Engineering and Economic Analysis for Electric Vehicle Charging Infrastructure — Placement, Pricing, and Market Design

Doctoral Dissertation

Abstract

The objective of this dissertation is to study the interplay between large-scale electric vehicle (EV) charging and the power system. In particular, we address three important issues pertaining to EV charging and integration into the power system: (1) charging station placement, (2) pricing policy and energy management strategy, and (3) electricity trading market and distribution network design to facilitate integrating EV and renewable energy source (RES) into the power system.

Regarding the charging station placement problem, we propose a multi-stage consumer behavior based placement strategy with incremental EV penetration rates and model the EV charging industry as an oligopoly where the entire market is dominated by a few charging service providers (oligopolists). A nested logit model is employed to characterize the charging preference of the EV owners. The optimal placement policy for each service provider is obtained by solving a Bayesian game. We also developed a simulation toolkit called \The EV Virtual City" based on Repast. We observe that service providers prefer clustering instead of separation in the EV charging market.

As for the problem of pricing and energy management of EV charging stations, we provide guidelines for charging service providers to determine charging price and manage electricity reserve to balance the competing objectives of improving profitability, enhancing customer satisfaction, and reducing impact on the power system. In the presence of renewable energy integration and energy storage system, EV charging service providers must deal with a number of uncertainties, e.g., charging demand volatility, inherent intermittency of renewable energy generation, and wholesale electricity price fluctuation. We propose a new metric to assess the impact on power system without needing to solve complete power flow equations. Two algorithms — stochastic dynamic programming (SDP) algorithm and greedy algorithm (benchmark algorithm) are applied to derive the pricing and electricity procurement strategy. We find that the charging service provider is able to reshape spatial-temporal charging demands to reduce the impact on power grid via pricing signals.

The last technical contribution of this dissertation is on the design of a novel electricity trading market and distribution network, which provides a platform to support seamless RES integration, grid to vehicle (G2V), vehicle to grid (V2G), vehicle to vehicle (V2V), and distributed generation (DG) and storage. We apply a sharing economy model to the electricity sector to stimulate different entities to exchange and monetize their underutilized electricity. We propose an online advertisement-based peer-to-peer (P2P) electricity trading mechanism. A fitness-score (FS)-based supply-demand matching algorithm is developed by considering consumer surplus, electricity network congestion, and economic dispatch. We compare the FS matching algorithm with the first-come-first-serve (FCFS) algorithm. The simulation results show that the FS matching algorithm outperforms the FCFS algorithm in terms of network congestion management, electricity delivery delay probability, energy efficiency, and consumer surplus.

Attributes

Attribute NameValues
Author Chao Luo
Contributor Yih-Fang Huang, Research Director
Degree Level Doctoral Dissertation
Degree Discipline Electrical Engineering
Degree Name Doctor of Philosophy
Defense Date
  • 2017-08-08

Submission Date 2017-10-19
Subject
  • dynamic programming

  • machine learning

  • clean energy

  • consumer behavior analysis

  • data mining

  • electricity wholesale market

  • dynamic pricing

  • renewable energy

  • multi-objective optimization

  • electric vehicle

  • EV charging station

  • electricity market

Record Visibility Public
Content License
Departments and Units

Files

Please Note: You may encounter a delay before a download begins. Large or infrequently accessed files can take several minutes to retrieve from our archival storage system.