Ensemble Control of Cycling Energy Loads: Markov Decision Approach
Ensemble Control of Cycling Energy Loads: Markov Decision Approach
复制标题
循环能量负荷的集合控制：马尔可夫决策方法
DOI：
10.1007/978-1-4939-7822-9_15
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
V. Chernyak
中科院分区：
文献类型：
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作者：
M. Chertkov;V. Chernyak
采用马尔可夫决策过程（MDP）框架来表示具有循环能耗模式的设备的总体控制，例如，恒温控制负载。具体来说，我们利用和开发类的MDP模型先前创造的线性可解的MDP，描述最佳动态的概率分布的合奏许多循环设备。两个主要不同的设置进行了讨论。首先，我们考虑集成聚合器的最佳策略之间的操作成本最小化和最小化的集成福利惩罚，后者表示为KL发散的合奏的实际和正常的概率分布。然后，第二，我们转移到需求响应设置建模聚合器的任务，以最大限度地减少福利惩罚的条件下，聚合的消费相匹配的系统运营商所要求的目标时变消费。我们讨论了修改这两个设置，旨在鼓励或约束不同状态之间的过渡。所得到的修改后的MDP的动态规划功能始终保留，但是，“线性可解性”完全或部分地丢失，这取决于修改的类型。我们还进行了一些（范围有限）的数值实验，使用第一设置的配方。最后，我们讨论未来的推广和应用。
A Markov decision process (MDP) framework is adopted to represent ensemble control of devices with cyclic energy consumption patterns, e.g., thermostatically controlled loads. Specifically we utilize and develop the class of MDP models previously coined linearly solvable MDPs, that describe optimal dynamics of the probability distribution of an ensemble of many cycling devices. Two principally different settings are discussed. First, we consider optimal strategy of the ensemble aggregator balancing between minimization of the cost of operations and minimization of the ensemble welfare penalty, where the latter is represented as a KL-divergence between actual and normal probability distributions of the ensemble. Then, second, we shift to the demand response setting modeling the aggregator’s task to minimize the welfare penalty under the condition that the aggregated consumption matches the targeted time-varying consumption requested by the system operator. We discuss a modification of both settings aimed at encouraging or constraining the transitions between different states. The dynamic programming feature of the resulting modified MDPs is always preserved; however, “linear solvability” is lost fully or partially, depending on the type of modification. We also conducted some (limited in scope) numerical experimentation using the formulations of the first setting. We conclude by discussing future generalizations and applications.