Search

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]

Sustainable energy legislation in the modern world is the primary strategy that has raised the benchmark for energy and environmental security globally. The rapid growth in the human population has led to rising energy needs, which are predicted to increase by at least 50% by 2030. Waste management and environmental pollution present the biggest challenge to developing countries. The improvement of energy efficiency while ensuring higher nutritional evacuation wastewater treatment plants (WWTPs) is a significant problem for many wastewater treatment plants. Some treatment techniques require high energy input, which makes them expensive to remediate water use. Pollutants like chemical pesticides, hydrocarbons, colorants (dyes), surfactants, and aromatic compounds are present in wastewater and are contributing to other problems. Israel consumes 10% of the global energy supply, significantly more than other countries. The lagoon and trickling filters are the most widely used technologies in South African WWTPs, where the electricity intensity ranges from 0.079 to 0.41 kWh.m-3 (Wang et al. 2016). Korea and India use almost the same energy (0.24 kWh.m-3). An estimated one-fifth of the energy used in a municipality's WWTPs is used for overall public utilities, and this percentage is anticipated to rise by 20% over the next 15 years owing to expanding consumption of water and higher standards. In this review paper, we examined the potential for creating energy-self-sufficient WWTPs and discussed how much energy is currently consumed by WWTPs. The desirable qualities of microalgae, their production on a global level, technologies for treating wastewater with biogas and solar power, its developments, and issues for sustainable development are highlighted. The scientific elaboration of the mechanisms used for pollutant degradation using solar energy, as well as their viability, are the key issues that have been addressed. [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]

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]

In order to more accurately predict the global solar energy consumption, a new grey prediction model FDGM(1,1, t α ) is proposed in this paper. The grey wolf optimizer (GWO) is used to optimize the fractional-order r and the time power α in the model. The proposed FDGM(1,1, t α ) model, two statistical models and other five existing grey models are used to simulate and predict the solar energy consumption in the four economies (France, South Korea, OECD and Asia Pacific region) from 2010 to 2029. The simulation results show that our proposed FDGM(1,1, t α ) has higher accuracy than the other seven models. The prediction results based on FDGM(1,1, t α ) show that in 2029, the solar energy consumption in South Korea, the OECD and the Asia Pacific region will reach 33,935.32 Ten-trillion J, 1,222,123.45 Ten-trillion J and 2,297,274.45 Ten-trillion J, respectively, but that in France will slowly increase in the next few years and will gradually decrease after reaching a peak of 14,727.34 Ten-trillion J in 2026. The better forecasting solar energy consumption by using the proposed model can provide useful information for the policy-makers to formulate and improve energy policies and measures. [ABSTRACT FROM AUTHOR]

Recent developments in power electronics, energy storage systems, and renewable energy; increased market demands for more efficient and cleaner electric power to meet stricter environmental regulations; and development in gigawatt (GW)-class DC (direct current) transmission systems for transmission of greater power over longer distances than similar alternative current (AC) systems, have supported the development of the DC grid, making it a promising solution for both the onshore and offshore industries. This paper presents an experimental study on the effectiveness of an engine speed reduction strategy on exhaust gas emission and fuel consumption when applied to a 4-stroke generator engine equipped with a cam-driven plunger diesel injection system. The experiments were performed on an 8-cylinder V-type 4-stroke generator engine installed in the MASTC laboratory, which is the only demonstration testbed for the ship's electric propulsion system in Korea. Experimental results showed that fuel consumption decreased, but emission mass fraction in exhaust gas increased when maintaining engine power while reducing engine speed. This study has shown economic benefits in reducing fuel consumption, but incurred penalties for the emission performance of 4-stroke generator engines equipped with cam-driven plunger diesel injection systems when applying the engine speed reduction strategy. [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]

With the rapid expansion of renewable energy and digital devices, there is a need for direct current (DC) distribution technology that can increase energy efficiency. As a result, DC distribution research is actively underway to cope with the sudden digitization and decentralization of load environment and power supply. To verify the possibility of DC distribution, Korea Electric Power Corporation (KEPCO) Research Institute made a DC distribution system connected with a real power system in Gwangju. The construction of the demonstration area mainly includes design of protection and grounding systems, operating procedures of insulation monitoring device (IMD), and construction of power converters. Furthermore, this paper goes beyond the simulation and the lab testing to apply DC distribution to a real system operation in advance. It is designed as a long-distance low-loaded customer for rural areas and operated by the DC distribution. In addition, safety and reliability are confirmed through field tests of DC distribution elements such as power conversion devices, protection and grounding systems. In particular, to improve the reliability of non-grounding system, the insulation monitoring device was installed and the algorithms of its operational procedures are proposed. Finally, this paper analyzes the problems caused by operating the actual DC distribution and suggests solutions accordingly. [ABSTRACT FROM AUTHOR]

Featured Application: Energy saving hull-form design for a fishing vessel. In response to the increasing number of free trade agreements (FTA) and the Trans-Pacific Partnership (TTP), it is necessary to develop next-generation fishing vessels for the littoral sea of the Republic of Korea. The main objectives of such fishing vessels are to enhance the comfort of the crew and to improve fuel efficiency under the newly established principal dimension. This paper employs the hull form variation method to obtain a series of forebody hull forms of the 19 gross tonnage class of a coastal composite fishing vessel. Chine breadth modification has been used for hull form variations. The objective function is the minimum value of wavemaking resistance. A parametric study was conducted to observe the effects of design parameters on the objective functions, and hull-form optimization was performed. In addition to the hull-form optimization, the effect of the stern flap was investigated in terms of both resistance and operational performances. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to examine whether salience of neighbor comparison information attracts more attention from residents and consequently leads to significant energy conservation. An eye-tracking experiment on 54 residents in a local apartment complex in Korea found that the average time of attention to the neighbor comparison information increased to 277 ms when the size of the information was four times larger and the information was located to the far left. However, the interviews with the subjects suggest that salience of the information is seemingly unrelated to energy conservation, because most of them did not agree with the social consensus that individuals need to refrain from consuming energy when they know that they have consumed more than the neighbor's average. Utility data on 502 households in the apartments revealed that, of the households notified that they consumed more than their neighbors, only less than 50% reduced their energy consumption, which supports the interview results. Therefore, it was concluded that neighbor comparison information did not lead to significant energy conservation effects in the community, although salience of the information contributed to attracting more attention to the information. Unavailable household data remained as limitation to clarify the effect by households. [ABSTRACT FROM AUTHOR]

