Impacts of Dynamic Probabilistic Reserve Sizing Techniques on Reserve Requirements and System Costs

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1 Impacts of Dynamic Probabilistic Reserve Sizing Techniques on Reserve Requirements and System Costs Michael Bucksteeg, Lenja Niesen, Christoph Weber Enerday 2015, 17 April 2015, Dresden

2 Motivation Integration of RES leads to an increasing variability and uncertainty TSOs responsible for maintaining system balance Despite of differentiation of reserve products and time slices procured reserve levels are held constant Static reserve sizing and provision ignores variability of uncertainties dependency of forecast errors on the forecast Average 1h-forecast errors of uncertainties in 2013 Proposal: dynamic reserve sizing with convolution of conditional error distributions 2

3 Overview Motivation Probabilistic Reserve Sizing Static and Dynamic Approach Results of Reserve Sizing Modeling of Spot and Reserve Power Markets Economic Impacts Conclusion 3

4 General Approach I. Expected/realized load, wind, pv II. Clustering and estimation of conditional error distributions III. Dispatch model Conditional generation unavailabilities IV. Loss of load probability Probabilistic reserve sizing Joint conditional error distribution Reserve requirements V. Scheduling model Reserve provision costs 4

5 Computation of conditional errors Grouping of empirical data Cluster analysis using k-means algorithm Forecast error depends on expected feed-in respectively load overestimation underestimation Conditional forecast error: ε i,t = x t x t where x t C i 5

6 Estimation of the distributions Statistical characteristics of load, wind and solar forecast error Increasing standard deviation, decreasing skewness, kurtosis > 3 Kernel-based nonparametric density estimation f i ε i = 1 T Th t=1 K ε i ε i,t with K( ) as the kernel smoothing function and i h i bandwidth h i (fixed normal optimal smoothing parameter) 6

7 Estimation of the distributions Conventional Generation Unavailabilities Failure probabilities of active generation units affect positive reserve requirements Expected generation output based on day-ahead forecasts of residual load Determination of dispatch according to marginal generation costs Conditional outage probabilities based on historical generation unavailabilities 7

8 Probabilistic Reserve Sizing Convolution of sources of system imbalances Load and RES forecast error, load and RES noise, schedule shifts, unplanned generation unavailabilities Recursive convolution LOLP Positive reserves 1 - LOLP Negative reserves GRL = SRL + MRL probability density function (pdf) of all uncertainties SRL pdf of short-term disturbances (noise of load and RES infeed, schedule shifts and generation unavailabilities) 8

9 Results of static reserve sizing Development of future reserve requirements (static approach) Reserve requirements in MW LOLP 99,975% TSOsa) calc. calc. Total reserve Pos % (GRL = SRL + MRL) Neg % Secondary reserve (SRL) Pos % Neg % Tertiary reserve (MRL) Pos % Neg % Increase of reserve requirements of about 30% Higher increase of negative reserves due to underestimation of RES infeed Imbalances caused by RES are driven by RES expansion and development of forecast quality a) cf. Monitoring-Report 2014 of BNetzA 9

10 Results of dynamic reserve sizing 10

11 Results of dynamic reserve sizing Dynamic reserve requirements for a week in April

12 WILMAR Joint Market Model Detailed hourly optimization of power plant dispatch for 8760 hours Objective function minimizes variable generation costs subject to electricity and heat demand Detailed formulation of technical restrictions (start up-costs, part-load efficiency losses, CHP plants, pumped storages, reserves, grid restrictions, ) Rolling planning approach with two planning horizons 1) Optimization of dispatch on a day-ahead basis 2) Readjustment of power plant dispatch based on new intraday information Geographical scope: EU-28, Norway, Switzerland, Balkans Modeling of reserve power markets Spinning Reserve (SRL) Non-Spinning Reserve (MRL) Hourly products Reserves procured in the day-ahead period Provision of reserves reserve requirements Marginal Pricing Without explicit consideration of the activation of reserves 12

13 Economic impacts dynamic vs static Capacity prices are driven by opportunity costs on the spot market Lower reserve requirements lower capacity and spot prices reserve prov. costs welfare changes + system producer consumer costs rent rent stat dyn Pos. SRL Neg. SRL Yearly average of static and dynamic reserve levels and capacity prices of positive spinning reserves 2030 Yearly average spot and capacity prices of negative spinning reserves

14 Conclusion Increase of future reserve requirements Induced by expansion of RES Besides an improvement of forecast quality dynamic reserve sizing techniques can reduce reserve requirements Spinning reserves can be reduced by up to 500 MW Non-spinning reserves are decreased by up to 2500 MW Further development of reserve power markets Shorter contract durations in order to counter the increasing variability Differentiation of time slices with varying reserve levels More efficient provision of reserves through dynamic reserve sizing would lead to lower capacity and spot prices and system costs 14

15 Thank you for your attention! Any questions, remarks or comments? Contact: M.Sc. Michael Bucksteeg Phone: /

16 References L. Hirth and I. Ziegenhagen, Balancing power and variable renewables: Three Links, in Renewable & Sustainable Energy Reviews (submitted), H. Holttinen, M. Milligan, E. Ela, N. Menemenlis, J. Dobschinski, B. Rawn, R.J. Bessa, D. Flynn, E. Gomez Lazaro, and N. Detlefsen, Methodologies to Determine Operating Reserves due to Increased Wind Power, in Proc IEEE Power and Energy Society General Meeting, pp K. Bruninx and E. Delarue, "A Statistical Description of the Error on Wind Power Forecasts for Probabilistic Reserve Sizing," IEEE Trans. Sustain. Energy, vol. 5, no. 3, pp , July S. Kippelt, T. Schlueter, C. Rehtanz, Flexible dimensioning of control reserve for future energy scenarios, PowerTech (POWERTECH), 2013 IEEE Grenoble, pp.1-6, June D. Jost, M. Speckmann, F. Sandau, and R. Schwinn, A new method for day-ahead sizing of control reserve in Germany under a 100% renewable energy sources scenario, in Electric Power Systems Research, vol. 119, pp , Feb A. Tuohy, P. Meibom, E. Denny, and M. O Malley, Unit Commitment for Systems With Significant Wind Penetration, IEEE Trans. Power Syst., vol. 24, no. 2, pp , May P. Meibom, R. Barth, B. Hasche, H. Brand, C. Weber, M. O'Malley, Stochastic Optimization Model to Study the Operational Impacts of High Wind Penetrations in Ireland, in Power Systems, IEEE Transactions on, vol. 26, no. 3, pp , Aug German TSOs, Transparency data, [Online]. Available: Accessed on: Apr. 06, European Network of Transmission System Operators for Electricity (ENTSO-E), Transparency platform, [Online]. Available: Accessed on: Apr. 06, German TSOs, Internet platform for control reserve tendering, [Online]. Available: Accessed on: Apr. 06, S. Just, Appropriate contract durations in the German markets for on-line reserve capacity, in J Regul Econ, vol. 39, pp , S. Just, C. Weber, Pricing of reserves: Valuing system reserve capacity against spot prices in electricity markets, in Energy Economics, vol. 30, pp ,

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