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

Mathematics

Muhammed Basheer Jasser¹  Akash Saxena²  Kavita Jain²  Muzaffar Hamzah³  Ali Wagdy Mohamed⁴ 

[1] Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Petaling Jaya 47500, Selangor, Malaysia;Department of Electrical Engineering, Swami Keshvanand Institute of Technology, Management and Gramothan, Jaipur 302017, Rajasthan, India;Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu 88450, Sabah, Malaysia;Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt;

关键词: electricity market;    optimal bidding;    Harris Hawk Optimization;    multi layered neural network;    bi-level optimization;    strategic bidding;   

DOI  :  10.3390/math10122094

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown