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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]

In South Korea, Renewable Energy Sources (RES) have been increasing with the application of energy policies, such as Feed in Tariff (FIT) and the Renewable Portfolio Standard (RPS). However, a rapid increase in RES supply leads to an uncertain power supply due to the intermittent output of RES. A representative example is the curtailment of Wind Turbines (WT), which frequently occurs in Jeju Island, South Korea. The proportion of RES power on Jeju Island is 67%, and there are cases where WT is curtailed among the operable sections to maintain the balance of power supply and demand. This paper applies Power-to-Gas (P2G) technology to hydrogenate, store, and utilize unused power to solve this problem. In this paper, Aewol-eup in Jeju Island is selected as a target site for case study. An Integrated Energy System (IES) for various energy operations is designed to control RES output. This paper proposes the optimal facility configuration and finally drives the optimal design and operation solution of IES by analyzing the objective functions and focusing on the Independent Power Producer (IPP) perspective. [ABSTRACT FROM AUTHOR]

In accordance with South Korea's recent 2030 Carbon Neutral Plan, an 8GW offshore wind farm is planned for construction in the South-west Sea. Therefore, it is expected that large-scale wind turbines will be installed, and these turbines must operate stably, even when there are instantaneous voltage fluctuations in the power system. The grid code is described for the low-voltage-ride-through (LVRT) and high-voltage-ride-through (HVRT) functions, and a test facility that can perform both LVRT and HVRT tests is essential. In the case of LVRT/HVRT test facilities developed by the existing RLC (impedance component) method, it may be difficult to test large-scale wind turbines due to problems such as power quality, frequent failures and narrow short-circuit capacity ranges. Therefore, to solve such problems, this paper proposes an LVRT/HVRT test facility of the autotransformer type, which is capable of outputting the desired voltage range by changing the wiring method and tap position. Specifically, in order to implement the test facility of the autotransformer type, which is able to output the desired voltage range by changing the wiring method and tap according to the LVRT/HVRT test status, this paper presents an impedance determination algorithm (two-step layer impedance determination algorithm) of auto-transformer based on the fault-current analysis and operation strategy at a real LVRT/HVRT testing evaluation facility. [ABSTRACT FROM AUTHOR]

This paper presents economic value estimation of improved biogas utilization systems of public wastewater treatment plants in Republic of Korea. Since a large amount of biogas produced at digestion facilities is being wasted as a by-product, the biogas energy utilization system needs to be enhanced. In this paper, three operating options able to utilize the produced biogas are proposed, and then their monetary benefits are estimated by means of net present value calculation. Real operational data from the public wastewater treatment plant located in Sejong city, Republic of Korea, is used to reflect a variation of the rated daily gas production and its concentration according to the weather and seasons, resulting in calculating more reliable results. Additionally, to minimize the estimation errors due to uncertainties of the gas concentration and the gas selling price, a Monte Carlo simulation considering the variation of critical input data is carried out. As a result, the proposed approach can lead to better decisions in selecting the suitable biogas utilization system by forecasting the ranges of possible economic values. [ABSTRACT FROM AUTHOR]

Recently, the generalization of P2P (peer-to-peer) technology with enhanced security due to blockchain technology and the expansion of renewable energy-based distributed energy resources have led to blockchain technology being applied in power transactions, thus giving the potential to become a new platform for DC microgrid operation. Meanwhile, the voltage of a DC microgrid represents the balance of energy supply and demand and also serves as a stability index. The balance is represented as a steady state; the stability is represented during and after events. This paper examines the stability of the DC microgrid built on a university campus in Korea and, in particular, the blockchain technology-based power transactions performed in the DC microgrid. The test is based on the pre-planned transaction schedule applied in the DC microgrid. The transaction schedule has used day-ahead and real-time bidding data. Although many technologies are included in the project, this paper focuses on the voltage stability of the DC microgrid. In addition, the DC protection is applied and evaluated. To consider general DC protection, the DC breaker was simplified with several IGBTs, diodes, capacitors, and arrestors and was designed to interrupt the fault current within five milliseconds. The stability was evaluated using a PSCAD/EMTDCTM. [ABSTRACT FROM AUTHOR]

To supply stable and high-quality power according to the advancement of industrial growth, electric power companies have performed maintenance of power facilities using various methods. In the case of domestic power distribution facilities, there are limitations in performing diagnostic management on all facilities owing to the large number of facilities; therefore, old facilities are managed using the health index assessment method. The health index assessment comprises only facility operation data and external environmental data and is managed only for four types of distribution facilities including overhead/underground transformers and switchgears. In the case of high voltage overhead lines, there are a large number of wires such as transformers and switchgears connected to the lines, and the ripple effect of power outages is large. However, in Korea, there is no overhead line health index standard. In overseas cases, a health index for overhead lines was developed, but only the material characteristics and surrounding environment of the overhead lines were considered and economic feasibility was not considered. Therefore, in this paper, we developed a health index evaluation methodology for ultra-high voltage overhead lines that considers economic feasibility. In this paper, unlike the existing health index evaluation method that uses only operational data and external environmental data to determine facility performance evaluation and aging replacement standards, we developed an economic health index evaluation methodology that additionally considers failure probability and risk costs. Using the health index assessment methodology developed in this paper, it is possible to expect a reduction in facility operating costs and investment costs from the perspective of the electric power companies through the replacement of old extra-high voltage overhead cables. In addition, from the perspective of consumers, it is expected to increase power reliability and reduce the ripple effect of failure by preferentially replacing equipment with a high probability of failure. [ABSTRACT FROM AUTHOR]

An SMR (small modular reactor) is expected to be able to operate flexibly in conjunction with a high renewable energy penetration grid as a result of improved safety and easy power control compared to large nuclear power plants. SMRs, such as South Korea's System-integrated Modular Advanced ReacTor (SMART), are designed to use canned motors (CMs) for their reactor coolant pumps (RCPs) to enhance their safety. CMs passively enhance the reactor's safety by preventing the leakage of radioactive reactor coolant. However, with motor sizes designed to be as small as possible, and the increased air gap of CMs between the stator and rotor, the starting torque may be insufficiently low compared to that of similar-sized induction motors (IMs). Thus, CMs may require variable frequency drives (VFDs) to start. This paper compares the torque characteristics of CMs with those of IMs for SMART's RCPs. ETAP is then used to perform a motor-starting analysis for CMs activated with and without VFDs. The results are presented and analyzed to find out if VFDs can deal with the CM starting torque issue. [ABSTRACT FROM AUTHOR]

