[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] To investigate and analyze the driving role of the green behavior of multiple loads on the evolution of a new type of power system and to enhance the flexibility and resource optimization allocation capacity of a new type of power system， this study conducted a simulation study of the green cooperative market behavior of multiple loads and constructed a simulation model of the green cooperative market behavior of multiple loads under the optimization of the evolution of a new power system. [Methods] First， the green cooperative market behavior and bidding behavior of multiple loads were analyzed and a multiple-load market bidding decision model was constructed. Second， the decision-making process of multiple loads and the entire transaction simulation process of the power market were constructed， and the deep reinforcement learning DQN algorithm was applied to analyze the behavior of multiple loads participating in market transactions and obtain their optimal bidding strategies. Finally， using a regional power system as a case study， the simulation simulated the entire transaction process of the multiple-load power market and derived the optimal strategy that offers results of multiple loads. [Results] Compared to the traditional Q-learning algorithm， the simulation model constructed in this study can reduce the average power purchase price of multiple loads by 8.5%， increase the demand response revenue by 13.7%， and increase the consumption rate of new energy by 5%. In addition， a sensitivity analysis of the new energy penetration rate was conducted and a higher sensitivity of demand response to the new energy penetration rate was identified. [Conclusions] The results showed that the proposed model can effectively simulate the green cooperative market behavior of multiple loads， improve the economic benefits of multiple loads and the new energy consumption rate， enhance the resource optimization and allocation ability of the new power system， and provide theoretical support and application reference for the market-oriented operation of multiple loads in a new type of power system and policy formulation.

[Objective] To improve the low-carbon economic performance of scheduling strategies for virtual power plants， this study proposes a distributionally robust low-carbon scheduling model that incorporates emerging distributed resources and electricity-carbon trading. [Methods] First， this study established an electricity-carbon trading framework for a virtual power plant. Second， two emerging distributed resources （e.g.， electric hydrogen production system and carbon capture system） were modeled within virtual power plants， along with traditional distributed resources （e.g.， energy storage， wind power， and photovoltaics）. Next， to minimize costs and consider the impact of electricity carbon trading， a low-carbon scheduling model for virtual power plants was established. Owing to the difficulty in obtaining accurate probability distributions of wind and solar power outputs and electric hydrogen loads， an uncertainty set of probability distributions was constructed using the 1-norm and infinite-norm. To avoid the complex iterations required in traditional multiple discrete-scenario distributionally robust optimization methods， this study solves the proposed model using a strong duality. Finally， the effectiveness of the proposed model in addressing source-load uncertainty and improving economic and low-carbon performance was verified based on numerical examples.[Results] Electricity-carbon trading reduced costs by approximately 24.7% compared to no electricity-carbon trading. Excess renewable energy could be sold entirely to the electricity market to obtain profitable operational results. Considering both the carbon capture system and the electric hydrogen production system， both abandoned electricity and operating costs are further respectively reduced by about 34.7% and 28.1% when only considering the carbon capture system， and respectively by about 2.6% and 1.8% when only considering the electric hydrogen production system. The total profit error of the proposed distributionally robust optimization method was approximately 1.7%， and the solving speed improved by approximately 40%.[Conclusions] Electricity-carbon trading and the integration of electric hydrogen production system and carbon capture system can jointly reduce scheduling costs， abandoned electricity， and carbon emissions. Moreover， the proposed distributionally robust optimization method showed high accuracy in decision-making results and significantly improved the solving speed.

[Objective] Virtual power plants （VPPs） centered on air-conditioning loads are susceptible to uncertainties， such as control delays and discrepancies between models and measurements， leading to deviations in the efficacy of demand response （DR） strategies from anticipated outcomes. A key contributor to this phenomenon is the reliance of existing DR strategies on static target load profiles， hindering their adaptability to dynamic operational environments.[Methods] To address this issue， this study introduced an adaptive control methodology for flexible-load VPPs participating in peak-shaving DR， utilizing a large-scale split-type inverter air conditioner on campuses as a case study. This approach allowed the adjustment of target load profiles for subsequent DR periods within the permissible range of the DR invitation based on the current operational environment， thereby enhancing the economic and robust nature of peak-shaving DR. In the proposed closed-loop control model， the controlled process was decoupled into a small-scale linear progress deviation model and a peak-shaving electricity correction model， each placed within the controller and feedback loop. The progress deviation model allocated planned peak shaving electricity to air conditioners， ensuring compliance with power constraints and user comfort levels. The peak-shaving electricity correction model， with the actual response to the peak-shaving DR， adaptively adjusted the target load profile for subsequent control moments to mitigate the adverse effects of uncertainties on control effectiveness.[Results] The case study focused on four types of inverter air conditioner clusters and examined the impact of different peak-shaving strategies， models， measurement errors， and control delays on the participation of the VPP in peak-shaving DR under market and invitation modes. This study verified the proposed method’s economic efficiency and robustness.[Conclusions] The results show that the proposed adaptive control method for peak-shaving DR based on a dynamic target load curve can autonomously adjust the target load curve based on the actual response conditions， demonstrating superior performance in terms of control accuracy， economic benefits， and robustness.

