[Objective] The increasing penetration rate of renewable energy has decreased the inertia of power systems. The traditional fixed-parameter frequency control strategy makes it difficult to effectively support system frequency stability requirements. Frequency stability faces severe challenges， especially in renewable energy sending systems. [Methods] To enhance the frequency support performance of VSC-HVDC for sending-end systems with renewable energy， an adaptive dual droop control strategy based on changes in system frequency and DC voltage is proposed. First， a model of renewable energy transmitted by a VSC-HVDC system was established. For frequency droop control， a frequency-adaptive coefficient was designed based on the logistic function of frequency deviation to enable the system to dynamically adjust the droop coefficient and respond flexibly to frequency changes. For the DC voltage droop control， via the coupling relationship between DC voltage and active power using virtual inertia technology， the DC adaptive coefficient was designed as the adaptive virtual inertia coefficient based on the frequency deviation and the rate of change of frequency to enhance the system’s ability to suppress frequency fluctuations. Finally， a simulation model based on the PSCAD/EMTDC platform was established， and a comparison study with the traditional method in different scenarios was performed. [Results] The results show that the proposed strategy reduces the maximum frequency deviation by more than 10% in various scenarios while increasing the maximum utilization of the VSC-HVDC system by over 10%. [Conclusions] Compared with traditional methods， the proposed strategy can significantly reduce the maximum frequency deviation and effectively improve the frequency support performance of VSC-HVDC systems.

[Objective] As renewable power bases gradually expand from load centers to remote areas such as deep offshore， deserts， and barren lands， using grid-forming voltage source converter-based high voltage direct current （VSC-HVDC） transmission technology is a promising solution for integrating renewable power to the grid. Considering power quality and fault current limitations， current grid-forming control usually adopts a dual closed-loop structure of AC voltage and AC current. This control structure relies on the voltage-current integral relationship of the AC-side filter capacitor and grid current feedforward to achieve stable control of the AC voltage. However， the AC side of the modular multilevel converter （MMC） usually lacks a centralized filter capacitor， and obtaining AC-side current feedforward to decouple from the grid is difficult， leading to degraded AC voltage control performance. [Methods] To solve this problem， this study proposes a novel dual-closed-loop AC voltage-control strategy for grid-forming VSC-HVDC converters. The strategy does not rely on the AC capacitance but adjusts the AC voltage of the PCC by controlling the voltage drop on the grid impedance. [Results] Simulation verification based on PSCAD/EMTDC showed that， under the proposed control mode， the grid-forming MMC exhibited good operational stability and active/reactive power dynamic control performance under different grid conditions. [Conclusions] The proposed control strategy can effectively improve the AC voltage-AC current dual-closed-loop control performance， operational stability， and grid adaptability of grid-forming MMC.

[Objective] With the increase in the proportion of new energy connected to the grid， issues with power system stability associated with weak grids are becoming increasingly prominent. Static var compensator （SVC）， an important device for improving the dynamic response of power systems， may trigger sub-synchronous and super-synchronous interaction （S²SI） in weak power grids， causing system oscillation. This paper aims to study the mechanism of sub- and super-synchronous interactions induced by SVC in a weak current network and proposes an effective oscillation suppression strategy. [Methods] First， a frequency-coupled impedance model （FCIM） was used to analyze the frequency coupling characteristics between the SVC and weak grid， revealing a strong coupling relationship between the sub-synchronous and super-synchronous frequencies. Second， based on the stability criterion of impedance crossing， the influence of the controller parameters on the oscillation mode is studied， and a supplementary sub-synchronous damping controller （SSDC） is proposed. Finally， an electromagnetic transient time-domain simulation was performed using the PSCAD/EMTDC platform to verify the correctness of the theoretical analysis and effectiveness of the control strategy. [Results] The results show that SVC can cause S²SI in a weak power network， with the oscillation frequency closely related to the controller parameters. The oscillation phenomenon can be effectively suppressed by optimizing the controller parameters. The simulation results show that the proposed SSDC control strategy can significantly reduce the amplitude of sub-super-synchronous oscillation and improve system stability. [Conclusions] The results reveal the mechanism of S²SI triggered by SVCs in a weak power grid at the frequency-domain impedance level. The proposed control strategy improves the oscillation suppression effect while ensuring the regulation rate of the SVC， thus highlighting significant engineering application value.

