Online Learning and Distributed Control for Residential Demand Response
Online Learning and Distributed Control for Residential Demand Response
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DOI：
10.1109/tsg.2021.3090039
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发表时间：
2020-10
影响因子：
9.6
通讯作者：
Xin Chen;Yingying Li;Jun Shimada;Na Li
中科院分区：
文献类型：
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作者：
Xin Chen;Yingying Li;Jun Shimada;Na Li
研究了激励型住宅需求响应中空调负荷自动调节的控制方法。关键的挑战是，客户对负荷调整的反应是不确定的，在实践中是未知的。在本文中，我们制定的AC控制问题在DR事件作为一个多周期的随机优化，集成了室内热动力学和客户选择退出状态转换。具体而言，机器学习技术，包括高斯过程和逻辑回归分别学习未知的热动力学模型和客户选择退出行为模型。我们考虑两个典型的DR目标的AC负载控制：1）最小化的总需求，2）密切跟踪调节功率轨迹。基于汤普森采样框架，我们提出了一种在线DR控制算法来学习客户行为，并制定实时AC控制方案。该算法考虑了各种环境因素对客户行为的影响，并以分布式方式实现，以保护客户的隐私。数值仿真结果表明，该算法的控制最优性和学习效率。
This paper studies the automated control method for regulating air conditioner (AC) loads in incentive-based residential demand response (DR). The critical challenge is that the customer responses to load adjustment are uncertain and unknown in practice. In this paper, we formulate the AC control problem in a DR event as a multi-period stochastic optimization that integrates the indoor thermal dynamics and customer opt-out status transition. Specifically, machine learning techniques including Gaussian process and logistic regression are employed to learn the unknown thermal dynamics model and customer opt-out behavior model, respectively. We consider two typical DR objectives for AC load control: 1) minimizing the total demand, 2) closely tracking a regulated power trajectory. Based on the Thompson sampling framework, we propose an online DR control algorithm to learn customer behaviors and make real-time AC control schemes. This algorithm considers the influence of various environmental factors on customer behaviors and is implemented in a distributed fashion to preserve the privacy of customers. Numerical simulations demonstrate the control optimality and learning efficiency of the proposed algorithm.