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Optimal bidding strategy of wind power producers in pay-as-bid power markets

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  • Afshar, Karim
  • Ghiasvand, Farshad Shamsini
  • Bigdeli, Nooshin

Abstract

This paper presents a method to determine the optimal bidding strategy of the wind power producers with market power for a strategic presence in the day-ahead market with pay as bid method. Since the wind power producer is not capable of exact prediction of his power production, he has to trade the difference between the amount won in the day-ahead market and the actual production value in the balancing market. Uncertainties related to power generation is modeled by likely scenarios. However in order to model the punitive effect of trade in balancing market, the balancing market price is considered as a factor of the day-ahead market's clearing price. In the proposed model, optimal bidding strategy is formulated via a bi-level problem including the upper-level and lower-level sub-problems. The purpose of the upper-level sub-problem is to maximize the wind power producer's earning while the purpose of the lower-level sub-problem is to clear the day-ahead market. To solve both upper-level and lower-level problems, particle swarm optimization algorithm is applied. The results of three-bus test system and IEEE 24-bus RTS shows the efficiency of the proposed method.

Suggested Citation

  • Afshar, Karim & Ghiasvand, Farshad Shamsini & Bigdeli, Nooshin, 2018. "Optimal bidding strategy of wind power producers in pay-as-bid power markets," Renewable Energy, Elsevier, vol. 127(C), pages 575-586.
    • Handle: RePEc:eee:renene:v:127:y:2018:i:c:p:575-586
    DOI: 10.1016/j.renene.2018.05.015
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    1. Azadeh, A. & Skandari, M.R. & Maleki-Shoja, B., 2010. "An integrated ant colony optimization approach to compare strategies of clearing market in electricity markets: Agent-based simulation," Energy Policy, Elsevier, vol. 38(10), pages 6307-6319, October.
    2. Liu, Zhen & Zhang, Xiliang & Lieu, Jenny, 2010. "Design of the incentive mechanism in electricity auction market based on the signaling game theory," Energy, Elsevier, vol. 35(4), pages 1813-1819.
    3. Genc, Talat S. & Reynolds, Stanley S., 2011. "Supply function equilibria with capacity constraints and pivotal suppliers," International Journal of Industrial Organization, Elsevier, vol. 29(4), pages 432-442, July.
    4. Bunn, Derek W. & Martoccia, Maria & Ochoa, Patricia & Kim, Haein & Ahn, Nam-Sung & Yoon, Yong-Beom, 2010. "Vertical integration and market power: A model-based analysis of restructuring in the Korean electricity market," Energy Policy, Elsevier, vol. 38(7), pages 3710-3716, July.
    5. Laia, R. & Pousinho, H.M.I. & Melíco, R. & Mendes, V.M.F., 2016. "Bidding strategy of wind-thermal energy producers," Renewable Energy, Elsevier, vol. 99(C), pages 673-681.
    6. 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.
    7. Ahn, Nam-sung & Niemeyer, Victor, 2007. "Modeling market power in Korea's emerging power market," Energy Policy, Elsevier, vol. 35(2), pages 899-906, February.
    8. Shivaie, Mojtaba & Ameli, Mohammad T., 2015. "An environmental/techno-economic approach for bidding strategy in security-constrained electricity markets by a bi-level harmony search algorithm," Renewable Energy, Elsevier, vol. 83(C), pages 881-896.
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