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Machine Learning Approaches in a Real Power System and Power Markets

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Application of Machine Learning and Deep Learning Methods to Power System Problems

Part of the book series: Power Systems ((POWSYS))

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Abstract

The last developments in the electric power system such as liberalization of the electricity markets, a surge in demands of the electricity and its impacts on power grid effectiveness and quality. The growth of renewable energy resources, dispersed production and growing number of interconnections, and power exchanges between electricity companies clarify the demand for regeneration in the planning, operating, and control of electric power system. On the other hand, a significant increase in competition in power production and transmission area is the biggest issue facing in the power system industry nowadays. Also, the power market due to its nature is very challenging due to its inability to store generated energy and technical limitations such as congestion of transmission line (TL) which can lead to the isolation of sub-markets. Machine learning (ML) approaches can be an alternative solution for related problems replacing the traditional analytical techniques. In the past 30 years, ML techniques such as artificial neural networks (ANNs), fuzzy systems, evolutionary programming, and other artificial intelligence (AI) techniques have been presented in the power system community.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, University of Ferdowsi, Mashhad, Iran

    Peyman Razmi

  2. Faculty of Economics, Kharazmi University, Tehran, Iran

    Mahdi Ghaemi Asl

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  1. Peyman Razmi
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  1. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Morteza Nazari-Heris

  2. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Somayeh Asadi

  3. Department of Energy Technology Aalborg University, Aalborg, Denmark

    Behnam Mohammadi-Ivatloo

  4. Deakin University, Geelong, VIC, Australia

    Moloud Abdar

  5. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Houtan Jebelli

  6. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Milad Sadat-Mohammadi

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Razmi, P., Ghaemi Asl, M. (2021). Machine Learning Approaches in a Real Power System and Power Markets. In: Nazari-Heris, M., Asadi, S., Mohammadi-Ivatloo, B., Abdar, M., Jebelli, H., Sadat-Mohammadi, M. (eds) Application of Machine Learning and Deep Learning Methods to Power System Problems. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-77696-1_17

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  • DOI: https://doi.org/10.1007/978-3-030-77696-1_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77695-4

  • Online ISBN: 978-3-030-77696-1

  • eBook Packages: EnergyEnergy (R0)

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