Challenges for Future Price Modelling European Forum on Electricity Pricing Berlin, 27th of May, 2013 1
Agenda The FfE The expansion of RES The influence of RES on German Power Markets Day-Ahead Intraday Control Reserve Summary Outlook: Modelling the residual load - A top-down approach 22
Company Portrait 33 Founded in 1949 Located in Munich since 1969 Members from power industry, industry as well as science Independent institution which deals with current questions in power engineering and economics Post-graduate education of more than 300 scientists since 1949
44 What do we do?
Executive Committee and Management Executive Committee Dipl.-Ing. Stephan Schwarz (Chairman) Stadtwerke München GmbH, Managing Director Utilities and Technology Dr. Werner Brinker (Vice Chairman) CEO of the EWE AG, Oldenburg Prof. Dr. Wolfram Münch (Vice Chairman) EnBW AG Karlsruhe, Director Research and Development Management Prof. Dr.-Ing. Ulrich Wagner (Scientific Director) DLR Cologne, Member of the Executive Board, Director of Energy and Transport 55 Prof. Dr.-Ing. Wolfgang Mauch (Managing Director) Research Center for Energy Economics, Munich
Elected Managing Committee Dipl.-Ing. Dipl.-Wi.-Ing. Karl-Heinz Backhaus Vaillant GmbH, Remscheid Director of Association Dr. Frank May Vattenfall Europe Wärme AG, Berlin CEO Dr. Marcus Bollig BMW Group, Munich Vice President Efficient Dynamics Dipl.-Ing. Helmut Mennel Illwerke vkw, Bregenz Member of the Executive Board Dr. Udo Brockmeier Stadtwerke Düsseldorf AG CEO Dipl.-Ing. Michael Riechel Thüga AG, Munich Member of the Executive Board Dr. Frank-Detlef Drake RWE AG, Essen Director Research and Development Dr. h.c. Norbert Schürmann LEW AG, Augsburg Member of the Executive Board Dipl.-Ing. Martin Fuchs TenneT TSO GmbH, Bayreuth CEO Dr. Thomas Unnerstall N-ERGIE AG, Nuremberg Member of the Executive Board Dipl.-Ing. Erik Höhne ENERVIE AG, Hagen Director Technology Dr. Egon Westphal E.ON Bayern AG, Regensburg Member of the Executive Board 66
Agenda The FfE The expansion of RES The influence of RES on German Power Markets Day-Ahead Intraday Control Reserve Summary Outlook: Modelling the residual load - A top-down approach 77
The expansion of RES - Germany 88 Tremendous increase of solar-capacity during last 3 years, in average 7.5 GW/year. Wind capacity increase approx. 2 GW per year. In the next years we estimate a growth of at least 3 GW per year.
The expansion of RES - Europe 70 Installed Capacity of RES in GW 60 2020 2013 50 40 30 20 10 0 biomass wind solar biomass wind solar biomass wind solar biomass wind solar biomass wind solar biomass wind solar biomass wind solar biomass wind solar biomass wind solar biomass wind solar biomass wind solar DE AT BE CZ DK FR GB IT NL PL SE Data: ScenarioOutlookAndForecast 2013 EU2020Secenario 99
The expansion of RES and its consequences for power markets Thesis Renewables change the nature of price modelling Question Are there already visible effects on price building mechanisms due to the significant increase in renewable power? 10
Agenda The FfE The expansion of RES The influence of RES on German Power Markets* Day-Ahead Intraday Control Reserve Summary Outlook: Modelling the residual load - A top-down approach 11 * Data in scatter plots from 2012
Day-Ahead-Analysis Price follows predominantly the residual load Several occurences of negative prices 12 Conclusion: No predominant influence of RES in price building mechanism visible
Events of negative prices 13 Extrema of the day in GW Solar Wind Coal Lignite Nuclear Min 0 10.229 1.147 9.186 8.564 Max 4.843 18.416 2.506 12.258 11.121 DA-Price = Day-Ahead Price, Data-Source: transparency.eex.com
