基于电力负荷可调特性的城市楼宇配网优化运行  

Optimization Operation of Urban Building Network Based on Load Adjustable Characteristics

作　　者：吕志盛 程如昊 杨浩文 王晓璨 LÜ Zhisheng;CHENG Ruhao;YANG Haowen;WANG Xiaocan(Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control,Xiamen University of Technology,Xiamen,Fujian 361024,China)

机构地区：[1]厦门市高端电力装备及智能控制重点实验室(厦门理工学院),福建厦门361024

出　　处：《南方电网技术》2024年第9期151-160,共10页Southern Power System Technology

基　　金：国家自然科学基金资助项目(51407151);国家重点研发计划资助项目(2022YFE019900);福建省教育厅重点项目(JZ230048)。

摘　　要：城市楼宇是有效促进电能节约和合理利用的场所,有利于加快发展低碳经济。从经济性和可接受度两个方面着手,提出了一种智能楼宇电力负荷的多目标优化模型。首先,针对可转移负荷和可缩减负荷建立与时间/负荷相关的指标,用于描述用户参与负荷调节的接受度。然后,以统计周期内负荷峰谷差率最小、可接受度最高、电费最低为目标,建立楼宇负荷组合数学优化模型。最后,基于连续型Hopfield神经网络(continuous Hopfield neural network,CHNN)进行模型的求解。算例分析表明,多目标优化结果能实现用电可接受度条件下有效降低用电费用。Urban buildings are places that effectively promote the conservation and rational use of energy resources,which are conducive to accelerate the development of a circular low-carbon economy.A multi-objective optimization model for intelligent building load optimization from two aspects of economy and acceptability is proposed.Firstly,time/load-related indicators are established to describe the users′acceptance of participating in load regulation for the transferable and reducible load.Then,with the goal of minimizing the peak-to-valley load difference in the statistical period,achieving the highest acceptability,and minimizing electricity costs,the building load combined mathematical model is established.Finally,it is proposed to solve the model based on continuous Hopfield neural network(CHNN).The analysis of the calculation example shows that the multi-objective optimization model can effectively reduce the electricity cost and ensure the acceptability of electricity.

关 键 词：城市楼宇 负荷可调 峰谷差 经济性 可接受度 连续型HOPFIELD神经网络 

分 类 号：TM732[电气工程—电力系统及自动化]