[Objective] To enhance the flexibility and low-carbon performance of integrated energy systems （IES）， this study proposes an optimal scheduling strategy that accounts for various electric vehicle （EV） charging modes and supply-demand flexibility. [Methods] First， from the demand perspective， an ordered charging strategy is developed based on the dynamic state of charge. This applies to three charging modes： conventional slow charging， fast charging， and power exchange. Second， flexibility-enhancement strategies are further explored in coordination with integrated energy demand. On the supply side， the Kalina cycle （KC） is introduced as an improvement over the organic Rankine cycle. The KC enables thermoelectric decoupling and supports flexible， efficient output from the CHP unit. Finally， an IES low-carbon economic optimization model is constructed. It incorporates stepwise carbon trading， EV charging modes， demand response， and the KC， while considering carbon emission constraints. [Results] By comparing and analyzing multiple scenarios， the proposed strategy reduces the total system cost by 16.22%. It also improves the flexibility of IES supply and demand， enables orderly charging across various EV charging modes， and lowers both economic costs and carbon emissions. [Conclusions] The key innovations include sequential charging tailored to different EV charging modes and strategies to enhance supply and demand flexibility. These findings offer new insights into improving IES flexibility and can be integrated with other flexibility resources to further maximize system performance.

[Objective] Photovoltaic power forecasting is a key technology to improve the efficiency of solar energy utilization and reduce operating costs. However， the traditional model has the problem of insufficient time-trend learning ability and error accumulation in multistep photovoltaic power prediction， which limits the improvement of prediction accuracy. [Methods] This paper presents a multistep prediction model for photovoltaic power generation based on a temporal convolutional network （TCN） and DLinear combined model. First， it improves the complete ensemble empirical mode decomposition with adaptive noise and ICEEMDAN decomposes multivariate meteorological sequences to reveal their potential features and obtain multidimensional subsequences that make it easier to learn multiscale features. Second， the TCN is used to model the local time sequence information and mine short-time sequence features. Finally， DLinear decomposes the sequence into trend and residual components， learns multiscale features through linear networks， and directly outputs multistep （four-step prediction， 15 min per step） photovoltaic power prediction results. [Results] Experimental results show that each module of the proposed method can significantly improve the prediction performance of the model. Compared with ICEEMDAN-CNN-BiLSTM， Informer， and Autoformer， the normalized root mean square error （NRMSE） decreased by 22.455%， 6.139%， and 8.504% on average， respectively， with obvious advantages. [Conclusions] Through the improved ICEEMDAN decomposition and TCN-DLinear combined model， this study effectively solves the shortcomings of traditional methods in multistep prediction and significantly improves the accuracy and reliability of photovoltaic power prediction. The research results provide a new technical path for the accurate prediction of photovoltaic power generation， theoretical support for the efficient management and operation of solar power generation， and data support for the safe and stable operation of new power systems. In the future， the generalizability of this model under different meteorological conditions and geographical environments should be further explored to promote the development of solar power generation technology.

[Objective] Factors such as road networks， temperature， and electric vehicle （EV） type affect the spatial and temporal distribution of EV charging loads. To improve prediction accuracy， a spatiotemporal EV charging load prediction model is developed by integrating multiparty information. [Methods] By introducing the model of temperature and vehicle speed on energy consumption， the impact of the external environment on EV range is quantified. A charging demand gravity model is also used， incorporating factors such as station size， electricity price， time cost， and gravity parameters. These are used to dynamically adjust user behavior in choosing charging stations. Additionally， the Dijkstra algorithm is improved to plan charging paths more effectively by including real-time road condition data. Finally， the total charging load is accumulated and superimposed. [Results] The MATLAB simulation results showed a significant difference between the charging load distributions of private cars and cabs. The charging loads of private cars in residential， working， and commercial areas are concentrated during nighttime， daytime working hours， and off-duty hours， respectively. In contrast， the charging loads of cabs are characterized by morning and evening peaks， valleys， and small peaks at noon due to operational demand. The proposed improved Dijkstra's algorithm improves the efficiency of path planning by dynamically adjusting road section weights， reducing driving time by 3.9% for the same destination. The proposed charging demand gravity model optimizes users' charging station selection behavior by integrating factors such as charging station size， electricity price， and user time cost， resulting in a more reasonable spatial and temporal distribution of the charging load[Conclusions] This study constructed a spatial and temporal distribution prediction model for electric vehicle charging loads by integrating information from multiple sources. It reveals the differences in the charging behaviors of different types of EVs， their temperature sensitivity， and the dynamic characteristics of user decision-making. The results provide theoretical support for grid load scheduling， charging station planning， and the development of an orderly charging strategy.

