Eom, Haneul (엄하늘)

Zhang, Xuehan

Kim, Hyun-Tae (김현태)

Lee, Moonjeong (이문정)

Lee, Junwoo (이준우)

Lee, Jeongsoo (이정수)

Shin, Dongyob (신동엽)

Alkaabi, Abdulla

Lee, Cheolmin (이철민)

Noh, Juan (노주안)

Yoo, Jaehyun (유재현)

Kim, Haelee (김해리)

Gham, Seungjun (감승준)

Jang, Joonhyeok (장준혁)

Lee, Juye (이주예)

Han, Seungman (한승만)

The research group focuses on the next generation of intelligent power distribution systems. Key areas of interest include smart grids, microgrids, and the planning and operation of advanced distribution systems. The group also focuses on the integration of artificial intelligence, data-driven prediction, optimization, protection, and stability analysis into modern power systems, aiming to create smarter, more resilient, and sustainable power networks by combining engineering fundamentals with computational methods.

본 연구 그룹은 차세대 지능형 배전 시스템에 중점을 둔다. 주요 연구 분야는 스마트 그리드, 마이크로그리드, 첨단 배전 시스템의 계획 및 운영을 포함한다. 인공 지능, 데이터 기반 예측, 최적화, 보호 및 안정성 분석을 현대 전력 시스템에 통합하여 엔지니어링 기초와 계산 방법을 결합함으로써 더욱 스마트하고 탄력적이며 지속 가능한 전력 네트워크를 구축하는 것을 목표로 한다.

A framework to identify and map uncertainties in distribution system planning

Protection, control, and operation of microgrids

Interval analysis based coordinated dispatch of battery energy storage systems and flexible loads for distribution systems considering extreme operating scenarios

Optimal electrolyzer placement strategy via probabilistic voltage stability assessment in renewable-integrated distribution systems

Impact of short circuit ratio on harmonic distortion in offshore wind farm integration

Safety analysis of thermal runaway in LTO battery cells under operational fault conditions in railway vehicles

The development of a MATLAB/Simulink-SCADA/EMS-integrated framework for microgrid pre-validation

A pricing strategy for key customers: a method considering disaster outage compensation and system stability penalty

Design and validation of a SiC-based single-to-three-phase converter for low-voltage distribution systems

Selecting an installation site for MW-scale water electrolysis systems based on grid voltage stability

Strategic intentional islanding method considering temporary overvoltage in disaster situations

Safe deep reinforcement learning-based real-time operation strategy in unbalanced distribution system

An enhanced continuation power flow method using hybrid parameterization

Analysis of underground distribution system models for secondary substations

Renewable-dominated mobility-as-a-service framework for resilience delivery in hydrogen-accommodated microgrids

Optimization of energy storage scheduling considering variable-type minimum SOC for enhanced disaster preparedness

A wind power scenario generation method based on copula functions and forecast errors

Enhanced non-communication-based protection coordination and advanced verification method using fault impedance in networked distribution systems

A study on a communication-based algorithm to improve protection coordination under high-impedance fault in networked distribution systems

An interval power flow method for radial distribution systems based on hybrid second-order cone and linear programming

Systematic planning of power-to-gas for improving photovoltaic acceptance rate: Application of the potential RES penetration index

LSTM–GAN based cloud movement prediction in satellite images for PV forecast

Optimal expansion planning of electric vehicle fast charging stations

Analysis of temporary overvoltage due to inverter-based distributed generation in networked distribution systems

Three-stage flexibility provision framework for radial distribution systems considering uncertainties

Mitigating subsynchronous torsional interaction using geometric feature extraction method

Evaluation of false data injection attacks on automatic generation control considering wind power dynamics

Impact of transformer topology on short-circuit analysis in distribution systems with inverter-based distributed generations

Stochastic second-order conic programming for optimal sizing of distributed generator units and electric vehicle charging stations

Cloud cover forecast based on correlation analysis on satellite images for short-term photovoltaic power forecasting

