Although virtual asynchronous machine (VAM) control has been proposed for virtual energy storage systems (VESSs), research into its secondary control applications is still limited. Thus, a distributed secondary frequency restoration control strategy based on VAMs is presented for VESSs. First, the VAM control is introduced, and a detailed electro-thermal coupling model of the VESS is developed. This model includes indoor-outdoor temperature differences, heat transfer through building envelope (walls, windows, and roof), solar radiations, ventilation losses, and electric boiler dynamics. It effectively captures the coupling between indoor temperature regulation and grid power balancing. Next, a distributed secondary frequency restoration control strategy based on VAM is proposed. It addresses parameter heterogeneity within a nonlinear multi-agent framework among VESSs. The nonlinear dynamics are converted into a linear reference model, which simplifies controller design and stability analysis. Using only local and neighboring information, the proposed strategy restores frequency and ensures active power sharing. Furthermore, the proposed strategy coordinates thermal power regulation to maintain indoor temperature balancing across VESSs within seasonal thermal comfort ranges. This improves thermal comfort without compromising dynamic response. Finally, the stability of the proposed strategy is verified using Lyapunov method, and simulation results from an islanded microgrid (MG) test system under parameter variations, communication imperfections, and winter/summer operating scenarios validate the effectiveness and robustness of the proposed strategy.

In this article, we research the output feedback consensus tracking control problem of nonlinear multiagent systems with unknown disturbance under event-triggered communication. An event-triggered mechanism with dynamic threshold utilizing only output information is proposed to reduce the controller updates and communication load between neighboring agent. This mechanism is combined with the state and disturbance observer to derive an output feedback-based adaptive backstepping event-triggered control protocol via dynamic filtering technique, which avoids the differentiation of virtual control protocol and eliminates Zeno behavior. Based on Lyapunov stability theory, it is proven that all signals of the closed-loop systems are bounded and output consensus tracking can be achieved. The effectiveness of the proposed control scheme is verified via a physical system example.

Understanding the operational characteristics of wind-solar energy generation proves crucial for power system planning and operation. Based on the historical new energy power generation and load data of a region in northern China, this paper systematically analyzes the fluctuation characteristics of new energy and net load at multiple time scales and their operation rules in extreme scenarios. On the intra-day time scale, the kernel density estimation(KED) method and the box plot are used to reveal the characteristics of the shift of the peak-valley period distribution of the net load and the aggravation of the fluctuation of the peak-valley difference after the new energy is connected, and the guaranteed capacity and consumption space of the new energy in each period of the day are analyzed. Over multi-day timescales, statistical characterization is performed on spatiotemporal distribution patterns of persistent low-output events in renewable generation, with particular emphasis on their manifestation under three meteorological conditions: cold wave weather, high temperatures, and atmospheric stagnation weather. On monthly timescales, seasonal fluctuation patterns in renewable generation are identified, accompanied by an inverse complementary relationship between renewable output and load demand. The findings demonstrate that renewable integration in northern China has substantially altered the temporal characteristics of net load, manifesting prominent challenges including intensified short-term volatility, anti-peak regulation effects, and seasonal supply-demand mismatches. The synergistic deterioration of wind-solar generation and load demand during extreme weather events exacerbates system supply-demand imbalance.

In this paper, a neural network-based adaptive fault-tolerant finite-time control scheme is proposed for stochastic active electromagnetic suspension systems in the presence of actuator failures and disturbances. The scheme improves the riding comfort of the vehicle. In addition, the active electromagnetic suspension system with actuator dynamic model is considered and a method based on differential operator is introduced to transform the state space expression into a stochastic non-strict feedback system. Based on the Lyapunov function, an adaptive neural network finite-time control algorithm is designed. A signal compensation tracking error system based on command filtering technology is developed to prevent “complexity explosion” and singularity problems caused by the repeated differentiation. This ensures the performance requirements of the active electromagnetic suspension system. Finally, simulation results are provided to demonstrate the effectiveness of the proposed strategy.

The rapid development of electric heating and combined heat and power generation for improving the level of renewable energy accommodation has necessitated integrated analysis of the electric power system and district heating networks. However, the thermal dynamic model is governed by partial differential equations related to time and space variables. Its complicated features make it hard to perform an efficient integrated analysis with renewable fluctuation. To address this issue, this paper propose an equivalent model for an accurate and concise integrated dynamic analysis. First, an analytical formulation is derived based on Laplace transform to explicitly describe the relation between the port temperature. The transform avoids the discretization in time domain, which accurately captures the thermal dynamics. On this basis, a space discretization strategy is introduced to further keep track of the dynamics. Meanwhile, the multiple cascaded space pipeline segments are aggregated to form an equivalent model for a concise analysis. Furthermore, to reduce the model complexity and computational burden of the high-order Laplacian "s" in the equivalent process, a reduction strategy is developed by preserving the low-frequency thermal dynamic feature. Then, the analytical expression of state fluctuation can be conveniently derived to analyse the embedded impact and interaction between EPS and DHN. Finally, case studies are conducted to prove the effectiveness of proposed model.

