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Prof. Dr. Mun Kyeom Kim
School of Energy System Engineering, Chung-Ang University, 84, Heukseok-ro Dongjak-gu, Seoul 06974, Republic of KOREA

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Research Keywords & Expertise

0 Smart hybrid AC/DC power systems
0 Microgrid operation techniques
0 AI-based smart power networks
0 Stochastic generation scheduling
0 Big Data-based stochastic optimal operation

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Hybrid demand response

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Journal article
Published: 08 July 2021 in International Journal of Electrical Power & Energy Systems
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Microgrids account for a relatively high proportion of renewable energy utilization. Therefore, an operation method that ensures flexible resources to cope with the variability and uncertainty in renewable energy is indispensable for microgrid operators (MGOs). Here, we focus on the necessity of an optimal generation scheduling model for MGOs by considering flexible resources, namely flexible ramping products (FRPs). We propose a microgrid scheduling model including a method of utilizing electric vehicles (EVs) as a flexible resources and an operation method to secure FRPs in microgrids by applying allocation indices. The states and available capacities of EVs during each period are estimated using a Markov chain. The net load scenarios, which are considered as the target for the supply–demand balancing in the proposed method, are generated considering the uncertainty in forecasting load and renewable energy. The effectiveness and merits of the proposed model are validated through experiments on a microgrid test system that allows transactions between utilities through the tie-line. The results confirm that the proposed optimal generation scheduling model affords a reduction in microgrid operating costs and enables the stabilization of variability.

ACS Style

Dam Kim; Kyung-Bin Kwon; Mun-Kyeom Kim. Application of flexible ramping products with allocation rates in microgrid utilizing electric vehicles. International Journal of Electrical Power & Energy Systems 2021, 133, 107340 .

AMA Style

Dam Kim, Kyung-Bin Kwon, Mun-Kyeom Kim. Application of flexible ramping products with allocation rates in microgrid utilizing electric vehicles. International Journal of Electrical Power & Energy Systems. 2021; 133 ():107340.

Chicago/Turabian Style

Dam Kim; Kyung-Bin Kwon; Mun-Kyeom Kim. 2021. "Application of flexible ramping products with allocation rates in microgrid utilizing electric vehicles." International Journal of Electrical Power & Energy Systems 133, no. : 107340.

Journal article
Published: 06 July 2021 in Electronics
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This work describes an optimum utilization of hybrid photovoltaic (PV)—wind energy for residential buildings on its occurrence with a newly proposed autonomous fuzzy controller (AuFuCo). In this regard, a virtual model of a vertical axis wind turbine (VAWT) and PV system (each rated at 2 kW) are constructed in a MATLAB Simulink environment. An autonomous fuzzy inference system is applied to model primary units of the controller such as load forecasting (LF), grid power selection (GPS) switch, renewable energy management system (REMS), and fuzzy load switch (FLS). The residential load consumption pattern (4 kW of connected load) is allowed to consume energy from the grid and hybrid resources located at the demand side and classified as base, priority, short-term, and schedulable loads. The simulation results identify that the proposed controller manages the demand side management (DSM) techniques for peak load shifting and valley filling effectively with renewable sources. Also, energy costs and savings for the home environment are evaluated using the proposed controller. Further, the energy conservation technique is studied by increasing renewable conversion efficiency (18% to 23% for PV and 35% to 45% for the VAWT model), which reduces the spending of 0.5% in energy cost and a 1.25% reduction in grid demand for 24-time units/day of the simulation study. Additionally, the proposed controller is adapted for computing energy cost (considering the same load pattern) for future demand, and it is exposed that the PV-wind energy cost reduced to 6.9% but 30.6% increase of coal energy cost due to its rise in the Indian energy market by 2030.

ACS Style

MohanaSundaram Anthony; Valsalal Prasad; Raju Kannadasan; Saad Mekhilef; Mohammed Alsharif; Mun-Kyeom Kim; Abu Jahid; Ayman Aly. Autonomous Fuzzy Controller Design for the Utilization of Hybrid PV-Wind Energy Resources in Demand Side Management Environment. Electronics 2021, 10, 1618 .

AMA Style

MohanaSundaram Anthony, Valsalal Prasad, Raju Kannadasan, Saad Mekhilef, Mohammed Alsharif, Mun-Kyeom Kim, Abu Jahid, Ayman Aly. Autonomous Fuzzy Controller Design for the Utilization of Hybrid PV-Wind Energy Resources in Demand Side Management Environment. Electronics. 2021; 10 (14):1618.

Chicago/Turabian Style

MohanaSundaram Anthony; Valsalal Prasad; Raju Kannadasan; Saad Mekhilef; Mohammed Alsharif; Mun-Kyeom Kim; Abu Jahid; Ayman Aly. 2021. "Autonomous Fuzzy Controller Design for the Utilization of Hybrid PV-Wind Energy Resources in Demand Side Management Environment." Electronics 10, no. 14: 1618.