[Objective] Constructing renewable energy transmission systems is a key approach for addressing the uneven spatial distribution of resources. However， integrating a high proportion of renewable energy poses significant challenges to the frequency security and control of power systems at the sending-end base. [Methods] To address these challenges， this study proposes a frequency control and parameter optimization method tailored for the coordinated operation of heterogeneous multi-source systems in a new high-proportion energy-sending-end base. First， for active frequency support in a heterogeneous multi-source sending-end base， a coupled model of system parameters and security indicators was developed for parameter optimization. Second， a multi-objective model-predictive automatic generation control method was designed， considering system frequency deviations， generation costs， and emission costs. This method enables the rational allocation of frequency regulation commands， reduces the overall system costs， and enhances new energy utilization. [Results] The theoretical analysis and simulation results demonstrated that the proposed method effectively stabilized the system frequency response indicators within a safe range. Moreover， compared with traditional frequency control methods， the proposed strategy exhibits significant advantages in terms of frequency deviation suppression， dynamic response speed， and economic efficiency. [Conclusions] The proposed collaborative control and parameter optimization approach offers an innovative solution for frequency stability control in high-penetration renewable energy sending-end systems. While ensuring the secure operation of the system， this approach also considers economic efficiency and frequency regulation performance， thus providing theoretical support for the design and optimization of the energy-sending-end system.

[Objective] Power grids face challenges in managing steady-state automatic and collaborative prevention and control measures， particularly owing to issues such as DC blocking， large unit tripping， and reliance on manual scheduling for the rapid response from resources including pumped storage and electrochemical energy storage. These have resulted in challenges in ensuring the frequency safety， regional control deviations， and minimizing the risk of load shedding. To address these issues， an intelligent processing method is proposed for high-power shortage in receiving-end power grids comprising multiple DC inputs. [Methods] This method automatically detects power grid shortage faults， calculates the total regulation demand， and allocates the demand based on prioritized criteria， including resource availability， operating conditions， and unit capabilities， considering the characteristics of the control object and various safety constraints. Control instructions are generated by employing strategies that incorporate available capacity statistics， shortage startup judgment， regulation demand allocation， control of pumped and energy storage systems， safety constraints， and control recovery. This approach facilitates the remote activation of multiple types of fast regulation resources， enabling rapid stabilization of the grid during steady-state power shortage. [Results] Using this method， the first domestic high-power-shortage intelligent processing system for power grids was developed and successfully implemented in a large-scale DC receiving-end provincial power grid. The control effect and reliability satisfied the actual usage requirements. [Conclusions] The application of the proposed methodology in handling steady-state high-power shortages in power grids represents a significant shift from manual telephone scheduling to automatic intelligent processing. This transition reduces decision-making time from minutes to seconds， thereby substantially improving fault-handling efficiency， and ensuring the stability of the power grid frequency and interconnection line power. Operational testing demonstrated the practicality and high reliability of the system， underscoring its potential for broader adoption and implementation in other power grid systems.

