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Distributionally robust planning for power distribution network considering multi-energy station enabled integrated demand response

Author

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  • Gao, Hongjun
  • Li, Yunman
  • He, Shuaijia
  • Tang, Zhiyuan
  • Liu, Junyong

Abstract

—To copy with the peak valley difference and peak load problem of power distribution network (PDN), this paper proposes a distributionally robust planning model for PDN considering the flexible management via the multi-energy station (MES) based integrated demand response (IDR). The planning covers reinforcement of the existing substation and feeders as well as the investment of MES and IDR. The IDR involves the incentive interruptible load response (ILR), transferable load response (TLR) and price-based power-gas substitution (PGS) which is implemented on MES. The MES integrates multiple energy storages and energy coupling devices, which is served as a platform for PDN operator to centrally track the final demand and improve the response potential of IDR. The proposed planning method aims at minimizing the present value of PDN investment cost and operation cost during the planning period. To handle the uncertainty of PDN end-use loads, a two-stage Kullback–Leibler divergence (KLD)-based distributionally robust optimization (DRO) planning model is formulated. Finally, the columns and constraints generation algorithm is utilized to solve the KLD-DRO planning model. Case studies are carried out on a practical 152-bus urban distribution system to validate the effectiveness of the proposed methodology and quantitatively analyze the key influence factors of PDN investment.

Suggested Citation

  • Gao, Hongjun & Li, Yunman & He, Shuaijia & Tang, Zhiyuan & Liu, Junyong, 2024. "Distributionally robust planning for power distribution network considering multi-energy station enabled integrated demand response," Energy, Elsevier, vol. 306(C).
    • Handle: RePEc:eee:energy:v:306:y:2024:i:c:s0360544224022345
    DOI: 10.1016/j.energy.2024.132460
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    References listed on IDEAS

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    1. Zhang, Jinliang & Liu, Ziyi, 2024. "Low carbon economic scheduling model for a park integrated energy system considering integrated demand response, ladder-type carbon trading and fine utilization of hydrogen," Energy, Elsevier, vol. 290(C).
    2. Zhang, Meijuan & Yan, Qingyou & Guan, Yajuan & Ni, Da & Agundis Tinajero, Gibran David, 2024. "Joint planning of residential electric vehicle charging station integrated with photovoltaic and energy storage considering demand response and uncertainties," Energy, Elsevier, vol. 298(C).
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    Citations

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    Cited by:

    1. Yejin Liu & Min Ye, 2025. "Digital Twin Technology and Energy Sustainability in China: A Regional and Spatial Perspective," Energies, MDPI, vol. 18(16), pages 1-40, August.
    2. Liu, Jia & Tang, Zao & Liu, Yikui & Zhou, Yue & Zeng, Peter Pingliang & Wu, Qiuwei, 2025. "Region-inspired distributed optimal dispatch of flexibility providers in coordinated transmission-distribution framework," Energy, Elsevier, vol. 319(C).
    3. Sepehrzad, Reza & Langeroudi, Amir Saman Godazi & Al-Durra, Ahmed & Anvari-Moghaddam, Amjad & Sadabadi, Mahdieh S., 2025. "Demand response-based multi-layer peer-to-peer energy trading strategy for renewable-powered microgrids with electric vehicles," Energy, Elsevier, vol. 320(C).
    4. Liu, Xingnan & Lu, Hao & Zhao, Wenjun & Chen, Yuhang & Shao, Shiru, 2025. "Research on optimal scheduling and source-network-load correlation matching of integrated energy system considering uncertainty," Energy, Elsevier, vol. 321(C).
    5. Wu, Biao & Zhang, Shaohua & Yuan, Chenxin & Wang, Xian & Wang, Fei & Zhang, Shengqi, 2024. "Cooperative energy and reserve trading strategies for multiple integrated energy systems based on asymmetric nash bargaining theory," Energy, Elsevier, vol. 313(C).
    6. Yue Chen & Qiuyu Lu & Kaiyue Zeng & Yinguo Yang & Pingping Xie, 2024. "Flexibility-Oriented AC/DC Hybrid Grid Optimization Using Distributionally Robust Chance-Constrained Method," Energies, MDPI, vol. 17(19), pages 1-18, September.

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