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Enhancing Wind Power Forecasting: Market Mechanisms and Case Study Insights

This study explores the impact of prediction errors on the imbalance costs for wind power generators, specifically analyzing the Estinnes Wind Power Plant. It evaluates how market mechanisms, such as postponed market closure and intraday markets, can facilitate improved forecasting models and reduce imbalance costs. The case studies highlight the effectiveness of enhanced prediction tools and suggest recommendations for optimizing market bids. This research provides crucial insights into achieving better balance between power generation and demand, a key factor in system security.

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Enhancing Wind Power Forecasting: Market Mechanisms and Case Study Insights

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  1. Value of Market Mechanisms Enabling Improved Wind Power Predictions A Case Study for the Estinnes Wind Power Plant Kristof De Vos*, Johan Driesen – K.U.Leuven AthaniosKyriazis – 3E

  2. Presentation Objectives • Insight in the impact of prediction errors on imbalance costs for wind power generators. • Insight in the impact of market mechanisms facilitating improved forecast models on these imbalance costs. • Postponed market closure • Intraday markets • 15’ market products EWEA 2011

  3. Table of Contents • Imbalance Settlement & Wind Power Predictions • Case Study I: Estinnes wind power plant • Case Study II: Improved forecasting model • Conclusions EWEA 2011

  4. Imbalance Settlement • Wind = variable RES-E • Limited controllability • Limited predictability • SYSTEM IMBALANCES • System balance = real-time balance between off-take and injections • Prerequisite for system security • Responsibility of the TSO • Activation of reserves EWEA 2011

  5. Imbalance Settlement • Imbalance Settlement Mechanism = costs are accounted to responsible market players. • Balancing Responsible Party (BRP) • Real-time portfolio balance (15’) • Day-Ahead nominations (Gate Closure: 14h00 D-1) • Prediction Tools: • Prediction error • Imbalance volume • Imbalance cost NREL, 2011 EWEA 2011

  6. Imbalance Settlement • Improving prediction tools: • Accuracy, Resolution, Horizon • Requires adapted market mechanisms: • Postponed market closure • Later prediction horizons (improved accuracy) • Intraday trading • Later prediction horizons • Intraday price risk • 15’ market products (~ imbalance settlement) • Accurate market bidding EWEA 2011

  7. Case Study: Estinnes • Predictions 3E: • 4 daily runs  7 prediction horizons • DA: 00h, 06h, 12h, 18h • ID: 00h, 06h,12h • Availability: + 6h • Electricity trading: • Belpex DAM • Imbalance Tariff Elia EWEA 2011

  8. Case Study Estinnes • 2/4/2010 – 28/09/2010 (180 days) • No wind power plant effects • No unavailability Wind speed Measurements Real-time output ENERCON E-126 7.5 MW power curve Prediction error Wind Speed Predictions Output prediction EWEA 2011

  9. Market Prices • Day-Ahead Market: Belpex • Imbalance Tariffs: Elia • Intraday-Prices: Synthetic Average intraday prices in Euro as a linear interpolation of the Belpex DAM and imbalance tariffs EWEA 2011

  10. Market Closure Timing • Reference: 11h D-1 (00H run) • Alternatively: 12h, 18h D-1 (06, 12H run) • Increasing imbalance costs due to asymmetric improvement: • Impact asymmetric imbalance tariffs • Take into account trends in imbalance prices EWEA 2011

  11. Intraday Trading • Reference: 11h D-1 • Alternatively: 6 intraday slots • Increasing imbalance costs due to non-converging improvements: • Increasing trading volumes • Large negative value when using rolling intraday EWEA 2011

  12. 15’ market products • Reference: 11h D-1 • Alternatively: 11 D-1, 15’ Belpex DAM product • Insignificant impact on imbalance costs: • Constant Belpex DAM prices • Averaging EWEA 2011

  13. Improved Forecasting Model • New day-ahead prediction model 3E • Synthetic intraday predictions • Linear converging from day-ahead prediction (00h run) until real-time output. EWEA 2011

  14. Improved Forecasting Model • Results: • Decreasing imbalance costs, increasing profits • Positive value market mechanisms EWEA 2011

  15. Conclusions • Value suggested market mechanisms depends strongly on forecast model characteristics. • Requires: • Symmetric improvements • Converging intraday predictions • Recommendations for further research: • Further improve prediction models • Optimizing market bids taking into account uncertainty balancing tariff. EWEA 2011

  16. Questions? Thankyouforyourattention! Kristof De Vos Kristof.DeVos@esat.kuleuven.be • More information @ http://www.7mw-wec-by-11.eu EWEA 2011

  17. Appendix 1 EWEA 2011

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