Design and Optimal Operation of a Virtual Power Plant with Bidirectional Electric Vehicle Chargers

  • Author / Creator
    Rahman, Saidur
  • Virtual power plants (VPPs) can enhance reliability and efficiency of power systems with a high share of renewables. However, their adoption largely depends on their profitability, which is difficult to maximize due to the heterogeneity of their components, different sources of uncertainty and potential profit streams. In this thesis, we study a VPP that aggregates a fleet of electric vehicles (EVs), EV chargers with vehicle-to-grid (V2G) support, and possibly renewable energy systems, such as solar panels. This VPP generates profit by trading energy in day-ahead and imbalance electricity markets.

    In the first part of this thesis, we assume that the VPP owns and operates the EVs in addition to bidirectional chargers. We propose two profit-maximizing operating strategies for this VPP. Both strategies solve a two-stage stochastic optimization problem. In the first stage, energy bids are placed by solving a sequence of linear programs, each formulated for a specific forecast scenario. In the second stage, given the day-ahead commitments and real-time measurements, the decisions with respect to charging or discharging EVs are made sequentially for every hour, and adjustments to the day-ahead commitments are settled in the imbalance market. The two strategies differ in how they solve the sequential decision-making problem in the second stage. But, they both foresee the effect of their current (dis)charge decisions on the feasibility of fulfilling the EV charging demands using a one-step lookahead technique. The first strategy employs a heuristic algorithm to find a feasible charging schedule for every EV that is connected to a charger. The second one utilizes a soft actor-critic reinforcement learning method with a differentiable projection layer that enforces constraint satisfaction. We empirically evaluate the proposed operating strategies using real market prices, solar traces, and EV charging sessions obtained from a network of chargers in the Netherlands, and analyze how the uptake of V2G could affect the profitability of this VPP.

    In the second part of this thesis, we study this VPP under a more realistic assumption that the EVs are independently-owned, hence the VPP does not own or operate them. As a result, EV owners must be incentivized to participate in the VPP, and the VPP itself must remain profitable. We use contract theory to design optimal, incentive-compatible contracts between the VPP and EV owners, where each contract defines the maximum amount of energy that can be discharged from the battery in a fixed period of time, and the compensation the owner receives in return. We then propose a scheduling algorithm for the optimal operation of such a VPP that participates in a two-stage electricity market. This algorithm aims to maximize the VPP's profit, while (a) respecting the contracts that are accepted and currently valid, and (b) fulfilling the charging demand of each EV before it disconnects from the charger. We show that this algorithm increases the profitability of this VPP and allows EVs to offset the cost of charging their battery by enhancing grid reliability.

  • Subjects / Keywords
  • Graduation date
    Spring 2023
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.