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Interaction-based virtual power plant operation methodology for distribution system operator’s voltage management

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  • Park, Sung-Won
  • Son, Sung-Yong

Abstract

The importance of distribution system operator (DSO) continues to increase owing to the widespread use of distributed energy resources (DERs) in power distribution system. A virtual power plant (VPP) that mostly relies on small DERs tends to make operations more difficult for DSO, because the VPP attempts to maximize its profits regardless of the status of the corresponding distribution system if an appropriate cooperation mechanism is not present. In this study, an interaction-based VPP operation methodology using distribution system constraints (DSCs) is proposed for DSO voltage management, assuming that the VPP primarily participates in the wholesale energy market (WEM). For efficient cooperative operations between a DSO and VPP, the VPP's DERs are grouped according to the DERs locations in distribution lines. Then, distributed system constraints that represent appropriate power outputs for the specific resource groups are calculated through the interactions between the DSO and the VPP. Both Volt-Watt control (VWC) and Volt-var control (VVC) are considered for optimal DSC calculation. The VWC and VVC-based operation results are compared to evaluate the technical and economic effects from both the DSO and VPP perspectives. The case study results show that the proposed methodology keeps the distribution system's voltage within the target range, reduces economic losses by approximately 24% compared to traditional methodologies, and reduces the DSC calculation rate of DSOs by approximately half. These results indicate that the proposed methodology could be effectively utilized in DSO and VPP operations.

Suggested Citation

  • Park, Sung-Won & Son, Sung-Yong, 2020. "Interaction-based virtual power plant operation methodology for distribution system operator’s voltage management," Applied Energy, Elsevier, vol. 271(C).
  • Handle: RePEc:eee:appene:v:271:y:2020:i:c:s0306261920307340
    DOI: 10.1016/j.apenergy.2020.115222
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    References listed on IDEAS

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    Cited by:

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    7. Mei Cai & Suqiong Hu & Ya Wang & Jingmei Xiao, 2022. "A Dynamic Social Network Matching Model for Virtual Power Plants and Distributed Energy Resources with Probabilistic Linguistic Information," Sustainability, MDPI, vol. 14(22), pages 1-33, November.
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    9. Song, Meng & Gao, Ciwei & Ma, Sisi & Meng, Jing & Chen, Kang, 2022. "Distributed scheduling of HVACs based on transactive energy and ADMM," Applied Energy, Elsevier, vol. 325(C).

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