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Model-free control of thermostatically controlled loads connected to a district heating network

机译:连接到区域供热网络的恒温负载的无模型控制

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Optimal control of thermostatically controlled loads connected to a district heating network is considered a sequential decision-making problem under uncertainty. The practicality of a direct model-based approach is compromised by two challenges, namely scalability due to the large dimensionality of the problem and the system identification required to identify an accurate model. To help in mitigating these problems, this paper leverages on recent developments in reinforcement learning in combination with a market-based multi-agent system to obtain a scalable solution that obtains a significant performance improvement in a practical learning time. In a first step, all relevant (and practically available) state information is collected from which a limited set of features is extracted, resulting in a low-dimensional representation of the system state. In a second step, a control action for the entire cluster is extracted from a policy determined offline. In a third and final step, this control action is dispatched over the different thermostatically controlled loads using a market-based multi-agent system. This process is repeated following a receding horizon approach. The control approach is applied to a scenario comprising 100 thermostatically controlled loads connected to a radial district heating network supplied by a central combined heat and power plant. Both for an energy arbitrage and a peak shaving objective, the control approach requires 60 days to obtain a performance within 65% of a theoretical lower bound on the cost. (C) 2017 Published by Elsevier B.V.
机译:在不确定性下,对与区域供热网络相连的恒温控制负载的最佳控制被认为是一个顺序决策问题。基于直接模型的方法的实用性受到两个挑战的折衷,即由于问题的大维度导致的可伸缩性以及识别准确模型所需的系统识别。为了帮助减轻这些问题,本文利用了强化学习的最新发展,并结合了基于市场的多智能体系统来获得可扩展的解决方案,该解决方案可以在实际学习时间内显着提高性能。第一步,收集所有相关(且实际上可用)的状态信息,从中提取有限的一组功能,从而得到系统状态的低维表示。在第二步中,从离线确定的策略中提取整个群集的控制操作。在第三个也是最后一个步骤中,使用基于市场的多代理系统将控制操作分配到不同的恒温控制负载上。后退的视野方法将重复此过程。该控制方法适用于包含100个恒温控制负载的场景,这些负载连接到由中央联合热电厂提供的径向区域供热网络。对于能源套利和调峰目标而言,控制方法都需要60天才能在理论成本下限的65%之内获得性能。 (C)2017由Elsevier B.V.发布

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