Wind farm incorporated optimal power flow solutions through multi-objective horse herd optimization with a novel constraint handling technique

Optimal power flow plays an important role in integrating wind power into electric power networks. Because of its complexities, standard formulae are insufficient for the present scenario. Therefore, the multi-objective optimal power flow problems for wind farm incorporated power systems have been explored in this paper. The objectives are to minimize the generation costs, pollutant emissions, power losses, and voltage deviations. This paper proposes the development of a multi-objective meta-heuristic horse herd optimization for solving multi-objective optimal power flow problems. For this, a decomposition concept is introduced to the proposed algorithm leading to decomposition based multi-objective horse herd optimization algorithm. The constraint handling techniques in the previous papers have been found to be inefficient for optimal power flow problems. Therefore, a novel constraint handling technique is proposed in this paper to effectively control the variables out of bounds. In order to validate the performance and suitability of the proposed algorithm, seven case studies are examined. The algorithm is tested on wind farm incorporated IEEE-30, IEEE-57, and IEEE-118 bus test systems to demonstrate the efficiency in solving multi-objective optimal power flow problem for various problem sizes. The experimental results are demonstrating the efficiency of the proposed algorithm in solving complex optimization problems of various scales with multiple objectives.