Experiences of Modelling of Intermittent Renewable Energy

Transcription

1 Experiences of Modelling of Intermittent Renewable Energy Tom Kober (ECN) JRC workshop on Addressing Flexibility in Energy System Models Petten, 4 Dec

2 Rationale Energy system models - strong tools for long-term energy analysis Renewable energy (RE) assessment requires modelling innovation No single model covers all facets of the integration of RE o How can energy system models be improved to better represent intermittent RE? Linkage with power models Adopt model structure & data Sensitivity analysis

3 Wind production Germany: hourly profile vs. 12 time slices 100.0% 90.0% 80.0% 70.0% 60.0% 50.0% Winter Spring Summer Autumn Winter 40.0% 30.0% 20.0% 10.0% 0.0% % DE onshore DE offshore times_de onshore times_de offshore

4 Power systems models Detailed representation of the electricity system What can energy system models learn? How can they be linked? Two examples: COMPETES (ECN) E2M2s (IER)

5 COMPETES electricity market model (ECN) Optimization-based model (e.g. LP/MIP) Formulations for different goals: 1. OPF Static Economic dispatch model with perfect competition (LP) 2. OPF Static Unit Commitment model with perfect competition (MIP) 3. Dynamic model (LP): Two-period under perfect competition Investments in the first period (generation + transmission) Dispatch in the second period

6 Modelling intermittent RE in COMPETES Deterministic approach using hourly power factors or capacity factors per country or node Capacity factors based on historic data: SODA Database, TradeWind Database, Websites European TSO s Future wind and solar profiles are similar to historic data Future availability factors are scaled-up to reflect technological advancements (EWEA Pure Power report) Curtailment allowed

7 COMPETES Unit Commitment Model Objective: Minimize Total variable generation cost+ Min-Load costs+ Startup costs + load-shedding costs Integer decisions subject to Power balance constraints: These constraints ensure demand and supply is balanced at each node at any time. Generation capacity constraints: These constraints limit the maximum available capacity of a generating unit. Cross-border transmission constraints: These limit the power flows between the countries for given NTC values. Ramping up and Down constraints : These limit the maximum increase/decrease in generation of a unit between two consecutive hours Minimum Load Constraints: These set the min generation level of a unit when it is committed (Relaxed for neighboring countries with aggregated capacities) Minimum up and down times (Only for NL)

8 LHV efficiency as % of max efficiency Minimum load and corresponding costs for each unit in COMPETES - Min Load Costs are incurred at Qmin - Relaxation on minimum load for neighboring countries 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% Part-load LHV efficiency curves Nuclear PC PC-CCS IGCC NGCC NGCC-CCS OCGT 0% 20% 40% 60% 80% 100% Production as % of max production

9 COMPETES flexibility assumptions Technology Decade of commissioning Minimum load (% of max capacity) Ramp rate (% of max capacity/hour) Start-up cost a ( /MWinstalled per start) Min up time Min down time Nuclear < ± ± > ± Lignite and PC < ± ± > ± IGCC < ± ± > ± NGCC < ± ± > ± OCGT < ± ±8 1 1 > ±8 1 1 CHP < ± ±8 1 1 > ±8 1 1 Sources [1-9] [1-8, 10] [11] [11] [11] Sources: [1] (Jeschke et al., 2012); [2] (Dijkema et al., 2009); [3] (OECD/IEA, 2012b); [4] (IEAGHG, 2012a); [5] (Klobasa et al., 2009); [6] (Balling, 2010); [7] (Hundt et al., 2010); [8] (Isles, 2012); [9] (Stevens et al., 2011); [10] (NETL, 2012b); [11] (Lew et al., 2012). a) Warm start-up costs are assumed for all technologies but OCGT. For OCGT, a cold start is assumed.