The paper aims to investigate the holistic environmental benefits of using a battery system on a roll on/roll off (ro-ro) passenger ship which was originally fitted with a diesel engine engaged in Korean coastal service. The process of this research has multiple layers. First, the operating profiles of the case ship were collected, such as speed, output, operation time and the configuration of the diesel propulsion system. Second, the full battery propulsion system, in place of the diesel system, was modelled and simulated on a power simulation software (PSIM) platform to verify the adequacy of the proposed battery propulsion system. Then, the life cycle assessment method was applied to comprehensively compare the environmental footprint of the diesel-mechanical and fully battery-powered vessels. A focus was placed on the life cycle of the energy sources consumed by the case ship in consideration of the South Korea's current energy importation and production status. Three life cycle stages were considered in the analysis: 'production', 'transport' and 'use'. With the aid of Sphera GaBi Software Version 2019 and its extensive data library, the environmental impacts at the energy production and transport stages were evaluated, while the same impacts at the use stage were determined based on actual laboratory measurements. The environmental performance of the two scenarios in four impact categories was discussed: global warming potential (GWP), acidification potential (AP), eutrophication potential (EP) and photochemical ozone creation potential (POCP). Results of the comparative analysis are presented based on estimates of the overall reduction in the environmental impact potential, thereby demonstrating the overall benefits of using a battery driven propulsion, with a decrease of the GWP by 35.7%, the AP by 77.6%, the EP by 87.8% and the POCP by 77.2%. A series of sensitivity analyses, however, has delivered the important message that the integration of batteries with marine transportation means may not always be the best solution. The types of energy sources used for electricity generation will be a key factor in determining whether the battery technology can ultimately contribute to cleaner shipping or not. By casting doubts on the benefits of battery propulsion, this paper is believed to offer a meaningful insight into developing a proper road map for electrifying ship propulsion toward zero emission of shipping. [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]

This study examines the convergence between green technology and building construction in Korea using both input-output and network analysis from 1990 to 2015. The industry type of the input-output tables used in the Bank of Korea is reclassified into 20 categories. The analytical results are summarized as follows: First, the construction industry is expanding its production area by adopting green technologies (KRW 2245 billion → KRW 7842 billion). Second, the impact of green technologies on the growth rate of the construction industry is greater than that of traditional construction technologies (technical coefficient 0.5410 → 0.5831). Third, the results of the analysis show that smart green technology enhances efficiency in the construction industry (multiplier coefficient 2.3673 → 2.4972). Our input-output model reveals that the smart green technology coefficient input to construction is relatively small, but the output is bigger in effects. Also, the results of the input-output analysis show that both hardware and software smart technologies continuously increase energy demand. Finally, the network analysis demonstrates the rapid convergence of smart technologies in the construction industry (pathway 13 → 22). These results demonstrate that smart green technology leads to a high value-added output in the construction industry. [ABSTRACT FROM AUTHOR]

We explore the relationship between carbon dioxide (CO2) emissions, fossil fuels energy consumption (FE), home patents (HP), foreign patents (FP), foreign direct investment (FDI), and Gross Domestic Product (GDP) in South Korea over the period 1980–2018 by employing the autoregressive distributed lag (ARDL) approach. Results reveal that FE, HP, and GDP have positive impacts on CO2 emissions, while FP have a negative impact. Furthermore, FE and FP act positively on GDP, whereas HP and CO2 emissions act negatively on it. In addition, HP, FDI, and GDP have positive effects on FP, and FE harms FP. Granger causality findings reveal that in the long run there will be two-way causality between CO2 emissions, GDP, HP, and FE, while, in the short run, there is one-way causality running from HP, FE, and FP to GDP; from FP to HP; from HP and FP to FE, and from HP and FP to CO2 emissions. Since foreign patents enhance environmental quality and boost economic growth, the South Korean government should encourage the use of foreign patents and promote clean technology home patents. [ABSTRACT FROM AUTHOR]

Coastal regions worldwide face increasing air pollution due to maritime activities. This technical note focuses on assessing the air pollution in the Daesan port area, Republic of Korea, using hourly emission measurements. Leveraging Automatic Identification System (AIS) data, we estimate vessel-induced air pollutant emissions and correlate them with real-time measurements. Vessel navigational statuses are categorized from the AIS data, enabling an estimation of fuel oil consumption. Random Forest models predict specific fuel oil consumption and maximum continuous ratings for vessels with unknown engine details. Using emission factors, we calculate the emissions (CO2, NO2, SO2, PM-10, and PM-2.5) from vessels visiting the port. These estimates are compared with actual air pollutant concentrations, revealing a qualitative relationship with an average correlation coefficient of approximately 0.33. [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]

For apartment complexes receiving medium-voltage electrical energies, the apartments can choose an electricity charging method between the single and general contracts in the Republic of Korea. In the single contract, a residential high-voltage rate is applied to the total electrical energy consumptions of households and common areas. On the other hand, in the general contract, different rate plans are applied to the electrical energy consumptions of households and their common areas, where a generic high-voltage rate plan is applied to the common consumption. Hence, depending on the amounts and composition of the consumptions, both contracts have their own strengths and weaknesses in terms of the total electricity charge. The management office of an apartment complex can select its preferred contract considering the amount and composition of the power consumptions on an annual basis. In this paper, we first formulate a model for the contracts and analyze their properties based on Monte-Carlo simulations. We then observe the contract properties through actual metering data from 30 apartment complexes in Korea. From the analysis of this paper, we can select appropriate contract for a given apartment complex and have guidelines for saving electricity charges. The greater the consumption of the electrical energy and the common area portion, the more advantageous the general contract is in terms of reducing electricity charges. [ABSTRACT FROM AUTHOR]

