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This paper presents an efficient approach for solving the optimal reactive power dispatch problem. It is a non-linear constrained optimization problem where two distinct objective functions are considered. The proposed approach is based on the hybridization of the particle swarm optimization method and the tabu-search technique. This hybrid approach is used to find control variable settings (i.e., generation bus voltages, transformer taps and shunt capacitor sizes) which minimize transmission active power losses and load bus voltage deviations. To validate the proposed hybrid method, the IEEE 30-bus system is considered for 12 and 19 control variables. The obtained results are compared with those obtained by particle swarm optimization and a tabu-search without hybridization and with other evolutionary algorithms reported in the literature.

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Summary For accurate economic dispatch solution, it is necessary to periodically estimate the parameters of fuel cost function. This paper proposes an improved differential evolution algorithm for computing the optimal parameters of fuel cost functions for thermal power plants. A new mutation strategy is suggested to enhance convergence rate and improve solution quality of original differential evolution. The proposed approach is examined on different test systems with several generator cost curve models involving smooth and nonsmooth/nonconvex functions. The results using the proposed approach are compared to those available in recent literature. The results show the efficiency of the proposed estimation approach for obtaining accurate fuel cost parameters without any restriction on the mathematical model of the generator cost curve.

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