10 Demand to ramp down (GWh) Demand to ramp up(gwh) Example: generation flexibility in the Netherlands 3000,0 Supply of domestic flexibility per technology (GWh) 2000,0 1000,0 0,0-1000,0 Decentralized CHP Res-e Nuclear Gas Other Gas GT Gas CHP Gas CCGT Coal -2000,0-3000, Source: ECN-E (2014)

11 E2M2s (IER, Uni Stuttgart) Electricity market model for Germany All generation units Inter-temporal optimisation 144 time slices per year Stochastic electricity production for wind and solar technology Flexibility parameters for power plants Ramp-up/down time & costs Minimum load Minimum down time

12 Link energy system model and power market model Electricity consumption CHP electricity generation Fuel prices European TIMES model (PanEU) Long-term 12 timeslices LP Power plant costs RE-generation (policy) Power market model (E2M2s) Long-term 144 timeslices Stochastics Capacity credit for wind and solar System reserve capacity Generation from flexible units

13 Capacity credit [%] Example: wind capacity credit Germany (power market model) ~150 TWh & ~60 GW in 2030 Source: IER Energieprognose 2009

14 Adopting the energy system structure in TIMES Energy system and technology parameters of intermittent RE depend on the technology s market diffusion Unless RE deployment is exogenous to the model, introduce different model processes to control parameters Parameter set z Parameter set y Parameter set x

15 Improved data for TIMES energy system model Capacity credit NCAP_PKCNT System reserve capacity COM_PKRSV Generation from flexible units User constraints helps to model system flexibility that cannot be captured with low time resolution 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Positive reserve energy from storages and flexible power plants Negative balancing energy into storages or flexible demand 0%

16 User constraints for flexible generation Determine energy production from flexible units as share (p,n) of production from intermittent RE (e.g. based on power model) per time slice per level of RE deployment (different technology processes) User constraint for positive energy: ElcGen(storages, GT, IC) p% ElcGen(wind, pv) User constraint for negative energy: ElcCons(storages, flex demand) n% ElcGen(wind, pv)

17 Storages in TIMES Pump storage Compressed air Natural gas-caes Adiabate CAES Stationary battery systems Natrium-Sulfid Redox Flow Elektro mobility E-vehicles loading from the grid only E-vehicles to grid (V2G) Hydrogen storage Power-to-gas + storage

18 CAES storages Base: natural gas caverns Existing storages: 36 Cavern storage projects: 38 Major storage regions: Germany, UK, Poland, France, Portugal, Spain Max CAES capacity estimated: 19 GW (of which 6 GW in Germany) source: Gillhaus 2007

19 Cost for transmission system extension [Euro/kWnew capacity] Electricity infrastructure investments Implemented via grid processes and user constraints Grid processes = solar and wind sector fuel processes (TIMES) 6 stages with costs up to 400 Euro/kW 100 refer to new installed capacity Wind Solar PV Installed capacity [GW] Good proxy but no trade-off between infrastructure investments and flexible generation / demand

20 Annual availability The extreme timeslice Problem: hours of negative residual load level out when annual wind/solar power generation profiles are reduced to 12 time slices (no negative electricity prices in the model) Introduce daynite timeslice per season that characterizes this condition (equivalent to peak time slice) and/or change distribution of annual profile to timeslices Analysis of wind/solar peaks and the load during these hours RD RN RP SD SN SP FD FN FP WD WN WP

21 Conclusion Model coupling is valuable TIMES offers model framework to introduce flexibility mechanisms Model link enables improved parameters for the energy system model (data and model structure to be adopted) Challenge: incorporate trade-off between infrastructure investments and system flexibility