Schemes of Remuneration The German Renewable Energy Act offers choice of the scheme of remuneration for feed-in as of 2012. Earnings by Feed-In-Tariff = Remuneration for Feed-In Rate * Feed-In 14 Earnings by Direct Selling = Revenue at EEX + Market Bonus = Remuneration for Feed-In Rate + Management Bonus - Monthly Weighted Average Price * Feed-In +/- Imbalance Energy Payments
Change in Management-Bonus Management-Bonus in ct/kwh 2012 2013 2014 2015 Biomass, Hydro and others 0,3 0,275 0,25 0,225 Wind and Solar w.o. remote control 1,2 0,65 0,45 0,3 Wind and Solar w. remote control - 0,75 0,6 0,5 Feed-In cut-off-price: Market-Bonus >= Day-Ahead-Price Remuneration for Feed-In Rate + Management Bonus - Monthly Weighted Average = Market Bonus 91.00 /MWh + 12.00 /MWh - 21.98 /MWh = 81.02 /MWh 15 25.12.2012: Prices should not have fallen below: -81 /MWh
Development in the capacity of Direct-Selling 16 Hydro Gas Biomass Wind Offshore Solar Total in MW Jan 2012 344 67 933 12.062 48 59 13.513 Jun 2012 392 42 1.433 19.884 238 828 22.817 Nov 2012 445 43 1.836 23.409 308 1.961 28.002 Apr 2013 451 57 2.328 24.484 333 3.012 30.670
Agenda The FfE The expansion of RES The influence of RES on German Power Markets* Day-Ahead Intraday Control Reserve Summary Outlook: Modelling the residual load - A top-down approach 17 * Data in scatter plots from 2012
Intraday-Analysis Intraday Price in /MWh 300 200 100 0 300 200 100 0 100 200 300 100 200 300 Day Ahead Price in /MWh FfE MOS-KOSI_00048 Traded Volume in TWh 18 16 14 12 10 8 6 4 2 0 EEX EPEX FfE MOS-KOSI_00036 2009 2010 2011 2012 Day-Ahead and average Intraday- Prices still correlate quite well in 2012 still enough short-term flexibility available Increase in traded ID-Volume until 2011 2012 ID-Volume decreased although volatile RES-Production increased by 15 % Conclusion: No predominant influence of RES visible 18
Intraday-Analysis Intraday Price in /MWh 300 200 y = 0,0035x + 43,117 100 0 100 200 FfE MOS-KOSI_00081 300 10000 5000 0 5000 10000 Day Ahead Intraday Error in MW Traded Volume in GWh/h 8 7 6 5 4 3 2 1 0 y = 0.4152x + 1205 FfE MOS-KOSI_00080 0 2 4 6 8 10 Abs(DA ID Error) in GWh/h ID-Price correlates with DA ID-Error Gradient of 0.0035 ( /MWh) / MW Traded volume increases with DA-ID- Error 19 Conclusion: An ID-variaton of the demand approximately doubles the slope of the regression line price vs. residual load compared to DA-Price vs. residual load (for comparison see slide 12) Day-Ahead Intraday Error = DA-Prognosis ID-Prognosis
Intraday Analysis Monthly evolution of price range Average Monthly Difference between ID High and ID Low in /MWh 45 40 35 30 25 20 15 10 5 0 FfE MOS-KOSI_00047 Increase of gap between Intraday-High-Price and Intraday-Low-Price 20 Conclusion: Increase of price risk in intraday-market
Day-Ahead- & Intraday-Error Exante Expost in GW 8 6 4 2 0 2 4 6 8 FfE MOS_00111 0 5 10 15 20 25 30 Wind+Solar Production in GW Feed-In > 15 GW no increase in max. DA-Prognosis error Intraday-prognosis improvement compared to Day-Ahead-prognosis 21 Conclusion: (1) A simple extrapolation of the expected error yields wrong conclusions (2) The improved intraday prognosis still shows strong deviations at several points
Agenda The FfE The expansion of RES The influence of RES on German Power Markets Day-Ahead Intraday Control Reserve Summary Outlook: Modelling the residual load - A top-down approach 22
