A multi-objective optimisation algorithm with swarm intelligence for contingency surveillance

摘要

This paper proposes a new multi-objective optimization model to minimize congestion cost, load curtailment and generation cost simultaneously to restore the equilibrium of operating point of the system under contingency. The solution algorithms of the proposed method are based on the Particle Swarm Optimization (PSO) in which load curtailment and generation cost have been optimized without breaching line flow constraints for congestion management. The significance of the proposed method has been presented in this paper by a comparative study with the conventional cost optimization method in terms of operating cost considering VOLL (Value of lost load) and two more proposed analytical indices namely Value of Congestion Cost (VOCC) and Value of Excess Loss (VOEL) in contingent states of power system. It has been depicted that the proposed method effectively reduces the operating cost volatility in spot power market with respect to the conventional methods. The applicability of the developed method has been tested on the IEEE 30 bus system.