A Novel Optimized Fuzzy Approach Based on Monte Carlo Method for System Load, Wind Turbine and Photovoltaic Unit Uncertainty Modeling in Unit Commitment

Abstract Developing usage of renewable resources, such as wind turbine and photovoltaic units, and their output uncertainty beside the system total load forecasting imprecision leads to the uncertainty of unit commitment solution. This article presents a new fuzzy approach for modeling of these uncertainty sources behavior in a power system including wind turbine, photovoltaic, and thermal units. For the first time, based on Monte Carlo simulation, a new concept called Monte Carlo mismatch index is proposed as a criterion for uncertainty modeling precision, which is used as a fitness function for optimization of the proposed fuzzy system parameters by genetic algorithm. Then this stochastic-optimized fuzzy system is applied for uncertainty modeling. The fuzzy system output is that part of the power system total load, which should be distributed among thermal units. Then, this output is fed to a crisp unit commitment program based on particle swarm optimization. Finally, optimal thermal units generation cost and scheduling is presented and the proposed method performance is compared with other uncertainty modeling methods.