Back to Search
Start Over
A Deep Reinforcement Learning Method for Pricing Electric Vehicles With Discrete Charging Levels.
- Source :
-
IEEE Transactions on Industry Applications . Sep-Oct2020, Vol. 56 Issue 5, p5901-5912. 12p. - Publication Year :
- 2020
-
Abstract
- The effective pricing of electric vehicle (EV) charging by aggregators constitutes a key problem toward the realization of the significant EV flexibility potential in deregulated electricity systems and has been addressed by previous work through bi-level optimization formulations. However, the solution approach adopted in previous work cannot capture the discrete nature of the EV charging/discharging levels. Although reinforcement learning (RL) can tackle this challenge, state-of-the-art RL methods require discretization of state and/or action spaces and thus exhibit limitations in terms of solution optimality and computational requirements. This article proposes a novel deep reinforcement learning (DRL) method to solve the examined EV pricing problem, combining deep deterministic policy gradient (DDPG) principles with a prioritized experience replay (PER) strategy and setting up the problem in multi-dimensional continuous state and action spaces. Case studies demonstrate that the proposed method outperforms state-of-the-art RL methods in terms of both solution optimality and computational requirements and comprehensively analyze the economic impacts of smart-charging and vehicle-to-grid (V2G) flexibility on both aggregators and EV owners. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00939994
- Volume :
- 56
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Industry Applications
- Publication Type :
- Academic Journal
- Accession number :
- 146012359
- Full Text :
- https://doi.org/10.1109/TIA.2020.2984614