Journal article
Published: 12 May 2021 in IEEE Access
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Adequate policies or incentives are needed to support investment in renewable energy industries. However, renewable power plant operators in South Korea may face the risk of finding it difficult to recoup their capital cost in the absence of a peak-biased incentive for the power plant with energy storage systems (ESS). Incentives for the forecast accuracy of the power plant will be adopted after the abolition of the incentive. This study explores the impact of incentives on power plant operations. In this study, we propose an ESS optimization model combined with a photovoltaic power plant. We design a coordinated model of the power plant to model the structures of revenues and costs based on actual power generation and a forecast error ratio. Optimization problems from the model are formulated as mixed integer linear programming to maximize revenue with the incentive for forecast accuracy. The revenue of the incentive is designed to reflect the unit price of the incentive, varying with the forecast error ratio. The effects of incentive adoption are evaluated in comparison with the absence of incentives.

ACS Style

Woong Ko; Mun-Kyeom Kim. Operation Strategy for Maximizing Revenue of an Energy Storage System With a Photovoltaic Power Plant Considering the Incentive for Forecast Accuracy in South Korea. IEEE Access 2021, 9, 71184 -71193.

AMA Style

Woong Ko, Mun-Kyeom Kim. Operation Strategy for Maximizing Revenue of an Energy Storage System With a Photovoltaic Power Plant Considering the Incentive for Forecast Accuracy in South Korea. IEEE Access. 2021; 9 (99):71184-71193.

Chicago/Turabian Style

Woong Ko; Mun-Kyeom Kim. 2021. "Operation Strategy for Maximizing Revenue of an Energy Storage System With a Photovoltaic Power Plant Considering the Incentive for Forecast Accuracy in South Korea." IEEE Access 9, no. 99: 71184-71193.

Journal article
Published: 10 May 2021 in Electronics
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In recent years, non-linear loads on the distribution side are increasing rapidly. Notably, the electric arc furnace (EAF) is the most used non-linear load due to its diverse applications for industrial needs. However, EAF has some disadvantages like uneven distribution of heat inside the furnace, release of unwanted gases, increased level of harmonics, and Flickers in voltages. Specifically, power quality concerns are more and need comprehensive solutions. In this work, a matrix converter (MC) along with static VAR compensator (SVC) is proposed, and the hybrid exponential-hyperbolic furnace model is adapted in MATLAB platform. Simulations are carried out for different cases and the observed results are compared with existing methodologies. It was perceived that the power quality parameters such as peak current and voltages, total harmonic distortions (THDs), voltage flickers, and power factors are enhanced compared with existing methodologies. Precisely, the THD of current and voltage attains a prime rate of about 2.85% and 29.54%, respectively. Moreover, the proposed model’s voltage flicker and power factor offer a grander scale of about 1.26% and 0.9975, respectively. The enhanced scheme provides more significant advantages to the large-scale steel manufacturing plant with EAF.

ACS Style

Bharath Jebaraj; Jaison Bennet; Raju Kannadasan; Mohammed Alsharif; Mun-Kyeom Kim; Ayman Aly; Mohamed Ahmed. Power Quality Enhancement in Electric Arc Furnace Using Matrix Converter and Static VAR Compensator. Electronics 2021, 10, 1125 .

AMA Style

Bharath Jebaraj, Jaison Bennet, Raju Kannadasan, Mohammed Alsharif, Mun-Kyeom Kim, Ayman Aly, Mohamed Ahmed. Power Quality Enhancement in Electric Arc Furnace Using Matrix Converter and Static VAR Compensator. Electronics. 2021; 10 (9):1125.

Chicago/Turabian Style

Bharath Jebaraj; Jaison Bennet; Raju Kannadasan; Mohammed Alsharif; Mun-Kyeom Kim; Ayman Aly; Mohamed Ahmed. 2021. "Power Quality Enhancement in Electric Arc Furnace Using Matrix Converter and Static VAR Compensator." Electronics 10, no. 9: 1125.

Journal article
Published: 29 April 2021 in IEEE Access
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Distributed energy resources (DERs) such as wind turbines (WTs), photovoltaics (PVs), energy storage systems (ESSs), local loads, and demand response (DR) are highly valued for environmental protection. However, their volatility poses several risks to the DER aggregator while formulating a profitable strategy for bidding in the day-ahead power market. This study proposes a data-driven bidding strategy framework for a DER aggregator confronted with various uncertainties. First, a data-driven forecasting model involving gated recurrent unit–enhanced learning particle swarm optimization (GRU-ELPSO) with improved mutual information (IMI) is employed to model renewables and local loads. It is critical for a DER aggregator to accurately estimate these components before bidding in the day-ahead power market. This aids in reducing the penalty costs of forecasting errors. Second, an optimal bidding strategy that is based on the information gap decision theory (IGDT) is formulated to address market price uncertainty. The DER aggregator is assumed to be risk-averse (RA) or risk-seeker (RS), and the corresponding bidding strategies are formulated according to the risk preferences thereof. Then, an hourly bidding profile is created for the DER aggregator to bid successfully in the day-ahead power market. The proposed data-driven bidding framework is evaluated using an illustrative system wherein a dataset is obtained from the PJM market. The results reveal the effectiveness of handling uncertainty by providing accurate forecasting results. In addition, the DER aggregator can bid effectively in the day-ahead power market according to its preference for robustness or high profit, with a suitable bidding profile.

