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  • 1.
    Agredano Torres, Manuel
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Zhang, Mengfan
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Cornell, Ann M.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering, Applied Electrochemistry.
    Dynamic power allocation control for frequency regulation using hybrid electrolyzer systems2023In: 2023 IEEE Applied Power Electronics Conference And Exposition, APEC, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 2991-2998Conference paper (Refereed)
    Abstract [en]

    The increase in hydrogen production to support the energy transition in different sectors, such as the steel industry, leads to the utilization of large scale electrolyzers. These electrolyzers have the ability to become a fundamental tool for grid stability providing grid services, especially frequency regulation, for power grids with a high share of renewable energy sources. Alkaline electrolyzers (AELs) have low cost and long lifetime, but their slow dynamics make them unsuitable for fast frequency regulation, especially in case of contingencies. Proton Exchange Membrane electrolyzers (PEMELs) have fast dynamic response to provide grid services, but they have higher costs. This paper proposes a dynamic power allocation control strategy for hybrid electrolyzer systems to provide frequency regulation with reduced cost, making use of advantages of AELs and PEMELs. Simulations and experiments are conducted to verify the proposed control strategy.

  • 2.
    Agredano Torres, Manuel
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Zhang, Mengfan
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Decentralized Dynamic Power Sharing Control for Frequency Regulation Using Hybrid Hydrogen Electrolyzer Systems2024In: IEEE Transactions on Sustainable Energy, ISSN 1949-3029, E-ISSN 1949-3037, Vol. 15, no 3, p. 1847-1858Article in journal (Refereed)
    Abstract [en]

    Hydrogen electrolyzers are promising tools for frequency regulation of future power systems with high penetration of renewable energies and low inertia. This is due to both the increasing demand for hydrogen and their flexibility as controllable load. The two main electrolyzer technologies are Alkaline Electrolyzers (AELs) and Proton Exchange Membrane Electrolyzers (PEMELs). However, they have trade-offs: dynamic response speed for AELs, and cost for PEMELs. This paper proposes the combination of both technologies into a Hybrid Hydrogen Electrolyzer System (HHES) to obtain a fast response for frequency regulation with reduced costs. A decentralized dynamic power sharing control strategy is proposed where PEMELs respond to the fast component of the frequency deviation, and AELs respond to the slow component, without the requirement of communication. The proposed decentralized approach facilitates a high reliability and scalability of the system, what is essential for expansion of hydrogen production. The effectiveness of the proposed strategy is validated in simulations and experimental results.

  • 3.
    Berg, Petra
    et al.
    School of Marketing and Communication and VEBIC, University of Vaasa, Vaasa, Finland.
    Berlijn, Sonja Monica
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Eltahawy, Bahaa
    University of Vaasa, Digital Economy Research Platform, Vaasa, Finland.
    Hilber, Patrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering and Fusion Science.
    Karimi, Mazaher
    School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
    Klepper, Karina Barnholt
    The Norwegian Defence Research Establishment (FFI), Kjeller, Norway.
    Turtola, Linda
    University of Vaasa, Industrial management, Vaasa, Finland.
    Ulshagen, Andrea
    The Norwegian Defence Research Establishment (FFI), Kjeller, Norway.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Towards a Model for Assessing the Effects of Social-Cyber-Physical Threats on the Future Power Grid - Review and Workshop Results2024In: 2024 International Workshop on Artificial Intelligence and Machine Learning for Energy Transformation, AIE 2024, Institute of Electrical and Electronics Engineers (IEEE) , 2024Conference paper (Refereed)
    Abstract [en]

    The energy system, including the electrical power system, is currently undergoing major changes to meet increased demands and climate target plans, and to stand against potential malicious activities and all sorts of disruptions. Specifically, the electrical power system is drastically changing with regards to consumption, production, transmission, control, monitoring, markets, and digitalization. Such a change, however, makes the power system an attractive and vulnerable target to all kinds of disruptive events and social-cyber-physical attacks since the system is crucial for the functioning of the society and economy. In this work, to act against such events and to study the future power system's susceptibility and resilience towards social-cyber-physical attacks, the Resilient Digital Sustainable Energy Transition (REDISET) project has shown the need for a new model that is able to describe the future electrical power system in a way that reflects the future reality. In this paper, existing power system models, the changing landscape of power systems, the drivers for a new model, the suggested model that comprises 7 building blocks instead of today's 3, and finally a direction of future related work are presented.

  • 4.
    Bhadoria, Shubhangi
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Dijkhuizen, Frans
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems. Hitachi Energy Res, SE-72178 Västerås, Sweden..
    Raj, Rishabh
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Wang, Xiongfei
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Matioli, Elison
    Ecole Polytech Fed Lausanne EPFL, Inst Elect Engn, CH-1015 Lausanne, Switzerland..
    Kostov, Konstantin
    Res Inst Sweden, S-16440 Stockholm, Sweden..
    Nee, Hans-Peter
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Enablers for Overcurrent Capability of Silicon-Carbide-Based Power Converters: An Overview2023In: IEEE transactions on power electronics, ISSN 0885-8993, E-ISSN 1941-0107, Vol. 38, no 3, p. 3569-3589Article in journal (Refereed)
    Abstract [en]

    With the increase in penetration of power electronic converters in the power systems, a demand for overcurrent/ overloading capability has risen for the fault clearance duration. This article gives an overview of the limiting factors and the recent technologies for the overcurrent performance of SiC power modules in power electronics converters. It presents the limitations produced at the power module level by packaging materials, which include semiconductor chips, substrates, metallization, bonding techniques, die attach, and encapsulation materials. Specifically, technologies for overcurrent related temperatures in excess of 200 degrees C are discussed. This article also discusses potential technologies, which have been proven or may be potential candidates for improving the safe operating area. The discussed technologies are use of phase-change materials below the semiconductor chip, Peltier elements, new layouts of the power modules, control and modulation techniques for converters. Special attention has been given to an overview of various potential phase-change materials, which can be considered for high-temperature operations.

  • 5.
    Bhadoria, Shubhangi
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Wang, Xiongfei
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Nee, Hans-Peter
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Concept of Enabling Over-Current Capability of Silicon-Carbide-Based Power Converters with Gate Voltage Augmentation2024In: Energies, E-ISSN 1996-1073, Vol. 17, no 17, p. 4319-Article in journal (Refereed)
    Abstract [en]

    An increasing share of fluctuating and intermittent renewable energy sources can cause over-currents (OCs) in the power system. The heat generated during OCs increases the junction temperature of semiconductor devices and could even lead to thermal runaway if thermal limits are reached. In order to keep the junction temperature within the thermal limit of the semiconductor, the power module structure with heat-absorbing material below the chip is investigated through COMSOL Multiphysics simulations. The upper limits of the junction temperature for Silicon (Si) and Silicon Carbide (SiC) are assumed to be 175 and 250 ∘∘C, respectively. The heat-absorbing materials considered for analysis are a copper block and a copper block with phase change materials (PCMs). Two times, three times, and four times of OCs would be discussed for durations of a few hundred milliseconds and seconds. This article also discusses the thermal performance of a copper block and a copper block with PCMs. PCMs used for Si and SiC are LM108 and Lithium, respectively. It is concluded that the copper block just below the semiconductor chip would enable OC capability in Si and SiC devices and would be more convenient to manufacture as compared to the copper block with PCM.

  • 6.
    Cao, Haozhi
    et al.
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
    Xu, Yuecong
    A*STAR, Institute for Infocomm Research, Singapore, Singapore.
    Mao, Kezhi
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
    Xie, Lihua
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
    Yin, Jianxiong
    NVIDIA AI Tech Center, Singapore, Singapore.
    See, Simon
    NVIDIA AI Tech Center, Singapore, Singapore.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Yang, Jianfei
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
    Self-Supervised Video Representation Learning by Video Incoherence Detection2023In: IEEE Transactions on Cybernetics, ISSN 2168-2267, E-ISSN 2168-2275Article in journal (Refereed)
    Abstract [en]

    This article introduces a novel self-supervised method that leverages incoherence detection for video representation learning. It stems from the observation that the visual system of human beings can easily identify video incoherence based on their comprehensive understanding of videos. Specifically, we construct the incoherent clip by multiple subclips hierarchically sampled from the same raw video with various lengths of incoherence. The network is trained to learn the high-level representation by predicting the location and length of incoherence given the incoherent clip as input. Additionally, we introduce intravideo contrastive learning to maximize the mutual information between incoherent clips from the same raw video. We evaluate our proposed method through extensive experiments on action recognition and video retrieval using various backbone networks. Experiments show that our proposed method achieves remarkable performance across different backbone networks and different datasets compared to previous coherence-based methods.

