Optimal Location and Sizing of PV Generation Units in Electrical Networks to Reduce the Total Annual Operating Costs: An Application of the Crow Search Algorithm

This study presents a master–slave methodology to solve the problem of optimally locating and sizing photovoltaic (PV) generation units in electrical networks. is represented by means Mixed-Integer Nonlinear Programming (MINLP) model, whose objective function reduce total annual operating costs network for 20-year planning period. Such include (i) purchasing energy at conventional generators (the main supply node this particular case), (ii) investment PV units, (iii) their corresponding operation maintenance costs. In proposed method, master stage uses Discrete–Continuous version Crow Search Algorithm (DCCSA) define set nodes where will be installed (location), as well nominal power (sizing), slave employs successive approximation flow technique find value each individual provided stage. The numerical results obtained 33- 69-node test systems demonstrated its applicability, efficiency, robustness when compared other methods reported specialized literature, such vortex search algorithm, generalized normal distribution optimizer, particle swarm optimization algorithm. All simulations were performed MATLAB using our own scripts.