ACS Style

Hyung Joon Kim; Hyun Joon Kang; Mun Kyeom Kim. Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization. IEEE Access 2021, 9, 66420 -66435.

AMA Style

Hyung Joon Kim, Hyun Joon Kang, Mun Kyeom Kim. Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization. IEEE Access. 2021; 9 ():66420-66435.

Chicago/Turabian Style

Hyung Joon Kim; Hyun Joon Kang; Mun Kyeom Kim. 2021. "Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization." IEEE Access 9, no. : 66420-66435.

Journal article
Published: 12 April 2021 in International Journal of Electrical Power & Energy Systems
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This study evaluates a risk-based hybrid energy management problem by creating a staircase bidding profile for microgrid operators under a confidence-based incentive demand response program. Scenario-based modeling of photovoltaic, wind turbine, and local loads is achieved by implementing a stochastic/information gap decision theory-based optimization technique; the upstream grid price uncertainty is accounted for, based on the errors between the actual and predicted values. By employing a demand response aggregator, the proposed demand response can be applied to reduce the total expected operating cost and enhance the reliability of the microgrid peak-period load, primarily through peak-period load reduction. To demonstrate the applicability and validate the effectiveness of the proposed risk-based hybrid energy management problem, a case study is analyzed and solved by applying an improved particle swarm optimization algorithm. The results demonstrate that the proposed framework can pursue risk-neutral, risk-averse, and risk-seeker strategies to provide microgrid operator with more degrees of freedom for hedging against risks. In addition, to manage price uncertainty in the optimal scheduling of grid-connected microgrid, operators can build staircase bidding curves that can be effectively submitted to the day-ahead market. Further comparative analysis reveals that the proposed method demonstrates superior solution quality and diversity with a reduced computational burden.

ACS Style

H.J. Kim; M.K. Kim. Risk-based hybrid energy management with developing bidding strategy and advanced demand response of grid-connected microgrid based on stochastic/information gap decision theory. International Journal of Electrical Power & Energy Systems 2021, 131, 107046 .

AMA Style

H.J. Kim, M.K. Kim. Risk-based hybrid energy management with developing bidding strategy and advanced demand response of grid-connected microgrid based on stochastic/information gap decision theory. International Journal of Electrical Power & Energy Systems. 2021; 131 ():107046.

Chicago/Turabian Style

H.J. Kim; M.K. Kim. 2021. "Risk-based hybrid energy management with developing bidding strategy and advanced demand response of grid-connected microgrid based on stochastic/information gap decision theory." International Journal of Electrical Power & Energy Systems 131, no. : 107046.

Journal article
Published: 11 April 2021 in Electronics
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Renewable energy (RE) resource assessment is essential for planners and investors to increase its penetration capacity, and improve social and economic security. Integration of renewable power generations (RPGs) and reactive power compensators (RPCs) offer potential benefits to the existing power system network by providing a prospect for voltage control, reduction in power losses, sustainability, and reliability improvement. There are proven outcomes with these RPGs and RPCs placement in distribution systems. This work proposes a candidature location and sizing of RPGs and RPCs optimally in the Indian utility transmission power system network. The foremost purpose of this integrated operation at multiple nodes is to increase the performance of the power system concerning power loss and voltage deviation reductions, and voltage stability improvement. The loss sensitivity factor (LSF) based particle swarm optimization (PSO) technique is adapted for finding the candidature locations and sizing the RPGs and RPCs under five different configurations. Simulation outcomes display the proposed methodology can lead to extensive performance enhancement in the power system towards the sustainable development of electric energy transactions. Further, renewable resource assessment is carried out to find the viability of the candidature locations. The potential of wind and solar energy resources is assessed widely and suitable tools are used to evaluate the power extraction through RE at these selected locations. The results show that the candidature locations have great potential to evacuate the energy, which can effectively improve the existing power system technically and economically. Additionally, it is attested that the RPGs can also be utilized for power system enhancement.

ACS Style

Chandrasekaran Venkatesan; Raju Kannadasan; Mohammed Alsharif; Mun-Kyeom Kim; Jamel Nebhen. Assessment and Integration of Renewable Energy Resources Installations with Reactive Power Compensator in Indian Utility Power System Network. Electronics 2021, 10, 912 .

AMA Style

Chandrasekaran Venkatesan, Raju Kannadasan, Mohammed Alsharif, Mun-Kyeom Kim, Jamel Nebhen. Assessment and Integration of Renewable Energy Resources Installations with Reactive Power Compensator in Indian Utility Power System Network. Electronics. 2021; 10 (8):912.

Chicago/Turabian Style

Chandrasekaran Venkatesan; Raju Kannadasan; Mohammed Alsharif; Mun-Kyeom Kim; Jamel Nebhen. 2021. "Assessment and Integration of Renewable Energy Resources Installations with Reactive Power Compensator in Indian Utility Power System Network." Electronics 10, no. 8: 912.