Energy is considered the most costly and scarce resource, and demand for it is increasing daily. Globally, a significant amount of energy is consumed in residential buildings, i.e., 30–40% of total energy consumption. An active energy prediction system is highly desirable for efficient energy production and utilization. In this paper, we have proposed a methodology to predict short-term energy consumption in a residential building. The proposed methodology consisted of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, real data collected from 4 multi-storied buildings situated in Seoul, South Korea, has been used. The collected data is provided as input to the data acquisition layer. In the pre-processing layer afterwards, several data cleaning and preprocessing schemes are applied to the input data for the removal of abnormalities. Preprocessing further consisted of two processes, namely the computation of statistical moments (mean, variance, skewness, and kurtosis) and data normalization. In the prediction layer, the feed forward back propagation neural network has been used on normalized data and data with statistical moments. In the performance evaluation layer, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) have been used to measure the performance of the proposed approach. The average values for data with statistical moments of MAE, MAPE, and RMSE are 4.3266, 11.9617, and 5.4625 respectively. These values of the statistical measures for data with statistical moments are less as compared to simple data and normalized data which indicates that the performance of the feed forward back propagation neural network (FFBPNN) on data with statistical moments is better when compared to simple data and normalized data. [ABSTRACT FROM AUTHOR]

The energy manufacturers are required to produce an accurate amount of energy by meeting the energy requirements at the end-user side. Consequently, energy prediction becomes an essential role in the electric industrial zone. In this paper, we propose the hybrid ensemble deep learning model, which combines multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM to improve the forecasting performance. These DL architectures are more popular and better than other machine learning (ML) models for time series electrical load prediction. Therefore, hourly-based energy data are collected from Jeju Island, South Korea, and applied for forecasting. We considered external features associated with meteorological conditions affecting energy. Two-year training and one-year testing data are preprocessed and arranged to reform the times series, which are then trained in each DL model. The forecasting results of the proposed ensemble model are evaluated by using mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Error metrics are compared with DL stand-alone models such as MLP, CNN, LSTM, and CNN-LSTM. Our ensemble model provides better performance than other forecasting models, providing minimum MAPE at 0.75%, and was proven to be inherently symmetric for forecasting time-series energy and demand data, which is of utmost concern to the power system sector. [ABSTRACT FROM AUTHOR]

This paper presents a delay- and energy-efficient approximate adder design exploiting an effective carry speculation scheme with error reduction. The proposed scheme reduces the delay and improves the energy efficiency without any significant accuracy degradation by effectively adding the predicted carry input using the OR operation. Additionally, the error reduction technique improves the overall computation accuracy at the expense of a few logic gates. As a result, the proposed adder achieves 3.84- and 7.79-times greater energy and energy-delay product (EDP) efficiencies than the traditional adder when implemented in 65-nm CMOS technology. In particular, when jointly analyzed with hardware accuracy, our design attains 69% and 70% reductions of the energy- and EDP-normalized mean error distance (NMED) products, respectively, compared to the other approximate adders under consideration. Furthermore, the proposed adder's efficacy over the existing adders is demonstrated by adopting it in a machine learning application. [ABSTRACT FROM AUTHOR]

We investigated the spatio-temporal variations in three key biomolecular compounds (carbohydrates, proteins, and lipids) in particulate organic matter (POM) in the Yellow Sea (YS), South Sea of Korea (SS), East China Sea (ECS), and East Sea (Sea of Japan; ES) in order to estimate the regional annual calorie production rates based on the seasonal data obtained in each region. Carbohydrates were found to be most dominant, followed by lipids across all seas. The euphotic-integral calorie contents of POM during the study period were determined as 53.5 ± 12.6 Kcal m−2 in the YS; 54.2 ± 7.5 Kcal m−2 in the SS; 35.7 ± 9.2 Kcal m−2 in the ECS; and 58.7 ± 6.2 Kcal m−2 in the ES. Utilizing seasonal carbon conversion factors and primary production rates, we estimated the annual calorie productions, which were highest in the ES (1705 Kcal m−2 y−1) and lowest in the ECS (403 Kcal m−2 y−1). This can be attributed to the significantly higher energy efficiency of phytoplankton and faster turnover rate of calorie content. However, caution must be taken when comparing these estimates regionally, considering potential variations in developmental phases among the four regions during the sampling period in 2018. The calorie production rates in this study provide valuable insights into the physiological condition of phytoplankton within specific regions. [ABSTRACT FROM AUTHOR]

Reducing fossil fuel energy consumption and greenhouse gas (GHG) emissions is vital to protecting life on the planet. Globally, emissions trading schemes are gaining traction as one way to curb emissions. However, the evidence of their effectiveness remains scarce. To address this gap, we examine the impact of Korea's Emissions Trading Scheme (KETS), the first nationally mandated cap-and-trade program in East Asia to reduce GHG emissions, relative to its pre-existing command-and-control regulation called the Target Management System for Greenhouse Gases and Energy (TMS). Using panel data for publicly traded firms between 2011 and 2017, we apply a combination of panel data estimators and matching methods. We find that KETS did not significantly reduce emissions by firms but may have improved the aggregate efficiency in energy use in the energy and manufacturing sectors. Given the low levels of noncompliance with the first phase of the policy, it is likely that firms purchased permits and offsets or used previously banked permits to meet policy targets. Our work is one of the first efforts to understand the impact of KETS and the mechanisms underpinning its impact. [ABSTRACT FROM AUTHOR]

