Harris Hawk Optimization-Based Deep Neural Networks Architecture for Optimal Bidding in the Electricity Market

Kavita Jain and Muhammed Basheer Jasser and Muzaffar Hamzah and Akash Saxena and Ali Wagdy Mohamed (2022) Harris Hawk Optimization-Based Deep Neural Networks Architecture for Optimal Bidding in the Electricity Market. Mathematics, 10 (2094). pp. 1-19. ISSN 2227-7390

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URL: https://www.mdpi.com/2227-7390/10/12/2094/htm

Abstract

In the power sector, competitive strategic bidding optimization has become a major challenge. Digital plate-form provides a superior technical base as well as backing for the optimization’s execution. The state-of-the-art frameworks used for simulating strategic bidding decisions in deregulated electricity markets (EM’s) in this article are bi-level optimization and neural networks. In this research, we provide HHO-NN (Harris Hawk Optimization-Neural network), a novel algorithm based on Harris Hawk Optimization (HHO) that is capable of fast convergence when compared to previous evolutionary algorithms for automatically searching for meaningful multilayered perceptron neural networks (MPNNs) topologies for optimal bidding. This technique usually demands a considerable amount of time and computer resources. This method sets up the problem in multi-dimensional continuous state-action spaces, allowing market players to get precise information on the effect of their bidding judgments on the market clearing results, as well as implement more valuable bidding decisions by utilizing a whole action domain and accounting for non-convex operating principles. Due to the use of the MPNN, case studies show that the suggested methodology delivers a much larger profit than other state-of-the-art methods and has a better computational performance than the benchmark HHO technique.

Item Type:	Article
Keyword:	Electricity market , Optimal bidding , Harris Hawk Optimization , Multi layered neural network , Bi-level optimization , Strategic bidding
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	SAFRUDIN BIN DARUN -
Date Deposited:	27 Sep 2022 10:51
Last Modified:	27 Sep 2022 10:51
URI:	https://eprints.ums.edu.my/id/eprint/34160

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