[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] With the continuous increase of flexibility resources in distribution networks， they can now participate in flexibility markets to provide active and reactive power flexibility support for transmission networks （TN）. This study thoroughly explored flexibility resources in distribution networks and proposes a two-stage distributed energy-flexibility market-clearing method for transmission-distribution networks， considering photovoltaic storage systems to enhance grid operational flexibility. [Methods] First， a PV storage system model was constructed by integrating energy market mechanisms with active and reactive power flexibility market mechanisms and analyzing its potential for active and reactive power support. Second， a two-stage market-clearing model for transmission-distribution networks was developed with the objective of maximizing the overall economic efficiency. Third， to preserve the privacy of the TN and distribution network information during computation， the proposed model was solved using the Alternating Direction Method of Multipliers （ADMM）. Finally， the method was validated using a test system that couples an IEEE 30-bus transmission network with two 33-bus distribution networks. [Results] The results show that when distribution system operators （DSO） participate in flexibility market transactions， the procurement costs for active and reactive flexibility resources in the TN decrease by 7.93% and the flexibility supply-demand balance index improves from 4.021 to 5.736. [Conclusions] The proposed method enhances the economic efficiency of the system， effectively reduces the total cost of procuring active and reactive flexibilities， and supports operational stability. Additionally， active distribution networks （ADN） can leverage their abundant active and reactive flexibility resources to provide flexibility services to transmission system operators （TSO）， thereby increasing ADN revenue and enabling the optimal allocation of flexibility resources across the grid. This study demonstrates the feasibility of coordinated flexibility trading between transmission and distribution networks under high renewable energy penetration conditions.

[Objective] The large-scale integration of electric vehicles （EVs） presents potential flexibility and operational uncertainty in power systems. Virtual power plants （VPPs）， as efficient paradigms for aggregating distributed energy resources， offer a feasible approach for coordinating EV participation in grid operations. This study proposed a bi-level optimization strategy based on a Stackelberg game to manage the interaction between VPPs and EV users under uncertainty.[Methods] A bi-level Stackelberg game model was developed in which the VPP acts as the leader and the EV users as followers. The upper-level model maximized the VPP profit while managing EV-related uncertainties via the conditional value at risk （CVaR）. It sets risk-aware charging and discharging prices. The lower-level model minimized user costs by responding to these prices using a utility function that captures both cost satisfaction and charging experience. A particle swarm optimization algorithm was employed to solve the coupled model and identify the equilibrium strategies.[Results] A case study of a VPP system with wind， solar， storage， and 300 EVs demonstrated the effectiveness of the proposed approach. Compared to benchmark strategies， the model reduced the peak-valley load gap by up to 36.9%， lowered the average user cost by 28.79%， and enhanced profit stability under uncertainty.[Conclusions] The CVaR-based bi-level game framework effectively balances the VPP profit， EV user satisfaction， and system stability. It provides a risk-aware， market-oriented approach for flexible resource management and offers practical insights into future EV-grid integration strategies.

[Objective] The increasing penetration of distributed resources into the distribution grid and their participation in coordinated operations across different levels of the power grid present several challenges. To address these challenges， this study proposes a joint clearing model for virtual power plants （VPPs） participating in local flexibility markets and spot markets. [Methods] Distribution service operators first aggregate the distributed resources into the VPP trading units in distribution network buses. Subsequently， at the distribution network level， a flexibility dispatching model for VPPs on the distribution network is established， which comprehensively considers the constraints of VPPs and the distribution network. Finally， the flexibility dispatching model and spot market-clearing model are co-optimized in a non-iterative manner. To validate the effectiveness of the proposed model， a case study was conducted based on IEEE standard test systems， and a comparative analysis with the market model was performed on the overall cost， transmission-distribution network operation， and clearing efficiency. [Results] Using the proposed method， the expected revenue of the transmission and distribution networks increased by 4.4% in the simulation and computation time reduced by 77.8%， compared with the non-cooperative scenario and iterative calculation method， respectively. [Conclusions] The results demonstrate that the proposed model can meet the flexibility needs of transmission and distribution networks and enhance the integration capacity of the system for renewables.