Journal article
Published: 17 March 2021 in Sustainability
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Distributed generation (DG) and capacitor bank (CB) allocation in distribution systems (DS) has the potential to enhance the overall system performance of radial distribution systems (RDS) using a multiobjective optimization technique. The benefits of CB and DG injection in the RDS greatly depend on selecting a suitable number of CBs/DGs and their volume along with the finest location. This work proposes applying a hybrid enhanced grey wolf optimizer and particle swarm optimization (EGWO-PSO) algorithm for optimal placement and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves. On the other hand, PSO is a swarm-based metaheuristic optimization algorithm that finds the optimal solution to a problem through the movement of the particles. The advantages of both techniques are utilized to acquire mutual benefits, i.e., the exploration ability of the EGWO and the exploitation ability of the PSO. The proposed hybrid method has a high convergence speed and is not trapped in local optimal. Using this hybrid method, technical, economic, and environmental advantages are enhanced using multiobjective functions (MOF) such as minimizing active power losses, voltage deviation index (VDI), the total cost of electrical energy, and total emissions from generation sources and enhancing the voltage stability index (VSI). Six different operational cases are considered and carried out on two standard distribution systems, namely, IEEE 33- and 69-bus RDSs, to demonstrate the proposed scheme’s effectiveness extensively. The simulated results are compared with existing optimization algorithms. From the obtained results, it is observed that the proposed EGWO-PSO gives distinguished enhancements in multiobjective optimization of different conflicting objective functions and high-level performance with global optimal values.

ACS Style

Chandrasekaran Venkatesan; Raju Kannadasan; Mohammed Alsharif; Mun-Kyeom Kim; Jamel Nebhen. A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems. Sustainability 2021, 13, 3308 .

AMA Style

Chandrasekaran Venkatesan, Raju Kannadasan, Mohammed Alsharif, Mun-Kyeom Kim, Jamel Nebhen. A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems. Sustainability. 2021; 13 (6):3308.

Chicago/Turabian Style

Chandrasekaran Venkatesan; Raju Kannadasan; Mohammed Alsharif; Mun-Kyeom Kim; Jamel Nebhen. 2021. "A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems." Sustainability 13, no. 6: 3308.

Journal article
Published: 19 February 2021 in Energies
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Owing to the intermittent nature of renewable energy systems, an improved power extraction technique and modernized power modulators are to be designed to overcome power quality challenges. Attesting to this fact, this work aims to enhance the efficiency of the photovoltaic (PV) system using the BAT algorithm (BA) and enhances the overall performance of the system using modified inverter topology. Specifically, a new power electronic modulator, i.e., a simplified high gain quasi-boost inverter (SHGqBI), is implemented to eliminate the downsides of the conventional system. The proposed inverter reduces the additional components that can condense the volume of the design with reduced conduction and switching losses. The combination of BA-based PV rated 250 W and novel inverter configuration pick the global peak power with enhanced power quality. Notably, BA extracts the maximum power from the panel meritoriously with about 98.8% efficiency. This is because BA uses the global input parameters to track the maximum power of the PV panel, whereas other conventional maximum power point tracking (MPPT) techniques used limited parameters. Further, the current and voltage total harmonic distortion (THD) of the proposed inverter are recorded, which show a commendable range of 2.7% and 10.2%, respectively. In addition, the efficiency of the inverter is found to be 97%. Consequently, the overall system efficiency is calculated and found to be 97.9%, providing greater advantages over a conventional system. The system is mathematically modelled using MATLAB/Simulink and validated through an experimental setup with the laboratory prototype model.

ACS Style

Mani Rajalakshmi; Sankaralingam Chandramohan; Raju Kannadasan; Mohammed Alsharif; Mun-Kyeom Kim; Jamel Nebhen. Design and Validation of BAT Algorithm-Based Photovoltaic System Using Simplified High Gain Quasi Boost Inverter. Energies 2021, 14, 1086 .

AMA Style

Mani Rajalakshmi, Sankaralingam Chandramohan, Raju Kannadasan, Mohammed Alsharif, Mun-Kyeom Kim, Jamel Nebhen. Design and Validation of BAT Algorithm-Based Photovoltaic System Using Simplified High Gain Quasi Boost Inverter. Energies. 2021; 14 (4):1086.

Chicago/Turabian Style

Mani Rajalakshmi; Sankaralingam Chandramohan; Raju Kannadasan; Mohammed Alsharif; Mun-Kyeom Kim; Jamel Nebhen. 2021. "Design and Validation of BAT Algorithm-Based Photovoltaic System Using Simplified High Gain Quasi Boost Inverter." Energies 14, no. 4: 1086.