Market for Control Reserve - Volume SCR & TCR Market Volume in Mio. 1200 1000 800 600 400 200 0 FfE BMWi-10#A PiV-Opt_eV_00029 2008 2009 2010 2011 2012 Conclusion: The Optimised Cooperation of Transmission System Operators (01.05.2010) reduces the total market volume 23 SCR/TCR = Secondary / Tertiary Control Reserve
Secondary Control Reserve (SCR) Accepted Volume The average accepted volume decreases for neg. and pos. SCR. 2011 and 2012 saturation in decrease Pos. SCR: Revenue correlates with accepted volume Neg. SCR: Revenue anticorrelates with accepted volume Conclusion: The Optimised Cooperation of Transmission System Operators (01.05.2010) reduced the average accepted volume. 24 c
Tertiary Control Reserve (TCR) Accepted Volume Pos. TCR-Volume: constant decrease Neg. TCR-Volume: slight increase Revenue from capacity fee correlates with the accepted volume Conclusion: If there is an effect of the Optimised Cooperation of Transmission System Operators (01.05.2010) it is only visible for the positive TCR 25 c
Secondary Control Reserve (SCR) Activated Volume 15 Minute avg. Activated Vol. Min Max 2008-2627 2665 2009-2860 2674 2010-2506 2907 2011-2281 2337 2012-2245 2111 Positive SCR-Volume: decreases Negative SCR-Volume: fluctuates with a decreasing tendency Decline in min and max values Conclusion: If there is an increase in the average activated volume due to RES it might be covered by the effect of the Optimised Cooperation of Transmission System Operators (01.05.2010) 26 c
Tertiary Control Reserve (TCR) Activated Volume 15 Minute avg. Activated Vol. Min Max 2008-2229 1467 2009-2437 2288 2010-2903 2171 2011-2578 2244 2012-2491 2426 Positive TCR-Volume increases Negative TCR-Volume fluctuates with a decreasing tendency starting from 2009 Slight increase of the maximum and a more or less constant minimum. Conclusion: If there is an increase in the average activated volume due to RES it might be covered by the effect of the Optimised Cooperation of Transmission System Operators (01.05.2010) 27 c
Secondary Control Reserve (SCR) - Prices Positive SCR: constant decrease Negative SCR: increase until 2010 and since 2011 decreasing Positive SCR: slight increase Negative SCR: constant low positive price pay for consumption Conclusion: No drastic change in prices. We even see a reduction in revenues. This might be due to a change in minimum supply offer from 10 to 5 MW and hence more market players. 28 c
Tertiary Control Reserve (TCR) - Prices Pos. TCR revenues decrease Neg. TCR revenues constant Pos. TCR revenues constant Neg. TCR revenues increase significantly receive money for consumption Conclusion: Except for pos. TCR revenues from capacity fee there is no evidence that the change in minimum supply offer from 15 to 5 MW has an influence on the prices. 29 c
Agenda The FfE The expansion of RES The influence of RES on German Power Markets Day-Ahead Intraday Ancillary Services Summary Outlook: Modelling the residual load - A top-down approach 30
Summary The biggest challenge for price modelling and trading will be: (a) to estimate the value of flexibility in Intraday-Trading (b) to know how fast a new framework like Direct-Selling and the Optimised Cooperation of TSO s will influence the market in which way. (c) to know threshold values of RES-production when prices will strongly be influenced by every extra gigawatt.* 31 *not part of this presentation
Agenda The FfE The expansion of RES The influence of RES on German Power Markets Day-Ahead Intraday Control Reserve Summary Outlook: Modelling the residual load - A top-down approach 32
Outlook Modelling the residual load - A top-down approach Thesis: Apart from RES-Scenarios the development of international transfer capacities is most important for modelling the residual load. 33
