Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea

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 pro...

Full description

Saved in:
Bibliographic Details
Published in:Energies (Basel) 2020-04, Vol.13 (7), p.1723
Main Authors: Jeong, Hyun Cheol, Jung, Jaesung, Kang, Byung O
Format: Article
Language:English
Subjects:
Adaptive systems
Algorithms
Alternative energy sources
Arbitrage
arbitrage operation
Classification
Clustering
customer load clustering
Customer services
Customers
Electric power
Electricity distribution
Energy consumption
Energy management
energy management in smart grid
Energy storage
energy storage system (ESS)
Feasibility studies
Load distribution
Optimization
peak shaving operation
Profits
Renewable resources
Self organizing maps
Smart grid technology
Square waves
Storage systems
Time of use electricity pricing
time-of-use (TOU) tariff
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13071723