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Floating offshore wind has received more attention due to its advantage of access to incredible wind resources over deep waters. Modeling of floating offshore wind farms is essential to evaluate their impacts on the electric power system, in which the floating offshore wind turbine should be adequately modeled for real-time simulation studies. This study proposes a simplified floating offshore wind turbine model, which is applicable for the real-time simulation of large-scale floating offshore wind farms. Two types of floating wind turbines are evaluated in this paper: the semi-submersible and spar-buoy floating wind turbines. The effectiveness of the simplified turbine models is shown by a comparison study with the detailed FAST (Fatigue, Aerodynamics, Structures, and Turbulence) floating turbine model. A large-scale floating offshore wind farm including eighty units of simplified turbines is tested in parallel simulation and real-time software (OPAL-RT). The wake effects among turbines and the effect of wind speeds on ocean waves are also taken into account in the modeling of offshore wind farms. Validation results show sufficient accuracy of the simplified models compared to detailed FAST models. The real-time results of offshore wind farms show the feasibility of the proposed turbine models for the real-time model of large-scale offshore wind farms.
Thanh-Dam Pham; Minh-Chau Dinh; Hak-Man Kim; Thai-Thanh Nguyen. Simplified Floating Wind Turbine for Real-Time Simulation of Large-Scale Floating Offshore Wind Farms. Energies 2021, 14, 4571 .
AMA StyleThanh-Dam Pham, Minh-Chau Dinh, Hak-Man Kim, Thai-Thanh Nguyen. Simplified Floating Wind Turbine for Real-Time Simulation of Large-Scale Floating Offshore Wind Farms. Energies. 2021; 14 (15):4571.
Chicago/Turabian StyleThanh-Dam Pham; Minh-Chau Dinh; Hak-Man Kim; Thai-Thanh Nguyen. 2021. "Simplified Floating Wind Turbine for Real-Time Simulation of Large-Scale Floating Offshore Wind Farms." Energies 14, no. 15: 4571.
The growth in number of electric vehicles (EVs) has resulted in increased dependence of transportation on the power sector. During power outages, especially for elongated time spans, the locally available energy may not be sufficient to fulfill the energy needs of all the EVs. Therefore, a multi-criteria EV prioritization scheme is proposed in this study to fairly allocate available energy among EVs during power outages. The five major factors considered for EV prioritization are trip purpose, EV occupants, energy gap, departure time, and customer behavior. With different combinations of the five prioritization factors, three indices are formulated, one each for social welfare, community wellbeing, and individual satisfaction of the EV owners. To this end, a multi -objective optimization problem is formulated, based on the three indices, to allocate the available power among EVs with higher index values. The formulated multi-objective optimization problem is solved using the lexicographic method, which has superior performance over the conventionally used weighted-sum method and ɛ-constraint method. The proposed method is not sensitive to the weights of individual functions and has the ability to handle multiple priority levels. In order to quantify the results, percent served and unserved indices are formulated for each of the three parameters (social welfare, community wellbeing, and individual satisfaction) and results of the proposed method are compared with those of the weighted-sum method and the ɛ-constraint method. Sensitivity analysis of different uncertain factors such as number of EVs, uncertainty in EV demand, uncertainty in renewable power, and error in battery state-of-charge estimation is also carried out. Simulation results have shown the superiority of the proposed method in allocating power to EVs during outages.
Akhtar Hussain; Hak-Man Kim. EV Prioritization and Power Allocation during Outages: A Lexicographic Method-Based Multi-Objective Optimization Approach. IEEE Transactions on Transportation Electrification 2021, PP, 1 -1.
AMA StyleAkhtar Hussain, Hak-Man Kim. EV Prioritization and Power Allocation during Outages: A Lexicographic Method-Based Multi-Objective Optimization Approach. IEEE Transactions on Transportation Electrification. 2021; PP (99):1-1.
Chicago/Turabian StyleAkhtar Hussain; Hak-Man Kim. 2021. "EV Prioritization and Power Allocation during Outages: A Lexicographic Method-Based Multi-Objective Optimization Approach." IEEE Transactions on Transportation Electrification PP, no. 99: 1-1.
