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

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  • 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
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    References listed on IDEAS

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    1. Yang, Jun & Su, Changqi, 2021. "Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty," Energy, Elsevier, vol. 223(C).
    2. 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).
    3. 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).
    4. 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).
    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.
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    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.

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