[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 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] 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] 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] 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] Demand-side technologies contribute to energy conservation and load regulation in integrated energy systems. However，most existing models focus solely on optimising the supply-side factors. To address this gap，a supply-demand co-optimisation model for integrated energy systems is developed. [Methods] On the demand side，building performance simulation is used to generate a set of envelope retrofitting scenarios，and a load-shifting-based demand response strategy is introduced to enhance system flexibility. On the supply side，clustering methods are applied to extract typical days to represent annual variations in energy demand. A mixed-integer linear programming model is established to integrate these components and achieve supply-demand co-optimisation. [Conclusions] A case study based on a community in Shanghai shows that the proposed co-optimisation model can reduce the total annual cost by approximately five percent compared to a supply-side-only optimisation approach. Moderate envelope retrofitting enables optimal system performance，and the associated costs can be offset by savings in operational，carbon emission，and installed capacity costs. [Conclusions] The proposed model provides theoretical support and practical reference for the efficient configuration and operational optimisation of integrated energy systems under carbon tax constraints.

[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] To enhance the representation of intra-interval power fluctuation and the allocation of flexibility resources，this paper provides a systematic review of the applications and existing challenges of continuous-time scheduling methods for flexible power system operation. [Methods] First，the mathematical formulations and solution paradigms of discrete-time and continuous-time scheduling are compared. Then，representative applications and problems are reviewed，organized according to four key domains：generation，transmission，storage，and demand. Finally，common challenges are synthesized from a mathematical point，and potential theoretical development directions are summarized. [Conclusions] In continuous-time scheduling，decision variables are modeled as continuous trajectories，and constraints involve derivatives，integrals，and differential equations. The main solution paradigms include optimal-control-inspired methods and Galerkin-projection-based approaches. Shared difficulties mainly arise from modeling uncertainty in continuous time，solving nonconvex constraints such as unit commitment and charge-discharge exclusivity，and handling differential-equation constraints. [Conclusions] Based on these findings，this paper outlines future application trends and follow-up research topics of continuous-time scheduling in multi-timescale scheduling coordination，planning assessment，and market mechanism design.

[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.

[Objective] To address the power imbalance between renewable generation（wind/solar）and load demand，this paper proposes a two-stage robust optimization-based economic dispatch method for integrated energy systems，incorporating hydrogen-ammonia storage and transportation. [Methods] First，to fully exploit the synergistic potential of hydrogen-based short- and long-term energy storage technologies，a hybrid energy storage model is established，integrating electro-thermal storage，short-term hydrogen storage，and seasonal hydrogen storage. Second，to enable large-scale and long-distance hydrogen transportation，a hydrogen-ammonia storage and transportation model is constructed within the hybrid energy storage framework via "hydrogen-ammonia-hydrogen" or "hydrogen-ammonia" conversion processes，enabling both cross-temporal hydrogen storage and cross-spatial transportation. Finally，considering uncertainties on both the source and load sides，a two-stage robust optimization-based economic dispatch model is developed with the objective of minimizing total system costs. [Conclusions] Simulation results demonstrate that，compared with conventional hydrogen storage methods，the proposed approach reduces hydrogen storage and transportation costs while improving renewable energy consumption，system economy，and robustness，thereby verifying its effectiveness and feasibility. [Conclusions] A hybrid energy storage model integrating electro/thermal storage with short-/long-term hydrogen storage is established，enabling spatiotemporal hydrogen utilization，enhancing renewable energy consumption，and reducing operational costs. An innovative ammonia-based hydrogen storage technology is adopted，where the "hydrogen-ammonia-hydrogen" or "hydrogen-ammonia" conversion process lowers storage and transportation costs. By accounting for source-load uncertainty，the system achieves flexible balance between economy and robustness. A current limitation of the model is that it does not integrate the electricity‑hydrogen‑ammonia with other energy carriers（e.g.，biomass，geothermal）to realize broader multi-energy synergy.