22 Thank you! Tom Kober Policy Studies Global Sustainability T: F: Radarweg 60, 1043 NT Amsterdam, The Netherlands

23 Supplementary material

24 ECN s experience on power markets in Europe (National projects based on COMPETES) The market value of large scale storage options (forthcoming) With COMPETES three types of storage options operating in the Dutch electricity system are analyzed and compared w.r.t. their utilization and (marginal) revenues, namely; Compressed Air Energy Storage (CAES), Power2Gas (P2G) and an Energy Island with hydro pumping. National Energy Outlook (2014) Within the National Energy Outlook Modelling System (NEOMS), COMPETES covers the developments in the Dutch electricity system. Hence, projections on for example the generation mix, e- prices and trade flows are based on calculations with the COMPETES model. North Sea Translational Grid The impact of wind offshore generation on the benefits of the major players in the electricity sector are analyzed from a social welfare perspective within a set of North Sea Transnational Grid scenarios. ECN uses COMPETES model for the economic analysis. Financing investments in new generation capacity Study on the incentives for investments in new generation capacity with an increasing share of renewable energy in the generation mix and the effects of introducing a national capacity market in Germany on the electricity markets in neighboring countries including the Netherlands. This has been examined with the European electricity model COMPETES This study developed a A Social Cost Benefit Analysis (SCBA) was developed to secure optimal contribution of the investments in interconnection to the social welfare of the involved countries. With COMPETES a case study was conducted of a fictitious but realistic investment project in interconnection to illustrate how certain social effects from the developed SCBA framework can be practically and concretely established. Reference projections and additional policies A national baseline scenario was developed for energy, greenhouse gases and air pollutants. The aim of the project was also to evaluate the Clean and Efficient programme of the Dutch Government. Three variants op the projections include without policies, with implemented policies and with proposed policies. On top of this, over 40 additional policy options were separately analyzed. In 2012, an update was done up to Dutch consortium aiming to make out a case for the role of the Netherlands w.r.t. sustainable use of energy resources. One of the goals of this project is to explore and understand the inter-market: interaction between the gas and electricity sector, via the technical infrastructure, power and carbon markets resulting from (changing) institutions and regulation. ECN has been developing a combined gas and market model to analyze the interactions between electricity and gas markets. Net benefits of a new Dutch Congestion Management System This study analysed the new connection policy that seeks to lift restrictions on grid connection. A scenario-based, quantitative analysis of the net benefits of the new connection policy was presented by using COMPETES model. Furthermore, pros and cons of several alternative designs for a congestion management system were identified and presented. Future electricity prices This study analyzed the impact of structural changes (e.g., fuel and CO2 prices, new investments in generation and transmission capacity) in the Northwest European electricity markets affecting the future wholesale electricity prices and exchanges between these markets. The results of the study supported Ministry s Energy Report in

25 ECN s experience on power markets in Europe (internat. projects of COMPETES) E-highways The project aims to develop a topdown planning methodology providing a modular and robust expansion of the Pan-European Network from 2020 to 2050, in line with the European energy policy pillars. The contribution of ECN to the project involves the scenario development, regulatory assessment, and economic modeling of electricity markets IRENE-40 The project aimed to identify strategies for investors and regulators to build a more secure, ecologically sustainable and competitive European electricity system. Main responsibilities of ECN included the roadmap with respect to electricity infrastructure that specifies actions needed to achieve public goals as well as the construction of generation and demand scenarios as a basis for network analyses SUSPLAN Development of strategies, recommendations and benchmarks for the integration of RES by within an Europe-wide context. Our work included reports on trans-national infrastructure developments on the electricity and gas market (ECN being responsible only for gas market modeling), and socio-economic approaches for integration of renewable energy sources into grid infra-structures Improgress Improvement of the Social Optimal Outcome of Market Integration of DG/RES in European Electricity Markets. The project analyzed the interactions of DG/RES operators with markets and networks, developed DG/RES integration scenarios for the EU-27, quantified the market and network impact of DG/RES integration in three case study networks (in Spain, Germany and the Netherlands) A nodal pricing analysis of the future German electricity market Scenario-based analysis of the impact of Germany's ambitious renewable agenda, disputed decommissioning of nuclear facilities and unbundling of TSOs as enforced by EU regulation on the future German power market while accounting for internal congestion. The analysis was done by using COMPETES model. 2008/2009 Impact of the EU ETS on electricity prices The project analyzed the implications of the EU ETS for the power sector, in particular it analyzed the pass through of the (opportunity) costs of CO2 emissions trading to electricity prices on spot and forward markets in various EU countries. 2007