A novel scheduling strategy for virtual power plant based on power market dynamic triggers

Yanjia Wang, Chao Xu, Da Xie, Chenghong Gu, Pengfei Zhao, Jinxia Gong, Mingjie Pan and Xitian Wang

Applied Energy, 2023, vol. 350, issue C, No S0306261923011224

Abstract: For the scheduling problem of virtual power plants (VPPs), this paper aims to propose a dynamic trigger scheduling strategy that integrates objective grid information and subjective judgment factors affecting VPP scheduling. The strategy design involves setting initial incentive electricity prices and optimizing price adjustment efficiency. Firstly, this paper establishes a reference incentive electricity price model to derive the initial reference electricity price for the dispatching process. Then, a response willingness model is proposed to establish the relationship between electricity price and VPP response, improving the accuracy of the initial reference electricity price and reducing the frequency of subsequent price adjustments in the actual trigger. Finally, this paper utilizes the groundbreaking dynamic trigger method to adjust the price based on the actual VPP response, obtaining the optimal VPP incentive electricity price. We use Matlab to verify this strategy, and the experimental results show that the average response error using this strategy is reduced by 3.33 kW compared to the contrastive literature. Moreover, scheduling task non-completion does not occur in this method. Thus, the accuracy and reliability of the method are verified.

Keywords: VPP scheduling; Dynamic trigger; Incentive electricity price; Response willingness (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1016/j.apenergy.2023.121758

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