[Objective] To address the issues of power angle instability and output current overload in grid-forming（GFM）inverter during symmetrical grid faults，this paper proposes a fault ride-through strategy based on power command constraints. [Methods] First，a transient model of a droop-controlled GFM inverter is established to analyze the transient characteristics of the system under grid voltage sag conditions，revealing the impact of power commands on transient stability. Second，based on the circuit relationship between the inverter and the grid，the characteristics of fault currents and their primary influencing factors are identified. Finally，a fault ride-through method based on active power command constraints is proposed，which only requires calculating and setting the active power command value to restore power angle stability and limit fault currents. [Conclusions] Simulations performed in MATLAB/Simulink demonstrate that the proposed strategy effectively enhances power angle stability，achieving fault ride-through. [Conclusions] The proposed fault ride-through strategy based on power command constraints effectively addresses power angle instability and overcurrent issues in GFM inverters during voltage sag by constraining active power commands，providing a feasible solution for enhancing the fault ride-through capability of renewable energy grid-connected systems.

[Objective] In response to the insufficient boost capability and excessive device stress when conventional converters are applied to hydrogen fuel cell grid connection， a low electrical stress high-gain single-switch converter based on a dual Z-source network （LEHGSSC-DZ） is proposed. [Methods] This converter places the switching transistor in the quasi-Z source converter upfront to reduce device stress. Simultaneously， one of the inductor components is replaced with a quasi-Z source network， forming a dual-Z source network structure to enhance the converter's boost capability. The operating principle and output characteristics of the converter are analyzed， and a comprehensive comparison is made between the LEHGSSC-DZ and several other high-gain boost converters. Component parameter design is provided based on its output characteristics. The correctness of theoretical analysis and the feasibility of LEHGSSC-DZ are verified through simulations and experiments. [Results] The results demonstrate that the LEHGSSC-DZtopology employs fewer devices and offers superior cost-effectiveness. Compared to conventional Z-source boost converters， it achieves a 43.8% increase in output voltage， while delivering an output voltage that is 5.1 times higher than that of conventional boost converters. Furthermore， it reduces switching device voltage stress by 30%. [Conclusions] The proposed converter offers the distinct advantages of low electrical stress， high gain， and minimal device count， achieving a maximum efficiency of 97.25%. This contributes to enhancing the operational efficiency of hydrogen fuel cell grid-connected systems.

[Objective] Extreme disasters can cause multiple physical failures in distribution networks as well as damage to road infrastructure. Road disruptions impede the mobility of repair crews，thereby affecting the efficiency of distribution network restoration. To improve the fault recovery efficiency of distribution networks post extreme disasters，this paper proposes an optimization strategy that couples road repair progress into multi-fault restoration planning for distribution networks. [Methods] A coupled model of the transportation network and distribution network is established，together with a disaster-impact model for the transportation network. A time-varying load model is then developed based on the spatiotemporal demand characteristics of three user categories. Building on these models，a coordinated restoration optimization framework is formulated that jointly schedules distribution-network repairs and road-repair activities，with the dual objectives of minimizing power loss and shortening overall restoration time. To solve this multi-objective optimization model，a parameter-adaptive Non-dominated Sorting Genetic Algorithm II （NSGA-II） algorithm enhanced by an inflection point strategy is employed. Simulation studies are conducted on a regional distribution line and a transportation network system with 17 nodes. [Conclusions] The results show that the proposed strategy dynamically allocates emergency repair resources based on the evolving restoration status of the coupled network，prevents repair delays，enables coordinated optimization of transportation-network recovery and distribution network restoration. The method effectively reduces the distribution-network repair time and outage load. [Conclusions] The proposed strategy effectively integrates road emergency repairs in the transportation network with fault restoration in the distribution network，enhances system resilience against extreme disasters，and offers valuable reference for distribution network emergency repair.