At present, renewable energy installations are expanding to solve both environmental problems and expensive energy fuel import prices in isolated areas. However, in a small-scale power system, rapid output fluctuations of renewable energy may cause power quality problems such as voltage and frequency fluctuations in the power system. To solve this problem, the local government of Jeju Island in South Korea implemented a megawatt (MW)-class pilot project to stabilize the output power of wind turbines using an energy storage system (ESS). In this project, a 0.5 MWh lithium-ion battery was connected to a 3 MW wind turbine via 1 MW power conversion system (PCS). In this paper, the field test results were divided into four categories as follows. First, the performance of stabilizing the output of the wind turbine using ESS was confirmed. Second, the control performance of the ESS was confirmed when the wind turbine suddenly stopped due to an accident. Third, it was confirmed that the ESS discharged energy into the power system after the stabilization of the wind turbine output. Fourth, the reasons for the failure of the ESS to control output stabilization of the wind turbine were analyzed through MATLAB simulation. [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]

One positive impact of smart cities is reducing energy consumption and CO2 emission through the use of information and communication technologies (ICT). Energy transition pursues systematic changes to the low-carbon society, and it can benefit from technological and institutional advancement in smart cities. The integration of the energy transition to smart city development has not been thoroughly studied yet. The purpose of this study is to find empirical evidence of smart cities' contributions to energy transition. The hypothesis is that there is a significant difference between smart and non-smart cities in the performance of energy transition. The Smart Energy Transition Index is introduced. Index is useful to summarize the smart city component's contribution to energy transition and to enable comparison among cities. The cities in South Korea are divided into three groups: (1) first-wave smart cities that focus on smart transportation and security services; (2) second-wave smart cities that provide comprehensive urban services; and (3) non-smart cities. The results showed that second-wave smart cities scored higher than first-wave and non-smart cities, and there is a statistically significant difference among city groups. This confirms the hypothesis of this paper that smart city development can contribute to the energy transition. [ABSTRACT FROM AUTHOR]

In the recent past, the agricultural industry has rapidly digitalized in the form of smart farms through the broad usage of data analysis and artificial intelligence. Commonly, high operating costs in a smart farm are primarily due to inefficient energy usage. Therefore, accurate estimation of agricultural energy usage and environmental factors is considered as one of the significant tasks for crop growth control. The growth sequences of crops in agricultural environments like smart farms are related to agricultural energy usage and consumption. This study aims to develop and validate an algorithm that can interpret the crop growth rate response to environmental and solar energy factors based on machine learning, and to evaluate the algorithm's accuracy compared to the base model. The proposed model was determined through a comparative experiment of three representative machine learning techniques, which are random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM), considering the energy usage for environmental control is highly associated with the paprika crop growth. Through the experiment performance with real data gathered from a paprika smart farm in South Korea, the multi-level RF can effectively predict paprika growth with an accuracy of 0.88, considering data analysis of factors that use solar energy. As a result of the experiment with the suggested model, the growth factors such as leaf length, leaf width, and environmental factors were found. Furthermore, the proposed algorithm can contribute to the development of applications through analysis of the crop growth big data for various plants in agricultural environments such as a smart farm. [ABSTRACT FROM AUTHOR]

As Korea has declared to realize a net-zero emission by 2050 in the Paris Agreement, the country has begun to implement national energy efficiency policies through the Green Standard for Energy and Environmental Design (G-SEED) certification and revision of building insulation thickness standard. However, some studies have reported the ineffectiveness of G-SEED certification and insulation thickness standard in reducing the energy consumption in certain buildings. Therefore, this study investigated the effectiveness of G-SEED certification and the revision of buildings' insulation thickness standard, and evaluated the energy consumption of courthouse buildings. In addition, this study investigated the total annual energy consumption (electricity, gas, and heating energy) per gross floor area of courthouse buildings located in the central and southern regions of South Korea. Although many studies about the energy consumption analysis of non-residential buildings have been performed previously, a study evaluating the effectiveness of green certification and building insulation thickness standard on the energy consumption of courthouse buildings was performed for the first time. The results revealed that the revision of building insulation thickness standard and G-SEED certification resulted in an energy consumption efficiency of 34.61 and 31.14%, respectively. These results indicated the effectiveness of G-SEED certification and the revision of the building insulation thickness standard for enhancing energy efficiency in Korean courthouse buildings. However, some negative results were observed in the southern area, indicating that it is essential to increase the effectiveness of the building insulation thickness standard and G-SEED certification implementation. [ABSTRACT FROM AUTHOR]

This study presents an incentive model for a renewable energy source (RES) supplier, based on forecasting accuracy. The incentive model imposes responsibility of forecasting to RES suppliers by providing an additional revenue or penalty, according to forecasting accuracy. For this, a profit analysis reflecting the current environment was performed. The structure of the incentive model is designed based on the results of profit analysis. Subsequently, the price parameter was determined using an artificially generated forecasting error of the RES. In addition, the utilization of energy storage system (ESS) is analyzed, including the operation algorithm and optimal sizing, to verify the utility of the ESS when the incentive model is applied. In the case study, the actual price data and RES data from South Korea were used. The results show that the incentive model can provide a high level of profit or penalty for the RES supplier, according to the forecasting accuracy. It is also shown that the optimal capacity of the ESS based on econometrics can be reduced by more than 50% compared with the current environment, thereby reducing the burden on the ESS investment of RES suppliers. [ABSTRACT FROM AUTHOR]

