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Ka Wing Chan
Electrical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, none

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Journal article
Published: 26 July 2021 in IEEE Transactions on Power Systems
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In this letter, the evolutionary game theory (EGT) with replication dynamic equations (RDEs) is adopted to explicitly determine the factors affecting energy providers (EPs) willingness of using the market power to uplift the price in the bidding procedure, which could be simulated using the win-or-learn-fast policy hill climbing (WoLF-PHC) algorithm as a multi-agent reinforcement learning (MARL) method. Firstly, empirical and numerical connections between WoLF-PHC and RDEs is proved. Then, by formulating RDEs of the bidding procedure, three factors affecting the bidding strategy preference are revealed, including the load demand, severity of congestion, and the price cap. Finally, the impact of these factors on the converged bidding price is demonstrated in case studies, by simulating the bidding procedure driven by WoLF-PHC.

ACS Style

Ziqing Zhu; Ka Wing Chan; Siqi Bu; Siu Wing Or; Xiang Gao; Shiwei Xia. Analysis of Evolutionary Dynamics for Bidding Strategy Driven by Multi-Agent Reinforcement Learning. IEEE Transactions on Power Systems 2021, PP, 1 -1.

AMA Style

Ziqing Zhu, Ka Wing Chan, Siqi Bu, Siu Wing Or, Xiang Gao, Shiwei Xia. Analysis of Evolutionary Dynamics for Bidding Strategy Driven by Multi-Agent Reinforcement Learning. IEEE Transactions on Power Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Ziqing Zhu; Ka Wing Chan; Siqi Bu; Siu Wing Or; Xiang Gao; Shiwei Xia. 2021. "Analysis of Evolutionary Dynamics for Bidding Strategy Driven by Multi-Agent Reinforcement Learning." IEEE Transactions on Power Systems PP, no. 99: 1-1.

Journal article
Published: 27 March 2021 in Energies
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There is an increasing interest in low voltage direct current (LVDC) distribution grids due to advancements in power electronics enabling efficient and economical electrical networks in the DC paradigm. Power flow equations in LVDC grids are non-linear and non-convex due to the presence of constant power nodes. Depending on the implementation, power flow equations may lead to more than one solution and unrealistic solutions; therefore, the uniqueness of the solution should not be taken for granted. This paper proposes a new power flow solver based on a graph theory for LVDC grids having radial or meshed configurations. The solver provides a unique solution. Two test feeders composed of 33 nodes and 69 nodes are considered to validate the effectiveness of the proposed method. The proposed method is compared with a fixed-point methodology called direct load flow (DLF) having a mathematical formulation equivalent to a backward forward sweep (BFS) class of solvers in the case of radial distribution networks but that can handle meshed networks more easily thanks to the use of connectivity matrices. In addition, the convergence and uniqueness of the solution is demonstrated using a Banach fixed-point theorem. The performance of the proposed method is tested for different loading conditions. The results show that the proposed method is robust and has fast convergence characteristics even with high loading conditions. All simulations are carried out in MATLAB 2020b software.

ACS Style

Zahid Javid; Ulas Karaagac; Ilhan Kocar; Ka Chan. Laplacian Matrix-Based Power Flow Formulation for LVDC Grids with Radial and Meshed Configurations. Energies 2021, 14, 1866 .

AMA Style

Zahid Javid, Ulas Karaagac, Ilhan Kocar, Ka Chan. Laplacian Matrix-Based Power Flow Formulation for LVDC Grids with Radial and Meshed Configurations. Energies. 2021; 14 (7):1866.

Chicago/Turabian Style

Zahid Javid; Ulas Karaagac; Ilhan Kocar; Ka Chan. 2021. "Laplacian Matrix-Based Power Flow Formulation for LVDC Grids with Radial and Meshed Configurations." Energies 14, no. 7: 1866.

Journal article
Published: 03 March 2021 in IEEE Transactions on Industrial Informatics
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A microgrid formed by distributed generation (DG) units is capable of operating in islanded and grid-connected modes. Traditionally, by using model predictive control (MPC), these two operation modes can be achieved with two separate cost functions, which brings in control complexity and hence, compromises reliability. In this paper, a unified model predictive voltage and current control (UMPVIC) strategy is proposed. Specifically, the cost function is kept unified with voltage and current taken into account without altering the control architecture. A high-quality voltage is generated in islanded mode and a bidirectional power flow is achieved in grid-connected mode. In addition, by only using DGs own and neighbouring information, a secondary distributed fuzzy cooperative algorithm is developed to mitigate voltage/frequency deviations. The fuzzy controller can optimize the secondary control coefficients for further voltage quality improvement. Comprehensive tests under various scenarios demonstrate the merits of the proposed control strategy over traditional methods.