[Objective] With the increasing penetration of distributed wind and photovoltaic generation in distribution networks， their inherent variability and intermittency have imposed higher requirements on network flexibility. Inadequate flexibility may lead to issues such as wind and solar power curtailment and power flow violations. Enhancing flexibility by exploiting the adjustable capabilities of nodal hosting and grid transfer capacity is regarded as an effective approach. This paper proposes a coordinated planning method for nodal-grid flexibility resources in active distribution networks.[Methods] First， flexibility resource models at both the nodal and grid levels were developed to accurately represent the nodal hosting capacity and grid transmission capability. Second， a flexibility demand model and a corresponding evaluation index system were constructed to quantify the system's flexibility supply capacity. Then， four types of flexibility resources—soft open points （SOP）， hybrid energy storage systems （HESS）， demand response （DR）， and network reconfiguration—were considered， and an optimization strategy for their coordinated allocation was proposed. To account for operational uncertainties， time-series-correlated scenarios were generated. The resulting optimization problem was formulated as a mixed-integer second-order cone programming model using the big-M method and second-order cone relaxation techniques.[Results] Case studies based on an improved IEEE 33-bus distribution system demonstrated that the proposed time-series-correlated scenario generation approach more accurately reflects the operational characteristics of distribution networks compared to traditional uncertainty-handling methods. Although the coordinated allocation strategy increased annual investment to some extent， it reduced the total annual cost by 16.9%， ensured a balance between flexibility supply and demand， and mitigated power flow fluctuations in the grid.[Conclusions] The proposed method fully leverages the complementary strengths of various flexibility resources， significantly enhancing distribution network flexibility and alleviating grid congestion. This work provides valuable insights and technical support for planning distribution networks with high penetration of distributed wind and solar generation.

[Objective] Constrained by practical operating conditions， load forecasting often faces the dual challenges of low data quality and variable load distribution. To address this， an ultra-short-term load forecasting method considering historical data missing and load temporal heterogeneity is proposed in this paper.[Methods] First， to reconstruct missing time-series data such as load， a Convolutional Neural Network-Bidirectional Long Short-Term Memory （CNN-BiLSTM） neural network is embedded within the Generative Adversarial Imputation Network （GAIN） to capture the spatiotemporal dependencies. During training， to prevent the model from focusing solely on the imputation error of observable values， random noise is introduced to replace partial observable values， enabling explicit measurement of the generation bias corresponding to the aforementioned noise. Second， to reveal load heterogeneity at finer temporal scales， clustering is applied to load input samples for the forecasting model in the training set to identify different load distribution patterns. Finally， a unified forecasting model is fine-tuned according to different pattern-specific samples， constructing personalized sub-models. During online forecasting， the most suitable sub-model is dynamically selected based on the similarity between the current load input sample and the centers of each pattern， eliminating reliance on external information such as calendar and weather data.[Results] Case studies demonstrate that the proposed missing data reconstruction method achieves lower reconstruction errors compared with traditional methods. Based on this， the constructed forecasting model yields higher prediction accuracy. Incorporating the construction and selection strategy for personalized forecasting sub-models further decreases forecasting errors.[Conclusions] Experimental results confirm the practical engineering value of the proposed method in real-world operational scenarios.

[Objective] To address the issues of insufficient wind power integration and the high energy consumption and pollution associated with coal-fired captive power plants， conducting generation rights trading between wind power and captive power plants represents a feasible solution. Although extensive research exists on generation rights trading involving wind power and captive power plants， studies on decision-making behavior that involves refined modeling of uncertainties across multiple time scales remain limited. To bridge this gap， this paper proposes a generation rights trading method for wind farms and captive power plants based on multi-time-scale coupling. [Methods] First， the costs and benefits of each market entity before and after participating in generation rights trading are analyzed to calculate the profit margin of such trading. Second， based on multi-time-scale wind curtailment forecasts， the Copula function is used to compute the conditional probability density distribution model of actual wind curtailment. This enables an analysis of the decision-making behavior of wind farms under multi-time-scale conditions and facilitates the implementation of generation rights trading. Finally， operational data from a provincial power system are used in a case study to validate the rationality of the proposed method. [Results] Compared with the traditional generation rights trading model， the multi-time-scale coupled generation rights trading model increases wind power integration by 3.7% and improves economic benefits by 3.3%. [Conclusions] The proposed multi-time-scale coupling model promotes wind power integration more effectively than traditional generation rights trading， while further exploring potential profit margins and maximizing the overall social benefits of generation rights trading.

