计及离散控制偏差的典型温控负荷态势感知及调控策略
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摘要
针对温控负荷离散化控制模式导致的临界温度效应累计偏差,提出一种计及离散控制偏差的温控负荷集群调控策略。通过引入修正因子改进负荷可消纳能力评估模型,并以热水器群为主要研究对象,将用户用水行为引入统一负荷模型,提升模型与实际环境的拟合性;提出负荷态势计算指标,基于其实时状态,以温度裕度、时间裕度和容量裕度为子指标进行综合,实现对于负荷调控能力的评价。仿真结果证明:综合考虑用户用能需求情况下,引入修正因子和用户行为分析的感知调控策略,可实现精准负荷调控,降低负荷资源储备需求。
Discretization control mode for thermostatically controlled load(TCLs) leads to the critical temperature effect(CTE), which causes cumulative deviation. An awareness and control strategy for TCLs considering discrete control deviation was proposed to improving the load capacity evaluation model by introducing a correction factor. In addition, focus on the water heater group here as research object, the user's water using behavior was introduced to construct the uniform load model, and the fitting performance of the model and the actual environment was improved. Further, a load situation calculation index was proposed. Based on the real-time state, temperature margins, time margins, and capacity margins were integrated as the sub-indicators to evaluate load control capabilities. The simulation results show that the control strategy can realize accurate load control and reduce the demand of load resource reserve.
Discretization control mode for thermostatically controlled load(TCLs) leads to the critical temperature effect(CTE), which causes cumulative deviation. An awareness and control strategy for TCLs considering discrete control deviation was proposed to improving the load capacity evaluation model by introducing a correction factor. In addition, focus on the water heater group here as research object, the user's water using behavior was introduced to construct the uniform load model, and the fitting performance of the model and the actual environment was improved. Further, a load situation calculation index was proposed. Based on the real-time state, temperature margins, time margins, and capacity margins were integrated as the sub-indicators to evaluate load control capabilities. The simulation results show that the control strategy can realize accurate load control and reduce the demand of load resource reserve.
引文
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[35]Zeng Lukun,Sun Yi,Ye Qiuzi,et al.A centralized demand response control strategy for domestic electric water heater group based on appliance cloud platform[J].IEEJ Transactions on Electrical and Electronic Engineering,2017,12(S2):S16-S22.
[2]Simon H.Tindemans,Vincenzo Trovato,Goran Strbac.Decentralized control of thermostatic loads for flexible demand response[J].IEEE Transactions on Control Systems Technology,2015,23(5):1685-1700.
[3]李娜,王晓亮.集群空调负荷提供微电网调频备用研究[J].电力系统保护与控制,2015,43(19):101-105Li Na,Wang Xiaoliang.Research of air conditioners providing frequency controlled reserve for microgrid[J].Power System Protection and Control,2015,43(19):101-105(in Chinese).
[4]薛禹胜,雷兴,薛峰,等.关于风电不确定性对电力系统影响的评述[J].中国电机工程学报,2014,34(29):5029-5040.Xue Yusheng,Lei Xing,Xue Feng,et al.A Review on impacts of wind power uncertainties on power systems[J].Proceedings of the CSEE,2014,34(29):5029-5040(in Chinese).
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[7]曾鸣,武赓,王昊婧,等.智能用电背景下考虑用户满意度的居民需求侧响应调控策略[J].电网技术,2016,40(10):2917-2923.Zeng Ming,Wu Geng,Wang Haojing,et al.Demand side response control strategy of residents considering user satisfaction under intelligent power use background[J].Power System Technology,2016,40(10):2917-2923(in Chinese).
[8]Hu Jianqiang,Cao Jinde,Yong Taiyou,et al.Demand response load following of source and load systems[J].IEEE Transactions on Control Systems Technology,2017,25(5):1586-1598.