Journal article
Published: 18 February 2021 in Sustainability
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This work demonstrates a techno-economical assessment of wind energy potential for four passes of Tamil Nadu (Aralvaimozhi, Shencottah, Palghat, and Cumbum) with uncertainty factors. First, a potential assessment was carried out with time-series data, and the Weibull parameters, such as c (scale) and k (shape), were determined using the modern-era retrospective analysis for research and applications (MEERA) data set. Using these parameters, the mean speed, most probable speed, power density, maximum energy-carrying speed of wind power were determined. From the analysis, it was observed that all four passes had better wind parameters; notably, the Aralvaimozhi pass attained a better range of about 6.563 m/s (mean wind speed), 226 w/m2 (wind power density), 6.403 m/s (most probable wind speed), and 8.699 m/s (max wind speed). Further, uncertainty factors, such as the probability of exceedance (PoE), wind shear co-efficient (WSC), surface roughness, and wake loss effect (WLE), were evaluated. The value of PoE was found to be within the bound for all the locations, i.e., below 15%. In addition, the ranged of WSC showed a good trend between 0.05 and 0.5. Moreover, the surface length of the passes was evaluated and recorded to be 0.0024 m with a 73% energy index. Further, output power, annual energy production (AEP), capacity factor (CF), and cost of wind energy of all four passes were computed using different wind turbine ratings in two cases, i.e., with and without WLE. It was observed that there was a huge profit in loss from all the four locations due to WLE that was estimated to be Rupees (Rs.) 10.07 crores without considering interest components and Rs. 13.66 crores with interest component at a 10% annual rate of interest.

ACS Style

Varadharajan Balaguru; Nesamony Swaroopan; Kannadasan Raju; Mohammed Alsharif; Mun-Kyeom Kim. Techno-Economic Investigation of Wind Energy Potential in Selected Sites with Uncertainty Factors. Sustainability 2021, 13, 2182 .

AMA Style

Varadharajan Balaguru, Nesamony Swaroopan, Kannadasan Raju, Mohammed Alsharif, Mun-Kyeom Kim. Techno-Economic Investigation of Wind Energy Potential in Selected Sites with Uncertainty Factors. Sustainability. 2021; 13 (4):2182.

Chicago/Turabian Style

Varadharajan Balaguru; Nesamony Swaroopan; Kannadasan Raju; Mohammed Alsharif; Mun-Kyeom Kim. 2021. "Techno-Economic Investigation of Wind Energy Potential in Selected Sites with Uncertainty Factors." Sustainability 13, no. 4: 2182.

Journal article
Published: 25 January 2021 in Energies
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Owing to the increases of energy loads and penetration of renewable energy with variability, it is essential to determine the optimum capacity of the battery energy storage system (BESS) and demand response (DR) within the microgrid (MG). To accomplish the foregoing, this paper proposes an optimal MG operation approach with a hybrid method considering the game theory for a multi-agent system. The hybrid method operation includes both BESS and DR methods. The former is presented to reduce the sum of the MG operation and BESS costs using the game theory, resulting in the optimal capacity of BESS. Similarly, the DR method determines the optimal DR capacity based on the trade-off between the incentive value and capacity. To improve optimization operation, multi-agent guiding particle swarm optimization (MAG-PSO) is implemented by adjusting the best global position and position vector. The results demonstrate that the proposed approach not only affords the most economical decision among agents but also reduces the utilization cost by approximately 8.5%, compared with the base method. Furthermore, it has been revealed that the proposed MAG-PSO algorithm has superiority in terms of solution quality and computational time with respect to other algorithms. Therefore, the optimal hybrid method operation obtains a superior solution with the game theory strategy.

ACS Style

Ji-Won Lee; Mun-Kyeom Kim; Hyung-Joon Kim. A Multi-Agent Based Optimization Model for Microgrid Operation with Hybrid Method Using Game Theory Strategy. Energies 2021, 14, 603 .

AMA Style

Ji-Won Lee, Mun-Kyeom Kim, Hyung-Joon Kim. A Multi-Agent Based Optimization Model for Microgrid Operation with Hybrid Method Using Game Theory Strategy. Energies. 2021; 14 (3):603.

Chicago/Turabian Style

Ji-Won Lee; Mun-Kyeom Kim; Hyung-Joon Kim. 2021. "A Multi-Agent Based Optimization Model for Microgrid Operation with Hybrid Method Using Game Theory Strategy." Energies 14, no. 3: 603.

Journal article
Published: 17 January 2021 in Energies
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As renewable penetration increases in microgrids (MGs), the use of battery energy storage systems (BESSs) has become indispensable for optimal MG operation. Although BESSs are advantageous for economic and stable MG operation, their life degradation should be considered for maximizing cost savings. This paper proposes an optimal BESS scheduling for MGs to solve the stochastic unit commitment problem, considering the uncertainties in renewables and load. Through the proposed BESS scheduling, the life degradation of BESSs is minimized, and MG operation becomes economically feasible. To address the aforementioned uncertainties, a scenario-based method was applied using Monte Carlo simulation and the K-means clustering algorithm for scenario generation and reduction, respectively. By implementing the rainflow-counting algorithm, the BESS charge/discharge state profile was obtained. To formulate the cycle aging stress function and examine the life cycle cost (LCC) of a BESS more realistically, the nonlinear cycle aging stress function was partially linearized. Benders decomposition was adopted for minimizing the BESS cycle aging, total operating cost, and LCC. To this end, the general problem was divided into a master problem and subproblems to consider uncertainties and optimize the BESS charging/discharging scheduling problem via parallel processing. To demonstrate the effectiveness and benefits of the proposed BESS optimal scheduling in MG operation, different case studies were analyzed. The simulation results confirmed the superiority and improved performance of the proposed scheduling.