Although the evaluation of system strength under high penetration of renewable energy sources (RESs) has been widely studied, traditional short-circuit ratio (SCR) indicators mainly focus on the interactions among RESs and are concentrated on steady-state research, neglecting the dynamic regulation effect of energy storage devices (ESDs) on system stability. In addition, most of the existing optimization methods aim to improve economic efficiency and lack system stability analysis based on SCR quantification. Therefore, this paper proposes an ESD-considered short-circuit ratio (ECSCR) that incorporates the contribution of ESDs to the short-circuit capacity of nodes. A bi-layer optimization strategy for the active support long-and short-term energy storage device is developed. The upper-layer optimizes the installation locations of ESDs by maximizing the minimum ECSCR (MinECSCR) on the RES buses, while the lower-layer uses the Newton-Raphson algorithm to dynamically adjust the power distribution of ESD to balance stability and economy. Specifically, when the ECSCR is less than the critical short-circuit ratio (CSCR), the fast-response flywheel energy storage (FES) and battery energy storage (BES) prioritize the restoration of stability; when the ECSCR is greater than the CSCR, the lower-cost thermal energy storage (TES) serves as the main power generation unit to minimize operating costs. The verification of the improved IEEE 9-bus, 39-bus system, and 14-bus system shows that compared with traditional SCR indicators, the proposed ECSCR can significantly enhance the nodal strength and reduce operating costs while ensuring system stability.

The angle constraint and vibration suppression issues of flexible manipulator (FM) systems subjected to intermittent faults are addressed in this article. Firstly, integral barrier Lyapunov functions (BLFs) that can directly constrain the angular position are introduced, eliminating the feasibility conditions of traditional BLFs. Secondly, a triggering mechanism with dynamic variables is provided to reduce the transmission of redundant information, thereby saving communication resources. To mitigate the impact of intermittent faults and handle system, the boundary estimation method and the neural networks (NNs) technology considering the influence of approximation error are adopted, which reduces the conservatism of the developed control algorithm. Through Lyapunov stability theory and Hamiltonian principle, a dynamic event-based fault-tolerant controller is designed, suppressing the offset of the FM while ensuring that the angular position asymptotically tracks the ideal position without exceeding the given constraint boundary. Eventually, the simulation results demonstrate the rationality of the developed control scheme.

Affected by the current limitation, grid-forming inverters (GFMs) will transition to a current source under grid faults, which makes the inverter-based microgrid lose the voltage support and then be switched to a current source interconnection microgrid (CSIM). It may deteriorate the system's transient synchronization stability (TSS). This issue has not been studied previously. To this end, this article proposes an analysis and enhancement method of TSS of CSIM. First, a large signal synchronization model of CSIM is derived, which takes into account the effect of the current limitation strategy of GFMs and the dynamic interactions between GFMs and grid-following inverters (GFLs). Second, by constructing the Lyapunov energy function, the criterion of TSS is obtained and the impacts of parameters on stability are investigated. Based on the stability criterion, the feasible region of current references of GFLs is clearly described. Therefore, a dynamic current control method of GFLs is proposed. It can well address the absence of equilibrium points (EPs) and strengthen the transient stability. Meanwhile, a novel antiwindup strategy of GFMs considering synchronization stability constraint is proposed, which can contribute to the fault recovery of GFMs. Finally, simulation and experimental results verify the effectiveness of the proposed methods.

With of the growing emphasis on refined management of lithium-ion batteries (LIBs), there is a significant demand for low-cost estimation of the state of charge (SOC) at the individual LIB cell level. Following the emerging concept of smart batteries, a data and model dual-driven high-accuracy SOC estimation solution is proposed in this article. In particular, a cost-effective quasi-redundant current sensor configuration is proposed first, which incorporates the least-squares current adjustment technique to enable the fusion-based accurate current sensing of cells. Building upon this, an SOC estimation algorithm based on the iterative extended Kalman filter is proposed using smart battery modeling, which innovatively incorporates the cell electrical coupling for information enhancement in cell-level SOC estimation. Experimental results demonstrate that the integration of the sensing and algorithm enables precise SOC estimation, with a maximum SOC estimating error of only 1% for all in-pack cells.

