Experiences of Modelling of Intermittent Renewable Energy Tom Kober - - PowerPoint PPT Presentation

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Experiences of Modelling of Intermittent Renewable Energy

Tom Kober (ECN) JRC workshop on Addressing Flexibility in Energy System Models Petten, 4 Dec 2014

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
• How can energy system models be improved to better represent

intermittent RE?

• Linkage with power models
• Adopt model structure & data
• Sensitivity analysis

Wind production Germany: hourly profile vs. 12 time slices

• 10.0%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 DE onshore DE offshore times_de onshore times_de offshore

Winter Spring Summer Autumn Winter

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)

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

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

COMPETES Unit Commitment Model

Objective: Minimize Total variable generation cost+ Min-Load costs+ Startup costs + load-shedding costs 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)

Integer decisions

Minimum load and corresponding costs for each unit in COMPETES

• Min Load Costs are incurred at Qmin
• Relaxation on minimum load for neighboring

countries

50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% 0% 20% 40% 60% 80% 100% LHV efficiency as % of max efficiency Production as % of max production

Part-load LHV efficiency curves

Nuclear PC PC-CCS IGCC NGCC NGCC-CCS OCGT

COMPETES’ flexibility assumptions

Technology Decade of commissioning Minimum load (% of max capacity) Ramp rate (% of max capacity/hour) Start-up costa (€/MWinstalled per start) Min up time Min down time Nuclear <2010 50 20 46 ±14 8 4 2010 50 20 46 ±14 8 4 >2010 50 20 46 ±14 8 4 Lignite and PC <2010 40 40 46 ±14 8 4 2010 35 50 46 ±14 8 4 >2010 30 50 46 ±14 8 4 IGCC <2010 45 30 46 ±14 8 4 2010 40 40 46 ±14 8 4 >2010 35 40 46 ±14 8 4 NGCC <2010 40 50 39 ±20 1 3 2010 30 60 39 ±20 1 3 >2010 30 80 39 ±20 1 3 OCGT <2010 10 100 16 ±8 1 1 2010 10 100 16 ±8 1 1 >2010 10 100 16 ±8 1 1 CHP <2010 10 90 16 ±8 1 1 2010 10 90 16 ±8 1 1 >2010 10 90 16 ±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.

Example: generation flexibility in the Netherlands

• 3000,0
• 2000,0
• 1000,0

0,0 1000,0 2000,0 3000,0 2012 2017 2023 2012 2017 2023

Supply of domestic flexibility per technology (GWh)

Decentralized CHP Res-e Nuclear Gas Other Gas GT Gas CHP Gas CCGT Coal

Demand to ramp up(GWh) Demand to ramp down (GWh)

Source: ECN-E--14-039 (2014)

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

Link energy system model and power market model

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

Example: wind capacity credit Germany (power market model)

Capacity credit [%] ~150 TWh & ~60 GW in 2030

Source: IER Energieprognose 2009

Adopting the energy system structure in TIMES

• Energy system and technology parameters of intermittent RE depend
• n the technology’s market diffusion
• Unless RE deployment is exogenous to the model, introduce different

model processes to control parameters

Parameter set x Parameter set y Parameter set z

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

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 4200 4300 4400 4500 4600 4700

Positive reserve energy from storages and flexible power plants Negative balancing energy into storages or flexible demand

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)

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

CAES storages

source: Gillhaus 2007

• 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)

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

refer to new installed capacity

50 100 150 200 250 300 350 400 450 100 200 300 400 Cost for transmission system extension [Euro/kWnew capacity] Installed capacity [GW] Wind Solar PV

 Good proxy but no trade-off between infrastructure investments and flexible generation / demand

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

.000 .050 .100 .150 .200 .250 .300

RD RN RP SD SN SP FD FN FP WD WN WP

Annual availability

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

Thank you!

Tom Kober Policy Studies | Global Sustainability T: +31 88 515 4105 | F: +31 224 56 83 38 Radarweg 60, 1043 NT Amsterdam, The Netherlands kober@ecn.nl

• Supplementary material

ECN’s experience on power markets in Europe (National projects based on COMPETES)

2010-2014 Dutch consortium aiming to make

• ut a case for the role of the

Netherlands w.r.t. sustainable use

• f 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. 2008 Future electricity prices This study analyzed the impact

• f 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 2008. 2009-2012 Reference projections and additional policies 2010-2020 A national baseline scenario was developed for energy, greenhouse gases and air pollutants. The aim

• f the project was also to evaluate

the Clean and Efficient programme

• f 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 2030. 2009 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. 2012 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. . 2012-2013 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

• f North Sea Transnational Grid
• scenarios. ECN uses

COMPETES model for the economic analysis. 2012 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

• f 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. 2014 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. 2014 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.

ECN’s experience on power markets in Europe (internat. projects of COMPETES)

2009-2012 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 2008/2009 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. 2007 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. 2007-2010 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) 2008-2011 SUSPLAN Development of strategies, recommendations and benchmarks for the integration of RES by 2030- 2050 within an Europe-wide

• context. Our work included reports
• n trans-national infrastructure

developments on the electricity and gas market (ECN being responsible

• nly for gas market modeling), and

socio-economic approaches for integration of renewable energy sources into grid infra-structures. 2012-2014 E-highways The project aims to develop a top- down 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.