[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] Airborne wind energy（AWE）technology utilizes faster and more stable wind speeds at higher altitudes and offers higher energy density and power generation efficiency than traditional wind power generation. This study explored the current status and prospects of AWE technology，with a particular focus on parachute-based ground-generated high-altitude wind power technology. [Methods] This article outlines the technological routes of AWE systems（AWESs）using two main approaches（ground-gen and air-gen）and discusses their respective technical challenges and the current status of development. Special attention is paid to the parachute-based ground-gen AWES，with a detailed introduction to its working principle，system composition，and engineering case analysis. Parachute-based technology effectively captures and converts wind energy through the coordinated operation of aerial，traction，and ground components. By analyzing the specific implementation of the Jixi high-altitude wind power project in China，this article demonstrates the practical application and effectiveness of parachute-based ground-gen AWE technology. [Results] The project successfully achieved high-altitude wind power generation，which could output kilowatt-level power at low altitudes and megawatt levels over 5 km，thus verifying the feasibility and advantages of the technology. [Conclusions] The Jixi Project proved the feasibility of this technology，which features scalability，high safety，and high resource utilization efficiency. It also achieves a high wind energy conversion efficiency and can capture wind resources at altitudes over 1 km by increasing the length of the tether and adjusting the launch angle. In the “Three North” regions with abundant wind resources，this technology can achieve MW-level power generation at an altitude of 1000 m and further upgrade the power generation capacity by increasing the number of doing-work parachutes，holding significant implications for renewable energy development.

[Objective] In response to the increasingly diversified charging demands arising from the rapid development of electric vehicles （EVs）， this study investigates a planning method for charging stations based on the collaboration of multiple types of charging posts.[Methods] From the perspective of EVs， transportation networks， and power grids， a siting and sizing planning method for charging stations under vehicle-road-grid coupling is first established based on the graph theory. The charging behavior characteristics of EV users are then explicitly modeled， and four types of charging posts are selected as the main facilities： slow charging post （SCP）， fast charging post （FCP）， mobile charging post （MCP）， and ultrafast charging post （UCP）. A planning model is constructed with the objective of minimizing the annualized total social cost， incorporating constraints from multiple scenario conditions and multiple charging post types. The planning problem is then reformulated as a mixed-integer second-order cone programming （MISOCP） problem via scenario transformation and the second-order cone relaxation techniques and solved using the Gurobi optimizer.[Results] The simulation results demonstrated the high efficiency and effectiveness of the proposed model. The results indicated that the planning solution considering SCP， FCP， UCP， and MCP was optimal. Notably， the integration of MCPs provided an effective emergency response during peak charging demand periods and reduced the overall planning cost by 17.82%.[Conclusions] In the proposed planning model， EV users can select among multiple types of charging posts based on specific principles. The coordinated configuration of diverse charging posts offers greater flexibility than single-type configurations， enabling the satisfaction of charging demands while reducing the annualized total social cost.

[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] Amidst the global energy transition， addressing the insufficient regulation capacity of the new electricity system， this study systematically examines the characteristics and applications of bounded rationality behaviors of users. It aims to bridge the theoretical gap in conventional demand-side management caused by oversimplified behavioral assumptions， establish a theoretical foundation for supply-demand interaction optimization， and provide actionable insights for unlocking the potential of demand-side resources.[Methods] An interdisciplinary framework integrating economics and behavioral science theories was developed to analyze bounded rationality in electricity consumption， explicitly distinguishing it from traditional rational behavioral paradigms. User behavior was characterized by four dimensions： the price elasticity of demand， consumer psychology principles， behavioral economics mechanisms， and data-driven behavioral modeling. Furthermore， the implications of bounded rationality were investigated across three key domains： demand response potential assessment， load forecasting accuracy improvement， and user-centric energy decision optimization. The study concluded with a critical evaluation of the current research gaps and proposed methodological advancements for future behavioral characterization studies.[Conclusions] This study contributes to an interdisciplinary framework to enable precision regulation of demand-side resources， fostering innovation in adaptive market mechanisms and dynamic control strategies. Future studies can integrate data-driven analyses of user electricity consumption data to rationally identify critical parameters. This will deepen the fundamental understanding of user energy behaviors， thereby enabling the dual achievement of enhanced system regulation capacity and reduced user energy costs under the “Dual Carbon” goals.