Peak Values of Exchange-Data in Germany Sum of all Countries in GW At the same time in GW Export Import Export Import Saldo Physical Flow 2012 20 25 13 9 11 Cross-Border-Schedules 2012 18 20 14 12 12 NTC 2012 10 1 15 1 10 1 14 1 - NTC 2023 (NEP) 29 31 - - - NTC 2033 (NEP) 41 41 - - - Flow-based NTC determination Increase in transfer capacities Examplary export capacities in GW NTC CBS Delta 2012 20 12,0 2012 (flow based) 14,4 + 2,4 GW 2023 30 21,6 2 + 9,6 GW Drastic increase in cross-border trading? 34 1 The NTC values between Germany and Austria are not published by EntsoE. On top data for Sweden is missing. This can explain the differences between the CBS and the physical flow values. 2 Calcultated via linear extrapolation
Simplified Determination of the Residual Load Determination of the Residual Load : Residual Load = Load - Wind - Solar + Saldo of CBS* Determination of the Residual Load for a given scenario: Maxmial Saldo of CBS = CBS (2012) - NPP (sc)* + Solar (sc) - Solar (2012) + Wind (sc) - Wind (2012) Saldo of CBS (sc) = min(max(maxmial Saldo of CBS, -ITC (sc)*), ITC (sc)*) Residual Load (sc) = Load (2012) - Wind (sc) - Solar (sc) + Saldo of CBS (sc) 35 *CBS : Cross-Border Commercial Schedules *NPP (sc) : Decommissioning of Nuclear Power Plants scenario *ITC (sc) : Installed Transfer Capacity scenario
Simplified Determination of the Residual Load 15 Scenario for 2017: Export balance in GW 10 5 0 CBS 2012 5 CBS scenario CBS 2012 10 CBS scenario incl. CBS scenario PP evolution 2000 40 GW 40 GW 6 GW + 1.5 GW - 2.5 GW FfE MOS-KOSI_00078 MOS-KOSI_00075 15 0 Installed wind capacity: Installed solar capacity: Newly installed power plants: Installed Transfer Capacity: NPP-Decomissioning: 4000 6000 8000 10000 90 Hour of the year Scenario including power plant evolution: Export balance of 62 TWh Residual Load in GW Scenario: Export balance of 13 TWh ResidualResidual Load 2012 Load 2012 Residual Load scenario Load scenario ResidualResidual Load scenario incl. PP evolution 80 70 60 50 40 30 20 10 FfE MOS-KOSI_00077 MOS-KOSI_00076 0 0 36 CBS = Cross-Border Commercial Schedules 1000 2000 3000 4000 5000 Hour of the year 6000 7000 8000
Simplified Determination of the Residual Load 15 Scenario for 2022: Export balance in GW 10 5 0 CBS 2012 5 CBS scenario 10 CBS scenario incl. PP evolution 2000 60 GW 60 GW 6 GW + 1.5 GW - 12.5 GW FfE MOS-KOSI_00079 15 0 Installed wind capacity: Installed solar capacity: Newly installed power plants: Installed Transfer Capacity: NPP-Decomissioning: 4000 6000 8000 10000 Hour of the year 90 Scenario: Export balance of -47 TWh 80 Starting 2020, the change of the Merit Order gets more important 70 Residual Load in GW Scenario including power plant portfolio: Export balance of 2 TWh Residual Load 2012 Residual Load scenario Residual Load scenario incl. PP evolution 60 50 40 30 20 10 0 FfE MOS-KOSI_00073 10 0 37 CBS = Cross-Border Commercial Schedules 1000 2000 3000 4000 5000 Hour of the year 6000 7000 c 8000
38 Why to use this approach? Quick-and-dirty estimation of influences of expansion scenarios and change in transfer capacities on the residual load Depending on how the addition is chosen of extra feed-in by renewables you get min-/max-scenarios for the flattening of the residual load. It can be referenced with complex models The approach is only valid for timescales of about 10 years as the power plant portfolio is more or less stable on this timescale
Thank you for your attention and the support of 39 Christoph Pellinger: cpellinger@ffe.de / +49-89-158-121-70