ACS Style

Yinghao Shan; Jiefeng Hu; Ka Wing Chan; Syed Islam. A Unified Model Predictive Voltage and Current Control for Microgrids With Distributed Fuzzy Cooperative Secondary Control. IEEE Transactions on Industrial Informatics 2021, 17, 8024 -8034.

AMA Style

Yinghao Shan, Jiefeng Hu, Ka Wing Chan, Syed Islam. A Unified Model Predictive Voltage and Current Control for Microgrids With Distributed Fuzzy Cooperative Secondary Control. IEEE Transactions on Industrial Informatics. 2021; 17 (12):8024-8034.

Chicago/Turabian Style

Yinghao Shan; Jiefeng Hu; Ka Wing Chan; Syed Islam. 2021. "A Unified Model Predictive Voltage and Current Control for Microgrids With Distributed Fuzzy Cooperative Secondary Control." IEEE Transactions on Industrial Informatics 17, no. 12: 8024-8034.

Journal article
Published: 01 February 2021 in IEEE Transactions on Industrial Informatics
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Large-scale renewable energy suppliers and electric vehicles (EVs) are expected to become dominated participants in future electricity market. A competitive bidding strategy is formulated here for wind power plants (WPPs) and EV aggregators in a pool-based day-ahead electricity market. A bi-level multi-agent based model is proposed to study their bidding behaviors, with market clearing competition in the lower level and revenue maximization in the upper level. A stochastic framework is developed to incorporate the uncertainties in bid prices and power production of WPPs and EV aggregators. The process of bidding decision is formulated as a stochastic game with incomplete information and explored by a multi-agent reinforcement learning algorithm named win or learn fast policy hill climbing (WoLF-PHC). The feasibility and effectiveness of the proposed model and the WoLF-PHC solution approach are successfully illustrated using a modified IEEE 6-bus system and a modified 118-bus system with different numbers of market players.

ACS Style

Xiang Gao; Ka Wing Chan; Shiwei Xia; Xiao Shun Zhang; Kuan Zhang; Jiahan Zhou. A Multiagent Competitive Bidding Strategy in a Pool-Based Electricity Market With Price-Maker Participants of WPPs and EV Aggregators. IEEE Transactions on Industrial Informatics 2021, 17, 7256 -7268.

AMA Style

Xiang Gao, Ka Wing Chan, Shiwei Xia, Xiao Shun Zhang, Kuan Zhang, Jiahan Zhou. A Multiagent Competitive Bidding Strategy in a Pool-Based Electricity Market With Price-Maker Participants of WPPs and EV Aggregators. IEEE Transactions on Industrial Informatics. 2021; 17 (11):7256-7268.

Chicago/Turabian Style

Xiang Gao; Ka Wing Chan; Shiwei Xia; Xiao Shun Zhang; Kuan Zhang; Jiahan Zhou. 2021. "A Multiagent Competitive Bidding Strategy in a Pool-Based Electricity Market With Price-Maker Participants of WPPs and EV Aggregators." IEEE Transactions on Industrial Informatics 17, no. 11: 7256-7268.

Journal article
Published: 10 November 2020 in IEEE Transactions on Smart Grid
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An operation model for distribution companies (DISCOs) is proposed to reduce their operation costs by fully utilizing the flexibility of electric vehicle aggregators (EVAs). In the proposed model, linear decision rules approximation is adopted to achieve mathematical tractability, and distributionally robust optimization is applied to evaluate costs affected by uncertainties in renewable power outputs and EVA charging demands. Case studies are conducted under various settings. With the proposed model, using EVAs to mitigate uncertainties is achieved and is further classified into delaying uncertainties and eliminating uncertainties. As a result, average penalties for DISCO’s deviations from its planned energy portfolio are reduced. Besides, EVA charging demands are shifted to hours with lower energy prices to reduce energy costs of DISCO. Using EVAs to mitigate uncertainties and shifting EVA charging demands are properly coordinated under the proposed model. Moreover, power losses in EVA charging and discharging are utilized to reduce the scale of uncertainties, which decreases average penalties for energy deviations of DISCO.

ACS Style

Xi Lu; Ka Wing Chan; Shiwei Xia; Mohammad Shahidehpour; Wing Ho Ng. An Operation Model for Distribution Companies Using the Flexibility of Electric Vehicle Aggregators. IEEE Transactions on Smart Grid 2020, 12, 1507 -1518.

AMA Style

Xi Lu, Ka Wing Chan, Shiwei Xia, Mohammad Shahidehpour, Wing Ho Ng. An Operation Model for Distribution Companies Using the Flexibility of Electric Vehicle Aggregators. IEEE Transactions on Smart Grid. 2020; 12 (2):1507-1518.

