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Van-Hai Bui
Research Institute for Northeast Asian Super Grid, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Korea

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Journal article
Published: 11 January 2021 in Sustainability
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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.

ACS Style

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 Style

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 (2):624.

Chicago/Turabian Style

Van-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.

Journal article
Published: 29 December 2020 in Energies
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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.

ACS Style

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 Style

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 (1):142.

Chicago/Turabian Style

Asfand 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.

Journal article
Published: 09 September 2020 in IEEE Access
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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.

ACS Style

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 Style

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 (99):173136-173146.

Chicago/Turabian Style

Van-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.

Journal article
Published: 02 January 2020 in Energies
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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.

ACS Style

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 Style

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 (1):230.

Chicago/Turabian Style

Akhtar 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.

Journal article
Published: 02 January 2020 in Energies
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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.

ACS Style

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 Style

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 (1):220.

Chicago/Turabian Style

Thai-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.

Journal article
Published: 09 December 2019 in Energies
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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.

ACS Style

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 Style

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 (24):4672.

Chicago/Turabian Style

Van-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 article
Published: 27 November 2019 in Energies
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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.

ACS Style

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 Style

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 (23):4516.

Chicago/Turabian Style

Akhtar 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.

Journal article
Published: 07 November 2019 in Energies
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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.

ACS Style

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 Style

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 (22):4242.

Chicago/Turabian Style

Van-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.

Journal article
Published: 27 September 2019 in Energies
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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.

ACS Style

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 Style

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 (19):3701.

Chicago/Turabian Style

Haesum 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.

Journal article
Published: 18 July 2019 in IEEE Transactions on Industrial Informatics
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An effort-based reward approach is proposed in this paper for the allocation of load shedding amount in interconnected microgrids. In this study, effort is defined as the relative contribution of a microgrid to the network with respect to its capacity. In contrast to the absolute contribution-based reward methods, where smaller microgrids are discriminated, the proposed effort -based reward method provides a fair chance for microgrids of all sizes. Historic data of efforts of each microgrid are recorded and an index (effort index) is formulated. The effort index is used as a measure to allocate load shedding to microgrids dur-ing emergencies, where microgrids with higher effort indices receive lesser load shedding and vice versa. Different weights are defined for efforts of microgrids depending on the operation mode of the network due to the difference in the importance of power sharing in each mode. In addition, a reward compensa-tion algorithm is devised to mitigate gaining of benefits for longer times while making lesser contributions. The proposed method is realized by using a multiagent system in JADE via agent communication language messages. The performance of the proposed method is compared with existing load shedding allocation algorithms, i.e. proportional method, bankruptcy Talmud rule, and absolute contribution-based reward method.

ACS Style

Akhtar Hussain; Van-Hai Bui; Hak-Man Kim. An Effort-Based Reward Approach for Allocating Load Shedding Amount in Networked Microgrids Using Multiagent System. IEEE Transactions on Industrial Informatics 2019, 16, 2268 -2279.

AMA Style

Akhtar Hussain, Van-Hai Bui, Hak-Man Kim. An Effort-Based Reward Approach for Allocating Load Shedding Amount in Networked Microgrids Using Multiagent System. IEEE Transactions on Industrial Informatics. 2019; 16 (4):2268-2279.

Chicago/Turabian Style

Akhtar Hussain; Van-Hai Bui; Hak-Man Kim. 2019. "An Effort-Based Reward Approach for Allocating Load Shedding Amount in Networked Microgrids Using Multiagent System." IEEE Transactions on Industrial Informatics 16, no. 4: 2268-2279.

Journal article
Published: 20 June 2019 in IEEE Transactions on Smart Grid
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Q-learning-based operation strategies are being recently applied for optimal operation of energy storage systems, where, a Q-table is used to store Q-values for all possible state-action pairs. However, Q-learning faces challenges when it comes to large state space problems, i.e., continuous state space problems or problems with environment uncertainties. In order to address the limitations of Q-learning, this paper proposes a distributed operation strategy using double deep Q-learning method. It is applied to managing the operation of a community battery energy storage system (CBESS) in a microgrid system. In contrast to Q-learning, the proposed operation strategy is capable of dealing with uncertainties in the system in both grid-connected and islanded modes. This is due to the utilization of a deep neural network as a function approximator to estimate the Q-values. Moreover, the proposed method can mitigate the overestimation that is the major drawback of the standard deep Q-learning. The proposed method trains the model faster by decoupling the selection and evaluation processes. Finally, the performance of the proposed double deep Q-learning-based operation method is evaluated by comparing its results with a centralized approach-based operation.