[Objective] To enhance the role of the capacity tariff mechanism in facilitating energy transition and address the issues of imprecise compensation and insufficient incentives for low-carbon and highly flexible coal-fired generating units under the current mechanism，this study develops a capacity tariff optimization model that incorporates energy transition objectives for coal-fired units. [Methods] The study first analyzes the cost pass-through path of coal-fired units and constructs a three-layer nested modeling framework consisting of a unit operation decision model，a power market clearing model，and a capacity tariff optimization model，thereby forming a dynamic feedback chain among the “unit-market-region-mechanism”."On this basis，an incentive model for coal-fired units targeting energy transition is proposed，in which the incentive targets and compensation levels are determined based on unit flexibility and carbon emission levels，using a sorting algorithm. Simulation analyses are conducted for typical days in new energy-rich and hydropower-rich regions，considering both regional and seasonal variations. [Conclusions] Simulation results show that the flexibility-based incentive strategy can effectively promote energy transition in scenarios with high ancillary service demand，but may inhibit transition in low-demand scenarios. In contrast，the carbon-based incentive strategy demonstrates stable and positive effects on energy transition across all regions and scenarios，indicating broad applicability. All proposed incentive strategies lead to only marginal increases in end-user electricity prices（within 0.038 yuan/kWh），demonstrating their economic viability. [Conclusions] The capacity tariff mechanism for coal-fired power units should be implemented with differentiated regional and seasonal strategies，taking into account variations in power source composition and load characteristics. It is recommended to initially adopt a carbon-based incentive scheme in the current stage and progressively introduce joint incentives based on both carbon emissions and flexibility as renewable energy penetration increases.

[Objective] Building and improving the mid-long-term electricity market between provinces is one of the fundamental links in promoting the development of green electricity and implementing the “dual carbon” goals. However，the randomness of green power output affects the physical feasibility of medium- and long-term clearing schemes for inter-provincial electricity transactions，and the participation of large-scale green power increases the burden of solving the clearing model. This paper proposes a fast-clearing method for mid- to long-term inter-provincial electricity transactions that considers the low-carbon value and stochastic characteristics of green electricity to improve the feasibility of clearing schemes and the efficiency of clearing calculations. [Methods] This paper constructs an inter-provincial electricity mid- to long-term transaction-clearing model that considers the low-carbon value and stochastic characteristics of green electricity. The objective function includes the cost of carbon emissions from power generation and the non-executable electricity penalty established based on the distribution characteristics of green electricity output，enabling clean and low-carbon green electricity to gain an advantage in market competition and improve the enforceability of green electricity-winning bids. Subsequently，an acceleration strategy based on fixed transaction variables is proposed. Based on the calculation results of the period decoupling model，transaction variables with a high probability of not closing are fixed，thereby constructing a smaller-scale clearing model and improving the efficiency of the clearing calculation. [Conclusions] Simulation examples based on actual transaction data from China show that the proposed model can balance the feasibility of the green electricity winning bid quantity and winning bid electricity. In addition，the proposed acceleration strategy can improve the computational efficiency by an average of 1.91 times while ensuring feasibility and accuracy （with an average relative error of only 0.4% compared with the optimal solution in multiple examples）. [Conclusions] This translation accurately reflects the technical meaning and structure of the original Chinese sentence while being fluent and appropriate for an academic context.

[Objective] To address the challenges of accurate monitoring and accounting of carbon flows in current building-level energy system carbon emission indices and optimization analysis，this paper proposes a modeling and analysis method for carbon flows in zero-carbon power supply stations. [Methods] First，the connection principles between the load of the power supply station’s load and the upstream power grid，as well as the access method for the DC load of power supply stations，are defined，establishing typical application scenarios for zero-carbon power supply stations. Next，based on the basic network structure of power supply to the station via the transformer area + DC microgrid，a method for calculating the carbon potential at the building-level busbar is proposed. This method comprehensively accounts for factors such as power flow between busbars，renewable energy generation，and the charging and discharging states of energy storage equipment. A model is then developed for allocating and calculating system losses and load carbon flow rates in zero-carbon power supply stations. Based on the measured power at various points and the corresponding busbar carbon potential，carbon flow rates are allocated to system losses and loads according to established principles. The models consider both scenarios of insufficient and surplus renewable energy generation，as well as the impact of energy storage charging and discharging states on carbon potential calculations. Finally，a specific case is presented to demonstrate the detailed process of calculating and allocating the busbar carbon potential and load carbon flow rates in the power supply station. [Conclusions] The calculation results show that when there is insufficient new energy generation，the carbon potential of the AC busbar remains at a maximum of 0.451 kg/kWh； When there is an excess of new energy generation，the carbon potential of the AC busbar drops to a minimum of 0.042 kg/kWh. In addition，energy storage discharge is considered as a power source and has a significant impact on the carbon potential of the DC busbar. [Conclusions] This paper provides theoretical and methodological support for carbon flow analysis in zero-carbon power supply stations，contributing to the advancement of low-carbon operation and energy optimization management in future power supply service centers.

[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.