Under the double pressure of the Ukrainian–Russian war and the COVID-19 pandemic, the global energy crisis has also engulfed the Korean economy. Based on this context, this article examines the macroeconomic implications of energy prices, using Korea as an example. According to an empirical study using the impulse response function, the results show that an energy price shock causes a decline in production, labor supply, capital stock, and energy consumption, as well as an increase in consumption, wages, the goods price level, inflation, and the deposit interest rate. Meanwhile, variance decomposition findings indicate that the energy price shock has a greater impact on the Korean macroeconomy than other shocks. In addition, the findings of three types of robustness tests validate the reliability and accuracy of the conclusions reached in this work. In conclusion, the information presented in this study may aid Korean policymakers in implementing appropriate countermeasures against macroeconomic volatility caused by the energy price shock. [ABSTRACT FROM AUTHOR]

[Display omitted] • Thermal potential estimation of water source and their energy systems are reviewed. • Practical potential mainly depends on the geographical and temporal potential. • Pumping-well system requires high installation costs but can use high temperature. • Critical parameters for improving performance of entire system were investigated. In recent years, the utilization of a water source heat pump system (WSHP) has been proposed in South Korea as a solution to reduce energy consumption of buildings. To practically utilize the thermal energy of a water source, it is necessary to estimate the thermal potential of water sources and apply suitable energy conversion systems. However, there are extremely limited studies that conduct in-depth analyses to estimate the thermal potential of water sources and the energy conversion systems of water source heat pumps. In this context, this paper focuses on the review of the main issues in thermal potential estimation and energy conversion systems of water source heat pump systems. The estimation of thermal potential was reviewed in terms of theoretical, geographical, environmental, technical, and temporal potentials. The components constituting the system were investigated by dividing into heat pumps, water intake systems, and air handling units. In addition, the critical parameters for improving the performance of the entire system, such as building type, water source, and water intake distance, were investigated. It is expected that this study will be a valuable supplement to enable the thermal energy utilization of water sources to meet the growing energy demands. [ABSTRACT FROM AUTHOR]

Using province-level data for South Korea, we analyze the dynamic relationship between economic growth and several energy parameters. Specifically, we decompose the growth effect into scale, composition, and technique effects, and control for regional spillovers through the use of a dynamic GMM estimator for spatial panel data models. The analyzed period, ranging from 2000 to 2017, allows us to look for changes in the regional growth effects following the implementation of the National Strategy for Green Growth in 2009. Our estimates show that the scale and composition effect tended to increase both per capita final energy use and energy intensity, outweighing reductions through the technique effect. In contrast, when considering renewable energy production, the scale and technique effect increased and the composition effect decreased the corresponding figures. Thereby, the technique effect was the main driver of increases in renewable energy production. Despite the larger, yet comparatively small share of renewables in Korea's energy mix, no considerable change of the growth effects can be observed since 2009. Therefore, to reduce the risks for the economy and achieve the political objectives of the green growth strategy throughout the whole country and in a timely manner, a stronger commitment seems to be required. [ABSTRACT FROM AUTHOR]

The relevance of the optimal design of water distribution networks lies in its applicability. Thus, the design must be as efficient and affordable as possible, meaning that it achieves a minimum level of serviceability using an appropriate amount of resources. In this context, an ideal water network requires the use of minimum-size elements to reach the minimum head pressure required for each node of the system with the lowest energy consumption. For this purpose, a relatively new meta-heuristic algorithm, called Harmony Search, was used in this study to optimize the water distribution network of Cheongna International City, Korea, in terms of the pipe diameter. Furthermore, using the same algorithm under a different approach, an assessment of the deterioration of conduits over time was conducted by analyzing the optimal roughness coefficient for the original pipe material, which provides a useful tool for decision making. Other heuristic and meta-heuristic algorithms have been applied to this type of problem; however, Harmony Search provides a convenient implementation at a reasonable computational cost. In this study, Harmony Search is demonstrated to be a valuable tool for water distribution network optimization as well as for pipe aging assessment. [ABSTRACT FROM AUTHOR]

Windows with low visible light transmittance (VLT) and g-values are preferred to reduce the building's energy consumption. However, low VLT and g-value can create an unpleasant indoor daylight environment. A glass with freely adjustable VLT and g-value as required has been developed and is called smart glass. In this study, the recently developed VLT adjustable smart glass is targeted. Some studies were conducted on the VLT adjustment status of smart windows to create an appropriate indoor lighting environment. Although research on smart glass has been conducted through ecofriendly building certification systems such as LEED in the US, BREEAM in the UK, CASBEE in Japan, and GSEED in South Korea, it was pointed out that there is a limit to creating a uniformity. Therefore, the previous study analyzed the VLT conditions to create a minimal indoor daylight environment. The purpose of this study is to propose and analyze a louver-type electrochromic façade that can create a uniform indoor illuminance. A simulation method was used, and a range of changes in indoor illuminance that could be controlled through an electrochromic louver was derived. The simulation was performed using the Rhino 6′s Grasshopper program based on the Radiance engine. Electrochromic is a class of smart glass with high VLT variable range and durability. The conditions for deriving the optimal daylight environment according to the composition, VLT, and angle of the electrochromic louver were analyzed. The evaluation was made against the criteria of LEED v4.1. Data on the composition, VLT, and angle adjustment of the electrochromic louver that can obtain a high LEED v4.1 daylight score were derived, and organized in tables. Considering the composition and angle, it was found that the daylight environment of the electrochromic louver adjusted with a VLT of 25% to 45% was excellent in composition. [ABSTRACT FROM AUTHOR]

Our study aims to deepen the understanding of personalized digital nudges by evaluating their effects on energy‐saving behavior. We conducted a field experiment with a leading smart metering company in South Korea to investigate whether customers save more energy when a personalized goal and feedback are provided, and how the impacts of nudges vary according to the types of misperception. Specifically, we focused on the behavior of customers who underestimate or overestimate their past electricity usage compared to their actual consumption. We merged daily energy consumption with a pre‐experiment survey for the customers. We found that goal‐setting and feedback mechanisms have a markedly different impact on each type of misperception. Underestimating customers reduced energy consumption only under the "goal‐setting with feedback" treatment. Conversely, overestimating customers reduced energy consumption even under the "goal‐setting without feedback" condition. The underlying mechanism is suggested as updating biased beliefs toward goal achievement. Overall, the results demonstrate that personalized nudges lead to heterogeneous behavioral responses and that service providers and policymakers can use these signals to enrich their planning of behavioral nudges. [ABSTRACT FROM AUTHOR]

