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Distributed Economic Dispatch of Virtual Power Plant under a Non-Ideal Communication Network

Author

Listed:
  • Chi Cao

    (College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Jun Xie

    (College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Dong Yue

    (College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Chongxin Huang

    (Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Jixiang Wang

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Shuyang Xu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

  • Xingying Chen

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China)

Abstract

A virtual power plant (VPP) is aimed to integrate distributed energy resources (DERs). To solve the VPP economic dispatch (VPED) problem, the power supply-demand balance, power transmission constraints, and power output constraints of each DER must be considered. Meanwhile, the impacts of communication time delays, channel noises, and the time-varying topology on the communication networks cannot be ignored. In this paper, a VPED model is established and a distributed primal-dual sub-gradient method (DPDSM) is employed to address the presented VPED model. Compared with the traditional centralized dispatch, the distributed dispatch has the advantages of lower communication costs and stronger system robustness, etc. Simulations are realized in the modified IEEE-34 and IEEE-123 bus test VPP systems and the results indicate that the VPED strategy via DPDSM has the superiority of better convergence, more economic profits, and stronger system stability.

Suggested Citation

  • Chi Cao & Jun Xie & Dong Yue & Chongxin Huang & Jixiang Wang & Shuyang Xu & Xingying Chen, 2017. "Distributed Economic Dispatch of Virtual Power Plant under a Non-Ideal Communication Network," Energies, MDPI, vol. 10(2), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:235-:d:90551
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    Citations

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    Cited by:

    1. Furquan Nadeem & Mohd Asim Aftab & S.M. Suhail Hussain & Ikbal Ali & Prashant Kumar Tiwari & Arup Kumar Goswami & Taha Selim Ustun, 2019. "Virtual Power Plant Management in Smart Grids with XMPP Based IEC 61850 Communication," Energies, MDPI, vol. 12(12), pages 1-20, June.
    2. Jingmin Wang & Wenhai Yang & Huaxin Cheng & Lingyu Huang & Yajing Gao, 2017. "The Optimal Configuration Scheme of the Virtual Power Plant Considering Benefits and Risks of Investors," Energies, MDPI, vol. 10(7), pages 1-12, July.
    3. 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.
    4. Jun Xie & Chi Cao, 2017. "Non-Convex Economic Dispatch of a Virtual Power Plant via a Distributed Randomized Gradient-Free Algorithm," Energies, MDPI, vol. 10(7), pages 1-12, July.
    5. Jiaqi Liu & Hongji Hu & Samson S. Yu & Hieu Trinh, 2023. "Virtual Power Plant with Renewable Energy Sources and Energy Storage Systems for Sustainable Power Grid-Formation, Control Techniques and Demand Response," Energies, MDPI, vol. 16(9), pages 1-28, April.
    6. Wafa Nafkha-Tayari & Seifeddine Ben Elghali & Ehsan Heydarian-Forushani & Mohamed Benbouzid, 2022. "Virtual Power Plants Optimization Issue: A Comprehensive Review on Methods, Solutions, and Prospects," Energies, MDPI, vol. 15(10), pages 1-20, May.
    7. Jing Qiu & Junhua Zhao & Dongxiao Wang & Yu Zheng, 2017. "Two-Stage Coordinated Operational Strategy for Distributed Energy Resources Considering Wind Power Curtailment Penalty Cost," Energies, MDPI, vol. 10(7), pages 1-19, July.
    8. Liwei Ju & Peng Li & Qinliang Tan & Zhongfu Tan & GejiriFu De, 2018. "A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses," Energies, MDPI, vol. 11(11), pages 1-28, October.

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