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The use of renewable energy sources to achieve carbon neutrality is increasing. However, the uncertainty and volatility of renewable resources are causing problems in power systems. Flexible and low-carbon resources such as Energy Storage Systems (ESSs) are essential for solving the problems of power systems and achieving greenhouse gas reduction goals. However, ESSs are not being installed because of Korea's fuel-based electricity market. To address this issue, this paper presents a method for determining the optimal investment timing of Battery Energy Storage Systems (BESSs) using the Least Squares Monte Carlo (LSMC) method. A case study is conducted considering the System Marginal Price (SMP) and Capacity Payment (CP), which are electricity rates in Korea. Revenue is calculated through the arbitrage of a 10 MW/40 MWh lithium-ion BESS, and linear programming optimization is performed for ESS scheduling to maximize revenue. The ESS revenue with uncertainty is modeled as a stochastic process using Geometric Brownian Motion (GBM), and the optimal time to invest in an ESS is determined using an LSMC simulation considering investment costs. The proposed method can be used as a decision-making tool for ESS investors to provide information on facility investments in arbitrage situations. [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]

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 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 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]

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]

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, 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]

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]

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]

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]

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]

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]

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]

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 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]

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]

Coal is currently a major electric power generation source in South Korea when considering that forty-three percent of electricity that was generated during the first quarter of 2017 came from coal. The amount of CO2 emissions from coal-fired power plant is two times larger than that from natural gas (NG)-fired power plant in the country. In this regard, the Korean government is trying to replace some amount of coal that is used for generation with NG to reduce CO2 emissions. However, the cost of NG-fired generation is about 1.25 times higher than that of coal-fired generation. Thus, the policy-makers demand information about the household willingness to pay (WTP) for the replacement to mitigate CO2 emissions. This paper applies the contingent valuation (CV) approach, and assesses the household's WTP for replacing one kWh of coal-fired power with that of NG-fired power. For this purpose, a total of 1000 South Korean households were involved in the CV survey employing a dichotomous choice question. In addition to the current electricity price, KRW 121.52 (USD 0.11) per kWh, the respondents were willing to pay KRW 25.35 (USD 0.02) per kWh. The costs of NG-fired and coal-fired generation are KRW 100.13 and 78.05, respectively, per kWh. The difference between the two is KRW22.08 per kWh, which is smaller than the mean additional WTP (KRW25.35 per kWh). The household's additional WTP is bigger than the actual additional cost. It is concluded that the switch of power generation source from coal to NG to reduce CO2 emissions can be supported by South Korean households. [ABSTRACT FROM AUTHOR]

Since the demand response (DR) market was introduced in Korea, load aggregators have also been allowed to participate in the electricity market. However, a risk-management-based method for the efficient operation of demand response resources (DRRs) has not been studied from the load aggregators' perspective. In this paper, a systematic DRR allocation method is proposed for load aggregators to operate DRRs using mean-variance portfolio theory. The proposed method is designed to determine the lowest-risk DRR portfolio for a given level of expected return using mean-variance portfolio theory from the perspective of load aggregators. The numerical results show that the proposed method can be used to reduce the risk compared to that obtained by the baseline method, in which all individual DRRs are allocated in a DRR group by maximum curtailment capability. [ABSTRACT FROM AUTHOR]

This paper aims to address the sustainability of power resources and environmental conditions for telecommunication base stations (BSs) at off-grid sites. Accordingly, this study examined the feasibility of using a hybrid solar photovoltaic (SPV)/wind turbine generator (WTG) system to feed the remote Long Term Evolution-macro base stations at off-grid sites of South Korea the energy necessary to minimise both the operational expenditure and greenhouse gas emissions. Three key aspects have been discussed: (i) optimal system architecture; (ii) energy yield analysis; and (iii) economic analysis. In addition, this study compares the feasibility of using a hybrid SPV/WTG system vs. a diesel generator. The simulation results show that by applying the proposed SPV/WTG system scheme to the cellular system, the total operational expenditure can be up to 48.52% more efficient and sustainability can be ensured with better planning by providing cleaner energy. [ABSTRACT FROM AUTHOR]