Chicago/Turabian Style

Xi Lu; Ka Wing Chan; Shiwei Xia; Mohammad Shahidehpour; Wing Ho Ng. 2020. "An Operation Model for Distribution Companies Using the Flexibility of Electric Vehicle Aggregators." IEEE Transactions on Smart Grid 12, no. 2: 1507-1518.

Review article
Published: 08 October 2020 in Renewable and Sustainable Energy Reviews
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The development of microgrids is an advantageous option for integrating rapidly growing renewable energies. However, the stochastic nature of renewable energies and variable power demand have created many challenges like unstable voltage/frequency and complicated power management and interaction with the utility grid. Recently, predictive control with its fast transient response and flexibility to accommodate different constraints has presented huge potentials in microgrid applications. This paper provides a comprehensive review of model predictive control (MPC) in individual and interconnected microgrids, including both converter-level and grid-level control strategies applied to three layers of the hierarchical control architecture. This survey shows that MPC is at the beginning of the application in microgrids and that it emerges as a competitive alternative to conventional methods in voltage regulation, frequency control, power flow management and economic operation optimization. Also, some of the most important trends in MPC development have been highlighted and discussed as future perspectives.

ACS Style

Jiefeng Hu; Yinghao Shan; Josep M. Guerrero; Adrian Ioinovici; Ka Wing Chan; Jose Rodriguez. Model predictive control of microgrids – An overview. Renewable and Sustainable Energy Reviews 2020, 136, 110422 .

AMA Style

Jiefeng Hu, Yinghao Shan, Josep M. Guerrero, Adrian Ioinovici, Ka Wing Chan, Jose Rodriguez. Model predictive control of microgrids – An overview. Renewable and Sustainable Energy Reviews. 2020; 136 ():110422.

Chicago/Turabian Style

Jiefeng Hu; Yinghao Shan; Josep M. Guerrero; Adrian Ioinovici; Ka Wing Chan; Jose Rodriguez. 2020. "Model predictive control of microgrids – An overview." Renewable and Sustainable Energy Reviews 136, no. : 110422.

Journal article
Published: 15 September 2020 in IEEE Transactions on Industrial Electronics
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In microgrids, intermittency of renewable energy sources (RES) and uncertain state-of-charge (SoC) of energy storage systems (ESS) can cause power deficiency to some distributed generation units (DGs). In this case, DGs with power deficiency may not meet the power demand, resulting in voltage collapse or frequency divergence. Unfortunately, this is seldom considered in inverter control development in existing literature and thus, in-depth investigation is urgently needed. In this paper, an adaptive droop and adaptive virtual impedance control strategy is proposed. Unlike conventional droop control where the droop coefficients are fixed by assuming the DGs can always meet the load demand, the droop coefficients here are adjusted according to actual solar PV power output. In this way, proper power sharing among DGs can be actually achieved under renewable energy variation. Furthermore, the impact of varying DG capacities on system stability is mathematically investigated. An adaptive virtual impedance is then incorporated into the adaptive droop method to deal with the system instability caused by renewable energy variations. The proposed strategy is analyzed theoretically and validated in MATLAB/Simulink simulation and laboratory experiment. The results demonstrate the advantages of the proposed method over conventional approaches under various scenarios.

ACS Style

Zilin Li; Ka Wing Chan; Jiefeng Hu; Josep M. Guerrero. Adaptive Droop Control Using Adaptive Virtual Impedance for Microgrids With Variable PV Outputs and Load Demands. IEEE Transactions on Industrial Electronics 2020, 68, 9630 -9640.

AMA Style

Zilin Li, Ka Wing Chan, Jiefeng Hu, Josep M. Guerrero. Adaptive Droop Control Using Adaptive Virtual Impedance for Microgrids With Variable PV Outputs and Load Demands. IEEE Transactions on Industrial Electronics. 2020; 68 (10):9630-9640.

Chicago/Turabian Style

Zilin Li; Ka Wing Chan; Jiefeng Hu; Josep M. Guerrero. 2020. "Adaptive Droop Control Using Adaptive Virtual Impedance for Microgrids With Variable PV Outputs and Load Demands." IEEE Transactions on Industrial Electronics 68, no. 10: 9630-9640.

Journal article
Published: 07 July 2020 in IEEE Access
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Traditional speed control of permanent magnet synchronous motors (PMSMs) includes a cascaded speed loop with proportional-integral (PI) regulators. The output of this outer speed loop, i.e. electromagnetic torque reference, is in turn fed to either the inner current controller or the direct torque controller. This cascaded control structure leads to relatively slow dynamic response, and more importantly, larger speed ripples. This paper presents a new dual cost function model predictive direct speed control (DCF-MPDSC) with duty ratio optimization for PMSM drives. By employing accurate system status prediction, optimized duty ratios between one zero voltage vector and one active voltage vector are firstly deduced based on the deadbeat criterion. Then, two separate cost functions are formulated sequentially to refine the combinations of voltage vectors, which provide two-degree-of-freedom control capability. Specifically, the first cost function results in better dynamic response, while the second one contributes to speed ripple reduction and steady-state offset elimination. The proposed control strategy has been validated by both Simulink simulation and hardware-in-the-loop (HIL) experiment. Compared to existing control methods, the proposed DCF-MPDSC can reach the speed reference rapidly with very small speed ripple and offset.