[Objective] When uncoordinated control strategies are employed during the parallel-connected operation of multiple energy storage converters in new-type power systems, they cause unbalanced state of charge (SOC) distribution among energy storage systems, as well as frequency overshoot and low-frequency oscillations. To address these problems, this paper proposes an improved virtual synchronous generator (VSG) control strategy that combines SOC-based power balancing and adaptive fundamental damping design. [Methods] First, a grid-forming parallel-connected energy storage system model is established using voltage-source-type VSG control as the grid-forming outer loop. Second, a set of active power balancing coefficients is derived based on the real-time SOC of energy storage systems. Third, a control strategy combining fundamental damping with adaptive damping is designed on the basis of these balancing coefficients. Finally, the performance and effectiveness of the proposed control strategy are verified and analyzed using the MATLAB/Simulink digital simulation environment. [Results] Using an aluminum battery pack as the energy storage system, after introducing the proposed improved VSG control strategy, a power distribution difference of 5% to 7% is observed between the high-SOC and low-SOC systems. Under typical disturbance conditions, the frequency drop amplitude is reduced by approximately 20% and the overshoot amplitude is reduced by approximately 30%; under complex operating conditions, the frequency deviation is reduced by 10% to 15%. [Conclusions] The proposed control strategy maintains SOC balance among parallel-connected units by setting balancing coefficients, thereby reasonably optimizing power distribution. Furthermore, by incorporating an adaptive damping strategy based on the obtained balancing coefficients to correct frequency deviations, the proposed approach effectively enhances the dynamic response performance, frequency stability and multi-machine coordinated operation capability of energy storage systems. Therefore, the proposed control strategy holds certain theoretical significance and engineering application value for the construction of new-type power systems with high penetration of renewable energy.

[Objective] Due to insufficient measurement devices, the observability of distribution networks is relatively poor, which undermines the accuracy of state estimation. To address this issue, this paper proposes a prior-enhanced state estimation method for weakly observable distribution networks based on dynamic partitioning, which enables full-node state estimation utilizing measurements from observable areas. [Methods] First, node observability analysis and real-time dynamic partitioning are performed in accordance with the available measurement data. On this basis, a regional state mapping model is constructed to realize real-time state estimation in unobservable areas. Second, leveraging the prior state information from historical system states, a prediction error covariance matrix is formulated to establish a prior-enhanced extended Kalman filter (PEEKF) model. This method avoids updating the error covariance during posterior estimation, thereby improving estimation efficiency while maintaining accurate estimation states in the observable areas. Finally, the states of observable areas are mapped to the states of unobservable areas through the state mapping model, yielding the state estimates for the unobservable areas of the distribution networks. [Results] Simulation tests are conducted on the IEEE 33-node and 95-node systems. The average absolute percentage error of the algorithm remains consistently below 0.4% in both systems. Compared with the Extended Kalman Filter, Unscented Kalman Filter, and Adaptive Interpolation Kalman Filter, the proposed method achieves significantly higher estimation accuracy. [Conclusions] The proposed method can effectively perform dynamic partitioning and state mapping, and achieve high real-time and high-precision state tracking of weakly observable distribution networks.

[Objective] The large-scale connection of electric vehicles （EVs） and distributed power sources has exacerbated the three-phase imbalance in distribution networks，and the market-driven orderly charging and discharging of EVs is one of the initiatives to manage the three-phase imbalance，but the traditional management strategy does not take into account the benefits of both operators and users. [Methods] In this regard，this paper proposes a two-stage optimization strategy for EV charging and discharging that takes into account the benefits of both supply and demand sides and the three-phase imbalance management of distribution networks. First，the scheduling incentive mechanism and the user response willingness assessment method are proposed； Second，a two-stage optimization model is established for the day-ahead and intraday stages，where the scheduling parameters are configured based on the management objectives and costs in the day-ahead stage，and the EV charging and discharging strategy is derived by combining the users' benefits and the actual imbalance degree in the intraday stage； Finally，the EV charging and discharging strategy is validated based on the IEEE 13-node arithmetic case for simulation. [Results] The results show that the proposed strategy can effectively reduce the overall voltage imbalance of distribution networks. And compared with the single-objective strategy，in Option 2，the user charging satisfaction indexes are improved by 24.3% and 42.8%，respectively，and the operator realizes arbitrage； in Option 1，the satisfaction indexes are further improved，the operator's revenue is increased by a factor of 3.84，and robust is shown in different operating scenarios. [Conclusions] The proposed strategy can meet the imbalance management needs of operators while taking into account the economic benefits of both the supply and demand sides，providing a flexible and efficient imbalance management solution for distribution networks with a high EV penetration.