  • 7.
    Cui, Chenggang
    et al.
    Shanghai Univ Elect Power, Coll Automat Engn, Intelligent Autonomous Syst Lab, Shanghai 200090, Peoples R China..
    Yang, Tianxiao
    Shanghai Univ Elect Power, Coll Automat Engn, Intelligent Autonomous Syst Lab, Shanghai 200090, Peoples R China..
    Dai, Yuxuan
    Shanghai Univ Elect Power, Coll Automat Engn, Intelligent Autonomous Syst Lab, Shanghai 200090, Peoples R China..
    Zhang, Chuanlin
    Shanghai Univ Elect Power, Coll Automat Engn, Intelligent Autonomous Syst Lab, Shanghai 200090, Peoples R China..
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Implementation of Transferring Reinforcement Learning for DC-DC Buck Converter Control via Duty Ratio Mapping2023In: IEEE Transactions on Industrial Electronics, ISSN 0278-0046, E-ISSN 1557-9948, Vol. 70, no 6, p. 6141-6150Article in journal (Refereed)
    Abstract [en]

    The reinforcement learning (RL) control approach with application to power electronics systems has become an emerging topic, while the sim-to-real issue remains a challenging problem as very few results can be referred to in the literature. Indeed, due to the inevitable mismatch between simulation models and real-life systems, offline-trained RL control strategies may sustain unexpected hurdles in practical implementation during the transfer procedure. In this article, a transfer methodology via a delicately designed duty ratio mapping is proposed for a dc-dc buck converter. Then, a detailed sim-to-real process is presented to enable the implementation of a model-free deep reinforcement learning controller. As the main contribution of this article, the proposed methodology is able to endow the control system to achieve: 1) voltage regulation and 2) adaptability and optimization abilities in the presence of uncertain circuit parameters and various working conditions. The feasibility and efficacy of the proposed methodology are demonstrated by comparative experimental studies.

  • 8.
    Fu, Zuhang
    et al.
    College of Mechanical and Vehicle Engineering, The State Key Laboratory of Mechanical Transmissions, Chongqing Automotive Collaborative Innovation Centre, Chongqing University, Chongqing 400044, China.
    Lu, Lei
    School of Business, Macau University of Science and Technology, Taipa, Macao SAR, China.
    Zhang, Caizhi
    College of Mechanical and Vehicle Engineering, The State Key Laboratory of Mechanical Transmissions, Chongqing Automotive Collaborative Innovation Centre, Chongqing University, Chongqing 400044, China.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Zhang, Xiaoyu
    China Shipbuilding Power Engineering Institute Co., Ltd, 200129, China.
    Gao, Zuchang
    School of Engineering, Temasek Polytechnic, 529757, Singapore.
    Li, Jun
    Institute of Engineering Thermophysics, School of Energy and Power Engineering, Chongqing University, Chongqing 400030, China.
    Fuel cell and hydrogen in maritime application: A review on aspects of technology, cost and regulations2023In: Sustainable Energy Technologies and Assessments, ISSN 2213-1388, E-ISSN 2213-1396, Vol. 57, article id 103181Article in journal (Refereed)
    Abstract [en]

    Currently, fuel cell and hydrogen technology are attracting more and more attention as a kind of green and clean energy technology in the context of the increasingly stringent carbon emission requirements of the shipping industry. However, there are still many obstacles to their maritime application due to high costs and a lack of infrastructure. This paper conducts a literature survey of fuel cell maritime applications from four aspects: key technologies of fuel cell and hydrogen maritime applications, cost and standards. It can be concluded that ships powered by hydrogen fuel cells will have a broad application prospect with the maturity of the hydrogen industry chain and the improvement of standards and regulations. Two main contributions of this paper are to fully understand a whole-process perspective of hydrogen and fuel cell maritime application, and to provide the problems and future direction in this field, which can help relevant research institutions and scholars evaluate the development status of the industrial chain and find new valuable research topics.

  • 9.
    Guo, Fanghong
    et al.
    Department of Automation, Zhejiang University of Technology, Hangzhou, China.
    Wang, Lei
    School of Electrical Engineering and Computing, University of Newcastle, Newcastle, Australia.
    Wen, Changyun
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
    Zhang, Dan
    Department of Automation, Zhejiang University of Technology, Hangzhou, China.
    Xu, Qianwen
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
    Distributed Voltage Restoration and Current Sharing Control in Islanded DC Microgrid Systems Without Continuous Communication2020In: IEEE Transactions on Industrial Electronics, ISSN 0278-0046, E-ISSN 1557-9948, Vol. 67, no 4, p. 3043-3053Article in journal (Refereed)
    Abstract [en]

    This paper presents a new distributed control scheme to achieve both accurate voltage restoration and precise current sharing for islanded dc microgrid (MG) system only with limited noncontinuous communication among the distributed generators (DGs). A two-layer multiagent framework is employed for this MG system, which consists of a physical layer and a cyber layer. A distributed voltage restoration control scheme is proposed in the cyber layer, where no overall system information is required and only dc bus voltage feedback is needed. Furthermore, by employing the idea of event-triggered communication, our proposed approach only relies on limited aperiodic communication, which greatly reduces the communication cost in the cyber layer. The stability of proposed method is analyzed through a Lyapunov function based approach and we also demonstrate that the Zeno behavior can be excluded if a proper event-triggered condition is established. Our proposed method is validated in an islanded dc MG test system built in the Simulink environment and an experimental prototype consisting of three DGs simultaneously.

  • 10.
    Guo, Guodong
    et al.
    State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China.
    Zhang, Mengfan
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Gong, Yanfeng
    School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, China.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Safe multi-agent deep reinforcement learning for real-time decentralized control of inverter based renewable energy resources considering communication delay2023In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 349, article id 121648Article in journal (Refereed)
    Abstract [en]

    The increasing penetration of distributed renewable energy resources brings a great challenge for real-time voltage security of distribution grids. The paper proposes a safe multi-agent deep reinforcement learning (MADRL) algorithm for real-time control of inverter-based Volt-Var control (VVC) in distribution grids considering communication delay to minimize the network power loss, while maintaining the nodal voltages in a safe range. The multi-agent VVC is modeled as a constrained Markov game, which is solved by the MADRL algorithm. In the training stage, the safety projection is added to the combined policy to analytically solve an action correction formulation to promote more efficient and safe exploration. In the real-time decision-making stage, a state synchronization block is designed to impute the data under the latest timestamp as the input of the agents deployed in a distributed manner, to avoid instability caused by communication delay. The simulation results show that the proposed algorithm performs well in safe exploration, and also achieves better performance under communication delay.

  • 11.
    Jiang, Tao
    et al.
    Northeast Electric Power University, Jilin 132012, China.
    Parisio, Alessandra
    University of Manchester, Manchester M13 9PL, UK.
    Liu, Guodong
    Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Guo, Qinglai
    Tsinghua University, Beijing 100084, China.
    Bai, Feifei
    The University of Queensland, Brisbane, QLD 4072, Australia.
    Rather, Zakir
    Indian Institute of Technology Bombay, Mumbai 400076, India.
    Li, Gengfeng
    Xi'an Jiaotong University, Xi'an 710049, China.
    Terzija, Vladimir
    Newcastle University, School of Engineering, Newcastle Upon Tyne NE1 7RU, UK.
    Guest Editorial: Special issue on flexible and resilient urban energy systems2023In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 154, p. 109439-, article id 109439Article, review/survey (Refereed)
    Abstract [en]

    This guest editorial summarizes the topics and the papers selected for the Special Issue on Flexible and Resilient Urban Energy Systems. After rigorous reviewing process, 21 papers are accepted for publication. These 21 accepted papers cover various aspects of urban energy systems and are distributed as following: situational awareness of urban energy systems (2 papers), quantification metrics of flexibility and resilience of urban energy systems (3 papers), vulnerability modeling of urban energy systems under various extreme events (3 papers), planning of flexible and resilient urban energy systems (4 papers), robust and resilient operation and control of urban energy systems (4 papers), recovery and restoration strategy of urban energy systems (2 papers), and coordination and interoperability of interconnected energy systems (3 papers). The Guest Editorial Board hopes this Special Issue can provide a valuable information for future research and advancements in the field of flexible and resilient urban energy systems.