A recent increase in the number of diverse leisure activities and family outdoor activities has increased the need for the automobile-embedded vacuum cleaner. To date, this technology has not been applied in Korea and development efforts are not underway. Many of the existing portable cleaners connecting to the lighter jack of the vehicle use a direct current motor (DC motor). However, they do not have sufficient suction power to satisfy consumers; moreover, they have low durability and efficiency. In this paper, we therefore propose a technology for increasing the efficiency of the low-voltage automobile vacuum cleaner by replacing the existing DC motor with a switched reluctance motor (SRM), which has superior durability and efficiency. [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]

In this paper, we present the results of an economic feasibility study and propose a system structure to test and maintain electrical stability. In addition, we present real operation results after constructing a remote microgrid on an island in South Korea. To perform the economic feasibility study, a commercial tool called HOMER was used. The developed remote microgrid consists of a 400 kW wind turbine (WT) generator, 314 kW photovoltaic (PV) generator, 500 kVA × 2 grid forming inverter, 3 MWh lithium ion battery, and an energy management system (EMS). The predicted renewable energy fraction was 91% and real operation result was 82%. The frequency maintaining rate of the diesel power plants was 57% but the remote microgrid was 100%. To improve the operating efficiency of the remote microgrid, we investigated the output range of a diesel generator. [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]

The most important thing to operate a power system is that the power supply should be close to the power demand. In order to predict the amount of electric power transaction (EPT), it is important to choose and decide the variable and its starting date. In this paper, variables that could be acquired one the starting day of prediction were chosen. This paper designated date, temperature and special day as variables to predict the amount of EPT of the Korea Electric Power company. This paper also used temperature data from a year ago to predict the next year. To do this, we proposed single deep learning algorithms and hybrid deep learning algorithms. The former included multi-layer perceptron (MLP), convolution neural network (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), support vector machine regression (SVR), and adaptive network-based fuzzy inference system (ANFIS). The latter included LSTM + CNN and CNN + LSTM. We then confirmed the improvement of accuracy for prediction using pre-processed variables compared to original variables We also assigned two years of test data during 2017–2018 as variable data to measure high prediction accuracy. We then selected a high-accuracy algorithm after measuring root mean square error (RMSE) and mean absolute percent error (MAPE). Finally, we predicted the amount of EPT in 2018 and then measured the error for each proposed algorithm. With these acquired error data, we obtained a model for predicting the amount of EPT with a high accuracy. [ABSTRACT FROM AUTHOR]

Renewable energy generation capacity in Korea is expected to reach about 63.8 GW by 2030 based on calculations using values from a power plan survey (Korea's renewable energy power generation project plan implemented in September 2017) and the "3020" implementation plan prescribed in the 8th Basic Plan for Long-Term Electricity Supply and Demand that was announced in 2017. In order for the electrical grid to accommodate this capacity, an appropriate power system reinforcement plan is critical. In this paper, a variety of scenarios are constructed involving renewable energy capacity, interconnection measures and reinforcement measures. Based on these scenarios, the impacts of large-scale renewable energy connections on the future power systems are analyzed and a reinforcement plan is proposed based on the system assessment results. First, the scenarios are categorized according to their renewable energy interconnection capacity and electricity supply and demand, from which a database is established. A dynamic model based on inverter-based resources is applied to the scenarios here. The transmission lines, high-voltage direct current and flexible alternating current transmission systems are reinforced to increase the stability and capabilities of the power systems considered here. Reinforcement measures are derived for each stage of renewable penetration based on static and dynamic analysis processes. As a result, when large-scale renewable energy has penetrated some areas in the future in Korean power systems, the most stable systems could be optimally configured by applying interconnection measure two and reinforcement measure two as described here. To verify the performance of the proposed methodology, in this paper, comprehensive tests are performed based on predicted large-scale power systems in 2026 and 2031. Database creation and simulation are performed semi-automatically here using Power System Simulator for Engineering (PSS/E) and Python. [ABSTRACT FROM AUTHOR]

