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

Manuel S. Alvarez-Alvarado (), Johnny Rengifo, Rommel M. Gallegos-Núñez, José G. Rivera-Mora, Holguer H. Noriega, Washington Velasquez, Daniel L. Donaldson and Carlos D. Rodríguez-Gallegos
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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

Energies, 2022, vol. 15, issue 22, 1-12

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.

Keywords: particle swarm optimization; PV system; power system stability; optimization wind generation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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