ACS Style

Yan-Hai Bui; Akhtar Hussain; Hak-Man Kim. Double Deep $Q$ -Learning-Based Distributed Operation of Battery Energy Storage System Considering Uncertainties. IEEE Transactions on Smart Grid 2019, 11, 457 -469.

AMA Style

Yan-Hai Bui, Akhtar Hussain, Hak-Man Kim. Double Deep $Q$ -Learning-Based Distributed Operation of Battery Energy Storage System Considering Uncertainties. IEEE Transactions on Smart Grid. 2019; 11 (1):457-469.

Chicago/Turabian Style

Yan-Hai Bui; Akhtar Hussain; Hak-Man Kim. 2019. "Double Deep $Q$ -Learning-Based Distributed Operation of Battery Energy Storage System Considering Uncertainties." IEEE Transactions on Smart Grid 11, no. 1: 457-469.

Journal article
Published: 20 May 2019 in Energies
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An optimal operation scheme for a building microgrid with a rooftop greenhouse in islanded mode is proposed in this paper. In islanded mode, the fulfillment of entire demand is challenging due to the absence of connection with the utility grid and the scarcity of local resources. The situation becomes more challenging when one or more pieces of equipment fail during the islanded mode. Therefore, in addition to islanded mode operation, component outage and recovery are also considered in this paper. In order to use the available energy efficiently, prioritization of building loads and control parameters of the greenhouse are proposed. A priority weight matrix is adopted to decide the supply of energy to fulfill the requirements of control parameters in the case of insufficient energy. In addition to the normal operation bounds, new bounds are defined to operate the control parameters if the resources are not sufficient. Additional penalties are imposed if the new bounds are chosen, due to violation of the normal operation range. The microgrid system is rescheduled if any component outage or recovery is detected from the outage point to the end of the scheduling horizon. The performance of the proposed method is evaluated by carrying out several simulations including component outage, component recovery, and simultaneous outage of two or more types of equipment. Numerical simulation results have demonstrated the effectiveness of the proposed operation scheme for optimal operation of building microgrids with a rooftop greenhouse in islanded mode.

ACS Style

Se-Hyeok Choi; Akhtar Hussain; Hak-Man Kim; Choi; Kim. Optimal Operation of Building Microgrids with Rooftop Greenhouse Under Component Outages in Islanded Mode. Energies 2019, 12, 1930 .

AMA Style

Se-Hyeok Choi, Akhtar Hussain, Hak-Man Kim, Choi, Kim. Optimal Operation of Building Microgrids with Rooftop Greenhouse Under Component Outages in Islanded Mode. Energies. 2019; 12 (10):1930.

Chicago/Turabian Style

Se-Hyeok Choi; Akhtar Hussain; Hak-Man Kim; Choi; Kim. 2019. "Optimal Operation of Building Microgrids with Rooftop Greenhouse Under Component Outages in Islanded Mode." Energies 12, no. 10: 1930.

Journal article
Published: 10 May 2019 in Energies
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Energy management systems (EMSs) of microgrids (MGs) can be broadly categorized as centralized or decentralized EMSs. The centralized approach may not be suitable for a system having several entities that have their own operation objectives. On the other hand, the use of the decentralized approach leads to an increase in the operation cost due to local optimization. In this paper, both centralized and decentralized approaches are combined for managing the operation of a distributed system, which is comprised of an MG and a community battery storage system (CBESS). The MG is formed by grouping all entities having the same operation objective and is operated under a centralized controller, i.e., a microgrid EMS (MG-EMS). The CBESS is operated by using its local controller with different operation objectives. A Q-learning-based operation strategy is proposed for optimal operation of CBESS in both grid-connected and islanded modes. The objective of CBESS in the grid-connected mode is to maximize its profit while the objective of CBESS in islanded mode is to minimize the load shedding amount in the entire system by cooperating with the MG. A comparison between the Q-learning-based strategy and a conventional centralized-based strategy is presented to show the effectiveness of the proposed strategy. In addition, an adjusted epsilon is also introduced for epsilon-greedy policy to reduce the learning time and improve the operation results.

ACS Style

Van-Hai Bui; Akhtar Hussain; Hak-Man Kim. Q-Learning-Based Operation Strategy for Community Battery Energy Storage System (CBESS) in Microgrid System. Energies 2019, 12, 1789 .

