Abstract
This study proposes a methodology to develop adaptive operational strategies of customer-installed Energy Storage Systems (ESS) based on the classification of customer load profiles. In addition, this study proposes a methodology to characterize and classify customer load profiles based on newly proposed Time-of-Use (TOU) indices. The TOU indices effectively distribute daily customer load profiles on multi-dimensional domains, indicating customer energy consumption patterns under the TOU tariff. The K-means and Self-Organizing Map (SOM) sophisticated clustering methods were applied for classification. Furthermore, this study demonstrates peak shaving and arbitrage operations of ESS with current supporting polices in South Korea. Actual load profiles accumulated from customers under the TOU rate were used to validate the proposed methodologies. The simulation results show that the TOU index-based clustering effectively classifies load patterns into ‘M-shaped’ and ‘square wave-shaped’ load patterns. In addition, the feasibility analysis results suggest different ESS operational strategies for different load patterns: the ‘M-shaped’ pattern fixes a 2-cycle operation per day due to battery life, while the ‘square wave-shaped’ pattern maximizes its operational cycle (a 3-cycle operation during the winter) for the highest profits.
Highlights
Smart grid circumstances employ various Internet of Thing (IoT) devices to provide efficient energy management for electric power consumers [1]
This study proposes a methodology to develop the optimized operational strategy of customer-installed Energy Storage Systems (ESS) depending on the cluster derived from the classification of load profiles under the TOU tariff structure
This study proposes a methodology to characterize and classify the customer load profiles based on the newly proposed TOU indices consisting of off-peak, mid-peak, and on-peak indices
Summary
Smart grid circumstances employ various Internet of Thing (IoT) devices to provide efficient energy management for electric power consumers [1]. A smart meter is a representative IoT device that measures electrical energy data of customers in real time and transmits these data through network communication. The smart meter in a smart grid is utilized to collect electric load profiles of clients and enables electric power suppliers to identify the energy information of their clients [2,3]. The efficient analysis of customer load profiles can be effectively utilized to optimize energy management for all types of electricity customers. Clustering approaches are frequently used to efficiently analyze and classify different types of customer load profiles. Several studies have introduced methodologies to classify load profiles accumulated from different customer types.
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