In order to deeply explore the role of virtual power plant in carbon emission reduction and realize the effective operation of low carbon economy， a low carbon economic dispatch model of virtual power plants considering carbon trading and demand response is proposed. Firstly， a model of the virtual power plant participating in the carbon trading market is constructed to restrict its carbon emissions. Secondly， according to the characteristics of load demand response， price demand response model and alternative demand response model are established respectively. Finally， a low carbon economic dispatch model is designed to minimize the total operating cost of the virtual power plant. Through the comparative analysis of the results of the four scenarios， the effectiveness of the model is verified. In addition， the influence of carbon trading price and demand response parameters on system operation is investigated. The results show that considering carbon trading and demand response simultaneously can not only significantly reduce the total operating cost of the system， but also reduce the actual carbon emissions. The total operating cost of the system is positively correlated with the carbon trading price， while the actual carbon emissions are negatively correlated with it. At the same time， the change of demand response parameters will also have a certain impact on the operating cost and carbon emissions. In the process of virtual power plant scheduling， the model takes into account the economy and low carbon of system operation， realizes the effect of “peak clipping and valley filling”， and improves the flexibility of system operation.

In response to the “dual carbon” strategic goals， the interactive linkage of tiered carbon trading， green certificate trading， and demand response holds significant importance. Their interaction can reduce carbon emissions and operational costs in parks. Firstly， a mathematical model for green certificate-tiered carbon trading is constructed. Secondly， a demand response mechanism is incorporated to guide user electricity behavior， promote the integration of renewable energy， and reduce system operating costs. Then， the total system operating cost and total carbon emissions are set as the objective functions for multi-objective optimization scheduling. The model is solved to obtain a Pareto solution set， and the technique for order preference by similarity to ideal solution （TOPSIS） combined with grey relational analysis is employed to determine the ideal solution from the Pareto set. Finally， multiple scenarios are configured for comparative analysis， verifying the practicality and effectiveness of the proposed model.

Aiming at the problems of single energy product types， high pollutant gas emissions and poor economic efficiency in traditional integrated energy systems， a configuration of electric-thermal combined supply integrated energy system with power to X （P2X） technologies such as power to hydrogen （P2H）， hydrogen to gas （H2G） and hydrogen to ammonia （H2A） is proposed. Firstly， in terms of system modelling， a power to hydrogen/ gas/ ammonia （P2H/G/A） coupled system is constructed by introducing technologies such as carbon capture， utilization and storage （CCUS） and oxygen enriched/ammonia mixed combustion in thermal power units. Secondly， in the low-carbon economic transformation of the power system， a comprehensive energy system objective function is constructed， which considers the carbon reduction effects and improved heating economic benefits of the oxygen enriched combustion- H2G coupling model， as well as the reduction of coal consumption costs and coal-related carbon emissions by the H2A- ammonia mixed combustion coupling model. Finally， an example is constructed based on a demonstration site in Inner Mongolia， the economic benefits and carbon reduction effects of different energy conversion technologies are compared and analyzed. The conclusion shows that the proposed integrated system can significantly optimize the energy structure and achieve multi-energy cooperative low-carbon economic operation. Compared with traditional integrated energy systems， the economic cost is reduced by 7.5 × 10⁵ Yuan （19.5%） and the environmental cost is reduced by 5.0 × 10⁵ Yuan （11.5%）.

