Optimal reliability planning for a composite electric power system based on Monte Carlo simulation using particle swarm optimization
A methodology for planning the optimal reliability indices of system components for a composite electric power system based on state sampling non-sequential Monte Carlo simulation using particle swarm optimization (PSO) algorithm is presented. The indices designed are forced outage rate of system components and expected demand not served (EDNS) of the system. The optimal reliability planning problem has been formulated as an optimization problem of minimizing the system interruption cost and the component investment cost. The cost functions are modeled as a function of forced outage rate and EDNS. The EDNS of the system for a particular system reliability level is evaluated based on state sampling non-sequentialMonte Carlo simulation and the dc load flow based load curtailment model. PSO algorithm is employed to minimize the reliability planning model. The applications of the proposed methodology are illustrated through case studies carried out using Modified Stagg and El-Abiad 5-bus system and IEEE 14-bus system. The effectiveness of this approach is validated by comparing the results obtained with the solution of reliability planning model using genetic algorithm optimizer.
reliability planning monte carlo simulation particle swarm optimization composite power system reliability index