ACS Style

Ming Liu; Jiefeng Hu; Ka Wing Chan; Siu Wing Or; Siu Lau Ho; Wenzheng Xu; Xian Zhang. Dual Cost Function Model Predictive Direct Speed Control With Duty Ratio Optimization for PMSM Drives. IEEE Access 2020, 8, 126637 -126647.

AMA Style

Ming Liu, Jiefeng Hu, Ka Wing Chan, Siu Wing Or, Siu Lau Ho, Wenzheng Xu, Xian Zhang. Dual Cost Function Model Predictive Direct Speed Control With Duty Ratio Optimization for PMSM Drives. IEEE Access. 2020; 8 ():126637-126647.

Chicago/Turabian Style

Ming Liu; Jiefeng Hu; Ka Wing Chan; Siu Wing Or; Siu Lau Ho; Wenzheng Xu; Xian Zhang. 2020. "Dual Cost Function Model Predictive Direct Speed Control With Duty Ratio Optimization for PMSM Drives." IEEE Access 8, no. : 126637-126647.

Journal article
Published: 18 June 2020 in IEEE Transactions on Power Systems
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In this paper, a transient stability margin is proposed in terms of the kinetic energy of power systems in extremely unstable conditions. A unified energy-based transient stability constraint is formed for both normal and extremely unstable conditions in the proposed transient stability-constrained optimal power flow (TSCOPF) model. A divide-and-conquer approach is presented to solve TSCOPF by decomposing it into optimal power flow and transient stability-constrained generation sub-problems. The former is solved by an interior point method and the latter is derived by an energy sensitivity technique. Furthermore, an accuracy-based perturbation strategy is proposed to address the system over-stabilization issue, and a parallel calculation technique is implemented to speed up the TSCOPF solution. The effectiveness of the proposed approach is investigated and the results are validated using the New England 10-generator and IEEE 50-generator systems under extremely unstable conditions.

ACS Style

Shiwei Xia; Zhaohao Ding; Mohammad Shahidehpour; Ka Wing Chan; Siqi Bu; Gengyin Li. Transient Stability-Constrained Optimal Power Flow Calculation With Extremely Unstable Conditions Using Energy Sensitivity Method. IEEE Transactions on Power Systems 2020, 36, 355 -365.

AMA Style

Shiwei Xia, Zhaohao Ding, Mohammad Shahidehpour, Ka Wing Chan, Siqi Bu, Gengyin Li. Transient Stability-Constrained Optimal Power Flow Calculation With Extremely Unstable Conditions Using Energy Sensitivity Method. IEEE Transactions on Power Systems. 2020; 36 (1):355-365.

Chicago/Turabian Style

Shiwei Xia; Zhaohao Ding; Mohammad Shahidehpour; Ka Wing Chan; Siqi Bu; Gengyin Li. 2020. "Transient Stability-Constrained Optimal Power Flow Calculation With Extremely Unstable Conditions Using Energy Sensitivity Method." IEEE Transactions on Power Systems 36, no. 1: 355-365.

Journal article
Published: 18 May 2020 in IEEE Transactions on Industry Applications
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This paper studies Electric Vehicle (EV) potential to participate in the energy market and provide flexible ramping products (FRPs). EV traffic flows are predicted by the deep belief network, and the availability of flexible EVs is estimated based on the predicted EV traffic flows. Then, a novel market mechanism in distribution system is proposed to encourage the dispatchable EV demand to react to economic signals and provide ramping services. The designed market model is based on locational marginal pricing (LMP) of energy, and marginal pricing of FRPs. System ramping capacity constraints and EV operation constraints are incorporated in the proposed model to achieve the balance between the system social cost minimization and the EV traveling convenience. Moreover, typical uncertainties are considered by the scenario-based approach. Finally, simulations are conducted to verify the effectiveness of the established model and demonstrate the contributions of EVs to the system reliability and flexibility.

ACS Style

Xian Zhang; Jiefeng Hu; Huaizhi Wang; Guibin Wang; Ka Wing Chan; Jing Qiu. Electric Vehicle Participated Electricity Market Model Considering Flexible Ramping Product Provisions. IEEE Transactions on Industry Applications 2020, 56, 5868 -5879.