As the scale of livestock breeding continues to improve， dairy farming is developing in a direction characterized by specialization， intensification and standardization. In light of the “double carbon” objective， the energy supply paradigm of advanced dairy farming has also undergone a transition towards green energy. In order to address the issue of efficient energy utilization and reduction of electricity production costs in the context of large-scale dairy farming operations， an optimal scheduling method for energy systems in such farms is proposed， and this method is based on source-load-storage coordination and interaction. This method classifies and models the primary production energy-using equipment according to the demand and response characteristics of dairy farm production electricity， establishing an optimal scheduling model of a large-scale dairy farm energy system that considers the cost of electricity. In the context of time-of-use electricity pricing， this study proposes an optimal scheduling strategy for dynamic adjustment of the source-load-storage system. Based on the actual production conditions of a large-scale dairy farm， a case study is conducted， comparing the power consumption effects before and after the implementation of the optimal scheduling method. The results of the case study demonstrate that the proposed optimal scheduling strategy can effectively reduce the daily electricity costs of the large-scale dairy farm while meeting production demands， thereby maximizing the economic benefits of electricity in dairy farms.

In low-voltage distribution networks with large-scale integration of distributed photovoltaic （PV） systems， existing residual current devices （RCDs） cannot distinguish between abnormal PV leakage currents and electric shock currents in residual current circuits， leading to frequent misoperations. This poses risks to electrical safety and power supply reliability. To solve this problem， this study proposes a leakage fault identification method based on support vector machine （SVM） and an electric shock current detection method based on extreme gradient boosting （XGBoost）. Firstly， variational mode decomposition （VMD） is used to extract components of residual current signals under different leakage scenarios， establishing a fault feature dataset. Then， using these features as input， an SVM model optimized by the sparrow search algorithm （SSA） is developed to identify leakage fault types. For cases where residual currents fail to reflect real electric shock conditions， an XGBoost regression model optimized by grid search and cross-validation （GSCV） is built to accurately extract electric shock currents from residual currents. Test results show that compared to standard SVM and kernel extreme learning machine （KELM） models， the SSA-SVM model achieves the highest leakage fault identification accuracy， with an average of 99.28%. The GSCV-XGBoost model accurately fits the extracted electric shock currents to real values. This work provides a theoretical basis for developing new RCDs with leakage fault identification and electric shock current detection capabilities.

The integration of high proportion of new energy into distribution networks poses significant challenges for voltage control of distribution network. Traditional voltage control methods cannot ensure satisfactory control performance in scenarios with high penetration of new energy. Additionally， traditional voltage control relies on mechanical voltage regulation equipment， and frequent operation of these equipment significantly affects their lifespan. To address these issues， this paper proposes a multilayer voltage control method based on model predictive control (MPC). In the proposed method， mechanical voltage regulation equipment， such as transformers and shunt capacitors， is controlled in the upper layer with a longer control period， while active and reactive power outputs of distributed generations are rapidly adjusted in the lower layer with a shorter timescale. The objective of the upper-layer control is to reduce the number of operation of mechanical voltage regulation equipment， while the lower-layer control aims to minimize network losses and active power curtailment， ensuring the economic operation of the distribution network. The proposed method considers both the current and future states of the distribution network， ensuring that voltage operates within allowed limits under high penetration of new energy. Simulation results show that the proposed method can effectively address the voltage violation issues introduced by the high penetration of new energy in the distribution network， decreasing the system voltage mean square error from 3.9% to 0.97%. Additionally， the proposed method significantly reduces operation number of mechanical voltage regulation equipment， which is only 40% and 16.4% of those under traditional voltage control methods， respectively.

To reduce the carbon emissions of the park's integrated energy system， an integrated energy system scheme based on electric energy substitution technology is designed according to the park's energy demand and load characteristics. A full-working condition simulation model is also established. Firstly， the non-dominated sorting genetic algorithm （NSGA） and Gurobi solver are employed to address the multi-objective cooperative optimization problem. Subsequently， the entropy weight-solution distance method is utilized for comprehensive evaluation and decision-making， thereby determining the optimal system capacity allocation and operational strategy. Case analysis demonstrates that the proposed scheme decreases the park's carbon emission intensity by 77%， satisfying the requirements for a near-zero carbon park. Additionally， the annual net energy cost is reduced by 61.2%， while the energy self-sufficiency rate is increased to 71.3%. The optimized integrated energy system significantly enhances energy efficiency and security， reduces carbon emissions， and strengthens the park's energy independence.

