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Supply curve bidding of electricity in constrained power networks

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  • Al-Agtash, Salem Y.

Abstract

This paper presents a Supply Curve Bidding (SCB) approach that complies with the notion of the Standard Market Design (SMD) in electricity markets. The approach considers the demand-side option and Locational Marginal Pricing (LMP) clearing. It iteratively alters Supply Function Equilibria (SFE) model solutions, then choosing the best bid based on market-clearing LMP and network conditions. It has been argued that SCB better benefits suppliers compared to fixed quantity-price bids. It provides more flexibility and better opportunity to achieving profitable outcomes over a range of demands. In addition, SCB fits two important criteria: simplifies evaluating electricity derivatives and captures smooth marginal cost characteristics that reflect actual production costs. The simultaneous inclusion of physical unit constraints and transmission security constraints will assure a feasible solution. An IEEE 24-bus system is used to illustrate perturbations of SCB in constrained power networks within the framework of SDM. By searching in the neighborhood of SFE model solutions, suppliers can obtain their best bid offers based on market-clearing LMP and network conditions. In this case, electricity producers can derive their best offering strategy both in the power exchange and the long-term contractual markets within a profitable, yet secure electricity market.

Suggested Citation

  • Al-Agtash, Salem Y., 2010. "Supply curve bidding of electricity in constrained power networks," Energy, Elsevier, vol. 35(7), pages 2886-2892.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:7:p:2886-2892
    DOI: 10.1016/j.energy.2010.03.019
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    1. Li, Gong & Shi, Jing & Qu, Xiuli, 2011. "Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market–A state-of-the-art review," Energy, Elsevier, vol. 36(8), pages 4686-4700.
    2. Yu, Liying & Wang, Peng & Chen, Zhe & Li, Dewen & Li, Ning & Cherkaoui, Rachid, 2023. "Finding Nash equilibrium based on reinforcement learning for bidding strategy and distributed algorithm for ISO in imperfect electricity market," Applied Energy, Elsevier, vol. 350(C).
    3. Ghaninejad, Mousa, 2020. "عرضه، تقاضا، و پیشنهاد قیمت در بازار برق ایران [Supply, Demand, and Bidding in Iran’s Electricity Market]," MPRA Paper 105340, University Library of Munich, Germany.
    4. Mestre, Guillermo & Sánchez-Úbeda, Eugenio F. & Muñoz San Roque, Antonio & Alonso, Estrella, 2022. "The arithmetic of stepwise offer curves," Energy, Elsevier, vol. 239(PE).
    5. Kamiński, Jacek, 2011. "Market power in a coal-based power generation sector: The case of Poland," Energy, Elsevier, vol. 36(11), pages 6634-6644.
    6. Huiru Zhao & Yuwei Wang & Sen Guo & Mingrui Zhao & Chao Zhang, 2016. "Application of a Gradient Descent Continuous Actor-Critic Algorithm for Double-Side Day-Ahead Electricity Market Modeling," Energies, MDPI, vol. 9(9), pages 1-20, September.
    7. Bubak, Baran, 2020. "تخمین نرخ بهینه عوارض تراکم برای آزادراه‏های ایران با استفاده از مدل قیمت‏گذاری ارزش [Estimation of Optimal Rate for Compact Tariffs in Highways across Iran applying the Value Pricing Methodology]," MPRA Paper 105490, University Library of Munich, Germany.
    8. Wang, Jianhui & Zhou, Zhi & Botterud, Audun, 2011. "An evolutionary game approach to analyzing bidding strategies in electricity markets with elastic demand," Energy, Elsevier, vol. 36(5), pages 3459-3467.
    9. Ying-Yi Hong & Ching-Ping Wu, 2012. "Day-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Analysis Network," Energies, MDPI, vol. 5(11), pages 1-15, November.

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