Real-Time Load Variability Control Using Energy Storage System for Demand-Side Management in South Korea

Kyo Beom Han, Jaesung Jung and Byung O Kang
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Kyo Beom Han: Department of Electrical Engineering, Dong-A University, Saha-gu, Busan 49315, Korea
Jaesung Jung: Department of Energy Systems Research, Ajou University, Suwon 16499, Korea
Byung O Kang: Department of Electrical Engineering, Dong-A University, Saha-gu, Busan 49315, Korea

Energies, 2021, vol. 14, issue 19, 1-17

Abstract: 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 controlling 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.

Keywords: demand-side management (DSM); energy storage system (ESS); maximum demand control; synthetic load generation; peak shaving (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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