Analysis on execution of VPPs for commercial buildings in Shanghai based on decision tree

doi:10.3969/j.issn.2097-0706.2023.06.009

Abstract

Abstract:

A commercial building virtual power plant（VPP）lying in Huangpu district，the centre of Shanghai， is a zero-emission power plant. The VPP can lower the power load of commercial buildings during peak periods through autonomous control， that is to say， it can generate power with "data" as fuels. The wide distribution of commercial buildings in central urban areas， the wide variety of electrical equipment in different buildings， and the diverse capabilities of management personnel lead to various problems to VPPs in performing demand-response tasks. Based on the raw data from 50 commercial buildings participating in demand responses via the VPP in Huangpu district， the execution capacity of the virtual generator set in the VPP is analysed by decision tree algorithm， to improve the prediction accuracy of the VPP’s power generation execution in demand responses. Through the training and verification of the algorithm， it shows that the accuracy of the algorithm reaches 72.3% under current scenarios， and it is possible to further improve the accuracy by taking random forest， Bayesian and other algorithms in optimization. Therefore， the decision tree model algorithm is considered to be of good application value in the scenario， and can be used to predict the execution capacity of VVPs.

Key words: commercial building, virtual power plant, decision tree, information entropy, demand response, demand-side management, zero emission

CLC Number:

TK01：TP399

XIONG Zhenzhen. Analysis on execution of VPPs for commercial buildings in Shanghai based on decision tree[J]. Integrated Intelligent Energy, 2023, 45(6): 66-72.

