IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v264y2023ics036054422202521x.html
   My bibliography  Save this article

Consensus-based distributed optimal power flow using gradient tracking technique for short-term power fluctuations

Author

Listed:
  • Zhang, Zhaoyi
  • Shang, Lei
  • Liu, Chengxi
  • Lai, Qiupin
  • Jiang, Youjin

Abstract

This paper proposes a consensus-based distributed optimal power flow (CD-OPF) scheme to fast track the sub-optimal operating point, considering the power systems’ state deviance caused by short-term fluctuations of renewable generations. By combining the chance-constraint optimal power flow (CC-OPF) and gradient tracking technique (GTT), the proposed scheme could achieve more precise optimization for power system. Firstly, for a time interval, the optimal power flow is applied to obtain the optimal state by chance-constrained programming according to the measured operating states. Then, for the moments within the time interval, a communication-less distributed GTT is performed at each local renewable generation unit to track the sub-optimal point considering its generation fluctuations. Next, the CD-OPF is achieved based on the consensus that each renewable generation unit performs its own tracking optimization by GTT independently, so as to reduce the dependence on global information and fast communication. Finally, the simulations on the IEEE-39 bus power system, the IEEE-118 bus power systems and the wind farm validate the effectiveness of proposed scheme. The results show that the proposed method can decrease the power loss, prevent the voltage violation, and reduce the time-cost.