Control strategy for line overload and short circuit current of networked distribution systems

Probabilistic stability evaluation based on confidence interval in distribution systems with inverter-based distributed generations

Optimal scheduling of battery energy storage systems and demand response for distribution systems with high penetration of renewable energy sources

Affine-arithmetic-based microgrid interval optimization considering uncertainty and battery energy storage system degradation

Intentional islanding method with temporary overvoltage analysis

Transient stability data driven special protection scheme using deep reinforcement learning

Deep reinforcement learning-based mitigation of subsynchronous oscillation due to inverter-based resources in weak grids

Hybrid AC/DC transmission expansion planning considering offshore windfarms in Korea

Deep reinforcement learning-based operation strategy for high resilience distribution system

Optimal scheduling model for renewable energy source-assisted electric and hydrogen vehicle charging station in HPS market

A study of mid-to long-term distribution planning based on PV installation forecasting

A study on the application of game theory for optimal operation of electric vehicle charging station

A study on meshed distribution system and protection coordination using HILS system

Application of reinforcement learning to operate the unbalanced distribution system

EV charging demands and problem formulation for the optimization of the hydrogen-integrated electric vehicle charging station: a review

Non-communication protection scheme for meshed distribution systems using a new time-current-voltage characteristic

Method and apparatus for optimally operating hydrogen and electric vehicle charging station by considering HPS market

Device and method for resolving voltage imbalance in power distribution system on basis of reinforcement training model considering physical constraints

Method for selecting optimal location for electric vehicle charging station for charging load balancing

Device and method for operating electrical vehicle charging station based on game theory

Physics-informed reinforcement learning model for compensating unbalanced voltage in the distribution system

A new TSO-DSO coordination framework under high penetration levels of renewable energy sources

Device and method for parameter estimation of transformer frequency response model using machine learning

Device and method for protecting distribution system using impedance relay

Optimization method to compensate unbalanced voltage for control active power and reactive power

Method of optimal placement of electric vehicle charging station for charging demand dispersion

Apparatus and method for predicting of energy generation

Device and method for forecasting renewable energy generation using ensemble machine learning

Intelligent equipment protection method based on dynamic state estimation

Device and method for locating oscillation sources in power systems based on waveform measurement unit data using the dissipating energy flow method

Reinforcement learning-based auxiliary damping control apparatus and method for mitigating power system oscillations

Device and method for GAN-based synthetic data generation for enhancing deep learning classification performance

Device and method for a series arc fault and high impedance fault detection in low-voltage direct current system

Device and method for hybrid control for mitigation of IBR SSO considering optimized control parameters

Device and method for optimal scheduling for electric and hydrogen vehicle charging station in hydrogen portfolio standard market

Distribution system reactor for mitigating flicker

Development of system stability analysis considering potential distributed resources in future power system

A study on hybrid AC/DC transmission system planning for improving the hosting capacity of offshore windfarms

Analysis and modeling of real world inverter-based resources sub-synchronous oscillation cases

Development of a distribution system analysis program considering various distributed generations and distributed generation connection codes

Protection coordination test in network distribution systems using HILS

Massive wildfires throughout Korean eastern coast and sub-synchronous oscillation events

Parameter estimation of transformer frequency response model using machine learning methods

Economic design of grounding systems through the application of new grounding resistance reduction materials

Analysis and modeling of real world inverter based resources sub-synchronous oscillation cases

Short-circuit analysis based on interconnection method of photovoltaic systems in an industrial network

A method of selecting the capacity of the reactive power compensators at the connection point of the offshore wind farm

A Study on electrolyzer placement evaluation method considering renewable generation scenario

Maximum hosting capacity estimation in AC/DC hybrid distribution networks

Stability analysis for microgrid operation in distribution system with GFM inverter

A framework for the determination of optimal operating point of multi-terminal DC for transfering surplus power in HoNam area

Design of the physics-informed reinforcement learning model to mitigate subsynchronous oscillation

Choi, Sungyun

This paper presents a new smart grid infrastructure for active distribution systems that will allow continuous and accurate monitoring of distribution system operations and customer utilization of electric power. The infrastructure allows a complete array of applications. The paper discusses four specific applications: a) protection against downed conductors; b) load levelization; c) loss minimization; and d) reliability enhancement.