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References 24

[1]	刘红岩, 陈剑, 陈国青. 数据挖掘中的数据分类算法综述[J]. 清华大学学报(自然科学版), 2002, 42(6):727-730.
	LIU Hongyan, CHEN Jian, CHEN Guoqing. Review of classification algorithms for data mining[J]. Journal of Tsinghua University (Science and Technology), 2002, 42(6):727-730.
[2]	RICHARDO D. 模式分类[M]. 北京: 机械工业出版, 2003.
[3]	关于加强国家机关办公建筑和大型公共建筑节能管理工作的实施意见[Z]. 2007.
[4]	王德文, 孙志伟. 电力用户侧大数据分析与并行负荷预测[J]. 中国机电工程学报, 2015, 35(3):527-537.
	WANG Dewen, SUN Zhiwei. Big data analysis and parallel load forecasting of electric power user side[J]. Proceedings of the CSEE, 2015, 35(3):527-537.
[5]	罗静. 电力负荷数据预测方法模型设计与分析[D]. 上海: 复旦大学, 2008.
	LUO Jing. Design and analysis of a model for predicting power load data[D]. Shanghai: Fudan University, 2008.
[6]	国家发展改革委办公厅关于上海市城区黄浦商业建筑需求侧管理示范项目的复函发改办运行〔2016〕1802号[Z]. 2016.
[7]	朱肖晶. 基于电力需求侧管理的需求响应机制研究[J]. 能源研究与利用, 2014, 160(6):44-46.
[8]	王蓓蓓, 李扬, 高赐威. 智能电网框架下的需求侧管理展望与思考[J]. 电力系统自动化, 2009, 33(20):17-22.
	WANG Beibei, LI Yang, GAO Ciwei. Demand side management outlook under smart grid infrastructure[J]. Automation of Electric Power System, 2009, 33(20):17-22.
[9]	田世明, 王蓓蓓, 张晶. 智能电网条件下的需求响应关键技术[J]. 中国电机工程学报, 2014, 34(22):3576-3589.
	TIAN Shiming, WANG Beibei, ZHANG Jing. Key technologies for demand response in smart grid[J]. Proceedings of the CSEE, 2014, 34(22):3576-3589.
[10]	李响, 黎灿兵, 曹一家, 等. 短期负荷预测的解耦决策树新算法[J]. 电力系统及其自动化学报, 2013, 25(3):13-19.
	LI Xiang, LI Canbing, CAO Yijia, et al. New algorithm of short-term load forecasting according to decision tree and decoupling[J]. Proceedings of the CSU-EPSA, 2013, 25(3):13-19.
[11]	马坤隆. 基于大数据的分布式短期负荷预测方法[D]. 长沙: 湖南大学, 2014.
	MA Kunrong. Short term distributed load forecasting method based on big data[D]. Changsha: Hunan University, 2014.
[12]	高赐威, 梁甜甜, 李慧星, 等. 开放式自动需求响应通信规范的发展和应用综述[J]. 电网技术, 2013, 37(3):692-698.
	GAO Ciwei, LIANG Tiantian, LI Huixing, et al. Development and application of open automated demand response[J]. Power System Technology, 2013, 37(3):692-698.
[13]	上海开展第一次电力需求响应试点试验工作[EB/OL].(2014-09-10)[2023-04-09]http://sh.people.com.cn/n/2014/0910/c347298-22262291.html.
[14]	张雪纯, 高广玲, 张智晟, 等. 基于需求响应的建筑楼宇综合能源系统优化调度[J]. 电力需求侧管理, 2019, 21(4):28-34.
	ZHANG Xuechun, GAO Guangling, ZHANG Zhisheng, et al. Optimal scheduling of building integrated energy system based on demand response[J]. Power Demand Side Management, 2019, 21(4):28-34.
[15]	赵晓东, 王娟, 周伏秋, 等. 构建新型电力系统亟待全面推行电力需求响应——基于11省市电力需求响应实践的调研[J]. 宏观经济管理, 2022(6):52-60.
	ZHAO Xiaodong, WANG Juan, ZHOU Fuqiu, et al. Building a new power system urgently needs to fully implement power demand response—Based on the investigation of power demand response practice in 11 provinces and cities[J]. Macroeconomic Management, 2022(6):52-60.
[16]	屠盛春, 刘晓春, 张皓. 上海市黄浦区商业建筑虚拟电厂典型应用[J]. 电力需求侧管理, 2020, 22(1):52-57.
	TU Shengchun, LIU Xiaochun, ZHANG Hao. Typical implementation of commercial building virtual power plant in Huangpu district of Shanghai[J]. Power Demand Side Management, 2020, 22(1):52-57.
[17]	孔祥玉, 刘超, 王成山, 等. 基于深度子领域自适应的需求响应潜力评估方法[J]. 中国电机工程学报, 2022, 42(16):5786-5797.
	KONG Xiangyu, LIU Chao, WANG Chengshan, et al. Demand response potential assessment method based on deep subdomain adaptation network[J]. Proceedings of the CSEE, 2022, 42(16):5786-5797.
[18]	罗为, 许鹏, 李为林, 等. 单体建筑需求响应研究[J]. 建筑节能, 2019, 47(11):31-35.
	LUO Wei, XU Peng, LI Weilin, et al. Case study of demand response in a large office building[J]. Building Energy Efficiency, 2019, 47(11):31-35.
[19]	宗世奇. 计及需求响应不确定性的调度优化研究[D]. 北京: 北京建筑大学, 2019
	ZONG Shiqi. Research on scheduling optimization considering uncertainty of demand response[D]. Beijing: Beijing University of Civil Engineering and Architecture, 2019.
[20]	刘芮含. 决策树算法在变电站故障诊断中的应用研究[D]. 阜新: 辽宁工程技术大学, 2020.
	LIU Ruihan. Application of decision tree algorithm in substation fault diagnosis[D]. Fuxing: Liaoning Technical University, 2020.
[21]	申明尧, 韩萌, 杜诗语, 等. 数据流决策树集成分类算法综述[J]. 计算机应用与软件, 2022, 39(9):1-10.
	SHEN Mingxiao, HANG Meng, DU Shiyu, et al. A survey of decision tree ensemble classification algorithms of data streams[J]. Computer Applications and Software, 2022, 39(9):1-10.
[22]	郑庆荣, 陆颖杰, 向佳, 等. 用户侧可调资源分类与特性分析研究[J]. 综合智慧能源, 2022, 44(11):50-55. doi: 10.3969/j.issn.2097-0706.2022.11.007
	ZHENG Qingrong, LU Yingjie, XIANG Jia, et al. Classification and characteristic analysis of adjustable resources on the user side[J]. Integrated Intelligent Energy, 2022, 44(11):50-55. doi: 10.3969/j.issn.2097-0706.2022.11.007
[23]	钟永洁, 纪陵, 李靖霞, 等. 虚拟电厂基础特征内涵与发展现状概述[J]. 综合智慧能源, 2022, 44(6):25-36. doi: 10.3969/j.issn.2097-0706.2022.06.003
	ZHONG Yongjie, JI Ling, LI Jingxia, et al. Overview on the characteristics,connotation and development status of virtual power plants in China[J]. Integrated Intelligent Energy, 2022, 44(6):25-36. doi: 10.3969/j.issn.2097-0706.2022.06.003
[24]	张凯杰, 丁国锋, 闻铭, 等. 虚拟电厂的优化调度技术与市场机制设计综述[J]. 综合智慧能源, 2022, 44(2):60-72. doi: 10.3969/j.issn.2097-0706.2022.02.009
	ZHANG Kaijie, DING Guofeng, WEN Ming, et al. Review of optimal dispatching technology and market mechanism design for virtual power plants[J]. Integrated Intelligent Energy, 2022, 44(2):60-72. doi: 10.3969/j.issn.2097-0706.2022.02.009

空调系统形式	建筑数量/台	占比/%
风机盘管+新风	37	74
全空气机组	5	10
风机盘管+新风，全空气机组	4	8
VRV系统	4	8

项目	说明
事件	事件相关性
建筑	建筑相关性
基准值	需求响应事件发生前的基准负荷
实际值	需求响应事件发生时间的负荷
削减量	削减量=基准值-实际值
削减率	削减率=削减量/基准值×100%
温度	需求响应发生时刻的平均温度
湿度	需求响应发生时刻的平均湿度
年代	该建筑的建设年代
类型	该建筑的商业类型
面积	该建筑的面积
装机容量	该建筑的电力装机容量
空调系统	该建筑的空调系统类型
末端	该建筑的空调末端类型

有效削减	次数	概率	熵
是	95	0.75	-0.307
否	31	0.25	-0.498

条件	熵取值	条件	熵取值
温度	0.664 7	空调	0.799 7
建筑	0.4185	能耗	0.577 1
类型	0.782 6	机组	0.606 6
容量	0.816 2

互信息	取值	影响因素
温度	0.140 2	第3因素
建筑	0.386 4	第1因素
类型	0.022 3
容量	-0.011 3
空调	0.005 2
能耗	0.227 8	第2因素
机组	0.198 3