Stochastic Distribution Expansion Planning with Wind Power Generation and Electric Vehicles Considering Carbon Emissions

Abstract

Conventional power distribution system is evolving with the growth of distributed generation and electric vehicle integration. The methods of this multidisciplinary planning under uncertainty have not yet been closely examined. In this work, we propose a framework for distribution network expansion planning considering the stochastic nature of DGs, charging stations associated with carbon impact. The proposed model aims to minimize the overall investment cost, the operation and maintenance cost, energy losses and carbon emissions by optimizing alternative feeder routes, the reinforcement of existing substations or new constructions, and the deployment of DGs and charging stations. A multiobjective mixed-integer nonlinear programme is formulated and recast as a two-stage stochastic problem based on analytical probabilistic approach. The model is solved with Tchebycheff decomposition method based evolutionary algorithm. The proposed approach is examined with a modified case-54 distribution and node-25 transportation system. Sensitivity analysis proves carbon emissions can influence the overall investment cost up to 21%. System cost and energy loss has the potential of 1.5% reduction by integrating wind generators. Numerical results obtained effectively demonstrate the capability and feasibility of proposed method.