An Ant colony optimization for solving the Optimal Power Flow Problem in medium-scale electrical net
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Dec 12, 2005
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Abstract
Abstract- Real ants are capable of finding the shortest path from their nest to a food source without visual sensing. They are also able to adapt to changes in the environment. Ant Colony Algorithms try to make use of these real ant abilities
to solve various optimization problems. This paper presents solution of optimal power flow (OPF) problem of a power system via an Ant Colony Optimization Metaheuristic method. The objective is to minimize the total fuel cost of thermal generating units and also conserve an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt
capacitors/reactors, transformers tap-setting and power flow of transmission lines. Simulation results on the 25-bus Electrical Network show that the Ant Colony Optimization method converges quickly to the global optimum.
to solve various optimization problems. This paper presents solution of optimal power flow (OPF) problem of a power system via an Ant Colony Optimization Metaheuristic method. The objective is to minimize the total fuel cost of thermal generating units and also conserve an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt
capacitors/reactors, transformers tap-setting and power flow of transmission lines. Simulation results on the 25-bus Electrical Network show that the Ant Colony Optimization method converges quickly to the global optimum.