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1.智慧城市物联网全国重点实验室(澳门大学),澳门 999078
2.贵州电网有限责任公司,贵州省贵阳市 550005
3.电力传输与功率变换控制教育部重点实验室,上海非碳基能源转换与利用研究院(上海交通大学),上海市 200240
Received:27 April 2025,
Revised:2025-09-02,
Online First:15 September 2025,
Published:25 October 2025
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蒋思宇,惠红勋,胡厚鹏等.建筑虚拟电厂可信调节能力概念与量化方法[J].电力系统自动化,2025,49(20):62-72.
JIANG Siyu,HUI Hongxun,HU Houpeng,et al.Concept and Quantification Method for Credible Regulation Capability of Building Virtual Power Plants[J].Automation of Electric Power Systems,2025,49(20):62-72.
蒋思宇,惠红勋,胡厚鹏等.建筑虚拟电厂可信调节能力概念与量化方法[J].电力系统自动化,2025,49(20):62-72. DOI: 10.7500/AEPS20250427004.
JIANG Siyu,HUI Hongxun,HU Houpeng,et al.Concept and Quantification Method for Credible Regulation Capability of Building Virtual Power Plants[J].Automation of Electric Power Systems,2025,49(20):62-72. DOI: 10.7500/AEPS20250427004.
随着虚拟电厂规模快速扩增,现有虚拟电厂在多元不确定性下的调节能力量化结果,难以满足电力系统风险感知调度与电力市场报量报价的需求。建筑虚拟电厂作为虚拟电厂的重要实现形式,通过聚合建筑负荷资源,具备快速响应特性和高可靠性。其中,空调作为建筑虚拟电厂的主要调节设备,其可信调节能力的量化对建筑虚拟电厂可靠运行具有重要意义。为此,提出一种以空调为例面向建筑虚拟电厂的可信调节能力概念与量化方法。首先,构建了基于数据-物理融合驱动模型的建筑虚拟电厂可信调节能力量化框架,并提出了可信调节能力的概念,明确了可信调节能力区间的含义;其次,提出了基于Informer和共形分位数回归的能耗基线概率预测模型,通过时序特征提取、构建非对称残差集,生成可靠的基线概率区间;进一步,基于建筑空调传热模型,量化不同置信度下建筑空调的上调和下调能力。最后,以中国澳门某建筑空调的真实数据进行仿真分析。结果表明,所提方法可在给定置信区间内有效量化建筑空调的可信调节能力,并通过量化区间揭示了建筑空调调节能力的动态波动特征。
With the rapid expansion of virtual power plants (VPPs) in scale
the existing quantification results of regulation capability for VPPs under multiple uncertainties fail to meet the requirements of risk-aware dispatch of power systems and bidding in electricity markets. Building VPP
as an important form of VPPs
possess the characteristics of rapid regulation capability and high reliability by aggregating building load resources. Among these resources
as the primary regulation equipment in building VPPs
the quantification of credible regulation capability for air conditioning is of significant importance for the reliable operation of building VPPs. To this end
the concept and a quantification method for the credible regulation capability of building VPPs are proposed by taking air conditioning as an example. Firstly
the quantification framework of credible regulation capability for building VPPs is constructed based on hybrid data-physical-driven model
and the concept of credible regulation capability is proposed
and the connection of the credible regulation capability interval is clarified. Secondly
a probabilistic prediction model for the energy consumption baseline is developed based on the Informer architecture and conformal quantile regression (Informer-CQR)
By extracting temporal features
asymmetric residual sets are constructed to generate reliable baseline probability intervals. Furthermore
based on a thermal transfer model of building air conditioning
the upward and downward regulation capabilities of building air conditioning are quantified at different confidence levels. Finally
simulation analysis is are conducted with actual data from a public building in Macau
China. The results demonstrate that the proposed method can effectively quantify the credible regulation capability of building air conditioning within predefined confidence intervals
and reveal the dynamic fluctuation behavior of the regulation capability of air conditioning through quantification intervals.
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