• This paper analyzed the effect of improving energy, IEQ before and after full EER. • By applying the passive details, thermal bridges and air leakage was minimized. • IEQ were measured with technical equipment to increase the objectivity of results. • Effect of full EER were compared as the differences in CO2 and energy use costs. The purpose of this study was to analyze energy performance along with the effect on the improvement in the indoor environment and air quality through the empirical case of the Full Energy Efficiency Retrofit (EER) in Korea. To increase the improvement effect objectivity, changes before/after the Full EER were measured and compared through various special equipment. The Full EER implementation through energy diagnosis, indoor environment, and air quality measurement led to the derivation of seven areas of improvement. Considering the efficiency of energy cost, the optimal full EER model of alternatives based on the area was selected, and passive design techniques were applied to enhance improvement effect during design and construction. After building the Full EER, the Predicted Mean Vote (PMV)/Predicted Percentage of Dissatisfied (PPD) satisfied the criteria (before the Full EER: unsatisfied) regarding the indoor environment. Concerning indoor air quality, more than 30% improvement was observed in all the items except CO2, and criteria were met. Considering changing heat source equipment, improvement in energy performance was compared by converting the usage of energy into carbon emission; it was reduced by 5.9% and 2.7% on a cooling/heating basis, respectively, and by approximately 12% ($14,000) regarding annual energy consumption cost. This result is expected to facilitate future Full EER activation by mitigating uncertainty regarding energy consumption cost for the Full EER improvement effect and demonstrating additional improvements related to the indoor environment and air quality. [ABSTRACT FROM AUTHOR]

• A practical multi-agent problem in the automobile industry is defined. • A comprehensive approach including System Dynamics and game theory is provided. • Energy efficiency, environmental awareness, and social welfare are considered. • A function for the average of vehicle kilometers traveled in a unit of time is suggested. • Several managerial insights for both manufacturers and governments are presented. This paper looks at the effects that environmental policies have on car manufacturers. A standing problem for car manufacturers is determining both a car's price and its level of energy efficiency. The South Korean government has sustainable goals that encompass saving energy, seeking revenue, and advancing social welfare. This study highlights four decision variables in two groups for the government. The government determines the optimal value of each goal, while maintaining a threshold for the two other goals as constraints. The four decision variables are determining the car sales tax, determining the fuel tax, determining the cost of educating the public about environmental awareness, and determining the percentage of government's cooperation in the manufacturer's research and development expenditure. For the purpose of this study, a new function, average of vehicle kilometer traveled in a unit of time (AVKT), is suggested for calculating the average distance driven by a car over a given period of time, based on the cost of fuel, the car's energy efficiency, the driver's environmental awareness quotient, and the rebound effect. This newly defined multi-agent problem is solved by looking at the structure of the Stackelberg game, and equilibrium solutions are offered by implementing the backward induction procedure. By considering demand and profit for fuel, this study offers the most efficient goals and strategies for both car manufacturers and fuel producers alike. Finally, this study offers several results and insights. [ABSTRACT FROM AUTHOR]

In order to keep pace with the global trend of controlled and proper energy usage, the Korean government has enforced its energy-related regulations. However, the firms under these regulations are of the view that this approach may threaten their financial performance. In response to the growing interest of industry in Korea, this paper investigates whether energy efficiency has a positive relationship with firms' financial performance. For the purpose of accommodating various factors for measuring energy efficiency, we extended an existing two-stage network DEA (Data Envelopment Analysis) model that distinguished pure-energy efficiency and economy efficiency by developing a multi-period model with the aim to identify any change in efficiency during each period. An empirical analysis on Korean firms reveals interesting findings; First, while it is true that energy efficiency has changed over time, there is a difference in the magnitude of changes by industry. Firms in more energy-intensive industry have experienced more changes in their energy efficiency performance. Second, it is impossible to improve both categories of efficiency (i.e., pure-energy and economic efficiencies) at the same time. Thus, a firm should understand its current position in order to determine the extent and direction of efficiency improvements. Third, the energy efficiency has been found to have a significant relationship with financial performance. However, firms whose pure-energy efficiency was found to be relatively high did not always achieve better financial performance. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to explore the impact of institutional quality on growth-emissions nexus in a panel of three East Asian countries over the period from 1990 to 2016. We also incorporated energy consumption and trade openness as important indicators in the model to avoid variable bias. The results of unit root tests show that the variables are stationary at first difference. The panel cointegration test confirmed that all the variables are cointegrated. We employed Fully Modified Ordinary Least Squares and Dynamic Ordinary Least Squares methods to estimate the long-run effects of explanatory variables on economic growth. The main findings are: (i) a positive significant interaction variable between carbon emission and institutional quality indicates that efficient and impartial domestic institutions are very important to increase economic growth and decrease carbon emissions, simultaneously. (ii) Institutional quality, energy use and trade openness stimulate economic growth. (iii) The VECM granger causality test results show that there is a one-way causality runs from institutional quality to economic growth, carbon emission and energy consumption, from trade openness to carbon emissions in both short-run and long-run, from energy use to trade openness, from energy use to carbon emission in both short-run and long-run. Moreover, feedback effect is present between economic growth and carbon emission, and between economic growth and energy use in both short-run and long-run. It is therefore, necessary to regulate and strengthen the role and effectiveness of local institutions with the aim of lowering carbon emissions in the course of economic development. Image 1 • Impartial and effective domestic institutions are very important to reduce carbon emissions in the course of economic growth. • Energy consumption and trade openness do contribute to enhance economic growth. • Feedback effect is present between economic growth, carbon emissions and energy consumption in both short run and long run. [ABSTRACT FROM AUTHOR]

