T. Bouktir, L. Slimani, and B. Mahdad
Load flow, optimal power flow, genetic algorithm, evolutionary programming
This paper presents solution of optimal power flow (OPF) problem of large distribution systems via two stochastic search algorithms. A simple genetic algorithm (GA) and evolutionary programming (EP) are used to minimize the fuel cost and keep the power outputs of generators, bus voltages, shunt capacitors/reactors and transformers tap-setting in their secure limits. CPU times can be reduced by decomposing the optimization constraints to "active constraintsa nd "passive constraints . The active constraints which affect directly the cost function are manipulated by GA or EP. The passive constraints which affect indirectly this function are maintained in their soft limits using a conventional constraint load flow. If the objective used in the OPF problem formulation is the minimization of the total cost of real power then the active constraints are the active power generations only and the passive constraints are generator bus voltages, transformer taps and reactive power generations. The two stochastic search algorithms are applied to IEEE 30-bus model system (6 generators, 41 lines and 20 loads) and to IEEE 118- bus model system (54 generators, 186 (line + transformer) and 99 loads). The numerical results have demonstrated the effectiveness of the stochastic search algorithms because it can provide accurate dispatch solutions with reasonable time.
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