基于NILE算法量化热水器参与需求响应的灵活性

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。

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

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.