Application of MA with RMSLS to probabilistic distribution network expansion planning

This paper proposes a meta-heuristic method for probabilistic distribution network expansion planning (DNEP). It has been studied for a long time, but recently system planners are faced with uncertainty under competitive power systems. A more flexible method is required to deal with the complicated distribution systems. This paper considers the uncertainty of the nodal specified values and multi-objective optimization. In this paper, a new Memetic Algorithm (MA) that consists of Genetic algorithm (GA) and local search (LS) is proposed to deal with multi-objective optimization. The uncertainty of the nodal specified values is considered in the Monte-Carlo Simulation (MCS). As a multi-objective solver, the epsiv-constraint method is employed to solve a multi-objective problem while Random Multi Start Local Search (RMSLS) is used to evaluate local solutions efficiently. The proposed method is successfully applied to a sample system.