Environmental and Economic Optimization of Multi-Source Power Real-Time Dispatch Based on DGADE-HDJ

Considering the special features of dynamic environment economic dispatch of power systems with high dimensionality, strong coupling, nonlinearity, and non-convexity, a GA-DE multi-objective optimization algorithm based on dual-population pseudo-parallel genetic algorithm-differential evolution is proposed in this paper. The algorithm is based on external elite archive and Pareto dominance, and it adopts the cooperative co-evolution mechanism of differential evolution and genetic algorithm. Average entropy and cubic chaotic mapping initialization strategies are proposed to increase population diversity. In the proposed method, we analyze the distribution of neighboring solutions and apply a new Pareto solution set pruning approach. Unlike traditional models, this work takes the transmission losses as an optimization target and overcomes complex model constraints through a dynamic relaxation constraint approach. To solve the uncertainty caused by integrating wind and photovoltaic energy in power system scheduling, a multi-objective dynamic environment economical dispatch model is set up that takes the system spinning reserve and network highest losses into account. In this paper, the DE algorithm is improved to form the DGAGE algorithm for the objective optimization of the overall power system, The DE algorithm part of DGAGE is combined with the JAYA algorithm to form the system scheduling HDJ algorithm for multiple energy sources connected to the grid. The effectiveness of the proposed method is demonstrated using CEC2022 and CEC2005 test functions, showing robust optimization performance. Validation on a classical 10-unit system confirms the feasibility of the proposed algorithm in addressing power system scheduling issues. This approach provides a novel solution for dynamic power dispatch systems.