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Price-based Demand Response Supported Three-stage Hierarchically Coordinated Voltage Control for Microgrids

Journal of Modern Power Systems and Clean Energy 2025年第1期13卷 338-350页

作者： Bo Wang Cuo Zhang Xingying Chen Yan Xu Kun Yu Haochen Hua Zhao Yang Dong IEEE the school of Electrical and Power engineering Hohai University the school of Electrical and Computer engineering The University of Sydney the Center for Power engineering (CPE) School of Electrical and Electronic Engineering Nanyang Technological University the Department of Electrical engineering City University of Hong Kong

Photovoltaic(PV) inverter, as a promising voltage/var control(VVC) resource, can supply flexible reactive power to reduce microgrid power loss and regulate bus voltage. Meanwhile, active power plays a significant role in microgrid voltage profile. Price-based demand response(PBDR) can shift load demand via determining time-varying prices, which can be regarded as an effective means for active power shifting. However, due to the different characteristics, PBDR and inverter-based VVC lack systematic coordination. Thus, this paper proposes a PBDR-supported three-stage hierarchically coordinated voltage control method, including day-ahead PBDR price scheduling, hour-ahead reactive power dispatch of PV inverters, and realtime local droop control of PV inverters. Considering their mutual influence, a stochastic optimization method is utilized to centrally or hierarchically coordinate adjacent two stages. To solve the bilinear constraints of droop control function, the problem is reformulated into a second-order cone programming relaxation model. Then, the concave constraints are convexified, forming a penalty convex-concave model for feasible solution recovery. Lastly, a convex-concave procedure-based solution algorithm is proposed to iteratively solve the penalty model. The proposed method is tested on 33-bus and IEEE 123-bus distribution networks and compared with other methods. The results verify the high efficiency of the proposed method to achieve power loss reduction and voltage regulation.

关键词： Voltage control Microgrids Inverters Reactive power Demand response Voltage measurement Load modeling

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Series-parallel Sequence Impedance Models of Multi-loop Grid-forming Converters

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Journal of Modern Power Systems and Clean Energy 2025年第1期13卷 29-41页

作者： Xiaokuan Jin Jianhua Wang Han Yan Xijun Ni Zhendong Ji Baojian Ji Ding Wan school of Electrical engineering Southeast University school of Electric Power engineering Nanjing Institute of Technology school of Automation Nanjing University of Science and Technology

The gradual penetration of grid-forming(GFM) converters into new power systems with renewable energy sources may result in the emergence of small-signal instability issues. These issues can be elucidated using sequence impedance models, which offer a more tangible and meaningful interpretation than dq-domain impedance models and state-space models. However, existing research has primarily focused on the impact of power loops and inner control loops in GFM converters, which has not yet elucidated the precise physical interpretation of inner voltage and current loops of GFM converters in circuits. This paper derives series-parallel sequence impedance models of multi-loop GFM converters, demonstrating that the voltage loop can be regarded as a parallel impedance and the current loop as a series impedance. Consequently, the corresponding small-signal stability characteristics can be identified through Bode diagrams of sequence impedances or by examining the physical meanings of impedances in series and in parallel. The results indicate that the GFM converter with a single power loop is a candidate suitable for application in new power systems, given its reduced number of control parameters and enhanced low-frequency performance, particularly in weak grids. The results of PLECS simulations and corresponding prototype experiments verify the accuracy of the analytical analysis under diverse grid conditions.

关键词： Analytical models Power system stability Frequency conversion Stability analysis Circuit stability State-space methods Impedance Integrated circuit modeling Voltage control Grid forming

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A Multi-Condition Sequential Network Ensemble for Industrial Energy Storage Prediction Considering the Condition Switching Characteristics

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 369-380页

作者： Tianyu Wang Fan Zhou Yangjie Wu Jun Zhao Wei Wang IEEE the school of Control Science and engineering Dalian University of Technology the school of Information Science and engineering Dalian Polytechnic University

As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system scheduling. Considering that the continuous switching of the pressure and valve status(mechanism knowledge) would bring about multiple working conditions of the equipment, a multi-condition time sequential network ensembled method is proposed. In order to especially consider the time dependence of different conditions, a centralwise condition sequential network is developed, where the network branches are specially designed based on the condition switching sequences. A branch combination transfer learning strategy is developed to tackle the sample imbalance problem of different condition data. Since the condition or status data are real-time information that cannot be recognized during the prediction process, a pre-trained and ensemble learning approach is further proposed to fuse the outputs of the multi-condition networks and realize a transient-state involved prediction. The performance of the proposed method is validated on practical energy data coming from a domestic steel plant, comparing with the state-of-the-art algorithms. The results show that the proposed method can maintain a high prediction accuracy under different condition switching cases, which would provide effective guidance for the optimal scheduling of the industrial energy systems.

