[Paper Review] Placement of EV Charging Stations --- Balancing Benefits among Multiple Entities

Chao Luo, Yih-Fang Huang+1 more|RePEc: Research Papers in Economics|Jan 7, 2018

Electric Vehicles and Infrastructure41 citations

TL;DR

The paper develops a multi-stage framework for placing EV charging stations in an oligopolistic market, using a nested logit model to predict demand and a Bayesian game to derive optimal placements under QoS and grid impact constraints.

ABSTRACT

This paper studies the problem of multi-stage placement of electric vehicle (EV) charging stations with incremental EV penetration rates. A nested logit model is employed to analyze the charging preference of the individual consumer (EV owner), and predict the aggregated charging demand at the charging stations. The EV charging industry is modeled as an oligopoly where the entire market is dominated by a few charging service providers (oligopolists). At the beginning of each planning stage, an optimal placement policy for each service provider is obtained through analyzing strategic interactions in a Bayesian game. To derive the optimal placement policy, we consider both the transportation network graph and the electric power network graph. A simulation software --- The EV Virtual City 1.0 --- is developed using Java to investigate the interactions among the consumers (EV owner), the transportation network graph, the electric power network graph, and the charging stations. Through a series of experiments using the geographic and demographic data from the city of San Pedro District of Los Angeles, we show that the charging station placement is highly consistent with the heatmap of the traffic flow. In addition, we observe a spatial economic phenomenon that service providers prefer clustering instead of separation in the EV charging market.

Motivation & Objective

• Balance profits among multiple charging-service providers, consumer satisfaction, and grid reliability in EV charging expansion.
• Model EV owner charging choices with a nested logit to predict station-level demand.
• Analyze strategic interactions among providers via a Bayesian game to determine optimal station placements and pricing.

Proposed method

• Model the EV charging market as a three-provider oligopoly with Level 1, Level 2, and Level 3 charging services.
• Use a nested logit framework to predict aggregated demand at candidate charging stations.
• Incorporate a power grid impact metric and a grid-stress penalty into a provider utility function.
• Formulate station placement and pricing as a Bayesian game to obtain equilibrium strategies.
• Develop The EV Virtual City 1.0 simulator to study interactions among EV owners, transport network, power grid, and charging stations.

Figure 1: The Architecture of The EV Virtual City 1.0

Experimental results

Research questions

• RQ1 How can planned charging locations maximize provider profits while satisfying QoS constraints and minimizing grid disturbance?
• RQ2 How can EV owner charging demand be predicted from travel and station attributes using a nested logit model?
• RQ3 What is the nature of strategic interactions among service providers in station placement and pricing under incomplete information?
• RQ4 How do mobility, road network, and power grid interdependencies influence optimal charging station deployment?
• RQ5 Does clustering or dispersion of providers emerge in equilibrium under the Bayesian game framework?

Key findings

• Charging station placement aligns with traffic heatmaps, showing consistency between demand and traffic flow patterns.
• Providers tend to cluster rather than separate in the EV charging market under the Bayesian game.
• The model demonstrates a trade-off between profit and grid disturbance governed by a tunable weight w in the utility.
• A nested logit model enables estimation of station-level demand and informs pricing and placement decisions.
• The EV Virtual City 1.0 simulator facilitates analysis of interactions among users, roads, power grid, and charging infrastructure.

Figure 2: Roads and Buildings of San Pedro District