智能小区可削减柔性负荷实时需求响应策略
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摘要
随着我国电力体制改革以及智能电网技术的发展,含柔性负荷的智能小区成为参与需求响应的重要主体之一。综合考虑不同可削减柔性负荷设备用电的不确定性,结合目前中国居民需求响应现状及未来发展趋势,针对新型智能小区提出一种适用于负荷聚合商的可削减柔性负荷实时需求响应调度策略。该策略利用混合整数线性优化与基于Copula的蒙特卡洛模拟相结合的方法对各负荷设备进行每小时一次的滚动优化,从而实现居民可削减柔性负荷的实时随机调度。通过仿真验证,该策略可在不影响用户满意度情况下有效降低用户用电成本,减小负荷峰值及用电量,使智能小区中的可削减柔性负荷能够有效参与到需求响应中。
With the reformation of electric power market and the development of smart grid technology in China, the smart residential community which contains flexible load has become one of the crucial entities participating in demand response.This paper presents a curtailable flexible load real-time demand response scheme for the novel smart residential community,which is compatible with load aggregator, considering the uncertainties of different residential curtailable load and incorporating both the current circumstances and future trends of the demand response programs in China. The proposed scheme focuses on the curtailable load among the residential flexible load. Mixed integer linear programming and Copula based Monte Carlo simulation are combined for the hourly rolling optimization of each load to realize the real-time scheduling of residential curtailable flexible load. The proposed strategy can significantly reduce the power consumption cost,the peak load, and the total energy consumption of the residential load without interfering the residents' comfort. The curtailable load can efficiently participate in the demand response program through the presented scheme in this paper.
With the reformation of electric power market and the development of smart grid technology in China, the smart residential community which contains flexible load has become one of the crucial entities participating in demand response.This paper presents a curtailable flexible load real-time demand response scheme for the novel smart residential community,which is compatible with load aggregator, considering the uncertainties of different residential curtailable load and incorporating both the current circumstances and future trends of the demand response programs in China. The proposed scheme focuses on the curtailable load among the residential flexible load. Mixed integer linear programming and Copula based Monte Carlo simulation are combined for the hourly rolling optimization of each load to realize the real-time scheduling of residential curtailable flexible load. The proposed strategy can significantly reduce the power consumption cost,the peak load, and the total energy consumption of the residential load without interfering the residents' comfort. The curtailable load can efficiently participate in the demand response program through the presented scheme in this paper.
引文
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[17]AHMED M S,MOHAMED A,KHATIB T,et al.Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm[J].Energy and Buildings,2017,138:215-227.
[18]孙毅,裴俊亦,景栋盛.考虑用户行为不确定性的智能家电控制策略[J].电力系统保护与控制,2018,46(17):109-117.SUN Yi,PEI Junyi,JING Dongsheng.Smart home appliance control strategy considering user behavior uncertainty[J].Power System Protection and Control,2018,46(17):109-117.
[19]WU Z,ZHOU S,LI J,et al.Real-time scheduling of residential appliances via conditional risk-at-value[J].IEEE Transactions on Smart Grid,2014,5(3):1282-1291.
[20]BINA M T,AHMADI D.Stochastic modeling for the next day domestic demand response applications[J].IEEETransactions on Power Systems,2015,30(6):2880-2893.
[21]SILVERMAN B W.Density estimation for statistics and data analysis[J].Density Estimation for Statistics and Data Analysis,1986(1951):1-22.
[22]RAYENS B,NELSEN R B.An introduction to Copulas[J].Technometrics,2000,42(3):317.
[23]DUPACOVA J,GROWE-KUSKA N,ROMISCH W.Scenario reduction in stochastic programming-an approach using probability metrics[J].Mathematical Programming,2003,95(3):493-511.
[2]YOON J H,BALDICK R,NOVOSELAC A.Dynamic demand response controller based on real-time retail price for residential buildings[J].IEEE Transactions on Smart Grid,2014,5(1):121-129.
[3]蔡德华,陈柏熹,程乐峰,等.实施需求侧管理对提高发电系统可靠性的影响探究[J].电力系统保护与控制,2015,43(10):51-56.CAI Dehua,CHEN Baixi,CHENG Lefeng,et al.Effective study about the implementation of demand side management on improving reliability of generation system[J].Power System Protection and Control,2015,43(10):51-56.
[4]COLE W J,RHODES J D,GORMAN W,et al.Community-scale residential air conditioning control for effective grid management[J].Applied Energy,2014,130:428-436.
[5]王珂,姚建国,姚良忠,等.电力柔性负荷调度研究综述[J].电力系统自动化,2014,38(20):127-135.WANG Ke,YAO Jianguo,YAO Liangzhong,et al.Survey of research on flexible loads scheduling technologies[J].Automation of Electric Power Systems,2014,38(20):127-135.