  • 12.
    Li, Biao
    et al.
    Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China..
    Wan, Can
    Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China..
    Yu, Peng
    Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China..
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Song, Yonghua
    Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Peoples R China.;Univ Macau, State Key Lab Internet Things Smart City, Macau, Peoples R China..
    Voltage-Price Coupling in Distribution Networks2024In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 15, no 2, p. 1438-1449Article in journal (Refereed)
    Abstract [en]

    Demand-side users are practical and economical resources for participating in voltage regulation of distribution networks, whose regulation effect is closely related to the price. This paper proposes a novel voltage-price coupling (VPC) mechanism to construct a fair voltage-based nodal pricing method for distribution networks to encourage demand-side users to participate in voltage regulation to improve voltage quality. The voltage impact factor is established to fairly characterize the impact of each nodal power on the voltage deviation in the distribution network. Further, the VPC mechanism is constructed to highly couple the nodal price with the voltage in both spatial and time dimensions by establishing distribution rules of prices at different nodes and different horizons. The VPC mechanism forms a fair and adaptable voltage-based nodal pricing method for distribution networks, which also considers the impact on the existing pricing mechanism, and the balance between the voltage regulation effect and user experience. Then, a price-driven voltage regulation (PVR) model is proposed for the distribution network to reduce voltage deviations and user costs, which integrates the demand-side resource management, voltage regulation, and voltage-based nodal pricing. Case studies verify the effectiveness of the VPC mechanism and the PVR model.

  • 13.
    Li, Boda
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    A Machine Learning-Assisted Distributed Optimization Method for Inverter-Based Volt-VAR Control in Active Distribution Networks2024In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 39, no 2, p. 2668-2681Article in journal (Refereed)
    Abstract [en]

    The number of smart inverters in active distribution networks is growing rapidly, making it challenging to realize a fast, distributed Volt/Var control (VVC). This work proposes a machine learning-assisted distributed algorithm to accelerate the solution of the VVC strategy. We first observe the convergence process of the Alternating Direction Method of Multipliers (ADMM)-based VVC problem and explore the potential relationships between the convergence and time-series regression. Then, the long short-term memory (LSTM) technique is applied to learn the convergence process and regress the converged values of the dual and global variables with previous ADMM observations. After that, the LSTM-assisted ADMM algorithm is proposed, where the regressions are used for ADMM parameter updates. In this algorithm, the inputs of the LSTM-model are carefully designed since the complementary conditions implied in the conventional ADMM should be considered. Unlike existing methods, the proposed method does not use the LSTM to determine the VVC strategy directly, indicating that it is non-intrusive and can satisfy all safety constraints during operations. The proof of its optimality and convergence is also given. The numerical simulations on the 33-bus distribution system demonstrate the effectiveness and efficiency of the proposed method.

  • 14.
    Li, Boda
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Multiple System Function Supports with Inverter-Dominated Virtual Power Plant2023In: 2023 IEEE Power and Energy Society General Meeting, PESGM 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023Conference paper (Refereed)
    Abstract [en]

    Recently, renewables equipped with smart inverters are being integrated into distribution networks on a large scale. To address the problem that renewables are too scattered and hard to dispatch uniformly, virtual power plant (VPP) technique has been developed. In this work, we proposed an optimization model to coordinate different system functions and allocate them to various devices in a VPP agent. Peak shaving, congestion management, frequency and voltage regulation are all considered. Then, this scenario-based model is solved with the scenario selection method. Two types of scenarios are carefully selected to form the model, aiming at reducing the computation burden and increasing the strategy's robustness. The model is tested on a VPP agent modified from the 33-bus system. The simulation demonstrates the effectiveness and efficiency of our work.

  • 15. Li, X.
    et al.
    Wen, C.
    Chen, C.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Adaptive Resilient Secondary Control for Microgrids With Communication Faults2021In: IEEE Transactions on Cybernetics, ISSN 2168-2267, E-ISSN 2168-2275, p. 1-11Article in journal (Refereed)
    Abstract [en]

    In this article, we consider the resilience problem in the presence of communication faults encountered in distributed secondary voltage and frequency control of an islanded alternating current microgrid. Such faults include the partial failure of communication links and some classes of data manipulation attacks. This practical and important yet challenging issue has been taken into limited consideration by existing approaches, which commonly assume that the measurement or communication between the distributed generations (DGs) is ideal or satisfies some restrictive assumptions. To achieve communication resilience, a novel adaptive observer is first proposed for each individual DG to estimate the desired reference voltage and frequency under unknown communication faults. Then, to guarantee the stability of the closed-loop system, voltage and frequency restoration, and accurate power sharing regardless of unknown communication faults, sufficient conditions are derived. Some simulation results are presented to verify the effectiveness of the proposed secondary control approach. 

  • 16.
    Li, X.
    et al.
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
    Blaabjerg, F.
    Aalborg University, Aalborg, Denmark.
    Adaptive Resilient Secondary Control for Islanded AC Microgrids With Sensor Faults2021In: IEEE Journal of Emerging and Selected Topics in Power Electronics, ISSN 2168-6777, E-ISSN 2168-6785, Vol. 9, no 5, p. 5239-5248Article in journal (Refereed)
    Abstract [en]

    In this article, we investigate the adaptive resilient secondary voltage and frequency control problem for islanded ac microgrids (MG) in the presence of sensor faults. Sensor faults or data attacks have a great impact on the stability and quality of MG. Existing methods commonly assume that the sensing information from distributed generations (DGs) is healthy or satisfies some ideal conditions. To achieve resilient secondary voltage restoration, a novel adaptive fault tolerant control scheme is proposed. Since the bounds of the time-varying faults are unknown, sufficient conditions are first derived to guarantee the voltage tracking errors to be uniformly ultimately bounded. It is also theoretically shown that the frequency restoration and power sharing can be ensured stable of the overall ac MG system by using the same control scheme. Compared with the existing distributed control methods for secondary control of MG, the considered problem with unknown boundaries sensor faults is more challenging and still not well explored.

  • 17.
    Li, Xiaolei
    et al.
    School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore..
    Chen, Ci
    Department of Automatic Control, Lund University, Sweden.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Wen, Changyun
    School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore..
    Resilience for Communication Faults in Reactive Power Sharing of Microgrids2021In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 12, no 4, p. 2788-2799Article in journal (Refereed)
    Abstract [en]

    In this paper, we consider the distributed reactive power sharing control problem for an autonomous inverter-based microgrid with resilience for communication faults, which may be caused by partial communication link failures or some channel manipulation attacks. Under the standard decoupling approximation for bus angle differences, the reactive power flow of each inverter can be controlled by manipulating the voltage amplitudes of itself and its neighbour inverters. By designing an adaptive resilient cooperative control scheme, accurate reactive power sharing can be guaranteed even in the presence of communication faults. Some simulation results are presented to verify the superiority of the proposed control approach compared with the existing methods.

  • 18.
    Liu, Xiao-Kang
    et al.
    School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China ; Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, China..
    Wen, Changyun
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798..
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Wang, Yan-Wu
    School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China ; Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, China..
    Resilient Control and Analysis for DC Microgrid System under DoS and Impulsive FDI Attacks2021In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 12, no 5, p. 3742-3754Article in journal (Refereed)
    Abstract [en]

    This paper proposes a resilient controller for DC microgrid to achieve current sharing and voltage restoration under discrete-time false data injection (FDI) and denial-of-service (DoS) attacks. Switching and impulsive signals are used to model the dynamic system of DC microgrid under DoS and FDI. To deal with the cyber attacks, a combined error of current and voltage is proposed and a switching secondary controller is designed. Based on the stability analysis method on hybrid systems, we establish a sufficient condition for selecting control parameters in relation to the average dwell time of FDI attack and the normal communication rate under DoS attack. Furthermore, an adaptive gain based control scheme is proposed to relax the requirement on knowledge of the cyber attacks in control parameter design. The utility of the results is illustrated through case studies on a tested DC microgrid.

  • 19.
    Lu, Yizhou
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Ghandhari Alavijh, Mehrdad
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Composite Control Scheme Based on Practical Droop and Tube Model Predictive Control for Electric Vehicles in Grid Frequency Regulation2024In: ICIT 2024 - 2024 25th International Conference on Industrial Technology, Institute of Electrical and Electronics Engineers (IEEE) , 2024Conference paper (Refereed)
    Abstract [en]

    Power imbalances between generation and consumption will cause frequency deviations in the grid. In modern power systems, intermittent renewable energy sources (RES) have resulted in more frequent frequency violations, as traditional power plants cannot compensate for power gaps timely. Electric vehicles (EVs) can participate in load frequency control (LFC) through aggregators and are capable of reacting faster to control commands than conventional frequency control reserves (FCR) in generators. Thus EVs hold great promise in assisting with LFC. This paper proposes a composite control scheme that fully utilizes EVs for LFC in both normal scenarios and contingencies. The designed droop control can greatly reduce instantaneous frequency deviations (IFD) in emergencies, while the tube model predictive control (Tube MPC) can ensure smooth frequency trajectories during normal operations. Based on realistic models, simulation results illustrate the effectiveness of the proposed method.

