Comparative Analysis of Cluster Specific Approach for Energy Management Using Meta-Heuristic Techniques
Abstract
Demand-side management (DSM) has emerged as a pivotal approach in addressing the increasing global electricity demand amidst integrating smart grid technologies. DSM can reduce the peak load, enhance the grid reliability, and lower operational costs. This paper investigates the different optimization techniques, especially for residential customers, to minimize the total cost of electricity consumption and optimize consumption patterns. The following study considers two optimization techniques: whale optimization algorithm (WOA) and grey wolf optimization (GWO) and also investigates the impact of K-means clustering on the performance of the proposed scheme. Simulations are conducted on a realistic dataset of residential loads and compares the results of the proposed scheme with and without clustering. The simulation results show that GWO outperforms WOA regarding cost minimization and convergence speed. Furthermore, clustering can improve cost minimization and cause a significant dip from the peak cost.
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