面向负荷转移应用的电网交互式热水器模型预测控制策略设计
Designing model predictive control strategies for grid-interactive water heaters for load shifting applications
AI总结：
本文构建住宅热水器MPC负荷转移框架，研究模型保真度、传感器配置及用水估算与预测等设计因素的影响，并提出无流量计用水估算方法。
摘要：
模型预测控制（MPC）策略使住宅热水器能够根据动态价格信号转移负荷。关键在于，此类策略的性能对各种算法设计选择十分敏感。在本研究中，我们开发了一个用于在住宅热水器上实施模型预测控制以实现负荷转移应用的框架。我们使用该框架分析四个不同设计因素如何影响控制性能和热舒适：（i）控制模型保真度，（ii）温度传感器配置，（iii）用水估算方法，以及（iv）用水预测方法。我们提出了无需使用流量计即可估算用水模式的新方法。通过使用高保真水箱模型和真实用水数据进行仿真，在两种不同的时变价格信号下对MPC策略进行比较。结果表明，控制模型保真度和温度传感器数量对电费影响最大，而用水预测方法对热舒适和热水耗尽事件频率具有显著影响。研究结果为家庭能源管理系统中热水器的有效MPC设计提供了实用见解。
英文摘要：
Model predictive control (MPC) strategies allow residential water heaters to shift load in response to dynamic price signals. Crucially, the performance of such strategies is sensitive to various algorithm design choices. In this work, we develop a framework for implementing model predictive controls on residential water heaters for load shifting applications. We use this framework to analyze how four different design factors affect control performance and thermal comfort: (i) control model fidelity, (ii) temperature sensor configuration, (iii) water draw estimation methodology, and (iv) water draw forecasting methodology. We propose new methods for estimating water draw patterns without the use of a flow meter. MPC strategies are compared under two different time-varying price signals through simulations using a high-fidelity tank model and real-world draw data. Results show that control model fidelity and the number of temperature sensors have the largest impact on electricity costs, while the water draw forecasting methodology has a significant impact on thermal comfort and the frequency of runout events. Results provide practical insight into effective MPC design for water heaters in home energy management systems.
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