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An intelligent wind power plant coalition formation model achieving balanced market penetration growth and profit increase

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  • Seok, Hyesung
  • Chen, Chen

Abstract

Forming coalitions of wind power plants (WPPs) has frequently been studied as a solution for effective renewable energy management [14–22]. Due to the volatility of renewable energy generation, WPPs tend to conservatively bids in a day-ahead market to avoid huge penalties for failure to deliver the appointed amount of energy. Hence, such a coalition among WPPs lessens the risk of delivery failure and motivates WPPs to generate more energy. However, as the size of the coalition increases, the total supply bid grows and therefore the wholesale unit price is decreasing [15]. Hence, WPPs tend to strategically lower their bid to keep market prices from decreasing too much to avoid less profit. It harms the market penetration growth of renewable energy. In our study, we propose an intelligent coalition formation model to deal with the trade-off between the profits of individual WPPs increase and a market penetration growth. Our model is especially applicable to heterogeneous WPPs. We validate that our model achieves more profit of WPPs and higher market penetration than other models under specific conditions. The fairness regarding WPP profits are also improving as the heterogeneity of WPPs is larger, and the number of WPPs is increasing.

Suggested Citation

  • Seok, Hyesung & Chen, Chen, 2019. "An intelligent wind power plant coalition formation model achieving balanced market penetration growth and profit increase," Renewable Energy, Elsevier, vol. 138(C), pages 1134-1142.
  • Handle: RePEc:eee:renene:v:138:y:2019:i:c:p:1134-1142
    DOI: 10.1016/j.renene.2019.02.064
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    References listed on IDEAS

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    1. Mahmoudi, Nadali & Saha, Tapan K. & Eghbal, Mehdi, 2014. "Modelling demand response aggregator behavior in wind power offering strategies," Applied Energy, Elsevier, vol. 133(C), pages 347-355.
    2. Kapsali, M. & Kaldellis, J.K., 2010. "Combining hydro and variable wind power generation by means of pumped-storage under economically viable terms," Applied Energy, Elsevier, vol. 87(11), pages 3475-3485, November.
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    Cited by:

    1. Carolina G. Marcelino & João V. C. Avancini & Carla A. D. M. Delgado & Elizabeth F. Wanner & Silvia Jiménez-Fernández & Sancho Salcedo-Sanz, 2021. "Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms," Sustainability, MDPI, vol. 13(21), pages 1-20, October.
    2. Galina S. CHEBOTAREVA & Wadim STRIELKOWSKI & Viktor A. BLAGININ, 2019. "The renewable energy market: Companies’ development and profitability," Upravlenets, Ural State University of Economics, vol. 10(3), pages 58-69, July.
    3. Manjulata Badi & Sheila Mahapatra, 2023. "Optimal reactive power management through a hybrid BOA–GWO–PSO algorithm for alleviating congestion," 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. 14(4), pages 1437-1456, August.
    4. Dirin, Sepehr & Rahimiyan, Morteza & Baringo, Luis, 2023. "Optimal offering strategy for wind-storage systems under correlated wind production," Applied Energy, Elsevier, vol. 333(C).

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