1.School of Electrical & Electronic Engineering, North China Electric Power University, Baoding 071003, China;2.College of Artificial Intelligence, Shenzhen Polytechnic, Shenzhen 518055, China

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.