Due to the uncertainty in output power of wind farm (WF) systems, a certain reserve capacity is often required in the power system to ensure service reliability and thereby increasing the operation and investment costs for the entire system. In order to reduce this uncertainty and reserve capacity, this study proposes a multi-objective stochastic optimization model to determine the set-points of the WF system. The first objective is to maximize the set-point of the WF system, while the second objective is to maximize the probability of fulfilling that set-point in the real-time operation. An increase in the probability of satisfying the set-point can reduce the uncertainty in the output power of the WF system. However, if the required probability increases, the set-point of the WF system decreases, which reduces the profitability of the WF system. Using the proposed method helps the WF operator in determining the optimal set-point for the WF system by making a trade-off between maximizing the set-point of WF and increasing the probability of fulfilling this set-point in real-time operation. This ensures that the WF system can offer an optimal set-point with a high probability of satisfying this set-point to the power system and thereby avoids a high penalty for mismatch power. In order to show the effectiveness of the proposed method, several case studies are carried out, and the effects of various parameters on the optimal set-point for the WF system are also analyzed. According to the parameters from the transmission system operator (TSO) and wind speed profile, the WF operator can easily determine the optimal set-point using the proposed strategy. A comparison of the profits that the WF system achieved with and without the proposed method is analyzed in detail, and the set-point of the WF system in different seasons is also presented.
Van-Hai Bui; Akhtar Hussain; Thai-Thanh Nguyen; Hak-Man Kim. Multi-Objective Stochastic Optimization for Determining Set-Point of Wind Farm System. Sustainability 2021, 13, 624 .
AMA StyleVan-Hai Bui, Akhtar Hussain, Thai-Thanh Nguyen, Hak-Man Kim. Multi-Objective Stochastic Optimization for Determining Set-Point of Wind Farm System. Sustainability. 2021; 13 (2):624.
Chicago/Turabian StyleVan-Hai Bui; Akhtar Hussain; Thai-Thanh Nguyen; Hak-Man Kim. 2021. "Multi-Objective Stochastic Optimization for Determining Set-Point of Wind Farm System." Sustainability 13, no. 2: 624.
A large number of wind turbine generators in the large-scale offshore wind farm system poses a challenge to the wind farm control system due to the computational burden in the central control methods or the complexity of the communication network in the decentralized control strategies. A hybrid control method based on the distributed consensus control and the central model predictive control is proposed in this study to overcome the problem. Typically, the wind turbine generators in the large-scale offshore wind farm system are clustered into several groups. The consensus-based distributed reactive power coordination control is proposed to each cluster and the centralized predictive voltage control is used to manage the total reactive powers of all clusters and regulate the voltage at the point of common coupling. The gradient-descent algorithm for the optimal design of the consensus-based cluster control is presented firstly. Based on the convergence property of the consensus control, the equivalent model of the total reactive power response of each cluster is identified, which is used for the design of the centralized predictive voltage control. Eigenvalue analysis of the proposed predictive control strategy is carried out to verify the stability of the distributed and predictive control systems. The robustness of the proposed predictive controller is evaluated in the conditions of significant model errors due to the communication delay in each cluster. A comparison study with the full distributed control based on consensus algorithm is presented to demonstrate the effectiveness of the proposed control method. The feasibility of the proposed predictive controller is evaluated by the control-hardware-in-the-loop simulation using OPAL-RT Technologies. An additional comparison study in term of computation time with the central control method is also carried out. Real-time simulation results show the superior performance of the proposed hybrid method compared to the full distributed consensus controller or the central control strategies.
Thai-Thanh Nguyen; Hak-Man Kim. Cluster-Based Predictive PCC Voltage Control of Large-Scale Offshore Wind Farm. IEEE Access 2020, 9, 4630 -4641.
AMA StyleThai-Thanh Nguyen, Hak-Man Kim. Cluster-Based Predictive PCC Voltage Control of Large-Scale Offshore Wind Farm. IEEE Access. 2020; 9 ():4630-4641.
Chicago/Turabian StyleThai-Thanh Nguyen; Hak-Man Kim. 2020. "Cluster-Based Predictive PCC Voltage Control of Large-Scale Offshore Wind Farm." IEEE Access 9, no. : 4630-4641.
The increased intensity and frequency of natural disasters have attracted the attention of researchers in the power sector to enhance the resilience of power systems. Microgrids are considered as a potential solution to enhance the resilience of power systems using local resources, such as renewable energy sources, electric vehicles (EV), and energy storage systems. However, the deployment of an additional storage system for resilience can increase the investment cost. Therefore, in this study, the usage of existing EVs in microgrids is proposed as a solution to increase the resilience of microgrids with outages without the need for additional investment. In the case of contingencies, the proposed algorithm supplies energy to islanded microgrids from grid-connected microgrids by using mobile EVs. The process for the selection of EVs for supplying energy to islanded microgrids is carried out in three steps. Firstly, islanded and networked microgrids inform the central energy management system (CEMS) about the required and available energy stored in EVs, respectively. Secondly, CEMS determines the microgrids among networked microgrids to supply energy to the islanded microgrid. Finally, the selected microgrids determine the EVs for supplying energy to the islanded microgrid. Simulations have shown the effectiveness of the proposed algorithm in enhancing the resilience of microgrids even in the absence of power connection among microgrids.