Gas turbine units will play an important supporting and regulating role in the new power system for a long time. Conducting precise monitoring of carbon emissions from gas turbine units can not only optimize the operation and management of these units but also support their participation in the carbon market trading. Based on the common carbon emission measurement methods （monitoring method and emission factor method）， a digital monitoring method based on machine learning and online monitoring and an online calculation method based on supervisory information system （SIS） online data and emission factors of power plants are proposed. Taking a gas unit as the research object， CO₂ emission are obtained through four measurement methods （manual accounting based on guidelines， online accounting， online monitoring based on cold and dry method and dilution method， and digital monitoring） during the statistical period. The accuracy of data from different monitoring methods is compared， and the conclusions are as follows： Carbon emission online accounting based on SIS online data and emission factor method is comparable with manual accounting CO₂ emission amount， with an error rate of less than 1%. They can be used as references to achieve internal data verification within the factory； Both dilution and cold-dry online monitoring technologies can obtain accurate carbon emission volumes， but dilution need regular calibration to maintain measurement accuracy； A digital monitoring model established by a large number of operational data of the unit achievs real-time prediction of carbon emissions with high accuracy. When online monitoring data miss， it can provide guidance for unit operation and is expected to be promoted to similar types of units.

Source-load uncertainty is one of the typical characteristics of new power systems， so it is necessary to calculate the probabilistic tidal currents of power grids considering source-load variations. Aiming at the blindness of stochastic response surface method （SRSM） in constructing polynomial chaotic expansion by random sampling to generate samples， this paper adopts stochastic reduced order method （SROM） to select important samples in order to improve the accuracy of calculation results. The light intensity， wind speed parameter and load are taken as random input variables， the source-load probability model is established and its correlation is processed， and the polynomial chaotic expansion of probabilistic tidal current is generated by using SROM to select the important samples of the three-dimensional input variables； the probabilistic tidal current computation method of the electric power system based on SROM-SRSM is proposed and the detailed computation process is given； the improved IEEE 33-node system is used as an example to the simulation time and accuracy under different polynomial orders are compared； the 3rd order polynomial chaotic expansion is used as the basis of probabilistic tidal current calculation； the results of probabilistic tidal current calculation under different calculation methods are compared and the probability distribution of tidal current state variables is obtained. The results show that compared with the traditional Monte Carlo method， the SROM-SRSM proposed in this paper reduces the computation time， and the accuracy of the probabilistic tidal current calculation results is improved by adopting the stochastic descending order method to optimize the samples.

Accurate power forecasting for distributed photovoltaic (PV) power plants is essential to address output uncertainty. Distributed PV is characterized by a large number and geographical distribution， if a power prediction system is configured for each distributed PV plant， it will bring high operating costs. For this reason， a short-term power prediction method for distributed PV clusters based on dynamic clustering of multi-source forecasts is proposed. Firstly， the local public weather forecast information of the forecast day is digitally encoded， and the encoded information is fused with the numerical weather prediction (NWP) data of the region through an improved self-encoder for feature extraction to achieve the fusion of multi-source forecast data； Secondly， the fused features of the multi-source forecast data of the forecast day are taken as the clustering features， and self-organizing mapping (SOM) network clustering is utilized to realize the dynamic division of the clusters； Finally， the clusters are predicted by the 1D convolutional neural network (1DCNN)， and the cluster prediction results are accumulated to achieve the power prediction of regional distributed photovoltaic. The results show that the proposed method can obtain more accurate and reliable prediction.

