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

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...

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Bibliographic Details
Published in:IEEE transactions on sustainable energy 2020-01, Vol.11 (1), p.524-533
Main Authors: Alvarez, Maria Alejandra Zuniga, Agbossou, Kodjo, Cardenas, Alben, Kelouwani, Sousso, Boulon, Loic
Format: Article
Language:English
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
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Summary: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.
ISSN:1949-3029
1949-3037
DOI:10.1109/TSTE.2019.2897288