Abstract Developments in simulation and distributed control of distributed energy resources require increasingly granular characterization and modeling of load behavior. Previous work has demonstrated the viability of voltage-based power control of residential appliances to enable load flexibility without service interruption. However, conventional static and dynamic load models fail to capture the coupled voltage and state dynamics relevant for this type of control. We develop a new class of dynamic load models for residential appliances. The input-output dynamics are learned by varying input voltage, which is enabled by custom hardware capable of controlling single-phase AC voltage and collecting high-resolution measurements. We estimate model parameters using nonlinear least squares regression and particle swarm optimization. The RMSE of power predictions is significantly reduced for loads with coupled time and voltage dynamics relative to traditional models. Using these models for voltage-based power control can help improve the ability of DERs to provide grid services.

中文翻译：

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用于电器级电网边缘仿真和控制的时域和电压域负载模型

摘要 分布式能源仿真和分布式控制的发展需要对负载行为进行日益细化的表征和建模。以前的工作已经证明了基于电压的家用电器电源控制的可行性，可以在不中断服务的情况下实现负载灵活性。然而，传统的静态和动态负载模型无法捕捉与此类控制相关的耦合电压和状态动态。我们为家用电器开发了一类新的动态负载模型。输入-输出动态是通过改变输入电压来学习的，这是由能够控制单相交流电压和收集高分辨率测量值的定制硬件实现的。我们使用非线性最小二乘回归和粒子群优化来估计模型参数。与传统模型相比，具有耦合时间和电压动态的负载的功率预测的均方根误差显着降低。将这些模型用于基于电压的电力控制有助于提高分布式能源提供电网服务的能力。