  • 20.
    Lu, Yizhou
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Nordström, Lars
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Data- driven decentralized volt/var control for smart PV inverters in distribution systems2022In: 2022 24Th European Conference On Power Electronics And Applications (EPE'22 ECCE EUROPE), IEEE , 2022Conference paper (Refereed)
    Abstract [en]

    The growing penetration of renewable energy sources (RES) in modern grids may result in severe voltage violation problems due to high stochastic features. Conventional centralized approaches could provide optimal solutions for voltage regulation while with great communication burdens. Control methods based on local information usually have non-optimal results and cannot always guarantee voltage security. This paper proposes a neural network-based decentralized strategy for volt/var control using inverter reactive power capacity. Learning from optimal power flow (OPF) results of historical data, the developed controller can provide optimal results approximate to centralized solutions and outperform local control methods in minimizing the power loss. The proposed method is tested on the IEEE 33-bus system and simulation results illustrate the effectiveness in voltage regulation and loss minimization.

  • 21.
    Lu, Yizhou
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Zhang, Mengfan
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Nordström, Lars
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    An Online Digital Twin based Health Monitoring Method for Boost Converter using Neural Network2023In: 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 3701-3706Conference paper (Refereed)
    Abstract [en]

    This paper proposes a neural network-based digital twin for online health monitoring of vulnerable components in converters. The proposed digital twin consists of a physics-informed model with uncertain parameters, and a neural network (NN) for real-time model updating and health monitoring of components. This method is noninvasive, without extra circuits, and can identify parameters in real-time with high efficiency. Simulation and experiment are conducted to validate the effectiveness of the proposed method in accurate parameter identification and degradation monitoring of capacitor and MOSFET.

  • 22.
    Luo, Xiaoyuan
    et al.
    Yanshan Univ, Sch Elect Engn, Qinhuangdao 066004, Peoples R China..
    Gao, Ruiyang
    Yanshan Univ, Sch Elect Engn, Qinhuangdao 066004, Peoples R China..
    Li, Xiaolei
    Yanshan Univ, Sch Elect Engn, Qinhuangdao 066004, Peoples R China..
    Fu, Yuliang
    Yanshan Univ, Sch Elect Engn, Qinhuangdao 066004, Peoples R China..
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Guan, Xinping
    Shanghai Jiao Tong Univ, Inst Elect Informat & Elect Engn, Shanghai 200240, Peoples R China..
    Event-Based Attack Detection and Mitigation for DC Microgrids via Adaptive LQR Approach2024In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 15, no 4, p. 4196-4206Article in journal (Refereed)
    Abstract [en]

    Data manipulation attacks have become one of the main threats to cyber-physical direct current (DC) microgrids, but how to ensure voltage and current restoration under cyber attacks has not been well explored. In this paper, the event-based attack detection and mitigation problem for DC microgrids is considered. Specifically, an attack detection mechanism is designed to detect whether an attack has occurred. Then the proposed resilient secondary control strategy is only activated when the detection mechanism generates an attack event. For unknown types of attacks that aim at tampering with the information transmitted in the communication network, an adaptive linear quadratic regulator (LQR) based control strategy is designed to mitigate the effects such that the voltage and current restoration is achieved. Finally, the effectiveness of the proposed strategy is verified through simulationthis.

  • 23.
    Mao, Jia
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Zhang, Mengfan
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    CNN and LSTM based Data-driven Cyberattack Detection for Grid-connected PV Inverter2022In: IEEE International Conference on Control and Automation, ICCA, Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 704-709Conference paper (Refereed)
    Abstract [en]

    Growing penetration of renewables comes with increased cyber security threat due to inherent low inertia characteristic and sophisticated control and communication networks of power electronics. This paper proposes a data-driven cyberattack detection strategy for grid-connected photovoltaic (PV) inverters. Ideas of long short term memory (LSTM) and convolutional neural network (CNN) as the core of detection achieve time series classification to diagnose the target and mode of cyberattack. Input de-redundancy and hyperparameter selection are conducted to optimize the detection. Meanwhile, well-designed cyberattack toolboxes of false data injection (FDI), denial-of-service (DoS) and delay are applied upon the communication of both sampled signals and issued commands in a grid-connected inverter model. By observing system performance via electrical measurements, this case study evaluates the LSTM, CNN-LSTM and convolutional LSTM based detection and obtains stable high quality of classification. 

  • 24.
    Meng, Xiangqi
    et al.
    Taiyuan Univ Technol, Shanxi Key Lab Power Syst Operat & Control, Taiyuan 030024, Peoples R China..
    Jia, Yanbing
    Taiyuan Univ Technol, Shanxi Key Lab Power Syst Operat & Control, Taiyuan 030024, Peoples R China..
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Ren, Chunguang
    Taiyuan Univ Technol, Shanxi Key Lab Power Syst Operat & Control, Taiyuan 030024, Peoples R China..
    Han, Xiaoqing
    Taiyuan Univ Technol, Shanxi Key Lab Power Syst Operat & Control, Taiyuan 030024, Peoples R China..
    Wang, Peng
    Nanyang Technol Univ, Dept Elect & Elect Engn, Singapore 639798, Singapore..
    A Novel Intelligent Nonlinear Controller for Dual Active Bridge Converter With Constant Power Loads2023In: IEEE Transactions on Industrial Electronics, ISSN 0278-0046, E-ISSN 1557-9948, Vol. 70, no 3, p. 2887-2896Article in journal (Refereed)
    Abstract [en]

    The stability of dual active bridge converter (DAB) is threatened when feeding the constant power loads (CPLs). This article proposes a deep reinforcement learning-based backstepping control strategy to solve this problem. First, a nonlinear disturbance observer is adopted to estimate the large-signal nonlinear disturbance. Then, a backstepping controller is used to stabilize the voltage response of the DAB under the large-signal disturbance. Finally, a compensation method based on deep reinforcement learning is developed to intelligently minimize output voltage tracking error and improve the operating efficiency of the system. The proposed controller can guarantee system stability under the large-signal disturbance of the CPL and achieve a fast dynamic response with accurate voltage tracking; it is more adaptive by using the deep reinforcement learning technique through the learning of its neural networks. The effectiveness of the proposed controller is verified by experiments.

  • 25.
    Shang, Ke
    et al.
    School of Electrical Engineering, Yanshan University, Qinhuangdao, China.
    Li, Xiaolei
    School of Electrical Engineering, Yanshan University, Qinhuangdao, China,.
    Luo, Xiaoyuan
    School of Electrical Engineering, Yanshan University, Qinhuangdao, China.
    Wang, Jiange
    School of Electrical Engineering, Yanshan University, Qinhuangdao, China.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Multi-Objective Optimal Dispatching for Heterogeneous Multienergy Ship Microgrid2023In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 6576-6581Conference paper (Refereed)
    Abstract [en]

    With the growth of energy and transportation demand, the integrated energy dispatching of ship power grid has become the focus of researchers. The optimization technique is used to reduce the total energy consumption and pollutant emissions of ships, optimizing the ship power generation planning. The purpose is to achieve environmental protection and energy saving while ensuring the continuous and reliable power supply of ships. However, heterogeneous ship microgrid poses new challenges to integrated energy dispatch. This paper proposes an integrated energy scheduling scheme that integrates photovoltaic, wind power, diesel engine, gas turbine, and battery for a heterogeneous multienergy ship microgrid. Under the system constraints, a multi-objective optimal scheduling model including operating costs and pollutant emissions is established, then the gravity search algorithm is applied to solve such an issue. The simulation results show that the scheme can effectively reduce the cost of energy consumption and pollutant emissions of ships, improving the economy, reliability and energy conservation, which verify the advantages of the proposed scheme.