[Objective] To address the challenge of accurately evaluating the regulation capability of battery-swapping stations （BSSs） and optimizing charging schedules under uncertain battery swapping demand， this paper proposes a BSS regulation capability assessment and optimal control method that explicitly considers the seasonal number of surplus battery slots. [Methods] Based on measured data from 77 BSSs in Northeast China， the study identifies that stations face significant charging and swapping pressure in winter， while possessing surplus regulation capacity in spring， summer， and autumn. By leveraging the inherent regularity of taxi battery-swapping behavior， we improve the cyclic utilization efficiency of battery slots and minimize the number of slots required for cyclic swapping. The number of seasonally surplus battery slots is subsequently used as a quantitative metric for the time-shiftable regulation capability of BSSs. On this basis， a delayed charging strategy for sealed battery slots is developed using mixed-integer linear programming， incorporating an innovative risk-threshold protection mechanism to maximize the potential regulation capability of surplus battery slots and batteries. [Results] Case studies demonstrate that during each peak-valley electricity price transition period in spring， summer， and autumn， the proposed strategy unlocked regulation potential equivalent to 25% of the total battery capacity of a given swapping station， reducing its electricity procurement and operational costs by approximately 9%. [Conclusions] The proposed strategy can effectively reduce electricity procurement and operational costs for BSSs while ensuring the timeliness of battery swapping services. This approach offers new insights and methodologies for research on the participation of BSSs in power grid demand response and the accommodation of renewable energy， and holds significant theoretical significance and practical value.

[Objective] The deployment of energy storage in industrial parks not only reduces electricity costs and demand power charges but also generates additional revenue through participation in grid peak shaving and frequency regulation services. However， the stochastic volatility of photovoltaic （PV） generation presents significant challenges for the collaborative application of energy storage systems in demand power management and peak shaving. While electrochemical energy storage demonstrates millisecond response capabilities in mitigating the random fluctuations of PV generation， its high investment costs and limited cycle lifespan restrict its large-scale application on the demand side. In contrast， gravitational energy storage is highly competitive in terms of daily investment costs due to its low cost and long operational lifespan. To address these issues， this study proposes an innovative two-layer iterative robust planning method for a hybrid energy storage system aimed at managing demand power charges and facilitating peak shaving and frequency regulation in industrial parks. [Methods] The upper-layer model， based on information gap decision theory （IGDT）， seeks to minimize the annual operating costs of the system by optimizing the capacity allocation of gravitational and electrochemical energy storage， with the results transmitted to the lower-layer model. The lower-layer model employs model predictive control to achieve adaptive dynamic control of demand charges through rolling optimization. The success of demand power defense serves as the criterion for determining whether feedback is needed for capacity reallocation in the upper layer. [Results] Simulation results indicate that the proposed approach enhances the accuracy of demand management by 49.4%， improves the comprehensive performance index for peak shaving and frequency regulation by 42%， and reduces the annual operating costs of the system by 21%. [Conclusions] The proposed method provides significant theoretical support for the planning of energy storage systems in industrial parks.

[Objective] The significant anti-peak regulation characteristics of renewable energy sources like wind and solar power lead to large-scale curtailment during low-load periods， which is detrimental to the economic and low-carbon operation of integrated energy systems （IES）. Hydrogen and ammonia， with their zero-carbon and high-energy-density features， hold great significance for promoting the energy transition when integrated into IES. To fully leverage their advantages in reducing emissions and enhancing economic performance， this study aims to develop an optimal scheduling model. [Methods] This paper proposes an optimal scheduling model for an IES that incorporates dynamic co-firing of hydrogen and ammonia， and couples power-to-gas （P2G） with carbon capture and storage （CCS）. Equipment models， including P2G， power-to-ammonia （P2A）， electric boilers， and energy storage systems， are constructed. The hydrogen produced by P2G serves as an energy link， enabling gas turbines to co-fire hydrogen and coal-fired units to co-fire ammonia. Furthermore， a tiered carbon trading mechanism is introduced to enhance the flexibility of carbon emission reduction. Targeting at minimizing the total operating cost， an analysis is made on the impact of different hydrogen/ammonia co-firing ratios on the system's economy and carbon emissions. [Results] Simulation results indicate that the P2G-CCS coupling combined with a fixed 20% hydrogen/ammonia co-firing ratio minimizes the total operating cost and carbon emissions. Adopting a dynamic co-firing ratio further reduces the total cost by 11.65% and carbon emissions by 33.63 tons. [Conclusions] The tiered carbon trading mechanism combined with a dynamic hydrogen/ammonia co-firing strategy can effectively enhance both the economic and low-carbon performance of the IES， providing a viable solution for its optimal scheduling.

