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Joint planning of distributed generations and energy storage in active distribution networks: A Bi-Level programming approach

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  • Li, Yang
  • Feng, Bo
  • Wang, Bin
  • Sun, Shuchao

Abstract

In order to improve the penetration of renewable energy resources for distribution networks, a joint planning model of distributed generations (DGs) and energy storage is proposed for an active distribution network by using a bi-level programming approach in this paper. In this model, the upper-level aims to seek the optimal location and capacity of DGs and energy storage, while the lower-level optimizes the operation of energy storage devices. To solve this model, an improved binary particle swarm optimization (IBPSO) algorithm based on chaos optimization is developed, and the optimal joint planning is achieved through alternating iterations between the two levels. The simulation results on the PG & E 69-bus distribution system demonstrate that the presented approach manages to reduce the planning deviation caused by the uncertainties of DG outputs and remarkably improve the voltage profile and operational economy of distribution systems.

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  • Li, Yang & Feng, Bo & Wang, Bin & Sun, Shuchao, 2022. "Joint planning of distributed generations and energy storage in active distribution networks: A Bi-Level programming approach," Energy, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:energy:v:245:y:2022:i:c:s0360544222001293
    DOI: 10.1016/j.energy.2022.123226
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    6. Zhou, Siyu & Han, Yang & Yang, Ping & Mahmoud, Karar & Lehtonen, Matti & Darwish, Mohamed M.F. & Zalhaf, Amr S., 2022. "An optimal network constraint-based joint expansion planning model for modern distribution networks with multi-types intermittent RERs," Renewable Energy, Elsevier, vol. 194(C), pages 137-151.
    7. Gang Liang & Bing Sun & Yuan Zeng & Leijiao Ge & Yunfei Li & Yu Wang, 2022. "An Optimal Allocation Method of Distributed PV and Energy Storage Considering Moderate Curtailment Measure," Energies, MDPI, vol. 15(20), pages 1-19, October.
    8. Hu, Junjie & Wang, Yudong & Dong, Lei, 2024. "Low carbon-oriented planning of shared energy storage station for multiple integrated energy systems considering energy-carbon flow and carbon emission reduction," Energy, Elsevier, vol. 290(C).
    9. Zahedmanesh, Arian & Verbic, Gregor & Rajarathnam, Gobinath & Weihs, Gustavo Fimbres & Shikata, Kentaro & Matsuda, Naohiko & Abbas, Ali, 2024. "Optimal design of greenfield energy hubs in the context of carbon neutral energy supply," Energy, Elsevier, vol. 305(C).
    10. Umme Mumtahina & Sanath Alahakoon & Peter Wolfs, 2023. "A Literature Review on the Optimal Placement of Static Synchronous Compensator (STATCOM) in Distribution Networks," Energies, MDPI, vol. 16(17), pages 1-38, August.
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    12. Zhou, Siyu & Han, Yang & Chen, Shuheng & Yang, Ping & Mahmoud, Karar & Darwish, Mohamed M.F. & Matti, Lehtonen & Zalhaf, Amr S., 2023. "A multiple uncertainty-based Bi-level expansion planning paradigm for distribution networks complying with energy storage system functionalities," Energy, Elsevier, vol. 275(C).
    13. Li, Yang & Wang, Ruinong & Li, Yuanzheng & Zhang, Meng & Long, Chao, 2023. "Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach," Applied Energy, Elsevier, vol. 329(C).
    14. Li, Yang & Han, Meng & Shahidehpour, Mohammad & Li, Jiazheng & Long, Chao, 2023. "Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response," Applied Energy, Elsevier, vol. 335(C).
    15. Hamdi, Mohamed & El Salmawy, Hafez A. & Ragab, Reda, 2024. "Incorporating operational constraints into long-term energy planning: The case of the Egyptian power system under high share of renewables," Energy, Elsevier, vol. 300(C).

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