Abstract
An optimal power flow (OPF) solution considering cost of wind and solar power uncertainty using Particle Swarm Optimization (PSO) techniques is proposed. A Monte Carlo approach is used to simulate the uncertainty, by Weibull and Normal distribution models for wind and solar, respectively. Wind generation power is determined using a wind turbine mathematical model while solar power is calculated using PV and inverter models. The costs of wind and solar uncertainty consists of the opportunity cost of renewable power shortage and the opportunity cost of renewable power surplus. They reflect the additional spinning reserve and benefit loss caused by the unavailability of renewable power. These uncertainty costs are integrated in to conventional OPF problem, and then solved by four types of PSO algorithms. The simulation results from different PSO techniques are compared and the PSO with time variant inertia and acceleration coefficients and mutation based PSO provide the superior results.
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Samakpong, T., Ongsakul, W., Nimal Madhu, M. (2020). Optimal Power Flow Considering Cost of Wind and Solar Power Uncertainty Using Particle Swarm Optimization. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2019. Advances in Intelligent Systems and Computing, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-33585-4_19
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DOI: https://doi.org/10.1007/978-3-030-33585-4_19
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