This paper proposes a practical framework for developing a net-zero electricity mix scenario (NEMS), which considers detailed conditions for supply of each energy. NEMS means a path scenario for power generation amount by year of each generation resource required to achieve carbon neutrality in 2050. NEMS framework refers to a methodological framework that contains procedures and requirements to continuously update the NEMS by comprehensively reflecting policy changes. For evaluation of NEMS, indicators such as a system inertia resource ratio (SIRR) and a fuel conversion rate (FCR) are proposed. The proposed framework and indicators are applied for the 2050 NEMS in Korea's electricity sector. The SIRR, indicating the ratio of inertial resources to total resources, projects values of 49% and 15% for the years 2030 and 2050, respectively. Furthermore, the FCR, reflecting the ratio of fuel conversion for resources undergoing this process, predicts that all targeted resources will have completed conversion by the year 2043. [ABSTRACT FROM AUTHOR]

As the scale of electricity consumption grows, the peak electricity consumption prediction of campus buildings is essential for effective building energy system management. The selection of an appropriate model is of paramount importance to accurately predict peak electricity consumption of campus buildings due to the substantial variations in electricity consumption trends and characteristics among campus buildings. In this paper, we proposed eight deep recurrent neural networks and compared their performance in predicting peak electricity consumption for each campus building to select the best model. Furthermore, we applied an attention approach capable of capturing long sequence patterns and controlling the importance level of input states. The test cases involve three campus buildings in Incheon City, South Korea: an office building, a nature science building, and a general education building, each with different scales and trends of electricity consumption. The experiment results demonstrate the importance of accurate model selection to enhance building energy efficiency, as no single model's performance dominates across all buildings. Moreover, we observe that the attention approach effectively improves the prediction performance of peak electricity consumption. [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]

Plastics follow a linear economic structure, leading to detrimental environmental effects, such as global warming and ecosystem destruction, through incineration and accumulation throughout their life cycle. This study examined the types, potential generation amounts, and properties of recycled plastics from waste electrical and electronic equipment (WEEE) to assess the feasibility of the closed-loop recycling of plastics from WEEE under South Korea's EPR system. Actual data from companies were used to determine the factors, such as the processing volume of WEEE. As of 2021, acrylonitrile-butadiene-styrene (53,363 tons), polypropylene (14,320 tons), and polystyrene (8199 tons) were the recycled plastics produced by both large and small WEEE. The properties of recycled plastics meet the specifications of new electrical and electronic products. In addition, an analysis using Life Cycle Assessment (LCA) methodology showed that the recycling effect (avoided emissions) reached 262,033 tons of CO2 eq. per year. Therefore, closed-loop recycling is the most suitable and effective method for reducing greenhouse gases. This paper presents the potential amount of recycled plastics generated from WEEE within South Korea's regulatory framework, providing valuable foundational data for policy development for promoting the use of recycled plastics. [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]

Recently, thanks to various support mechanisms, residential photovoltaics (PVs) for self-consumption are proliferating at a rapid pace. The net-metering scheme, one of the prevailing support mechanisms for self-consumption PVs, contributes to the proliferation of residential PVs by enhancing the economic benefits of PV adopters, but it suffers from certain disadvantages, such as missing network revenue of utilities and spread of cross-subsidies between the customers. This paper analyzes the cross-subsidy effect of residential PV proliferation across customer groups segmented according to their PV adoption and electricity consumption level under the net-metering scheme in the Korean electricity market. The results show that missing network revenue of utilities increase by about 0.83% for every 1% increase of residential PV penetration, and customers in the lowest usage tier provide more cross-subsidies toward customers in the higher usage tiers as with higher proliferation of residential PVs. In addition, this paper suggests that the cross-subsidy effect between customers can be reduced by introducing a new network charge design that is more consistent with the cost-causality principle as well as targeted deployment policies for self-consumption PVs. [ABSTRACT FROM AUTHOR]

This paper proposes a wind power probabilistic model (WPPM) using the reflection method and multi-kernel function kernel density estimation (KDE). With the increasing penetration of renewable energy sources (RESs) into power systems, several probabilistic approaches have been introduced to assess the impact of RESs on the power system. A probabilistic approach requires a wind power scenario (WPS), and the WPS is generated from the WPPM. Previously, WPPM was generated using a parametric density estimation, and it had limitations in reflecting the characteristics of wind power data (WPD) due to a boundary bias problem. The paper proposes a WPPM generated using the KDE, which is a non-parametric method. Additionally, the paper proposes a reflection method correcting for the boundary bias problem caused by the double-bounded characteristic of the WPD and the multi-kernel function KDE minimizing the effect of tied values. Six bandwidth selectors are used to calculate the bandwidth for the KDE, and one is selected by analyzing the correlation between the normalized WPD and the calculated bandwidth. The results were validated by generating WPPMs with WPDs in six regions of the Republic of Korea, and it was confirmed that the accuracy and goodness-of-fit are improved when the proposed method is used. [ABSTRACT FROM AUTHOR]

