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Optimal Operation of a Photovoltaic Integrated Captive Cogeneration Plant with a Utility Grid Using Optimization and Machine Learning Prediction Methods

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  • B. Koti Reddy

    (School of Electronics and Electrical Engineering, Lovely Professional University, Jallandhar 144402, India)

  • Amit Kumar Singh

    (School of Electronics and Electrical Engineering, Lovely Professional University, Jallandhar 144402, India)

Abstract

The World Energy Council, in its 2019 World Energy Scenarios Report, advised policymakers to identify innovative opportunities for the integration of renewable energy resources into existing electrical power systems to achieve a fast and affordable solution. However, large-scale industries with cogeneration units are facing problems in handling the higher penetration levels of intermittent renewable energies. This paper addresses large-size photovoltaic power integration problems and their optimal operation. This work considers the case of a chemical industry having both cogeneration power and solar photovoltaics. Here, a modified firefly algorithm and a hybrid power resource optimization solver are proposed. The results of the proposed method are compared with other benchmark techniques, to confirm its advantages. The proposed techniques can be used in industries having cogeneration power plants with photovoltaics for better optimization and to meet the guidelines specified in IEEE 1547. The voltage ramp index is proposed to determine the voltage ramp up and down with intermittent solar irradiance. Additionally, a machine learning technique is used to predict the cogeneration plant efficiency at different loads and the solar irradiance under varying weather conditions. Finally, this paper proposes the effectiveness of the modified heuristic technique and certain guidelines, including solvers for industrial use.

Suggested Citation

  • B. Koti Reddy & Amit Kumar Singh, 2021. "Optimal Operation of a Photovoltaic Integrated Captive Cogeneration Plant with a Utility Grid Using Optimization and Machine Learning Prediction Methods," Energies, MDPI, vol. 14(16), pages 1-28, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4935-:d:613028
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