Quantifying Flexibility of Water Heater Participating in Demand Response Based on Non-intrusive Load Extracting Algorithm

Hongshan ZHAO; Xihui YAN; Xiang DAI; Haiyan WEN

doi:10.7500/AEPS20190516005

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Quantifying Flexibility of Water Heater Participating in Demand Response Based on Non-intrusive Load Extracting Algorithm

Basic Research | 更新时间：2025-06-16

- Quantifying Flexibility of Water Heater Participating in Demand Response Based on Non-intrusive Load Extracting Algorithm
- Automation of Electric Power Systems Vol. 44, Issue 3, Pages: 98-104(2020)
- 作者机构：
  
  1. 华北电力大学电气与电子工程学院，河北省保定市 071003
  2. 深圳职业技术学院人工智能学院，广东省深圳市 518055
- 作者简介：
- 基金信息：
- DOI：10.7500/AEPS20190516005
  CLC：
- Received：16 May 2019，
  
  Revised：2019-09-02，
  
  Published Online：30 November 2019，
  
  Published：25 March 2020
- 稿件说明：
移动端阅览
赵洪山,闫西慧,戴湘等.基于NILE算法量化热水器参与需求响应的灵活性[J].电力系统自动化,2020,44(03):98-104.

ZHAO Hongshan,YAN Xihui,DAI Xiang,et al.Quantifying Flexibility of Water Heater Participating in Demand Response Based on Non-intrusive Load Extracting Algorithm[J].Automation of Electric Power Systems,2020,44(03):98-104.
赵洪山,闫西慧,戴湘等.基于NILE算法量化热水器参与需求响应的灵活性[J].电力系统自动化,2020,44(03):98-104. DOI： 10.7500/AEPS20190516005.

ZHAO Hongshan,YAN Xihui,DAI Xiang,et al.Quantifying Flexibility of Water Heater Participating in Demand Response Based on Non-intrusive Load Extracting Algorithm[J].Automation of Electric Power Systems,2020,44(03):98-104. DOI： 10.7500/AEPS20190516005.

摘要

居民电热水器(EWH)因其功耗与日负荷模式高度相关且占家庭负荷比重高等特点，在需求响应(DR)市场中极具潜力。识别住宅侧EWH集群的负荷模式及量化其参与DR的灵活性有助于电网运营商制定合理的调控策略。首先，对居民EWH不同时间类型下的负荷模式(用电事件的起止时间和用电时长)建立了概率统计模型。然后，提出了一种基于负荷印记和功率块极值的无训练过程的非侵入式负荷提取(NILE)算法，其可自动分离不同额定功率的EWH负荷。最后，建立了电价激励的DR模型以优化EWH的负荷模式，根据其在优化前后使用行为的变化情况量化其灵活性。此外，在真实数据集上验证了所提算法的有效性，并基于分离的负荷数据在不同情况下量化了EWH集群参与DR的灵活性。

Abstract

Domestic electric water heater (EWH) has great potential in the demand response (DR) market because its power consumption is highly correlated with daily load patterns and it accounts for a high proportion of the household consumption. Recognizing load patterns of residential EWHs and quantifying their flexibility in DR help grid operators develop reasonable regulatory strategies. Firstly

probability statistic models are established for load patterns (the start time

the end time and the duration of the power event) of residential EWH with different time types. Secondly

a training-less non-intrusive load extracting (NILE) algorithm based on load signatures and power block extremum is proposed

which can automatically separate EWH loads with different rated power levels. Finally

an incentive price-based DR model is established to optimize load patterns of EWH

and flexibility of EWH is quantified based on changes in their usage behavior before and after optimization. Furthermore

the validity of the proposed algorithm is verified with an actual dataset

and the flexibility of EWH participating DR in different conditions is quantified based on the separated load data.

关键词

Keywords

references

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 .

孙毅 , 石墨 , 许鹏 , 等 . 基于混合逻辑动态模型的热水器群态势感知及调控策略 [J]. 中国电机工程学报 , 2018 , 38 ( 17 ): 5043 - 5051 .

SUN Yi , SHI Mo , XU Peng , et al . Situation awareness and control strategy of water heater group based on mixed logical dynamic model [J]. Proceedings of the CSEE , 2018 , 38 ( 17 ): 5043 - 5051 .

AL-JABERY K , XU Z Z , YU W J , et al . Demand-side management of domestic electric water heaters using approximate dynamic programming [J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , 2017 , 36 ( 5 ): 775 - 788 .

HARI CHARAN CHERUKURI S , SARAVANAN B . A novel DSM strategy for micro grids consisting of higher penetration of water heater loads [C]// 2018 National Power Engineering Conference (NPEC) , March 9 - 10 , 2018 , Madurai, India: 1 - 6 .

NEL P J C , BOOYSEN M J , VAN DER MERWE B . Energy perceptions in South Africa: an analysis of behavior and understanding of electric water heaters [J]. Energy for Sustainable Development , 2016 , 32 : 62 - 70 .

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 .

祁兵 , 董超 , 武昕 , 等 . 基于DTW算法与稳态电流波形的非侵入式负荷辨识方法 [J]. 电力系统自动化 , 2018 , 42 ( 3 ): 70 - 76 . DOI: 10.7500/AEPS20170103001 http://dx.doi.org/10.7500/AEPS20170103001 .