ACS Style

Yong-Rae Lee; Hyung-Joon Kim; Mun-Kyeom Kim. Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids. Energies 2021, 14, 470 .

AMA Style

Yong-Rae Lee, Hyung-Joon Kim, Mun-Kyeom Kim. Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids. Energies. 2021; 14 (2):470.

Chicago/Turabian Style

Yong-Rae Lee; Hyung-Joon Kim; Mun-Kyeom Kim. 2021. "Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids." Energies 14, no. 2: 470.

Journal article
Published: 06 December 2020 in Energies
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Owing to the growing interest in environmental problems worldwide, it is essential to schedule power generation considering the effects of pollutants. To address this, we propose an optimal approach that solves the combined economic emission dispatch (CEED) with maximum emission constraints by considering demand response (DR) program. The CEED consists of the sum of operation costs for each generator and the pollutant emissions. An environment-based demand response (EBDR) program is used to implement pollutant emission reduction and facilitate economic improvement. Through the weighting update artificial bee colony (WU-ABC) algorithm, the penalty factor that determines the weighting of the two objective functions is adjusted, and an optimal operation solution for a microgrid (MG) is then determined to resolve the CEED problem. The effectiveness and applicability of the proposed approach are demonstrated via comparative analyses at a modified grid-connected MG test system. The results confirm that the proposed approach not only satisfies emission constraints but also ensures an economically superior performance compared to other approaches. These results present a useful solution for microgrid operators considered environment issues.

ACS Style

Ho-Sung Ryu; Mun-Kyeom Kim. Combined Economic Emission Dispatch with Environment-Based Demand Response Using WU-ABC Algorithm. Energies 2020, 13, 6450 .

AMA Style

Ho-Sung Ryu, Mun-Kyeom Kim. Combined Economic Emission Dispatch with Environment-Based Demand Response Using WU-ABC Algorithm. Energies. 2020; 13 (23):6450.

Chicago/Turabian Style

Ho-Sung Ryu; Mun-Kyeom Kim. 2020. "Combined Economic Emission Dispatch with Environment-Based Demand Response Using WU-ABC Algorithm." Energies 13, no. 23: 6450.

Journal article
Published: 19 November 2020 in Energies
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Owing to the increasing utilization of renewable energy resources, distributed energy resources (DERs) become inevitably uncertain, and microgrid operators have difficulty in operating the power systems because of this uncertainty. In this study, we propose a two-stage optimization approach with a hybrid demand response program (DRP) considering a risk index for microgrids (MGs) under uncertainty. The risk-based hybrid DRP is presented to reduce both operational costs and uncertainty effect using demand response elasticity. The problem is formulated as a two-stage optimization that considers not only the expected operation costs but also risk expense of uncertainty. To address the optimization problem, an improved multi-layer artificial bee colony (IML-ABC) is incorporated into the MG operation. The effectiveness of the proposed approach is demonstrated through a numerical analysis based on a typical low-voltage grid-connected MG. As a result, the proposed approach can reduce the operation costs which are taken into account uncertainty in MG. Therefore, the two-stage optimal operation considering uncertainty has been sufficiently helpful for microgrid operators (MGOs) to make risk-based decisions.

ACS Style

Ho-Sung Ryu; Mun-Kyeom Kim. Two-Stage Optimal Microgrid Operation with a Risk-Based Hybrid Demand Response Program Considering Uncertainty. Energies 2020, 13, 6052 .

AMA Style

Ho-Sung Ryu, Mun-Kyeom Kim. Two-Stage Optimal Microgrid Operation with a Risk-Based Hybrid Demand Response Program Considering Uncertainty. Energies. 2020; 13 (22):6052.

Chicago/Turabian Style

Ho-Sung Ryu; Mun-Kyeom Kim. 2020. "Two-Stage Optimal Microgrid Operation with a Risk-Based Hybrid Demand Response Program Considering Uncertainty." Energies 13, no. 22: 6052.

Journal article
Published: 12 August 2020 in International Journal of Electrical Power & Energy Systems
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This study proposes a two-stage stochastic p-robust optimal energy trading management for microgrid, including photovoltaic, wind turbine, diesel engine, and micro turbine. To achieve optimal energy management for an microgrid, a hybrid demand response, which combines improved incentive-based and price-based demand responses, is incorporated to reduce peak period load while ensuring the reliability of the microgrid. A multi-scenario tree method is used to generate scenarios for uncertain parameters such as wind turbine, photovoltaic, loads, and market-clearing prices, where each probability density function has been discretized by certain intervals. Then, using a scenario reduction technique, a differential evolution clustering, a set of reduced scenarios can be obtained. The proposed energy management combines a Gaussian-based regularized particle swarm optimization with a fuzzy clustering technique to solve the optimization problem and determine the best compromise solution according to cost-effectiveness and reliability. The effectiveness of the proposed approach has been analyzed for a typical microgrid test system, and then the results demonstrate that the robustness can be improved substantially while guaranteeing the economical operation of microgrid. Therefore, the proposed energy trading management determines the most reasonable solution in terms of economic and reliability issues for the microgrid operator.