Renewable energy generation capacity in Korea is expected to reach about 63.8 GW by 2030 based on calculations using values from a power plan survey (Korea's renewable energy power generation project plan implemented in September 2017) and the "3020" implementation plan prescribed in the 8th Basic Plan for Long-Term Electricity Supply and Demand that was announced in 2017. In order for the electrical grid to accommodate this capacity, an appropriate power system reinforcement plan is critical. In this paper, a variety of scenarios are constructed involving renewable energy capacity, interconnection measures and reinforcement measures. Based on these scenarios, the impacts of large-scale renewable energy connections on the future power systems are analyzed and a reinforcement plan is proposed based on the system assessment results. First, the scenarios are categorized according to their renewable energy interconnection capacity and electricity supply and demand, from which a database is established. A dynamic model based on inverter-based resources is applied to the scenarios here. The transmission lines, high-voltage direct current and flexible alternating current transmission systems are reinforced to increase the stability and capabilities of the power systems considered here. Reinforcement measures are derived for each stage of renewable penetration based on static and dynamic analysis processes. As a result, when large-scale renewable energy has penetrated some areas in the future in Korean power systems, the most stable systems could be optimally configured by applying interconnection measure two and reinforcement measure two as described here. To verify the performance of the proposed methodology, in this paper, comprehensive tests are performed based on predicted large-scale power systems in 2026 and 2031. Database creation and simulation are performed semi-automatically here using Power System Simulator for Engineering (PSS/E) and Python. [ABSTRACT FROM AUTHOR]

As the importance of renewable generating resources has grown around the world, South Korea is also trying to expand the proportion of renewable generating resources in the power generation sector. Among the various renewable energy sources, wind generating resources are emerging as a key alternative to conventional power generations in the electricity sector in Korea accounted for 17.7 GW of total capacity by 2030. As wind generating resources are gradually replacing traditional generating resources, the system security and reliability are negatively affected because of the variability, due to intermittent outputs. Therefore, existing power grids will need to be correctly re-measured to cover the large scale of renewable energy, including wind generation. To expand the grid, we must understand the characteristics of renewable energy and the impact of its adoption in the grid. In this paper, we analyze various characteristics of wind power generation, and then we propose a probabilistic power output modeling method to consider the uncertainty of wind power generation. For the probabilistic approach, Monte-Carlo simulation is used in the modeling method. The modeled wind power outputs can help planning for the reinforcement and expansion of power systems to expand the capacity for large-scale renewable energy in the future. To verify the proposed method, some case studies were performed using empirical data, and probabilistic power flow calculation was performed by integrating large-scale wind power generation to the Jeju Island power system. The probabilistic method proposed in this paper can efficiently plan power system expansion and play a key strategy of evaluating the security of the power system through the results of stochastic power flow calculation. [ABSTRACT FROM AUTHOR]

As renewable energy resources such as wind and solar power are developing and the penetration of electric vehicles (EVs) is increasingly integrated into existing systems, uncertainty and variability in power systems have become important issues. The charging demands for EVs and wind power output are recognized as highly variable generation resources (VGRs) with uncertainty, which can cause unexpected disturbances such as short circuits. This can deteriorate the reliability of existing power systems. In response, research is required to identify the uncertainties presented by VGRs and is required to examine the ability of power system models to reflect those uncertainties. The deterministic method, which is the most basic method that is currently in use, does not reflect the uncertainty of system components. Therefore, this paper proposes a probabilistic method to assess the steady-state security of power systems, reflecting the uncertainty of VGRs using Monte Carlo simulation (MCS). In the proposed method, the empirical EVs charging demand and wind power output data are modeled as a probability distribution, and then MCS is performed, integrating the power system operation to represent the steady-state security as a probability index. To verify the method proposed in this paper, a security analysis was performed based on the systems in Jeju Island, South Korea, where the penetration of wind power and EVs is expanding rapidly. [ABSTRACT FROM AUTHOR]

