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Participation of photovoltaic power producers in short-term electricity markets based on rescheduling and risk-hedging mapping

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  • Sánchez de la Nieta, Agustín A.
  • Paterakis, Nikolaos G.
  • Gibescu, Madeleine

Abstract

Optimal bidding that considers different electricity market floors can increase the financial gains of photovoltaic (PV) power producers. However, the current approach to trading PV power essentially consists of committing to sell the forecasted PV generation. To analyze profits and investigate new business opportunities for PV power producers, this paper proposes two novel stochastic programming-based methods for scheduling and rescheduling for trading the PV generated energy in day-ahead and intraday electricity markets. Risk-hedging is also considered in terms of co-optimizing the expected profit with the Conditional Value-at-Risk (CVaR) metric. As a consequence of the structure and organization of the market floors and due to different market windows, rescheduling is necessary to exploit the most recent information. Updated rescheduling progressively reveals actual profits or losses, risk-hedging possible engagement in business transactions, and the final effect of strategic bidding. A case study in the Spanish electricity market based on actual data is presented. The analysis of the case study shows the influence of the three market floors (day-ahead, intraday, and imbalance), the participation in multiple intraday sessions, risk-hedging, and rescheduling on the profits of the PV producer.

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  • Sánchez de la Nieta, Agustín A. & Paterakis, Nikolaos G. & Gibescu, Madeleine, 2020. "Participation of photovoltaic power producers in short-term electricity markets based on rescheduling and risk-hedging mapping," Applied Energy, Elsevier, vol. 266(C).
    • Handle: RePEc:eee:appene:v:266:y:2020:i:c:s0306261920302531
    DOI: 10.1016/j.apenergy.2020.114741
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    3. Benedikt Finnah, 2022. "Optimal bidding functions for renewable energies in sequential electricity markets," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 1-27, March.
    4. Jiang, Hou & Lu, Ning & Yao, Ling & Qin, Jun & Liu, Tang, 2023. "Impact of climate changes on the stability of solar energy: Evidence from observations and reanalysis," Renewable Energy, Elsevier, vol. 208(C), pages 726-736.
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    6. Rajesh Panda & Prashant Kumar Tiwari, 2022. "An economic risk based optimal bidding strategy for various market players considering optimal wind placements in day-ahead and real-time competitive power market," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 347-362, February.
    7. Angel L. Cedeño & Reinier López Ahuar & José Rojas & Gonzalo Carvajal & César Silva & Juan C. Agüero, 2022. "Model Predictive Control for Photovoltaic Plants with Non-Ideal Energy Storage Using Mixed Integer Linear Programming," Energies, MDPI, vol. 15(17), pages 1-21, September.
    8. du Plessis, A.A. & Strauss, J.M. & Rix, A.J., 2021. "Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour," Applied Energy, Elsevier, vol. 285(C).
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