Machine learning-based time-series forecasting has recently been intensively studied. Deep learning (DL), specifically deep neural networks (DNN) and long short-term memory (LSTM), are the popular approaches for this purpose. However, these methods have several problems. First, DNN needs a lot of data to avoid over-fitting. Without sufficient data, the model cannot be generalized so it may not be good for unseen data. Second, impaired data affect forecasting accuracy. In general, one trains a model assuming that normal data enters the input. However, when anomalous data enters the input, the forecasting accuracy of the model may decrease substantially, which emphasizes the importance of data integrity. This paper focuses on these two problems. In time-series forecasting, especially for photovoltaic (PV) forecasting, data from solar power plants are not sufficient. As solar panels are newly installed, a sufficiently long period of data cannot be obtained. We also find that many solar power plants may contain a substantial amount of anomalous data, e.g., 30%. In this regard, we propose a data preprocessing technique leveraging convolutional autoencoder and principal component analysis (PCA) to use insufficient data with a high rate of anomaly. We compare the performance of the PV forecasting model after applying the proposed anomaly detection in constructing a virtual power plant (VPP). Extensive experiments with 2517 PV sites in the Republic of Korea, which are used for VPP construction, confirm that the proposed technique can filter out anomaly PV sites with very high accuracy, e.g., 99%, which in turn contributes to reducing the forecasting error by 23%. [ABSTRACT FROM AUTHOR]

The energy industry has been trying to reduce the use of fossil fuels that emit carbon and to proliferate renewable energy as a way to respond to climate change. The attempts to reduce carbon emissions resulting from the process of generating the electric and thermal energy needed by a building were bolstered with the introduction of the concept of nZEB (nearly zero-energy building). In line with such initiatives, the South Korean government made it mandatory for new buildings to have an nZEB certificate as a way to promote the supply of renewable energy. The criteria for Energy Independence Rate, which is one of the nZEB certification criteria in South Korea, is to maintain the share of renewable energy as at least 20% of the primary energy sources for the building. For a new building in South Korea to have an nZEB certificate, it is required to establish an energy plan that would allow the building to meet the Energy Independence requirement. This optimally reflects the cost of installation for renewable energy facilities and the cost of purchasing energy from external sources, such as the national grid or district heating companies. In South Korea, the base retail rate of energy is calculated based on the peak demand per hour over the year, rather than the contracted energy. This has produced difficulties in standardizing the process with a mathematical model; in addition, there have not been many preceding studies that could be used as a reference. In this regard, this paper analyzed a modeling strategy for developing a realistic yet optimized energy plan in consideration of the unique conditions of the retail energy rates of South Korea, and analyzed the impact of the rates based on peak demands upon the total energy plan. In this study, our research team analyzed the electric billing system, conducted a case study, and analyzed the impact of the billing system that is based on the peak demand upon the optimal cost. By utilizing the restrictions for reaching the 20% Energy Independence goal, this paper calculated the proper energy supply facility capacity for renewable energy. Then, the cases in which the maximum demand modeling was used and the cases without one were compared to confirm the cost benefits observable when the suggested model is added or implemented. [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]

Republic of Korea has unique geographical characteristics similar to those of an island, resulting in an isolated power system. For this reason, securing sufficient operating reserves for the system's stability and reliability in the face of the intermittency of increasing variable renewable energy (VRE) is paramount, and this will pave the way to achieving the nation's decarbonization target and carbon neutrality. However, the current reserve-operation method in Republic of Korea does not take into account energy-system conditions, such as the intermittency of the VRE. Therefore, this paper presents an analysis of the impact of changes in reserve-operation methods on the electricity market in the future Republic of Korean power system, with the increased levels of VRE that are currently envisioned. Specifically, three reserve-operation methods, including Korea's current reserve-power-operation standards, were applied to the two power-system plans announced by the Korean government to analyze the annual generator operation and costs. The analysis results show that securing reserves proportional to the VRE would exert negative effects, such as increased power-generation costs and the curtailment of nuclear and VRE generation. These results can contribute to the estimation of operational reserves needed for high levels of VRE and to the design of new the Korean reserve market, to be introduced in 2025. [ABSTRACT FROM AUTHOR]

A novel energy-saving hybrid cooling system that combines a forced-cooling cycle and a free-cooling cycle was developed to increase the energy efficiency of cooling systems in year-round operation of an internet data center with high heat loads. This system effectively utilizes a dam deep water source to reduce energy consumption in internet data centers. The hybrid cooling system operates in forced-cooling mode when the entering water temperature exceeds the mode change temperature of 9 °C, but switches to free-cooling mode when the ambient temperature falls below the mode change temperature. In this paper, the cooling performance of the hybrid system was assessed under various operating conditions based on entering water temperature fluctuation. Because the cooling effectiveness of this type of system is highly dependent on the outside climate, its usefulness and suitability for different periods and zones must be investigated. The annual energy saving performance of the new system was estimated and compared to a conventional cooling system in terms of the integrated coefficient of performance, based on the hourly weather air temperature and water temperature bin data collected from 16 cities of different climate zones in South Korea. The experimental findings revealed that the novel hybrid internet data center cooling system showed a 67% annual operating performance over a conventional air source internet data center cooling system due to the adoption of a dam deep water source. [ABSTRACT FROM AUTHOR]

A monopile is the most conventional structure foundation for offshore wind turbines (OWTs) in the world. However, the Korean offshore wind industry has mostly been using the jacket type of foundation. The main reason for the current situation in Korea is that most of the marine soil consists of weak layers of sand and clay. Thus, the monopile foundation depth has to be deep enough to satisfy the intended serviceability design requirement of the monopile and the rotation limit at the seabed; a conventional monopile design concept alone might be insufficient in Korean offshore conditions, or otherwise could be very expensive, e.g., resulting in a rock socket installation at the tip of the monopile. The main objective of this paper is to introduce a novel hybrid monopile that is composed of a monopile and a supplemental support with three buckets, followed by assessing the lateral resistance of the hybrid system through physical experiments and finite element (FE) simulations. Namely, 1/64.5 small-scaled monopile and hybrid physical models with a monopile diameter of 7 m for a 5.5 MW OWT were loaded monotonically. The results show that the hybrid monopile improves the lateral bearing capacity regarding the initial lateral stiffness and ultimate load. The FE analyses of the corresponding physical models were also implemented to support the results from the physical model test. The numerical results, such as the structural member forces and soil deformation, were analyzed in detail. Additionally, a case study using FE analysis was conducted for the 5.5 MW OWT hybrid monopile support installed in a representative Korean weak soil area. The results show that the hybrid monopile foundation has a larger lateral resistance and stiffness than the monopile. [ABSTRACT FROM AUTHOR]

