论文标题
一种无模型的方法，用于学习载荷的灵活性能力，提供网格支持
A model-free method for learning flexibility capacity of loads providing grid support
论文作者
论文摘要
灵活的负载是未来平衡权威（BA）的资源，以帮助平衡电源和需求。为了用作资源，BA必须知道灵活负载的能力，以改变其在基线上不违反消费者服务质量（QoS）的能力需求。现有关于容量表征的工作是基于模型的：他们需要将功耗与决定QoS的变量（例如空调系统中的温度）相关的模型。但是，在许多情况下，模型参数尚不清楚或难以获得。在这项工作中，我们构成了不需要模型信息的数据驱动能力表征方法，它只需要访问模拟器即可。能力的特征是需求偏差的一组可行光谱密度（SDS）。提出的方法是我们最近基于SD的容量表征的工作的扩展，该工作仅限于载荷的线性时间不变（LTI）动力学。这里提出的方法适用于非线性动力学。提供了该方法的数值评估，包括与LTI情况的基于模型的解决方案进行了比较。
Flexible loads are a resource for the Balancing Authority (BA) of the future to aid in the balance of power supply and demand. In order to be used as a resource, the BA must know the capacity of the flexible loads to vary their power demand over a baseline without violating consumers' quality of service (QoS). Existing work on capacity characterization is model-based: They need models relating power consumption to variables that dictate QoS, such as temperature in case of an air conditioning system. However, in many cases the model parameters are not known or difficult to obtain. In this work, we pose a data driven capacity characterization method that does not require model information, it only needs access to a simulator. The capacity is characterized as the set of feasible spectral densities (SDs) of the demand deviation. The proposed method is an extension of our recent work on SD-based capacity characterization that was limited to linear time invariant (LTI) dynamics of loads. The method proposed here is applicable to nonlinear dynamics. Numerical evaluation of the method is provided, including a comparison with the model-based solution for the LTI case.
