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Electric distribution network operation planning using chu and beasley genetic algorithm and particle swarm optimization

Abstract:

The operation planning problem consists of finding the best network control variable settings to improve its performance, while meeting its physical and operational constraints, given the daily load change. The presence of continuous (such as distributed generation) and discrete control variables (such as substation transformer taps, voltage regulators and switchable capacitor banks), along with the nonlinearity of the objective function and constraints, results in a very complex optimization problem. Those difficulties make room for opportunities for the development of new solution approaches and their application through efficient optimization tools. In this chapter, voltage regulator tap positions and capacitors banks have been used as discrete control variables to provide the distribution system operator with alternative measures to minimize real power losses. The presence of distributed generation as continuous variables has also been taken into account. Additionally, four metaheuristic optimization tools have been proposed and compared, namely the Chu-Beasley Genetic Algorithm and three Particle Swarm Optimization variants. Results are presented using data from both test and realistic electric distribution networks.