Single- and Multiobjective Optimal Power Flow with Stochastic Wind and Solar Power Plants Using Moth Flame Optimization Algorithm

Abstract

ABSTRACT The proposed article recommends a method for the solution of single and multiobjective optimal power flow without and with integrating renewable energy resources along with traditional coal-based generating stations. In the first part, the different objectives of optimal power flow problem with a single- as well as conflicting multiobjective manners are optimized. The efficiency of the recommended technique has been verified on three diverse standard test systems like IEEE-30 bus system, IEEE-57 bus system and large system like IEEE-118 bus network with the statistical analysis. The simulated results are equated to other reported meta heuristic methods. The second part consists of optimal power flow problem with the incorporation of solar and wind output energy. For forecasting solar and wind production, the proposed approach uses log-normal and Weibull probability density functions, combined. Penalties costs for undervaluation and a backup fee for oversimplification of unusual nonconventional power sources are included in the objective feature. The optimization problem is formulated using a nondominated multiobjective moth flame optimization method. To find the best compromise solution, the fuzzy decision-making technique is used. The results are confirmed using an updated IEEE-30 bus test system that includes wind and solar power plants.