  • 26.
    Wang, Benfei
    et al.
    Sun Yat Sen Univ, Sch Intelligent Syst Engn, Shenzhen Campus, Shenzhen 518107, Peoples R China..
    Li, Zhipeng
    Sun Yat Sen Univ, Sch Intelligent Syst Engn, Shenzhen Campus, Shenzhen 518107, Peoples R China..
    Fan, Hongru
    Sun Yat Sen Univ, Sch Intelligent Syst Engn, Shenzhen Campus, Shenzhen 518107, Peoples R China..
    Wan, Xinmao
    Sun Yat Sen Univ, Sch Intelligent Syst Engn, Shenzhen Campus, Shenzhen 518107, Peoples R China..
    Xian, Liang
    Huawei Digital Power Co Ltd, Singapore 486066, Singapore..
    Zhang, Mengfan
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Higher Order Sliding Mode Observer Based Fast Composite Backstepping Control for HESS in DC Microgrids2024In: IEEE Transactions on Sustainable Energy, ISSN 1949-3029, E-ISSN 1949-3037, Vol. 15, no 3, p. 1627-1639Article in journal (Refereed)
    Abstract [en]

    Hybrid energy storage system (HESS) is effective to compensate for fluctuation power in renewables and fast fluctuation loads in DC microgrids. To regulate DC bus voltage, a power management strategy is an essential issue. In the meantime, the increasing integration of constant power loads (CPLs) in DC microgrids brings great challenges to stable operation due to their negative incremental impedance. In this paper, a fast composite backstepping control (FBC) method is proposed for the HESS to achieve faster dynamics, smaller voltage variations, and large-signal stabilization. In the FBC method, a higher order sliding mode observer (HOSMO) is adopted to estimate the coupled disturbances. Furthermore, the FBC method is integrated with the droop control; so that the FBC-based decentralized power allocation (FBC-DPA) strategy for HESS in DC microgrids is developed. The proposed FBC method is designed based on the Lyapunov function to ensure its stability. Moreover, the design guidelines are provided to facilitate the application of the proposed method. Both simulation and experimental studies under different operating scenarios show that the proposed method achieves faster voltage recovery and smaller voltage variations than the conventional backstepping control method.

  • 27.
    Wang, Xiaoyu
    et al.
    School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, China.
    Huang, Jingjing
    School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, China.
    Cao, Ye
    School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, China.
    Yang, Tianxiao
    Shanghai University of Electric Power, Intelligent Autonomous System Lab, Shanghai, China.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Zhang, Chuanlin
    Shanghai University of Electric Power, Intelligent Autonomous System Lab, Shanghai, China.
    Zhang, Aimin
    School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, China.
    A Novel Output-Constrained Controller for DC/DC Buck Converter Feeding Constant Power Loads in DC Microgrids2023In: IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society, Institute of Electrical and Electronics Engineers (IEEE) , 2023Conference paper (Refereed)
    Abstract [en]

    DC microgrids have emerged as a promising solution for efficient and reliable electricity distribution. In DC microgrids, when power electronic loads and motor drives are tightly regulated, they behave as constant power loads (CPLs) and may lead to the instability issue. In this paper, a novel output-constrained controller for the DC/DC buck converter feeding CPLs is proposed. By introducing the output-constrained technique into the backstepping method, the proposed control scheme can keep the DC bus working within the pre-specific boundary even when large-signal disturbances happen. Relevant theoretical analyses are conducted by employing Laypunov stability theorem. Simulations in Matlab/Simulink are presented to verify the proposed controller.

  • 28.
    Weiss, Xavier
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Nordström, Lars
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Energy Management of Smart Homes with Electric Vehicles Using Deep Reinforcement Learning2022In: 2022 24th european conference on power electronics and applications (EPE'22 ECCE europe), IEEE, 2022Conference paper (Refereed)
    Abstract [en]

    The proliferation of electric vehicles (EVs) has resulted in new charging infrastructure at all levels, including domestically. These new domestic EVs can potentially provide vehicle to home (V2H) services where EVs are used as energy storage systems (ESSs) for the home when they are not in use. Energy management systems (EMSs) can control these EVs to minimize the electricity cost to the owner but must satisfy constraints. Uncertainty in EV availability and the microgrid environment is also a challenge and can be addressed through real-time operation. Hence this paper formulates the EV charge/discharge scheduling problem as a Markov Decision Process (MDP). A safe implementation of Proximal Policy Optimization (PPO) is proposed for real-time optimization and compared to a day-ahead Mixed Integer Linear Programming (MILP) benchmark. The resulting PPO agent is able to minimize RA and SD costs for a typical EV user 3% better than the MILP solution. It obtains a 39% higher electricity cost than MILP, but unlike MILP does not require accurate forecasting data and operates in real-time.

  • 29. Xing, L.
    et al.
    Xu, Qianwen
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
    Wen, C.
    Tian, Y. -C
    Mishra, Y.
    Ledwich, G.
    Song, Y.
    Robust Event-Triggered Dynamic Average Consensus Against Communication Link Failures With Application to Battery Control2020In: IEEE Transactions on Control of Network Systems, E-ISSN 2325-5870, Vol. 7, no 3, p. 1559-1570Article in journal (Refereed)
    Abstract [en]

    Dynamic average consensus (DAC) has found applications in various systems. The existing event-triggered DAC algorithms have not well addressed the issue of key communication link failures that lead to the separation of the initial communication topology. This article presents a modified event-triggered DAC algorithm that is independent of its initial conditions. As a result, it is robust against key communication link failures. In this algorithm, each agent decides locally when to transmit signals to its neighbors. In this way, the communication burden among the neighboring agents is reduced. A numerical example is provided to illustrate the effectiveness of the proposed algorithm. Moreover, the proposed algorithm is applied to a state-of-charge balance control problem of batteries in energy systems, and both simulations and hardware-in-the-loop tests are provided to demonstrate the control performance.

  • 30.
    Xing, Lantao
    et al.
    Nanyang Technological University, 54761 Singapore, Singapore, 639798.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems. School of Electrical and Electronic Engineering, Nanyang Technological University, 54761 Singapore, Singapore, 639798.
    Guo, Fanghong
    Zhejiang University of Technology, 12624 Hangzhou, China, 310014.
    Wu, Zheng-Guang
    Zhejiang University, 12377 Hangzhou, Zhejiang, China, 310063.
    Liu, Meiqin
    Zhejiang University, 12377 Hangzhou, Zhejiang, China.
    Distributed Secondary Control for DC Microgrid with Event-triggered Signal Transmissions2021In: IEEE Transactions on Sustainable Energy, ISSN 1949-3029, E-ISSN 1949-3037, Vol. 12, no 3, p. 1801-1810Article in journal (Refereed)
    Abstract [en]

    The distributed control of DC microgrid is becoming increasingly important in modern power systems. One important control objective is to ensure DC bus voltage stability and proper current sharing with a reduced communication burden. This paper presents a new event-triggered distributed secondary control strategy for single-bus DC microgrid. Through this strategy, both current sharing and bus voltage regulation can be guaranteed. Moreover, through the event-triggering mechanism, each converter can decide locally when to transmit signals to its neighbours. In this way, the communication burden among converters is significantly reduced. Compared to existing results, the proposed strategy also enables various types of loads, including both linear and nonlinear loads, to be connected to the DC microgrid. Simulation and experiment results illustrate the effectiveness of the proposed strategy.

  • 31.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Overview of stability analysis methods in power electronics2021In: Control of Power Electronic Converters and Systems: Volume 3, Elsevier BV , 2021, p. 169-197Chapter in book (Other academic)
    Abstract [en]

    In this chapter, the stability analysis methods for power electronic-based power systems are reviewed. First, modeling methods and small signal stability analysis methods, i.e., eigenvalue method and impedance-based method, are illustrated with detailed procedures. Next, large-signal stability analysis tools are discussed. Then two case studies are presented with small-signal stability analysis and large-signal stability analysis. Finally, conclusions are drawn.

  • 32.
    Xu, Qianwen
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems. Department of Energy Technology, Aalborg University, 1004 Aalborg, Denmark, 9220.
    Dragicevic, T.
    Xie, L.
    Blaabjerg, F.
    Artificial Intelligence based Control Design for Reliable Virtual Synchronous Generators2021In: IEEE transactions on power electronics, ISSN 0885-8993, E-ISSN 1941-0107, p. 1-1Article in journal (Refereed)
    Abstract [en]

    Virtual synchronous generator (VSG) is a promising solution for inertia support of the future electricity grid to deal with the frequency stability issues caused by the high penetration of renewable generations. However, the power variation in power electronic interface converters caused by VSG emulation increases the stress on power semiconductor devices and hence has a negative impact on their reliability. Unlike existing works that only consider stability for VSG control design, this article proposes a double-artificial neural network (ANN)-based method for designing VSG inertia parameter considering simultaneously the reliability and stability. First, a representative frequency profile is generated to extract various VSG power injection profiles under different inertia values through detailed simulations. Next, a functional relationship between inertia parameter (H) and lifetime consumption (LC) of VSG is established by the proposed double-ANN reliability model: ANN t provides fast and accurate modeling of thermal stress in the semiconductor devices from a given operating profile; with the aid of ANN t , ANN LC is built for fast and accurate estimation of LC for different inertia parameters in the next step. The proposed approach not only provides a guideline for parameter design given a certain LC requirement, but can also be used for optimal design of VSG parameter considering reliability and other factors (e.g., inertia support in this article). The proposed technique is applied to a grid-connected VSG system as a demonstration example.

