文章摘要
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. 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. DOI: 10.7500/AEPS20190516005.
基于NILE算法量化热水器参与需求响应的灵活性
Quantifying Flexibility of Water Heater Participating in Demand Response Based on Non-intrusive Load Extracting Algorithm
DOI:10.7500/AEPS20190516005
关键词: 电热水器  负荷模式  非侵入式负荷提取  需求响应  灵活性
KeyWords: electric water heater  load pattern  non-intrusive load extracting  demand response  flexibility
上网日期:2019-11-30
基金项目:
作者单位E-mail
赵洪山 华北电力大学电气与电子工程学院 zhaohshcn@ncepu.edu.cn 
闫西慧 华北电力大学电气与电子工程学院 2318247982@qq.com 
戴湘 深圳职业技术学院 dxdx678@163.com 
文海艳 华北电力大学电气与电子工程学院 2172213210@ncepu.edu.cn 
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 heaters (EWHs) have great potential in the demand response (DR) market because their power consumption is highly correlated with daily load patterns and EWHs account for a high proportion of the household consumption. Recognizing residential EWH load patterns and quantifying their flexibility in DR can help grid operators develop reasonable regulatory strategies. Firstly, probability statistic models are established for EWH load patterns (the start time, the end time, and the duration of the power event) under different time types. Then, a training-less non-intrusive load extracting (NILE) algorithm based on load signatures and power block extremums is proposed, which can automatically separate EWH loads of different rated power levels. Finally, a price-based DR model is established to optimize EWH usage behavior, and EWHs’ flexibility is quantified based on changes in their usage behavior before and after optimization. Furthermore, the validity of the proposed algorithm is verified with a ground-truth dataset, and EWHs’ flexibility under different conditions is quantified based on the separated load data.
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