In recent years, electric vehicles (EVs), hybrid electric vehicles (HEVs), and plug-in electric vehicles (PEVs) have become very popular. Therefore, the use of secondary batteries exponentially increased in EV systems. Battery fuel gauges determine the amount of charge inside the battery, and how much farther the vehicle can drive itself under specific operating conditions. It is very important to provide accurate state-of-charge (SOC) information of the battery module to the driver, since inaccurate fuel gauges will not be tolerated. In this paper, a model-based approach is proposed to estimate the SOCs of multiple lithium-ion (Li-ion) battery cells, connected in a module in series, by using a nonlinear state observer (NSO) and an online parameter identification algorithm. A simple method of estimating the impedance and SOC of each cell in a module is also presented in this paper, by employing a ratio vector with respect to the reference value. A battery model based on an autoregressive model with exogenous input (ARX) was used with recursive least squares (RLS) for parameter identification, in an effort to guarantee reliable estimation results under various operating conditions. The validity and feasibility of the proposed algorithm were verified by an experimental setup of six Li-ion battery cells connected in a module in series. It was found that, when compared with a simple linear state observer (LSO), an NSO can further reduce the SOC error by 1%.

Maintaining high facility reliability in power plants is essential to secure long-term electricity supply. This paper applies the survival analysis to the actual unit level power generation data in Korea to estimate the relationship between facility reliability and the preventive maintenance. Duration of generators between forced outages is used to measure plant reliability. the empirical analysis shows that preventive maintenance cost, planned outage for maintenance, use rate, and reserve margin lead to the longer duration of generators and, in turn, the lower forced outage rates. We uncover that the marginal benefit of the preventive maintenance cost is decreasing at an increasing rate. It indicates that the marginal benefit of the “current” maintenance cost is minimal. Results in the paper imply that power plants in Korea might be spending unnecessarily high maintenance costs considering already having world’s lowest forced outage rates.

This study explores the international natural gas market integration using the Engle–Granger cointegration and error correction model. Previous studies have suggested that liquefied natural gas (LNG) and oil-linked pricing with a long-term contract have played key roles in gas market integration, especially between European and Asian markets. There is, however, little discussion of the role of the emergence of a swing supplier. A swing supplier, e.g., Qatar or Russia, is flexible to unexpected changes in supply and demand in both European and Asian markets and adapts the gas production/exports swiftly to meet the changes in the markets. Qatar has been a swing supplier since 2005 in the global natural gas market. In 2009, Qatar’s global LNG export share reached above 30% and has remained around 25% since then. Empirical results indirectly support that the emergence of a swing supplier may tighten market integration between Europe and Asia. The swing supplier may have accelerated the degree of market integration as well, particularly after 2009.

This study examines the effectiveness of smart meters in reducing residential electricity consumption, focusing on how smart meter feedback interacts with consumers’ usage patterns. While real-time feedback can help consumers make informed energy decisions, previous research shows its impact is often minimal. Our analysis of smart meter data from Korea reveals that the effect of demand response depends on baseline consumption. Households with lower usage increased consumption, while those with higher usage reduced it after receiving feedback. These findings suggest that smart meters’ effectiveness is influenced by the gap between actual and optimal consumption. This study contributes to understanding mixed research outcomes and emphasizes the importance of consumer behavior in designing effective energy-saving strategies.

This study compares the cost efficiency of two LNG procurement methods in Korea: private direct imports and the Korea Gas Corporation (KOGAS) average tariff system, under both buyer's and seller's market conditions. Using monthly Japan Korea Marker (JKM) spot prices from January 2015 to May 2023, we identify structural changes via the Zivot-Andrews test and classify the sample into buyer's (January 2015–May 2021) and seller's (June 2021–May 2023) regimes. Panel fixed-effects regressions examine differences in unit heat costs between procurement methods. While a simple comparison of contract structures might suggest that KOGAS-supplied generators could become more cost-competitive during high-price periods, the results show that direct import generators achieve significantly lower unit heat costs in both regimes. The cost advantage appears to widen during the seller's market period, though this regime-specific effect is marginally significant. The direct import model exhibits greater market responsiveness and cost efficiency, with implications for energy security and electricity market design in Korea and other LNG-importing countries.