[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] In the context of high renewable energy penetration， the collaborative operation of multiple virtual power plants （VPPs） faces dual challenges： uncertainty risks and conflicts in benefit distribution. This study proposes a collaborative optimization strategy for multiple VPPs that integrates risk quantification with hybrid game theory by combining conditional value-at-risk （CVaR） and a multi-agent game framework. This approach provides a new perspective for collaborative VPP optimization in scenarios with high renewable energy integration.[Methods] First， a scenario analysis method combining Latin hypercube sampling （LHS） and Manhattan probability distance was designed to address the uncertainties in wind and solar output as well as electricity prices. CVaR was adopted to measure the impact of these uncertainty risks. Second， a Stackelberg game framework was constructed between the distribution system operator （DSO） and the VPP alliance， where the VPP alliance， based on cooperative game theory， established an asymmetric Nash bargaining model incorporating energy contributions. The model was then decomposed into two subproblems： maximizing alliance benefits and distributing cooperative benefits. Finally， the hybrid game model was solved using a combination of the bisection method and the alternating direction method of multipliers （ADMM）.[Results] Simulation results demonstrate that the proposed coordinated optimization strategy for VPPs effectively enhances the operational economy of the VPP alliance and improves operational reliability and security under uncertainty.[Conclusions] The proposed strategy increased the flexibility of coordinated operations among multiple VPPs. By incorporating CVaR for risk quantification and multi-agent game theory， the strategy not only enhances overall system benefits but also ensures a fair distribution of cooperative gains. Moreover， VPPs can balance the risk-benefit trade-off based on their risk aversion coefficients， providing a valuable reference for rational dispatch decision-making.

[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] A two-stage optimal operation strategy under multi-market coupling is proposed to coordinate the interests of various trading entities of large-scale energy bases under the coupling of power， carbon， and green certificate multi-markets， promote the development of new energy， and achieve carbon emission reduction and energy transition.[Methods] First， based on the system dynamics model， the coupling relationship between the markets of large energy bases is analyzed， and the trading mechanism of each market and the causal loop of each trading body under multi-market coupling are clarified. To guarantee the interests of each trading body， we construct a two-stage optimal operation strategy for large-scale energy bases under the joint market of electricity， carbon， and green certificates. The first stage is the distribution of electricity before the day of balanced internal profit for each trading subject in the joint trading market. The second stage is optimal intraday dispatching to balance the overall economy and low-carbon nature of outgoing transmission.[Results] Considering a large energy-based outgoing system as an example， the results of the verification showed that the strategy enabled the profit of traditional thermal power reach 87% of its own interest maximization through the profit equilibrium constraint.[Conclusions] Based on the balanced interests of all trading entities， a large energy base achieves an effective balance between economy and low carbon emissions and realizes the goals of carbon emission reduction and energy structure adjustment.

[Objective] Multi-energy microgrids（MEMGs）can integrate multiple energy carriers to improve energy efficiency，thereby contributing to the achievement of "dual carbon" goals. [Methods] This study proposes a data-driven distributionally robust optimization scheduling method for MEMGs that considers uncertainty correlations and outlier data. First，an ambiguity set incorporating uncertainty correlations is introduced using the copula function. A distributionally robust optimization scheduling model with opportunity constraints is then formulated，integrating the ambiguity set and opportunity constraints to address the uncertainty correlations. Second，because the distributionally robust optimization model cannot be solved directly，a worst-case transformation method is derived for the proposed ambiguity set using dual theory，McCormick relaxation，and conditional value-at-risk approximation. This transforms the distributionally robust model into a linear deterministic model，thereby enabling an efficient solution using optimization solvers. Finally，a sample-pruning algorithm is proposed，which iteratively generates subsamples from the original dataset by removing the outliers and extreme data points. This approach mitigates the adverse effects of such data on distributionally robust opportunity constraint scheduling results. [Results] Case simulations demonstrate that the proposed distributionally robust model effectively eliminates unrealistic distributions in the ambiguity set，resulting in an 8.16% reduction in out-of-sample costs. The proposed sample-pruning algorithm further reduces the out-of-sample costs by 3.33%. [Conclusions] The proposed method enhances the scheduling efficiency and ensures reliability，which collectively demonstrates its superiority.

