Akademik Veri Yönetim Sistemi
ELECTRICAL ENGINEERING, cilt.108, sa.6, 2026 (SCI-Expanded, Scopus)
Home Energy Management Systems (HEMS) are attracting increasing attention because of the significant financial benefits they provide to users. However, the random and unorganized response of HEMS users under the same dynamic pricing scheme when operating independently causes operational problems at the grid level, such as unpredictable events, imbalances, and rebound peaks. To overcome these problems and reduce the grid stress, the coordination of multiple HEMS becomes important. This study aimed to develop a coordinated approach for the efficient operation of multiple HEMS in a residential area with five houses with different demographics served by a single transformer. The proposed strategy aims to reduce the electricity bill of each homeowner by considering user preferences. This strategy also regulates the overall load profile of the neighborhood, thus avoiding the occurrence of rebound peaks and transformer overload. In this study, 45 different appliances were used, and their electrical parameters were obtained from real measurements with a resolution of 1 s. The daily load schedule of each house was determined by the Grey Wolf Optimizer (GWO) using the load-shifting method for different optimization strategies. In the optimization process, the load profiles of the coordinated load management scenarios were obtained based on real high-resolution measurements from residential appliances with a 1-minute appliance scheduling interval. While uncoordinated load management has been observed to cause rebound peaks, coordinated load management prevents these peaks from occurring, smoothing the load curve of the transformer serving the neighborhood, and effectively reduces the cost of each house owner's bill. This study provides the first real-data-based assessment of the real-world performance of neighborhood energy management system (NEMS) strategies, utilizing high-resolution field-measured data. This approach eliminates the inaccuracies introduced by theoretical and simulation-based appliance models by using high-resolution real measurement data in both the optimization model and the scenario generation process, thereby providing a realistic representation of NEMS strategies in real-world conditions.