Optimization of economic dispatch using updated differential evolution algorithm

In the operation of power systems, economic dispatch (ED) plays a vibrant role. Its key aim is to find the best efficient distribution of power between generating units. Also, it converted into non-convex nature due to considering many practical restrictions like maximum and minimum power output, prohibited operating zones, transmission line capacity, and ramp rate limits. Therefore, solving non-convex economic dispatch issue poses a significant challenge. Recently, many evolutionary techniques have been developed to address this type of problems. Among them differential evolution (DE) is effectively solved in several complex optimization issues. However, its presentation is affected by stagnation and slow convergence. It happens due to improper selection of mutation strategy and control parameter. This paper advised an updated DE (UDE), to improve the presentation DE and solving economic dispatch issue. It has a new mutation strategy (based on particle swarm optimization concept), to balance the population diversity. Also, it incorporated novel mutant control parameters (formed by time-varying criteria) on suggested mutation strategy, to avoid stagnation and keep evolving. Using the memory and modified factor in suggested mutation strategy, UDE follows the higher convergence speed and well balanced exploration and exploitation ability. To investigate the suggested UDE performance, a collection of IEEE CEC2006 benchmark suite are solved. Furthermore, the applicability of the UDE are further demonstrated on three different unit cases of ED problem. The experimental and statistical test outcomes, collectively indicate that compared to other modern optimization algorithms, overall UDE exhibits superior performance.