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.
Highlights
Accepted: 30 September 2021Around the world, smart grids have been widely adopted to respond to environmental problems, enhance energy efficiency, and improve electric services [1]
As a result of this study, we proposed an Energy Storage System (ESS)-based demand-side management (DSM) method that developed by applying ESS-based maximum demand control to the conventional DSM operations of customer-installed ESSs
DSM for all four customers using a proposed feasibility parameter, benefit per battery (BPB), calculated by annual savings with the DSM application based on battery size
Summary
Smart grids have been widely adopted to respond to environmental problems, enhance energy efficiency, and improve electric services [1]. The ESS-based DSM methods have been developed with reference to samples of customers’ historical load profiles representing an accumulated duration, which has been 15 min in South Korea and cannot show real-time load variability This limitation brings the developed method to schedule ESS to discharge fixed amounts of power during such accumulated durations To overcome such limited resolution, effective estimation techniques capable of predicting load variations are needed that can generate synthetic load profiles with higher resolutions. Ekonomou et al utilized temperature, humidity, and other meteorological parameters to develop an ANN-based estimation model for synthetizing sequences of demand and more accurate estimation via training, verification, and testing processes [27] All of those methods of estimating variability in power systems developed with stochastic or artificial algorithms provide practical guidance for effectively estimating load variation within the sampling limitation.
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