Optimal Investments in PV Sources for Grid-Connected Distribution Networks: An Application of the Discrete–Continuous Genetic Algorithm

The problem of the optimal siting and sizing photovoltaic (PV) sources in grid connected distribution networks is addressed this study with a master–slave optimization approach. In master stage, discrete–continuous version Chu Beasley genetic algorithm (DCCBGA) employed, which defines locations sizes for PV sources. slave successive approximation method used to evaluate fitness function value each individual provided by stage. objective simultaneously minimizes energy purchasing costs substation bus, investment operating planning period 20 years. numerical results IEEE 33-bus 69-bus systems demonstrate that proposed methodology, it possible eliminate about 27% annual operation both three After 100 consecutive evaluations DCCBGA, was observed 44% solutions found system were better than those BONMIN solver General Algebraic Modeling System (GAMS package). case system, DCCBGA ensured, 55% probability, values mean solution GAMS found. Power generation curves slack source confirmed create duck curve power required main grid; addition, voltage profile show regulation always maintained between ±10% all time periods under analysis. All validations carried out MATLAB programming environment package.