodology Input parameters of the simulations: Thermal capacity margin Share of flexible capacity (CCGT vs Nuclear) Storage Power [MW] Storage Capacity [MWh] Wind penetration PV penetration Latin hypercube: Definition of 245 Runs Simulation in Dispa-SET

Simulation in Dispa-SET Methodology Input parameters of the simulations: Thermal capacity margin Share of flexible capacity (CCGT vs Nuclear) Storage Power [MW] Storage Capacity [MWh] Wind penetration PV penetration Latin hypercube: Definition of 245 Runs Simulation in Dispa-SET

Simulation in Dispa-SET Methodology Input parameters of the simulations: Thermal capacity margin Share of flexible capacity (CCGT vs Nuclear) Storage Power [MW] Storage Capacity [MWh] Wind penetration PV penetration Latin hypercube: Definition of 245 Runs Simulation in Dispa-SET

Methodology Input parameters of the simulations: Thermal capacity margin Share of flexible capacity (CCGT vs Nuclear) Storage Power [MW] Storage Capacity [MWh] Wind penetration PV penetration Latin hypercube: Definition of 245 Runs Simulation in Dispa-SET Cleaning up: discarding simulations with lost load => 219 results

Methodology Input parameters of the simulations: Thermal capacity margin Share of flexible capacity (CCGT vs Nuclear) Storage Power [MW] Storage Capacity [MWh] Wind penetration PV penetration Latin hypercube: Definition of 245 Runs Simulation in Dispa-SET Cleaning up: discarding simulations with lost load => 219 results Gaussian Processes regression of the results to build the most likely response surface

Methodology Input parameters of the simulations: Thermal capacity margin Share of flexible capacity (CCGT vs Nuclear) Storage Power [MW] Storage Capacity [MWh] Wind penetration PV penetration Latin hypercube: Definition of 245 Runs Simulation in Dispa-SET Cleaning up: discarding simulations with lost load => 219 results Linearization of the results e.g.: Excess power vs thermal capacity, flexible share, storage, wind, PV Gaussian Processes interpolation of the results to build the most likely response surface

Methodology Input parameters of the simulations: Thermal capacity margin Share of flexible capacity (CCGT vs Nuclear) Storage Power [MW] Storage Capacity [MWh] Wind penetration PV penetration Latin hypercube: Definition of 245 Runs Simulation in Dispa-SET Cleaning up: discarding simulations with lost load => 219 results Implementation in TIMES Linearization of the results e.g.: Excess power vs thermal capacity, flexible share, storage, wind, PV Gaussian Processes interpolation of the results to build the most likely response surface

Regression results (examples)

JRC-EU-TIMES Variable Renewable Energy (VRE) Parametrization based on detailed analysis outside JRC-EU-TIMES Applied to all timeslices

Conclusions A methodology has been defined to extract simplified flexibility constraints from a large number of runs from a power system model Demonstrated for the excess power, but also used to limit the dispatch of baseload generation Could be an alternative to soft or hard-linking, or to the increase of the simulation time-resolution Work in progress! Future work will focus on: Implementation and extensive testing in the TIMES model Validating the current approach in other conditions Integration of interconnections All methods and models are released as open-source (Dispa-SET side): https://joinup.ec.europa.eu/software/dispaset/

Evaluating flexibility and adequacy in future EU power systems: model coupling and long-term forecasting Sylvain Quoilin, Wouter Nijs and Andreas Zucker Joint Research Center (JRC)

Dispa-SET 2.1 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