论文标题
数据驱动的学习和负载集合控制
Data-Driven Learning and Load Ensemble Control
论文作者
论文摘要
需求响应（DR）计划旨在参与分布的小规模柔性负载，例如恒温控制负载（TCLS），以提供各种网格支持服务。线性解决的Markov决策过程（LS-MDP）是传统MDP的一种变体，用于对汇总的TCL进行建模。然后，应用一种称为Z-Learning的无模型增强学习技术来学习价值函数，并为DR聚合器控制TCLS的最佳策略提供了最佳策略。学习过程与估计汇总TCL的被动动态产生的不确定性具有鲁棒性。通过模拟住宅房屋的测试床附近的供暖，冷却和通风（HVAC）单元，证明了这种数据驱动的学习的效率。
Demand response (DR) programs aim to engage distributed small-scale flexible loads, such as thermostatically controllable loads (TCLs), to provide various grid support services. Linearly Solvable Markov Decision Process (LS-MDP), a variant of the traditional MDP, is used to model aggregated TCLs. Then, a model-free reinforcement learning technique called Z-learning is applied to learn the value function and derive the optimal policy for the DR aggregator to control TCLs. The learning process is robust against uncertainty that arises from estimating the passive dynamics of the aggregated TCLs. The efficiency of this data-driven learning is demonstrated through simulations on Heating, Cooling & Ventilation (HVAC) units in a testbed neighborhood of residential houses.
