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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. Ma, Qianyi & Ye, Yujian & Liu, Zifa & Liu, Xiao & Strbac, Goran, 2025. "Carbon cap based multi-energy sharing among heterogeneous microgrids using multi-agent safe reinforcement learning method with credit assignment and sequential update," Applied Energy, Elsevier, vol. 393(C).
    2. Wang, Zhaoqi & Lu, Gang & Liu, Jun & Wu, Shengyu & Wu, Cong, 2026. "Equilibrium allocation strategy of distributed ESSs balancing ecology and economics in DNs considering carbon taxation systems," Applied Energy, Elsevier, vol. 402(PB).
    3. Li, Liang & Zhao, Jian & Deng, Kai, 2026. "Edge-based real-time tracking of carbon emission flow in power systems with dynamic network pruning," Applied Energy, Elsevier, vol. 402(PB).
    4. Xuan Ruan & Lingyun Zhang & Jie Zhou & Zhiwei Wang & Shaojun Zhong & Fuyou Zhao & Bo Yang, 2025. "DynaG Algorithm-Based Optimal Power Flow Design for Hybrid Wind–Solar–Storage Power Systems Considering Demand Response," Energies, MDPI, vol. 18(17), pages 1-25, August.

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