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Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer

Author

Listed:
  • Arul Rajagopalan

    (School of Electrical Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India)

  • Karthik Nagarajan

    (Department of Electrical & Electronics Engineering, Hindustan Institute of Technology & Science, Chennai 601301, Tamil Nadu, India)

  • Oscar Danilo Montoya

    (Grupo de Compatibilidad e Interferencia Electromágnetica, Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia
    Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, Cartagena 131001, Colombia)

  • Seshathiri Dhanasekaran

    (Department of Computer Science, UiT The Arctic University of Norway, 9037 Tromsø, Norway)

  • Inayathullah Abdul Kareem

    (School of Electrical Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India)

  • Angalaeswari Sendraya Perumal

    (School of Electrical Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India)

  • Natrayan Lakshmaiya

    (Department of Mechanical Engineering, Saveetha School of Engineering, SIMATS, Chennai 602107, Tamilnadu, India)

  • Prabhu Paramasivam

    (Department of Mechanical Engineering, College of Engineering and Technology, Mattu University, Mettu 318, Ethiopia)

Abstract

Optimal energy management has become a challenging task to accomplish in today’s advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It is possible to operate the microgrids under grid-connected, as well as isolated modes. The authors presented a new optimization algorithm, i.e., Oppositional Gradient-based Grey Wolf Optimizer (OGGWO) in the current study to elucidate the optimal operation in microgrids that is loaded with sustainable, as well as unsustainable energy sources. With the integration of non-Renewable Energy Sources (RES) with microgrids, environmental pollution is reduced. The current study proposes this hybrid algorithm to avoid stagnation and achieve premature convergence. Having been strategized as a bi-objective optimization problem, the ultimate aim of this model’s optimal operation is to cut the costs incurred upon operations and reduce the emission of pollutants in a 24-h scheduling period. In the current study, the authors considered a Micro Turbine (MT) followed by a Wind Turbine (WT), a battery unit and a Fuel Cell (FC) as storage devices. The microgrid was assumed under the grid-connected mode. The authors validated the proposed algorithm upon three different scenarios to establish the former’s efficiency and efficacy. In addition to these, the optimization results attained from the proposed technique were also compared with that of the results from techniques implemented earlier. According to the outcomes, it can be inferred that the presented OGGWO approach outperformed other methods in terms of cost mitigation and pollution reduction.

Suggested Citation

  • Arul Rajagopalan & Karthik Nagarajan & Oscar Danilo Montoya & Seshathiri Dhanasekaran & Inayathullah Abdul Kareem & Angalaeswari Sendraya Perumal & Natrayan Lakshmaiya & Prabhu Paramasivam, 2022. "Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer," Energies, MDPI, vol. 15(23), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9024-:d:987495
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    References listed on IDEAS

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    Cited by:

    1. Arun S. Loganathan & Vijayapriya Ramachandran & Angalaeswari Sendraya Perumal & Seshathiri Dhanasekaran & Natrayan Lakshmaiya & Prabhu Paramasivam, 2022. "Framework of Transactive Energy Market Strategies for Lucrative Peer-to-Peer Energy Transactions," Energies, MDPI, vol. 16(1), pages 1-16, December.
    2. Luis Fernando Grisales-Noreña & Bonie Johana Restrepo-Cuestas & Brandon Cortés-Caicedo & Jhon Montano & Andrés Alfonso Rosales-Muñoz & Marco Rivera, 2022. "Optimal Location and Sizing of Distributed Generators and Energy Storage Systems in Microgrids: A Review," Energies, MDPI, vol. 16(1), pages 1-30, December.

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