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Dr. Akhtar Hussain
Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 22012, Korea

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Research Keywords & Expertise

0 Distribution Automation
0 Energy management in buildings
0 Resilience of power systems
0 Integration of EVs and DR
0 Microgrid operation

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Journal article
Published: 26 March 2021 in Sustainability
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Grid-connected rooftop and ground-mounted solar photovoltaics (PV) systems have gained attraction globally in recent years due to (a) reduced PV module prices, (b) maturing inverter technology, and (c) incentives through feed-in tariff (FiT) or net metering. The large penetration of grid-connected PVs coupled with nonlinear loads and bidirectional power flows impacts grid voltage levels and total harmonic distortion (THD) at the low-voltage (LV) distribution feeder. In this study, LV power quality issues with significant nonlinear loads were evaluated at the point of common coupling (PCC). Various cases of PV penetration (0 to 100%) were evaluated for practical feeder data in a weak grid environment and tested at the radial modified IEEE-34 bus system to evaluate total harmonic distortion in the current (THDi) and voltage (THDv) at PCC along with the seasonal variations. Results showed lower active, reactive, and apparent power losses of 1.9, 2.6, and 3.3%, respectively, with 50% solar PV penetration in the LV network as the voltage profile of the LV network was significantly improved compared to the base case of no solar. Further, with 50% PV penetration, THDi and THDv at PCC were noted as 10.2 and 5.2%, respectively, which is within the IEEE benchmarks at LV.

ACS Style

Syed Ahsan; Hassan Khan; Akhtar Hussain; Sarmad Tariq; Nauman Zaffar. Harmonic Analysis of Grid-Connected Solar PV Systems with Nonlinear Household Loads in Low-Voltage Distribution Networks. Sustainability 2021, 13, 3709 .

AMA Style

Syed Ahsan, Hassan Khan, Akhtar Hussain, Sarmad Tariq, Nauman Zaffar. Harmonic Analysis of Grid-Connected Solar PV Systems with Nonlinear Household Loads in Low-Voltage Distribution Networks. Sustainability. 2021; 13 (7):3709.

Chicago/Turabian Style

Syed Ahsan; Hassan Khan; Akhtar Hussain; Sarmad Tariq; Nauman Zaffar. 2021. "Harmonic Analysis of Grid-Connected Solar PV Systems with Nonlinear Household Loads in Low-Voltage Distribution Networks." Sustainability 13, no. 7: 3709.

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: 01 October 2020 in Sustainability
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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.

ACS Style

Akhtar Hussain; Hak-Man Kim. Goal-Programming-Based Multi-Objective Optimization in Off-Grid Microgrids. Sustainability 2020, 12, 8119 .

AMA Style

Akhtar Hussain, Hak-Man Kim. Goal-Programming-Based Multi-Objective Optimization in Off-Grid Microgrids. Sustainability. 2020; 12 (19):8119.

Chicago/Turabian Style

Akhtar Hussain; Hak-Man Kim. 2020. "Goal-Programming-Based Multi-Objective Optimization in Off-Grid Microgrids." Sustainability 12, no. 19: 8119.

Journal article
Published: 13 March 2020 in IEEE Transactions on Transportation Electrification
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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.

ACS Style

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 Style

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 (2):453-463.

Chicago/Turabian Style

Akhtar 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.

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: 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.

Research article
Published: 05 April 2019 in IET Smart Grid
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A power distribution network is a critical infrastructure in any society and any disruption has an enormous impact on the economy and daily lives. Therefore, the objective of this study is to transform the conventional power distribution systems into resilient autonomous microgrid networks by optimally sizing and siting the distributed generators (DGs). First, N main DGs are placed to transform an existing network into an autonomous microgrid network. Second, all the possible combinations of the initially deployed DGs are made and then the outage of 1 to N − 1 DGs is considered. Considering the outage of DGs in each combination (one at a time), the resiliency of the network is analysed. Amount of load shedding, total power loss in the network, and voltage limits are analysed in this step. Finally, based on the resiliency analysis, additional DGs are placed to enhance the resiliency of the transformed network. Heuristic methods (particle swarm optimisation and genetic algorithm) are used for both sizing and siting of DGs during the first and the second steps. The objective of the formulation is to minimise load shedding, total power loss (active and reactive), and voltage deviations in the network during DG outages.

ACS Style

Akhtar Hussain; Syed Danial Ali Shah; Syed Muhammad Arif. Heuristic optimisation‐based sizing and siting of DGs for enhancing resiliency of autonomous microgrid networks. IET Smart Grid 2019, 2, 269 -282.

AMA Style

Akhtar Hussain, Syed Danial Ali Shah, Syed Muhammad Arif. Heuristic optimisation‐based sizing and siting of DGs for enhancing resiliency of autonomous microgrid networks. IET Smart Grid. 2019; 2 (2):269-282.

Chicago/Turabian Style

Akhtar Hussain; Syed Danial Ali Shah; Syed Muhammad Arif. 2019. "Heuristic optimisation‐based sizing and siting of DGs for enhancing resiliency of autonomous microgrid networks." IET Smart Grid 2, no. 2: 269-282.

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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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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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: 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.

Journal article
Published: 03 October 2018 in Energies
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The optimal operation of microgrids is challenging due to the presence of various uncertain factors, i.e., renewable energy sources, loads, market price signals, and arrival and departure times of electric vehicles (EVs). In order to incorporate these uncertainties into the operation model of microgrids, an adaptive robust optimization-based operation method is proposed in this paper. In particular, the focus is on the uncertainties in arrival and departure times of EVs. The optimization problem is divided into inner and outer problems and is solved iteratively by introducing column and constraint cuts. The unit commitment status of dispatchable generators is determined in the outer problem. Then, the worst-case realizations of all the uncertain factors are determined in the inner problem. Based on the values of uncertain factors, the generation amount of dispatchable generators, the amount of power trading with the utility grid, and the charging/discharging amount of storage elements are determined. The performance of the proposed method is evaluated using three different cases, and sensitivity analysis is carried out by varying the number of EVs and the budget of uncertainty. The impact of the budget of uncertainty and number of EVs on the operation cost of the microgrid is also evaluated considering uncertainties in arrival and departure times of EVs.

ACS Style

Se-Hyeok Choi; Akhtar Hussain; Hak-Man Kim. Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles. Energies 2018, 11, 2646 .

AMA Style

Se-Hyeok Choi, Akhtar Hussain, Hak-Man Kim. Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles. Energies. 2018; 11 (10):2646.

Chicago/Turabian Style

Se-Hyeok Choi; Akhtar Hussain; Hak-Man Kim. 2018. "Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles." Energies 11, no. 10: 2646.