  • 33.
    Xu, Qianwen
    et al.
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
    Jiang, W.
    Blaabjerg, F.
    Zhang, C.
    Zhang, X.
    Fernando, T.
    Backstepping Control for Large Signal Stability of High Boost Ratio Interleaved Converter Interfaced DC Microgrids With Constant Power Loads2020In: IEEE transactions on power electronics, ISSN 0885-8993, E-ISSN 1941-0107, Vol. 35, no 5, p. 5397-5407Article in journal (Refereed)
    Abstract [en]

    With the penetration of renewable generation and tightly regulated power electronic loads, the power quality and stability of modern dc microgrids are greatly challenged. To solve this problem, energy storage systems (ESSs) are widely proposed. Unfortunately, most of the existing control methods for ESSs can only ensure the stability of dc microgrids with small signal disturbances, which may not be satisfying for real-time applications where large signal disturbances exist. Moreover, they are usually designed for low boost ratio and low power converters, negating their suitability for grid-scale applications. To ensure the large signal stability of dc microgrids using high boost ratio interleaved converter interfaced ESSs, this article proposes a new backstepping control strategy with finite-time disturbance observers. Its effectiveness is verified by simulation and experiments carried out on an interleaved double dual boost converter.

  • 34.
    Xu, Qianwen
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Vafamand, N.
    Chen, L.
    Dragičević, T.
    Xie, L.
    Blaabjerg, F.
    Review on Advanced Control Technologies for Bidirectional DC/DC Converters in DC Microgrids2021In: IEEE Journal of Emerging and Selected Topics in Power Electronics, ISSN 2168-6777, E-ISSN 2168-6785, Vol. 9, no 2, p. 1205-1221Article in journal (Refereed)
    Abstract [en]

    DC microgrids encounter the challenges of constant power loads (CPLs) and pulsed power loads (PPLs), which impose the requirements of fast dynamics, large stability margin, high robustness that cannot be easily addressed by conventional linear control methods. This necessitates the implementation of advanced control technologies in order to significantly improve the robustness, dynamic performance, stability and flexibility of the system. This article presents an overview of advanced control technologies for bidirectional dc/dc converters in dc microgrids. First, the stability issue caused by CPLs and the power balance issue caused by PPLs are discussed, which motivate the utilization of advanced control technologies for addressing these issues. Next, typical advanced control technologies including model predictive control, backstepping control, sliding-mode control, passivity-based control, disturbance estimation techniques, intelligent control, and nonlinear modeling approaches are reviewed. Then the applications of advanced control technologies in bidirectional dc/dc converters are presented for the stabilization of CPLs and accommodation of PPLs. Finally, advanced control techniques are explored in other high-gain nonisolated (e.g., interleaved, multilevel, cascaded) and isolated converters (e.g., dual active bridge) for high-power applications.

  • 35.
    Xu, Qianwen
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xie, L.
    Dragicevic, T.
    Distributed Finite-time Power Management for Hybrid Energy Storage Systems in DC microgrids2020In: 2020 IEEE 11th International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2020, Institute of Electrical and Electronics Engineers (IEEE) , 2020, p. 170-174Conference paper (Refereed)
    Abstract [en]

    Hybrid energy storage systems (HESSs) with batteries and supercapacitors (SCs) provide an effective solution to compensate fluctuations of renewable resources and fast loads in DC microgrid. This paper proposes a distributed power management strategy for HESSs. In primary level, a virtual resistance/capacitance droop control strategy is implemented to achieve decentralized low-and high-frequency power sharing of batteries and supercapacitors (SCs). Then a distributed finite-time secondary control strategy is proposed to address the issues of DC bus deviation, state-of-charge (SoC) balancing of batteries and SoC recovery of SCs. With the proposed finite-time controller, DC bus voltage is restored to the nominal value and SoC balancing of batteries is achieved in finite time with information from neighbouring batteries; SoCs of SCs are restored to their initial values autonomously. The proposed power management strategy only requires limited communication among batteries and does not require communication among SCs. Simulations are conducted to verify the effectiveness of the proposed method.

  • 36.
    Xu, Qianwen
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Y.
    Xu, Z.
    Xie, L.
    Blaabjerg, F.
    Erratum to “A Hierarchically Coordinated Operation and Control Scheme for DC Microgrid Clusters Under Uncertainty”2021In: IEEE Transactions on Sustainable Energy, ISSN 1949-3029, E-ISSN 1949-3037, Vol. 12, no 2, p. 1497-1497Article in journal (Refereed)
  • 37.
    Xu, Qianwen
    et al.
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
    Xu, Y.
    Zhang, C.
    Wang, P.
    A Robust Droop-Based Autonomous Controller for Decentralized Power Sharing in DC Microgrid Considering Large-Signal Stability2020In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 16, no 3, p. 1483-1494, article id 8886385Article in journal (Refereed)
    Abstract [en]

    The high penetration of power electronic converter loads in dc microgrid causes system stability issue, or also known as constant power load issue, due to their negative impedance characteristics. The stability concern will be more complicated for a self-disciplined microgrid that allows plug and play of various distributed generations (DGs). This article proposes a robust droop-based controller for decentralized power sharing in a dc microgrid considering large-signal stability. For each DG interface converter subsystem, the interactions with other DG interface converters and loads are estimated by a nonlinear disturbance observer (NDO) utilizing the subsystem's own information to achieve decentralized power sharing and fast voltage regulation. With the uncertainties of circuit parameters modeled as a lumped disturbance term and compensated by an NDO, the proposed controller can significantly enhance the robustness against the uncertainties of circuit parameters. The large-signal stability of the whole interconnected system is proved by the backstepping algorithm and Lyapunov theorem. The efficacy and large-signal stability of the proposed approach are verified by both simulations and experiments. 

  • 38.
    Xu, Qianwen
    et al.
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
    Xu, Yan
    Xu, Zhao
    Xie, Lihua
    Blaabjerg, Frede
    A Hierarchically Coordinated Operation and Control Scheme for DC Microgrid Clusters Under Uncertainty2021In: IEEE Transactions on Sustainable Energy, ISSN 1949-3029, E-ISSN 1949-3037, Vol. 12, no 1, p. 273-283, article id 9080125Article in journal (Refereed)
    Abstract [en]

    In the existing works of microgrid clusters, operation and real-time control are normally designed separately in a hierarchical architecture, with the real-time control in the primary and secondary levels, and operation in the tertiary level. This article proposes a hierarchically coordinated control scheme for DC MG clusters under uncertainty. In each MG, the tertiary level controller optimizes the operating cost in the MG by taking into account the real-time uncertainties of renewable generations and loads deviated from the forecasting data; and the primary controller responds to the real-time power fluctuations through an optimised droop curve. The hierarchically coordinated optimization problem is formulated to optimize the power set points and droop curve coefficients simultaneously under uncertainties using an adjustable robust optimization model. For the MG cluster, the energy sharing of each MG in the cluster is optimized to minimize the total operating cost and the transmission loss. The overall optimization problem is solved in a distributed manner by alternating direction method of multipliers (ADMM) where each MG entity only exchanges boundary information (i.e. the power exchange of MG entity with the MG cluster), thus information privacy and plug-and-play feature of each MG are guaranteed. The proposed approach optimally coordinates the operation and real-time control layers of a DC MG cluster with uncertainties; it achieves decentralized power sharing at the real-time control layer and distributed optimization at the operation layer, featuring high scalability, reliability and economy. Case studies of a DC MG cluster are conducted in Matlab/Simulink in order to demonstrate the effectiveness of the proposed approach. 

  • 39.
    Xu, Qianwen
    et al.
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.
    Yan, Y.
    Zhang, C.
    Dragicevic, T.
    Blaabjerg, F.
    An Offset-Free Composite Model Predictive Control Strategy for DC/DC Buck Converter Feeding Constant Power Loads2020In: IEEE transactions on power electronics, ISSN 0885-8993, E-ISSN 1941-0107, Vol. 35, no 5, p. 5331-5342Article in journal (Refereed)
    Abstract [en]

    The high penetration of power electronic converters into dc microgrids may cause the constant power load stability issues, which could lead to large voltage oscillations or even system collapse. On the other hand, dynamic performance should be satisfied in the control of power electronic converter systems with small overshoot, less oscillations, and smooth transient performance. This article proposes an offset-free model predictive controller for a dc/dc buck converter feeding constant power loads with guaranteed dynamic performance and stability. First, a receding horizon optimization problem is formulated for optimal voltage tracking. To deal with the unknown load variation and system uncertainties, a higher order sliding mode observer is designed and integrated into the optimization problem. Then an explicit closed-loop solution is obtained by solving the receding horizon optimization problem offline. A rigorous stability analysis is performed to ensure the system large signal stability. The proposed controller achieves optimized transient dynamics and accurate tracking with simple implementation. The effectiveness of the proposed controller is validated by simulation and experimental results.