[Objective] Grid-forming control （GFM） energy storage converters based on virtual synchronous generator （VSG） control can effectively provide voltage and frequency support for the system； however， current research has rarely considered the support capability of VSG energy storage converters in the scenario of new energy devices connected to weak power grids. [Methods] This study analyzed the stability laws between photovoltaic systems， VSG energy storage systems， and weak electricity grids. First， small signal models for photovoltaic and VSG energy storage systems under weak AC grids were established. Second， the eigenvalue analysis method was used to comprehensively analyze the impact of changes in photovoltaic output and SCR on the system's eigenvalues. Third， we identified the high-sensitivity control parameters that affect system stability by studying the oscillation modes and participation factors. Finally， a method for adjusting control parameters to improve system stability was proposed by studying the root locus of the dominant mode at the critical steady state. Finally， the accuracy of the stability impact law of the system was verified through a MATLAB/Simulink simulation under different operating conditions. The time-domain simulation model constructed using MATLAB/Simulink verified the effectiveness of the theoretical analysis. [Results] The simulation results showed that the small signal model established in this paper can accurately identify the control parameters that significantly impact system stability under various operating conditions， such as photovoltaic output fluctuations （0.5~2.0 p.u.） and changes in grid short-circuit ratio （SCR=0.6~2.0）. By implementing the proposed parameter adjustment scheme， the photovoltaic output of the system increased from 1.2 p.u. to 2.0 p.u. and remained stable at SCR=0.6. [Conclusions] When the output of the photovoltaic system changes， the q-axis pi parameters of the VSG energy storage system and the photovoltaic system have high sensitivity； when SCR changes， the internal and external q-axis pi parameters of the VSG energy storage system have high sensitivity. Orderly adjustment of the control parameters with different sensitivities improved the static stability of the system， thereby providing an effective solution for improving the stability of a high proportion of new energy grids.

[Objective] Driven by the “dual carbon” goal and the ongoing development of new energy systems， renewable sources and novel loads—such as distributed generation， electric vehicles， and controllable user-side resources—have expanded rapidly， reaching unprecedented levels. The fluctuation and randomness of these new energy sources and loads pose significant challenges for the safe operation and flexible regulation of distribution networks. Consequently， there is an urgent need to modernize and enhance the intelligence of the distribution networks. [Methods] This study analyzes the characteristics and underlying principles of modern smart distribution networks and outlines the intelligence requirements and development priorities for these networks. Given the diversity in distribution network construction， the key technologies for upgrading conventional distribution networks to modern and intelligent systems are explored across five representative scenarios： coordinated microgrid development； efficient integration of charging facilities； optimal utilization of new energy storage technologies； modernization of urban and rural distribution networks； and seamless coordination of generation， network， load， and storage. Based on these connotations， characteristics， and development priorities of modern smart distribution networks， the technical development trajectory and construction priorities for future distribution networks are forecasted. [Results] It is imperative to upgrade the distribution network to a “modern smart distribution network” via high-quality development and construction. Such upgrades will significantly enhance the network’s capability to ensure reliable power supply， support clean energy consumption， accommodate diverse loads， and optimize resource allocation. [Conclusions] As an critical element of the new power system， the modern intelligent distribution network integrates all fundamental components of the new power system. It will continue to facilitate the seamless integration of emerging technologies and foster the development of novel business structures and operational models.

[Objective] This study proposes a low-carbon economic operation strategy for industrial parks， aiming to accurately account for carbon emissions across all operational processes in distribution networks and clarify carbon responsibility sharing among stakeholders， thereby achieving low-carbon economic operation. [Methods] The proposed strategy involved first calculating the carbon flow distribution within the distribution network at each time interval， applying the proportional-sharing principle based on optimal economic dispatch results， thereby enabling the dynamic evaluation of nodal carbon potential at the park level. Subsequently， an extended carbon emissions trading model was developed incorporating indirect carbon emissions within the existing carbon trading market framework. Finally， smart charging modeling for electric vehicles （EVs） and conventional loads within the park was conducted. A multi-type load demand response method in electricity-carbon coupled markets is proposed， which integrates time-of-use electricity pricing with nodal carbon potential signals. [Results] Case studies conducted using a modified IEEE 33-node system and typical park models on the GAMS platform demonstrate that the proposed strategy yielded substantial carbon reduction benefits. Specifically， Park 1 exhibited only a 1.4% increase in total costs compared with conventional economic dispatch modes， while achieving a 7.1% reduction in indirect carbon emissions during the dispatch period. Furthermore， EV smart charging demonstrates lower dispatch costs and higher flexibility than that of conventional adjustable loads， functioning as generalized energy storage and exhibiting substantial carbon reduction potential. [Conclusions] The proposed strategy offers a dynamic approach to reflect the carbon emission responsibilities of park operations and optimizes load interactions based on carbon intensity signals and time-of-use pricing. It facilitates peak shaving and valley filling， reduces indirect carbon emissions， and ensures low-carbon and cost-effective system operation.

