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In recent years, the frequency of power demand imbalance and negative price phenomenon has risen due to the rapid expansion of renewable energy sources (RES). Because of this, a means to reduce the curtailment of RES by utilizing surplus energy is essential. This paper focuses on reducing the curtailment of wind turbines (WT) with high output intermittency and minimizing the investment cost of IES via an integrated energy system (IES). The IES operation seeks to improve the acceptability and efficiency of the RES as it supports the integration of various energies mix, such as electricity, heat, hydrogen. This paper proposes an optimal facility combination set (FCS) of IES that satisfies the requirements of ISO and IPP using Multi-Objective Optimization Programming (MOP). The case study is based on a wind farm in South Korea, set in Aewol-eup, Jeju-Island. The case study results provide the best configuration of the IES energy mix with the best economic value and efficiency while satisfying ISO and IPP perspectives. • Derive the optimal design and operation solution for integrated energy systems. • Derive the optimal solution using multi-objective optimization programming. • Propose the optimal facility combination set of the integrated energy system. • Propose the consensus point between investors and users of IES. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of incorporating spatiotemporal wind data on the performance of wind forecasting neural networks. While previous studies have shown that including spatial data enhances the accuracy of such models, limited research has explored the impact of different spatial and temporal scales of input wind data on the learnability of neural network models. In this study, convolutional neural networks (CNNs) are employed and trained using various scales of spatiotemporal wind data. The research demonstrates that using spatiotemporally correlated data from the surrounding area and past time steps for training a CNN favorably affects the predictive performance of the model. The study proposes correlation analyses, including autocorrelation and Pearson correlation analyses, to unveil the influence of spatiotemporal wind characteristics on the predictive performance of different CNN models. The spatiotemporal correlations and performances of CNN models are investigated in three regions: Korea, the USA, and the UK. The findings reveal that regions with smaller deviations of autocorrelation coefficients (ACC) are more favorable for CNNs to learn the regional and seasonal wind characteristics. Specifically, the regions of Korea, the USA, and the UK exhibit maximum standard deviations of ACCs of 0.100, 0.043, and 0.023, respectively. The CNNs wind prediction performances follow the reverse order of the regions: UK, USA, and Korea. This highlights the significant impact of regional and seasonal wind conditions on the performance of the prediction models. [Display omitted] • 3D CNN-based wind velocity prediction. • Estimation of CNN learnability through spatiotemporal wind correlation analysis. • Discovery of influence of local geometric and seasonal wind on CNN prediction. • The proposed correlation analysis can aid in selecting yaw-control wind farm sites. [ABSTRACT FROM AUTHOR]

Fuel cell electric vehicle (FCEV) is a disruptive technology for traditional vehicles. High cost and inadequate infrastructure are the primary shortcomings of adopting FCEV, which prevents the commercialization of FCEV technology. It is an exciting issue whether a new wave of developing FCEV is present. The paper explores FCEV technology sources globally by combing multiple patent information. Our study finds the development of FCEV technology has been reviving slowly since 2014. The U.S., Japan, Germany, China, and South Korea are the core sources of FCEV technology, but FCEV technology sources are being reconstructed. Moreover, dominant incumbents, including large automobile manufacturers and component suppliers, are essential technology sources of FCEV development. Our research enriches the literature on the wave of developing FCEV technology and helps policy-makers or managers consider whether they should develop FCEV technology in the face of FCEV development reviving. • A method of combing multiple patent information to detect technology sources. • The development of Fuel cell electric vehicles has been reviving slowly since 2014. • The U.S., Japan, Germany, China, and South Korea are the core technology sources. • Large car manufacturers and component suppliers are the essential technology sources. [ABSTRACT FROM AUTHOR]

This paper tested the relation among macroeconomic variables such as economic growth, current account balance, energy imports and non-economic variable as militarization for the selected countries by employing Markov Switching-Bayesian Vector Auto Regressive approach for China, Israel and South Korea. These countries have high rate of export and import of weapons, the current account surplus, and higher rate of energy imports. The results showed that the relation between the selected variables differs in these countries with respect to their energy and weapon import levels. According to Markov-Switching Bayesian Granger Causality results, if the governments determine the polices in the context of the results of the traditional causality test, it will be impossible to obtain the estimated impacts from the policies. Results determined that Militarization, energy imports, economic growth and current account balance relate to each other for all countries. Energy imports increase in the effect of militarization races and economic growth. The current account of the selected countries are vulnerable to the negative energy shocks and militarization. • Markov-Switching Bayesian Granger Causality test and traditional Granger causality test were applied. • Militarization, energy imports, economic growth and current account balance relate to each other. • Energy imports increase in the effect of militarization races and economic growth. • The current account balances are vulnerable to the negative energy shocks and militarization. [ABSTRACT FROM AUTHOR]

This paper aims to develop the mathematical model of electricity based on renewable energy and large-scale utility (eRELU) networks to achieve a low-carbon economy. Huge petrochemical industries allocated in South Korea are considered to evaluate the proposed model by techno-economic and environmental assessment subject to Korean renewable energy policy. The suggested mathematical model consists of two parts: the deterministic model to optimize industrial-scale utility networks and the stochastic model to construct clean electricity networks using variable renewable energy coupled with energy storage systems to provide feasible quantities of renewable electricity required from the optimized utility network. The resulting model is complemented by carbon capture and storage (CCS) systems in doing so the inevitable amount of greenhouse gases from boilers in utility networks can be significantly captured. Diverse scenarios under the uncertain parameters such as facility investment/operating costs and capacity factors of renewable energy are applied to the developed model, and the results show that the best scenario-based eRELU-CCS network reduces 16% of the total costs and capture/mitigate 114 tCO 2 /d comparing to the base case. It is expected that the proposed model will play an essential role in advancing the country's energy transition. [Display omitted] • Mathematical model developed for renewable energy and large-scale utility network. • CCS technology is combined with the optimized utility network for low-carbon economy. • A case study of huge petrochemical industries in South Korea were applied. • Scenario-based techno-economic and environmental assessment was conducted. • 16% of total economic costs and 114 tCO 2 /d of GHG emissions were reduced. [ABSTRACT FROM AUTHOR]