Pareto Optimization of Power System Reconstruction Using NSGA-II Algorithm

After blackout, power system reconstruction is a multiobjective optimization problem, especially for parallel restoration through power system partitioning. It includes optimal system partitioning strategy, units optimal starting-up sequence, time requirements for system reconstruction. A constrained multiobjective optimization model is proposed for power system reconstruction. Fast and elitist non-dominated sorting genetic algorithm (NSGA-II) is applied in order to avoid artificially balanced solutions. Priority-based genetic encoding and decoding are designed for NSGA-II. With non-dominated sorting and crowding distance calculation the means of dummy fitness are used to find Pareto optimal solutions. An elitist checking strategy is adopted to check Pareto optimal solutions. Simulation results on IEEE 30-bus system show that the proposed algorithms are able to find better spread of solutions, better convergence near the Pareto-optimal front and lower computational complexity compared to other power system reconstruction methods based on genetic algorithms. The effectiveness of the proposed algorithms is further validated by the numerical results on the power system of Shandong province, China.