AMA Style

Xian Zhang, Jiefeng Hu, Huaizhi Wang, Guibin Wang, Ka Wing Chan, Jing Qiu. Electric Vehicle Participated Electricity Market Model Considering Flexible Ramping Product Provisions. IEEE Transactions on Industry Applications. 2020; 56 (5):5868-5879.

Chicago/Turabian Style

Xian Zhang; Jiefeng Hu; Huaizhi Wang; Guibin Wang; Ka Wing Chan; Jing Qiu. 2020. "Electric Vehicle Participated Electricity Market Model Considering Flexible Ramping Product Provisions." IEEE Transactions on Industry Applications 56, no. 5: 5868-5879.

Journal article
Published: 27 April 2020 in IEEE Transactions on Industry Applications
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ACS Style

Guangzeng Sun; Gengyin Li; Shiwei Xia; Mohammad Shahidehpour; Xi Lu; Ka Wing Chan. ALADIN-Based Coordinated Operation of Power Distribution and Traffic Networks With Electric Vehicles. IEEE Transactions on Industry Applications 2020, 56, 5944 -5954.

AMA Style

Guangzeng Sun, Gengyin Li, Shiwei Xia, Mohammad Shahidehpour, Xi Lu, Ka Wing Chan. ALADIN-Based Coordinated Operation of Power Distribution and Traffic Networks With Electric Vehicles. IEEE Transactions on Industry Applications. 2020; 56 (5):5944-5954.

Chicago/Turabian Style

Guangzeng Sun; Gengyin Li; Shiwei Xia; Mohammad Shahidehpour; Xi Lu; Ka Wing Chan. 2020. "ALADIN-Based Coordinated Operation of Power Distribution and Traffic Networks With Electric Vehicles." IEEE Transactions on Industry Applications 56, no. 5: 5944-5954.

Journal article
Published: 13 April 2020 in IEEE Transactions on Industrial Informatics
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ACS Style

Bin Zhou; Kuan Zhang; Ka Wing Chan; Canbing Li; Xi Lu; Siqi Bu; Xiang Gao. Optimal Coordination of Electric Vehicles for Virtual Power Plants With Dynamic Communication Spectrum Allocation. IEEE Transactions on Industrial Informatics 2020, 17, 450 -462.

AMA Style

Bin Zhou, Kuan Zhang, Ka Wing Chan, Canbing Li, Xi Lu, Siqi Bu, Xiang Gao. Optimal Coordination of Electric Vehicles for Virtual Power Plants With Dynamic Communication Spectrum Allocation. IEEE Transactions on Industrial Informatics. 2020; 17 (1):450-462.

Chicago/Turabian Style

Bin Zhou; Kuan Zhang; Ka Wing Chan; Canbing Li; Xi Lu; Siqi Bu; Xiang Gao. 2020. "Optimal Coordination of Electric Vehicles for Virtual Power Plants With Dynamic Communication Spectrum Allocation." IEEE Transactions on Industrial Informatics 17, no. 1: 450-462.

Journal article
Published: 02 April 2020 in IEEE Transactions on Cybernetics
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With the emerging electric vehicle (EV) and fast charging technologies, EV load forecasting has become a concern for planners and operators of EV charging stations (CSs). Due to the nonstationary feature of the traffic flow (TF) and the erratic nature of the charging procedures, EV charging load is difficult to accurately forecast. In this article, TF is first predicted using a deep-learning-based convolutional neural network (CNN), and different forecast uncertainties are evaluated to formulate the TF prediction intervals (PIs). Then, the EV arrival rates are calculated according to the historical data and the proposed mixture model. Based on TF forecasting and arrival rate results, the EV charging process is studied to convert the TF to the charging load using a novel probabilistic queuing model that takes into consideration charging service limitations and driver behaviors. The proposed models are assessed using the actual TF data, and the results show that the uncertainties of the EV charging load can be learned comprehensively, indicating significant potential for practical applications.

ACS Style

Xian Zhang; Ka Wing Chan; Hairong Li; Huaizhi Wang; Jing Qiu; Guibin Wang. Deep-Learning-Based Probabilistic Forecasting of Electric Vehicle Charging Load With a Novel Queuing Model. IEEE Transactions on Cybernetics 2020, 51, 3157 -3170.

AMA Style

Xian Zhang, Ka Wing Chan, Hairong Li, Huaizhi Wang, Jing Qiu, Guibin Wang. Deep-Learning-Based Probabilistic Forecasting of Electric Vehicle Charging Load With a Novel Queuing Model. IEEE Transactions on Cybernetics. 2020; 51 (6):3157-3170.

Chicago/Turabian Style

Xian Zhang; Ka Wing Chan; Hairong Li; Huaizhi Wang; Jing Qiu; Guibin Wang. 2020. "Deep-Learning-Based Probabilistic Forecasting of Electric Vehicle Charging Load With a Novel Queuing Model." IEEE Transactions on Cybernetics 51, no. 6: 3157-3170.