Smart grid technologies for autonomous operation and control

The prosumer market, where energy surplus can be sold to consumers or utility, has emerged with the increasing penetration of renewable energy sources. The energy management system plays a key role in the prosumer market environment by controlling dispatchable sources-for example, the energy storage systems (ESSs)-to achieve operational goals, such as economic or stable operations. This paper, in this context, proposes the coordination scheme between day-ahead optimizing planning and real-time corrective operations of the ESS, aiming to achieve economic benefit-by reducing operational costs-and contract fulfillment-by providing designated power to customers in a grid-connected industrial complex at peak-load time. All in all, the ultimate goal of the proposed methodology is to maximize the profits of prosumers in various operating conditions, such as the cloudy weather and grid supporting. This …

Optimal scheduling and operation of the ESS for prosumer market environment in grid-connected industrial complex

Subsynchronous oscillation (SSO) is classified as subsynchronous resonance, torsional interaction, or control interaction, depending on which devices or controllers are involved. Researchers have conducted numerous studies and developed methodologies on how to analyze SSO cases in different types of power systems. For these diverse mechanisms, complicated systems, and analytical methods, the overall summary and categorization of the SSO phenomena have been crucial, and numerous reviews have been published to this end. However, with the emerging inverter-based and power-electronics-based devices, in addition to the high computational capability, more advanced analytical methods have recently been researched, and more general, up-to-date literature surveys are thus needed. This study reviews the various SSO types depending on the interaction mechanisms, before investigating a number …

Subsynchronous oscillation and advanced analysis: A review

This paper proposes an effective scheme for real-time operation and protection of microgrids based on the distributed dynamic state estimation (DDSE) that is applied to a single renewable distributed energy resource (DER) or other components. First, the DDSE can be used for setting-less component protection that applies dynamic state estimation on a component under protection with real-time measurements and dynamic models of the component. Based on the results, the well-known chi-square test yields the confidence level that quantifies the goodness of fit of models to measurements, indicating the health status of the component. With this approach, renewable DERs in microgrids can be protected on an autonomous and adaptive basis. Meanwhile, the estimated state variables of each component are converted to phasor data with time tags, and then collected to the distributed energy resources management …

Effective real-time operation and protection scheme of microgrids using distributed dynamic state estimation

Because of Korea's rapid expansion in photovoltaic (PV) generation, forecasting long-term PV generation is of prime importance for utilities to establish transmission and distribution planning. However, most previous studies focused on long-term PV forecasting have been based on parametric methodologies, and most machine learning-based approaches have focused on short-term forecasting. In addition, many factors can affect local PV production, but proper feature selection is needed to prevent overfitting and multicollinearity. In this study, we perform feature-selective long-term PV power generation predictions based on an ensemble model that combines machine learning methods and traditional time-series predictions. We provide a framework for performing feature selection through correlation analysis and backward elimination, along with an ensemble prediction methodology based on feature selection …

Feature-selective ensemble learning-based long-term regional PV generation forecasting

A new protection scheme is proposed that does not require settings or the settings are simple and the need to coordinate with other protective devices has been removed. The approach can be viewed as a generalization of differential protection and it is enabled with dynamic state estimation. Specifically, the proposed protection scheme is based on continuously monitoring terminal voltages and currents of the component and other possible quantities such as tap setting, temperature, etc. as appropriate for the component under protection. The monitored data are utilized in a dynamic state estimation that continuously provides the dynamic state of the component by fitting the data to the basic model equations of the device under protection. The dynamic state is then used to determine the health of the component. Tripping or no tripping is decided on the basis of the health of the component. The basic approach, the …