Hydrogen production from new energy power generation is an effective measure to achieve energy transformation, low carbon and clean hydrogen production. To reduce the cost of hydrogen (COH) production, improve the utilization rate of photovoltaic (PV) power generation and deal with PV uncertainty, this paper proposes a multi-time scale optimization strategy for PV-electrolyzers hydrogen production system based on model predictive control (MPC). Firstly, for the hydrogen production system, a rotation operation strategy is proposed based on the characteristics of alkaline electrolyzers (AELs), and an AEL management system(AEMS) is configured to manage the operation and health status of the electrolyzers. The multi-electrolyzer rotation operation strategy is integrated into the optimization strategy. Secondly, with the optimization objective of minimizing the cost, the electricity purchase cost function here is newly defined to guide the system to produce more hydrogen during low electricity price periods. Finally, a two-layer optimization scheduling framework based on MPC is proposed to improve system's economic efficiency while correcting deviations of day-ahead scheduling. The simulation results show that the power range of the electrolyzer is extended and the improved rotation operation strategy enhances the balance between electrolyzers. The MPC-based optimization strategy effectively coordinates the operation of electric and hydrogen hybrid energy storage. In addition, the proposed strategy increases the average hydrogen production of the hydrogen production system by 20% and reduces the cost of hydrogen (COH) by 3%.

To address the problem of acquiring high-quality data required for AI-based economic dispatch in data trading, this paper proposes a blockchain-enabled electricity data trading framework, which incorporates a comprehensive market mechanism that includes data valuation, pricing, and trading. The platform aims to facilitate the discovery of high-quality data in market transactions and enable the sharing of economic dispatch data. First, a novel valuation method is proposed to effectively assess the value attributes of electricity data, considering uncertainty, integrity, and timeliness in power system dispatching. On this basis, the market clearing price of electricity data, guided by the data valuation, is obtained through a game-theoretic approach that takes into account the supply and demand of the electricity data. Furthermore, transactions are completed using smart contracts, eliminating the need for third-party data servicers and effectively preventing data privacy leaks and realizing data trading without trust. Simulations are provided to illustrate the effectiveness of the proposed framework and mechanisms.

This paper proposes a time series prediction based industrial control honey network simulation method to address the issue that many current industrial control honey network solutions only consider the response interaction simulation of attackers within a certain period of time and cannot effectively achieve communication simulation and collaborative work between honeypots. Firstly, the deployment and basic interaction simulation of the industrial control honeynet have been achieved; Then, a communication simulation method between devices within the industrial control honey network was studied and implemented to simulate collaborative work scenarios between devices; Secondly, using time series prediction algorithms to generate simulation data for various devices within the industrial control honey network, improving the sustained response capability of the industrial control honey network; Finally, comparative experiments have shown that the method proposed in this article can improve the ability to trap attackers, provide more data for security analysis, and help users establish a comprehensive industrial control network security protection system.

The bi-directional energy conversion components such as gas-fired generators (GfG) and power-to-gas (P2G) have enhanced the interactions between power and gas systems. This paper focuses on the steady-state energy flow analysis of an integrated power-gas system (IPGS) with bi-directional energy conversion components. Considering the shortcomings of adjusting active power balance only by single GfG unit and the capacity limitation of slack bus, a multi-slack bus (MSB) model is proposed for integrated power-gas systems, by combining the advantages of bi-directional energy conversion components in adjusting active power. The components are modeled as participating units through iterative participation factors solved by the power sensitivity method, which embeds the effect of system conditions. On this basis, the impact of the mixed problem of multi-type gas supply sources (such as hydrogen and methane generated by P2G) on integrated system is considered, and the gas characteristics-specific gravity (SG) and gross calorific value (GCV) are modeled as state variables to obtain a more accurate operational results. Finally, a bi-directional energy flow solver with iterative SG, GCV and participation factors is developed to assess the steady-state equilibrium point of IPGS based on Newton-Raphson method. The applicability of proposed methodology is demonstrated by analyzing an integrated IEEE 14-bus power system and a Belgian 20-node gas system.

Abstract Distributed dynamic event‐triggered control (ETC) method is proposed to solve the stochastic and intermittent problem caused by distributed renewable energy generation unit (DG) in microgrid (MG). It aims at reducing communication cost while achieving proportional current sharing among DGs. Firstly, the state space function model of the MG system is established, considering the unknown disturbance caused by DGs and the uncertainty caused by the constant power load (CPL). Also, the system model is transformed into the standard linear heterogeneous multiagent systems (LHMAS). Subsequently, the distributed dynamic ETC method with consensus algorithm is proposed to achieve proportional current sharing among DGs. Compared with conventional ETC approaches, the authors’ method is based on directed topology, which reduces the controller updating frequency and the communication frequency among DGs. At the same time, the Zeno behaviour can be avoided. Additionally, the authors’ method is much easier to be implemented, which can also handle the uncertain loads and CPLs. Finally, the simulation results demonstrate the effectiveness of the proposed method.

