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

Author

Listed:
  • Kavita Jain

    (Department of Electrical Engineering, Swami Keshvanand Institute of Technology, Management and Gramothan, Jaipur 302017, Rajasthan, India)

  • Muhammed Basheer Jasser

    (Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Petaling Jaya 47500, Selangor, Malaysia)

  • Muzaffar Hamzah

    (Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu 88450, Sabah, Malaysia)

  • Akash Saxena

    (Department of Electrical Engineering, Swami Keshvanand Institute of Technology, Management and Gramothan, Jaipur 302017, Rajasthan, India)

  • Ali Wagdy Mohamed

    (Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt
    Department of Mathematics and Actuarial Science, School of Sciences Engineering, The American University in Cairo, New Cairo 11835, Egypt)

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.

Suggested Citation

  • Kavita Jain & Muhammed Basheer Jasser & Muzaffar Hamzah & Akash Saxena & Ali Wagdy Mohamed, 2022. "Harris Hawk Optimization-Based Deep Neural Networks Architecture for Optimal Bidding in the Electricity Market," Mathematics, MDPI, vol. 10(12), pages 1-19, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2094-:d:840471
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    References listed on IDEAS

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    Cited by:

    1. Jian Dong, 2023. "Preface to the Special Issue on “Recent Advances in Swarm Intelligence Algorithms and Their Applications”—Special Issue Book," Mathematics, MDPI, vol. 11(12), pages 1-4, June.

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