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

This paper proposes a method to improve the performance of a distribution system by optimizing volt–var function of a smart inverter to alleviate the voltage deviation problem due to distributed generation connection. In order to minimize voltage deviation and line losses which represent the performance of a distribution system, this paper proposes an algorithm that optimally sets the parameters of the volt–var function. In the process of optimizing the parameters of the volt–var function, the algorithm proposed in this paper considers minimizing the contribution of the reactive power in order not to affect the output of the distributed generation. In order to apply to the field, the distribution system in South Korea considering the configuration and operation regulation was selected as a test model for algorithm verification. As a result, the system performance was successfully improved by optimally setting the volt–var function of the smart inverter which is an effective way to solve the over-voltage problem caused by distributed generation connection. This paper verified the proposed method through OpenDSS, a quasi-static time-series simulation, for the test model considering the characteristics of the distribution system in South Korea. [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]

Not only the driving for offshore wind energy capacity of 12 GW by Korea's Renewable Energy 2030 plan but also the need for the rejuvenation of existing world-class shipbuilders' infrastructures is drawing much attention to offshore wind energy in Korea, especially to the diverse substructures. Considering the deep-sea environment in the East Sea, this paper presents detailed modeling and analysis of spar-type substructure for a 5 MW floating offshore wind turbine (FOWT). This process uses a fully coupled integrated load analysis, which was carried out using FAST, a widely used integrated load analysis software developed by NREL, coupled with an in-house hydrodynamic code (UOU code). The environmental design loads were calculated from data recorded over three years at the Ulsan Marine buoy point according to the ABS and DNVGL standards. The total 12 maximum cases from DLC 6.1 were selected to evaluate the structural integrity of the spar-type substructure under the three co-directional conditions (45°, 135°, and 315°) of wind and wave. A three-dimensional (3D) structural finite element (FE) model incorporating the wind turbine tower and floating structure bolted joint connection was constructed in FEGate (pre/post-structural analysis module based on MSC NASTRAN for ship and offshore structures). The FEM analysis applied the external loads such as the structural loads due to the inertial acceleration, buoyancy, and gravity, and the environmental loads due to the wind, wave, and current. The three-dimensional FE analysis results from the MSC Nastran software showed that the designed spar-type substructure had enough strength to endure the extreme limitation in the East Sea based on the von Mises criteria. The current process of this study would be applicable to the other substructures such as the submersible type. [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]

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 power industry is rapidly changing as demand for eco-friendly and stable power supply increases along with global greenhouse gas emission regulations. Small-capacity renewable power sources represented by photovoltaics and wind are continuously increasing as a form of microgrid to supply electric power to a community or island. As a result, microgrids based on renewable resources have come into wide usage around small areas or islands in Korea. In particular, the microgrid development policy of Korea is focused on electric power quality, as well as expansion in renewable energy supply for reducing greenhouse gas emissions. From 2009, the government began to develop independent carbon-free microgrids with photovoltaic and wind powers instead of traditional power diesel generators for small islands. The goal of this paper is to investigate a feasible economic microgrid topology for implementing the carbon-free island (CFI) under an acceptable level of reliability. First, we derive three scenarios of power systems including photovoltaics, wind, battery, and fuel cells. Next, we assess economic feasibility on top of the power supply reliability of the scenarios. Then, we perform a sensitivity test to suggest economic conditions for achieving the CFI goals. Finally, we present carbon-free-based microgrid models considering the CFI policy of Korea. [ABSTRACT FROM AUTHOR]

As a large number of distributed generations are connected to the distribution system, research on the hosting capacity is actively being conducted. In particular, various methods, such as smart inverter functionality, co-located energy storage systems (ESS), and the use of on-load tap changers (OLTC), have been proposed to improve the hosting capacity. In this paper, a method to improve the hosting capacity by utilizing a solid-state transformer (SST) and its unique control capability is proposed. Lastly, the proposed method is verified in the distribution system of the Republic of Korea using the OpenDSS program. [ABSTRACT FROM AUTHOR]

