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Profit maximization for large-scale energy storage systems to enable fast EV charging infrastructure in distribution networks

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  • Lai, Chun Sing
  • Chen, Dashen
  • Zhang, Jinning
  • Zhang, Xin
  • Xu, Xu
  • Taylor, Gareth A.
  • Lai, Loi Lei

Abstract

Large-scale integration of battery energy storage systems (BESS) in distribution networks has the potential to enhance the utilization of photovoltaic (PV) power generation and mitigate the negative effects caused by electric vehicles (EV) fast charging behavior. This paper presents a novel deep reinforcement learning-based power scheduling strategy for BESS which is installed in an active distribution network. The network includes fast EV charging demand, PV power generation, and electricity arbitrage from main grid. The aim is to maximize the profit of BESS operator whilst maintaining voltage limits. The novel strategy adopts a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and requires forecasted PV power generation and EV smart charging demand. The proposed strategy is compared with Deep Deterministic Policy Gradient (DDPG), Particle Swarm Optimization and Simulated Annealing algorithms to verify its effectiveness. Case studies are conducted with smart EV charging dataset from Project Shift (UK Power Networks Innovation) and the UK photovoltaic dataset. The Internal Rate of Return results with TD3 and DDPG algorithms are 9.46% and 8.69%, respectively, which show that the proposed strategy can enhance power scheduling and outperforms the mainstream methods in terms of reduced levelized cost of storage and increased net present value.

Suggested Citation

  • Lai, Chun Sing & Chen, Dashen & Zhang, Jinning & Zhang, Xin & Xu, Xu & Taylor, Gareth A. & Lai, Loi Lei, 2022. "Profit maximization for large-scale energy storage systems to enable fast EV charging infrastructure in distribution networks," Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:energy:v:259:y:2022:i:c:s0360544222017558
    DOI: 10.1016/j.energy.2022.124852
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    References listed on IDEAS

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    Cited by:

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    3. Zhao, Zhonghao & Lee, Carman K.M. & Huo, Jiage, 2023. "EV charging station deployment on coupled transportation and power distribution networks via reinforcement learning," Energy, Elsevier, vol. 267(C).
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    6. Pramote Jaruwatanachai & Yod Sukamongkol & Taweesak Samanchuen, 2023. "Predicting and Managing EV Charging Demand on Electrical Grids: A Simulation-Based Approach," Energies, MDPI, vol. 16(8), pages 1-22, April.

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