Asfand Yar Ali; Akhtar Hussain; Ju-Won Baek; Hak-Man Kim. Optimal Operation of Networked Microgrids for Enhancing Resilience Using Mobile Electric Vehicles. Energies 2020, 14, 142 .
AMA StyleAsfand Yar Ali, Akhtar Hussain, Ju-Won Baek, Hak-Man Kim. Optimal Operation of Networked Microgrids for Enhancing Resilience Using Mobile Electric Vehicles. Energies. 2020; 14 (1):142.
Chicago/Turabian StyleAsfand Yar Ali; Akhtar Hussain; Ju-Won Baek; Hak-Man Kim. 2020. "Optimal Operation of Networked Microgrids for Enhancing Resilience Using Mobile Electric Vehicles." Energies 14, no. 1: 142.
Renewable-based off-grid microgrids are considered as a potential solution for providing electricity to rural and remote communities in an environment-friendly manner. In such systems, energy storage is commonly utilized to cope with the intermittent nature of renewable energy sources. However, frequent usage may result in the fast degradation of energy storage elements. Therefore, a goal-programming-based multi-objective optimization problem has been developed in this study, which considers both the energy storage system (battery and electric vehicle) degradation and the curtailment of loads and renewables. Initially, goals are set for each of the parameters and the objective of the developed model is to minimize the deviations from those set goals. Degradation of battery and electric vehicles is quantified using deep discharging, overcharging, and cycling frequency during the operation horizon. The developed model is solved using two of the well-known approaches used for solving multi-optimization problems, the weighted-sum approach and the priority approach. Five cases are simulated for each of the methods by varying weight/priority of different objectives. Besides this, the impact of weight and priority values selected by policymakers is also analyzed. Simulation results have shown the superiority of the weighted-sum method over the priority method in solving the formulated problem.
Akhtar Hussain; Hak-Man Kim. Goal-Programming-Based Multi-Objective Optimization in Off-Grid Microgrids. Sustainability 2020, 12, 8119 .
AMA StyleAkhtar Hussain, Hak-Man Kim. Goal-Programming-Based Multi-Objective Optimization in Off-Grid Microgrids. Sustainability. 2020; 12 (19):8119.
Chicago/Turabian StyleAkhtar Hussain; Hak-Man Kim. 2020. "Goal-Programming-Based Multi-Objective Optimization in Off-Grid Microgrids." Sustainability 12, no. 19: 8119.
In the conventional operation of a wind farm (WF) system, the operation point of each wind turbine generator (WTG) is determined to capture maximum energy individually using maximum power point tracking (MPPT) algorithm. However, this operation strategy might not ensure the maximum output power of WF due to wake effect among WTGs. Therefore, this paper develops a multi-agent-based cooperative learning strategy among WTGs using deep reinforcement learning to enhance the overall efficiency of WF by minimizing the wake effect. WTG agents are learnable units and they interact with others as an extensive-form game based on a cooperative model to achieve a common goals (i.e. maximum output power of the WF). In this game, WTG agents carry out their actions sequentially and measure a common reward which is used to update the knowledge of all agents. During the training process, WTG agents use different deep neural networks (DNNs) to improve their actions for achieving the higher reward in the long run by optimizing the weights of DNNs in each learning step. After the training process, WTG agents are able to determine optimal set-points with different input information to minimize the wake effect and to maximize the output power of the WF. Moreover, an operation strategy for the entire WF system is proposed to ensure that the WF always complies with grid-code constraints from transmission system operators, including the requirement of limited power and reserve power. In order to show the effectiveness of the proposed method, a comparison between the results using the proposed method and the conventional MPPT method is also presented in different cases, and the results show that the proposed method can increase the output power of the WF in the range of 1.99% to 4.11% with different layouts.
Van-Hai Bui; Thai-Thanh Nguyen; Hak-Man Kim. Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach. IEEE Access 2020, 8, 173136 -173146.
AMA StyleVan-Hai Bui, Thai-Thanh Nguyen, Hak-Man Kim. Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach. IEEE Access. 2020; 8 (99):173136-173146.
Chicago/Turabian StyleVan-Hai Bui; Thai-Thanh Nguyen; Hak-Man Kim. 2020. "Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach." IEEE Access 8, no. 99: 173136-173146.
