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As the penetration of renewable energy sources into a power system increases, the significance of precise short-term forecasts for wind power generation becomes paramount. However, the erratic and non-periodic nature of wind poses challenges in accurately predicting the output. This paper presents a comprehensive investigation into forecasting wind power generation for the following day, using three machine learning models: long short-term memory (LSTM), convolutional neural network-bidirectional LSTM (CNN-biLSTM), and light gradient boosting machine (LGBM). In addition, this paper proposes a method to improve the prediction performance of LGBM by separating data according to the distribution of features, and training and testing each separated dataset with a distinct model. This study includes a comparative analysis of the performance of the proposed models in predicting wind turbine output, offering valuable insights into their respective efficiencies. The results of this investigation were analyzed for two geographically distinct wind farms (Korea and the UK). The findings of this study are expected to facilitate the selection of efficient prediction models within the forecast accuracy auxiliary service market and assist grid operators in ensuring reliable power supply for the grid. [ABSTRACT FROM AUTHOR]

Recently, the proportion of renewable energy (RE) sources in a power system has been increasing worldwide to reduce carbon emissions. To effectively accommodate renewable energy, there is a growing need to develop integrated planning strategies for both renewable energy sources and transmission lines in a power system, taking into account the location-specific characteristics of renewable energy. Economically viable and rapid system expansion plans are required to resolve the problem of delayed integration of renewable energy into existing power system, which arises due to inadequate transmission facilities. To address these problems, this paper presents an integrated economic evaluation method that considers the inherently uncertain output characteristics of renewable energy, contingent on location, the costs associated with installing transmission lines, and environmental benefits. The case study is conducted using a modified power system in Korea. In the case study, it is assumed that by integrating solar power generators in a low-demand area and wind power generators in a high-demand area, wind power plants have higher construction costs than solar power generators. However, the transmission costs for wind power plants are lower than those for solar power generators due to the regional characteristics of the power grid in the installation areas because renewable energy in high-demand areas typically requires less additional power infrastructure than in low-demand areas. The results of this study show that the net benefit for the wind power plant is estimated at USD 225.4 M, while the solar power plant yields a net benefit of USD 22.9 M in the example system. These results demonstrate the effectiveness of the proposed method, underscoring its potential to mitigate the challenges faced in the practical implementation of renewable energy projects. [ABSTRACT FROM AUTHOR]

This study intends to shed light on the environmental impacts of energy decisions in South Korea by analyzing the correlation between energy consumption patterns and environmental indicators such as carbon dioxide emissions. In 2021, global CO2 emissions increased by 6%—to the highest ever level of 36.3 billion tons—according to the International Energy Agency (IEA). This increase in CO2 emissions is a big problem for all countries around the world. The aim of this article is an analyze the impact of fossil fuels, renewable energy, and nuclear energy on South Korea's environment based on the STIRPAT (stochastic impact by regression on population, affluence, and technology) model. Exploring the intricate nexus between economic and energy indicators and environmental outcomes, this study employs the STIRPAT model to analyze the influence of GDP, population dynamics, fossil fuels, renewable energy, and nuclear energy on South Korea's environment. The yearly data from 1972 to 2021 are analyzed in this paper using an autoregressive distributed lag (ARDL) model. The reliability of this study is also examined by employing FMOLS (fully modified ordinary least squares) and CCR (canonical cointegrating regression) estimators. This study confirms the findings of previous research by showing that the rising South Korea GDP and population can lead to higher CO2 emissions and that a strategy switching to renewable energy can cut down on CO2 emissions in Korea, as it exhibits a coefficient of −0.085 *. The robustness results of FMOLS and CCR's findings support baseline ARDL findings. [ABSTRACT FROM AUTHOR]

