论文标题
在线学习和分布式控制以响应住宅需求
Online Learning and Distributed Control for Residential Demand Response
论文作者
论文摘要
本文研究了基于激励型住宅需求响应（DR）的调节空调（AC）负载的自动控制方法。关键的挑战是，在实践中，客户对负载调整的响应尚不确定且未知。在本文中，我们将DR事件中的交流控制问题作为多周期随机优化，该优化整合了室内热力学和客户选择退出状态过渡。具体而言，使用包括高斯流程和逻辑回归在内的机器学习技术分别用于学习未知的热动力学模型和客户选择退出行为模型。我们考虑了两个用于交流负载控制的典型DR目标：1）最大程度地减少总需求，2）密切跟踪受调节的功率轨迹。基于汤普森采样框架，我们提出了一种在线DR Control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，并以分布式方式实施以保护客户的隐私。数值模拟证明了所提出算法的控制最佳性和学习效率。
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.