[6]赵鸿图,周京阳,于尔铿.支撑高效需求响应的高级量测体系[J].电网技术,2010,34(9):13-20.ZHAO Hongtu,ZHOU Jingyang,YU Erkeng.Advanced metering infrastructure supporting effective demand response[J].Power System Technology,2010,34(9):13-20.
[7]曹筱欧,王冬,周静,等.含储能及新能源发电的居民侧需求响应[J].信息与控制,2017,46(3):379-384.CAO Xiaoou,WANG Dong,ZHOU Jing,et al.Residential demand side response considering energy storages and renewable energy sourced generators[J].Information and Control,2017,46(3):379-384.
[8]马红伟,甄立敬,毛建容,等.区域柔性负荷参与功率调度的评价体系构建及其应用研究[J].电力系统保护与控制,2018,46(17):54-60.MA Hongwei,YAN Lijing,MAO Jianrong,et al.Evaluation system construction and application research of regional flexible load participation in power scheduling[J].Power System Protection and Control,2018,46(17):54-60.
[9]陆俊,彭文昊,朱炎平,等.基于粒子校正优化的智能小区需求响应调度策略[J].电网技术,2017,41(7):2370-2377.LU Jun,PENG Wenhao,ZHU Yanping,et al.A scheduling strategy based on particle correction for intelligent power demand response[J].Power System Technology,2017,41(7):2370-2377.
[10]许道林,谢松.基于模糊线性规划的居民直接负荷控制[J].电力需求侧管理,2007,9(1):15-18,34.XU Daolin,XIE Song.Direct residential load control based on fuzzy linear programming[J].Power Demand Side Management,2007,9(1):15-18,34.
[11]汤奕,鲁针针,宁佳,等.基于电力需求响应的智能家电管理控制方案[J].电力系统自动化,2014,38(9):93-99.TANG Yi,LU Zhenzhen,NING Jia,et al.Management and control scheme for intelligent home appliances based on electricity demand response[J].Automation of Electric Power Systems,2014,38(9):93-99.
[12]付蔚,敬章浩,罗志勇,等.基于分时电价的智能家电控制方案[J].电网技术,2015,39(3):717-723.FU Wei,JING Zhanghao,LUO Zhiyong,et al.Smart home appliances control scheme based on TOU price[J].Power System Technology,2015,39(3):717-723.
[13]王庆泽,陈星莺,颜庆国,等.分时电价环境下的办公建筑空调负荷双层柔性优化控制策略[J].电力系统保护与控制,2017,45(21):43-50.WANG Qingze,CHEN Xingying,YAN Qingguo,et al.Two-layer flexible optimal strategy for air-conditioning of office building under TOU price[J].Power System Protection and Control,2017,45(21):43-50.
[14]YOON J H,BALDICK R,NOVOSELAC A.Dynamic demand response controller based on real-time retail price for residential buildings[J].IEEE Transactions on Smart Grid,2014,5(1):121-129.
[15]SHARMA I,DONG J,MALIKOPOULOS A A,et al.Amodeling framework for optimal energy management of a residential building[J].Energy and Buildings,2016,130:55-63.
[16]张禹森,孔祥玉,孙博伟,等.基于电力需求响应的多时间尺度家庭能量管理优化策略[J].电网技术,2018,42(6):1811-1819.ZHANG Yusen,KONG Xiangyu,SUN Bowei,et al.Multi-time scale home energy management strategy based on electricity demand response[J].Power System Technology,2018,42(6):1811-1819.
[17]AHMED M S,MOHAMED A,KHATIB T,et al.Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm[J].Energy and Buildings,2017,138:215-227.
[18]孙毅,裴俊亦,景栋盛.考虑用户行为不确定性的智能家电控制策略[J].电力系统保护与控制,2018,46(17):109-117.SUN Yi,PEI Junyi,JING Dongsheng.Smart home appliance control strategy considering user behavior uncertainty[J].Power System Protection and Control,2018,46(17):109-117.
[19]WU Z,ZHOU S,LI J,et al.Real-time scheduling of residential appliances via conditional risk-at-value[J].IEEE Transactions on Smart Grid,2014,5(3):1282-1291.
[20]BINA M T,AHMADI D.Stochastic modeling for the next day domestic demand response applications[J].IEEETransactions on Power Systems,2015,30(6):2880-2893.
[21]SILVERMAN B W.Density estimation for statistics and data analysis[J].Density Estimation for Statistics and Data Analysis,1986(1951):1-22.
[22]RAYENS B,NELSEN R B.An introduction to Copulas[J].Technometrics,2000,42(3):317.
[23]DUPACOVA J,GROWE-KUSKA N,ROMISCH W.Scenario reduction in stochastic programming-an approach using probability metrics[J].Mathematical Programming,2003,95(3):493-511.