Journal article
Published: 30 March 2020 in IEEE Transactions on Transportation Electrification
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Three-phase Z-source inverters provide a solution of voltage boosting by a single-stage topology. They are also capable of bi-directional operation as rectifiers, thus have great potential for applications in the field of transportation electrification such as Vehicle-to-Grid (V2G) chargers. In this paper, three new modulation schemes for three-phase Z-source converters are proposed and investigated. The best performed one is further developed to a closed-loop PI control method. While the voltage conversion ratio is flexible, the output voltage Total Harmonics Distortion (THD) is below 3% within the voltage ratio range of 0.5 to 2.5. The effectiveness of the proposed method has been fully validated in MATLAB/Simulink simulations and RTLAB Hardware-In-Loop (HIL) experiments based on the realtime simulator OPAL-RT OP4510. Compared to existing control methods, the proposed one performs better with reduced harmonics, flexible voltage gain, and simpler control algorithm.

ACS Style

Wenzheng Xu; Ka Wing Chan; Siu Wing Or; Siu Lau Ho; Ming Liu. A Low-Harmonic Control Method of Bidirectional Three-Phase Z-Source Converters for Vehicle-to-Grid Applications. IEEE Transactions on Transportation Electrification 2020, 6, 464 -477.

AMA Style

Wenzheng Xu, Ka Wing Chan, Siu Wing Or, Siu Lau Ho, Ming Liu. A Low-Harmonic Control Method of Bidirectional Three-Phase Z-Source Converters for Vehicle-to-Grid Applications. IEEE Transactions on Transportation Electrification. 2020; 6 (2):464-477.

Chicago/Turabian Style

Wenzheng Xu; Ka Wing Chan; Siu Wing Or; Siu Lau Ho; Ming Liu. 2020. "A Low-Harmonic Control Method of Bidirectional Three-Phase Z-Source Converters for Vehicle-to-Grid Applications." IEEE Transactions on Transportation Electrification 6, no. 2: 464-477.

Journal article
Published: 03 March 2020 in IEEE Transactions on Power Delivery
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Inverter-Based Resources (IBRs), including Wind turbine generators (WTGs), exhibit substantially different negative-sequence fault current characteristics compared to synchronous generators (SGs). These differences may cause misoperation of customary negative-sequence-based protective elements set under the assumption of a conventional SG dominated power system. The amplitude of the negative-sequence fault current of a WTG is smaller than that of an SG. This may lead to misoperation of the negative-sequence overcurrent elements 50Q/51Q. Moreover, the angular relation of the negative-sequence current and voltage is different under WTGs, which may result in the misoperation of directional negative-sequence overcurrent element 67Q. This paper first studies the key differences between the WTGs and SG by comparing their equivalent negative-sequence impedances with SG's. Then, simulation case studies are presented showing the misoperation of 50Q and 67Q due to wind generation and the corresponding impact on communication-assisted protection and fault identification scheme (FID). The impact on directional element is also experimentally validated in a hardware-in-the-loop real-time simulation set up using a physical relay. Finally, the paper studies the impact of various factors such as WTG type (Type-III/Type-IV) and Type-IV WTG control scheme (coupled/decoupled sequence) to determine the key features that need to be considered in practical protection studies. The objective is to show potential protection misoperation issues, identify the cause, and propose potential solutions.

ACS Style

Aboutaleb Haddadi; Mingxuan Zhao; Ilhan Kocar; Ulas Karaagac; Ka Wing Chan; Evangelos Farantatos. Impact of Inverter-Based Resources on Negative Sequence Quantities-Based Protection Elements. IEEE Transactions on Power Delivery 2020, 36, 289 -298.

AMA Style

Aboutaleb Haddadi, Mingxuan Zhao, Ilhan Kocar, Ulas Karaagac, Ka Wing Chan, Evangelos Farantatos. Impact of Inverter-Based Resources on Negative Sequence Quantities-Based Protection Elements. IEEE Transactions on Power Delivery. 2020; 36 (1):289-298.

Chicago/Turabian Style

Aboutaleb Haddadi; Mingxuan Zhao; Ilhan Kocar; Ulas Karaagac; Ka Wing Chan; Evangelos Farantatos. 2020. "Impact of Inverter-Based Resources on Negative Sequence Quantities-Based Protection Elements." IEEE Transactions on Power Delivery 36, no. 1: 289-298.