Temporary housing must be developed to support the long-term residence needs of disaster victims. The present study assesses a temporary housing unit, the so-called Mobile Energy Shelter House (MeSH), incorporating the "Korean Dwelling Insulation Standard" in order to reduce energy usage for cooling and heating. To assess energy performance, the characteristics of the indoor thermal environment were measured during the winter and summer seasons. In summer, at maximum insolation, the outdoor temperature was 37.6 °C and the indoor temperature of the MeSH ranged from 18 to 24 °C when the cooling system was not used. Conversely, during winter, the average outdoor temperature was -11.3 °C and the indoor temperature ranged from 16.09 to 20.63 °C when a temperature-controlled floor-heating was installed. Furthermore, the Predicted Mean Vote (PMV) was adopted to determine whether the ISO 7730 comfort criterion (i.e., PMV range from -0.5 to +0.5) was achieved. Based on the calculations presented here, PMV in summer ranged from -1.21 to +1.07 and that in winter ranged from -0.08 to -0.85, suggesting that the thermal environment is not always comfortable for occupants in either summer or winter. Nevertheless, the ISO comfort criterion can be achieved through varying air velocity in summer and changing clothing characteristics in winter. A comparison between yearly energy demand of existing characteristic temporary housing (Shelter House) and the MeSH modules used in this study was performed. The simulation results show a 60% difference in energy demand between MeSH and existing temporary housing shelter houses. [ABSTRACT FROM AUTHOR]

In South Korea, heating degree-days (HDD) and cooling degree-days (CDD) have been widely used as climatic indicators for the assessment of the impact of climate change, but arbitrary or customary base temperatures have been used for calculation of HDD and CDD. The purpose of this study is to determine real base temperatures to accurately calculate HDD and CDD for South Korea, using monthly electric energy consumption and mean temperature data from 2001 to 2010. The results reveal that the regional electricity demand generally depends on air temperature in a V-shaped curve in urban settings but in an L-shaped curve in rural settings, indicating that the sensitivity of the electricity demand to the temperature change is affected by the size of cities. The South Korean regional base temperatures, defined by a piecewise linear regression method, range from 14.7° to 19.4°C. These results suggest that the assessment of climate change impacts on the energy sector in South Korea should be carried out on a regional scale. [ABSTRACT FROM AUTHOR]

Context: Energy utilization is one of the most closely related factors affecting many areas of the smart farm, plant growth, crop production, device automation, and energy supply to the same degree. Recently, 4th industrial revolution technologies such as IoT, artificial intelligence, and big data have been widely used in smart farm environments to efficiently use energy and control smart farms' conditions. In particular, machine learning technologies with big data analysis are actively used as one of the most potent prediction methods supporting energy use in the smart farm. Purpose: This study proposes a machine learning-based prediction model for peak energy use by analyzing energy-related data collected from various environmental and growth devices in a smart paprika farm of the Jeonnam Agricultural Research and Extension Service in South Korea between 2019 and 2021. Scientific method: To find out the most optimized prediction model, comparative evaluation tests are performed using representative ML algorithms such as artificial neural network, support vector regression, random forest, K-nearest neighbors, extreme gradient boosting and gradient boosting machine, and time series algorithm ARIMA with binary classification for a different number of input features. Validate: This article can provide an effective and viable way for smart farm managers or greenhouse farmers who can better manage the problem of agricultural energy economically and environmentally. Therefore, we hope that the recommended ML method will help improve the smart farm's energy use or their energy policies in various fields related to agricultural energy. Conclusion: The seven performance metrics including R-squared, root mean squared error, and mean absolute error, are associated with these two algorithms. It is concluded that the RF-based model is more successful than in the pre-others diction accuracy of 92%. Therefore, the proposed model may be contributed to the development of various applications for environment energy usage in a smart farm, such as a notification service for energy usage peak time or an energy usage control for each device. [ABSTRACT FROM AUTHOR]

Most apartment buildings in South Korea use internal insulation systems to reduce building energy demand. However, thermal bridges such as balcony slabs in apartment buildings still lead to significant heat loss in winter, because the internal insulation system is not continuous in the balcony slab structure, and floor heating systems are commonly used in residential buildings. Therefore, this study investigates two types of thermal break elements, namely thermal break (TB) and thermal break-fiber glass reinforced polymer (TB-GFRP), to improve the thermal resistance of a balcony thermal bridge. To understand the effects of balcony thermal bridges with and without thermal break elements, the linear thermal transmittances of different balcony thermal bridges were analyzed using Physibel simulations. Then, the heating demand of a model apartment under varying thermal bridge conditions was evaluated using TRNSYS simulations. To understand the effect of insulation systems on heat loss through a balcony thermal bridge, apartments with internal and external insulation systems were studied. Whether the apartment was heating was also considered in the thermal transmittance analysis. Thus, the linear thermal transmittance of the thermal bridges with thermal break elements was reduced by more than 60%, and the heating energy demands were reduced by more than 8%. [ABSTRACT FROM AUTHOR]

This study evaluated the energy saving potential of renewable energy generation systems based on integrated solar energy in an urban environment. The solar city concept was implemented using photovoltaic (PV) and solar thermal systems. As a case study, the Sejong national pilot smart city in South Korea was selected to evaluate the renewable energy penetration rate. For evaluating the proposed renewable energy systems, the electrical and thermal loads of the smart city were estimated using field measurement data. Then, the renewable energy penetration rate of the city was evaluated. The HomerPro software was used to analyze the PV generation and operating energy consumption of the natural gas (NG) generator with a district heating network. The thermal load-supporting potential of the solar thermal system was estimated using the TRNSYS software. The results showed that the proposed urban integrated renewable energy system could meet over 30% of the renewable energy penetration rate and the levelized cost of energy and total net present cost was 7% lower than the base case system (i.e., NG generator). The proposed system also exhibited 38% less CO2 emissions than the base case system. [ABSTRACT FROM AUTHOR]