[Objective] With the increasing penetration of power electronic devices in the new power systems， the system is gradually evolving from a “physical synchronization” paradigm dominated by synchronous generators to a “control-based synchronization” paradigm dominated by grid-connected inverters. Due to the strong nonlinearity， limited overcurrent capability， and multi-inverter coupling characteristics of grid-connected inverters， synchronization stability issues exhibit multi-level features， extending from local dynamics to system-wide interactions.[Methods] To address this， this paper presents a comprehensive review from two perspectives， single-machine infinite-bus systems and hybrid systems. For the single-machine infinite-bus systems， virtual power angle dynamic models describing the synchronization behavior of grid-following （GFL） inverters， virtual synchronous generator-based， and virtual oscillator-based grid-forming （GFM） inverters with the grid are introduced. Based on the virtual power angle curves， the key factors affecting the synchronization stability of different inverter types are analyzed， and representative stabilization control strategies are summarized. For hybrid systems， considering the interactive coupling among control loops of GFL and GFM inverters， equivalent power angle dynamic models under both islanded and grid-connected modes are established， and the mechanisms of synchronization instability caused by multi-machine interactions， together with corresponding stabilization control strategies， are systematically reviewed.[Conclusions] Based on an analysis of existing research progress， future research directions on the synchronization stability of grid-connected inverters are discussed from the perspectives of multi-time-scale modeling， multi-scenario stability assessment， and coordinated stabilization control.

[Objective] To address the practical challenges confronting China’s power and green finance sectors， which are characterized by prominent data barriers， irregular information disclosure， inconsistent standards and data collection constraints， and to resolve the resulting obstacles to low-carbon energy transition， this study proposes a cross-sectoral data sharing and information disclosure collaborative solution that is both theoretically grounded and practically applicable. [Methods] Based on institutional economics and technological innovation theory， and by integrating case studies regarding integrated development of power and green finance sectors from Chongqing and other regions， this study systematically analyzes the current status， core bottlenecks and underlying contradictions of cross-sectoral data sharing and information disclosure from the perspectives of incomplete data collection scenarios and inefficient information utilization， while clarifying the rights and responsibilities of participating entities. A trinity collaborative mechanism framework integrating "organizational structure， operational rules and technical support" is established. Specifically， it proposes a “top-level coordination + tiered implementation” cross-sectoral data sharing system and a “mandatory disclosure + voluntary supplementation” information disclosure model. Furthermore， the operational pathways for cost allocation and benefit distribution are elaborated in detail， the compatibility verification of multi-scenario technical solutions is implemented， and a comprehensive set of safeguarding measures is provided to ensure the mechanism’s operability. [Results] The proposed solution effectively improves the efficiency of data sharing and the quality of information disclosure. [Conclusions] The proposed collaborative mechanism and operational pathway effectively unblock the data transmission chain connecting electricity， carbon and finance. This provides a supporting pathway with both theoretical value and practical feasibility for promoting the low-carbon energy transition， and holds significant importance for addressing industry development challenges and improving the governance system in related fields.

[Objective] Digital twin power grid is an important component of the new power system. Its current construction is confronted with problems such as diverse demand scenarios and insufficient coordination of technical routes. Therefore，a deeper analysis of the connotations underlying different application scenarios and strengthened systematic planning of the technical framework are essential. [Methods] Based on the summary of the practical experience of State Grid Corporation of China （SGCC） in recent years，this paper proposes the "five states" of digital twin power grids and interprets their connotations. From the perspective of different application scenarios，the construction requirements and key technologies of each state are discussed，and systematic construction frameworks are formed. [Results] The "planning state" of digital twin power grid strengthens the unification of the standard system throughout the full life cycle and the hierarchical construction of the model，which is the foundation for the construction of digital twin power grid. The "growth state" empowers power grid engineering construction，enhancing the quality and efficiency of three-dimensional design and construction. The "completed state" is oriented towards the digital transfer of power grid engineering achievements，strengthening the completeness of information and the consistency of diagrams and models. The "intelligent state" focuses on the intelligent management of equipment and enhances the intelligent perception capabilities of equipment and systems. The "emergency state" focuses on emergency repair of power grids，supporting the prevention and response to disasters such as ice，wind，lightning，line galloping，earthquake and fire. [Conclusions] The construction of digital twin power grid is a systematic project. It is not only the integrated application of technology but also the reconstruction of business processes and management models.

[Objective] Under China’s “dual carbon” goals，as energy decarbonization accelerates and renewable energy deployment enters a fast-growth phase，low-probability but high-risk extreme weather poses significant challenges to the safe and reliable operation of new power systems with high renewable energy penetration. Flexible power resources—such as electric vehicles and distributed generation—offer solutions to enhance system resilience during extreme weather. [Methods] This paper outlines the conceptual characteristics of power system resilience and examines the impact of extreme weather on new power systems. It reviews the resilience research of new power systems under extreme weather from three aspects：system component modeling under extreme weather，system resilience analysis methods，and resilience indicator frameworks. Furthermore，by analyzing the adjustable capacity of flexible power resources during extreme weather，the paper proposes strategies for enhancing the resilience of new power systems considering flexibility and extreme weather from four perspectives（generation，grid，load，and storage），and across three stages（prevention，emergency control，and rapid power restoration）. [Conclusions] Finally，the paper identifies research directions on the resilience of new power systems with flexible resources under extreme weather，aiming to establish a closed-loop risk management and resilience enhancement framework，and provide a theoretical basis for ensuring power supply during extreme weather.

