Challenges for Future Price Modelling. European Forum on Electricity Pricing Berlin, 27th of May, 2013

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1 Challenges for Future Price Modelling European Forum on Electricity Pricing Berlin, 27th of May,

2 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

3 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

4 44 What do we do?

Executive Committee and Management Executive Committee

Stephan Schwarz (Chairman) Stadtwerke München GmbH,

Werner Brinker (Vice Chairman) CEO of the EWE AG,

Wolfram Münch (Vice Chairman) EnBW AG Karlsruhe,

-Ing. Ulrich Wagner (Scientific Director) DLR Cologne,

5 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

6 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

7 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

5 GW/year. Wind capacity increase approx. 2 GW per year.

8 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.

9 The expansion of RES - Europe 70 Installed Capacity of RES in GW 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

10 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

11 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

12 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.

13 Events of negative prices 13 Extrema of the day in GW Solar Wind Coal Lignite Nuclear Min Max DA-Price = Day-Ahead Price, Data-Source: transparency.eex.com

14 Schemes of Remuneration The German Renewable Energy Act offers choice of the scheme of remuneration for feed-in as of 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

15 Change in Management-Bonus Management-Bonus in ct/kwh 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 /MWh /MWh /MWh = /MWh : 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.

16 Development in the capacity of Direct-Selling 16 Hydro Gas Biomass Wind Offshore Solar Total in MW Jan Jun Nov Apr

17 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

18 Intraday-Analysis Intraday Price in /MWh Day Ahead Price in /MWh FfE MOS-KOSI_00048 Traded Volume in TWh EEX EPEX FfE MOS-KOSI_ Day-Ahead and average Intraday- Prices still correlate quite well in 2012 still enough short-term flexibility available Increase in traded ID-Volume until ID-Volume decreased although volatile RES-Production increased by 15 % Conclusion: No predominant influence of RES visible 18

19 Intraday-Analysis Intraday Price in /MWh y = 0,0035x + 43, FfE MOS-KOSI_ Day Ahead Intraday Error in MW Traded Volume in GWh/h y = x FfE MOS-KOSI_ Abs(DA ID Error) in GWh/h ID-Price correlates with DA ID-Error Gradient of ( /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

20 Intraday Analysis Monthly evolution of price range Average Monthly Difference between ID High and ID Low in /MWh 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.

21 Day-Ahead- & Intraday-Error Exante Expost in GW FfE MOS_ 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

22 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

23 Market for Control Reserve - Volume SCR & TCR Market Volume in Mio FfE BMWi-10#A PiV-Opt_eV_ Conclusion: The Optimised Cooperation of Transmission System Operators ( ) 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.

24 Secondary Control Reserve (SCR) Accepted Volume The average accepted volume decreases for neg. and pos. SCR 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 ( ) reduced the average accepted volume. 24 c

25 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 ( ) it is only visible for the positive TCR 25 c

26 Secondary Control Reserve (SCR) Activated Volume 15 Minute avg. Activated Vol. Min Max 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 ( ) 26 c

27 Tertiary Control Reserve (TCR) Activated Volume 15 Minute avg. Activated Vol. Min Max 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 ( ) 27 c

price pay for consumption Conclusion: No drastic change in prices.

28 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

TCR revenues increase significantly receive money for consumption Conclusion: Except for

29 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

30 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

31 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

32 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

33 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

34 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 Cross-Border-Schedules NTC NTC 2023 (NEP) NTC 2033 (NEP) Flow-based NTC determination Increase in transfer capacities Examplary export capacities in GW NTC CBS Delta , (flow based) 14,4 + 2,4 GW , ,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

35 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

36 Simplified Determination of the Residual Load 15 Scenario for 2017: Export balance in GW CBS CBS scenario CBS CBS scenario incl. CBS scenario PP evolution GW 40 GW 6 GW GW GW FfE MOS-KOSI_00078 MOS-KOSI_ Installed wind capacity: Installed solar capacity: Newly installed power plants: Installed Transfer Capacity: NPP-Decomissioning: 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 FfE MOS-KOSI_00077 MOS-KOSI_ CBS = Cross-Border Commercial Schedules Hour of the year

37 Simplified Determination of the Residual Load 15 Scenario for 2022: Export balance in GW CBS CBS scenario 10 CBS scenario incl. PP evolution GW 60 GW 6 GW GW GW FfE MOS-KOSI_ Installed wind capacity: Installed solar capacity: Newly installed power plants: Installed Transfer Capacity: NPP-Decomissioning: 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 FfE MOS-KOSI_ CBS = Cross-Border Commercial Schedules Hour of the year c 8000

38 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

39 Thank you for your attention and the support of 39 Christoph Pellinger: /

FRAUNHOFER INSTITUTE FOR SOLAR ENERGY SYSTEMS ISE

FRAUNHOFER INSTITUTE FOR SOLAR ENERGY SYSTEMS ISE Power generation in Germany assessment of 2017 Prof. Dr. Bruno Burger Fraunhofer Institute for Solar Energy Systems ISE Freiburg, Germany, 02.01.2018 www.ise.fraunhofer.de