Ist die Talsole erreicht? - Die Auswirkungen des PV Ausbaus auf die Preisvarianz und Wirtschaftlichkeit von Speichern im Stromsystem

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1 Ist die Talsole erreicht? - Die Auswirkungen des PV Ausbaus auf die Preisvarianz und Wirtschaftlichkeit von Speichern im Stromsystem 14 revenues [ /kw] h 8h 2h 2h Michael Hartner Andreas Permoser TU Wien Energy Economics Group (EEG) hartner@eeg.tuwien.ac.at share of PV in total electricity generation Vienna University of Technology, EEG 1

2 Research question and scope What is the effect of PV penetration on the revenues of storage plants operating on the spot market? What is the effect of PV penetration on price volatility and spot market prices in general? What is the effect of PV penetration on the operation schedules of storage plants Vienna University of Technology, EEG 2

3 Historical revenues for storage plants Dispatch model for storage plants Storage revenues serve as a measure for price variance Simple storage optimization model is used to derive maximum values for hourly spot market prices from 22 to 214 (EXXA prices) For various storage level vs. turbining power ratios (4h, 8h, 2h, 2h) Objective: s.t. Constraints: TT max qq tttttttt tt pp tt qq pppppppp tt pp tt tt=1 SSSS tt = SSSS tt 1 + qq tt pppppppp ηη pppppppp + qq tt tttttttt qq tt tttttttt min (QQ tt tttttttt, SSSS tt 1 ) ηη tttttttt qq tt pppppppp min (QQ tt pppppppp, SSCC mmmmmm SSSS tt 1 ) SSSS tt SSCC mmmmmm rr = SSCC mmmmmm QQ tt tttttttt MMMMh MMMM Vienna University of Technology, EEG 3

4 Historical revenues for storage plants 14 revenue [ /kw] h 8h 2h 2h Year - Revenues for storage plants on the spot market have been volatile - Influencing factors: fuel prices, CO 2 prices, spread between natural gas and coal prices, weather, installed capacity of renewable - What was the effect of PV? Vienna University of Technology, EEG 4

5 Effect of PV on price volatility? 65 Average electricity prices for each hour of the day in 25 and 212 average hourly electricity prices [ /MWh] average prices 212 average prices hour - The literature on that issue does not fully agree - In general more volatile prices are expected with increasing shares of renewables - Some econometric studies have shown a negative effect of PV feed-in on price volatility: Explanation can be seen above! - Wozabal et. al. (214) point out that the effect depends on the RES penetration level and the shape of the merit order curve Vienna University of Technology, EEG 5

6 Modelling approach to derive storage revenues at higher PV penetration level hourly dispatch model - linear Storage PV Wind 3 Merit order - CO 2 price: 1 [ /t] 25 Demand marginal costs [ /MWh] Nuclear Lignite Hard coal Natural Gas Oil coal/nuclear/ccgt/gas-turbine installed capacity [GW] - Spot market prices are derived from the model as shadow prices of the demand constraint - Annual feed-in profiles of PV are used and installed capacitiy is increased to derive prices for several PV penetration levels - Storage optimization model is used to derive revenues for storage plants optimzed against the modelled prices at each PV penetration level - Sensititivy analysis on various fuel/co 2 price combinations and wind feed-in are performed Vienna University of Technology, EEG 6

7 Comparison of modelled and historical prices spot market price [ /MWh] modelled prices historic prices hours Extreme values are underestimated (typical for fundamental model) but the price variations and trends are captured by the model Vienna University of Technology, EEG 7

8 Model results for storage revenues 14 revenues [ /kw] h 8h 2h 2h share of PV in total electricity generation Until 5% of PV share storage revenues decrease At around 1% storage revenues are at initial levels without PV penetration At higher PV shares revenues for storage plants increase significantly reflecting the need for additional storage Vienna University of Technology, EEG 8

9 Interpretation of results Average prices for each hour Effects storage revenues 7 14 price [ /MWh] % 5% 1% 15% revenues [ /kw] h 8h 2h 2h hour share of PV in total electricity generation - At 5% PV share average prices around noon are flattend out reducing revenues - At 1% average prices around noon are below average prices creating new revenue opportunities - This trend increases for higher shares, at 15% prices around noon are frequently during summer even at hours with high demand Vienna University of Technology, EEG 9

10 Interpretation of results: residual loads Average residual load load [GW] probability density original demand residual load 5% PV residual load 1% PV residual load 15% PV hour - Peaks around noon disappear and will not be critical peak load hours even in winter - The number of peak hours is reduced significantly but maximum loads are hardly affected full backup is needed irrespective of PV share - The number of low residual loads increases dramatically for PV shares around 15% - challange for system stability load [GW] load [GW] PV share % hour Load [GW] PV share 1% hour hour Vienna University of Technology, EEG 1 load [GW] load [GW] PV share 5% PV share 15% hour

11 Change in dispatch patterns of storage plants 1 average charging power [MW] discharge charge hours - Pattern of dispatch between discharging and charging of storage plants changes from turbing to pumping around noon and less charging at night - At very high shares of PV (>1%) storage patterns and flexibility needs are increasingly dominated by irradiation patterns Vienna University of Technology, EEG 11

12 Conclusion and Discussion Conclusions: There is a non linear relationship between PV deployment and storage revenues Price variance and therefore storage revenues decrease with low shares of PV and rise with higher shares estimated minimum around 5% PV share in total electricity generation for Austria and Germany Current shares in Germany suggest that storage revenues should rise from now on as current PV shares are around 5% to 6% Discussion: Incentives for storage investments might be low are subsidies justified and when? What are effects on balancing prices when storage revenues on the spot market increase? Vienna University of Technology, EEG 12

13 Thanks for your attention! Contact: Michael Hartner Vienna University of Technology Institute of Energy Systems and Electrical Drives Energy Economics Group EEG Tel: +43() Web: Vienna University of Technology, EEG 13

14 Additonal Slides Vienna University of Technology, EEG 14

15 Source: Wozabal D., Graf C., Hirschmann D.: The effect of intermittent renewables on the electricity price variance, Vienna University of Technology, EEG 15

16 Storage revenues ratio of revenues compared to PV feed in share of PV in total electricity generation Vienna University of Technology, EEG 16

17 Additional insights: 6 PV market value 12 CO2 emission reduction Market value [ /MWh] Marginal CO2 Emission reduction per year [t/mw] Base Scenario No Nuclear Wind x Additional PV installed [GW] Additional PV installed [GW] Vienna University of Technology, EEG 17

18 PV maximum output Source: own calculations based on data from: Vienna University of Technology, EEG 18

19 PV system or market value Here, the system value (v) is defined as reduced short run costs (fuel+emissions) which equal reduced production of conventional power plants (ignoring all other costs, benefits and restrictions like grid losses and congestion, balance costs etc.) t 1_pv_opt t 2_pv_opt marginal costs/market price [ /MWh] p 2 p 1 e 1 e 2 A1 A2 v = T e( t) p( t) dt v. market/system value e power output at time t p price at time t Total cost reduction: v = A1+A2 Quantity [MWh] Vienna University of Technology, EEG 19

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