This paper proposed a distributed reactive power coordination and voltage control of offshore wind farm based on the leader-following diffusion algorithm. Designating several wind turbine generators (WTGs) as the leaders to receive the information of voltage at point of common coupling (PCC), the reactive power generations required to minimize the voltage deviation could be computed by these leaders. The required reactive power generations are diffused throughout all WTGs by the diffusion algorithm, resulting in the coordinated operation of WTGs to regulate the PCC voltage. The proposed offshore wind farm controller is based on the hierarchical control strategy, which consists of primary and secondary layers. The primary layer is responsible for the inner current, voltage or power regulations whereas the secondary layer is based on the proposed diffusion algorithm to achieve the coordinated operation among WTGs. The proposed strategy could maintain accurate reactive power sharing among WTGs and regulate the PCC voltage. A comparison study with the conventional consensus-based control is presented to show the effectiveness of the proposed diffusion controller. The comparison results show the better performance of the proposed method in terms of dynamic responses of PCC voltage and reactive power coordination. Simulation scenarios of constant wind speed, variable wind speed, and voltage sag in the utility grid are carried out to evaluate the performance of proposed method. The proposed diffusion control is tested either in a small or large offshore wind farm systems. Effect of communication delay on the performance of proposed diffusion control is also described. An experiment of the small-scale wind farm system is conducted to show the feasibility of proposed diffusion strategy.
Thai-Thanh Nguyen; Hak-Man Kim. Leader-Following Diffusion-Based Reactive Power Coordination and Voltage Control of Offshore Wind Farm. IEEE Access 2020, 8, 149555 -149568.
AMA StyleThai-Thanh Nguyen, Hak-Man Kim. Leader-Following Diffusion-Based Reactive Power Coordination and Voltage Control of Offshore Wind Farm. IEEE Access. 2020; 8 (99):149555-149568.
Chicago/Turabian StyleThai-Thanh Nguyen; Hak-Man Kim. 2020. "Leader-Following Diffusion-Based Reactive Power Coordination and Voltage Control of Offshore Wind Farm." IEEE Access 8, no. 99: 149555-149568.
This paper proposes a distributed control of the microgrid (MG) system based on the diffusion algorithm. Unlike the existing decentralized strategy that focuses on the economic operation of the MG system, the proposed strategy performs secondary frequency regulation in addition to the optimization of the MG system. The hierarchical control technique is employed in this study, where the primary layer is responsible for power control and the secondary layer is responsible for the frequency control and economic operation of the MG system. A tested MG system with four distributed generations (DGs) is considered. Three types of communication topologies are evaluated in this study, which are line, ring, and full topologies. The proposed controller is compared to the conventional consensus controller to show the effectiveness of the proposed diffusion controller. Simulation results show that the proposed diffusion strategy improves the convergence speed of the distributed control, resulting in the improvement of power responses and frequency quality of the MG system. The tested system is implemented in the MATLAB/Simulink environment to show the feasibility of the proposed diffusion controller.
Su-Been Hong; Thai-Thanh Nguyen; Jinhong Jeon; Hak-Man Kim. Distributed Operation of Microgrids Considering Secondary Frequency Restoration Based on the Diffusion Algorithm. Energies 2020, 13, 3207 .
AMA StyleSu-Been Hong, Thai-Thanh Nguyen, Jinhong Jeon, Hak-Man Kim. Distributed Operation of Microgrids Considering Secondary Frequency Restoration Based on the Diffusion Algorithm. Energies. 2020; 13 (12):3207.
Chicago/Turabian StyleSu-Been Hong; Thai-Thanh Nguyen; Jinhong Jeon; Hak-Man Kim. 2020. "Distributed Operation of Microgrids Considering Secondary Frequency Restoration Based on the Diffusion Algorithm." Energies 13, no. 12: 3207.
The existing distributed operation schemes for microgrids lack the ability to determine the power selling to the grid during normal operation mode and are unable to provide service reliability to critical loads, during islanded operation mode. In order to overcome these issues, in this study, we have proposed a distributed operation method for both grid-connected and islanded modes of microgrids. Unlike the existing studies, where the utility grid is considered as a dispatchable generator, the bi-directional flow of power with the grid is considered in this study. Similarly, different load agents are considered for different priority loads to assure the service reliability to the critical loads during islanding. A two-step operation method is proposed for both grid-connected and islanded mode operations. During the first step, each agent in the network shares information with its neighboring agents to determine the total load and available renewable power in the network. Whereas, in the second step, each agent in the network determines the optimal operation points based on the local information received from the neighboring agents. Moreover, a modified cost function for the battery is also proposed in this study, which utilizes the information of market price and load to enhance the battery operation. A comparison is made between the centralized method, conventional distributed method, and the proposed distributed operation method. Simulation results have proved the effectiveness of the proposed method for realizing distributed operation for microgrids in both grid-connected and islanded modes.
