A Deep Neural Network Coordination Model for Electric Heating and Cooling Loads Based on IoT Data
A Deep Neural Network Coordination Model for Electric Heating and Cooling Loads Based on IoT Data
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DOI：
10.17775/cseejpes.2019.01700
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发表时间：
2020-03-01
影响因子：
7.1
通讯作者：
Chen, Zhe
中科院分区：
文献类型：
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作者：
Jin, Hongyang;Teng, Yun;Chen, Zhe
随着泛在电力物联网（UEPIOT）的发展和物联网数据的增加，传统的负荷调度中心调度模式面临着各种挑战，如实时优化计算、时变特征提取和制定协调调度策略等。提出了一种基于恒温控制负荷（TCL）态势感知（SA）的电冷热负荷深度神经网络协调模型。首先，使用滑动窗口对具有不确定性的物联网节点数据进行自适应预处理。根据个人热舒适度（PTC）和调峰贡献（PSC），建立了负荷动态模型，同时将个性化行为和消费者心理融入到热舒适度的柔性调节模型中。在此基础上，提出了一种基于深度Q网络（DQN）的序贯决策方法，该方法以热舒适度和电费为综合报酬函数，解决了序贯决策问题。最后，利用UEPIoT智能调度系统数据，设计了支持深度神经网络电冷热负荷协调模型有效性的仿真模型。实例研究表明，该方法可以有效地管理与大规模的电加热和冷却负荷的协调。
As the ubiquitous electric power internet of things (UEPIoT) evolves and IoT data increases, traditional scheduling modes for load dispatch centers have yielded a variety of challenges such as calculation of real-time optimization, extraction of time-varying characteristics and formulation of coordinated scheduling strategy for capacity optimization of electric heating and cooling loads. In this paper, a deep neural network coordination model for electric heating and cooling loads based on the situation awareness (SA) of thermostatically controlled loads (TCLs) is proposed. First, a sliding window is used to adaptively preprocess the IoT node data with uncertainty. According to personal thermal comfort (PTC) and peak shaving contribution (PSC), a dynamic model for loads is proposed; meanwhile, personalized behavior and consumer psychology are integrated into a flexible regulation model of TCLs. Then, a deep Q-network (DQN)-based approach, using the thermal comfort and electricity cost as the comprehensive reward function, is proposed to solve the sequential decision problem. Finally, the simulation model is designed to support the validity of the deep neural network coordination model for electric heating and cooling loads, by using UEPIoT intelligent dispatching system data. The case study demonstrates that the proposed method can efficiently manage coordination with large-scale electric heating and cooling loads.