A fuzzy binary clustered particle swarm optimization strategy for thermal unit commitment problem with wind power integration.

This paper presents a fuzzy-logic-based binary particle swarm optimization (BPSO) method for solving short-term thermal unit commitment problem integrated with an equivalent wind-battery system. As a renewable power source, wind power is injected stochastically with the model. To handle the uncertainty and intermittency due to the wind power integration, the trivial crisp problem formulations are modified by introducing fuzzy logic. Moreover, since it is also forecast, load demand along with the spinning reserve and production cost are taken under fuzzification. The potential solutions are distributed among several clusters based on a clustering scheme which exploits their associated fitness values. The fitness value is functionalized by combining the objective function, penalty function, and the aggregated fuzzy membership function. After clustering, each solution is updated according to the velocity and position BPSO refinement functions. The clustering scheme inherently introduces multipopulation-based search space exploration in PSO. Therefore, this algorithm allows the particles to explore a larger search space in the problem domain by diversification of particles. Simulation results are provided to show the effectiveness of the proposed method by scrutinizing two different power systems. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]