In predicting energy consumption, classic econometric and statistical models are used to forecast energy consumption. These models may have limitations in an increasingly fast-changing energy market, which requires big data analysis of energy consumption patterns and relevant variables using complex mathematical tools. In current literature, there are minimal comparison studies reviewing machine learning algorithms to predict energy consumption in Korea. To bridge this gap, this paper compared three different machine learning algorithms, namely the Random Forest (RF) model, XGBoost (XGB) model, and Long Short-Term Memory (LSTM) model. These algorithms were applied in Period 1 (prior to the onset of the COVID-19 pandemic) and Period 2 (after the onset of the COVID-19 pandemic). Period 1 was characterized by an upward trend in energy consumption, while Period 2 showed a reduction in energy consumption. LSTM performed best in its prediction power specifically in Period 1, and RF outperformed the other models in Period 2. Findings, therefore, suggested the applicability of machine learning to forecast energy consumption and also demonstrated that traditional econometric approaches may outperform machine learning when there is less unknown irregularity in the time series, but machine learning can work better with unexpected irregular time series data. [ABSTRACT FROM AUTHOR]

As renewable energy penetrates the power system, system operators are required to curtail output power from generation units to balance the power supply and demand. However, large curtailment from wind power plants (WPPs) may instantly cause excessive output power decrement, causing system frequency to drop significantly before reaching its nominal value. In order to solve this problem, this paper proposes a cooperative control framework to determine the operation of WPPs in two control methods, which are the stepwise inertial control (SIC) method and the curtailed control method. The proposed framework first determines the WPPs to operate in the curtailed control method to provide the required power curtailment. Next, it determines the WPPs to operate in the SIC method considering their releasable kinetic energy to provide an effective inertial response and compensate for the sudden excessive output power decrement caused by other WPPs operated in the curtailed control method. Therefore, each WPP is operated in one of two control methods to provide required power curtailment while reducing the sudden excessive output power decrement. To verify the effectiveness of the proposed cooperative control framework, several case studies are carried out on the practical South Korea electric power system. [ABSTRACT FROM AUTHOR]

Agrivoltaics can be used to supply energy and produce agricultural products in order to meet the growing demand for energy and food. The amount of power generation is affected by the solar panel direction, spacing, tilt, and panel technology; however, there is insufficient empirical data-based research on the operation of agrivoltaics. This study estimates the levelized cost of energy (LCOE) for a fence-based agrivoltaics system using bifacial modules. This study installed and operated photovoltaic (PV) systems on a rice paddy and saltern in South Korea to estimate the input variables that could affect their economic efficiency and LCOE. For the research methods, this study used Monte Carlo simulation (a stochastic analysis method that reflects the uncertainty of the input variables), a deterministic LCOE analysis, and a sensitivity analysis of the input variables. In terms of space utilization, the LCOE of the paddy system (139.07~141.19 KRW/kWh) was found to be relatively lower than that of the saltern system (145.43~146.18 KRW/kWh), implying that the PV system on the paddy was economically favorable. In terms of installation direction, it was more economical to operate the southwest-facing panels (139.07~145.43 KRW/kWh) than the southeast-facing panels (141.19~146.18 KRW/kWh). This study provides foundational policy data for the adoption of fence-based agrivoltaics and contributes to the widespread and active use of agrivoltaics. [ABSTRACT FROM AUTHOR]

The growing demand for electricity, driven by factors such as the shift to carbon neutrality and economic growth, is a challenge shared by South Korea and Japan. South Korea, a peninsula nation, and Japan, an island country, which are both heavily dependent on foreign energy sources and have manufacturing-based industrial structures, are actively working to secure stable power supplies for their economic development. This article carries out a quantitative analysis of the roles of the South Korean and Japanese electric power sectors (EPSs) in their respective economies, seeking to identify differences and generate actionable insights for decision making and policy formulation. Utilizing the input–output (IO) technique with the latest available data, the analysis includes a demand-side model, a supply-side model, and a price-side model to examine various effects of the EPSs. The key findings reveal differences in the production-inducing, value-added creation, and job-creation effects between the two countries, highlighting operational disparities in their electricity sectors. Additionally, South Korea exhibits higher wage-inducing, supply shortage, and price-side effects than Japan, because of its public enterprise-oriented high-wage structure and a substantial manufacturing sector. These quantitative results provide valuable reference material for future government decisions and policy development in the EPS and emphasize the significant role and impact of the power sector in both countries. [ABSTRACT FROM AUTHOR]

Energy poverty, defined as difficulty meeting the minimum requirements for a thermal environment, is becoming a significant social issue. To provide efficient welfare services, information provision and monitoring are required. However, characteristics of energy poverty, such as inconsistent residential patterns, small living spaces, and limited electricity and telecommunication resources, lead to a lack of information. This research introduces the empirical results of the development of the system. Based on the feedback from welfare workers and experts supporting energy poverty, a monitoring system combining various sensors was prototyped. This system measures temperature, humidity, illuminance, air velocity, CO2, black bulb temperature, occupancy, and noise and generates indicators for occupancy and thermal environment monitoring. Applicability assessment was conducted across 55 energy poverty households in Korea during the duration of cooling and heating. Subjects were living in spaces averaging 6.3 sqm within buildings over 43 years old and renting on a monthly or weekly basis. Electricity and communication are partially supplied. Based on the actual measurement data and field surveys, the configuration of an energy poverty monitoring system was proposed. In particular, the applicability of the simple methodology for the determination of black bulb temperature, metabolic rate, and clothing insulation required for a thermal environment evaluation was assessed. The proposed system can be efficiently used for taking care of energy poverty where the installation of conventional monitoring systems is restricted. [ABSTRACT FROM AUTHOR]