  • 40.
    Xu, Qianwen
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Zhang, Chuanlin
    Shanghai Univ Elect Power, Coll Automat Engn, Intelligent Autonomous Syst Lab, Shanghai, Peoples R China..
    Xu, Zhao
    Hong Kong Polytech Univ, Shenzhen Res Inst, Hong Kong, Peoples R China.;Hong Kong Polytech Univ, Dept Elect Engn, Hong Kong, Peoples R China.;Changsha Univ Sci & Technol, Changsha 410077, Peoples R China..
    Lin, Pengfeng
    Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore..
    Wang, Peng
    Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore..
    A Composite Finite-Time Controller for Decentralized Power Sharing and Stabilization of Hybrid Fuel Cell/Supercapacitor System With Constant Power Load2021In: IEEE Transactions on Industrial Electronics, ISSN 0278-0046, E-ISSN 1557-9948, Vol. 68, no 2, p. 1388-1400Article in journal (Refereed)
    Abstract [en]

    The hybrid fuel cell/supercapacitor (FC/SC) system is a promising onboard power supply system for more electric aircraft (MEA), where system stability is a critical issue due to the high penetration of constant power loads (CPLs) in MEA. This article proposes a composite finite-time controller for decentralized power sharing and stabilization of the hybrid FC/SC system with CPLs. It consists of an integral droop (ID) + finite-time controller for the SC converter and a proportional droop (PD) + finite-time controller for the FC converter. First, the coordination of PD and ID achieves decentralized power sharing between FC and SC such that SC only compensates fast fluctuations and FC provides smooth power at the steady state. Then, a finite-time observer is designed to provide feedforward compensation for the disturbances and enables accurate tracking with fast dynamics. Finally, a composite finite-time controller is constructed following a nonrecursive synthesis procedure with a rigorous large signal stability analysis. The proposed controller guarantees finite-time convergence even under large signal variations and can be easily implemented with a practical gain tuning procedure. Simulations and experiments are conducted to verify the proposed technique.

  • 41.
    Xu, Qianwen
    et al.
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
    Zhao, T.
    Xu, Y.
    Xu, Z.
    Wang, P.
    Blaabjerg, F.
    A Distributed and Robust Energy Management System for Networked Hybrid AC/DC Microgrids2020In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 11, no 4, p. 3496-3508Article in journal (Refereed)
    Abstract [en]

    Hybrid AC/DC microgrids (MGs) provide efficient integration of renewable sources into grids and the interconnection of multiple MGs can improve system reliability, efficiency and economy by energy sharing. In this paper, a distributed and robust energy management system is proposed for networked hybrid AC/DC MGs. For each individual MG, an adjustable robust optimization model is proposed to optimize its individual operational cost considering the uncertainty of the renewable generation and load demand. For the networked-MGs system, the energy sharing information of each MG is coordinated by the DC network to minimize the power transmission loss with network constraints. The overall optimization model is formulated, exactly convexified and solved in a distributed manner by the alternating direction method of multipliers (ADMM), where only limited information is required from each MG entity (i.e., the power injection to the network) and thus information privacy is guaranteed. Simulations of the networked hybrid AC/DC MGs are conducted to demonstrate the effectiveness of the proposed energy management system.

  • 42.
    Yang, Jianfei
    et al.
    Nanyang Technological University, School of Electrical and Electronics Engineering, Singapore.
    Chen, Xinyan
    Nanyang Technological University, School of Electrical and Electronics Engineering, Singapore.
    Zou, Han
    University of California at Berkeley, Department of Electrical Engineering and Computer Sciences, Berkeley, 94720, CA, United States.
    Wang, Dazhuo
    Nanyang Technological University, School of Electrical and Electronics Engineering, Singapore.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xie, Lihua
    Nanyang Technological University, School of Electrical and Electronics Engineering, Singapore.
    EfficientFi: Towards Large-Scale Lightweight WiFi Sensing via CSI Compression2022In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 9, no 15, p. 13086-13095Article in journal (Refereed)
    Abstract [en]

    WiFi technology has been applied to various places due to the increasing requirement of high-speed Internet access. Recently, besides network services, WiFi sensing is appealing in smart homes since it is device-free, cost-effective and privacy-preserving. Though numerous WiFi sensing methods have been developed, most of them only consider single smart home scenario. Without the connection of powerful cloud server and massive users, large-scale WiFi sensing is still difficult. In this paper, we firstly analyze and summarize these obstacles, and propose an efficient large-scale WiFi sensing framework, namely EfficientFi. The EfficientFi works with edge computing at WiFi APs and cloud computing at center servers. It consists of a novel deep neural network that can compress fine-grained WiFi Channel State Information (CSI) at edge, restore CSI at cloud, and perform sensing tasks simultaneously. A quantized auto-encoder and a joint classifier are designed to achieve these goals in an end-to-end fashion. To the best of our knowledge, the EfficientFi is the first IoT-cloud-enabled WiFi sensing framework that significantly reduces communication overhead while realizing sensing tasks accurately. We utilized human activity recognition and identification via WiFi sensing as two case studies, and conduct extensive experiments to evaluate the EfficientFi. The results show that it compresses CSI data from 1.368Mb/s to 0.768Kb/s with extremely low error of data reconstruction and achieves over 98% accuracy for human activity recognition.

  • 43.
    You, Yang
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Hierarchical Online Game-Theoretic Framework for Real-Time Energy Trading in Smart Grid2024In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 15, no 2, p. 1634-1645Article in journal (Refereed)
    Abstract [en]

    In this paper, the real-time energy trading problem between the energy provider and the consumers in a smart grid system is studied. The problem is formulated as a hierarchical game, where the energy provider acts as a leader who determines the pricing strategy that maximizes its profits, while the consumers act as followers who react by adjusting their energy demand to save their energy costs and enhance their energy consumption utility. In particular, the energy provider employs a pricing strategy that depends on the aggregated amount of energy requested by the consumers, which suits a commodity-limited market. With this price setting, the consumers' energy demand response strategies are designed under a non-cooperative game framework, where a unique generalized Nash equilibrium point is shown to exist. As an extension, the consumers are assumed to be unaware of their future energy consumption behaviors due to uncertain personal needs. To address this issue, an online distributed energy trading framework is proposed, where the energy provider and the consumers can design their strategies only based on the historical knowledge of consumers' energy consumption behavior at each bidding stage. Besides, the proposed framework can be implemented in a distributed manner such that the consumers can design their demand responses by only exchanging information with their neighboring consumers, which requires much fewer communication resources and would thus be more suitable for the practical operation of the grid. As a theoretical guarantee, the proposed framework is further proved to asymptotically achieve the same performance as the offline solution for both energy provider and consumers' optimization problems. The performance of practical designs of the proposed online distributed energy trading framework is finally illustrated in numerical experiments.

  • 44.
    You, Yang
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Ye, Yu
    School of Computer and Information Science, Southwest University, Chongqing, China.
    Xiao, Guoqiang
    School of Computer and Information Science, Southwest University, Chongqing, China.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Fast Incremental ADMM for Decentralized Consensus Multi-Agent Optimization2024In: 2024 IEEE 18th International Conference on Control and Automation, ICCA 2024, IEEE Computer Society , 2024, p. 473-477Conference paper (Refereed)
    Abstract [en]

    The alternating direction method of multipliers (ADMM) has been recently recognized as well-suited for solving distributed optimization problems among multiple agents. Nonetheless, there remains a scarcity of research exploring ADMM's communication costs. Especially for large-scale multi-agent systems, the impact of communication costs becomes more significant. On the other hand, it is well-known that the convergence property of ADMM is significantly influenced by the different parameters while tuning these parameters arbitrarily would disrupt the convergence of ADMM. To this end, inspired by the preliminary works on incremental ADMM, we propose a fast incremental ADMM algorithm that can solve large-scale multi-agent optimization problems with enhanced communication efficiency and fast convergence speed. The proposed algorithm can improve the convergence speed by introducing an extra adjustable parameter to modify the penalty parameter ? in both primal and dual updates of incremental ADMM. With several mild assumptions, we provide the convergence analysis of our proposed algorithm. Finally, the numerical experiments demonstrate the superiority of the proposed fast incremental ADMM algorithm compared to the other incremental ADMM-type methods.