[Objective] To completely exploit the coupling flexibility of the electric-hydrogen-gas-storage-demand response， a data-driven two-stage distributed robust collaborative planning model for integrated energy systems is proposed. [Methods] To address the problems of model inaccuracy and low solving efficiency of existing equipment modeling methods， a refined modeling method for an integrated energy system was proposed， which considered a refined model of distributed power supply， energy coupling equipment， hybrid energy storage， and demand response mechanism. A demand response incentive mechanism considering baseline uncertainty was developed. [Results] The MATLAB simulation results showed that the baseline load prediction model based on Gaussian process regression can calculate the baseline load more accurately and rapidly while simultaneously considering the response uncertainty. In addition， the equipment refinement model proposed in this study effectively reduced the comprehensive planning cost of the system， in which the operation， planning， carbon trading， and demand response costs were reduced by 2.55%， 10.78%， 1.08%， and 2.55%， respectively. Simultaneously， through the collaborative optimization of carbon trading and demand response mechanisms， the system could reduce the power purchased by the upper power grid and use flexible loads and distributed power sources to achieve a low-carbon and stable operation of the integrated energy system. The example showed that compared with the SO and RO methods， the proposed DRO planning method had more advantages in terms of the balance of economy and robustness and verified its applicability in integrated energy system planning. [Conclusions] The integrated energy system planning model based on demand response can significantly reduce the annual comprehensive cost of the system， improve the utilization rate of renewable energy， and reduce carbon emissions， providing ideas for subsequent research on the planning of the electric-hydrogen-gas integrated energy systems.

[Objective] To improve the accuracy of global horizontal irradiance （GHI） prediction and completely explore its application value in solar energy resource assessments， such as photovoltaic site selection， this study proposes a GHI prediction model that integrates the temporal convolutional network （TCN） with the former architecture. [Methods] To address the presence of anomalies in the GHI data， raw data were first cleaned and preprocessed to eliminate outliers and ensure data quality. Then， the model leveraged the temporal feature extraction capability of the TCN to perform deep representation learning on preprocessed multisource input data， whereas the former network was employed to capture long-term dependencies. A high-precision prediction framework driven by multiple features was constructed by incorporating environmental and geographical parameters into the model input to enhance the overall performance. [Results] Comparative experiments conducted on real-world datasets from multiple regions demonstrated that the proposed TCN-Informer model outperformed mainstream prediction models in terms of mean absolute error， mean absolute percentage error， and root mean square error. Compared with the second-best performing informer model， the proposed model achieved reductions of 24.0%， 23.1%， and 28.5% in the mean absolute error， mean absolute percentage error， and root mean square error， respectively. [Conclusions] The TCN-Informer model exhibited significant advantages in terms of accuracy and robustness for GHI prediction， enabling a more effective capture of temporal variation patterns in solar irradiance. It has a strong engineering application potential and provides solid data support for solar resource evaluation and photovoltaic site planning.

[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] With the continuous increase in the proportion of renewable energy in the overall energy mix， the inherent uncertainty and variability in power generation pose challenges to the stable operation and economic efficiency of microgrid systems. Demand response strategies have emerged as crucial measures for enhancing the integration capacity of renewable energy in microgrids.[Methods] First， to optimize the fitting ability of the demand response model to the user behavior， this study constructs a demand response model based on the endowment effect by analyzing the psychological factors of users participating in demand response. Then， based on this demand response model， an economic optimization operation strategy for microgrids is proposed. Considering the comprehensive satisfaction of users and the operation cost， a microgrid economic operation model is established. Pareto optimization technology combining the constraint and relaxation factor is adopted to solve the operation model， and the economic optimization of the microgrid is achieved under the constraints of the equipment operation power and grid interaction.[Results] The simulation analysis verified the effectiveness of the demand response model proposed in this study in improving the economic benefits of microgrids and its superiority over traditional demand response models， while also enhancing user satisfaction.[Conclusions] The demand response model based on behavioral economics theory proposed in this study can more accurately describe the user demand response behavior. The introduction of the endowment effect theory provides a new perspective for understanding and predicting consumer responses to energy price changes， enabling microgrid operators to more accurately adjust power supply strategies to cope with demand fluctuations and market changes. The demand response model proposed in this study can effectively promote user participation in peak shaving and valley filling and reduce the operation cost of the system.

