Cross-Entropy-Based Approach to Multi-Objective Electric Vehicle Charging Infrastructure Planning

TL;DR

This paper presents a multi-objective framework using a cross-entropy-based method to optimize electric vehicle charging station placement, considering traffic flow, costs, and network reliability, demonstrated on a real-world network.

Contribution

It introduces a novel multi-objective planning approach with a cross-entropy method for EV charging infrastructure, considering multiple stakeholder factors.

Findings

Provides viable planning options for different stakeholder objectives.

Demonstrates effectiveness on a real-world traffic and distribution network.

Enhances EV infrastructure planning with a comprehensive multi-objective model.

Abstract

Pure electric vehicles (PEVs) are increasingly adopted to decarbonize the transport sector and mitigate global warming. However, the inadequate PEV charging infrastructure may hinder the further adoption of PEVs in the large-scale traffic network, which calls for effective planning solutions for the charging station (CS) placement. The deployment of charging infrastructure inevitably increases the load on the associated power distribution network. Therefore, we are motivated to develop a comprehensive multi-objective framework for optimal CS placement in a traffic network overlaid by a distribution network, considering multiple stakeholders' interested factors, such as traffic flow, PEV charging time cost, PEV travel distance, and the reliability of the distribution network. We leverage a cross-entropy-based method to solve the optimal CS placement and evaluate our method in a real-world 183-node traffic network in Chengdu, China, overlaid by a 26-region distribution network. It is demonstrated that our work provides various viable planning options favoring different objectives for the stakeholders' decision-making in practice.