Haesum Ali; Akhtar Hussain; Van-Hai Bui; Hak-Man Kim. Consensus Algorithm-Based Distributed Operation of Microgrids During Grid-Connected and Islanded Modes. IEEE Access 2020, 8, 78151 -78165.
AMA StyleHaesum Ali, Akhtar Hussain, Van-Hai Bui, Hak-Man Kim. Consensus Algorithm-Based Distributed Operation of Microgrids During Grid-Connected and Islanded Modes. IEEE Access. 2020; 8 (99):78151-78165.
Chicago/Turabian StyleHaesum Ali; Akhtar Hussain; Van-Hai Bui; Hak-Man Kim. 2020. "Consensus Algorithm-Based Distributed Operation of Microgrids During Grid-Connected and Islanded Modes." IEEE Access 8, no. 99: 78151-78165.
In order to determine the optimal size of energy storage system (ESS) in a fast electric vehicle (EV) charging sta-tion, minimization of ESS cost, enhancement of EVs’ resilience, and reduction of peak load are considered in this study. Especially, the resilience aspect of the EVs is focused due to its significance for EVs during power outages. Firstly, stochastic load of the fast charging station (FCS) and the resilience load of the EVs are es-timated using probability distribution functions. This information is utilized to maintain energy level in the ESS to ensure resilience of EVs during power outages. Then, the annualized cost of the ESS is determined using the annual interest rate and life-time of ESS components. Finally, the optimal ESS size is determined using the annualized ESS cost, penalty cost for buying power during peak hours, and penalty cost for resilience violations. Simulations along with sensitivity analysis of uncertainties (market price, arrival time of EVs, and residual energy level of EVs), number of EVs in the FCS, and converter ratings are conducted. Simulation results have shown that increasing the penalty cost for peak intervals is a viable solution to decrease the peak load while controlling the cost of the FCS.
Akhtar Hussain; Van-Hai Bui; Hak-Man Kim. Optimal Sizing of Battery Energy Storage System in a Fast EV Charging Station Considering Power Outages. IEEE Transactions on Transportation Electrification 2020, 6, 453 -463.
AMA StyleAkhtar Hussain, Van-Hai Bui, Hak-Man Kim. Optimal Sizing of Battery Energy Storage System in a Fast EV Charging Station Considering Power Outages. IEEE Transactions on Transportation Electrification. 2020; 6 (2):453-463.
Chicago/Turabian StyleAkhtar Hussain; Van-Hai Bui; Hak-Man Kim. 2020. "Optimal Sizing of Battery Energy Storage System in a Fast EV Charging Station Considering Power Outages." IEEE Transactions on Transportation Electrification 6, no. 2: 453-463.
High temperature superconducting (HTS) power cables are a potential solution for the grid integration of offshore wind farms since the HTS cable can conduct bulk wind power at low voltage levels. However, the transient current through the HTS cable in cases of low voltage ride through (LVRT) operation has a negative impact on the HTS cable operation due to the quenching phenomenon. This paper analyzes the impact of LVRT control strategies on the HTS cable operation. In addition, a coordinated control of wind turbines for LVRT improvement of an offshore wind farm is proposed. The feasibility of the HTS cable application for the grid connection of offshore wind farms is also discussed in this study. The proposed controller is designed for the wind turbine generator based on a type-4 permanent magnet synchronous generator. In the proposed controller, the transient current through the HTS cable is reduced by regulating the machine side power during fault conditions. The feasibility of the proposed controller is validated in the PSCAD/EMTDC program (Manitoba Hydro International Ltd., Winnipeg, Manitoba, Canada, version 4.2.1). The effects of transient current on the cable temperatures and resistances are analyzed in this study. Simulation results show that the proposed control strategy could reduce the transient current and temperature rise of the HTS cable.
Thai-Thanh Nguyen; Hak-Man Kim; Hyung Suk Yang. Impacts of a LVRT Control Strategy of Offshore Wind Farms on the HTS Power Cable. Energies 2020, 13, 1194 .
AMA StyleThai-Thanh Nguyen, Hak-Man Kim, Hyung Suk Yang. Impacts of a LVRT Control Strategy of Offshore Wind Farms on the HTS Power Cable. Energies. 2020; 13 (5):1194.
Chicago/Turabian StyleThai-Thanh Nguyen; Hak-Man Kim; Hyung Suk Yang. 2020. "Impacts of a LVRT Control Strategy of Offshore Wind Farms on the HTS Power Cable." Energies 13, no. 5: 1194.