ACS Style

H.J. Kim; M.K. Kim; J.W. Lee. A two-stage stochastic p-robust optimal energy trading management in microgrid operation considering uncertainty with hybrid demand response. International Journal of Electrical Power & Energy Systems 2020, 124, 106422 .

AMA Style

H.J. Kim, M.K. Kim, J.W. Lee. A two-stage stochastic p-robust optimal energy trading management in microgrid operation considering uncertainty with hybrid demand response. International Journal of Electrical Power & Energy Systems. 2020; 124 ():106422.

Chicago/Turabian Style

H.J. Kim; M.K. Kim; J.W. Lee. 2020. "A two-stage stochastic p-robust optimal energy trading management in microgrid operation considering uncertainty with hybrid demand response." International Journal of Electrical Power & Energy Systems 124, no. : 106422.

Journal article
Published: 30 October 2019 in Energies
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This paper proposes an optimal energy management approach for a grid-connected microgrid (MG) by considering the demand response (DR). The multi-objective optimization framework involves minimizing the operating cost and maximizing the utility benefit. The proposed approach combines confidence-based velocity-controlled particle swarm optimization (CVCPSO) (i.e., PSO with an added confidence term and modified inertia weight and acceleration parameters), with a fuzzy-clustering technique to find the best compromise operating solution for the MG operator. Furthermore, a confidence-based incentive DR (CBIDR) strategy was adopted, which pays different incentives in different periods to attract more DR participants during the peak period and thus ensure the reliability of the MG under the peak load. In addition, the peak load shaving factor (PLSF) was employed to show that the reliability of the peak load had improved. The applicability and effectiveness of the proposed approach were verified by conducting simulations at two different scales of MG test systems. The results confirm that the proposed approach not only enhances the MG system peak load reliability, but also facilitates economical operation with better performance in terms of solution quality and diversity.

ACS Style

Hyung-Joon Kim; Mun-Kyeom Kim. Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response. Energies 2019, 12, 4142 .

AMA Style

Hyung-Joon Kim, Mun-Kyeom Kim. Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response. Energies. 2019; 12 (21):4142.

Chicago/Turabian Style

Hyung-Joon Kim; Mun-Kyeom Kim. 2019. "Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response." Energies 12, no. 21: 4142.

Journal article
Published: 10 October 2019 in Applied Sciences
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An optimal operation of new distributed energy resources can significantly advance the performance of power systems, including distribution network (DN). However, increased penetration of renewable energy may negatively affect the system performance under certain conditions. From a system operator perspective, the tie-line control strategy may aid in overcoming various problems regarding increased renewable penetration. We propose a bi-level optimization model incorporating an energy band operation scheme to ensure cooperation between DN and microgrid (MG). The bi-level formulation for the cooperation problem consists of the cost minimization of the DN and profit maximization of the MG. The goal of the upper-level is to minimize the operating costs of the DN by accounting for feedback information, including the operating costs of the MG and energy band. The lower-level aims to maximize the MG profit, simultaneously satisfying the reliability and economic targets imposed in the scheduling requirements by the DN system operator. The bi-level optimization model is solved using an advanced method based on the modified non-dominated sorting genetic algorithm II. Based on simulation results using a typical MG and an actual power system, we demonstrate the applicability, effectiveness, and validity of the proposed bi-level optimization model.

ACS Style

Ho-Young Kim; Mun-Kyeom Kim; Hyung-Joon Kim. Optimal Operational Scheduling of Distribution Network with Microgrid via Bi-Level Optimization Model with Energy Band. Applied Sciences 2019, 9, 4219 .

AMA Style

Ho-Young Kim, Mun-Kyeom Kim, Hyung-Joon Kim. Optimal Operational Scheduling of Distribution Network with Microgrid via Bi-Level Optimization Model with Energy Band. Applied Sciences. 2019; 9 (20):4219.

Chicago/Turabian Style

Ho-Young Kim; Mun-Kyeom Kim; Hyung-Joon Kim. 2019. "Optimal Operational Scheduling of Distribution Network with Microgrid via Bi-Level Optimization Model with Energy Band." Applied Sciences 9, no. 20: 4219.

