The improvement of a new optimization method for integration of renewable energy sources to smart grids

Today, the interest in renewable energy sources is increasing due to the finite fossil fuels and global warming. However, the placement of these energy sources in the networks is done with the analysis of some problems. In this context, the problem of optimal placement and sizing of renewable-based resources into the network is addressed. Moreover, this problem can be solved by heuristic methods. In this study, a new heuristic algorithm has been proposed, named an improved particle swarm optimization with eagle strategy algorithm (ESPSO). This method has been tested on some classical benchmark functions. In the next step, it has been modified for multi-objective problems and applied to some test functions. The results of proposed method have been compared with other algorithms. For comparison, particle swarm optimization (PSO), salp swarm algorithm (SSA) and firefly (FA) and chaotic firefly (CFA) algorithms have been used. Finally, they have been implemented to the optimal placement and sizing problem based on a multi-objective real world problem. In this problem, the minimizations of power loss, installation cost, the voltage deviation and gas emission are handled simultaneously. So the problem has become multi-objective problem. The achieving competitive results in test beds is evidence of the outperforming ability of the proposed method. It can be also shown that it is a superior method. Multi-objective test problems have a true Pareto front. In this study, both methods converged to this anterior surface. In the real world problem, the proposed method has success the placement of renewable energy sources with high capacity in different buses. This can also be a proof of its ability. Large-scale modified test systems have been used in the analysis of smart grid applications for the real world problem solved by the proposed method. In this study, the modified IEEE 30-bus system, the modified IEEE 57-bus system and the modified IEEE 118-bus system have been used. Keywords: Smart Electric Power Systems, Chaotic Firefly Algorithm, Eagle Strategy, Particle Swarm Optimization, Renewable Energy Sources.