Adaptive Control Method of Peak Shaving Demand Response Program for Flexible Load Virtual Power Plant
[Objective] Virtual power plants （VPPs） centered on air-conditioning loads are susceptible to uncertainties， such as control delays and discrepancies between models and measurements， leading to deviations in the efficacy of demand response （DR） strategies from anticipated outcomes. A key contributor...
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Power Construction
2025-07-01
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| Series: | Dianli jianshe |
| Subjects: | |
| Online Access: | https://www.cepc.com.cn/fileup/1000-7229/PDF/1750819471464-481702377.pdf |
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| Summary: | [Objective] Virtual power plants （VPPs） centered on air-conditioning loads are susceptible to uncertainties， such as control delays and discrepancies between models and measurements， leading to deviations in the efficacy of demand response （DR） strategies from anticipated outcomes. A key contributor to this phenomenon is the reliance of existing DR strategies on static target load profiles， hindering their adaptability to dynamic operational environments.[Methods] To address this issue， this study introduced an adaptive control methodology for flexible-load VPPs participating in peak-shaving DR， utilizing a large-scale split-type inverter air conditioner on campuses as a case study. This approach allowed the adjustment of target load profiles for subsequent DR periods within the permissible range of the DR invitation based on the current operational environment， thereby enhancing the economic and robust nature of peak-shaving DR. In the proposed closed-loop control model， the controlled process was decoupled into a small-scale linear progress deviation model and a peak-shaving electricity correction model， each placed within the controller and feedback loop. The progress deviation model allocated planned peak shaving electricity to air conditioners， ensuring compliance with power constraints and user comfort levels. The peak-shaving electricity correction model， with the actual response to the peak-shaving DR， adaptively adjusted the target load profile for subsequent control moments to mitigate the adverse effects of uncertainties on control effectiveness.[Results] The case study focused on four types of inverter air conditioner clusters and examined the impact of different peak-shaving strategies， models， measurement errors， and control delays on the participation of the VPP in peak-shaving DR under market and invitation modes. This study verified the proposed method’s economic efficiency and robustness.[Conclusions] The results show that the proposed adaptive control method for peak-shaving DR based on a dynamic target load curve can autonomously adjust the target load curve based on the actual response conditions， demonstrating superior performance in terms of control accuracy， economic benefits， and robustness. |
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| ISSN: | 1000-7229 |