[Objective] Addressing the challenge that grid-forming energy storage converters，operating in a single mode，struggle to adapt to variations in grid short-circuit ratio and complex fault disturbances，this paper proposes a dual-mode switching strategy based on amplitude and phase synchronization（APS），and uses the improved particle swarm optimization（IPSO）to identify its key parameters. [Methods] First，the limitations of conventional grid-forming/grid-following switching strategies are analyzed. The mechanism by which the cumulative voltage phase error in the power loop induces reactive power/voltage deviations，thereby amplifying transient impacts during mode switching，is revealed. Based on this，an APS compensation mechanism is proposed to simultaneously correct the voltage amplitude and phase signal during the mode switching process，ensuring smooth changes in the inner-loop current reference signal. Second，to overcome the difficulty in tuning the parameters of the conventional strategy's tracking loops，an IPSO algorithm based on nonlinear inertia weights and learning factors is used to adaptively identify the parameters of the four sets of tracking loops. This enhances the tracking performance and disturbance suppression effect of the energy storage converter on the operation points of the grid-following and grid-forming modes. [Conclusions] Validation was conducted via an electromagnetic transient model of a MW-level grid-forming energy storage system built in MATLAB/Simulink. The results showed that the proposed control strategy could successfully achieve a transient power impact of less than 0.02 p.u.，and could operate stably in the scenarios of continuous switching and operation point fluctuation. [Conclusions] Compared with the conventional switching strategy，the APS-IPSO-based strategy enables energy storage converters to achieve low-impact switching and high stability during grid-following to grid-forming transitions，providing a theoretical basis for the subsequent deployment of energy storage or new energy units with mode switching in new energy stations.

[Objective] To enhance the resilience of distribution networks under typhoon disasters and mitigate the risks of load interruptions and power supply losses caused by natural disasters， this paper proposes a resilience-oriented optimization strategy and evaluation method that accounts for load restoration priority and dynamic repair. [Methods] Typhoon-induced fault scenarios are constructed by integrating the Batts typhoon model with a line fault model， thereby capturing the impacts of typhoon intensity and trajectory on distribution networks. On this basis， a multi-source collaborative optimization model is developed with the core objective of prioritizing the restoration of critical loads. The model couples dynamic reconfiguration， fault repair， and the dynamic output characteristics of distributed energy resources （DERs） to enable rapid response and efficient resource dispatch during disasters. A set of resilience evaluation metrics of load average recovery level considering load weights is proposed to quantitatively evaluate system resilience under different scenarios. [Results] Case studies on a modified IEEE 33-bus distribution system demonstrate that the proposed strategy effectively reduces overall system load losses and significantly improves the restoration level of critical buses and essential users under typhoon scenarios. The simulation results also validate the applicability and effectiveness of the proposed evaluation metrics in distinguishing the merits and demerits of different recovery strategies. [Conclusions] The proposed strategy achieves dynamic optimization of distribution networks throughout the disaster impact and recovery process. Compared with conventional approaches， it exhibits distinct advantages in terms of load restoration speed， supply reliability， and resource utilization efficiency. In addition， the proposed resilience evaluation metrics provide a more scientific characterization of system resilience under disaster conditions， compensating for the limitations of conventional metrics. Overall， this paper offers valuable insights and references for fault recovery and resilience evaluation of future power systems under typhoon disasters.

