Multi-Objective Optimal Location and Sizing of Hybrid Photovoltaic System in Distribution Systems Using Crow Search Algorithm

The optimal size and location of Distributed generation (DG) in a distribution system can minimize the power loss and improve the voltage profile of the distribution system. But the random placement of DG will not solve the problem of power loss and voltage profile. Therefore optimization algorithms are used to find the optimal size and location of DG to minimize the power loss and to improve the voltage profile. This research paper introduces a new metaheuristic algorithm which is the Crow search algorithm (CSA) to calculate the optimal size and location of multiple solar photovoltaic (PV) units for reducing the power loss and improving the voltage profile. This paper also discusses the complete mathematical modeling of probabilistic PV generation, time-varying load modeling, loss calculation, voltage stability, and optimal PV size calculation. The method proposed in this paper is tested on IEEE 30 and 57 bus test systems and compared with other existing methods like Genetic algorithm (GA), Particle swarm optimization (PSO) and Hybrid GA-PSO method. CSA has only 2 settings parameters while GA and PSO have 6 and 3 settings parameters. Because of this, computation time and complexity of the algorithm are less in comparison to other methods and it gives a better result. Therefore by using the CSA method, the optimal size and location of the DG can be found which gives less power loss and improved voltage profile, less computational time and easy to implement compared to other existing methods proposed here.