[Objective] The integration of a high proportion of distributed generation into the distribution system leads to frequent and significant voltage fluctuations， which negatively affect the accuracy of voltage measurement data and increase the difficulty of parameter identification based on full measurement data of the distribution system. [Methods] This paper proposes a solution to the parameter identification challenge based on full measurement data in distribution systems by establishing an adaptive extended Kalman filtering （AEKF） model based on current measurements. Specifically， the state equations for the line parameters are derived by incorporating historical temperature data， whereas those for the transformer parameters are established using a first-order exponential smoothing method. By applying Kirchhoff's law， the current amplitude measurement equations for the lines and transformers are constructed. A voltage pseudomeasurement strategy is introduced to construct pseudomeasurement equations based on prior parameter estimates. An adaptive noise mechanism is designed in which the noise covariance matrix is dynamically estimated based on the differences in adjacent measurements， thereby enhancing the robustness of the algorithm against time-varying noise. [Results] The results showed that the relative mean identification errors for line resistance and reactance were 1.45% and 1.61%， respectively， a reduction of 61.2% and 61.7% compared with those of the traditional EKF method with errors of 3.7% and 4.2%. The maximum relative error decreased from 12.6% to 2.4%. The peak identification errors for transformer resistance and reactance were 5.2% and 5.53%， and the single iteration time was optimized from 2.2 to 1.04 s. [Conclusion] This method effectively addresses the impact of deficiencies in voltage measurement data quality on parameter identification by integrating current amplitude data with historical operational information. Pseudomeasurement modeling and adaptive noise estimation techniques work synergistically to enhance the stability and computational efficiency in complex power grid environments， helping address the challenges posed by the randomness and volatility of distributed generation and improving the fine-tuned control capabilities of the distribution system.

[Objective] The flexibility of the power control of grid-connected inverters in integrated photovoltaic storage power stations makes them a good resource for participating in distribution network harmonic control. [Methods] This study proposes a two-layer optimization model for a distribution network with an integrated photovoltaic storage power station considering harmonic control. The upper layer establishes the optimization model of the distribution network and integrated photovoltaic storage power station. The distribution network sets the harmonic compensation price to maximize revenue， thus guiding the integrated photovoltaic storage power station to optimize the multi-functional grid-connected inverter active grid-connected and harmonic control capacity. The lower layer establishes the optimization model of the distribution network and the demand user and the demand user according to the electricity price and the cost of electricity use of different harmonic control levels before developing a power purchase strategy to minimize the total electricity cost. Finally， an IEEE 14-node system was used as an example for analysis. [Results] The results show that the revenue of the integrated photovoltaic storage power station increased by 3 615.4 yuan after considering the harmonic control service， and the harmonic voltage distortion rate of each node was limited to less than 4%. [Conclusions] The results show that the proposed model can fully exploit the harmonic control potential of an integrated photovoltaic storage power station， reduce the harmonic voltage distortion rate of the distribution network， satisfy the differentiated power quality demand of the user， and improve the operational efficiency of each main body.

