A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor

Rongquan Zhang, Saddam Aziz, Muhammad Umar Farooq, Kazi Nazmul Hasan, Nabil Mohammed, Sadiq Ahmad and Nisrine Ibadah
Additional contact information
Rongquan Zhang: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518000, China
Saddam Aziz: College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518000, China
Muhammad Umar Farooq: Department of Business Studies, Namal Institute Mianwali, Mianwali 42201, Pakistan
Kazi Nazmul Hasan: School of Engineering, RMIT University, Melbourne 3000, Australia
Nabil Mohammed: School of Engineering, Macquarie University, Sydney 2019, Australia
Sadiq Ahmad: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Cantonment 47040, Pakistan
Nisrine Ibadah: 1 LRIT Laboratory, Faculty of Sciences, Mohammed V University, 10056 Rabat, Morocco

Energies, 2021, vol. 14, issue 11, 1-22

Abstract: As the integration of large-scale wind energy is increasing into the electricity grids, the role of wind energy suppliers should be investigated as a price-maker as their participation would influence the locational marginal price (LMP) of electricity. The existing bidding strategies for a wind energy supplier faces limitations with respect to the potential cooperation, other competitors’ bidding behavior, network loss, and uncertainty of wind production (WP) and balancing market price (BMP). Hence, to solve these problems, a novel bidding strategy (BS) for a wind power supplier as a price-maker has been proposed in this paper. The new algorithm, called the evolutionary game approach (EGA) inspired hybrid particle swarm optimization and improved firefly algorithm (HPSOIFA), has been proposed to handle the bidding issue. The bidding behavior of power suppliers, including conventional power suppliers, has been encoded as one species to obtain the equilibrium where the EGA can explore dynamically reasonable behavior changes of the opponents. Each species of behavior change has been exploited by the HPSOIFA to improve the optimization solutions. Moreover, a deep learning algorithm, namely deep belief network, has been implemented for improving the accuracy of the forecasting results considering the WP and BMP, and the uncertainty revealed in the WP and BMP has been modeled by quantile regression (QR). Finally, the Shapley value (SV) has been calculated to estimate the benefits of cooperative power suppliers. The presented case studies have verified that the proposed algorithm and the established bidding strategy exhibit higher effectiveness.

Keywords: cooperation; deep belief network; hybrid optimization; network loss; wind energy bidding (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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