Setting-less protection: Feasibility study

Owing to the high uncertainty and variability of renewable energy, power system operators require an accurate forecast method. Considering that the cloud cover significantly affects the photovoltaic (PV) generation, critical factors for accurate PV forecast are the future shape and trajectory of clouds, which weather information services hardly provide. The paper proposes an innovative PV generation forecast method based on future cloud image prediction, for which a hybrid deep learning technique combining the generative adversarial network (GAN) and the long short-term memory (LSTM) model is used. The role of GAN is to generate cloud images from random latent vectors while LSTM learns patterns of time-series input images. To verify the effectiveness of the proposed methodology, the paper compares it with various hybrid PV forecast models in terms of prediction accuracy, using field data of satellite images …

We propose an autonomous state estimation based on robotic concepts and advanced state estimation methods. An autonomous, intelligent monitoring infrastructure is proposed that reliably and automatically detects devices as they are plugged-in or -out; it identifies changes in system state and automatically updates the real-time model of the system. The real-time model is used for control, operation, and optimization of the system via application software that are not addressed in this paper. The proposed infrastructure uses modern intelligent electronic devices (IEDs) named universal monitoring protection and control units (UMPCUs) that are capable of handling three key data sets for the component that they are attached to: 1) connectivity, 2) device model, and 3) measurements. The connectivity data represent the connecting points where a device is connected to the power grid, the device model data provide …

Feasibility study: Autonomous state estimation in distribution systems

With the penetration rate of electric vehicles (EVs) increasing exponentially, the high charging load may cause new issues in future power system, e.g., voltage drop. Besides, EV Charging stations (EVCSs) located in inadequate places exacerbate these issues, causing charging demand to concentrate on a few EVCSs. Therefore, the charging demand estimation for EVCSs and their strategic placement is essential for the system operators and charging station owner. The paper proposes a method for optimal EVCS placement to achieve charging demand dispersion considering not only installation cost but also drivers’ preferences and existing charging stations. To optimally place EVCSs, this work first estimates the charging demand of existing EVCSs based on kernel density estimation. The charging demand for new and existing EVCSs is modeled using the nearest neighbor search to consider drivers’ preference …

The uncertainty has been one of the main obstacles in the operation of distribution systems with renewable energy resources whose power generation is, by nature, highly intermittent, often resulting in the unbalanced conditions of distributed networks. This unbalance degrades voltage profiles and, thus, aggravates power loss consumed in distributed networks. In this sense, the pragmatic coordination scheme between day-ahead and real-time optimization to achieve economic and stable operation is presented in this paper. At the day-ahead optimization stage, the objective aims to minimize operation costs by controlling the energy storage system such as battery systems. This stage is based primarily on historical data about the daily profiles of power demands or renewable distributed generations, so uncertain elements are always inevitable in operational procedures. The real-time monitoring system plays a …

Practical coordination between day-ahead and real-time optimization for economic and stable operation of distribution systems

Microgrids can effectively integrate renewable energy sources (RESs) and provide power for local customers. However, uncertainties of RESs and loads pose challenges to microgrid operation. The traditional point optimization method is unrealistic, and the widely used stochastic optimization (SO) method is time-consuming. Besides, battery energy storage systems (BESSs) are critical dispatchable devices to alleviate adverse effects of uncertainty, so an accurate nonlinear degradation cost model of BESSs should also be proposed. To handle such problems, the paper proposes an affine–arithmetic (AA)-based microgrid interval optimization (IO) method considering uncertainty and BESS degradation. First, the AA theory is introduced to model the RES and load variation ranges as intervals and calculate the interval uncertainty. Then, a nonlinear BESS degradation cost model is proposed, which can assess battery …

This paper presents a new approach for wide area dynamic monitoring of the system with many possible applications. One such application is discussed to provide real time stability controls. The new approach utilizes a substation based dynamic state estimation. The substation based dynamic state estimation uses data from relays, PMUs, meters, FDRs etc in the substation only thus avoiding all issues associated with transmission of data and associated time latencies. The substation based dynamic state estimator provides accurate representation of the dynamic state of the system. The dynamic state estimator runs at rates comparable to the suggested rates in the synchrophasors standard C37.118. Presently it has been implemented to execute 10 times per second thus providing the dynamic state of the substation 10 times per second. The results of the substation based dynamic state estimator are transmitted to …