[Objective] With advancements in energy storage technology and increasing demand for resilient distribution networks， mobile energy storage （MES） has gained traction in enhancing the resilience of distribution networks. However， existing research primarily focuses on the post-disaster scheduling of MES， neglecting the critical impact of traffic conditions， thereby failing to maximize the emergency response capabilities of MES. [Methods] Therefore， this study proposes an MES scheduling strategy that integrates both pre- and post-disaster dynamic scheduling， accounting for the influence of transportation networks. A dynamic transportation network model was developed employing the road weight matrix derived from the Dijkstra algorithm in conjunction with a speed-flow model， to fully capture the impact of traffic flow status on the emergency response capabilities of the MES. Subsequently， a pre-disaster scheduling model of the MES was developed with the objective of minimizing expected power outage losses under random scenarios， thereby aiming to pre-schedule the MES to candidate connection points. Following the disaster， a dynamic optimization scheduling model of the MES was developed for determine the optimal dynamic scheduling scheme of the MES. [Results] Simulation results demonstrate that the proposed MES scheduling strategy significantly outperforms alternative strategies that does not incorporate pre-scheduling or transportation network impacts， particularly in terms of load loss cost and scheduling time. These results highlight the effectiveness and superiority of the proposed strategy. The proposed MES scheduling strategy， which integrates pre-disaster scheduling and dynamic post-disaster scheduling， significantly reduces the time required for the MES to assist in power restoration and minimizes power outage losses. Furthermore， this strategy fully accounts for traffic flow changes in the transportation network， optimizes the selection of MES scheduling paths， reduces the negative impact of traffic congestion， and further improves the scheduling efficiency of the MES. [Conclusions] In conclusion， the proposed MES scheduling strategy comprehensively integrates the influence of prescheduling and traffic flow， optimally exploits the emergency response potential of the MES， and enhances the resilience level of distribution networks.

[Objective] This study addresses the challenges faced by wind farms， exhibiting varying construction costs， in their participation in electricity markets. It seeks to facilitate a seamless transition from a fixed-price procurement model to a competitive spot market model for wind power while accounting for the inherent uncertainty of wind power generation. Consequently， designing a robust transition mechanism is essential for wind power participation in electricity markets. [Methods] A medium- and long-term contract mechanism is proposed to adjust wind farm revenue by modifying contract coverage. A bi-level programming is employed to model participation in both the medium- and long-term and spot markets. The upper-level problem is to determine the optimal medium- and long-term contract coverage of wind power， with the objective of minimizing government subsidy costs and improving the fairness index of unit generation profits across all types of wind farms. The lower-level problem is framed as a joint clearing model for energy and reserve markets， accounting for the uncertainty of wind power output， which is represented by typical scenarios. The bi-level model is transformed into a single-level optimization model using the Karush-Kuhn-Tucker condition substitution and the big M method. Additionally， the corresponding linearization methods are proposed to handle the product terms involving price， continuous variables， and absolute value terms in the upper objective， eventually transforming the single-level optimization model into a mixed-integer linear programming model. To enhance computational efficiency， the check-add method is applied to address the capacity constraints of the transmission lines when solving the bi-level programming model. A simulation analysis of a real 44-unit， 1560-bus system containing two wind farms was conducted to validate the effectiveness of the proposed method. [Results] The contract hedging effect enables the wind farm to mitigate the risk of spot price fluctuations. Within the decision cycle， the subsidy amount is reduced by 1.34 million yuan， and the unit profit gap narrows by 0.015 yuan/kWh. Additionally， decision-makers can effectively adjust the policy impact by tuning the weighting factors. [Conclusions] The results indicate that By determining the optimal contract coverage ratio， the proposed approach effectively reduced government subsidy costs and narrowed the per-unit profit gap between wind farms， achieving a smooth transition to market participation.

[Objective] This study addresses the emerging oscillation risks posed by power electronic converter-dominated control systems in large-scale integration of offshore wind power. Specifically， it aims to enhance the operational stability of AC transmission systems by focusing on sub/super-synchronous oscillation mitigation in offshore wind farms connected via AC transmission systems. To address the limitations of conventional mitigation measures， such as the necessity for extensive upgrades in wind turbine generator and insufficient parameter adaptability of system parameters， an adaptive oscillation mitigation strategy is proposed. [Methods] The proposed method adopts a “real-time measurement-identification-control” framework. The measured data provide observable input signals for identifying the system sub/super-synchronous oscillation modes， followed by the real-time oscillation characterization of oscillation dynamics. Based on this identification， the control parameters are dynamically tuned， and the control structures are modified to meet the specified objectives. To validate this method， a prototype controller was developed. An electromagnetic transient simulation model of the oscillations was developed in the Real-time Digital Simulator， and closed-loop testing was conducted by integrating it with the controller. [Results] Hardware-in-the-loop simulation results demonstrated that the proposed method accurately identifies oscillation frequency shifts， such as variations resulting from changes in turbine numbers， wind speeds， or AC line lengths， owing to system condition variations. The online-adjusted control parameters effectively mitigated oscillations， and the optimized damping control parameters achieved enhanced oscillation suppression. [Conclusions] Grid-side parallel VSC-based damping control provides a centralized and efficient solution for managing offshore wind farm oscillations， thereby obviating the need for the implementation of individual wind turbine generators. The prototype controller exhibited adaptive suppression capabilities for varying oscillation characteristics in hardware-in-the-loop simulations， thereby establishing a robust foundation for practical engineering applications. This approach demonstrates significant potential for addressing real-world oscillation challenges in high-penetration renewable energy grids.