A dual-rotor yokeless and segmented armature (YASA)-type axial-flux permanent magnet (AFPM) motor with a surface-mounted permanent magnet (SPM) array type was developed for urban air mobility (UAM) aircraft in this work. The proposed AFPM motor had rated and peak output powers of 75.5 and 104 kW, respectively, with rated and peak rotational speeds of 1800 rpm. To achieve a high torque, a cobalt–iron alloy core material was used for the stator core. The prototype AFPM motor, developed by KSEP in the Republic of Korea, was successfully manufactured and verified through experimentation. Additionally, the thermal stability of the winding and permanent magnets (PMs) was confirmed with a water-cooling system. A structure analysis of the proposed AFPM motor was conducted due to the detachment of an uneven air-gap length in the prototype AFPM motor. An output performance comparison based on core materials for the stator and rotor was carried out to explore the material cost reduction. Subsequently, the design for performance improvement by applying a Halbach permanent magnet (HPM) array type was investigated for further research. [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]

Polymer electrolyte membrane fuel cells (PEMFCs) are employed in trucks and large commercial vehicles utilizing hydrogen as fuel due to their rapid start-up characteristics and responsiveness. However, addressing the requirement for high power output in the low-current section presents a challenge. To solve this issue, a multi-stack can be applied using two stacks. Furthermore, thermal management, which significantly affects the performance of the stacks, is essential. Therefore, in this study, a hydrogen electric truck system model was developed based on a Hyundai Xcient hydrogen electric truck model using MATLAB/SimscapeTM 2022b. In addition, the system's performance and thermal characteristics were evaluated and analyzed under different road environments and wind conditions while driving in 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]

Nuclear power is a major source of electricity in the international community. However, a significant problem with nuclear power is that, if a severe nuclear accident occurs, radiation may leak and cause great damage. As such, research on nuclear safety has become increasingly popular worldwide. In this paper, the structural integrity of a reactor cavity during a steam explosion—one kind of the aforementioned severe nuclear accidents—was evaluated. Steam explosions are primarily caused by fuel–coolant interactions (FCI), and result from issues in the cooling system that discharges the melt from the reactor core to the outside. A steam explosion can damage the nuclear power plant, and radiation leakage, the greatest concern, may occur. In the Chernobyl or Fukushima Daiichi accidents, significant radiation leakages resulted in damages extending beyond the country of origin. In this paper, a steam explosion was simulated using values given by the transient analysis code for explosive reactions (TRACER-II)—the only steam explosion code in Korea. The walls of the reactor cavity were modeled after the APR-1400 currently operating in Korea. The integrity of the concrete, rebars, and liner plate in the reactor cavity during a steam explosion was evaluated in terms of stress and ductile failure strain limits. [ABSTRACT FROM AUTHOR]

To reduce national greenhouse gas emissions, the South Korean government has encouraged new energy businesses and implemented policies to reduce energy consumption in buildings, and aims to construct all new buildings as zero-energy buildings by 2025. According to the promotion of policies on passive houses and zero-energy buildings, the thermal insulation and airtight performance of new buildings have been further enhanced. However, to enhance indoor comfort and air quality in new airtight buildings, it is critical to secure an adequate amount of ventilation. Heat recovery ventilators (HRVs) in South Korea have been used for more than 20 years as high-efficiency energy equipment; however, the high-efficiency standard 20 years ago (cooling efficiency 45%, heating efficiency 70%) is still being employed without any change. Most HRVs in the Korean market either meet or exceed this standard. This study examined HRV performance changes from 2010 to 2020 based on the data of 847 HRV performance certifications given by a certification agency. It also analyzed how institutional strategies and related laws contributed to the enhancement of such performance. As HRVs in South Korea are only required to satisfy the pre-defined efficiency criteria, the development and use of HRVs focus more on cost reduction rather than efficiency enhancement. Under such market conditions, it is challenging to research and design highly efficient HRVs along with customer satisfaction. If better market conditions are offered that would welcome HRVs with higher efficiency, the development of better HRVs, as compared to those analyzed in this research study, would be possible. [ABSTRACT FROM AUTHOR]

The energy-intensive steel industry, which consumes substantial amounts of electricity, meets its power demands through external electricity purchases and self-generation through the operation of its own generators. This study aimed to optimize boiler combustion efficiency and increase power generation output by deriving optimal operational values for O2 and CO within the boiler flue gas using machine learning (ML) with the aim of achieving maximum boiler efficiency. This study focuses on the power-generation boilers at steel mill P in Korea. First, 361 types of operation data from power generation equipment were collected and preprocessed. Subsequently, a partial least squares regression (PLSR) algorithm was used to develop a prediction model for O2 and CO values, known as the Boiler Flue Gas Prediction Model (BFG-PM). The prediction accuracy for O2 was notably high (83.2%), whereas that for CO was lower (53.4%). Nonetheless, the model's reliability was high because more than 90% of the predicted values were within a 10% error range. Finally, the correlation of the BFG-PM model was applied to the performance test code (PTC) 4.0 for the boiler efficiency calculations formula, deriving the optimal O2 and CO control points. Through a simulation, it was verified that the boiler efficiency was improved by controlling the combustion air. In addition, an average increase in boiler efficiency of 0.29% was confirmed by applying it directly to the generator operating on-site. The results of this study are expected to contribute to annual cost savings, with a reduction of USD 217,000 in electricity purchasing costs and USD 19,700 in greenhouse gas emissions trading expenses. [ABSTRACT FROM AUTHOR]

Many years after the introduction of quantum-dot-sensitized solar cells (QDSSCs), publications related to it have been widely disseminated in scientific archives. In this study, a bibliometric analysis was conducted to examine the bibliographic content of publications indexed in the Science Citation Index Expanded database from 2000 to 2021. Over the past two decades, 1557 articles have been published in the field of QDSSCs, covering studies on the photoanode, cathode, and electrolytes of the system. The most active journal, Electrochimica Acta, has published 6.48% of the total number of publications. The three most productive nations are China, South Korea, and India, accounting for 47.4%, 13%, and 9%, respectively, of publications from the top 20 nations. Through keyword analysis, our findings suggest that scholars have focused on controlling the optical and electrochemical properties of active materials, studying power conversion mechanisms, and investigating other internal kinetic mechanisms of QDSSCs. The authors and institutions are also classified based on their scientific performance over the past two decades to determine the intellectual base. This study assesses the general progression in QDSSC research and may provide baseline information to help scholars identify research trends in the field of QDSSCs. [ABSTRACT FROM AUTHOR]

