Optimization of Emergency Load Shedding Employing Social Learning-Based PSO

Abstract

Emergency load shedding (ELS) is an essential measure to prevent power system accidents from expanding. Economy and security need to be optimized comprehensively for ELS. In this paper, an ELS optimization model is established, which takes the minimum load shedding amount as the objective function and the transient angle security, transient voltage deviation acceptability, transient frequency deviation acceptability, maximum controllable load as constraints. The social learning-based particle swarm optimization (SL-PSO) algorithm is proposed to solve the ELS optimization problem, which adopts adaptive parameters. The portable and open-source power system dynamic simulation toolkit (STEPS) is used for numerical simulation to check the feasibility of the solution. Finally, the efficiency of the solution is improved by parallel computing. The proposed model and algorithm are validated with the IEEE 39 bus test system.