In order to minimize the peak load of electric vehicles (EVs) and enhance the resilience of fast EV charging stations, several sizing methods for deployment of the stationary energy storage system (ESS) have been proposed. However, methods for assessing the optimality of the obtained results and performance of the determined sizes under different conditions are missing. In order to address these issues, a two-step approach is proposed in this study, which comprises of optimality analysis and performance evaluation steps. In the case of optimality analysis, random sizes of battery and converter (scenarios) are generated using Monte Carlo simulations and their results are compared with the results of sizes obtained from sizing methods. In order to carry out this analysis, two performance analysis indices are proposed in this study, which are named the cost index and the power index. These indices respectively determine the performance of the determined sizes in terms of total network cost and performance ratio of power bought during peak intervals and investment cost of the ESS. During performance evaluation, the performance of the determined sizes (battery and converter) are analyzed for different seasons of the year and typical public holidays. Typical working days and holidays have been analyzed for each season of the year and suitability of the determined sizes is analyzed. Simulation results have proved that the proposed method is suitable for determining the optimality of results obtained by different sizing methods.
Akhtar Hussain; Van-Hai Bui; Ju-Won Baek; Hak-Man Kim. Stationary Energy Storage System for Fast EV Charging Stations: Optimality Analysis and Results Validation. Energies 2020, 13, 230 .
AMA StyleAkhtar Hussain, Van-Hai Bui, Ju-Won Baek, Hak-Man Kim. Stationary Energy Storage System for Fast EV Charging Stations: Optimality Analysis and Results Validation. Energies. 2020; 13 (1):230.
Chicago/Turabian StyleAkhtar Hussain; Van-Hai Bui; Ju-Won Baek; Hak-Man Kim. 2020. "Stationary Energy Storage System for Fast EV Charging Stations: Optimality Analysis and Results Validation." Energies 13, no. 1: 230.
The uses of high-temperature superconducting (HTS) cables pose a challenge of power system protection since the impedance of the HTS cable is varied during fault conditions. The protection systems should be designed properly to ensure the reliability and stability of the whole system. This paper presents a fault analysis of the co-axial HTS cable in the mesh system and proposes a coordinated protection system. In the proposed protection system, the main protection is the differential current relay whereas the backup protections are the overcurrent and directional overcurrent relays. The normal and abnormal relay operations are considered to analyze the transient fault current in the HTS cable and evaluate the performance of the proposed coordinated protection system. Characteristics of cable impedances and temperatures under various fault conditions are presented. The proposed protection scheme is validated by the simulation in the PSCAD/EMTDC program. Simulation results show that the coordinated protection scheme could successfully protect the HTS cables in both normal and abnormal relay operations.
Thai-Thanh Nguyen; Woon-Gyu Lee; Hak-Man Kim; Hyung Suk Yang. Fault Analysis and Design of a Protection System for a Mesh Power System with a Co-Axial HTS Power Cable. Energies 2020, 13, 220 .
AMA StyleThai-Thanh Nguyen, Woon-Gyu Lee, Hak-Man Kim, Hyung Suk Yang. Fault Analysis and Design of a Protection System for a Mesh Power System with a Co-Axial HTS Power Cable. Energies. 2020; 13 (1):220.
Chicago/Turabian StyleThai-Thanh Nguyen; Woon-Gyu Lee; Hak-Man Kim; Hyung Suk Yang. 2020. "Fault Analysis and Design of a Protection System for a Mesh Power System with a Co-Axial HTS Power Cable." Energies 13, no. 1: 220.
In this paper, a hybrid energy management system is developed to optimize the operation of a wind farm (WF) by combining centralized and decentralized approaches. A two-stage optimization strategy, including distributed information sharing (stage 1); and centralized optimization (stage 2) is proposed to find out the optimal set-points of wind turbine generators (WTGs) considering grid-code constraints. In stage 1, cluster energy management systems (CEMSs) and transmission system operator (TSO) interact with their neighboring agents to share information using diffusion strategy and then determine the mismatch power amount between the current output power of WF and the required power from TSO. This amount of mismatch power is optimally allocated to all clusters through the CEMSs. In stage 2, a mixed-integer linear programming (MILP)-based optimization model is developed for each CEMS to find out the optimal set-points of WTGs in the corresponding cluster. The CEMSs are responsible for ensuring the operation of WF in accordance with the requirements of TSO (i.e., grid-code constraints) and also minimizing the power deviation for the set-points of WTGs in each cluster. The minimization of power deviation helps to reduce the internal power fluctuations inside each cluster. Finally, to evaluate the effectiveness of the proposed method, several case studies are analyzed in the simulations section for operation of a WF with 20 WTGs in four different clusters.
Van-Hai Bui; Akhtar Hussain; Woon-Gyu Lee; Hak-Man Kim. Hybrid Energy Management System for Operation of Wind Farm System Considering Grid-Code Constraints. Energies 2019, 12, 4672 .