Journal article
Published: 17 January 2020 in Applied Energy
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While the number of plug-in electric vehicles (PEVs) increases rapidly, the application potential of PEVs should be accounted in electric power dispatch with several conflicting and competing objectives such as providing vehicle-to-grid (V2G) service or coordinating with wind power. To solve this highly constrained multi-objective optimization problem (MOOP), a multiple group search optimization based on decomposition (MGSO/D) is proposed considering the uncertainties of PEVs and wind power. Specifically, the decomposition approach effectively reduces the computational complexity, and the innovatively incorporated producer-scrounger model effectively improves the diversity and spanning of the Pareto-optimal front (PF). Meanwhile, the estimation error punishment is utilized to take into account of uncertainties. The performance of MGSO/D and the effectiveness of the uncertainty model are investigated on the IEEE 30-bus and 118-bus system with wind farms and PEV aggregators. Simulation results demonstrate the superiority of MGSO/D to solve this MOOP with practical uncertainties by comparing with well-established Pareto heuristic methods.

ACS Style

Xian Zhang; Ka Wing Chan; Huaizhi Wang; Bin Zhou; Guibin Wang; Jing Qiu. Multiple group search optimization based on decomposition for multi-objective dispatch with electric vehicle and wind power uncertainties. Applied Energy 2020, 262, 114507 .

AMA Style

Xian Zhang, Ka Wing Chan, Huaizhi Wang, Bin Zhou, Guibin Wang, Jing Qiu. Multiple group search optimization based on decomposition for multi-objective dispatch with electric vehicle and wind power uncertainties. Applied Energy. 2020; 262 ():114507.

Chicago/Turabian Style

Xian Zhang; Ka Wing Chan; Huaizhi Wang; Bin Zhou; Guibin Wang; Jing Qiu. 2020. "Multiple group search optimization based on decomposition for multi-objective dispatch with electric vehicle and wind power uncertainties." Applied Energy 262, no. : 114507.

Journal article
Published: 04 November 2019 in IEEE Transactions on Power Systems
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Electric vehicles (EVs) provide new options for energy balancing of power systems. One possible way to use EVs in energy balancing is to let each distribution system mitigate its forecast uncertainties through the flexibility of EVs. In consideration of the difficulties to directly govern a large number of EVs, it is more reasonable for distribution systems to dispatch electric vehicle aggregators (EVAs). Without influencing EVs driving activities in the next day, a model is established for distribution systems to make use of EVAs, whose contributions are delaying uncertainties through their temporal flexibility and thus creating opportunities for uncertainties from different hours to offset each other. In the established model, a scheme of uncertainty transferring is proposed to relieve interruption to EVAs and distributionally robust optimization is adopted to evaluate the operation plans' average performance with temporal and spatial uncertainty correlations considered. Comprehensive case studies are carried out based on charging demands of EVAs simulated from real traffic data to verify the effectiveness of the proposed model.

ACS Style

Xi Lu; Ka Wing Chan; Shiwei Xia; Xian Zhang; Guibin Wang; Furong Li. A Model to Mitigate Forecast Uncertainties in Distribution Systems Using the Temporal Flexibility of EVAs. IEEE Transactions on Power Systems 2019, 35, 2212 -2221.

AMA Style

Xi Lu, Ka Wing Chan, Shiwei Xia, Xian Zhang, Guibin Wang, Furong Li. A Model to Mitigate Forecast Uncertainties in Distribution Systems Using the Temporal Flexibility of EVAs. IEEE Transactions on Power Systems. 2019; 35 (3):2212-2221.

Chicago/Turabian Style

Xi Lu; Ka Wing Chan; Shiwei Xia; Xian Zhang; Guibin Wang; Furong Li. 2019. "A Model to Mitigate Forecast Uncertainties in Distribution Systems Using the Temporal Flexibility of EVAs." IEEE Transactions on Power Systems 35, no. 3: 2212-2221.

Journal article
Published: 14 August 2019 in IEEE Transactions on Intelligent Transportation Systems
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Extreme events can extensively damage power systems, causing customers to experience long-lasting outages. During such events, an electric vehicle (EV) can be used to directly power a house, i.e., vehicle-to-home (V2H). Specifically, the EV serves as a mobile energy storage system--running errands to ''transport'' energy from other places. Vehicle-to-grid (V2G) further allows cooperation among houses. It enables EV fleets to take turns running the errands so that sustained power supply is possible. Moreover, autonomous driving technology can also benefit system adequacy because the charging errands of EVs can be scheduled flexibly without being bonded to human activities. An emergency power supply strategy featuring scheduled EV charging errands as introduced above is proposed. It answers the questions whether and to what extent a system can survive an extended period of outage with the use of EVs only. An optimization problem is formulated with the purpose of maximizing the supply adequacy of the isolated system during the outage period. Both V2H and V2G scenarios are considered in the problem formulation, as well as self-driving capability. The complex optimization problems are solved with genetic algorithm. It is significant to find from the case study that the proposed strategy is able to fully restoring an islanded system when V2G and self-driving EVs are implemented.

