公共楼宇群体价格型负荷响应的仿真分析方法研究
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摘要
公共楼宇作为需求侧响应的主要参与者,因其复杂的响应机理而难以被电网公司、售电公司准确地调配使用。首先建立面向需求响应的楼宇照明系统、空调系统用户用电能耗模型;在此基础上,分别估算公共楼宇群体签订合同的人数和某次响应电价的整体功率;最后,在算例中对某一公共楼宇群体响应电价的曲线进行了仿真分析。结果表明,本文方法可以用于仿真公共楼宇群体响应价格型需求响应项目的特性。
As the main participators in demand side response,public buildings are difficult to be properly deployed by power grid companies and electricity retail companies in case of its complicated response mechanism. The energy consumption models of building lighting system and air-conditioning system faced to demand response are established. On this basis, the number of contracts signed by public buildings and the overall power of a response are estimated respectively. Finally, an example is given to simulate the response of a group of public buildings. The results show that this method can be used to simulate the characters of public building groups responds to price based demand response projects.
As the main participators in demand side response,public buildings are difficult to be properly deployed by power grid companies and electricity retail companies in case of its complicated response mechanism. The energy consumption models of building lighting system and air-conditioning system faced to demand response are established. On this basis, the number of contracts signed by public buildings and the overall power of a response are estimated respectively. Finally, an example is given to simulate the response of a group of public buildings. The results show that this method can be used to simulate the characters of public building groups responds to price based demand response projects.
引文
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[2] RAHMANI-ANDEBILI M. Nonlinear demand response programs for residential customers with nonlinear behavioral models[J]. Energy and Buildings, 2016, 119(3):352-362.
[3] MOHAJERYAMI S, MOGHADDAM I N, DOOSTAN M, et al. A novel economic model for price-based demand response[J]. Electric Power Systems Research, 2016, 135:1-9.
[4]阮文骏,王蓓蓓,李扬,等.峰谷分时电价下的用户响应行为研究[J].电网技术,2012(7):86-93.RUAN Wenjun, WANG Beibei, LI Yang, et al. Customer response behavior in time-of-use price[J]. Power System Technology, 2012(7):86-93.
[5] BINA M T, AHMADI D. Stochastic modeling for the next day domestic demand response applications[J]. IEEE Transactions on Power Systems, 2015, 30(6):2 880-2 893.
[6] TRAN N, PHAM C, NGUYEN M, et al. Incentivizing en-ergy reduction for emergency demand response in multi-tenant mixed-use buildings[J]. IEEE Transactions onSmart Grid, 2016(99):1.
[7] CHRISTANTONI D, OXIZIDIS S, FLYNN D, et al. Imple-mentation of demand response strategies in a multi-pur-pose commercial building using a whole-building simula-tion model approach[J]. Energy and Buildings, 2016(131):76-86.
[8]彭堃.公共楼宇日前用电负荷优化策略研究[D].南京:河海大学,2016.PENG Kun. Public buildings before the electricity loadoptimization strategy research[D]. Nanjing:Hohai Uni-versity, 2016.
[9] KROPYVNYTSKYY A. An empirical study of electricityprice and temperature[D]. Bergen:Norwegian School ofEconomics, 2016.
[10]朱峰.空调负荷需求响应特性及其调控策略研究[D].南京:东南大学,2016.ZHU Feng. Study on response characteristics and con-trol strategies of air conditioning load demand[D]. Nan-jing:Southeast University, 2016.
[11] HUISMAN R. The influence of temperature on spikeprobability in day-ahead power prices[J]. Energy Eco-nomics, 2008, 30(5):2 697-2 704.
[12]丁际刚,兰肇华.前景理论述评[J].经济学动态,2002(9):64-66.DING Jigang, LAN Zhaohua. A review of prospect theory[J], Economic Perspectives, 2002(9):64-66.
[13]宁艺飞,陈星莺.分时电价下大用户概率响应建模研究[J].电力需求侧管理,2016, 18(1):22-27.NING Yifei, CHEN Xingying. Research on probabilisticmodeling of large users response to TOU price[J]. Pow-er Demand Side Management, 2016, 18(1):22-27.