AMA Style

Van-Hai Bui, Akhtar Hussain, Hak-Man Kim. Q-Learning-Based Operation Strategy for Community Battery Energy Storage System (CBESS) in Microgrid System. Energies. 2019; 12 (9):1789.

Chicago/Turabian Style

Van-Hai Bui; Akhtar Hussain; Hak-Man Kim. 2019. "Q-Learning-Based Operation Strategy for Community Battery Energy Storage System (CBESS) in Microgrid System." Energies 12, no. 9: 1789.

Journal article
Published: 25 March 2019 in IEEE Access
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Power system decentralization has been an emerging topic for the past decade in an effort to improve power quality and environment protection via increased integration of renewable energy sources. Towards these objectives, decentralized microgrids have been proposed and thoroughly investigated in terms of technical capabilities and economic performance. In fact, the planning and actual operation of small-scale, decentralized microgrids has started in countries such as Canada, Japan, USA, UK and other countries. It is expected that the research in this area will progress and eventually take over the existing paradigm of large-scale power generation in the future. These small-size decentralized microgrids could be connected with nearby microgrids under normal operating conditions, but under special events, such as natural or man-made disasters, they would be disconnected and run in islanded mode. Under such high impact – low probability events, the microgrid must have resiliency to successfully re-connect with other microgrids and the main grid. In this paper, an Energy Management System (EMS) for a microgrid having a resiliency function, allowing to operate under islanded mode after an accident, is proposed. The proposed tool, called Resilient Energy Management System (ResEMS), aims at procuring reserve power into the microgrid’s Battery Energy Storage System (BESS) effectively, by importing it from the nearby connected power system. The quantity of power to be imported is decided considering the value of load, photovoltaic device generation, and the State Of Charge (SOC)of BESS. The accident is assumed to be a predictable natural disaster, which means that the accident occurrence date or time period can be estimated. The proposed ResEMS has been applied to a microgrid comprising of a BESS, a diesel generator and several photovoltaic devices. The simulation results verify its beneficial operation.

ACS Style

Halim Lee; Gil-Seong Byeon; Jin-Hong Jeon; Akhtar Hussain; Hak-Man Kim; Anastasios Oulis Rousis; Goran Strbac. An Energy Management System With Optimum Reserve Power Procurement Function for Microgrid Resilience Improvement. IEEE Access 2019, 7, 42577 -42585.

AMA Style

Halim Lee, Gil-Seong Byeon, Jin-Hong Jeon, Akhtar Hussain, Hak-Man Kim, Anastasios Oulis Rousis, Goran Strbac. An Energy Management System With Optimum Reserve Power Procurement Function for Microgrid Resilience Improvement. IEEE Access. 2019; 7 (99):42577-42585.

Chicago/Turabian Style

Halim Lee; Gil-Seong Byeon; Jin-Hong Jeon; Akhtar Hussain; Hak-Man Kim; Anastasios Oulis Rousis; Goran Strbac. 2019. "An Energy Management System With Optimum Reserve Power Procurement Function for Microgrid Resilience Improvement." IEEE Access 7, no. 99: 42577-42585.

Journal article
Published: 31 January 2019 in Energies
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Microgrids have the potential to withstand the power outages due to their ability of islanding and potential to sustain the penetration of renewables. Increased penetration of renewables can be beneficial but it may result in curtailment of renewables during peak generation intervals due to the limited availability of storage capacity while shedding loads during peak load intervals. This problem can be solved by adjusting the load profiles, i.e., demand response (DR) programs. In contrast to the existing studies, where DR is triggered by market price signals, a local resource-triggered survivability-oriented demand response program is proposed in this paper. The proposed DR program is triggered by renewable and load level of the microgrid with an objective to minimize the load shedding and curtailment of renewables. The uncertainties in load and renewables are realized via a robust optimization method and the worst-case scenario is considered. The performance of the proposed method is compared with two conventional operation cases, i.e., independent operation case and interconnected operation case without DR. In addition, the impact of renewable penetration level, amount of shiftable load, and load absorption capacity on the performance of the proposed method are also analyzed. Simulation results have proved the proposed method is capable of reducing load shedding, renewable curtailment, and operation cost of the network during emergencies.

ACS Style

Sung-Ho Park; Akhtar Hussain; Hak-Man Kim. Impact Analysis of Survivability-Oriented Demand Response on Islanded Operation of Networked Microgrids with High Penetration of Renewables. Energies 2019, 12, 452 .