[Objective] To enhance the economic efficiency of large-scale cross-regional and cross-provincial wind and solar power absorption，a multi-time-scale hierarchical optimization scheduling method for DC power transmission systems considering the source-load matching deviation is proposed. [Methods] A user electricity price correction method considering the source-load matching deviation is proposed. A load demand response model based on the Logistic function fuzzy response mechanism is established. The response-induced user load and transmission electricity price are obtained through dynamic electricity prices. In the day-ahead time scale，the transmission and receiving electricity willingness at the sending and receiving ends at different times is represented by the transmission and receiving electricity prices at the sending and receiving ends through the interconnection line，and a day-ahead optimization scheduling model considering the transmission willingness cost is proposed. In the intraday time scale，a wind and solar random component description method combining fuzzy parameters and t-location-scale distribution is proposed. It is combined with the day-ahead scheduling results as the input，and an intraday cross-regional and cross-provincial hierarchical optimization scheduling model is constructed based on the DC power adjustment strategy. [Conclusions] The case study verifies that the proposed dynamic correction method of user electricity price considering the source-load matching deviation can quantify the supply-demand deviation，and guide the load to actively track the curve of new energy power generation through the load demand response model based on the Logistic function fuzzy response mechanism； the scheduling model considering the transmission willingness cost can optimize the regional wind and solar resource allocation，reduce the output fluctuation of thermal power units and improve the system economic efficiency； the intraday hierarchical optimization model adjusts the scheduling mode dynamically，avoids local excessive adjustment，and improves the calculation efficiency by 22.82 seconds while achieving the coordinated optimization of economic efficiency and operational efficiency. [Conclusions] The proposed multi-time-scale hierarchical optimization scheduling method for DC power transmission systems considering source-load matching deviation can significantly enhance the wind and solar power absorption capacity when high penetrations of new energy are integrated into DC power transmission systems. It can not only shorten the scheduling calculation time but also effectively reduce the operational economic cost of the system.

[Objective] To enhance frequency stability in systems with a high penetration of converters，this paper investigates a dynamic evaluation and quantification method for determining the frequency support requirements of grid-forming（GFM）converters. [Methods] First，a multi-machine system frequency response model is developed by incorporating the power-frequency characteristics of coupled GFM converters. Second，considering both the rate of change of frequency（RoCoF）and the maximum transient frequency deviation under anticipated disturbances，a comprehensive assessment method is constructed to evaluate the frequency support requirements of GFM converters. Finally，based on the Brent iterative algorithm，the critical thresholds of virtual inertia and primary frequency regulation gain for GFM converters are dynamically assessed under various operating conditions. Based on the modified IEEE-39 bus system，multiple scenario-based case studies were conducted. [Conclusions] The results show that the introduction of GFM converters can raise the minimum system frequency to 49.5 Hz，significantly enhancing system stability and highlighting the necessity of deploying GFM converters in highly power-electronic systems. Furthermore，a performance comparison of three algorithms demonstrates that the proposed iterative method requires only 11 iterations on average，reducing the iteration count by over 20% compared to traditional approaches，thereby validating the effectiveness of the proposed assessment method and iterative algorithm. [Conclusions] The proposed assessment method clarifies，from a system-level perspective，the frequency support requirements imposed by the grid on GFM converters，providing a theoretical foundation for their optimal allocation and parameter tuning.

[Objective] With the integration of large-scale distributed photovoltaics，issues such as reverse power flow and overvoltage arise，posing challenges to the safe and stable operation of power systems. To address this issue，both domestically and internationally，regulations mandate that PV systems must have grid support capabilities，such as voltage-reactive control，which actively regulates reactive power based on voltage deviations. However，such regulations are typically designed for balanced voltage scenarios and unbalanced voltage conditions are rarely considered. [Methods] Therefore，using voltage-reactive power control（volt-var control，VVC）as an example，this study analyzes the influence of voltage imbalance on the reactive power output and proposes an improved voltage-support control method aimed at minimizing three-phase voltage deviation. Based on instantaneous power theory，the mathematical relationship between voltage imbalance and active/reactive power is derived，demonstrating the voltage regulation effect of reactive power and the existence of the minimum voltage point. Then，with the three-phase voltage deviation as an index，the positive sequence voltage and reactive power corresponding to such point are calculated，and dynamic adjustments to the voltage-support control curve are made using this point，voltage limits，and reactive power capacity of PV inverters. [Results] Matlab/Simulink simulation results demonstrate that the proposed improved voltage-support control method ensures that three-phase voltages remain within limits while achieving minimal deviation from the nominal voltage under the lowest reactive power output. This ensures economical and efficient regulation of the three-phase voltage in unbalanced scenarios. [Conclusions] The proposed control strategy effectively coordinates the traditional VVC control with negative-sequence current/power control. It maintains three-phase voltages within grid-connected standard limits despite changes in irradiance，temperature，or other factors causing photovoltaic power fluctuations or voltage-unbalance variations，significantly enhancing the adaptability of conventional VVC control functions.

