Capacity Expansion of Electric Vehicle Charging Network: Model, Algorithms and A Case Study

Abstract

Governments in many counties are taking measures to promote electric vehicles. An important strategy is to build enough charging infrastructures so as to alleviate drivers’ range anxieties. To help the governments make plans about the public charging network, we propose a multi-stage stochastic integer programming model to determine the locations and capacities of charging facilities over finite planning horizons. We use the logit choice model to estimate drivers’ random choices towards different charging stations nearby. The objective of the model is to minimize the expected total cost of installing and operating the charging facilities. Two simple algorithms are designed to solve this model, an approximation algorithm and a heuristic algorithm. A branch-and-price algorithm is also designed for this model, and some implementation details and improvement methods are explained. We do some numerical experiments to test the efficiency of these algorithms. Each algorithm has advantages over the CPLEX MIP solver in terms of solution time or solution quality. A case study of Oakville is presented to demonstrate the process of designing an electric vehicle public charging network using this model in Canada.