[Objective] The soft open point （SOP） is a key solution for addressing challenges such as distributed photovoltaic （PV） integration， as it can connect multiple low-voltage distribution networks and offer functions including flexible power regulation. However， existing SOPs require power data from distribution transformer service areas to formulate their operating strategies， necessitating additional detection devices， energy management systems， and communication infrastructure， which significantly increases system complexity. A pressing problem lies in enabling SOPs to operate autonomously without reliance on external commands. [Methods] This paper proposed a novel neural network-based power control method for SOPs. First， a neural network model is constructed using low-time-resolution historical power sampling data and local voltage sampling data from the SOP. Subsequently， the neural network is deployed within the SOP controller， enabling the device to predict the transformer service area power deficit using real-time voltage sampling data. Finally， the power outputs of the multiple ports are regulated according to the predicted power deficit to achieve real-time power balancing among the connected distribution transformer service areas. [Results] The neural network model is trained using real-world transformer service area data and deployed in both a field-installed SOP and a laboratory test platform. Experimental results demonstrate that the proposed method successfully achieves automatic power distribution among the distribution transformer service areas， with an average deviation of less than 2 kW compared to the ideal equal power distribution values. [Conclusions] The findings indicate that this method enables power balancing using only local voltage sampling， effectively reducing the operation and maintenance costs and complexity of SOPs. This facilitates the broader adoption of SOPs， and promotes the local consumption of distributed PV generation.

[Objective] With the large-scale integration of doubly-fed induction generator （DFIG） based wind farms via series-compensated transmission lines， the issue of sub-synchronous oscillation （SSO） induced by the interaction between units and the grid has become increasingly prominent. Existing sub-synchronous damping controllers （SSDC） face limitations in adaptability and robustness， making it difficult to cope with complex and varying operating conditions. To enhance the suppression of wide-band SSO， this paper proposes a model-data fusion damping control method featuring high reliability and strong adaptability. [Method] A model-data fusion damping control architecture based on energy storage is proposed. This architecture combines a mechanism-based traditional SSDC with a model-free adaptive control （MFAC） based SSDC， injecting damping signals via the same grid-side energy storage system. The traditional SSDC provides fundamental damping support near the fundamental frequency， while the MFAC-based SSDC tracks system dynamics in real-time and adapts to frequency deviations， thereby avoiding multi-device coordination and complex parameter tuning. [Results] Under various operating conditions， including parameter variations and large disturbance faults， the proposed controller can rapidly and smoothly suppress SSO at different frequencies compared to its constituent units. It demonstrates superior bandwidth adaptability and dynamic regulation performance. [Conclusions] The constructed fusion damping control architecture effectively combines the stability of fixed structures with the flexibility of data-driven approaches. It features a concise structure and ease of implementation， showing significant effectiveness in improving system dynamic performance and robustness.

[Objective] To address the problems of electrolyzer operation instability， frequent start-stop cycles， and efficiency reduction caused by strong wind power fluctuations in off-grid wind-storage hydrogen production systems， a power smoothing control strategy for multiple scenarios that does not rely on wind power forecasting is proposed. [Methods] Based on the principle of first-order low-pass filtering， a smoothed power command equation is constructed. According to the working and efficiency characteristics of alkaline electrolyzers， the operation process is divided into four typical scenarios： startup， high-efficiency， rated， and overload， with smoothing factors independently configured for each scenario. The energy storage system compensates in real-time for the power difference between wind power and the electrolyzer， and dynamic switching of operating scenarios is achieved using a state machine to build a multi-scenario coordinated control architecture， realizing adaptive smoothing of wind power fluctuations. [Results] Simulation results show that， compared with existing control methods， the proposed strategy can significantly suppress fluctuations in electrolyzer input power and effectively improve system operation stability and hydrogen production efficiency. [Conclusions] The strategy can greatly extend the high-efficiency operating time of the electrolyzer， reduce the number of start-stop cycles， and increase hydrogen production per unit energy consumption without substantially increasing the total system energy consumption， demonstrating good comprehensive performance. This provides a power smoothing solution with a clear structure， strong robustness， and no reliance on forecasting for off-grid wind-storage hydrogen production systems.

[Objective] To address practical issues such as limited transmission channels for large-scale wind-solar bases in desert and arid regions of China and insufficient local consumption, this paper proposes a collaborative planning method for an integrated electricity-hydrogen energy system (IEHS) that considers solid-state hydrogen transport interactions. The method aims to alleviate the coupling bottleneck between renewable energy curtailment and the disconnection across hydrogen production, storage, transport and utilization. [Methods] First, an IEHS model is established, comprising electrolyzers, hydrogen storage tanks, gas-solid conversion units, and hydrogen vehicle loads. Solid-state hydrogen transport vehicles (SHTVs) are introduced to enable efficient interregional hydrogen transport and energy interaction. A bilevel planning model for hydrogen production, storage, transport, and utilization is then formulated. The upper-level objective minimizes equipment capacity configuration costs and the lower-level objective minimizes system operation optimal dispatch costs. The bilevel model is transformed into a single-level linear model using the Karush-Kuhn-Tucker (KKT) conditions and the Big-M method. [Results] The results demonstrate that adopting SHTVs for cross-regional hydrogen interaction reduces annual electricity purchase costs and renewable energy (wind and solar power) curtailment costs. Compared with independent operation in each region, the annual total system cost decreases by RMB 74.79 million, representing a reduction of 17.7%. In addition, relative to hydrogen tube trailer (HT)-based interaction, the annual transport cost decreases by RMB 15.52 million, or 67.9%. [Conclusions] The proposed method facilitates the accommodation of wind and solar power while reducing CO₂ emissions by 9,450 t, thereby effectively improving the overall economic and environmental performance of the system.