  • 45.
    Zhang, Mengfan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Guo, Guodong
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Magnusson, Sindri
    Stockholm Univ, Dept Comp & Syst Sci, S-11419 Stockholm, Sweden..
    Pilawa-Podgurski, Robert C. N.
    Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA..
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Data Driven Decentralized Control of Inverter Based Renewable Energy Sources Using Safe Guaranteed Multi-Agent Deep Reinforcement Learning2024In: IEEE Transactions on Sustainable Energy, ISSN 1949-3029, E-ISSN 1949-3037, Vol. 15, no 2, p. 1288-1299Article in journal (Refereed)
    Abstract [en]

    The wide integration of inverter based renewable energy sources (RESs) in modern grids may cause severe voltage violation issues due to high stochastic fluctuations of RESs. Existing centralized approaches can achieve optimal results for voltage regulation, but they have high communication burdens; existing decentralized methods only require local information, but they cannot achieve optimal results. Deep reinforcement learning (DRL) based methods are effective to deal with uncertainties, but it is difficult to guarantee secure constraints in existing DRL training. To address the above challenges, this paper proposes a projection embedded multi-agent DRL algorithm to achieve decentralized optimal control of distribution grids with guaranteed 100% safety. The safety of the DRL training is guaranteed via an embedded safe policy projection, which could smoothly and effectively restrict the DRL agent action space, and avoid any violation of physical constraints in distribution grid operations. The multi-agent implementation of the proposed algorithm enables the optimal solution achieved in a decentralized manner that does not require real-time communication for practical deployment. The proposed method is tested in modified IEEE 33-bus distribution and compared with existing methods; the results validate the effectiveness of the proposed method in achieving decentralized optimal control with guaranteed 100% safety and without the requirement of real-time communications.

  • 46.
    Zhang, Mengfan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Guo, Guodong
    State Grid Economic and Technological Research Institute Co., Ltd., Beijing, China.
    Zhao, Tianyang
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    DNN Assisted Projection based Deep Reinforcement Learning for Safe Control of Distribution Grids2024In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 39, no 4, p. 5687-5698Article in journal (Refereed)
    Abstract [en]

    Deep reinforcement learning (DRL) is a promising solution for voltage control of distribution grids with high penetration of inverter-based renewable energy sources (RESs). Yet, when adopting the DRL-based control method, the safe and optimal operation of the system cannot be guaranteed at the same time, as the conventional DRL agent is not designed to solve the hard constraint problem. To address this challenge, this paper proposes a deep neural network (DNN) assisted projection based DRL method for safe control of distribution grids. First, a finite iteration projection algorithm is proposed to guarantee hard constraints by converting a non-convex optimization problem into a finite iteration problem. Next, a DNN assisted projection method is proposed to accelerate the calculation of projection and achieve the practical implementation of hard constraints in DRL problem. Finally, a DNN Projection embedded twin-delayed deep deterministic policy gradient (DPe-TD3) method is proposed to achieve optimal operation of distribution grids with guaranteed 100% safety of the distribution grid. The safety of the DRL training is guaranteed via the embedded Projection DNN in TD3 with participation in gradient return process, which could smoothly and effectively project the DRL agent actions into the feasible area, thus guaranteeing the safety of data driven control and the optimal operation at the same time. The case studies and comparisons are conducted in the IEEE 33 bus system to show the effectiveness of the proposed method.

  • 47.
    Zhang, Mengfan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Gómez, Pere Izquierdo
    Technical University of Denmark, Department of Electrical Engineering, Kongens Lyngby, 2800, Denmark.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Dragicevic, Tomislav
    organization=Technical University of Denmark, Department of Electrical Engineering, city=Kongens Lyngby, postcode=2800, country=Denmark.
    Review of online learning for control and diagnostics of power converters and drives: Algorithms, implementations and applications2023In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 186, article id 113627Article, review/survey (Refereed)
    Abstract [en]

    Power converters and motor drives are playing a significant role in the transition towards sustainable energy systems and transportation electrification. In this context, rich diversity of new power converters and motor drive products are developed and commissioned by the industry every year. However, to achieve efficient, reliable and stable operation of power converter and drive systems, there are challenges in condition monitoring, fault diagnosis, lifecycle estimation, stability evaluation and control. Online learning is an emerging technology that can serve as a powerful remedy to these challenges. This paper aims to provide a systematic study of algorithms, implementations, and applications of online learning for control and diagnostics in the area of power converters and drives. First, online learning problems are formulated for condition monitoring, fault detection, online stability assessment, model predictive control for power converter and drive applications. Next, guidelines are provided about how to develop online learning models and algorithms for these applications. Practical case studies are presented with experimental demonstrations. Finally, challenges and future opportunities are discussed about online learning for power converter and drive applications.

  • 48.
    Zhang, Mengfan
    et al.
    Aalborg Univ, Dept Energy Technol, Aalborg, Denmark..
    Wang, Xiongfei
    Aalborg Univ, Dept Energy Technol, Aalborg, Denmark..
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Data-Driven Modeling of Power-Electronics-Based Power System Considering the Operating Point Variation2021In: 2021 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE), Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 3513-3517Conference paper (Refereed)
    Abstract [en]

    Large-scale integrations of power-electronics devices have introduced the stability challenges to the conventional power system. The stability of the power-electronics-based power systems, which are modeled by a Multi-Input Multi-Output (MIMO) transfer function matrix, can be analyzed based on the Nyquist Criterion. However, since no or limited information about the internal control details, this matrix can only be obtained using the measured data. On the other hand, the elements of the matrix will change along with the operating point of each power-electronics converter, which introduces the challenge to guarantee the interaction stability of each inverter at different operating points. In this paper, a data-driven method is proposed to overcome this operating-point dependent challenge. An artificial neural network (ANN) is used to characterize the operating-point dependent model of power-electronics-based power systems. The comparison results confirm the accuracy of the impedance model obtained by this data-driven modeling method.

  • 49.
    Zhang, Mengfan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    An MPC based Power Management Method for Renewable Energy Hydrogen based DC Microgrids2023In: 2023 IEEE Applied Power Electronics Conference and Exposition, APEC, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 577-581Conference paper (Refereed)
    Abstract [en]

    The renewable energy hydrogen based dc microgrid is an attractive solution for renewables integration, as the hydrogen is a clean fuel, that extra renewable energy source generation can be stored as hydrogen through electrolysis technology, and be used later through fuel cell technology. However, the efficiency of the electrolyzer and fuel cell change significantly under the wide operation ranges, and they have different degradation mechanisms that are greatly impacted by current ripples. Moreover, to achieve consistent power supply with 100% RESs, the electrolyzer and fuel cell need to be optimally coordinated. To address the issues, this paper proposes an MPC based power management method to achieve smooth power sharing and reduce the current ripple, also can guarantee the system stability under uncertainties of the renewable energy source and load. It consists of a baseline MPC for optimized transient performance and a sliding mode observer to estimate system uncertainties. Both the simulation and experiment results can validate the effectiveness of the proposed method.

  • 50.
    Zhang, Mengfan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Xu, Qianwen
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Magnússon, Sindri
    Department of Computer and Systems Sciences Stockholm University Stockholm, Sweden.
    Pilawa-Podgurski, Robert C.N.
    Department of Electrical Engineering and Computer Sciences University of California, Berkeley Berkeley, USA.
    Guo, Guodong
    School of Electrical & Electronic Engineering North China Electric Power University Beijing, China.
    Multi-Agent Deep Reinforcement Learning for Decentralized Voltage-Var Control in Distribution Power System2022In: 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022, Institute of Electrical and Electronics Engineers (IEEE) , 2022Conference paper (Refereed)
    Abstract [en]

    With the large integration of renewables, the traditional power system becomes more sustainable and effective. Yet, the fluctuation and uncertainties of renewables have led to large challenges to the voltage stability in distribution power systems. This paper proposes a multi-agent deep reinforcement learning method to address the issue. The voltage control issue of the distribution system is modeled as the Markov Decision Process, while each grid-connected interface inverter of renewables is modeled as a deep neural network (DNN) based agent. With the designed reward function, the agents will interact with and seek for the optimal coordinated voltage-var control strategy. The offline-trained agents will execute online in a decentralized way to guarantee the voltage stability of the distribution without any extra communication. The proposed method can effectively achieve a communication-free and accurate voltage-var control of the distribution system under the uncertainties of renewables. The case study based on IEEE 33-bus system is demonstrated to validate the method.

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