With the widespread integration of electric vehicles as flexible resources on the user side and distributed photovoltaic generation units into the distribution network， serious issues such as voltage violations and line flow congestion may arise. These problems can severely impact the normal and stable operation of the distribution system， posing significant challenges for distribution system operators （DSOs）. To address this， this paper proposes a user-side electric vehicle regulation and local flexibility market clearing method for congestion management in distribution networks. Firstly， the operational mechanism of the local flexibility market（LFM） is established， and a flexibility bidding strategy model for flexibility aggregators is constructed to bid for flexibility based on the flexibility demand published by the LFM. Secondly， based on the alternating direction method of multipliers（ADMM） distributed clearing algorithm， the clearing of the LFM is ensured without disclosing the privacy information of DSOs and user-side flexible resources to the LFM operator. Finally， through case studies， the effectiveness of the proposed LFM mechanism and distributed clearing algorithm is verified， demonstrating that the proposed method can fully utilize the flexibility of user-side electric vehicles for congestion management in distribution networks.

In order to solve the problem that a single energy storage form of AC/DC hybrid microgrid cannot meet the power coordination of all subgrids， a multi-mode power coordination control strategy considering the changes of interlinking interface converter (IIC) and hybrid energy storage state of charge (SOC) is proposed. According to the subgrid normalized voltage and frequency indexes， the microgrid operation domain is divided， combined with the power state of each subgrid and the hybrid energy storage SOC， the IIC and the energy storage bidirectional interface converter (BIC) cooperate in power transmission under different operating conditions， to realize the overall power balance of the system as well as to control the storage SOC within a reasonable range， which improves the stability and reliability of the system. At the same time， the hybrid energy storage formed by supercapacitor and battery can not only give full play to the advantage of rapid response of supercapacitor， but also reduce the impact of overshoot and overdischarge on the battery. Finally， the inter-network power mutual aid scenarios with different operating modes are designed in the PSCAD environment to verify the effectiveness of the multi-mode power coordination control strategy.

A microgrid model is established to address the optimization and scheduling of microgrid in the context of new energy generation access and demand response. The objective function is constructed to consider the user's electricity discomfort caused by demand response and the operating cost of the system， and the user's transferable load is adjusted. Based on the randomness and volatility of wind and solar power output， the fuzzy K-means algorithm is used to cluster the wind and solar power output data and obtain typical wind and solar power output curves. Next， this paper improves the Harris hawks optimization (HHO) algorithm to address the issues of uneven population distribution and susceptibility to local optima. Firstly， Tent mapping is introduced in the initialization stage of the population to make the initial population coverage more comprehensive and avoid falling into local optima in the early stage. Then， Levy flight function is introduced in the search stage to enhance the global search ability of the algorithm. Finally， improved HHO (IHHO) algorithm is applied to optimization and compared with classical algorithms. The final results validate the effectiveness of the proposed strategy and the superiority of the IHHO algorithm.

As the key component of the DC microgrids， the performance of high-power bidirectional isolated DC-DC converters has a significant impact on the microgrid operation. When the microgrid starts and recovers after fault occurrence， the isolated DC-DC converter often generates a huge inrush current， which brings a great challenge to the system reliability. To address this issue， this paper presents a soft start method for a three-phase dual active bridge converter by combining the Buck mode with phase-shift control， which can greatly reduce the start-up inrush current. Besides， the output voltage regulator is also designed with the load current feedforward function， and the improved instantaneous current control is employed in the transient state， so that the phase shift angle will change smoothly. The simulation results finally confirm that this method can greatly reduce the inrush current and improve the dynamic performance in the black startup.

With the continuous growth of the proportion of renewable energy and the increasingly significant peak-valley difference in the load center grid， the development and utilization of distributed resources has become a research hotspot， which promotes the emergence of new entities such as producers and consumers and load aggregators. In view of the different optimization objectives of each stakeholder， this paper constructed a bi-level optimization model with the load aggregator as the electricity seller to participate in the electricity market. Firstly， the demand response mechanism of producers was introduced to form the master-slave game framework， and Karush-Kuhn tucker (KKT) conditions were used to integrate the lower level goals and constraints of the two-level model to the upper level to achieve a unified solution. Secondly， the conditional value at risk (CVaR) method was introduced to quantify the risk impact of electricity price uncertainty on the power purchasing strategy of load aggregators. Finally， the empirical example analysis shows that the mechanism can effectively encourage the user side adjustable resources to participate in the flexibility adjustment of the system， and promote the win-win cooperation pattern between the load aggregator and the producer and consumer.

