Predictive operation optimization of multi-energy virtual power plant considering behavior uncertainty of diverse stakeholders

Xiaojie Lin, Xueru Lin, Wei Zhong and Yi Zhou

Energy, 2023, vol. 280, issue C

Abstract: To address the inflexibility of traditional single-energy virtual power plants (VPPs) in accommodating high proportions of renewable energy, this study proposes a predictive optimization method for multi-energy VPPs. The method supports multi-energy complementary cooperative dispatch and participation in multiple markets, and is applicable to future multi-energy VPPs that integrate carbon capture technology, power-to-gas, and energy storage. The method takes into account the uncertain parameters of stakeholders' behavior and introduces sliding time windows to improve production stability and the feasibility of VPP dispatch schemes. The optimization scheme is obtained based on the scenario method when the risk is maximum. The case results show that the proposed method can increase the profit by 8.31% compared with that without considering the time window. After stabilization, the ramping power changes by 17.39%. It is also found that accounting for the uncertainty of stakeholders increased the maximum profit drop from 47.70% to 60.45%. The introduction of power-to-gas and carbon capture technology has effectively improved the overall economy of the VPP and reduced carbon emissions. The proposed VPP predictive optimization method and uncertainty analysis of stakeholders' behavior provide basis for operation control of multi-energy VPPs.

Keywords: Virtual power plant; Multiple stakeholders; Predictive optimization; Uncertainty; Power-to-gas (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:280:y:2023:i:c:s0360544223015244

DOI: 10.1016/j.energy.2023.128130

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