Demand Response Strategy Applied to Residential Electric Water Heaters Using Dynamic Programming and K-Means Clustering

• Published in: IEEE transactions on sustainable energy Vol. 11; no. 1; pp. 524 - 533
• Main Authors: Alvarez, Maria Alejandra Zuniga, Agbossou, Kodjo, Cardenas, Alben, Kelouwani, Sousso, Boulon, Loic
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analytical models; Cluster analysis; Clustering; Computer simulation; demand-side management; Dynamic programming; Energy management; Energy storage; Heat transfer; Hot water; k-means; load shifting; Mathematical model; Peak demand; Peak periods; Power consumption; Power demand; Resistance heating; silhouette; Vector quantization; Water availability; Water demand; water heater; Water heaters; Water heating
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2019.2897288

Previous studies have shown that electric water heaters (EWHs) have strong potential in demand-side management applications more precisely because they offer energy storage capability, so, can be employed as shift loads. However, the challenge of EWH curtailment strategies is to minimize the impact on the hot water availability while shaving the peak of consumption during critical periods. The success of such strategies depends highly on the knowledge of the consumption behavior of each user. Thus, appropriated modeling and consumption analysis could yield better management strategies. This study proposes an electric water heater control strategy based on the dynamic programming and power consumption profile classification. An adaptive clustering process allows recognizing the clients who contribute to the highest power consumption during the peak periods. The analysis and simulation indicate that an appropriate control on the group of users could be implemented to reduce the peak demand and to meet the hot water demand. A k-means clustering algorithm has been used for cluster analysis. The silhouette method has been applied to estimate the appropriate number of clusters.