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Particle Swarm Optimization for Optimal Frequency Response with High Penetration of Photovoltaic and Wind Generation

Author

Listed:
  • Manuel S. Alvarez-Alvarado

    (Escuela Superior Politécnica del Litoral (ESPOL), Guayaquil EC090112, Ecuador)

  • Johnny Rengifo

    (Escuela Superior Politécnica del Litoral (ESPOL), Guayaquil EC090112, Ecuador)

  • Rommel M. Gallegos-Núñez

    (Escuela Superior Politécnica del Litoral (ESPOL), Guayaquil EC090112, Ecuador)

  • José G. Rivera-Mora

    (Escuela Superior Politécnica del Litoral (ESPOL), Guayaquil EC090112, Ecuador)

  • Holguer H. Noriega

    (Escuela Superior Politécnica del Litoral (ESPOL), Guayaquil EC090112, Ecuador)

  • Washington Velasquez

    (Escuela Superior Politécnica del Litoral (ESPOL), Guayaquil EC090112, Ecuador)

  • Daniel L. Donaldson

    (Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, UK)

  • Carlos D. Rodríguez-Gallegos

    (Solar Energy Research Institute of Singapore (SERIS), National University of Singapore (NUS), Singapore 117574, Singapore)

Abstract

As the installation of solar-photovoltaic and wind-generation systems continue to grow, the location must be strategically selected to maintain a reliable grid. However, such strategies are commonly subject to system adequacy constraints, while system security constraints (e.g., frequency stability, voltage limits) are vaguely explored. This may lead to inaccuracies in the optimal placement of the renewables, and thus maximum benefits may not be achieved. In this context, this paper proposes an optimization-based mathematical framework to design a robust distributed generation system, able to keep system stability in a desired range under system perturbance. The optimum placement of wind and solar renewable energies that minimizes the impact on system stability in terms of the standard frequency deviation is obtained through particle swarm optimization, which is developed in Python and executed in PowerFactory-DIgSILENT. The results reveal that the proposed approach has the potential to reduce the influence of disturbances, enhancing critical clearance time before frequency collapse and supporting secure power system operation.

Suggested Citation

  • Manuel S. Alvarez-Alvarado & Johnny Rengifo & Rommel M. Gallegos-Núñez & José G. Rivera-Mora & Holguer H. Noriega & Washington Velasquez & Daniel L. Donaldson & Carlos D. Rodríguez-Gallegos, 2022. "Particle Swarm Optimization for Optimal Frequency Response with High Penetration of Photovoltaic and Wind Generation," Energies, MDPI, vol. 15(22), pages 1-12, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8565-:d:974220
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    References listed on IDEAS

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    1. Amirhossein Sajadi & Luka Strezoski & Vladimir Strezoski & Marija Prica & Kenneth A. Loparo, 2019. "Integration of renewable energy systems and challenges for dynamics, control, and automation of electrical power systems," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(1), January.
    2. Wu, Wei & Skye, Harrison M. & Domanski, Piotr A., 2018. "Selecting HVAC systems to achieve comfortable and cost-effective residential net-zero energy buildings," Applied Energy, Elsevier, vol. 212(C), pages 577-591.
    3. Ben Hamida, Imen & Salah, Saoussen Brini & Msahli, Faouzi & Mimouni, Mohamed Faouzi, 2018. "Optimal network reconfiguration and renewable DG integration considering time sequence variation in load and DGs," Renewable Energy, Elsevier, vol. 121(C), pages 66-80.
    4. Rai, Alan & Nunn, Oliver, 2020. "On the impact of increasing penetration of variable renewables on electricity spot price extremes in Australia," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 67-86.
    5. Luay Elkhidir & Khalid Khan & Mohammad Al-Muhaini & Muhammad Khalid, 2022. "Enhancing Transient Response and Voltage Stability of Renewable Integrated Microgrids," Sustainability, MDPI, vol. 14(7), pages 1-21, March.
    6. Diego Carrión & Edwin García & Manuel Jaramillo & Jorge W. González, 2021. "A Novel Methodology for Optimal SVC Location Considering N-1 Contingencies and Reactive Power Flows Reconfiguration," Energies, MDPI, vol. 14(20), pages 1-17, October.
    7. Ehsan, Ali & Yang, Qiang, 2018. "Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques," Applied Energy, Elsevier, vol. 210(C), pages 44-59.
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