Abstract：To guide residents to participate demand response, a shiftable load monitoring algorithm was proposed based on the analysis of load states set correlation. Firstly, a sliding average method was introduced to extract the load states, which extracts load’s working states from its current value and form the load states set. Then load states set correlation analysis model was established, load states correlation and load characteristic time could be obtained by using FP-Growth solver. The former leads to a strong-correlated loads set from the data of daily electricity demand, while the latter determines whether the loads set is shiftable and the time period of being shiftable. The daily shiftable loads monitoring was achieved from both results, which can provide necessary data to distributed control of resident demand response and optimization of household energy management.
王孝慈, 董树锋, 王莉, 余志文, 朱嘉麒. 基于电器状态关联分析的民可平移负荷辨识[J]. 电工技术学报, 2020, 35(23): 4961-4970.
Wang Xiaoci, Dong Shufeng, Wang Li, Yu Zhiwen, Zhu Jiaqi. Resident Shiftable Loads Monitoring Based on Load States Set Correlation Analysis. Transactions of China Electrotechnical Society, 2020, 35(23): 4961-4970.
[1] 董朝阳, 赵俊华, 文福拴, 等. 从智能电网到能源互联网:基本概念与研究框架[J]. 电力系统自动化, 2014, 38(15): 1-11. Dong Zhaoyang, Zhao Junhua, Wen Fushuang, et al.From smart grid to energy internet:basic concept and research framework[J]. Automation of Electric Power Systems, 2014, 38(15): 1-11. [2] 胡江溢, 祝恩国, 杜新纲, 等. 用电信息采集系统应用现状及发展趋势[J]. 电力系统自动化, 2014, 38(2): 131-135. Hu Juangyi, Zhu Enguo, Du Xingang, et al.Application status and development trend of power consumption information collection system[J]. Automation of Electric Power Systems, 2014, 38(2): 131-135. [3] 宋亚奇, 周国亮, 朱永利. 智能电网大数据处理技术现状与挑战[J]. 电网技术, 2013, 37(4):927-935. Song Yaqi, Zhou Guoliang, Zhu Yongli.Present status and challenges of big data processing in smart grid[J]. Power System Technology, 2013, 37(4): 927-935. [4] 陆俊, 朱炎平, 彭文昊, 等. 智能用电用户行为分析特征优选策略[J]. 电力系统自动化, 2017, 41(5): 58-63, 83. Lu Jun, Zhu Ynping, Peng Wenhao, et al.Feature selection strategy for electricity consumption behavior analysis in smart grid[J]. Automation of Electric Power Systems, 2017, 41(5): 58-63, 83. [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] 徐青山, 刘梦佳, 戴蔚莺, 等. 计及用户响应不确定性的可中断负荷储蓄机制[J]. 电工技术学报, 2019, 34(15): 3198-3208. Xu Qingshan, Liu Mengjia, Dai Weiying, et al.Interruptible load based on deposit mechanism considering uncertainty of customer behavior[J]. Transactions of China Electrotechnical Society, 2019, 34(15): 3198-3208. [7] Callaway D S, Hiskens I A.Achieving controllability of electric loads[J]. Proceedings of the IEEE, 2011, 99(1): 184-199. [8] 李童佳, 张晶, 祁兵. 居民用户需求响应业务模型研究[J]. 电网技术, 2015, 39(10): 2719-2724. Li Tongjia, Zhang Jing, Qi Bing.Studies on business model of demand response for residential users[J]. Power System Technology, 2015, 39(10): 2719-2724. [9] 孙川, 汪隆君, 许海林. 用户互动负荷模型及其微电网日前经济调度的应用[J]. 电网技术, 2016, 40(7): 2009-2015. Sun Chuan, Wang Longjun, Xu Hailin.An interaction load model and its application in microgrid day-ahead economic scheduling[J]. Power System Technology, 2016, 40(7): 2009-2015. [10] 阳小丹, 李扬. 家庭用电响应模式研究[J]. 