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This paper presents a hierarchical interactive particle swarm optimization (HIPSO) for solving the static multi objective optimal power flow (OPF) considering multi SVC controllers and dynamic economic dispatch (DED). This study introduces a variant of PSO using new mechanism search based dynamic adjustment of PSO parameters coordinated with a hierarchical strategy for reactive power planning stage to improve the performances of the original PSO in terms of solution quality and convergence characteristics. The HIPSO algorithm optimizes a combined control variables such as: active power generations, voltage control of generators, tap transformer and the reactive power of multi SVC Controllers installed at a specified buses to minimize fuel cost, total power loss and voltage deviation while satisfying the generators constraints and power system security. The proposed approach examined and applied to many practical test systems: IEEE 57-Bus, IEEE 118-Bus, 40 generating units and 10 generating units to solving; the static multi objective OPF with quadratic fuel cost function, the economic dispatch considering valve point effects and the dynamic economic dispatch considering both valve point effects and ramp rate limits. Simulation results compared to Matpower, to the standard PSO and others recent metaheuristic methods confirm the effectiveness of the proposed combined approach. [ABSTRACT FROM AUTHOR]

Optimal capacitor placement problem is a significant and precious strategy which can be supposed as an always appropriate solution to reduce the operating costs in the power systems. Nevertheless, as the result of nonlinear and discrete characteristics of this problem, traditional optimization methods can not solve it effectively. Therefore, this paper proposes a novel optimization framework based on shuffled frog leaping algorithm (SFLA) to solve the optimal capacitor placement and sizing problem properly. In this regard, a new modification approach based on the mutation and crossover operators is suggested to improve the local search ability of this algorithm. The effectiveness and satisfying performance of the proposed method is assessed on two IEEE test systems. Comparative results sow the superiority of the proposed optimization method than the other popular methods in the area. [ABSTRACT FROM AUTHOR]