Application of Multi-objective Memetic Algorithm with Solution Diversity to Probabilistic Distribution Network Expansion Planning

Abstract

This paper proposes a new efficient Multi-objective Memetic Algorithm (MOMA) for probabilistic distribution network expansion planning (DNEP). Recently, the deregulated and competitive power market brings about uncertainty, i.e., random output of distributed generation (DG) such as wind and photovoltaic power, load growths, etc. DG plays a key role to smooth distribution network planning. However, system planners are faced with new uncertain environment. This paper makes use of Monte-Carlo simulation to consider these uncertainties efficiently. Furthermore, a new method is proposed for multi-objective DNEP problems with MOMA that combines Multi-objective meta-heuristics with local search to obtain better solution sets. This paper proposes SPEA2 with Random Multi-start Variable neighborhood LS (RMSVLS) to consider the diversity and accuracy of solution sets. The proposed method is successfully applied to a sample system.