The Present Study Investigates the Coordinated Optimisation of Optimal Power Flow and Economic Dispatch in Power Systems based on Intelligent Optimisation Algorithms

Abstract

Against the backdrop of the “dual carbon” goals driving the development of a new power system, the high penetration of renewable energy and interconnections of cross-regional power grids have significantly increased the complexity of system operations. The synergistic optimization of safety constraints and economic objectives has become a critical challenge. The traditional “stepwise optimization of Optimal Power Flow (OPF) and Economic Dispatch (ED)” approach faces the contradiction of either unfeasible solutions or economic losses. This paper proposes a deep collaborative optimization framework: this study constructs a multi-objective optimization model by using the Lagrange multiplier to inherently couple the safety constraints of OPF (node voltage, line power flow) with the economic objectives of ED (generation cost, network losses); a hybrid intelligent algorithm combining the improved competitive group optimization algorithm (ICSO) and the improved Newton method is designed to enhance the solution efficiency and optimality of the solution for large-scale systems. A simulation system incorporating a high proportion of renewable energy is established on the Power World platform, and the advantages of the proposed method are validated through multi-scenario comparisons. Experimental results show that this framework can reduce total generation costs by 8%-12%, reduce line overlimit rates to below 5%, and improve convergence speed by over 30%, providing technical support for the safe and economical operation of new power systems.