Research on Multi-Objective Optimization Power Flow of Power System Based on Improved Remora Optimization Algorithm

In this paper, an improved remora optimization algorithm (IROA) is proposed to solve the multi-objective optimal power flow problem (MOOPF). The algorithm introduced the crossover strategy and variance strategy in the differential evolutionary (DE) algorithm. The use of these two strategies can increase the diversity of the remora optimization algorithm (ROA) population and jump out of the defect of being trapped in a local optimum. To better solve the MOOPF, this paper proposed constraint prioritization strategy (CPS), congestion distance ranking strategy (CDRS), and optimal compromise solution strategy (OCSS) to acquire a uniform Pareto optimal set (POS) and the best trade-off solution (BTS). Combined with practical applications, six kinds of objective functions are selected, namely, basic fuel cost, active power loss, emission, voltage deviation, voltage stability, and fuel cost with valve point. The above six objective functions are arranged and combined to obtain the MOOPF problems with dual or triple objectives for solving on IEEE30-bus, IEEE57-bus, and IEEE118-bus systems, which are used to demonstrate the capability of IROA. Furthermore, three performance metrics Hypervolume (HV), Spacing (SP), and Generational Distance (GD) were applied to verify the uniformity and diversity of the POS. The results of the IROA algorithm are compared with those of the non-dominated sorting genetic algorithm. (NSGA-II) and the multi-objective particle swarm optimization algorithm (MOPSO), and it is obtained that the IROA algorithm has a better competitive advantage in solving the MOOPF.