Based on the strong similarities between energy-resource-poor and fossil-fuel-centered economies (e.g., Taiwan, Japan, and South Korea) in terms of economy, culture, and energy usage characteristics, they should be analyzed collectively. This study adopted two-tier input-output structural decomposition analysis to identify the driving forces behind CO2 emissions to these countries to the formulation of effective environmental policy. Data from the World Input-Output Database was used to decompose relative changes in CO2 emissions into a range of technological advances, factor substitution, and final demand effects. Technological advances in energy (direct) contributed to a 77% reduction in Taiwan and a 34% reduction in South Korea. This is a clear indication that improving energy efficiency via technological advances should be a priority. In Japan in particular, there was a 22% reduction in CO2 emissions attributable to technological advances in materials; hence, it is recommended that Taiwan and South Korea work to extensively develop eco-industrial parks to create industry clusters to promote resource/energy efficiency and reductions in CO2 emissions. Decomposition results based on factor substitution revealed that a variety of strategies will be required, such as switching to fuels that are less carbon intensive, promoting the adoption of renewable energies, and implementing clean-coal technologies. [ABSTRACT FROM AUTHOR]

Gaining energy independence by utilizing new and renewable energy resources has become imperative for Korea. Energy recovery from Korean municipal solid waste (MSW) could be a promising option to resolve the issue, as Korean MSW is highly recyclable due to its systematic separation, collection and volume-based waste disposal system. In this study, gasification experiments were conducted on Korean municipal waste-derived solid fuel (SRF) using a fixed bed reactor by varying the equivalence ratio (ER) to assess the viability of syngas production. Experiments were also conducted on coal and biomass under similar conditions to compare the experimental results, as the gasification applicability of coal and biomass are long-established. Experimental results showed that Korean SRF could be used to recover energy in form of syngas. In particular, 50.94% cold gas efficiency and 54.66% carbon conversion ratio with a lower heating value of 12.57 MJ/Nm3 can be achieved by gasifying the SRF at 0.4 ER and 900 °C. However, compared to coal and biomass, the syngas efficiency of Korean SRF was less, which can be resolved by operating the gasification processes at high temperatures. If proper research and development activities are conducted on Korean SRF, it could be a good substitute for fossil fuels in the future. [ABSTRACT FROM AUTHOR]

Between 60% and 70% of the total energy load of a house or office occurs through the exteriors of the building, and in the case of offices, heat loss from windows and doors can approach 40%. A need for glass that can artificially control the transmittance of visible light has therefore emerged. Smart windows with suspended particle device (SPD) film can reduce energy consumption by responding to environmental conditions. To measure the effect of SPD windows on the energy requirements for cooling and heating in Korea, we installed a testbed with SPD windows. With TRNSYS18, the comparison between measurements and simulation has been made in order to validate the simulation model with respect to the modeling of an SPD window. Furthermore, the energy requirements of conventional and SPD-applied windows were compared and analyzed for a standard building that represented an actual office building. When weather for the city of Anseong and a two-speed heat pump were used to verify the simulation, the simulated electricity consumption error compared with the testbed was −1.0% for cooling and −0.9% for heating. The annual electricity consumption error was −0.9%. When TMY2 Seoul weather data were applied to the reference building, the decrease in electricity consumption for cooling in the SPD model compared with the non-SPD model was 29.1% and the increase for heating was 15.8%. Annual electricity consumption decreased by 4.1%. [ABSTRACT FROM AUTHOR]

A new government report on climate change shows that global emissions of greenhouse gases have increased to very high levels despite various policies to reduce climate change. Building energy accounts for 40% of the world's energy consumption and accounts for 33% of the world's greenhouse gas emissions. This study applied the LEAP (Long-range energy alternatives planning) model and Bass diffusion method for predicting the total energy consumption and GHG (Greenhouse Gas) emissions from the residential and commercial building sector of Sejong City in South Korea. Then, using the Bass diffusion model, three scenarios were analyzed (REST: Renewable energy supply target, BES: Building energy saving, BEP: Building energy policy) for GHG reduction. The GHG emissions for Sejong City for 2015–2030 were analyzed, and the past and future GHG emissions of the city were predicted in a Business-as-Usual (BAU) scenario. In the REST scenario, the GHG emissions would attain a 24.5% reduction and, in the BES scenario, the GHG emissions would attain 12.81% reduction by 2030. Finally, the BEP scenario shows the potential for a 19.81% GHG reduction. These results could be used to guide the planning and development of the new city. [ABSTRACT FROM AUTHOR]

Industrial equipment such as electric arc furnaces and steel mills are often associated with rapid and high load disturbances, so their power systems require additional control equipment to limit the frequency. However, proper ancillary service fees are not paid in these cases with extreme and variable load demands. The frequency regulation reserve equipment adds to power generation costs. Therefore, variable power generation loads lead to increases in the cost of energy production. We propose a load frequency control method that is applied on the customer end instead of the power supply end to reduce the operating reserve required to improve the energy efficiency of the power system. We analyzed the load fluctuation of steel mill customers using real data sampled at two-second intervals from the energy management system in Korea. We developed an automatic generation control program to simulate the power system's frequency characteristics. We also propose compensation techniques for mitigation of the system's frequency deviation at the customer end based on an energy storage system, pump storage hydro generator, customer generator, and plant process adjustment. To recover the frequency deviation, we calculated the compensation facility capacity and analyzed static characteristics, and we proved the feasibility via simulations. [ABSTRACT FROM AUTHOR]