[Objective] To address the current tracking challenges in the harmonic suppression of active power filters （APF）， a fast integral terminal sliding mode control （FITSMC） strategy based on a hippocampus-based fuzzy neural network （HBFNN） is proposed. [Methods] The FITSMC is employed to guarantee global robustness and finite-time convergence of the tracking error. To circumvent the dependence on accurate system parameters， the HBFNN is constructed to approximate unknown system dynamics online. By integrating the hippocampus mechanism with fuzzy theory， the HBFNN eliminates redundancy through feature selection and enhances anti-interference performance against time-varying signals via a double recurrent structure. [Results] Simulation and hardware experiments verify that the proposed HBFNN-FITSMC scheme tracks harmonic currents rapidly and accurately. In simulations， the total harmonic distortion （THD） of the grid-side current decreases from 40.30% to 1.25%， while in hardware experiments， it decreases from 32.73% to 2.96%. Compared with traditional methods， the proposed strategy significantly improves dynamic response and steady-state accuracy， while effectively suppressing system chattering. [Conclusion] By virtue of its information screening and double recurrent mechanism， the HBFNN demonstrates superior approximation and anti-interference capabilities， reducing reliance on precise mathematical models. This scheme achieves the complementary advantages of brain-inspired intelligence and sliding mode control， offering significant value for engineering applications.

[Objective] With the rapid development of microgrids， ensuring the safety of system frequency and voltage has become a critical issue that urgently needs to be addressed. However， traditional frequency and voltage control strategies fail to consider the coupling relationship between voltage control and frequency control， making it impossible to achieve optimal real-time coordinated control of system frequency and voltage. Therefore， a data-driven frequency and voltage control method for isolated microgrids is proposed. [Methods] Firstly， the microgrid system model is recursively approximated online in real-time using the weighted least squares （WLS） method. Secondly， based on the identified system model and combined with a feedback-based approximate gradient algorithm， the frequency-voltage coupling relationship is utilized. By adjusting the power of voltage-sensitive loads through voltage regulation， active power balance in the system is achieved， enabling optimal real-time coordinated control of microgrid frequency and voltage. [Results] A microgrid system is built on Matlab/Simulink to simulate and verify the proposed method， and the results show that， compared with traditional frequency and voltage control strategies， the proposed method can fully exploit the regulation potential of voltage-sensitive loads， reduce microgrid frequency fluctuations， and achieve a maximum system frequency deviation of only 0.75%. [Conclusions] The proposed frequency and voltage control method for isolated microgrid clusters fully takes into account the coupling relationship between frequency and voltage. By utilizing voltage-sensitive loads， it achieves real-time optimal control of system frequency and voltage through voltage regulation.

[Objective] To address the issue of high operational costs in existing distribution networks and multi-microgrids caused by insufficient exploitation of synergistic flexibility， this paper proposes a coordinated optimal scheduling strategy for distribution-microgrids considering multi-resource trading of energy， reserve， and carbon emissions.[Methods] First， by fully considering the heterogeneity in market access qualifications among individual microgrids， a novel asymmetric microgrid alliance （AMGA） framework is established to enable differentiated entities participating in spot and reserve market bidding. On this basis， a Stackelberg game strategy between the distribution network operator （DNO） and AMGA is designed， incorporating a multi-level market clearing mechanism. In the upper-level， the DNO fully accounts for the power flow constraints of the distribution network and implements dynamic pricing of energy， reserve， and carbon targeting different market entities. In the lower-level， AMGA explores the energy transfer potential of Electric Vehicles （EV） to achieve coordinated scheduling of multi-microgrids. Subsequently， an optimization algorithm integrating a bisection method is employed to iteratively solve the scheduling model.[Results] The proposed strategy effectively reduces the operational costs of distribution- microgrid system， improves system economic performance by 12.83%， and ensures efficient grid operation.[Conclusions] This method achieves collaborative optimization and fair benefit distribution among multiple entities while balancing economic efficiency and environmental sustainability.