【摘要】 空调负荷需求响应潜力的科学评估是指导柔性建筑和新型电力系统规划设计与运行调控的重要基础。现有研究往往聚焦于单体建筑，缺少面向区域级建筑群空调负荷需求响应潜力的规模化评估方法，且在计算精度和鲁棒性方面难以实现兼顾。为此，本研究提出了一种计及多源不确定性的区域级建筑群空调负荷需求响应潜力评估方法。首先，基于GIS地图数据进行区域级建筑群信息识别和提取；而后，建立EnergyPlus仿真模型，对模型中可能影响空调负荷的不确定性参数进行随机采样模拟，并通过实测数据进行模型有效性验证；最后，基于验证后的模型，开展不同需求响应策略下的空调负荷模拟，得到对应策略下的单位面积空调负荷需求响应潜力，并通过区域面积聚合方法得到空调负荷需求响应潜力总量。该方法应用于上海市某建筑面积为110.3万m~2的区域级办公建筑群，结果显示：室温设定值调节、预冷、预冷+室温设定值调节三种策略在夏季典型日的需求响应时段内可平均削减负荷8.6 MW、1.5 MW、8.9 MW,分别占对应时段内空调负荷基线平均值的33%、6%、35%。更多还原

【Abstract】 The scientific assessment of air conditioning demand response potential serves as a critical foundation for guiding the planning, design, and operation of flexible buildings and new power systems. Existing studies tend to focus on individual buildings, yet a scalable assessment method for evaluating the air conditioning demand response potential of regional-level building clusters remains lacking. Moreover, achieving a balance between computational accuracy and robustness has proven challenging. To address this gap, this study proposes an air conditioning demand response potential assessment method for regional-level building groups that takes into account multi-source uncertainties. Firstly, regional-level building information identification and extraction are carried out based on GIS map data; Then, the EnergyPlus simulation model is established. The uncertain parameters that might affect the air conditioning load in the model are randomly sampled and simulated, and the validity of the model is verified through the measured data. Finally, based on the verified model, simulations of air conditioning load under different demand response strategies are carried out to obtain the air conditioning load demand response potential per area under the corresponding strategies, and the total potential of air conditioning demand response is obtained through the regional area aggregation method. This method is applied in the case analysis of a regional-level office building group with a floor area of 1.103 million square meters in Shanghai. The results show that the three strategies of global temperature adjustment(GTA), pre-cooling, and pre-cooling combined with GTA can reduce the load by an average of 8.6 MW, 1.5MW, and 8.9 MW during the demand response period of typical summer days, accounting for 33%, 6%, and 35% of the baseline average value of air conditioning load in the corresponding periods respectively.更多还原