ACS Style

Ning Zhou Xu; Ka Wing Chan; Chi Yung Chung; Ming Niu. Enhancing Adequacy of Isolated Systems With Electric Vehicle-Based Emergency Strategy. IEEE Transactions on Intelligent Transportation Systems 2019, 21, 3469 -3475.

AMA Style

Ning Zhou Xu, Ka Wing Chan, Chi Yung Chung, Ming Niu. Enhancing Adequacy of Isolated Systems With Electric Vehicle-Based Emergency Strategy. IEEE Transactions on Intelligent Transportation Systems. 2019; 21 (8):3469-3475.

Chicago/Turabian Style

Ning Zhou Xu; Ka Wing Chan; Chi Yung Chung; Ming Niu. 2019. "Enhancing Adequacy of Isolated Systems With Electric Vehicle-Based Emergency Strategy." IEEE Transactions on Intelligent Transportation Systems 21, no. 8: 3469-3475.

Journal article
Published: 13 August 2019 in IEEE Access
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This paper proposes a series of new control methods for single-phase Z-source inverters. A detailed description of the concept and principle of each method is first presented, then a comparison among them is conducted comprehensively. Afterwards, an optimized closed-loop control scheme with better harmonic elimination performance is derived. Experimental results obtained from a 1kW un-isolated Z-source inverter prototype have demonstrated the effectiveness of the proposed control method. Compared to the conventional boost control, the proposed scheme has better performance with reduced harmonics, more flexible voltage gain, and simple algorithm.

ACS Style

Wenzheng Xu; Ming Liu; Junwei Liu; Ka Wing Chan; Ka Wai Eric Cheng. A Series of New Control Methods for Single-Phase Z-Source Inverters and the Optimized Operation. IEEE Access 2019, 7, 113786 -113800.

AMA Style

Wenzheng Xu, Ming Liu, Junwei Liu, Ka Wing Chan, Ka Wai Eric Cheng. A Series of New Control Methods for Single-Phase Z-Source Inverters and the Optimized Operation. IEEE Access. 2019; 7 ():113786-113800.

Chicago/Turabian Style

Wenzheng Xu; Ming Liu; Junwei Liu; Ka Wing Chan; Ka Wai Eric Cheng. 2019. "A Series of New Control Methods for Single-Phase Z-Source Inverters and the Optimized Operation." IEEE Access 7, no. : 113786-113800.

Journal article
Published: 12 July 2019 in IEEE Power and Energy Technology Systems Journal
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Utilities are under considerable pressure to increase the share of wind energy resources in their generation fleet. With the increasing share of wind energy resources, the dynamic behavior of power systems will change considerably due to fundamental differences in technologies used for wind and conventional generators. There is very little standardization in the ways to model wind turbines (WTs) and wind parks (WPs) in sharp contrast to conventional power plants. Hence, there is an international interest to deliver generic models (i.e. standardized and publicly available) for WTs and WPs that are able to capture all performance aspects as good as manufacturer-specific models. This paper presents an electromagnetic transient (EMT) simulation model for full size converter (FSC) WT based WPs that can be used for stability analysis and interconnection studies. The considered topology uses permanent magnet synchronous generator. Although the collector grid and the FSC WTs are represented with their aggregated models, the overall control structure of the WP is preserved. FSC WT and WP control systems include the non-linearities, and necessary transient and protection functions to simulate the accurate transient behavior of WPs.

ACS Style

U. Karaagac; J. Mahseredjian; R. Gagnon; H. Gras; H. Saad; L. Cai; I. Kocar; A. Haddadi; E. Farantatos; S. Bu; K. W. Chan; L. Wang. A Generic EMT-Type Model for Wind Parks With Permanent Magnet Synchronous Generator Full Size Converter Wind Turbines. IEEE Power and Energy Technology Systems Journal 2019, 6, 131 -141.

AMA Style

U. Karaagac, J. Mahseredjian, R. Gagnon, H. Gras, H. Saad, L. Cai, I. Kocar, A. Haddadi, E. Farantatos, S. Bu, K. W. Chan, L. Wang. A Generic EMT-Type Model for Wind Parks With Permanent Magnet Synchronous Generator Full Size Converter Wind Turbines. IEEE Power and Energy Technology Systems Journal. 2019; 6 (3):131-141.

Chicago/Turabian Style

U. Karaagac; J. Mahseredjian; R. Gagnon; H. Gras; H. Saad; L. Cai; I. Kocar; A. Haddadi; E. Farantatos; S. Bu; K. W. Chan; L. Wang. 2019. "A Generic EMT-Type Model for Wind Parks With Permanent Magnet Synchronous Generator Full Size Converter Wind Turbines." IEEE Power and Energy Technology Systems Journal 6, no. 3: 131-141.