电力系统保护与控制, 2014, 42(12): 51-56. Yang Xiaodan, Li Yang.Research on household electricity response mode[J]. Power System Protection and Control, 2014, 42(12): 51-56. [11] 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.Regulation strategies of demand response considering user satisfaction under smart power background[J]. Power System Technology, 2016, 40(10): 2917-2923. [12] 徐建军, 王保娥, 闫丽梅, 等. 混合能源协同控制的智能家庭能源优化控制策略[J]. 电工技术学报, 2017, 32(12): 214-223. Xu Jianjun, Wang Baoe, Yan Limei, et al.The strategy of the smart home energy optimization control of the hybrid energy coordinated control[J]. Transactions of China Electrotechnical Society, 2017, 32(12): 214-223. [13] 马丽. 基于博弈论的智能配用电系统优化运行方法[D]. 北京: 华北电力大学, 2017. [14] 孙毅, 刘迪, 李彬, 等. 基于家庭用电负荷关联度的实时优化策略[J]. 电网技术, 2016, 40(10): 1825-1829. Sun Yi, Liu Di, Li Bin, et al.Research on real-time optimization strategy based on correlation of household electrical load[J]. Power System Technology, 2016, 40(10): 1825-1829. [15] 范龙, 李献梅, 陈跃辉, 等. 激励CCHP参与需求侧管理双向峰谷定价模型[J]. 电力系统保护与控制, 2016, 44(17): 45-51. Fan Long, Li Xianmei, Chen Yuehui, et al.Pricing model of bidirectional peak-valley for motivating CCHP to participate in DSM[J]. Power System Protection and Control, 2016, 44(17): 45-51. [16] 程祥, 李林芝, 吴浩, 等. 非侵入式负荷监测与分解研究综述[J]. 电网技术, 2016, 40(10): 3108-3117. Cheng Xiang, Li Linzhi, Wu Hao, et al.A survey of the research on non-intrusive load monitoring and disaggregation[J]. Power System Technology, 2016, 40(10): 3108-3117. [17] 陈思运, 高峰, 刘烃, 等. 基于因子隐马尔可夫模型的负荷分解方法及灵敏度分析[J]. 电力系统自动化, 2016, 40(21): 128-136. Chen Siyuan, Gao Feng, Liu Ting, et al.Load disaggregation method based on factorial hidden markov model and its sensitivity analysis[J]. Automation of Electric Power Systems, 2016, 40(21): 128-136. [18] 徐青山, 娄藕蝶, 郑爱霞, 等. 基于近邻传播聚类和遗传优化的非侵入式负荷分解方法[J]. 电工技术学报, 2018, 33(16): 212-222. Xu Qingshan, Lou Oudie, Zheng Aixia, et al.A non-intrusive load decomposition method based on affinity propagation and genetic algorithm optimization[J]. Transactions of China Electrotechnical Society, 2018, 33(16): 212-222. [19] 燕续峰, 翟少鹏, 王治华, 等. 深度神经网络在非侵入式负荷分解中的应用[J]. 电力系统自动化, 2019, 43(1): 126-132, 167. Yan Xufeng, Zhai Shaopeng, Wang Zhihua, et al.Application of deep neutral network in non-intrusive load disaggregation[J]. Automation of Electric Power System, 2019, 43(1): 126-132, 167. [20] 晏杰, 亓文娟. 基于Aprior & FP-growth 算法的研究[J]. 计算机系统应用, 2013, 22(5):122-125. Yan Jie, Qi Wenjuan.Research based on aprior & FP-growth algorithm[J]. Computer Systems & Applications, 2013, 22(5): 122-125. [21] 王新宇, 杜孝平, 谢昆青. FP-growth算法的实现方法研究[J]. 计算机工程与应用, 2004, 40(9): 174-176. Wang Xxinyu, Du Xiaopig, Xie Kunqing.Research on implementation of the FP-growth algorithm[J]. Computer Engineering and Applications, 2004, 40(9): 174-176. [22] Makonin S, Popowich F, Bartram L, et al.AMPds: A public dataset for load disaggregation and eco-feedback research[C]// 2013 IEEE Electrical Power & Energy Conference, Halifax, NS, Canada, 2014, DOI: 10.1109/EPEC.2013.6802949. [23] Anderson K, Ocneanu A, Benitez D, et al.Blued:a fully labeled public dataset for event-based non-intrusive load monitoring research[C]//Proceedings of the 2nd KDD Workshop on Data Mining Applications in Sustainability, Beijing, China: 2012: 1-5. [24] Kolter J Z, Johnson M J.REDD: A public data set for energy disaggregation research[J]. Sustkdd, 2011, 25.
2020, Vol. 35 