Industrial crises exert considerable influence on a wide range of industries, national economies, and global economic landscapes. The primary objective of this study is to devise a crisis index specifically tailored for the petroleum sector—a vital component of South Korea's energy industry. An exhaustive analysis of the existing literature was conducted to extract pertinent elements and indicators, and indicator weights were determined using the analytic network process (ANP). Moreover, a combination of qualitative and quantitative methods was employed to rigorously evaluate the validity of the proposed crisis index. The implications derived from this study offer critical insights for stakeholders into the petroleum industry and demonstrate the potential applicability of a crisis index framework for other industries. [ABSTRACT FROM AUTHOR]

With the rapid expansion of the supply of renewable energy in accordance with the global energy transition policy, the wind power generation industry is attracting attention. Subsequently, various wind turbine control technologies have been widely developed and applied. However, there is a lack of research on optimal pitch control, which detects wind direction and changes the rotation angle of the blade in real time. In areas where the wind speed is not strong, such as South Korea, it is necessary to maintain the optimal angle in real time so that the rotating surface of the blade can face the wind direction. In this study, optimal pitch control was performed through real-time analysis of wind speed, direction, and temperature, which is the core of wind turbine maintenance, using fuzzy rules using FIS (Fuzzy Interface System) and WEKA data mining cluster analysis techniques. In order to prevent fires caused by the over-current of wind turbines, over-current control methods such as VCB (Vacuum Circuit Breaker) utilization, prototype utilization such as a modular MCB (Main Circuit Breaker) incorporating VI (Vacuum Interrupter), and vacuum degree change analysis methods using a PD (Partial Discharge) signal were proposed. The optimal control technique for wind turbine parts and facilities was put forth after judging and predicting the annual average wind distribution suitable for wind power generation using HRWPRM (Korea's High-Resolution Wind Power Resource Maps). Finally, the various wind turbine control methods carried out in this study were confirmed through computer simulation, such as remote diagnosis and early warning issuance, prediction of power generation increase and decrease situation, and automatic analysis of wind turbine efficiency. [ABSTRACT FROM AUTHOR]

The iron and steel industry is a large emitter of CO2 globally. This is especially true for the iron and steel industries in China, Japan, and Korea due to their production volumes and the prevalence of carbon-based steel production. With few low-carbon and commercially available alternatives, the iron and steel industry is truly a hard-to-abate sector. Each of the countries of interest have committed to a net-zero future involving the mitigation of emissions from steel production. However, few studies have investigated the means by which to achieve decarbonization beyond the inclusion of price signalling policies (e.g., carbon tax or emission trading schemes). Here, we use E3ME-FTT:Steel to simulate technology diffusion in the ISI under several policy environments and we investigate the likely impacts on the wider economy. The results show that penalizing carbon intensive processes can incentivize a transition towards scrap recycling, but it is relatively unsuccessful in aiding the uptake of low carbon primary steelmaking. A combination of support and penalizing policies can achieve deep decarbonisation (>80% emission reduction compared with the baseline). Mitigating the emissions in the iron and steel industry can lead to economic benefits in terms of GDP (China: +0.8%; Japan: +1.3%; Korea: +0.1%), and employment (Japan: +0.7%; Korea: +0.3%) with China, where job losses in the coal sector would negate job gains elsewhere, as the exception. [ABSTRACT FROM AUTHOR]

The most important design parameters of a moisture separator to optimize the efficiency of a nuclear power plant are the moisture carry over and pressure drop. An air-water test facility was constructed to evaluate the design parameters for various types of conventional moisture separators in Korea. In this study, a half scale of swirl-vane separator was designed, and test conditions were defined based on the scaling law to cover a wide range of operating conditions. The moisture carry over was measured using an isokinetic sampling system for 34 different flow conditions. The MCO was in range of 0.30–7.05% according to the test conditions. Single and two-phase flow experiments were also carried out to identify the two-phase pressure drop characteristics. Based on the obtained results, the proposed empirical correlation model was extended to a submerged separator and applying these results to a full scale system is discussed in this paper. [ABSTRACT FROM AUTHOR]

The Korean government is currently evaluating two alternatives, direct disposal and pyroprocessing, for the disposal of spent nuclear fuel. This paper presents the ranking results of comparing and evaluating direct disposal and pyro-SFR fuel cycle alternatives using multi-criteria decision-making methods such as AHP, TOPSIS, and PROMETHEE. In considering the various evaluation criteria involved in these two alternatives, we aimed to determine the optimal choice in terms of the economic and social conditions of Korea. The evaluation criteria considered were safety, resource availability, environmental impact, economics, nuclear proliferation resistance, and public acceptance. The results show that the pyro-SFR fuel cycle alternative is more advantageous than direct disposal in the AHP and TOPSIS methods, whereas direct disposal is more advantageous in the PROMETHEE method because the ranking is reversed. TOPSIS assigns the ideal value and the most negative value among the input values to each criterion as a parameter reflecting the concept of distance between the best alternative and the worst alternative. In contrast, the PROMETHEE method first selects the preference function including the preference threshold, and calculates the preferred outflow and the preferred inflow for the detailed evaluation indicators. Therefore, differences exist in the methodologies of multi-criteria decision making. Nonetheless, the analysis results of the back-end fuel cycle option can greatly contribute to establishing a nuclear policy for the back-end nuclear fuel cycle, and these efforts will enable sustainable nuclear power generation. [ABSTRACT FROM AUTHOR]

The increasing penetration level of distributed energy resources (DERs) increases the risk of congestion in the distribution network. To mitigate this, the concept of the small-scale DER aggregator was introduced as a change from uncoordinated to coordinated DERs. However, without appropriate network use cost allocation, the unwanted DER curtailment will be enforced by the network operator. Therefore, this paper proposes a new approach for congestion management by allocating the different network usage costs depending on how much congestion is caused by the DERs in the distribution network. For this, a modified Kirschen's tracing method is proposed and applied to the small-scale DER aggregator market. To verify the effectiveness of the proposed method, a simulation of the small-scale DER aggregator market in South Korea was performed under the IEEE 69-bus distribution network. The model was able to allocate the different network usage costs at different buses and, thus, encouraged the DERs to reduce their generation by charging the energy storage system (ESS) to mitigate congestion. An economic benefit analysis was also performed from the point of view of the aggregator concerning whether they should have an ESS or not. [ABSTRACT FROM AUTHOR]