Journal article
Published: 26 June 2019 in Energies
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A levelized cost of energy (LCOE) is a methodology for comparing power generation costs in the transition to renewable energy (RE). However, the major limitation of evaluating RE based on the LCOE is that it does not consider indirect costs, such as the environmental and curtailment effect. This paper proposes the real LCOE (rLCOE) approach that accounts for indirect and direct generation costs. The mathematical approach to estimating indirect costs is derived from economic theory. The indirect effects, which quantify all benefits generated due to RE, is related to the variability of the share RE in the energy generation mix. The rLCOE enhances the accuracy of the economic comparison of power generation costs and the derivation of the optimal quantities of RE because external effects are incorporated into the LCOE principles. This approach has taken into account electricity demand, fuel prices, and environmental costs for each energy source to adequately compare generation costs. Simulations have been performed to demonstrate the application of the rLCOE approach in the Korean power market. Here, the unit variation of costs with the RE share were analyzed. The results show that indirect cost savings of an additional unit of RE begin to fall in scenario 3 in contrast to the result of LCOE approach indicating higher generation costs with RE share, especially, the proportion of RE in the generation mix is higher than 20%. Thus, the optimal power generation can be evaluated using the rLCOE approach.

ACS Style

Sung-Hyun Hwang; Mun-Kyeom Kim; Ho-Sung Ryu. Real Levelized Cost of Energy with Indirect Costs and Market Value of Variable Renewables: A Study of the Korean Power Market. Energies 2019, 12, 2459 .

AMA Style

Sung-Hyun Hwang, Mun-Kyeom Kim, Ho-Sung Ryu. Real Levelized Cost of Energy with Indirect Costs and Market Value of Variable Renewables: A Study of the Korean Power Market. Energies. 2019; 12 (13):2459.

Chicago/Turabian Style

Sung-Hyun Hwang; Mun-Kyeom Kim; Ho-Sung Ryu. 2019. "Real Levelized Cost of Energy with Indirect Costs and Market Value of Variable Renewables: A Study of the Korean Power Market." Energies 12, no. 13: 2459.

Journal article
Published: 02 April 2019 in International Journal of Electrical Power & Energy Systems
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This paper presents a flexible risk control strategy with energy storage system to assist in taking a remedy action for removing a line overload in post-contingency. The problem is formulated as a flexible risk constrained-optimal power flow with multi-stage corrective action, which is classified into three types using the difference in ramp rate characteristics of the energy storage system unit and generator. The first-stage minimizes the operating costs in pre-contingency and the remaining stages help to mitigate the risk in post-contingency. With reference to the emergency transmission rating, each line has been set with different risk constraints. Here, the risk index is implemented by different types of a piecewise linear severity function according to the line overload. The proposed strategy can mitigate the risk constraint by taking a rapid corrective action using an storage units with a fast ramp rate, and it is proved using RTS 96 modified 24-bus and 73-bus systems. Based on the results of the case study, in which other strategies are compared, the proposed multi-stage risk control strategy with ESS is found yield a better performance.

ACS Style

S. Kim; Y.R. Lee; M.K. Kim. Flexible risk control strategy based on multi-stage corrective action with energy storage system. International Journal of Electrical Power & Energy Systems 2019, 110, 679 -695.

AMA Style

S. Kim, Y.R. Lee, M.K. Kim. Flexible risk control strategy based on multi-stage corrective action with energy storage system. International Journal of Electrical Power & Energy Systems. 2019; 110 ():679-695.

Chicago/Turabian Style

S. Kim; Y.R. Lee; M.K. Kim. 2019. "Flexible risk control strategy based on multi-stage corrective action with energy storage system." International Journal of Electrical Power & Energy Systems 110, no. : 679-695.

Journal article
Published: 13 September 2018 in Energies
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Investment options of transmission expansion planning (TEP) involve different lead times according to their length, technology, and environmental and social impacts. TEP planners can utilize the various lead times to deal with the risk of uncertainty. This paper proposes a novel framework for TEP under an uncertain environment, which includes investment options with various lead times. A multi-stage model is developed to reflect the different lead times in the planning method. The level of demand uncertainty is represented using a relative standard deviation. Demand uncertainty in the presented multi-stage model and its influence on the optimal decision are studied. The problem is formulated as a mixed integer linear problem to which stochastic programming is applied, and the proposed framework is illustrated from case studies on a modified Garver’s six-bus system. The case studies verify the effectiveness of the framework for TEP problems with a mathematically tractable model and demonstrates that the proposed method achieves better performance than other methods when the problems involve investment candidates with various lead times under uncertain conditions.

ACS Style

Wook-Won Kim; Jong-Keun Park; Yong-Tae Yoon; Mun-Kyeom Kim. Transmission Expansion Planning under Uncertainty for Investment Options with Various Lead-Times. Energies 2018, 11, 2429 .

AMA Style

Wook-Won Kim, Jong-Keun Park, Yong-Tae Yoon, Mun-Kyeom Kim. Transmission Expansion Planning under Uncertainty for Investment Options with Various Lead-Times. Energies. 2018; 11 (9):2429.

Chicago/Turabian Style

Wook-Won Kim; Jong-Keun Park; Yong-Tae Yoon; Mun-Kyeom Kim. 2018. "Transmission Expansion Planning under Uncertainty for Investment Options with Various Lead-Times." Energies 11, no. 9: 2429.