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Peak Shaving Methods of Distributed Generation Clusters Using Dynamic Evaluation and Self-Renewal Mechanism

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  • Hongwei Li

    (Educational Administration Center, State Grid of China Technology College, Jinan 250002, China)

  • Qing Xu

    (College of New Energy, Harbin Institute of Technology at Weihai, Weihai 264200, China)

  • Shitao Wang

    (Educational Administration Center, State Grid of China Technology College, Jinan 250002, China)

  • Huihui Song

    (College of New Energy, Harbin Institute of Technology at Weihai, Weihai 264200, China)

Abstract

As one of the power auxiliary services, peak shaving is the key problem to be solved in the power grid. With the rapid development of DGs, the traditional peak shaving scheduling method for centralized adjustable energy is no longer applicable. Thus, this paper proposes two-layer optimization methods of allocating the peak shaving task for DGs. Layer 1 mainly proposes four evaluation indexes and the peak shaving priority sequence can be obtained with modified TOPSIS, then the DG cluster’s task is allocated to the corresponding DGs. On the basis of dynamic evaluation and the self-renewal mechanism, layer 2 proposes a peak shaving optimization model with dynamic constraints which assigns peak shaving instructions to each cluster. Finally, the effectiveness of the method is verified by using the real DGs data of a regional power grid in China based on the MATLAB simulation platform. The results demonstrate that the proposed methods can simply the calculation complexity by ranking the DGs in the peak shaving task and update the reliable aggregate adjustable power of each cluster in time to allocate more reasonably.

Suggested Citation

  • Hongwei Li & Qing Xu & Shitao Wang & Huihui Song, 2022. "Peak Shaving Methods of Distributed Generation Clusters Using Dynamic Evaluation and Self-Renewal Mechanism," Energies, MDPI, vol. 15(19), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7036-:d:924565
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    References listed on IDEAS

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    1. Yinping Yang & Chao Qin & Yuan Zeng & Chengshan Wang, 2019. "Interval Optimization-Based Unit Commitment for Deep Peak Regulation of Thermal Units," Energies, MDPI, vol. 12(5), pages 1-21, March.
    2. Mehigan, L. & Deane, J.P. & Gallachóir, B.P.Ó. & Bertsch, V., 2018. "A review of the role of distributed generation (DG) in future electricity systems," Energy, Elsevier, vol. 163(C), pages 822-836.
    3. Wang, Jianxiao & Zhong, Haiwang & Wu, Chenye & Du, Ershun & Xia, Qing & Kang, Chongqing, 2019. "Incentivizing distributed energy resource aggregation in energy and capacity markets: An energy sharing scheme and mechanism design," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    4. Zhong Shi & Zhijie Wang & Yue Jin & Nengling Tai & Xiuchen Jiang & Xiaoyu Yang, 2018. "Optimal Allocation of Intermittent Distributed Generation under Active Management," Energies, MDPI, vol. 11(10), pages 1-19, September.
    5. Chang Ye & Kan Cao & Haiteng Han & Ziwen Liu & Defu Cai & Dan Liu & Dazhong Ma, 2022. "Cluster Partition Method of Large-Scale Grid-Connected Distributed Generations considering Expanded Dynamic Time Scenarios," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-10, February.
    6. Vasileios Papadopoulos & Jos Knockaert & Chris Develder & Jan Desmet, 2020. "Peak Shaving through Battery Storage for Low-Voltage Enterprises with Peak Demand Pricing," Energies, MDPI, vol. 13(5), pages 1-17, March.
    7. Md Masud Rana & Mohamed Atef & Md Rasel Sarkar & Moslem Uddin & GM Shafiullah, 2022. "A Review on Peak Load Shaving in Microgrid—Potential Benefits, Challenges, and Future Trend," Energies, MDPI, vol. 15(6), pages 1-17, March.
    8. Pedro Faria & João Spínola & Zita Vale, 2018. "Distributed Energy Resources Scheduling and Aggregation in the Context of Demand Response Programs," Energies, MDPI, vol. 11(8), pages 1-17, July.
    9. Rana, Md Masud & Romlie, Mohd Fakhizan & Abdullah, Mohd Faris & Uddin, Moslem & Sarkar, Md Rasel, 2021. "A novel peak load shaving algorithm for isolated microgrid using hybrid PV-BESS system," Energy, Elsevier, vol. 234(C).
    10. Yuxin Zhu & Dazuo Tian & Feng Yan, 2020. "Effectiveness of Entropy Weight Method in Decision-Making," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-5, March.
    11. Lin He & Chang-Ling Li & Qing-Yun Nie & Yan Men & Hai Shao & Jiang Zhu, 2017. "Core Abilities Evaluation Index System Exploration and Empirical Study on Distributed PV-Generation Projects," Energies, MDPI, vol. 10(12), pages 1-18, December.
    12. Guo, Yi & Ming, Bo & Huang, Qiang & Wang, Yimin & Zheng, Xudong & Zhang, Wei, 2022. "Risk-averse day-ahead generation scheduling of hydro–wind–photovoltaic complementary systems considering the steady requirement of power delivery," Applied Energy, Elsevier, vol. 309(C).
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