Wide area dynamic monitoring and stability controls

High penetration of Converter Interfaced Generations (CIGs) presents challenges in both microgrid (μGrid) circuit and other system with CIG resources, such as wind farms and PV plants. Specifically, protection challenges are mainly brought by the insufficient separation between fault and load currents, especially for μGrids in islanded operation, and the short connection length in μGrids. In addition, CIG resources exhibit limited inertia and weak coupling to any rotating machinery, which can result in large transients during disturbances. To address the above challenges, this paper proposes a Dynamic State Estimation (DSE) based algorithm for protection and control of systems with substantial CIG resources such as a μGrid. It requires a high-fidelity dynamic model and time domain (sampled value) measurements. For μGrid circuit protection, the algorithm dependably and securely detects internal faults by checking …

Protection and control of microgrids using dynamic state estimation

The penetration of renewable energy sources (RESs) is increasing in modern power systems. However, the uncertainties of RESs pose challenges to distribution system operations, such as RES curtailment. Demand response (DR) and battery energy storage systems (BESSs) are flexible countermeasures for distribution-system operators. In this context, this study proposes an optimization model that considers DR and BESSs and develops a simulation analysis platform representing a medium-sized distribution system with high penetration of RESs. First, BESSs and DR were employed to minimize the total expenses of the distribution system operation, where the BESS model excluding binary state variables was adopted. Second, a simulation platform based on a modified IEEE 123 bus system was developed via MATLAB/Simulink for day-ahead scheduling analysis of the distribution system with a high penetration of RESs. The simulation results indicate the positive effects of DR implementation, BESS deployment, and permission for electricity sales to the upper utility on decreasing RES curtailment and distribution system operation costs. Noticeably, the RES curtailments became zero with the permission of bidirectional power flow. In addition, the adopted BESS model excluding binary variables was also validated. Finally, the effectiveness of the developed simulation analysis platform for day-ahead scheduling was demonstrated.

Transmission system operators (TSOs) are procuring increasing services through distributed energy resources (DERs), many of which are distribution-level renewable energy sources (RESs). However, RES inherent uncertainties have negative impacts on distribution system operations, which is a critical problem when distribution system operators (DSOs) respond to the TSO's flexibility requirement. This raises the question of how the DSO can arrange flexible resources in each different time stage so that the DSO can offer the preset flexibility at as little economic loss as possible. In this context, a novel three-stage flexibility provision framework for radial distribution systems considering uncertainties is proposed. In the first stage, the DSO responds to the TSO's specific flexibility requirement by scheduling priced-based demand response (PBDR), battery energy storage systems (BESSs), and electric vehicles (EVs) on …

A power transformer is an essential device for stable and reliable power transfer to customers. Therefore, accurate modeling of transformers is required for simulation-based analysis with the model. The paper proposes an efficient and straightforward parameter estimation of power transformers based on sweep frequency response analysis (SFRA) test data. The method first develops a transformer model consisting of repetitive RLC sections and mutual inductances and then aligns the simulated SFRA curve with the measured one by adjusting parameters. Note that this adjustment is based on individual parameter impacts on the SFRA curve. After aligning the two curves, the final transformer model can be obtained. In this paper, actual single-phase, three-winding transformer model parameters were estimated based on field SFRA data, showing that SFRA curves simulated from the estimated model are consistent with the measured data.

