Multi-objective optimal load distribution based on improved gravitational search algorithm in power plants

With the development of power market, the current plant-level load distribution not only conforms with the requirements of load economy in scheduling, but also accords with the demands of rapid load adjustment time. Based on the existing economic model and rapidity model of optimal load distribution, a mathematical model of multi-objective fusion load distribution is established. Global memory and convergence accuracy are added to the standard Gravitational Search Algorithm (GSA). The simulation experimental results of GSA and Improvement Gravitational Search Algorithm (IGSA) is verified by the peak multidimensional test function. At the same time, a multi-objective continuous optimization gravitational search algorithm is proposed, which contains non-dominance sorting, crowding distance calculation and elite strategy in Nondominated Sorting Genetic Algorithm II (NSGA-II). IGSA was applied to multi-objective load distribution problems and compared with the NSGA-II algorithm. Pareto set was obtained through simulation experiments. A multi-attribute decision method based on pole and entropy is applied to select the optimal solution of load distribution. From the experimental results, IGSA can achieve better performance in multi-objective load distribution of power plant.