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

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