Stand-alone microgrids integrating renewable energy sources have emerged as an efficient energy solution for electrifying isolated sites, such as islands and remote areas. The design of a microgrid involves various influential factors, including technological development, economic feasibility, and environmental impacts, based on the conditions and regulations of a particular site. This paper proposes a comprehensive microgrid design framework based on power system analysis and techno-economic analysis. The obtained optimal microgrid configuration satisfies both the design objective and power system performance regulations. The proposed design approach focuses on using practical data and can adapt to any microgrid design problems based on the local characteristics of a specific site. The practicality and effectiveness of the design framework are validated by applying it to the design of a stand-alone microgrid for Deokjeok Island in South Korea. The case study results justify the importance of considering site-specific characteristics and the impacts of power system conditions on the optimal microgrid design. [ABSTRACT FROM AUTHOR]

This paper examines the solution to the problem of turbine arrangement in offshore wind farms. The two main objectives of offshore wind farm planning are to minimize wake loss and maximize annual energy production (AEP). There is more wind with less turbulence offshore compared with an onshore case, which drives the development of the offshore wind farm worldwide. South Korea's offshore wind farms, which are deep in water and cannot be installed far off the coast, are affected by land complex terrain. Thus, domestic offshore wind farms should consider the separation distance from the coastline as a major variable depending on the topography and marine environmental characteristics. As a case study, a 60 MW offshore wind farm was optimized for the coast of the Busan Metropolitan City. For the analysis of wind conditions in the candidate site, wind conditions data from the meteorological tower and Ganjeolgot AWS at Gori offshore were used from 2001 to 2018. The optimization procedure is performed by evolutionary algorithm (EA) and particle swarm optimization (PSO) algorithm with the purpose of maximizing the AEP while minimizing the total wake loss. The optimization procedure can be applied to the optimized placement of WTs within a wind farm and can be extended for a variety of wind conditions and wind farm capacity. The results of the optimization were predicted to be 172,437 MWh/year under the Gori offshore wind potential, turbine layout optimization, and an annual utilization rate of 26.5%. This could convert 4.6% of electricity consumption in the Busan Metropolitan City region in 2019 in offshore wind farms. [ABSTRACT FROM AUTHOR]

The RAST-K v2, a novel nodal diffusion code, was developed at the Ulsan National Institute of Science and Technology (UNIST) for designing the cores of pressurized water reactors (PWR) and performing analyses with high accuracy and computational performance by adopting state-of-the-art calculation models and various engineering features. It is a three-dimensional multi-group nodal diffusion code developed for the steady and transient states using microscopic cross-sections generated by the STREAM code for 37 isotopes. A depletion chain containing 22 actinides and 15 fission products and burnable absorbers was solved using the Chebyshev rational approximation method. A simplified one-dimensional single-channel thermal-hydraulic calculation was performed with various values for the thermal conductivity. Advanced features such as burnup adaptation and CRUD modeling capabilities are implemented for the multi-cycle analysis of commercial reactor power plants. The performance of RAST-K v2 has been validated with the measured data of PWRs operating in Korea. Furthermore, RAST-K v2 has been coupled with a sub-channel code (CTF), fuel performance code (FRAPCON), and water chemistry code for multiphysics analyses. In this paper, the calculation models and engineering features implemented in RAST-K v2 are described, and then the application status of RAST-K v2 is presented. [ABSTRACT FROM AUTHOR]

This paper proposes a flexible modeling approach to develop a theoretical reference building (RB) for energy analysis. We designed an RB for five non-residential buildings, using dynamic simulation from statistically analyzed data of building stock in South Korea. For modeling, four subsets of data—form, envelope, system, and operation—were assessed. This study uses the autosizing function within EnergyPlus, to develop the RB. The proposed approach allows for a flexible design where capacities and flow rates of the heating, ventilation, and air-conditioning (HVAC) system match the newly defined RB model. This approach would be ideal for closing the gap between the architectural data and equipment elements. The RB developed in this study allows for performing energy performance analysis by end-use. The analysis results by the end-use can provide support for country-level greenhouse gas (GHG)-mitigation-strategy development. [ABSTRACT FROM AUTHOR]