As environmental pollution deteriorates, the acceleration of decarbonization worldwide is underway. Several countries, including Korea, are incorporating new and renewable energy into existing systems, and various methods of decarbonization are being researched. With changes in the control environment, the expansion of the system linkage of new and renewable energy can cause system instability problems. This paper introduces an operation plan that uses the fast response characteristics of energy storage system(s) (ESS) to improve stability and review system stability as a result of the expansion of new and renewable energy on Jeju Island. Frequency stability was ensured through an analysis of frequency response characteristics in the event of a system accident, and an efficient ESS operation plan was simulated by changing the ESS parameters. Through this simulation, stable ESS operation and hunting in the frequency recovery process were minimized through cooperation between the existing and new ESS via a wide dead-band range and speed adjustment rate correction, and the lowest frequency was improved. [ABSTRACT FROM AUTHOR]

Alkaline electrolysis systems are currently considered to be suitable for large-scale hydrogen production. Previous research has primarily focused on integrating renewable energy sources such as solar and wind into water electrolysis systems. However, intermittent issues stemming from the sporadic nature of renewable energy sources have led to the introduction of energy storage systems (ESSs) to address these intermittent challenges. Extensive research has been conducted on the efficiency and operational aspects of these systems. In contrast to other renewable energy sources, hydropower offers the advantages of stable output and high utilization, making it a promising solution for overcoming intermittent issues. In this study, we propose the design of an optimized alkaline electrolysis system tailored for small-scale hydropower generation. This approach allowed us to confirm the efficiency of a small-scale hydropower-based hydrogen production facility and the analysis of hydrogen production costs under diverse scenarios. Notably, the optimal selling price per kilogram of hydrogen was determined to be USD 15.6 when the operational time exceeded 20 h, albeit indicating a challenging market supply. Under the consideration of various scenarios and government subsidies, this study revealed that a USD 10/kgH2 subsidy or 24 h of continuous operation achieved break-even points in the sixth and eighth years, respectively. Ultimately, the findings underscore the necessity for essential measures, including government backing and technological advancements in small-scale hydropower facilities, to enhance the economic viability of the green hydrogen market in South Korea. [ABSTRACT FROM AUTHOR]

When solar panels reach end-of-life, the disposal of solar panel waste is an issue of concern because it creates environmental pollution if it is improperly disposed of. It is expected that such waste will probably be increasing as the widespread use of renewable energy is adopted by taking measures associated with carbon neutrality. Thus, accurate prediction of solar panel waste with future demands for renewable energy is urgently needed for sustainable waste management. This study examined the amounts of solar panels to be retried by 2050 under three scenarios (S1-BAU, S2, S3). The amounts of solar panels to be retired are estimated by using the PBM (population balance model) with the Weibull distribution. According to the carbon neutral scenario (S3), the total amounts of solar panels to be retired are anticipated to be around 172 kt in 2030, 932 kt in 2040, and 3146 kt in 2050. The total volume of retired solar panels was projected to be 168 kt in 2030, 820 kt in 2040, and 2331 kt in 2050 under the government-led scenario (S2). The average recovery of end-of-life solar panels produced by the three scenarios in 2050 is 1531 kt, 337 kt, 535 kt, and 22 kt for glass, aluminum, silicon, and copper, respectively. Economic benefits by resource recovery of retired solar panels in 2050 range from $25.6 million in S1 to $519.1 million in S3. Based on the sensitivity analysis with the weight of solar panel (5% and 10% reduction), the results indicated that the annual volumes of retired solar panels mostly fell within the range of 4.9% to 10.0% in 2050. To confirm the predicted volumes of retired solar panels in this study, a further study is warranted because they can be influenced by other factors (e.g., weight, technology development, early loss rate, or reuse and recycling options). [ABSTRACT FROM AUTHOR]

APV systems producing both crops and electricity are becoming popular as an alternative way of producing renewable energy in many countries with land shortage issues (e.g., South Korea). This study aims at developing a hybrid performance model of an Agrophotovoltaic (APV) system that produces crops underneath the PV modules. In this study, the physical model used to estimate solar radiation is integrated with a polynomial regression approach to forecast the amount of electricity generation and crop production in the APV system. The model takes into account not only the environmental factors (i.e., daily temperature, precipitation, humidity, and wind speed) but also physical factors (i.e., shading ratio of the APV system) related to the performance of the APV system. For more accurate modelling, the proposed approach is validated based on field experiment data collected from the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea. As a result, the proposed approach can predict the electricity generation quantity in the APV system with an R2 of 80.4%. This will contribute to the distribution of the APV system, which will increase farmers' income as well as the sustainability of our society. [ABSTRACT FROM AUTHOR]