AMA Style

Sung-Ho Park, Akhtar Hussain, Hak-Man Kim. Impact Analysis of Survivability-Oriented Demand Response on Islanded Operation of Networked Microgrids with High Penetration of Renewables. Energies. 2019; 12 (3):452.

Chicago/Turabian Style

Sung-Ho Park; Akhtar Hussain; Hak-Man Kim. 2019. "Impact Analysis of Survivability-Oriented Demand Response on Islanded Operation of Networked Microgrids with High Penetration of Renewables." Energies 12, no. 3: 452.

Journal article
Published: 10 January 2019 in IEEE Access
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ACS Style

Akhtar Hussain; Anastasios Oulis Rousis; Ioannis Konstantelos; Goran Strbac; Jinhong Jeon; Hak-Man Kim. Impact of Uncertainties on Resilient Operation of Microgrids: A Data-Driven Approach. IEEE Access 2019, 7, 14924 -14937.

AMA Style

Akhtar Hussain, Anastasios Oulis Rousis, Ioannis Konstantelos, Goran Strbac, Jinhong Jeon, Hak-Man Kim. Impact of Uncertainties on Resilient Operation of Microgrids: A Data-Driven Approach. IEEE Access. 2019; 7 ():14924-14937.

Chicago/Turabian Style

Akhtar Hussain; Anastasios Oulis Rousis; Ioannis Konstantelos; Goran Strbac; Jinhong Jeon; Hak-Man Kim. 2019. "Impact of Uncertainties on Resilient Operation of Microgrids: A Data-Driven Approach." IEEE Access 7, no. : 14924-14937.

Journal article
Published: 28 December 2018 in IEEE Transactions on Smart Grid
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ACS Style

Leong Kit Gan; Akhtar Hussain; David A. Howey; Hak-Man Kim. Limitations in Energy Management Systems: A Case Study for Resilient Interconnected Microgrids. IEEE Transactions on Smart Grid 2018, 10, 5675 -5685.

AMA Style

Leong Kit Gan, Akhtar Hussain, David A. Howey, Hak-Man Kim. Limitations in Energy Management Systems: A Case Study for Resilient Interconnected Microgrids. IEEE Transactions on Smart Grid. 2018; 10 (5):5675-5685.

Chicago/Turabian Style

Leong Kit Gan; Akhtar Hussain; David A. Howey; Hak-Man Kim. 2018. "Limitations in Energy Management Systems: A Case Study for Resilient Interconnected Microgrids." IEEE Transactions on Smart Grid 10, no. 5: 5675-5685.

Journal article
Published: 16 December 2018 in Energies
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Multiple battery energy storage systems (BESSs) are used to compensate for the fluctuation in wind generations effectively. The stage of charge (SOC) of BESSs might be unbalanced due to the difference of wind speed, initial SOCs, line impedances and capabilities of BESSs, which have a negative impact on the operation of the wind farm. This paper proposes a distributed control of the wind energy conversion system (WECS) based on dynamic average consensus algorithm to balance the SOC of the BESSs in a wind farm. There are three controllers in the WECS with integrated BESS, including a machine-side controller (MSC), the grid-side controller (GSC) and battery-side controller (BSC). The MSC regulates the generator speed to capture maximum wind power. Since the BSC maintains the DC link voltage of the back-to-back (BTB) converter that is used in the WECS, an improved virtual synchronous generator (VSG) based on consensus algorithm is used for the GSC to control the output power of the WECS. The functionalities of the improved VSG are designed to compensate for the wind power fluctuation and imbalance of SOC among BESSs. The average value of SOCs obtained by the dynamic consensus algorithm is used to adjust the wind power output for balancing the SOC of batteries. With the proposed controller, the fluctuation in the output power of wind generation is reduced, and the SOCs of BESSs are maintained equally. The effectiveness of the proposed control strategy is validated through the simulation by using a MATLAB/Simulink environment.

ACS Style

Cao-Khang Nguyen; Thai-Thanh Nguyen; Hyeong-Jun Yoo; Hak-Man Kim. Consensus-Based SOC Balancing of Battery Energy Storage Systems in Wind Farm. Energies 2018, 11, 3507 .

AMA Style

Cao-Khang Nguyen, Thai-Thanh Nguyen, Hyeong-Jun Yoo, Hak-Man Kim. Consensus-Based SOC Balancing of Battery Energy Storage Systems in Wind Farm. Energies. 2018; 11 (12):3507.

