Optimal Planning of Distribution Network with DG Units and ESS Considering Costs
Аннотация
This paper focuses on the application of the artificial ecosystem-based optimizer opposition-based learning (AEO-OBL) technique to optimal integration of renewable energy sources (RES) based distributed generation (DG) units, namely wind turbine (WT), photovoltaic (PV) and energy storage system (ESS) in the 33-bus distribution network (DN). This study is designed to minimize the total costs associated with ESS, PV, and WT-based DG units, installation, operation, maintenance costs, energy loss cost, cost of purchasing energy from the grid and emission cost taking into account uncertainty in generation of power, and variable load demands. According to the simulation results, it has been demonstrated that the total costs associated with the operation of the integrated system can be significantly reduced by optimally connecting DG units and ESS. Furthermore, the proposed algorithm has been demonstrated to be more effective in solving the optimization problem compared to other techniques, such as artificial ecosystem-based optimization (AEO), moth-flame optimization (MFO), and particle swarm optimization (PSO). According to the results of the test, AEO-OBL proved to be more efficient than other techniques when compared with other techniques.