High-frequency modeling of a three-winding power transformer using sweep frequency response analysis

A phasor measurement unit (PMU) is a device that can directly measure the phase angles of voltages and currents, with high accuracy, using accurate time signals. Hence, PMUs have originally been used to improve the accuracy of transmission system state estimation. The merit of PMUs, further, extends the scope of its application to distribution system state estimation (DSSE), which has recently been studied because of the growing needs for distribution system management due to high levels of distributed energy resources. More importantly, the scarcity of measuring sensors in distribution systems highlights the role of PMUs for the DSSE. However, the full deployment of PMUs in distribution systems is practically impossible because of the high installation costs and geographically large size of these systems. Therefore, this paper investigates the adequate and economic performance of PMUs to meet the DSSE …

Performance criterion of phasor measurement units for distribution system state estimation

Photovoltaic power generation must be predicted to counter the system instability caused by an increasing number of photovoltaic power-plant connections. In this study, a method for predicting the cloud volume and power generation using satellite images is proposed. Generally, solar irradiance and cloud cover have a high correlation. However, because the predicted solar irradiance is not provided by the Meteorological Administration or a weather site, cloud cover can be used instead of the predicted solar radiation. A lot of information, such as the direction and speed of movement of the cloud is contained in the satellite image. Therefore, the spatio-temporal correlation of the cloud is obtained from satellite images, and this correlation is presented pictorially. When the learning is complete, the current satellite image can be entered at the current time and the cloud value for the desired time can be obtained. In the case of the predictive model, the artificial neural network (ANN) model with the identical hyperparameters or setting values is used for data performance evaluation. Four cases of forecasting models are tested: cloud cover, visible image, infrared image, and a combination of the three variables. According to the result, the multivariable case showed the best performance for all test periods. Among single variable models, cloud cover presented a fair performance for short-term forecasting, and visible image presented a good performance for ultra-short-term forecasting.

This paper proposes a novel method for integrating renewable energy sources (RES) and power-to-gas (P2G) technologies to optimize energy utilization efficiency as RES capacity increases. The method introduces the potential RES penetration index (PRPI) to select the appropriate capacity of P2G facilities for improved RES acceptance rates. In order to establish input constraints for RES and P2G facilities, this study determines the saturation point of RES (R-SP) and the saturation point of P2G (PG-SP). Additionally, the paper introduces the marginal capacity rate of RES to P2G (MCRG) as a useful tool for evaluating the adequacy of planned facilities. This paper's index is limited to PV as a renewable energy source. By incorporating PRPI into the input plan of P2G facilities, the proposed method improves PV's acceptance rate, contributes to facilities' economic planning, and streamlines optimization complexity …

Patent valuation is required to revitalize patent transactions, but calculating a reasonable value that consumers and suppliers could satisfy is difficult. When machine learning is used, a quantitative evaluation based on a large volume of data is possible, and evaluation can be conducted quickly and inexpensively, contributing to the activation of patent transactions. However, due to patent characteristics, securing the necessary training data is challenging because most patents are traded privately to prevent technical information leaks. In this study, the derived marketable value of a patent through event study is used for patent value evaluation, matching it with the semantic information from the patent calculated using latent Dirichlet allocation (LDA)-based topic modeling. In addition, an ensemble learning methodology that combines the predicted values of multiple predictive models was used to determine the prediction stability. Base learners with high predictive power for each fold were different, but the ensemble model that was trained on the base learners’ predicted values exceeded the predictive power of the individual models. The Wilcoxon rank-sum test indicated that the superiority of the accuracy of the ensemble model was statistically significant at the 95% significance level.

Marketable value estimation of patents using ensemble learning methodology: Focusing on US patents for the electricity sector

Smart Microgrid Lab

교수님 소개

최승연

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Interval analysis based coordinated dispatch of battery energy storage systems and flexible loads for distribution systems considering extreme operating scenarios

A framework to identify and map uncertainties in distribution system planning

Optimal electrolyzer placement strategy via probabilistic voltage stability assessment in renewable-integrated distribution systems

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

최승연

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Interval analysis based coordinated dispatch of battery energy storage systems and flexible loads for distribution systems considering extreme operating scenarios

A framework to identify and map uncertainties in distribution system planning

Optimal electrolyzer placement strategy via probabilistic voltage stability assessment in renewable-integrated distribution systems

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집