具有一定弹性的建筑热负荷被视为电-热综合能源系统运行优化的重要调节资源.考虑建筑热负荷具有规模大、单体容量小的特点,非侵入式的数据驱动方法成为量化建筑热负荷弹性的有效手段.然而,由于数据不足或模型精度不够,该方法将不可避免地产生误差,给电-热综合能源系统的优化调度带来认知不确定性.因此,提出一种考虑建筑热负荷弹性并兼容相关认知不确定性的电-热综合能源系统优化调度方法.分析基于数据驱动的建筑热负荷需求弹性评估方法,将评估过程中产生的误差建模为认知不确定性,并通过改进的D-S证据理论对多源误差进行融合;采用拉丁超立方抽样方法生成表征热负荷弹性认知不确定性的场景,并通过模糊聚类法进行场景削减;将构造的场景集嵌入电-热综合能源系统的协调优化调度中,实现对建筑热负荷弹性及相关认知不确定性的综合考虑.算例仿真结果表明,考虑建筑热负荷需求弹性及认知不确定性对减少弃风、提高电热综合能源系统的运行灵活性至关重要.

The flexibility of thermal loads of buildings is a valuable balancing resource for operation of the heat and electricity integrated energy system (HE-IES). Considering the characteristics of large scale and small single load capacity of the themal load, the non-intrusive data-driven method has become an effective means to quantify the flexibility of building thermal load. However, due to the inaccuracy of the model or the lack of data, this method inevitably produces errors and brings epistemic uncertainty to the optimal dispatch of the HE-IES. An optimal dispatch model of the HE-IES that is compatible with the epistemic uncertainty of demand flexibility in the thermal loads of buildings is proposed. First, a data-driven flexible demand assessment method for building thermal load is described. The measurement errors are modeled as epistemic uncertainty and the multiple error sources are combined by using the D-S evidence theory. Then, the representative scenarios are selected to represent the epistemic uncertainty of the demand flexibility based Latin hypercube sampling(LHS) method, and the scenarios are reduced by the fuzzy clustering method. Finally, the representative scenarios are embedded in the coordinated and optimized dispatch of the HE-IES to realize the comprehensive consideration of the thermal load flexibility and related epistemic uncertainty of the building. The results demonstrate that considering the epistemic uncertainties of the thermal load demand is crucial for reducing the wind power curtailments and improving the operational flexibility of HE-IES.