计及差异化用能需求的集群空调负荷优化控制策略  

An optimal control strategy of cluster air-conditioning loads considering differentiated energy consumption demand

作　　者：赵兵 王增平[1] 孙毅[1] Zhao Bing;Wang Zengping;Sun Yi(School of Electrical and Electronic Engineering,North China Electric Power University,Beijing 102206,China;China Electric Power Research Institute Co.,Ltd.,Beijing 100192,China)

机构地区：[1]华北电力大学电气与电子工程学院,北京102206 [2]中国电力科学研究院有限公司,北京100192

出　　处：《电测与仪表》2021年第9期22-27,共6页Electrical Measurement & Instrumentation

基　　金：国家重点研发计划资助项目(2016YFF0201201)。

摘　　要：随着电力物联网的加速建设,需求侧资源管理也愈加信息化和智能化,为引导用户节能降耗提供了精细化调控的基础。传统的集群负荷调控大多面向群体优化,而忽略了个体用户的用能偏好,难以同时满足用户的差异化舒适度需求和经济性需求。提出一种计及用户差异化用能需求的集群空调负荷协同控制策略,基于LSTM神经网络模拟单个用户行为特性,引入用能行为相似度量化对用户差异化需求的切合程度进行量化,进而采用DQN强化学习制定个性化用能策略,降低用户用能成本的同时满足各用户的差异化舒适需求,并且有效降低了峰谷差。最后,仿真结果验证了文章所提策略的有效性和优势。With the accelerated construction of the power Internet of Things,demand-side resource management has become more informative and intelligent,providing a basis for fine-grained regulation to guide users to save energy and reduce consumption.The traditional cluster load control is mostly oriented to group optimization,and ignores the energy consumption preference of individual users,and it is difficult to meet the differentiated comfort and economic needs of users at the same time.Therefore,this paper proposes a cluster air-conditioning load collaborative control strategy that considering the differentiated energy consumption needs of users.Based on LSTM neural network to simulate user behavior characteristics,this paper introduces the similarity of energy use consumption behavior to measure the degree of compliance with the differentiated needs of users,and then,adopts DQN reinforcement learning to formulate personalized energy consumption strategies,which can reduce user energy costs while meeting the comfort requirements of individual users,and effectively reduce the peak-to-valley difference.Finally,the simulation result verifies the effectiveness and advantages of the proposed strategy.

关 键 词：用能需求差异化 LSTM DQN 负荷精细化调控 

分 类 号：TM734[电气工程—电力系统及自动化]