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Wind farms participation in electricity markets considering uncertainties

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  • Dehghani, Hamed
  • Vahidi, Behrooz
  • Hosseinian, Seyed Hossein

Abstract

Rising global temperature and environmental pollution as well as the demand for energy consumption have made finding new and affordable clean energy resources a serious challenge for governments. A possible solution could be renewable resources such as solar, wind or geothermal energies. Restructuring and deregulation have provided a competitive environment which makes analysis of these new energy sources necessary. Wind farms have been receiving more attention from governments because of their noticeable generation capability. The stochastic nature of the wind inflicts uncertainty on the output generation of wind farms which then causes some limitations for the participation of these farms in the electricity market. Thus, in this paper the effects of uncertainty in predicting the wind farm's power on locational marginal price in the market have been studied. According to the advantages and disadvantages of wind farm's power uncertainties, a procedure to maximize the social welfare is presented. The studies have been done on an 8-bus network for 24 h in a day-ahead electricity market. To do this, the farm power is predicted using Neural Network and Wavelet Transform and its uncertainties are calculated using the asymmetric Quantile Regression method.

Suggested Citation

  • Dehghani, Hamed & Vahidi, Behrooz & Hosseinian, Seyed Hossein, 2017. "Wind farms participation in electricity markets considering uncertainties," Renewable Energy, Elsevier, vol. 101(C), pages 907-918.
    • Handle: RePEc:eee:renene:v:101:y:2017:i:c:p:907-918
    DOI: 10.1016/j.renene.2016.09.049
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    References listed on IDEAS

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    1. Xiaolin Ayón & María Ángeles Moreno & Julio Usaola, 2017. "Aggregators’ Optimal Bidding Strategy in Sequential Day-Ahead and Intraday Electricity Spot Markets," Energies, MDPI, vol. 10(4), pages 1-20, April.
    2. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    3. Konstantinos Konstas & Panos T. Chountalas & Eleni A. Didaskalou & Dimitrios A. Georgakellos, 2023. "A Pragmatic Framework for Data-Driven Decision-Making Process in the Energy Sector: Insights from a Wind Farm Case Study," Energies, MDPI, vol. 16(17), pages 1-26, August.
    4. Banaei, Mohsen & Oloomi-Buygi, Majid & Zabetian-Hosseini, Seyed-Mahdi, 2018. "Strategic gaming of wind power producers joined with thermal units in electricity markets," Renewable Energy, Elsevier, vol. 115(C), pages 1067-1074.
    5. Guglielmo D’Amico & Filippo Petroni & Salvatore Vergine, 2021. "An Analysis of a Storage System for a Wind Farm with Ramp-Rate Limitation," Energies, MDPI, vol. 14(13), pages 1-25, July.
    6. Chen, Tao & Pipattanasomporn, Manisa & Rahman, Imran & Jing, Zejia & Rahman, Saifur, 2020. "MATPLAN: A probability-based planning tool for cost-effective grid integration of renewable energy," Renewable Energy, Elsevier, vol. 156(C), pages 1089-1099.

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