AMA StyleVan-Hai Bui, Akhtar Hussain, Woon-Gyu Lee, Hak-Man Kim. Hybrid Energy Management System for Operation of Wind Farm System Considering Grid-Code Constraints. Energies. 2019; 12 (24):4672.
Chicago/Turabian StyleVan-Hai Bui; Akhtar Hussain; Woon-Gyu Lee; Hak-Man Kim. 2019. "Hybrid Energy Management System for Operation of Wind Farm System Considering Grid-Code Constraints." Energies 12, no. 24: 4672.
Journal of Animal Science and Technology (JAST) is a peer-reviewed, open access journal publishing original research, review articles and notes in all fields of animal science. Topics covered by the journal include: genetics and breeding, physiology, nutrition of monogastric animals, nutrition of ruminants, animal products (milk, meat, eggs and their by-products) and their processing, grasslands and roughages, livestock environment, animal biotechnology, animal behavior and welfare.
Taeyoung Kil; Hak-Man Kim; Minkyu Kim. The effectiveness of group combined intervention using animal-assisted therapy and integrated elderly play therapy. Journal of Animal Science and Technology 2019, 61, 371 -378.
AMA StyleTaeyoung Kil, Hak-Man Kim, Minkyu Kim. The effectiveness of group combined intervention using animal-assisted therapy and integrated elderly play therapy. Journal of Animal Science and Technology. 2019; 61 (6):371-378.
Chicago/Turabian StyleTaeyoung Kil; Hak-Man Kim; Minkyu Kim. 2019. "The effectiveness of group combined intervention using animal-assisted therapy and integrated elderly play therapy." Journal of Animal Science and Technology 61, no. 6: 371-378.
Optimal sizing of stationary energy storage systems (ESS) is required to reduce the peak load and increase the profit of fast charging stations. Sequential sizing of battery and converter or fixed-size converters are considered in most of the existing studies. However, sequential sizing or fixed-converter sizes may result in under or oversizing of ESS and thus fail to achieve the set targets, such as peak shaving and cost reduction. In order to address these issues, simultaneous sizing of battery and converter is proposed in this study. The proposed method has the ability to avoid the under or oversizing of ESS by considering the converter capacity and battery size as two independence decision variables. A mathematical problem is formulated by considering the stochastic return time of electrical vehicles (EVs), worst-case state of charge at return time, number of registered EVs, charging level of EVs, and other related parameters. The annualized cost of ESS is computed by considering the lifetime of ESS equipment and annual interest rates. The performance of the proposed method is compared with the existing sizing methods for ESS in fast-charging stations. In addition, sensitivity analysis is carried out to analyze the impact of different parameters on the size of the battery and the converter. Simulation results have proved that the proposed method is outperforming the existing sizing methods in terms of the total annual cost of the charging station and the amount of power buying during peak load intervals.
Akhtar Hussain; Van-Hai Bui; Ju-Won Baek; Hak-Man Kim. Stationary Energy Storage System for Fast EV Charging Stations: Simultaneous Sizing of Battery and Converter. Energies 2019, 12, 4516 .
AMA StyleAkhtar Hussain, Van-Hai Bui, Ju-Won Baek, Hak-Man Kim. Stationary Energy Storage System for Fast EV Charging Stations: Simultaneous Sizing of Battery and Converter. Energies. 2019; 12 (23):4516.
Chicago/Turabian StyleAkhtar Hussain; Van-Hai Bui; Ju-Won Baek; Hak-Man Kim. 2019. "Stationary Energy Storage System for Fast EV Charging Stations: Simultaneous Sizing of Battery and Converter." Energies 12, no. 23: 4516.
In this paper, a multi-objective optimization method is proposed to determine trade-off between conflicting operation objectives of wind farm (WF) systems, i.e., maximizing the output power and minimizing the output power fluctuation of the WF system. A detailed analysis of the effects of different objective’s weight values and battery size on the operation of the WF system is also carried out. This helps the WF operator to decide on an optimal operation point for the whole system to increase its profit and improve output power quality. In order to find out the optimal solution, a two-stage optimization is also developed to determine the optimal output power of the entire system as well as the optimal set-points of wind turbine generators (WTGs). In stage 1, the WF operator performs multi-objective optimization to determine the optimal output power of the WF system based on the relevant information from WTGs’ and battery’s controllers. In stage 2, the WF operator performs optimization to determine the optimal set-points of WTGs for minimizing the power deviation and fulfilling the required output power from the previous stage. The minimization of the power deviation for the set-points of WTGs helps the output power of WTGs much smoother and therefore avoids unnecessary internal power fluctuations. Finally, different case studies are also analyzed to show the effectiveness of the proposed method.