The development of regional integrated electric-thermal energy systems (RIETES) is considered a promising direction for modern energy supply systems. These systems provide a significant potential to enhance the comprehensive utilization and efficient management of energy resources. Therein, the real-time power balance between supply and demand has emerged as one pressing concern for system stability operation. However, current methods focus more on minute-level and hour-level power optimal scheduling methods applied in RIETES. To achieve real-time power balance, this paper proposes one virtual asynchronous machine (VAM) control using heat with large inertia and electricity with fast response speed. First, the coupling time-scale model is developed that considers the dynamic response time scales of both electric and thermal energy systems. Second, a real-time power balance strategy based on VAM control can be adopted to the load power variation and enhance the dynamic frequency response. Then, an adaptive inertia control method based on temperature variation is proposed, and the unified expression is further established. In addition, the small-signal stability of the proposed control strategy is validated. Finally, the effectiveness of this control strategy is confirmed through MATLAB/Simulink and HIL (Hardware-in-the-Loop) experiments.

Shared bikes are widely used in Chinese cities as a green and healthy solution to address the First/Last Mile issue in public transit access. However, usage declines in cold regions during winter due to harsh weather conditions. While climate factors cannot be changed, enhancing the built environment can promote green travel even in winter. This study uses data from Shenyang, China, to investigate how built environment attributes impact the travel satisfaction of shared bike users who utilize bikes as a First/Last Mile solution to access public transit in winter cities. By employing machine learning algorithms combined with Asymmetric Impact-Performance Analysis (AIPA) and grounded theory, we systematically identify the key attributes and rank them based on their asymmetric impact and urgency of improvement. The analysis revealed 19 key attributes, 17 of which are related to the built environment, underscoring its profound influence on travel satisfaction. Notably, factors such as the profile design of cycling paths and safety facilities along routes were identified as high priorities for improvement due to their significant potential to enhance satisfaction. Meanwhile, features like barrier-free access along paths and street greenery offer substantial opportunities for improvement with more modest efforts. Our research provides critical insights into the nuanced relationship between built environment features and travel satisfaction for First/Last Mile shared bike users. By highlighting priority improvements, we offer urban planners and policymakers a framework for creating livable, sustainable environments that support green travel even in harsh winter conditions.

As the number of inverter-interfaced distributed generators (IIDGs) increases, the decrease in the inertia of the microgrid affects the stability of microgrid voltage and current. The virtual synchronous generator (VSG) control method simulates the output characteristics of the synchronous machine to improve the inertia and damping of the microgrid. However, low-frequency oscillations can occur in systems with multi-VSGs connected in parallel, primarily when operating in island mode. Therefore, this article develops the small-signal state-space model for multi-inverters based on VSG and analyses the robust stability region (RSR) of the control parameters. Firstly, an extensible small-signal state-space model is established and simplified in the dq axis, which is a necessary preprocessing for the subsequent stability analysis. Secondly, the guardian map method based on bialternate product operation is constructed, and the RSR is established through translation mapping and rotation mapping. Therefore, the solution of RSR is transformed into the problem of identifying the Hurwitz matrix. Finally, the simulations and experiments verify that the calculated region can ensure stable operation during startup and meet the requirement to suppress low-frequency oscillations. The method of solving RSR proposed in this paper provides a basis for selecting controller parameters in practical engineering.

Post-industrial neighborhoods are valued for their historical and cultural significance but often contend with challenges such as physical deterioration, social instability, and cultural decay, which diminish residents’ satisfaction. Leveraging urban renewal as a catalyst, it is essential to boost residents’ satisfaction by enhancing the environmental quality of these areas. This study, drawing on data from Shenyang, China, utilizes the combined strengths of gradient boosting decision trees (GBDTs) and asymmetric impact-performance analysis (AIPA) to systematically identify and prioritize the built-environment attributes that significantly enhance residents’ satisfaction. Our analysis identifies twelve key attributes, strategically prioritized based on their asymmetric impacts on satisfaction and current performance levels. Heritage maintenance, property management, activities, and heritage publicity are marked as requiring immediate improvement, with heritage maintenance identified as the most urgent. Other attributes are categorized based on their potential to enhance satisfaction or their lack of immediate improvement needs, enabling targeted and effective urban revitalization strategies. This research equips urban planners and policymakers with critical insights, supporting informed decisions that markedly improve the quality of life in these distinctive urban settings.