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Dynamic Price-Based Demand Response through Linear Regression for Microgrids with Renewable Energy Resources

Author

Listed:
  • Muhammad Arshad Shehzad Hassan

    (Department of Electrical Engineering, The University of Faisalabad, Faisalabad 38000, Pakistan)

  • Ussama Assad

    (Department of Electrical Engineering, The University of Faisalabad, Faisalabad 38000, Pakistan)

  • Umar Farooq

    (Department of Electrical Engineering, The University of Faisalabad, Faisalabad 38000, Pakistan)

  • Asif Kabir

    (Department of CS & IT, University of Kotli, Azad Jammu & Kashmir, Azad Jammu and Kashmir 11100, Pakistan)

  • Muhammad Zeeshan Khan

    (Department of Electrical Engineering, The University of Faisalabad, Faisalabad 38000, Pakistan)

  • S. Sabahat H. Bukhari

    (School of Computer Science, Neijiang Normal University, Neijiang 641100, China)

  • Zain ul Abidin Jaffri

    (College of Physics and Electronic Information Engineering, Neijiang Normal University, Neijiang 641100, China)

  • Judit Oláh

    (Faculty of Economics and Business, University of Debrecen, 4032 Debrecen, Hungary
    College of Business and Economics, University of Johannesburg, Johannesburg 2006, South Africa)

  • József Popp

    (College of Business and Economics, University of Johannesburg, Johannesburg 2006, South Africa
    Hungarian National Bank—Research Center, John von Neumann University, Izsáki út 10, 6000 Kecskemét, Hungary)

Abstract

The green innovations in the energy sector are smart solutions to meet the excessive power requirements through renewable energy resources (RERs). These resources have forwarded the revolutionary relief in control of carbon dioxide gaseous emissions from traditional energy resources. The use of RERs in a heuristic manner is necessary to meet the demand side management in microgrids (MGs). The pricing scheme limitations hinder the profit maximization of MG and their customers. In addition, recent pricing schemes lack mechanistic underpinning. Therefore, a dynamic electricity pricing scheme through linear regression is designed for RERs to maximize the profit of load customers (changeable and unchangeable) in MG. The demand response optimization problem is solved through the particle swarm optimization (PSO) technique. The proposed dynamic electricity pricing scheme is evaluated under two different scenarios. The simulation results verified that the proposed dynamic electricity pricing scheme sustained the profit margins and comforts for changeable and unchangeable load customers as compared to fixed electricity pricing schemes in both scenarios. Hence, the proposed dynamic electricity pricing scheme can readily be used for real microgrids (MGs) to grasp the goal for cleaner energy production.

Suggested Citation

  • Muhammad Arshad Shehzad Hassan & Ussama Assad & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Dynamic Price-Based Demand Response through Linear Regression for Microgrids with Renewable Energy Resources," Energies, MDPI, vol. 15(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1385-:d:749140
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    References listed on IDEAS

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    Cited by:

    1. Sushmita Kujur & Hari Mohan Dubey & Surender Reddy Salkuti, 2023. "Demand Response Management of a Residential Microgrid Using Chaotic Aquila Optimization," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    2. Pourramezan, Ali & Samadi, Mahdi, 2023. "A system dynamics investigation on the long-term impacts of demand response in generation investment planning incorporating renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    3. Wenqiang Guo & Xinyi Xu, 2022. "Comprehensive Energy Demand Response Optimization Dispatch Method Based on Carbon Trading," Energies, MDPI, vol. 15(9), pages 1-17, April.

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