Since the importance and effects of national energy policies, plans, and roadmaps were presented in South Korea, the role of renewable energy resources has received great attention. Moreover, as there is significant reasoning for reducing and minimizing nuclear and fossil fuel usage in South Korean national energy plans, several academic scholars and implementers have expended significant effort to present the potential and feasibility of renewable energy resources in South Korea. This study contributes to these efforts by presenting potential sustainable configurations of renewable energy production facilities for a public building in South Korea. Based on economic, environmental, and technical information as well as the presented simulation results, it proposes an environmentally friendly renewable energy production facility configuration that consists of photovoltaic arrays, battery units, and a converter. Subsidies for installing and renovating such facilities are also considered. The potential configuration indicates $0.464 as the cost of energy, 100% of which is renewable. Potential limitations and future research areas are suggested based on the results of these simulations. [ABSTRACT FROM AUTHOR]

The Agrophotovoltaic (APV) system is a novel concept in the field of Renewable Energy Systems. This system enables the generation of solar energy via photo-voltaic (PV) modules above crops, to mitigate harmful impact on food production. This study aims to develop a performance evaluation model for an APV system in a temperate climate region, such as South Korea. To this end, both traditional electricity generation models (solar radiation-based model and climate-based model) of PV modules and two major machine learning (ML) techniques (i.e., polynomial regression and deep learning) have been considered. Electricity generation data was collected via remote sensors installed in the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea. Moreover, economic analysis in terms of cost and benefit of the subject APV system was conducted to provide information about the return on investment to farmers and government agencies. As a result, farmers, agronomists, and agricultural engineers can easily estimate performance and profit of their APV systems via the proposed performance model. [ABSTRACT FROM AUTHOR]

Increasing renewable energy penetration rate in a power grid leads to an increase in the variability of the generated energy, which increases the system integration cost. To handle the output variations in the generation, it is necessary to secure sufficient flexible resources, such as energy storage units. Flexible resources can adjust the output quickly, which helps to increase the system flexibility. However, the electricity generation cost of the flexible resources is usually high. Because the renewable energy expansion policy is being implemented worldwide, it is necessary to evaluate the ability to manage the short-term variations of the renewable energy outputs to obtain a cost-effective long-term plan. In this study, the variability of renewable energy in Korea over the past five years was analyzed. Additionally, the backup capacity is determined to manage the variability of renewable energy output. The backup capacity is affected by system flexibility. In general, increasing system flexibility decreases the backup capacity and increases the total electricity production cost. In this study, a backup capacity planning method is proposed considering the short-term variability of renewable energy output and flexibility deficit in a power system. The numerical results illustrated the effectiveness of the proposed backup capacity planning method. [ABSTRACT FROM AUTHOR]

To increase acceptance of new nuclear power plants (NPPs) by local communities, some countries offer those communities economic incentives. This study analyzes potential residents' preferences for economic incentives provided during the construction and operation of NPPs in South Korea. This study uses stated preference data through a discrete choice experiment and the mixed logit model to reflect the heterogeneity of respondents' preferences. The analysis results confirm heterogeneity by various incentive types, and show that respondents consider the distance between the NPP and the residential area as well as reduced electricity bills as crucial attribute of new NPPs. In addition, the result for the marginal willingness to be near to an NPP shows that reduced electricity bills, construction of new public facilities, and increased residents' participation are relatively more effective incentives than job creation or solar panel installation. In particular, increased residents' participation could greatly improve acceptance although it is not a direct form of financial support. The simulation analysis results indicate that acceptance of NPPs rapidly reduces as distance to the plant becomes shorter, although acceptance can change within a 0–30% range depending on the level of incentives. Several policy implications are suggested for policymakers based on the results. [ABSTRACT FROM AUTHOR]