Chicago/Turabian Style

Cao-Khang Nguyen; Thai-Thanh Nguyen; Hyeong-Jun Yoo; Hak-Man Kim. 2018. "Consensus-Based SOC Balancing of Battery Energy Storage Systems in Wind Farm." Energies 11, no. 12: 3507.

Journal article
Published: 27 November 2018 in IEEE Access
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A resilience-constrained operation strategy is proposed in this paper using battery energy storage system (BESS) as a resilience resource. A proactive operation scheme is proposed for the predisturbance phase by formulating resiliency cuts for the state-of-charge (SoC) of BESS units. These resiliency cuts can assure the survivability of critical loads for n intervals after the occurrence of an event. A resilience constraint index is formulated to access the feasibility of the operation model for each selected value of n. In emergency mode, different possible scenarios are analyzed and a unified survivability enhancement strategy is proposed, which comprises of dynamic penalty costs and target SoC. Dynamic penalty costs can potentially avoid the unnecessary load shedding by shifting it towards the possible end of the scheduling horizon. Similarly, the inclusion of target SoC for the last interval of the scheduling horizon can mitigate the load shedding of critical loads on the following day. A two-step adaptive robust optimization scheme is adopted to incorporate the prevailing uncertainties in loads and renewables in the operation model. Finally, the impact of various decision parameters, involved in the problem formulation, is analyzed with reference to the proposed resilience-constrained operation strategy for hybrid microgrids.

ACS Style

Akhtar Hussain; Van-Hai Bui; Hak-Man Kim. A Proactive and Survivability-Constrained Operation Strategy for Enhancing Resilience of Microgrids Using Energy Storage System. IEEE Access 2018, 6, 75495 -75507.

AMA Style

Akhtar Hussain, Van-Hai Bui, Hak-Man Kim. A Proactive and Survivability-Constrained Operation Strategy for Enhancing Resilience of Microgrids Using Energy Storage System. IEEE Access. 2018; 6 (99):75495-75507.

Chicago/Turabian Style

Akhtar Hussain; Van-Hai Bui; Hak-Man Kim. 2018. "A Proactive and Survivability-Constrained Operation Strategy for Enhancing Resilience of Microgrids Using Energy Storage System." IEEE Access 6, no. 99: 75495-75507.

Preprint
Published: 27 September 2018
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Power system decentralization has been an emerging topic for the past decade in an effort to improve power quality and environment protection via increased integration of renewable energy sources. Towards these objectives, decentralized microgrids have been proposed and thoroughly investigated in terms of technical capabilities and economic performance. In fact, the planning and actual operation of small-scale, decentralized microgrids has started in countries such as Canada, Japan, USA, UK and other countries. It is expected that the research in this area will progress and eventually take over the existing paradigm of large-scale power generation in the future. These small-size decentralized microgrids could be connected with nearby microgrids under normal operating conditions, but under special events, such as natural or man-made disasters, they would be disconnected and run in islanded mode. Under such high impact – low probability events, the microgrid must have resiliency to successfully re-connect with other microgrids and the main grid. In this paper, an Energy Management System (EMS) for a microgrid having a resiliency function, allowing to operate under islanded mode after an accident, is proposed. The proposed tool, called Resilient Energy Management System (ResEMS), aims at procuring reserve power into the microgrid’s Battery Energy Storage System (BESS) effectively, by importing it from the nearby connected power system. The accident is assumed to be a predictable natural disaster, which means that the accident occurrence time, duration and level of damage can be estimated. The proposed ResEMS has been applied to a microgrid comprising of a BESS, a diesel generator and several photovoltaic devices. The simulation results verify its beneficial operation.

ACS Style

Halim Lee; Gil-Seong Byeon; Jin-Hong Jeon; Akhtar Hussain; Hak-Man Kim; Anastasios Oulis Rousis; Goran Strbac. A Case Study on Increasing Microgrid Resiliency Using Reserve Power Procurement Method. 2018, 1 .

AMA Style

Halim Lee, Gil-Seong Byeon, Jin-Hong Jeon, Akhtar Hussain, Hak-Man Kim, Anastasios Oulis Rousis, Goran Strbac. A Case Study on Increasing Microgrid Resiliency Using Reserve Power Procurement Method. . 2018; ():1.

Chicago/Turabian Style

Halim Lee; Gil-Seong Byeon; Jin-Hong Jeon; Akhtar Hussain; Hak-Man Kim; Anastasios Oulis Rousis; Goran Strbac. 2018. "A Case Study on Increasing Microgrid Resiliency Using Reserve Power Procurement Method." , no. : 1.