This paper presents the results of various benefit–cost ratio (BCR) analyses of back-end nuclear fuel cycle alternatives. Korea is currently considering two alternatives for the disposal of spent nuclear fuel: direct disposal and pyroprocessing. Each of these two alternatives has advantages and disadvantages. To select one alternative, various evaluation criteria must be considered, since the superior alternative cannot be intuitively selected. A multi-criteria decision-making model can be a good methodology in this case. The analyses of benefit–cost ratios showed that the pyroprocessing alternative was more advantageous than direct disposal when using the results of the AHP and TOPSIS multi-criteria decision-making (MCDM) method. However, when using the results of the PROMETHEE method, the rank was reversed, and direct disposal was more advantageous than the Pyro-SFR fuel cycle. The results of BCR and MCDM can greatly contribute to establishing a nuclear policy for the back-end nuclear fuel cycle. [ABSTRACT FROM AUTHOR]

Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer's demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting. [ABSTRACT FROM AUTHOR]

As a consequence of the COVID-19 pandemic, Korea's economy has experienced significant setbacks. Thus, this article examines the implications of the COVID-19 pandemic on Korea's key macroeconomic indicators via the transmission channels of oil prices and production technology. Using Bayesian estimation and impulse response functions for empirical investigation, the results suggest that the COVID-19 pandemic has intensified the reduction in firm production, consumption of oil-based goods, employment, and investment. Increasingly, households rely on non-oil goods rather than oil-based ones. Similarly, the results suggest that the drop in production technology levels brought on by the COVID-19 pandemic has a stronger impact on business output and investment but a lesser influence on household employment. The COVID-19 pandemic has led to a decline in household non-oil consumption as well as household and business consumption of oil-based goods. To sum up, the existing Korean literature on this issue might be improved by including the findings offered in this article. [ABSTRACT FROM AUTHOR]

The amount of cattle manure generated accounts for over 40% of the livestock manure in South Korea. Most livestock manure is utilized as a fertilizer and a soil amendment. However, the soil nutrients have exceeded saturation in South Korea. Accordingly, cattle manure, including lignocellulosic biomass, was applied for solid fuel production in this study. The three different types of carbonization process, namely, hydrothermal carbonization, torrefaction, and carbonization (slow pyrolysis), were estimated for a comparison of the hydrochar, torrefied char, and biochar characteristics derived from cattle manure. The processes were performed at temperatures ranging from 190 to 450 °C. The evaluation of the hydrochar, torrefied char, and biochar produced by three processes was conducted by the proximate, ultimate, calorific value, fuel ratio, and energy yield, which were used for the analysis of fuel quality. Additionally, the ash properties, including silicon dioxide, chlorine, and base-to-acid ratio (B/A) on hydrochar, torrefied char, and biochar were investigated to predict ash deposition during combustion. These analyses are essential to stabilize the operation of the combustion chamber. The thermogravimetric analysis represented the upgraded quality of hydrochar, torrefied char, and biochar by three different carbonization processes. [ABSTRACT FROM AUTHOR]

While international trade drives countries' economic growth and promotes employment, it also has some environmental impact. To investigate the impact of trade on carbon emissions and employment, this study performs a detailed decomposition and measurement of embodied carbon emissions and employment in value-added trade between China, Japan, and Korea from 2007–2019. The current study established that, while China's trade with Japan and Korea created many domestic jobs, it also resulted in significant domestic carbon emissions. While Japan and Korea's trade with China reduced carbon emissions, employment in their own countries was reduced and replaced by employment in China. At the value chain route level, trade among the three countries through each value chain route either achieves employment promotion at the cost of increased carbon emissions or promotes domestic emissions reduction at the cost of employment loss. However, it is worth noting that, when trade between Japan and Korea was conducted through simple GVCs (route 2), it not only helped reduce Japan's carbon emissions, but also effectively promoted employment. This is the ideal trade route. The results of this study can provide useful reference information for developing countries such as China, to achieve sustainable economic growth, carbon emission reduction, and employment promotion in the context of trade globalization. [ABSTRACT FROM AUTHOR]

It has been mandated that 5% of the generation capacity of conventional fossil fuel power plants shall be used exclusively for frequency regulation (FR) purposes in South Korea. However, the rotational speed of generators cannot be controlled quickly, and thus the variation in the power generation for FR takes some time. Even during this short period of time, frequency fluctuations may occur, and the frequency may be out of range of its reference value. In order to overcome the limitations of the existing FR method, 374 MW (103 MWh) battery energy storage systems (BESSs) for FR have been installed and are in operation at 13 sites in South Korea. When designing the capacity of BESS for FR, three key factors, i.e., the deployment time, duration of delivery, and end of delivery, are considered. When these times can be reduced, the required capacity for BESS installation can be decreased, achieving the same operational effects with minimal investment in the facilities. However, because a BESS for FR (FR BESS) needs to be installed under a large capacity, providing a single output, a centralized control method is employed. The centralized control method has the advantage of being able to view and check the entire system at once, although in the case of FR BESS, a novel system design that can optimize the above three factors through a faster and more accurate control is required. Therefore, this paper proposes the implementation of a distributed autonomous control-based BESS for frequency regulation. For the proposed FR BESS, the central control system is responsible for the determination of external factors, e.g., power generation/demand forecasting; and the system is designed such that the optimal control method of renewable energy sources and BESS according to real-time frequency variations during practical operation is determined and operated using a distributed autonomous control method. Furthermore, this study was verified through the simulation that the proposed distributed autonomous control method conducts FR faster than an FR BESS with conventional centralized control, leading to an increase in the FR success rate, and a decrease in the deployment time required (e.g., 200 ms). [ABSTRACT FROM AUTHOR]