Hydrogen production from new energy is the most promising hydrogen production method under the targets of carbon peak and carbon neutrality. Aiming at the problem that the off-grid new energy hybrid hydrogen production system lacks a cooperative control strategy， this paper proposes a multi-level power replacement strategy based on the expected operation interval of the proton exchange membrane （PEM） electrolyzer. When the power command on the hydrogen production changes， the PEM electrolyzer first makes rapid adjustments to respond to the change. Then， a power replacement strategy is formulated in combination with the expected operation interval of the PEM electrolyzer. By utilizing the large-capacity support capability of the alkaline electrolyzer， the power outside the expected range of the PEM electrolyzer is gradually transferred to the alkaline electrolyzer， which fully exploits the multi-type and multi-time-scale response capabilities. Finally， the strategy proposed in this paper is tested on the self-developed integrated simulation platform for new energy hydrogen production. The operation results show that the strategy proposed in this paper can fully exert the multi-time scale response capabilities of the two types of electrolyzers， not only newly improves the response speed of the power command but also ensures the safety of the hydrogen production system. Moreover， it can meet the stability requirements of off-grid hydrogen production using new energy， and thus has high practical value.

Aiming at the challenges of insufficient scheduling flexibility and rising operational costs in multi-park electric-thermal systems under large-scale renewable energy integration，this paper proposes an optimal scheduling model for tiered dual-time-scale distributed electric-thermal system based on edge computing. Firstly，a three-tier collaborative architecture comprising a physical equipment layer，edge computing layer，and cloud layer is constructed. Edge computing facilitates rapid data processing and distributed decision-making among parks. Secondly，the improved analytical target cascading method is employed with a dual-time-scale strategy： the lower layer optimizes electrical energy interactions at a 5 min granularity，while the upper layer coordinates thermal energy interactions at a 1 h granularity. The augmented Lagrangian method is integrated to decouple and iteratively solve multi-time-scale optimization problems. Finally，a benefit redistribution mechanism based on energy contribution degrees is designed，utilizing an asymmetric mapping function to quantify each park’s contributions to electric-thermal exchanges and renewable energy consumption，ensuring equitable profit distribution. Case studies demonstrate that the proposed model reduces comprehensive operational costs by 34.46% compared to conventional methods，significantly improves renewable energy consumption rates，and achieves convergence within eight iterations. The findings confirm that the integration of edge computing and dual-time-scale strategies effectively addresses spatiotemporal disparities in energy flows，providing theoretical and practical insights for coordinated optimization in multi-energy-coupled systems.

In recent years，typhoons have occurred frequently in coastal cities，and the vulnerability of distribution network lines and loads affected by typhoons has been increasing day by day. Therefore，an optimal scheduling model of mobile emergency power supply vehicle based on hierarchical sequence method was proposed. Firstly，the failure rate model under extreme disaster conditions was constructed，and the Monte Carlo method was used to determine the line vulnerability model. At the same time，the synergy of multiple flexible resources such as distributed power sources，energy storage systems and mobile emergency power vehicles under different spatial and temporal scales was considered to formulate a reliable distribution network resilience assessment model combining islanding and reconfiguration. Finally，the original nonlinear problem was convexized into a standard mixed integer second-order cone problem that was easy to solve by the second-order cone relaxation technique. In this study，the hierarchical sequential solution algorithm is used to take the supply rate of important loads as the main goal，while reducing the overall load loss of the distribution network as much as possible as a sub-goal. The effectiveness of the strategy is verified by the example results. Compared with other scheduling methods，the accuracy of the algorithm are further proved.