[Objective] Following fault-induced outages in flexible interconnected distribution networks，devices such as Soft Open Points （SOPs） can enable rapid and coordinated power rerouting or support islanded operation. However，existing studies overlook the impact of different control states of flexible devices on restoration performance. Moreover，conventional predefined islanding schemes have limited capability in determining the effective support radius and restoration priority enabled by SOP. To address issues related to the feasible control strategies of interconnected devices，this paper proposes a fault restoration strategy based on the equivalent model of the SOP. [Methods] An alternating iterative power flow algorithm is developed to incorporate SOP with multiple control modes and to compute the post-restoration power and voltage distribution. By jointly optimizing SOP control mode selection and non-predefined network reconfiguration，a restoration model is established with the objective of minimizing weighted operational losses，while fully considering power flow constraints and multi-terminal SOP control limitations. To efficiently solve the resulting large-scale optimization problem，a genetic algorithm enhanced with a cooperative graph Laplacian operator is employed for efficient solution. Post-fault restoration performance was evaluated on an interconnected dual IEEE 33-bus test system. [Results] The results demonstrate that，in response to different topological changes and distributed generation outputs after line outages，the proposed method can dynamically generate appropriate non-predefined reconfiguration schemes. It also enables coordinated adjustment of SOP control strategies at different locations. Compared to conventional reconfiguration approaches，the proposed strategy improves the load restoration ratio by 14%. [Conclusions] The non-predefined network reconfiguration contributes to a higher post-fault load restoration ratio. When combined with optimized SOP control states，it yields a more favorable post-restoration voltage profile and supports high supply resilience in flexible interconnected distribution networks.

[Objective] In recent times，countries worldwide are actively promoting energy transition and low-carbon development，and the coupling of electricity，carbon，and hydrogen trading has become an inevitable trend in the development of China’s energy industry. However，the complexity of energy mutualization and information interaction across multiple systems is significantly enhanced introducing new challenges to the development of a coupled electricity-carbon-hydrogen trading market. [Methods] This study first introduced the existing research on the electricity-carbon and electricity-hydrogen markets and extensively analyzed the research methodology，coupling mechanism，trading mode，pricing，and clearing mechanism of the electricity-carbon-hydrogen market. Second，because of key challenges at both the physical and information levels that constrain market development，the a framework for the electricity-carbon-hydrogen coupling market within the Energy Internet was proposed to address these issues，along with its participation mechanisms and specific methods. Finally，the study summarized key technologies and research points for the electricity-carbon-hydrogen market from both technical and market levels. [Results] At the physical level，the Energy Internet resolved the electricity-carbon-hydrogen market’s challenge of clarifying the market trading mechanism and decision-making law，which arise due to complexity of resources and interactions. It also established clear laws and mechanisms for energy production and marketing. At the information level，based on information technology and data platforms，the Energy Internet realized data matching and sharing，information transparency，and openness among the electricity，carbon，and hydrogen markets； promoted autonomous and flexible distributed energy trading； and guided the optimal resource allocation. [Conclusions] Exploring the key technologies and research points of the coupled electricity-carbon-hydrogen market for the Energy Internet can overcome the dilemma of the development of the existing market and provide a feasible reference path for improving and developing the construction program of the electricity-carbon-hydrogen market.

[Objective] The large-scale integration of inverter-based renewable generation introduces new challenges for power-system stability. Although virtual synchronous generator（VSG）control imparts virtual inertia and damping，grid short-circuit faults can induce excessive currents that threaten synchronization and may cause inverter disconnection. This paper proposes a VSG fault ride-through strategy based on dynamic virtual velocity regulation. [Methods] The transient behavior of the VSG under fault conditions is analyzed to identify the source of overcurrent，and dynamic virtual angular velocity regulation is introduced to maintain power-angle stability. The reactive-power control loop is reinforced to improve reactive-power compensation，and a voltage-current dual-loop structure based on the balanced-current method is employed to ensure balanced three-phase output currents. [Conclusions] Matlab/Simulink simulations show that，during grid short-circuit faults，the virtual angular velocity and power angle stay close to their rated values； short-circuit current is limited to within 1.5 times the VSG’s rated current； the VSG provides reactive power in accordance with national grid connection codes； output currents remain three-phase balanced； and fault impact currents are effectively suppressed. [Conclusions] Compared with existing methods，the proposed strategy more effectively stabilizes virtual angular velocity and power angle，limits short-circuit and fault impact currents，accelerates transient response，maintains balanced three-phase outputs，and ensures secure，stable VSG operation under grid short-circuit conditions.