The South Korean government plans to switch from a centralized power generation method to a distributed one. However, due to opposition from local residents, construction of distributed power plants is frequently delayed or suspended. This study attempts to investigate whether proximity to a power plant negatively affects housing property values, using the hedonic pricing technique and quantitatively analyzing the level of impact. To this end, 2291 apartment sales data from a specific city in the South Korean Seoul Metropolitan area with a power plant were used. As a result of the analysis, it was found that proximity to a power plant had a negative effect on apartment prices, which was statistically significant at the significance level of 5%. The difference in apartment prices per 1 km direct distance from the power plant to the apartment was derived as KRW 8 million (USD 7.1 thousand). This value is about 0.7% of the average price of apartments in the area (KRW 1102 million = USD 0.98 thousand). The results of this study can be used as a useful reference when the government determines the size of subsidies for local residents near power plants. [ABSTRACT FROM AUTHOR]

In Korea's air pollutant inventory, construction machinery is a major emission source in the non-road sector. Since 2004, the Korean government has introduced and reinforced emission regulations to reduce the air pollutants emitted from their diesel engines. Since the engine dynamometer test method used in emission regulations has limitations in reflecting emission characteristics under the diverse working conditions of construction machinery, it is necessary to examine the effectiveness of emission regulations and the validity of the emission factors applied as inputs to the air pollutants inventory. This could be done by evaluating engine operation and emission characteristics under real-world working conditions. In this study, 14 units were selected among the excavators, wheel loaders, and forklifts that represent approximately 90% of the registered construction machines in Korea. They were equipped with a portable emission measurement system (PEMS) to measure gaseous emissions and collect engine data under various real-world working conditions. With the reinforcement of emission regulations for the construction machinery from K-tier3 to K-tier4 in Korea, exhaust after-treatment technologies, such as selective catalytic reduction and diesel oxidation catalyst, were applied. Real world NOx was reduced by approximately 83%, and THC 77% and CO by 73%, respectively. Real world NOx + THC of the K-tier3 machines exceeded the laboratory emission limit, but the K-tier4 machines considerably improved, 20% for excavator (124 kW), 61% for excavator (90 kW), 90% for wheel loader (202 kW) and 21% for Fork-lift (55 kW), despite some differences. The emission factors applied to the air pollutant inventory have been developed using the engine dynamometer test method, but they were considerably underestimated compared with emissions under real-world working conditions. The difference was even larger for the K-tier4 machines. In this study, the possibility of developing emission factor equations that use the engine load factor as a parameter was confirmed by using the engine work 1 g/kW·h segment moving averaging window (MAW) method. [ABSTRACT FROM AUTHOR]

Climate change has been turning into a climate crisis. Thus, we live in an era in which it is important to carry out the promise of 2050 carbon neutrality worldwide. South Korea is a country with a very large private education market. As the online education market has also recently expanded rapidly, interest in the electricity consumed by educational institutions is growing. One way to reduce the power consumption of private educational institutions, which is expected to gradually increase, is to replace the existing power system with a hybrid energy system based on renewable energy. This study aims to investigate an optimized renewable-energy-based hybrid system to supply adequate power to private educational institutions in Korea. We propose an optimal system using the HOMER (Hybrid Optimization Model for Electric Renewables) program. The result is that when private educational institutions adopt a hybrid renewable energy system, the renewable fraction is negligible in the grid-connected type, but it is analyzed that the NPC (Net Present Cost) will greatly increase because it exceeds 70% in the stand-alone type. The difference between on-grid and off-grid is significant, so it must be taken into account when devising renewable energy policies. [ABSTRACT FROM AUTHOR]

The conventional volt-VAR control (VVC) in distribution systems has limitations in solving the overvoltage problem caused by massive solar photovoltaic (PV) deployment. As an alternative method, VVC using solar PV smart inverters (PVSIs) has come into the limelight, which can respond quickly and effectively to solve the overvoltage problem by absorbing reactive power. However, the network power loss, that is, the sum of line losses in the distribution network, increases with reactive power. Dynamic distribution network reconfiguration (DNR), which hourly controls the network topology by controlling sectionalizing and tie switches, can also solve the overvoltage problem and reduce network loss by changing the power flow in the network. In this study, to improve the voltage profile and minimize the network power loss, we propose a control scheme that integrates the dynamic DNR with volt-VAR control of PVSIs. The proposed control scheme is practically usable for three reasons: Primarily, the proposed scheme is based on a deep reinforcement learning (DRL) algorithm, which does not require accurate distribution system parameters. Furthermore, we propose the use of a heterogeneous multiagent DRL algorithm to control the switches centrally and PVSIs locally. Finally, a practical communication network in the distribution system is assumed. PVSIs only send their status to the central control center, and there is no communication between the PVSIs. A modified 33-bus distribution test feeder reflecting the system conditions of South Korea is used for the case study. The results of this case study demonstrates that the proposed control scheme effectively improves the voltage profile of the distribution system. In addition, the proposed scheme reduces the total power loss in the distribution system, which is the sum of the network power loss and curtailed energy, owing to the voltage violation of the solar PV output. [ABSTRACT FROM AUTHOR]

A louver is a traditional environmental control device and passive architectural element based on an ecofriendly concept. Louvers are architectural elements that can be used to regulate natural lighting, thermal environment, and building energy use. To realize these integrated functionalities of louvers, they must be designed considering the climate and geographical characteristics of the target region. However, these aspects are typically not considered during building design in Korea, resulting in lovers being used as design elements with simple natural lighting control functions. Therefore, the objective of this study was to promote the integrated use of louvers by optimizing the louver angle according to the microclimate in Korea from the viewpoint of thermal energy use. We performed load and energy simulation planning and calculation and conducted optimization studies for the louver angle and range of motion for each region. The energy consumption in central and southern Korean regions was minimized when the angles of the fixed louvers were 45°–75° and 60°–90°, respectively. Kinetic louvers could enhance thermal energy management when installed at 30°–75° in spring, 135°–165° in summer, 75°–165° in autumn, and 45°–75° in winter. These findings can promote the realization of integrated functionalities of louvers from the perspective of indoor environment comfort based on the microclimates of the Korean regions. [ABSTRACT FROM AUTHOR]