Integrating demand response into virtual power plants can enhance their flexibility and economic efficiency，but the inherent uncertainty of demand response poses challenges for scheduling and operation. Moreover，research on applying multiple demand response types in virtual power plants remains limited. To address these issues，this paper proposes an incentive-based demand response model incorporating a benefit coefficient that adjusts incentive compensation to reduce payouts under unsatisfactory demand response performance，as well as a replaceable-based demand response model considering customer satisfaction to better reflect its influence on replaceable demand response. Finally，a multi-energy virtual power plants model integrating multiple demand response types is established，considering three demand response strategies to achieve superior optimization. Case studies demonstrate that the incentive-based demand response model considering the benefit-coefficient can improve economic efficiency of the system，and the replaceable-based demand response model considering customer satisfaction can more accurately capture potential load variations to enhance demand response precision，and the coordinated participation of multiple demand response types in virtual power plant scheduling yields optimal overall performance.

As a new type of power system scheduling mode，virtual power plant （VPP） can realize the efficient utilization of new energy power by aggregating distributed energy resources. However，the traditional scheduling strategy aiming at economy has been unable to meet the needs of current low-carbon development. Based on this，this paper proposes a multi-objective optimization scheduling strategy for VPP considering both economy and carbon emissions. Firstly，a post-combustion carbon capture device was introduced into the VPP system，and combined with a flexible carbon trading strategy，a multi-objective optimization scheduling model considering economic cost and carbon emissions was constructed. Secondly，to obtain the optimal solution of the model，the augmented ε-constraint method was used to solve the Pareto solution set，and the entropy weight - technique for order preference by similarity to ideal solution （TOPSIS） method was used to evaluate the solution set. Finally，multi-case simulation experiments were carried out around different carbon capture and carbon trading strategies to compare and analyze the differences in scheduling results between the single-objective model only considering economy or low-carbon characteristics and the multi-objective model considering both economy and carbon emissions. The experimental results show that when the ladder carbon trading mechanism and the corresponding carbon capture operation mode are adopted，the carbon emissions of VPP reach the lowest level. In addition，compared with the single objective model，the multi-objective optimization strategy considering both economy and carbon emissions can effectively reduce carbon emissions and improve economic benefits.

Under the goal of “Dual Carbon” strategy，how to realize the flexible interaction between virtual power plants and promote the low-carbon operation of virtual power plants through carbon price is a problem worthy of study. Therefore，the peer to peer （P2P） trading model of virtual power plants is studied based on the carbon flow theory. Firstly，based on the carbon emission flow theory，the distribution characteristics of carbon flow in the network are analyzed，and natural gas is introduced to form a multi-energy network，and a low-carbon economic scheduling model is established. Secondly，considering the privacy of each virtual power plant participating in the trading，a quantitative index method including the trading information of the bid volume and quotation is proposed，and a P2P trading model based on comprehensive priority is established. At the same time，combined with the carbon emission responsibility of virtual power plants in the network，carbon pricing method is introduced into the P2P trading mechanism，and a comprehensive price model of “energy-carbon” based on carbon tax is established. Finally，an example is given to verify that the proposed method can not only reduce the operating cost of the virtual power plant，but also effectively reduce the carbon emission.

With the in-depth promotion of the strategy of "carbon peak and carbon neutrality"，virtual power plant （VPP） has shown significant advantages in integrating and dispatching new energy. It is one of the effective means to improve the operation economy of VPP to deeply tap the huge potential of new energy in the field of reactive power support. Firstly，a set of new energy reactive power capacity evaluation system considering active power output and inverter constraints was constructed. Secondly，in order to improve the solving speed of the reactive power optimization model，according to the nonlinear characteristics of active network loss and power flow constraints，an active network loss estimation method based on power flow iteration and a power flow linearization method based on variable space optimization selection were proposed. A VPP linear power flow reactive power optimization model considering the reactive power potential of new energy sources and based on variable space optimization selection was constructed. Finally，the improved IEEE 33-node active distribution system was taken as an example to verify the effectiveness of the proposed model. The results show that when VPP makes full use of the reactive power support ability of new energy，the system voltage deviation is reduced by 0.673 pu，and the operating cost is reduced by 1 254.9 yuan.

