Solving the Reactive Power Dispatch Optimization for Large Scale System

Abstract

Reactive power dispatch (RPD) is a crucial optimization task in power system operation because it seeks the optimal settings of control variables, such as generator voltages, transformer tap positions, and shunt VAR compensators, to reduce active power loss while satisfying operating constraints [file:1]. This study investigates the application of the fmincon solver and particle swarm optimization (PSO) to the RPD problem on three benchmark IEEE test systems, namely the 30-bus, 57-bus, and 118-bus networks [file:1]. For the IEEE 30-bus system, the minimum active power loss obtained by fmincon and PSO was 4.5480 MW and 4.4858 MW, respectively, corresponding to loss reductions of 21.88% and 22.95% from the initial value of 5.8223 MW [file:1]. For the IEEE 57-bus system, the power loss was reduced from 0.2846 p.u. to 0.2473 p.u. by fmincon and to 0.2362 p.u. by PSO, equivalent to reductions of 13.11% and 17.01%, respectively [file:1]. These results show that both methods are effective for large-scale RPD optimization, while PSO generally provides the best performance among the compared techniques in terms of active power loss minimization. The contribution of this study lies in providing a focused comparison between a deterministic nonlinear programming solver and a population-based metaheuristic approach across different system sizes, thereby demonstrating their practical suitability for large-scale reactive power optimization