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Joint peak power and carbon emission shaving in active distribution systems using carbon emission flow-based deep reinforcement learning

Author

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  • Lee, Sangyoon
  • Prabawa, Panggah
  • Choi, Dae-Hyun

Abstract

Distribution optimal power flow (D-OPF) with peak load shaving function is crucial for guaranteeing economical and reliable operations of active distribution grids with various distributed energy resources. However, conventional D-OPF methods reduce only the power operation cost without considering carbon emission reduction, which may lead to a slowdown in achieving global carbon neutrality. To resolve this issue, this study proposes a deep reinforcement learning (DRL)-assisted D-OPF framework realizing dual-peak shaving of power and carbon emission for low-carbon active distribution system operations based on the notion of carbon emission flow (CEF). The proposed framework aims to minimize the total power operation costs of substation and gas-turbine (GT) generators. It also aims to reduce the total carbon emission cost via mitigation of peak power and carbon emission in the CEF-based D-OPF framework with both power and carbon emission peak constraints. A key feature of the proposed framework is the adoption of the DRL method for the CEF-based D-OPF problem to determine economical and eco-friendly peaks of power and carbon emission under dynamically changing distribution system operations. Furthermore, a D-OPF optimization-based reward function for the DRL agent is designed to yield no constraint violations for the D-OPF problem during the agent’s training phase. Numerical examples conducted on the IEEE 33-node and IEEE 69-node distribution systems with GT generators, solar photovoltaic systems, and energy storage systems demonstrate that, in contrast with CEF-free and CEF-integrated optimization methods with fixed power and/or carbon emission peaks, the proposed method further reduces the total carbon emission and cost.

Suggested Citation

  • Lee, Sangyoon & Prabawa, Panggah & Choi, Dae-Hyun, 2025. "Joint peak power and carbon emission shaving in active distribution systems using carbon emission flow-based deep reinforcement learning," Applied Energy, Elsevier, vol. 379(C).
  • Handle: RePEc:eee:appene:v:379:y:2025:i:c:s0306261924023274
    DOI: 10.1016/j.apenergy.2024.124944
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    References listed on IDEAS

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    1. Lyu, Yizheng & Gao, Hanbo & Yan, Kun & Liu, Yingjie & Tian, Jinping & Chen, Lyujun & Wan, Mei, 2022. "Carbon peaking strategies for industrial parks: Model development and applications in China," Applied Energy, Elsevier, vol. 322(C).
    2. Cheng Yang & Yupeng Sun & Yujie Zou & Fei Zheng & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods," Energies, MDPI, vol. 16(16), pages 1-42, August.
    3. De Mel, Ishanki & Klymenko, Oleksiy V. & Short, Michael, 2024. "Discrete optimal designs for distributed energy systems with nonconvex multiphase optimal power flow," Applied Energy, Elsevier, vol. 353(PB).
    4. Huang, Yan & Ju, Yuntao & Ma, Kang & Short, Michael & Chen, Tao & Zhang, Ruosi & Lin, Yi, 2022. "Three-phase optimal power flow for networked microgrids based on semidefinite programming convex relaxation," Applied Energy, Elsevier, vol. 305(C).
    5. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    6. Guo, Chenyu & Wang, Xin & Zheng, Yihui & Zhang, Feng, 2022. "Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    7. Wang, Yunqi & Qiu, Jing & Tao, Yuechuan, 2022. "Robust energy systems scheduling considering uncertainties and demand side emission impacts," Energy, Elsevier, vol. 239(PD).
    Full references (including those not matched with items on IDEAS)

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