关键词： Ensemble learning industrial energy prediction multi-working condition structural deep network transfer learning

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Praseodymium effects on greatly improving low-temperature hydrothermal stability of Cu/SAPO-34 NH3-SCR catalysts

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Journal of Rare Earths 2025年第4期43卷 707-718页

作者： Xiaojie Yu Xinyu Li Chen Wang Jianqiang Wang Meiqing Shen M.D.Tarikul Islam Gurong Shen school of Materials Science and engineering Tianjin University school of Chemical engineering and Technology Tianjin University school of Environmental and Safety engineering North University of China National Rare Earth Catalysis Research Institute

Based on the previous findings that the presence of hydroxyl groups on the outer surface is crucial for maintaining skeletal stability,we propose a strategy modified Cu/SAPO-34 using Pr ions in this ***,we conducted several measurements to investigate the effect of Pr ions on the lowtemperature hydrothermal stability of Cu/*** find that Pr exists only on the surface of Cu/SAPO-34 as ions and oxides,with Pr3+ions playing a protective role in occupying surface acidic *** addition of small amounts of Pr leads to the re-dispersion of Cu,resulting in improved lowtemperature selective catalytic reduction（SCR） activity in the as-synthesized ***,it enhances the resistance to decomposition of the Si-（OH）-Al framework during low-temperature hydrothermal aging,thereby preserving the framework structure and allowing detached active Cu species to return to exchangeable positions,ultimately restoring SCR ***,as the Pr content increases,the enhanced acidity causes some structural damage,gradually weakening the protective *** work demonstrates that Pr modification is a simple and effective solution to the issue of poor lowtemperature hydrothermal stability in Cu/SAPO-34,providing a promising way for the application of light rare earth elements.

关键词： NH 3 -SCR Pr modification Cu/SAPO-34 Low-temperature hydrothermal stability Rare earths

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Event-triggered leader-follower bipartite consensus control for nonlinear multi-agent systems under DoS attacks

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Science China(Information Sciences) 2025年第3期68卷 286-301页

作者： Wei SU Chaoxu MU Song ZHU Ben NIU Changyin SUN school of Control Science and engineering Tianjin University school of Mathematics China University of Mining and Technology school of Control Science and engineering Dalian University of Technology school of Automation Southeast University

Aiming at the consensus control problem of nonlinear multi-agent systems(MASs) under directed topology, a leader-follower bipartite consensus control strategy is proposed. This strategy takes into account the potential for denial-of-service(DoS) attacks and completely unknown system dynamics. Specifically, the bipartite consensus dynamics describes the cooperation and competition relationship between followers and the leader, that is, the follower chooses to move in accordance with or opposite to the leader according to its trajectory. In order to optimize the communication bandwidth and mitigate the impact of DoS attacks, the proposed consensus control scheme integrates the DoS attack detection mechanism and event-triggered mechanism. In addition, neural networks(NNs) are used to solve the nonlinear problem, and a speed function is designed to achieve the desired tracking performance, ensuring that all agents' tracking errors converge to a predefined set in a finite time. With the help of backstepping, graph theory, and Lyapunov stability theory, sufficient conditions for achieving bipartite consensus without Zeno behavior are established. Finally, the accuracy and feasibility of the theoretical analysis are verified by simulation cases.

关键词： multi-agent systems neural networks bipartite consensus event-trigger denial-of-service attack

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Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning

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Chinese Journal of Electronics 2025年第1期34卷 98-113页

作者： Hengmin Zhang Jian Yang Wenli Du Bob Zhang Zhiyuan Zha Bihan Wen school of Electrical and Electronic engineering Nanyang Technological University school of Computer Science and engineering Nanjing University of Science and Technology school of Information Science and engineering East China University of Science and Technology Department of Electrical and Computer engineering University of Macau

Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.

