In today’s power systems, the widespread adoption of smart grid applications requires sophisticated control of load variability for effective demand-side management (DSM). Conventional Energy Storage System (ESS)-based DSM methods in South Korea are limited to real-time variability control owing to difficulties with model development using customers’ load profiles from sampling with higher temporal resolution. Herein, this study thus proposes a method of con-trolling the variability of customers’ load profiles for real-time DSM using customer-installed ESSs. To optimize the reserved capacity for the proposed maximum demand control within ESSs, this study also proposes a hybrid method of load generation, which synthesizes approaches based on Markov Transition Matrix (MTM) and Artificial Neuron Network (ANN) to estimate load variations every 15 min and, in turn reserve capacity in ESSs. The proposed ESS-based DSM strategy primarily reserves capacity in ESSs based on estimated variation in load, and performs real-time maximum demand control with the reserved capacity during scheduled peak shaving operations. To validate the proposed methods, this study used load profiles accumulated from industrial and general (i.e., commercial) customers under the time-of-use (TOU) rate. Simulation verified the improved performance of the proposed ESS-based DSM method for all customers, and results of Kolmogorov-Smirnov (K–S) testing indicate advances in the proposed hybrid estimation beyond the stand-alone estimation using the MTM-or ANN-based approach.