[Objective] This study addresses the challenges posed by the increasing integration of high proportions of renewable energy on the safe and stable operation of power systems. The objective is to enhance reserve capacity during fault recovery process， thereby ensuring system frequency stability. A novel fault recovery model， incorporating frequency deviation under large-scale wind power integration， is proposed to optimize this process. [Methods] First， a power system frequency response model that incorporates large-scale wind power integration was developed. The frequency deviation was derived using the Laplace final value theorem， and power fluctuations were calculated. Second， to maximize the incorporation of generator regulation capacity， the minimum values of the power fluctuations， generator output boundary， and ramp rate were considered to determine the maximum adjustable reserve capacity of each unit. This ensures sufficient reserve capacity during fault recovery to address wind power fluctuations. Third， to address the multi-objective nonlinear nature of the fault recovery model， piecewise linearization and weighting methods were employed to solve the multi-objective function with load loss， generator operation cost， and reserve cost as the objectives. [Results] The model was validated using an IEEE 39-bus system. A comparison among conventional fault recovery （without considering frequency deviation）， reserve recovery （without considering frequency deviation）， and reserve recovery （considering frequency deviation） demonstrates that the proposed reserve recovery model enables more efficient reserve capacity allocation during fault recovery. Therefore， it ensures that the system frequency deviation remains within ± 0.2 Hz. Additionally， the model reduces system load loss by approximately 24.63% and the total cost by approximately 36.62% compared with traditional methods. [Conclusions] The proposed model significantly suppresses the frequency fluctuation before and after a fault， ensuring stable operation of the system frequency while achieving optimal economic benefits and reducing fault load loss.

[Objective] The growing concern over frequency stability is attributed to the decreasing overall inertia of power systems. To improve the analysis of frequency stability and performance of modern power systems， a frequency dynamic characteristic analysis method for ultralow frequency oscillations is proposed， in addition to a multimachine governor power system stabilizer （GPSS） parameter tuning method aimed at enhancing the frequency regulation performance. [Methods] The proposed method developed a frequency characteristic analysis model that integrates power system stability and frequency regulation performance from the frequency-domain perspective. Initially， a model was derived based on the rotor circuit， thereby demonstrating the properties of constant distribution vector elements and the approximate congruence of the speed response function matrix. Subsequently， leveraging these properties， the model was simplified to form a low-dimensional dynamic framework for analyzing ultralow-frequency oscillations. Finally， a parameter-tuning method for multimachine GPSS was developed based on this model. [Results] The proposed frequency dynamic analysis model was applied to practical power systems， and the GPSS mechanism for suppressing ultralow-frequency oscillations was revealed via vector margin analysis. The validity of the distribution vector and speed response function properties， as well as the overall model were validated using an IEEE 4-machine 11-node case study. The effectiveness of the GPSS parameter tuning method was validated via time-domain simulations and vector margin assessments for both a 10-machine 39-node system and an equivalent model of the Yunnan power grid. [Conclusions] The proposed frequency dynamic characteristic analysis method and its simplified model provide a theoretical foundation for the widely employed unified frequency models in engineering. This approach provides a novel tool for frequency stability analysis of power systems and is of great significance for advancing the frequency regulation capabilities of modern grids. Furthermore， the GPSS parameter-tuning method facilitates effective suppression of ultra-low-frequency oscillations， thereby enhancing the stability and performance of power systems.