[9]文刚,翁维华,赵岩,等.考虑负荷聚集商参与的源荷互动双层优化模型[J].电网技术,2017,41(12):3956-3963.Wen Gang,Weng Weihua,Zhao Yan,et al.A bi-level optimal dispatching model considering source-load interaction integrated with load aggregator[J].Power System Technology,2017,41(12):3956-3963(in Chinese).
[10]高赐威,张良杰,杨晓梅.中央空调负荷聚合及平抑风电出力波动研究[J].中国电机工程学报,2017,37(11):3184-3191.Gao Ciwei,Zhang Liangjie,Yang Xiaomei.Study on load polymerization of central air conditioning and fluctuation of wind power output[J].Proceedings of the CSEE,2017,37(11):3184-3191(in Chinese).
[11]沈瑜,岳园园,闫华光,等.地区电网需求响应资源聚合与调控策略研究[J].电网技术,2017,41(10):3341-3348.Shen Yu,Yue Yuanyuan,Yan Guanghua,et al.Research on demand response resource aggregation and regulation strategy of regional power grid[J].Power System Technology,2017,41(10):3341-3348(in Chinese).
[12]孙毅,杨艳敏,许鹏,等.基于云模型的用户群负荷调控算法[J].电网技术,2017,41(08):2611-2617.Sun Yi,Yang Yanmin,Xu Peng,et al.Load control algorithm for user group based on cloud model[J].Power System Technology,2017,41(08):2611-2617(in Chinese).
[13]Hu Jianqiang,Cao Jinde,Michael Z et al.Load following of multiple heterogeneous TCL aggregators by centralized control[J].IEEE Transactions on Power Systems,2017,32(4):3157-3167.
[14]蔡珑,顾洁,金之俭.居民用户需求响应行为影响因素辨识与响应特性提取[J].电网技术,2017,41(07):2378-2385.Cai Long,Gu Jie,Jin Zhijian.Identification and response characteristics extraction of response factors of residents'demand response behavior[J].Power System Technology,2017,41(07):2378-2385(in Chinese).
[15]陆俊,朱炎平,彭文昊,等.计及用电行为聚类的智能小区互动化需求响应方法[J].电力系统自动化,2017,41(17):113-120.Lu Jun,Zhu Yanping,Peng Wenhao,et al.An interactive demand response method for intelligent community based on electricity behavior clustering[J].Power system automation,2017,41(17):113-120(in Chinese).
[16]李江波,王波,高岩,等.马尔可夫决策过程下的智能电网实时电价模型[J].系统仿真学报,2016,28(11):2756-2763.Li Jiangbo,Wang Bo,Gao Yan,et al.Real time price model of smart grid under Markov's decision-making process[J].Journal of system simulation,2016,28(11):2756-2763(in Chinese).
[17]郝然,艾芊,肖斐.基于多元大数据平台的用电行为分析构架研究[J].电力自动化设备,2017,37(08):20-27.Hao Ran,Ai Qian,Xiao Fei.Research on electrical behavior analysis framework based on multivariable and large data platform[J].Power automation equipment,2017,37(08):20-27(in Chinese).
[18]Panwar L K,Reddy Konda S,Verma A,et al.Demand response aggregator coordinated two-stage responsive load scheduling in distribution system considering customer behavior[J].IET Generation,Transmission&Distribution,2017,11(4):1023-1032.
[19]戚野白,王丹,贾宏杰,等.基于需求响应的集群温控负荷建模及参与低频减载策略研究[J].中国电机工程学报,2017,37(03):751-760.Qi Yebai,Wang Dan,Jia Hongjie,et al.Modeling of cluster temperature control load based on demand response and Research on participation in low frequency load reduction strategy[J].Proceedings of the CSEE,2017,37(03):751-760(in Chinese).
[20]Wang Z,Paranjape R.Optimal residential demand response for multiple heterogeneous homes with real-time price prediction in a multiagent framework[J].IEEETransactions on Smart Grid,2017,8(3):1173-1184.