QI Bing , DONG Chao , WU Xin , et al . Non-intrusive load identification method based on DTW algorithm and steady-state current waveform [J]. Automation of Electric Power Systems , 2018 , 42 ( 3 ): 70 - 76 . DOI: 10.7500/AEPS20170103001 http://dx.doi.org/10.7500/AEPS20170103001 .

HOSSEINI S S , AGBOSSOU K , KELOUWANI S , et al . Non-intrusive load monitoring through home energy management systems: a comprehensive review [J]. Renewable and Sustainable Energy Reviews , 2017 , 79 : 1266 - 1274 .

BONFIGLI R , PRINCIPI E , FAGIANI M , et al . Non-intrusive load monitoring by using active and reactive power in additive factorial hidden Markov models [J]. Applied Energy , 2017 , 208 : 1590 - 1607 .

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 . DOI: 10.7500/AEPS20180629004 http://dx.doi.org/10.7500/AEPS20180629004 .

YAN Xufeng , ZHAI Shaopeng , WANG Zhihua , et al . Application of deep neural network in non-intrusive load disaggregation [J]. Automation of Electric Power Systems , 2019 , 43 ( 1 ): 126 - 132 . DOI: 10.7500/AEPS20180629004 http://dx.doi.org/10.7500/AEPS20180629004 .

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 ): 3868 - 3878 .

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 ): 3868 - 3878 .

BONFIGLI R , SQUARTINI S , FAGIANI M , et al . Unsupervised algorithms for non-intrusive load monitoring: an up-to-date overview [C]// 15th International Conference on Environment and Electrical Engineering (EEEIC) , June 10 - 13 , 2018 , Rome, Italy: 1175 - 1180 .

周明 , 宋旭帆 , 涂京 , 等 . 基于非侵入式负荷监测的居民用电行为分析 [J]. 电网技术 , 2018 , 42 ( 10 ): 3268 - 3276 .

ZHOU Ming , SONG Xufan , TU Jing , et al . Residential electricity consumption behavior analysis based on non-intrusive load monitoring [J]. Power System Technology , 2018 , 42 ( 10 ): 3268 - 3276 .

孙毅 , 崔灿 , 陆俊 , 等 . 基于遗传优化的非侵入式家居负荷分解方法 [J]. 电网技术 , 2016 , 40 ( 12 ): 3912 - 3917 .

SUN Yi , CUI Can , LU Jun , et al . A non-intrusive household load monitoring method based on genetic optimization [J]. Power System Technology , 2016 , 40 ( 12 ): 3912 - 3917 .

王轲 , 钟海旺 , 余南鹏 , 等 . 基于seq2seq和Attention机制的居民用户非侵入式负荷分解 [J]. 中国电机工程学报 , 2019 , 39 ( 1 ): 75 - 83 .

WANG Ke , ZHONG Haiwang , YU Nanpeng , et al . Nonintrusive load monitoring based on sequence-to-sequence model with Attention mechanism [J]. Proceedings of the CSEE , 2019 , 39 ( 1 ): 75 - 83 .

彭显刚 , 郑凯 , 林哲昊 , 等 . 基于谱图理论的居民用户非侵入式负荷分解 [J]. 电网技术 , 2018 , 42 ( 8 ): 2674 - 2680 .

PENG Xiangang , ZHENG Kai , LIN Zhehao , et al . Non-intrusive residential load monitor based on spectral graph theory [J]. Power System Technology , 2018 , 42 ( 8 ): 2674 - 2680 .

XU Z J , DIAO R S , LU S , 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 .

KAPSALIS V , HADELLIS L . Optimal operation scheduling of electric water heaters under dynamic pricing [J]. Sustainable Cities and Society , 2017 , 31 : 109 - 121 .

KAPSALIS V , SAFOURI G , HADELLIS L . Cost/comfort-oriented optimization algorithm for operation scheduling of electric water heaters under dynamic pricing [J]. Journal of Cleaner Production , 2018 , 198 : 1053 - 1065 .

PATERAKIS N G , ERDINC O , BAKIRTZIS A G , et al . Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies [J]. IEEE Transactions on Industrial Informatics , 2015 , 11 ( 6 ): 1509 - 1519 .

D’HULST R , LABEEUW W , BEUSEN B , et al . Demand response flexibility and flexibility potential of residential smart appliances: experiences from large pilot test in Belgium [J]. Applied Energy , 2015 , 155 : 79 - 90 .

Pecan Street Inc . Dataport residential data [EB/OL]. [ 2019-02-10 ]. https://dataport.pecanstreet.org/ https://dataport.pecanstreet.org/ .

ESA N F , ABDULLAH M P , HASSAN M Y . A review disaggregation method in non-intrusive appliance load monitoring [J]. Renewable and Sustainable Energy Reviews , 2016 , 66 : 163 - 173 .

AZAIEZ M N , AL SHARIF S S . A 0-1 goal programming model for nurse scheduling [J]. Computers & Operations Research , 2005 , 32 ( 3 ): 491 - 507 .

YIN R , KARA E C , LI Y , et al . Quantifying flexibility of commercial and residential loads for demand response using setpoint changes [J]. Applied Energy , 2016 , 177 : 149 - 164 .

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