Van-Hai Bui; Akhtar Hussain; Woon-Gyu Lee; Hak-Man Kim. Multi-Objective Optimization for Determining Trade-Off between Output Power and Power Fluctuations in Wind Farm System. Energies 2019, 12, 4242 .
AMA StyleVan-Hai Bui, Akhtar Hussain, Woon-Gyu Lee, Hak-Man Kim. Multi-Objective Optimization for Determining Trade-Off between Output Power and Power Fluctuations in Wind Farm System. Energies. 2019; 12 (22):4242.
Chicago/Turabian StyleVan-Hai Bui; Akhtar Hussain; Woon-Gyu Lee; Hak-Man Kim. 2019. "Multi-Objective Optimization for Determining Trade-Off between Output Power and Power Fluctuations in Wind Farm System." Energies 12, no. 22: 4242.
Integration of demand response programs in microgrids can be beneficial for both the microgrid owners and the consumers. The demand response programs are generally triggered by market price signals to reduce the peak load demand. However, during islanded mode, due to the absence of connection with the utility grid, the market price signals are not available. Therefore, in this study, we have proposed a distributed demand response program for an islanded multi-microgrid network, which is not triggered by market price signals. The proposed distributed demand response program is based on welfare maximization of the network. Based on the welfare function of individual microgrids, the optimal power is allocated to the microgrids of the network in two steps. In the first step, the total surplus power and shortage power of the network is determined in a distributed way by using the local surplus/shortage information of each microgrid, which is computed after local optimization. In the second step, the total surplus of the network is allocated to the microgrids having shortage power based on their welfare functions. Finally, the allocated power amount and the initial shortage amount in the microgrid is used to determine the amount of load to be curtailed. Diffusion strategy is used in both the first and the second steps and the performance of the proposed method is compared with the widely used consensus method. Simulation results have proved the effectiveness of the proposed method for realizing distributed demand response for islanded microgrid networks.
Haesum Ali; Akhtar Hussain; Van-Hai Bui; Jinhong Jeon; Hak-Man Kim. Welfare Maximization-Based Distributed Demand Response for Islanded Multi-Microgrid Networks Using Diffusion Strategy. Energies 2019, 12, 3701 .
AMA StyleHaesum Ali, Akhtar Hussain, Van-Hai Bui, Jinhong Jeon, Hak-Man Kim. Welfare Maximization-Based Distributed Demand Response for Islanded Multi-Microgrid Networks Using Diffusion Strategy. Energies. 2019; 12 (19):3701.
Chicago/Turabian StyleHaesum Ali; Akhtar Hussain; Van-Hai Bui; Jinhong Jeon; Hak-Man Kim. 2019. "Welfare Maximization-Based Distributed Demand Response for Islanded Multi-Microgrid Networks Using Diffusion Strategy." Energies 12, no. 19: 3701.
In wind farm (WF) systems, the set-points of wind turbine generators (WTGs) is determined by the wind farm operator. A change in the value of set-point in two consecutive intervals leads to power fluctuations in the WF system, i.e. power deviation. A large amount of power deviation may adversely affect the operation of the WF system. Therefore, this paper proposes a strategy for operation of a WF system to minimize the power deviation of WTGs during operation time. By using the proposed strategy, the change in set-points of WTGs is minimized and the output power of WTGs is therefore smoother and avoids unnecessary fluctuations. Several grid-code constraints are also considered for operation of the WF system. This ensures the WF system to operate in compliance with the requirements from transmission system operator. Besides, an additional strategy is also proposed to monitor all events in the WF system. Whenever an event occurs in the system, the set-points of WTGs are rescheduled considering the event. Therefore, the proposed operation strategy is also capable of handling events in the WF system. Finally, the simulation results with and without the proposed method are compared to show the effectiveness of the proposed method.
Van-Hai Bui; Akhtar Hussain; Hak-Man Kim. Optimal Operation of Wind Farm for Reducing Power Deviation Considering Grid-Code Constraints and Events. IEEE Access 2019, 7, 139058 -139068.
AMA StyleVan-Hai Bui, Akhtar Hussain, Hak-Man Kim. Optimal Operation of Wind Farm for Reducing Power Deviation Considering Grid-Code Constraints and Events. IEEE Access. 2019; 7 (99):139058-139068.
Chicago/Turabian StyleVan-Hai Bui; Akhtar Hussain; Hak-Man Kim. 2019. "Optimal Operation of Wind Farm for Reducing Power Deviation Considering Grid-Code Constraints and Events." IEEE Access 7, no. 99: 139058-139068.