[21]Vanouni M,Lu N.A reward allocation mechanism for thermostatically controlled loads participating in intra-hour ancillary services[J].IEEE Transactions on Smart Grid,2017,PP(99):1-1.
[22]刘晓峰,高丙团,罗京,等.基于非合作博弈的居民负荷分层调度模型[J].电力系统自动化,2017,41(14):54-60.Liu Xiaofeng,Gao Bingtuan,Luo Jing,et al.Resident load scheduling model based on non cooperative game[J].Power System Automation,2017,41(14):54-60(in Chinese).
[23]Cui Qiang,Wang Xiuli,Wang Xifan,et al.Residential appliances direct load control in real-time using cooperative game[J].IEEE Transactions on Power Systems,2016,31(1):226-233
[24]Chen Chen,Wang Jianhui,Kishore S.A distributed direct load control approach for large-scale residential demand response[J].IEEE Transactions on Power Systems,2014,29(5):2219-2228.
[25]陈宁,薛禹胜,丁杰,等.利用空间相关性的超短期风速预测[J].电力系统自动化,2017,41(12):124-130.Chen Ning,Xue Yusheng,Ding Jie,et al.Ultra-short term wind speed prediction using spatial correlation[J].Power System Automation,2017,41(12):124-130(in Chinese).
[26]乔颖,鲁宗相,闵勇.提高风电功率预测精度的方法[J].电网技术,2017,41(10):3261-3269.Qiao Ying,Lu Zongxiang,Min Yong.Research&application of raising wind power prediction accuracy[J].Power System Technology,2017,41(10):3261-3269(in Chinese).
[27]邹鹏,陈启鑫,夏清,等.国外电力现货市场建设的逻辑分析及对中国的启示与建议[J].电力系统自动化,2014,38(13):18-27Zou Peng,Chen Qixing,Xia Qing,et al.Logical analysis of electricity spot market design in foreign countries and enlightenment and policy suggestions for China[J].Power System Automation,2014,38(13):18-27(in Chinese).
[28]白杨,李昂,夏清.新形势下电力市场营销模式与新型电价体系[J].电力系统保护与控制,2016,44(5):10-16.Bai Yang,Li Ang,Xia Qing.Electricity business marketing modes in the new environment and new electricity pricing systems[J].Power System Protection and Control,2016,44(5):10-16(in Chinese).
[29]Bao Yuqiang,Hu Minqiang,Hong Yingyi,et al.Accuracy analysis and improvement of the statequeuing model for the thermostatically controlled loads[J].IET Generation,Transmission&Distribution,2017,11(5):1303-1310.
[30]Zhijie Xu,Ruisheng Diao,ShuaiLu,et al.Modeling of electric water heaters for demand response:a baseline PDE model[J].IEEE Transactions on Smart Grid,2014,5(5):2203-2210.
[31]Bruce Stephen,Stuart Galloway,Graeme Burt.Selflearning load characteristic models for smart appliances[J].IEEE Transactions on Smart Grid,2014,5(5):2432-2439.
[32]李彬,贾滨诚,曹望璋,等.边缘计算在电力需求响应业务中的应用展望[J].电网技术,2018,42(01):79-87.Li Bin,Jia Bincheng,Cao Wangzhang,et al.Application prospect of edge computing in power demand response business[J].Power System Technology,2018,42(1):79-87(in Chinese).
[33]Wolsey L A.Integer programming[M],New York:Wiley,1998.
[34]许鹏,孙毅,石墨,等.负荷态势感知:概念、架构及关键技术[J].中国电机工程学报,2018,38(10):2918-2926.Xu Peng,Sun Yi,Shi Mo,et al.Load situation awareness:concept,framework,and key technologies[J].Proceedings of the CSEE2018,38(10):2918-2926(in Chinese).
[35]Zeng Lukun,Sun Yi,Ye Qiuzi,et al.A centralized demand response control strategy for domestic electric water heater group based on appliance cloud platform[J].IEEJ Transactions on Electrical and Electronic Engineering,2017,12(S2):S16-S22.