关键词： Learning systems Image recognition Minimization Computational efficiency Complexity theory Matrix decomposition Optimization Image reconstruction Singular value decomposition Convergence

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The optimal control theory——a scientific approach to fundamentally solving control problems

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Science China(Information Sciences) 2025年第1期68卷 389-391页

作者： Huanshui ZHANG school of Electrical and Automation engineering Shandong University of Science and Technology school of Control Science and engineering Shandong University

The maximum principle has bridged mathematical optimization to optimal control,ushering in significant developments and refinements in optimal control theory,notably during the 1960s with the advent of linear quadratic (LQ)control and linear quadratic estimation (LQE).This progression propelled optimal control theory into further advancements,encompassing stochastic control,robust/H-infinity control,model predictive control (MPC),networked control,and reinforcement learning *** control,established upon a rigorous mathematical foundation,extends static optimization theory to dynamic systems,exhibiting scientific essence,unity,and ***,since its inception,optimal control theory has served as an indispensable core role across all control-related domains,including communication-constrained control in networked systems,consensus control,cooperative control,and reinforcement learning control.

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Revealing the effect of external pressure on solid-electrolyte interphase and lithium plating in lithium-ion batteries

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Journal of Energy Chemistry 2025年第3期102卷 734-744页

作者： Rui Xiong Haocheng Li Bing-Ang Mei Hongwen He Weixiang Shen National engineering Research Center of Electric Vehicles School of Mechanical Engineering Beijing Institute of Technology school of Science Computing and Engineering Technologies Swinburne University of Technology

Lithium plating is a detrimental phenomenon in lithium-ion cells that compromises both functionality and *** study investigates electro-chemo-mechanical behaviors of lithium plating in lithium iron phosphate pouch cells under different external *** force microscopy nanoindentation is performed on the graphite electrode to analyze the influence of external pressure on solid-electrolyte interphase(SEI),revealing that the mechanical strength of SEI,indicated by Young's modulus,increases with the presence of external ***,an improved phase field model for lithium plating is developed by incorporating electrochemical parameterization based on nonequilibrium *** results demonstrate that higher pressure promotes lateral lithium deposition,covering a larger area of ***,electrochemical impedance spectroscopy and thickness measurements of the pouch cells are conducted during overcharge,showing that external pressure suppresses gas generation and thus increases the proportion of lithium deposition among galvanostatic overcharge *** integrating experimental results with numerical simulations,it is demonstrated that moderate pressure mitigates SEI damage during lithium plating,while both insufficient and excessive pressure may exacerbate *** study offers new insights into optimizing the design and operation of lithium iron phosphate pouch cells under external pressures.

关键词： Electrochemical mechanics Lithium plating Lithium-ion batteries Overcharge Synergistic effects

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Asymptotic Analysis of Epidemics on Networks

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Journal of Systems Science & Complexity 2025年第1期38卷 338-348页

作者： SOLO Victor school of Electrical engineering and Telecommunications UNSW Sydney

Driven partly by cyber-security issues there is growing interest in modelling epidemics on networks. However it turns out that there are some gaps in the formulation of these network models that have precluded a proper study of stochastic fluctuations. The author revisits some popular models,rectify their shortcomings and then develops an asymptotic analysis of their behaviour.

关键词： Epidemic Markov process mean field theory stochastic stability

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Graph reinforcement learning with relational priors for predictive power allocation

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Science China(Information Sciences) 2025年第2期68卷 230-247页

作者： Jianyu ZHAO Chenyang YANG school of Electronics and Information engineering Beihang University

Deep reinforcement learning for resource allocation has been investigated extensively owing to its ability of handling model-free and end-to-end problems. However, its slow convergence and high time complexity during online training hinder its practical use in dynamic wireless systems. To reduce the training complexity, we resort to graph reinforcement learning for leveraging two kinds of relational priors inherent in many wireless communication problems: topology information and permutation properties. To harness the two priors, we first conceive a method to convert the state matrix into a state graph,and then propose a graph deep deterministic policy gradient(DDPG) algorithm with the desired permutation property. To demonstrate how to apply the proposed methods, we consider a representative problem of using reinforcement learning, predictive power allocation, which minimizes the energy consumption while ensuring the quality-of-service of each user requesting video streaming. We derive the time complexity required by training the proposed graph DDPG algorithm and fully-connected neural network-based DDPG algorithm in each time step. Simulations show that the graph DDPG algorithm converges much faster and needs much lower time and space complexity than existing DDPG algorithms to achieve the same learning performance.

关键词： reinforcement learning graph neural network relational priors resource allocation

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