An Efficient Multi-objective Meta-heuristic Method for Probabilistic Transmission Network Planning

In this paper, a new method is proposed for probabilistic transmission network expansion planning in Smart Grid. The proposed method makes use of Controlled Nondominated Sorting Genetic Algorithm (CNSGA-II) of multi-objective meta-heuristics (MOMH) to calculate a set of the Pareto solutions. In recent years, electric power networks increase the degree of uncertainties due to new environment of Smart Grid with renewable energy, distributed generation, Demand Response (DR), etc. Smart grid planners are interested in improving power supply reliability of transmission networks so that probabilistic expansion planning approaches are required. This paper focuses on a multi-objective problem in probabilistic transmission network expansion planning. The multi-objective optimization problem may be expressed as multi-metaheuristic formulation that evaluates a set of the Pareto solutions in Monte Carlo Simulation (MCS). In this paper, CNSGA-II is used to calculate a set of the Pareto Solutions. The proposed method is successfully applied to the IEEE 24-bus reliability test system.