[Objective] With the increasing penetration of power electronic devices，such as energy storage and photovoltaics，in microgrids，their low inertia and low damping characteristics pose challenges to the stable operation of microgrids（MGs）. To enhance the stability of inverter-based MGs，this study introduces a novel data-driven method for the coordinated and rapid local adjustment of inverter multicontrol parameters. [Methods] An offline eigenvalue-based optimization problem was formulated to compute the optimal multicontrol parameters using the osprey optimization algorithm（OOA）under various operating conditions. Subsequently，to minimize the reliance on global system information，a multilabel feature selection algorithm is employed to identify the most relevant local measurements that influence the adjustment of each control parameter. Finally，local measurements are treated as input variables and optimal control parameters as output variables. A novel deep learning algorithm based on northern goshawk optimization（NGO）and a bidirectional gated recurrent unit（BiGRU）is proposed to train the local parameter optimization model（LPOM）by learning the input-output mapping. [Results] The case study demonstrates that the designed LPOM can swiftly adjust controller parameters based on online measurement data，thereby enhancing microgrid stability. It also establishes that the proposed deep learning algorithm achieves higher accuracy in training the LPOM compared to traditional neural networks. The LPOM delivers faster computation speeds for parameter optimization. [Conclusions] The proposed method only requires local measurement data and rapidly enhances the small-signal stability of microgrids through online dynamic optimization of multiple inverter control parameters.

[Objective] Ensuring the normal operation of the price mechanism and auxiliary mechanisms is an important goal for the future regulation of China's electricity market. Summarizing the regulatory practices of mature international electricity markets in dealing with behaviors that disrupt the price mechanism and auxiliary mechanisms，and comparing the penalty rules for violations in different countries，is of great significance for improving China's electricity market system. [Methods] Grounding its analysis in penal theory，this study conducts a focused investigation into the jurisprudential rationales and operational methodologies employed by EU and U.S. electricity market regulators to sanction manipulative practices. Through a comparative examination of statutory frameworks，regulatory philosophies，and empirical enforcement cases，the research systematically contrasts jurisdictional variations in both legal thresholds for liability and gradations of punitive severity. The concluding synthesis assesses enforcement efficacy to derive comparative insights into regulatory paradigm effectiveness. [Results] While Europe and the U.S. differ significantly in specific legal provisions governing penalties，their electricity regulators apply identical thresholds for initiating enforcement actions against market manipulation. While the U.S. model presents more efficient for quantifiable illegal gains，the French model provides clearer outcomes for uncertain gains. When no illegal gains or harm have yet occurred，the French model exerts stronger deterrence against non-compliant firms. [Conclusions] In light of China's evolving electricity market landscape，this article advances three policy recommendations： excluding the element of intention for violation determination； establishing a fine base with dual deterrence and compensation functions，coupled with fines graded by harm severity； education and penalties are combined to enhance regulatory effectiveness.

[Objective] Low-probability，high-impact natural disasters have become increasingly frequent. Integrated energy systems with high renewable penetration often lack the self-adaptive optimization capability needed to manage unexpected disturbances. This paper proposes a vulnerability identification framework and a pre-disaster resilience-oriented dispatch strategy for integrated energy system under typhoon disasters. [Methods] A dynamic integrated energy systems model is constructed using a generalized phasor approach to represent heterogeneous energy flows. A line fault-restoration probability model under typhoon scenarios is developed to generate disaster-operational states. A vulnerability identification method is then introduced from the perspectives of system performance loss and power-flow transfer risk. Finally，a pre-disaster resilience scheduling strategy is formulated by integrating flexibility enhancement with power-flow optimization. [Conclusions] Simulation results show that the optimized resilience-oriented dispatch strategy increases equivalent energy-storage capacity by 31.68%，reduces the average loading of key transmission branches by 56.58%，and decreases total load loss by 26.27%. [Conclusions] The proposed framework effectively identifies high-risk，critical vulnerabilities under extreme scenarios and substantially enhances the disaster resilience and supply security of integrated energy systems.