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Regular Paper | Open Access

Stochastic Flexibility Evaluation for Virtual Power Plants by Aggregating Distributed Energy Resources

Siyuan Wang1, Wenchuan Wu1 ( ), Qizhan Chen2, Junjie Yu2, Peng Wang3
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Zhongshan Power Supply Bureau of the Guangdong Power Grid Corporation, Zhongshan 528400, China
Beijing Qingda Gaoke System Control Company, Beijing 102208, China
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Abstract

To manage a large amount of flexible distributed energy resources (DERs) in the distribution networks, the virtual power plant (VPP) is introduced into the industry. The VPP can optimally dispatch these resources in a cluster manner and provide flexibility for the power system operation as a whole. Most existing studies formulate the equivalent power flexibility of the aggregating DERs as deterministic optimization models without considering their uncertainties. In this paper, we introduce the stochastic power flexibility range (PFR) and time-coupling flexibility (TCF) to describe the power flexibility of VPP. In this model, both operational constraints and the randomness of the DERs' output are incorporated, and a combined model and data-driven solution is proposed to obtain the stochastic PFR, TCF, and cost function of VPP. The aggregating model can be easily incorporated into the optimization model for the power system operator or market bidding, considering uncertainties. Finally, a numerical test is performed. The results show that the proposed model not only has higher computational efficiency than the scenario-based methods but also achieves more economic benefits.

Keywords

virtual power plantChance constrained optimizationcombined model and data-drivenstochastic power flexibility

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CSEE Journal of Power and Energy Systems
Volume 10 Issue 3,
May 2024
Pages 988-999
DOI: 10.17775/CSEEJPES.2021.07410
Cite this article:
Wang S, Wu W, Chen Q, et al. Stochastic Flexibility Evaluation for Virtual Power Plants by Aggregating Distributed Energy Resources. CSEE Journal of Power and Energy Systems, 2024, 10(3): 988-999. https://doi.org/10.17775/CSEEJPES.2021.07410

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Received: 05 October 2021
Revised: 02 December 2021
Accepted: 28 December 2021
Published: 18 August 2022
© 2021 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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