Particle Swarm Optimization of Wind Farm Due to Non-greenhouse Gas Emission under Power Market Considering Uncertainty of Wind Speed Using Monte Carlo Method

Original scientific paper In this study a multi-objective formulation for optimal sizing and finally optimal operation of wind farm in distribution systems for maximizing net present worth of system is analysed. The proposed system in this study consists of upstream network i.e. 63 kV / 20 kV substation as main grid and wind turbines as DG to supply load. In this study, total net present worth as objective function consists of two parts including net present worth of distribution company (Disco) and of wind farm owner (WT-owner). In order to obtain accurate results, in this study, the uncertainty of wind speed is considered using the Monte Carlo method. The implemented technique is based on particle swarm optimization method (PSO) and weighting coefficient method. Simulation results on 33-bus distribution test system under power market operation are presented to show the effectiveness of the proposed procedure. The considered objective function is of highly non-convex manner, and also has several constraints. On the other hand due to significant computational time reduction and faster convergence of PSO in comparison with other intelligent optimization approaches such as Genetic Algorithm (GA) and Artificial Bee Colony (ABC) the simple version of PSO has been implemented. Of course other versions of PSO such as Adaptive PSO and combination of PSO with other methods due to complexity of this optimization problem have not been considered in this research.