Two‐stage scenario‐based DEP incorporating PEV using Benders’ decomposition

Abstract

This study proposes a novel scheme for dynamic distribution expansion planning (DEP) in the presence of plug-in electric vehicles (PEVs). The model considers investment, production and maintenance cost and identifies the substations and feeders to be built, reinforced or replaced. Owing to the increasing penetration of PEVs into the distribution network, traditional strategies to expand the network have to be updated to cope with the new uncertainties incurred by the PEVs integration. In this regard, a two-stage scenario-based strategy is presented, in which the uncertainties related to the PEVs are modelled via stochastic optimisation using Monte Carlo simulation. In the first stage, the binary decision variables (here and now decision variables) are determined, whereas in the second stage, the optimal production of substations and optimal charging of PEVs (wait and see decision variables) are identified in a day-ahead electricity market. Moreover, the daily electricity price and load volatilities have also been taken into account. The DEP problem is formulated as a mixed-integer linear programming problem and is solved using the efficient Benders’ decomposition algorithm. The results of the case study based on a 24-node distribution system show the feasibility, tractability and effectiveness of the proposed model.