基于粗数据的建筑柔性的易处理鲁棒建模
Tractable and Robust Modeling of Building Flexibility Using Coarse Data
摘要：
可控建筑负荷有可能增加电力系统的灵活性。开发有效和可实现的负载控制策略的关键步骤是对一组可行的建筑物负载配置文件进行建模。在本文中，我们认为建筑物的灵活性来源是他们的暖通空调负荷。我们提出了一个数据驱动的方法，经验估计一个强大的可行域的负载使用粗糙的数据，也就是说，只使用总建筑负荷和平均室内温度。所提出的方法使用易于收集的粗略数据，可以适应任何类型的建筑物。所得到的可行域模型对温度预测误差具有鲁棒性，并由线性约束描述。这些约束的数学简单性使得该模型适用于许多电力系统的应用，例如，经济调度和最优潮流。我们使用EnergyPlus的数据验证了我们的模型，并通过一个案例研究证明了它的实用性，在这个案例中，灵活的建筑负荷被用来平衡风力发电预测中的误差。
英文摘要：
Controllable building loads have the potential to increase the flexibility of power systems. A key step in developing effective and attainable load control policies is modeling the set of feasible building load profiles. In this paper, we consider buildings whose source of flexibility is their HVAC loads. We propose a data-driven method to empirically estimate a robust feasible region of the load using coarse data, that is, using only total building load and average indoor temperatures. The proposed method uses easy-to-gather coarse data and can be adapted to buildings of any type. The resulting feasible region model is robust to temperature prediction errors and is described by linear constraints. The mathematical simplicity of these constraints makes the proposed model adaptable to many power system applications, for example, economic dispatch, and optimal power flow. We validate our model using data from EnergyPlus and demonstrate its usefulness through a case study in which flexible building loads are used to balance errors in wind power forecasts.
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