With the rapid advancement of renewable energy， wind power has emerged as a significant clean energy source. Consequently， the accuracy of wind power forecasting is essential for ensuring both the stability and economic efficiency of the power system. To address the challenges of nonlinearity and non-stationarity in wind power forecasting and to enhance prediction accuracy and reliability， this study proposes a novel wind power forecasting model based on variational mode decomposition （VMD）， K-means clustering analysis algorithm， and TimesNet deep learning model. Firstly， VMD is employed to decompose nonlinear and non-stationary time series signals into multiple intrinsic mode functions （IMF）， facilitating the analysis and extraction of trends and periodic components from wind speed and generation data. Secondly， K-means clustering algorithm is utilized to classify the obtained IMFs， thereby identifying characteristic patterns of fluctuations in wind power. This process effectively enhances the model’s ability to capture variations in power under different wind conditions. Finally， the results processed through clustering are inputted into the TimesNet deep learning model for prediction. Comparative experiments with various existing wind power forecasting models demonstrate that the proposed forecasting model significantly reduces errors in predicting wind power output.

Dynamic equivalent modeling of large-scale wind farms is the foundation for studying wind power grid integration，while the clustering-based equivalent model of wind farms cannot fit the dynamic output characteristics with high accuracy，and the poor generalization ability in its application is an inherent defect of clustering based model. Aiming at this problem，this paper proposes a wind farm equivalent modeling method based on particle swarm optimization-long short term memory neural network-error correction model （PSO-LSTM-ECM）. Firstly，K-means clustering algorithm and capacity weighting method are used to cluster wind turbines in wind farms，and a clustering equivalent model of the wind farms is constructed； Then，ECM is constructed based on the transient response errors of the detailed model and the clustering equivalent model，and the correction model is obtained through the LSTM neural network training optimized by PSO，and the output value of the network is compensated to the clustering equivalent model； Finally，a joint simulation is conducted on PSCAD and Matlab platforms to compare and analyze the detailed wind farm model，clustering equivalent model，and the model proposed in this paper. The result proves the effectiveness and superiority of the proposed model.

The gigawatt-hour level large-capacity energy storage station provides critical support for the absorption of renewable energy， as well as enhancing system safety and operational control capabilities. However， with the significant increase in the number of energy storage units and individual battery cells within gigawatt-hour level battery energy storage stations， the risk of battery inconsistency has also intensified. This poses a serious threat to the safe and efficient operation of these storage facilities. To address this issue， an analysis is conducted on how the consistency of individual batteries and energy storage units affects the charging and discharging capabilities of energy storage stations. A method for calculating and evaluating these capabilities is proposed. A two-tier power distribution model for energy storage stations is developed， taking into account both station-level and virtual subsystem-level considerations. At the station level， based on the state of health （SOH） of each battery unit， the energy storage station is divided into several virtual subsystems. The overall charging and discharging strategy for each subsystem is designed according to their average state of charge （SOC） consistency， allowing for accurate calculation of total power demand across subsystems. At the virtual subsystem level， a power distribution strategy is established with an optimization goal aimed at minimizing SOC variance among stored units. The proposed power distribution method can delay lifespan degradation in low SOH energy storage units while accelerating SOC consistency convergence among them， thereby improving overall utilization rates within the energy storage station. Through simulation experiments conducted alongside analyses from Qinghai’s Togruoge 270 MW/1.080 GW·h battery energy storage project， we evaluated our proposed power distribution and management strategies. Results indicate that this approach demonstrates considerable effectiveness in achieving battery consistency management.