Suggested Citation

  • Zhang, Zhaoyi & Shang, Lei & Liu, Chengxi & Lai, Qiupin & Jiang, Youjin, 2023. "Consensus-based distributed optimal power flow using gradient tracking technique for short-term power fluctuations," Energy, Elsevier, vol. 264(C).
    • Handle: RePEc:eee:energy:v:264:y:2023:i:c:s036054422202521x
    DOI: 10.1016/j.energy.2022.125635
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422202521X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125635?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kang, Wenfa & Chen, Minyou & Guan, Yajuan & Wei, Baoze & Vasquez Q., Juan C. & Guerrero, Josep M., 2022. "Event-triggered distributed voltage regulation by heterogeneous BESS in low-voltage distribution networks," Applied Energy, Elsevier, vol. 312(C).
    2. Zhou, Yu & Li, Zhengshuo & Wang, Guangrui, 2021. "Study on leveraging wind farms' robust reactive power range for uncertain power system reactive power optimization," Applied Energy, Elsevier, vol. 298(C).
    3. Yang, Jun & Su, Changqi, 2021. "Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty," Energy, Elsevier, vol. 223(C).
    4. Zhao, Ning & You, Fengqi, 2022. "Sustainable power systems operations under renewable energy induced disjunctive uncertainties via machine learning-based robust optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    5. Shojaei, Amir Hossein & Ghadimi, Ali Asghar & Miveh, Mohammad Reza & Gandoman, Foad H. & Ahmadi, Abdollah, 2021. "Multiobjective reactive power planning considering the uncertainties of wind farms and loads using Information Gap Decision Theory," Renewable Energy, Elsevier, vol. 163(C), pages 1427-1443.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhu, Xingxu & Hou, Xiangchen & Li, Junhui & Yan, Gangui & Li, Cuiping & Wang, Dongbo, 2023. "Distributed online prediction optimization algorithm for distributed energy resources considering the multi-periods optimal operation," Applied Energy, Elsevier, vol. 348(C).
    2. Buxiang Zhou & Jiale Wu & Tianlei Zang & Yating Cai & Binjie Sun & Yiwei Qiu, 2023. "Emergency Dispatch Approach for Power Systems with Hybrid Energy Considering Thermal Power Unit Ramping," Energies, MDPI, vol. 16(10), pages 1-25, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sulman Shahzad & Muhammad Abbas Abbasi & Hassan Ali & Muhammad Iqbal & Rania Munir & Heybet Kilic, 2023. "Possibilities, Challenges, and Future Opportunities of Microgrids: A Review," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    2. Liu, Xingtao & Tang, Qinbin & Feng, Yitian & Lin, Mingqiang & Meng, Jinhao & Wu, Ji, 2023. "Fast sorting method of retired batteries based on multi-feature extraction from partial charging segment," Applied Energy, Elsevier, vol. 351(C).
    3. Xiangdong Wang & Lei Wang & Wenfa Kang & Tiecheng Li & Hao Zhou & Xuekai Hu & Kai Sun, 2022. "Distributed Nodal Voltage Regulation Method for Low-Voltage Distribution Networks by Sharing PV System Reactive Power," Energies, MDPI, vol. 16(1), pages 1-15, December.
    4. Wang, Yunqi & Qiu, Jing & Tao, Yuechuan, 2022. "Robust energy systems scheduling considering uncertainties and demand side emission impacts," Energy, Elsevier, vol. 239(PD).
    5. Aslani, Mehrdad & Mashayekhi, Mehdi & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2022. "Robust optimal operation of energy hub incorporating integrated thermal and electrical demand response programs under various electric vehicle charging modes," Applied Energy, Elsevier, vol. 321(C).
    6. Vahid-Ghavidel, Morteza & Shafie-khah, Miadreza & Javadi, Mohammad S. & Santos, Sérgio F. & Gough, Matthew & Quijano, Darwin A. & Catalao, Joao P.S., 2023. "Hybrid IGDT-stochastic self-scheduling of a distributed energy resources aggregator in a multi-energy system," Energy, Elsevier, vol. 265(C).
    7. Hu, Guoqing & You, Fengqi, 2022. "Renewable energy-powered semi-closed greenhouse for sustainable crop production using model predictive control and machine learning for energy management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    8. Kim, H.J. & Kim, M.K., 2023. "A novel deep learning-based forecasting model optimized by heuristic algorithm for energy management of microgrid," Applied Energy, Elsevier, vol. 332(C).
    9. Wang, Jiangjiang & Huo, Shuojie & Yan, Rujing & Cui, Zhiheng, 2022. "Leveraging heat accumulation of district heating network to improve performances of integrated energy system under source-load uncertainties," Energy, Elsevier, vol. 252(C).
    10. Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Masahiro Furukakoi & Paras Mandal & Tomonobu Senjyu, 2023. "Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty," Energies, MDPI, vol. 16(19), pages 1-25, September.
    11. Zheng, Yi & Wang, Jiawei & You, Shi & Li, Ximei & Bindner, Henrik W. & Münster, Marie, 2023. "Data-driven scheme for optimal day-ahead operation of a wind/hydrogen system under multiple uncertainties," Applied Energy, Elsevier, vol. 329(C).
    12. Mihaela Bukljaš & Kristijan Rogić & Vladimir Jerebić, 2022. "Distributionally Robust Model and Metaheuristic Frame for Liner Ships Fleet Deployment," Sustainability, MDPI, vol. 14(9), pages 1-18, May.
    13. Hwang Goh, Hui & Shi, Shuaiwei & Liang, Xue & Zhang, Dongdong & Dai, Wei & Liu, Hui & Yuong Wong, Shen & Agustiono Kurniawan, Tonni & Chen Goh, Kai & Leei Cham, Chin, 2022. "Optimal energy scheduling of grid-connected microgrids with demand side response considering uncertainty," Applied Energy, Elsevier, vol. 327(C).
    14. Shanmugarajah Vinothine & Lidula N. Widanagama Arachchige & Athula D. Rajapakse & Roshani Kaluthanthrige, 2022. "Microgrid Energy Management and Methods for Managing Forecast Uncertainties," Energies, MDPI, vol. 15(22), pages 1-22, November.
    15. Xie, Rui & Wei, Wei & Li, Mingxuan & Dong, ZhaoYang & Mei, Shengwei, 2023. "Sizing capacities of renewable generation, transmission, and energy storage for low-carbon power systems: A distributionally robust optimization approach," Energy, Elsevier, vol. 263(PA).
    16. Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
    17. Jiang, Sufan & Wu, Chuanshen & Gao, Shan & Pan, Guangsheng & Liu, Yu & Zhao, Xin & Wang, Sicheng, 2022. "Robust frequency risk-constrained unit commitment model for AC-DC system considering wind uncertainty," Renewable Energy, Elsevier, vol. 195(C), pages 395-406.
    18. Yan, Zhongzhen & Zhu, Xinyuan & Chang, Yiming & Wang, Xianglong & Ye, Zhiwei & Xu, Zhigang & Fars, Ashk, 2023. "Renewable energy effects on energy management based on demand response in microgrids environment," Renewable Energy, Elsevier, vol. 213(C), pages 205-217.
    19. Wang, Qipeng & Zhao, Liang, 2023. "Data-driven stochastic robust optimization of sustainable utility system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    20. Li, Bei & Li, Jiangchen, 2021. "Probabilistic sizing of a low-carbon emission power system considering HVDC transmission and microgrid clusters," Applied Energy, Elsevier, vol. 304(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:264:y:2023:i:c:s036054422202521x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.