The study explores the risk spillover effects among carbon markets， energy markets， and green finance markets， with a particular focus on the impact of extreme events on these markets. The aim is to provide insights for mitigating risks in the carbon market and promoting its healthy development. Based on an in-depth analysis of the risk spillover mechanisms among carbon， energy， and green finance markets， this research employs time-varying parameter vector autoregressive （TVP-VAR）-Diebold-Yilmaz（DY） and TVP-VAR-Baruník-Křehlík（BK） spillover index methods-and integrates them with a spillover network model. Utilizing historical data from 2018 to 2023 pertaining to China’s carbon market， energy market， and green finance market， this paper conducts an empirical investigation into the time-frequency risk spillover effects across these various markets. The results indicate that： （1） The spillover effects among the carbon-energy-green finance markets exhibit time variability and are susceptible to extreme sudden events. （2） Within different temporal domains and frequency cycles， both the green stock market and new energy market serve as primary sources of systemic risk transmission； conversely， while the carbon market acts as a recipient of risk in the short term， it transitions into a source of risk outflow over the long term. （3） The green stock market and new energy market constitute significant centers within the systemic spillover network.

The LCL filter is usually constructed by centrally deploying filter capacitors on the AC side of the megawatt multi-module converter. The stability of the system is affected by the communication delay between the control device and the power module and the number of power modules，and the traditional active damping design method is difficult to ensure stable operation. Therefore，a new design method of active damping was proposed. In this method，the equivalent active damping control model of the converter considering the communication transmission delay was established in the discrete domain，and the equivalent transfer function root locus method was introduced. The active damping feedback coefficient and quasi-resonant controller parameters were designed under different communication delays and power modules，and their influence on the selection and design of converter parameters was analyzed. The effectiveness of the proposed method was verified by simulation and experiment. The active damping design can quickly obtain the control parameters required for the stable operation of the high-power converter，thereby improving the efficiency of equipment development.

With the continuous advancement in the construction of new power systems， the issues related to insufficient new energy consumption capacity in Northwest China and electricity supply shortages in eastern regions have become increasingly prominent. Therefore， transporting surplus new energy from Northwest China to developed eastern regions has emerged as an effective means to achieve inter-regional energy balance. In response to the demand for long-distance and large-capacity new energy delivery， this paper proposes a collaborative approach utilizing both electricity and hydrogen for new energy transmission. First， we establish a framework for a new energy delivery system that encompasses both electric power transmission and hydrogen transport channels. Next， considering cost and loss factors associated with equipment at the source end， transmission end， and load end during the new energy delivery process， while imposing constraints on supply-demand balance， losses incurred during transportation capacity limits as well as delivery time， we formulate an optimization model aimed at minimizing total costs and losses within this collaborative planning context. Finally， we conduct simulation analyses using four cases from Gansu Province’s new energy delivery projects. The results indicate that implementing this collaborative planning scheme reduces overall system costs by approximately 5.72% to 7.74%， thereby validating the effectiveness and rationality of the proposed model.

The installed new energy capacity of new energy bases is usually equal to or slightly smaller than the capacity of collection engineering. Considering the randomness，fluctuation and intermittency of new energy units’ output，the capacity of collection engineering has not been fully utilized in most of the time. The potential scale of new energy that can be accepted by collection engineering could be further excavated from the perspective of national economy and rational new energy utilization rate. This paper studies reasonable accepted new energy scale of new energy bases in various voltage levels based on optimal levelized cost of electricity considering the characteristics of wind and solar power stations and regulation capability of power grid. The research of collection scene in north China region shows that the new method proposed in this paper can accept 50% larger capacity of wind and photovoltaic power and effectively reduces the levelized cost of electricity for new energy bases while meeting the requirements of new energy utilization rate. In addition，the acceptable new energy capacity of complementary collection stations can be significantly larger than pure wind or photovoltaic collection stations. Research in this paper can provide reference for acceptable new energy potential scale of collection engineering in subsequent new energy bases.