[Objective] The large-scale integration of distributed renewable energy generation （REG） has significantly enhanced the flexible regulation capabilities of distribution systems. However， the inherent randomness and volatility of REG output characteristics present serious challenges to the security and stability of distribution system operations. [Methods] To effectively improve the adaptability of day-ahead dispatch plans to uncertainties， this study proposes a distributionally robust day-ahead dispatch optimization method for active distribution networks （ADN） based on an improved conditional generative adversarial network （CGAN）. First， an improved CGAN model designed by three-dimensional convolution （Conv3D） is proposed to address the problem of generating day-ahead scenarios for wind turbines （WT） and photovoltaic （PV） outputs considering spatio-temporal correlation， which effectively reduces the conservatism of the generated scenario set. Second， based on the generated day-ahead scenario samples of the WT and PV outputs， a Wasserstein ambiguity set construction method based on kernel density estimation （KDE） is proposed， which realizes full utilization of the sample distribution information. On this basis， a two-stage distributionally robust day-ahead dispatch optimization （DRO） model for ADN is established， considering multiple grid-side resource coordination. The original model is reconstructed into a mixed-integer linear programming problem to obtain a solution based on the affine strategy and strong duality theory. [Results] The findings demonstrate that although the day-ahead dispatch plan cost of the proposed method increases by 1.87% and 0.21% compared with the deterministic optimization （DO） and stochastic optimization （SO） methods， the integrated operation cost decreases by 5.38% and 0.46% under the worst-case scenario， respectively. [Conclusions] The analysis revealed that the proposed DRO model exhibits better adaptability to REG uncertainty and can effectively decrease the operational adjustment cost of the day-ahead dispatch plan while maintaining robustness， especially under the worst-case scenario.

[Objective] The operational characteristics of the grid-forming modular multilevel converter （MMC） in offshore wind power transmission systems under short-circuit faults in the receiving-end grid are unclear， and the interaction mechanism between fault ride-through and grid-forming active support is not well understood， which poses a significant threat to the reliable operation of offshore wind power flexible direct current systems. In addition， traditional fault ride-through strategies face the issues of large transient impact currents and slow reactive power support responses during the switching of strategies before and after faults， in conjunction with grid control. [Methods] This study proposes a low-voltage fault internal potential reconstruction ride-through control strategy for grid-forming MMCs. Specifically， it analyzes the short-circuit current characteristics of MMCs under grid voltage dips， constructs a fault ride-through current-limiting boundary based on the voltage equation space vector of the grid-forming MMC， and reconstructs the internal potential using current optimization commands. This study aimed to demonstrate the capability of active voltage support in grid control under different grid voltage dips. [Results] The proposed fault ride-through strategy was validated using a semi-physical experimental platform， and the results indicated that low-voltage ride-through could be achieved under varying degrees of faults. As the grid-voltage dip deepens， the reactive power generated increases accordingly； however， the grid-forming current is limited at deeper dip levels. Compared with traditional fault ride-through strategies， this approach can reduce transient impact currents while quickly providing reactive power support and improving the recovery rate of the grid connection point voltage. The analysis revealed the operational characteristics of active voltage support in a grid-forming MMC and provided insights based on the vector angle correction of currents. [Conclusions] This study lays the foundation for the study of fault ride-through technology under severe voltage drop conditions in deep-sea environments， promoting the safe and reliable operation of large offshore wind power bases and effectively advancing innovation in the energy industry.

[Objective] As a flexible and controllable power regulation device， the flexibly interconnected soft open point can improve the power flow and increase the economy and voltage quality of distribution systems. [Methods] To mitigate the impact of unplanned load fluctuations on distribution systems， this study proposes adaptive optimal dispatching of a flexible interconnected distribution system based on dynamic weight. A two-stage adaptive optimization scheduling framework is established based on the system operation requirements across different time-scales and response speeds of various adjustment devices. This framework is adopted to facilitate collaborative optimization involving energy storage， soft open points， and distributed generation. Furthermore， considering the competition between the scheduling plan based on economic decision-making and that based on voltage fluctuation decision-making in the system operation process， a method for determining the weight coefficients based on the day-ahead optimization results during the intra-day stage is proposed. This method calculates the weight coefficients for multiple objectives of various nodes at different times by considering both the temporal and spatial dimensions. [Results] The effectiveness of the proposed optimal scheduling strategy is verified by the IEEE 33 system. The results show that the proposed strategy can reduce the distribution network operational cost and improve the voltage quality. [Conclusions] Compared with the single-time-scale optimal scheduling strategy， the proposed multi-time-scale optimal scheduling scheme has a better effect of suppressing source-load fluctuation， and the operation cost and voltage fluctuation are smaller， which effectively improves the economy and voltage quality of the distribution network operation. Meanwhile， compared with the traditional multi-objective optimization solving method， the proposed dynamic weight coefficient determination method can track the system structure and operational demand and realize the synergistic optimization of the system economy and voltage deviation.

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

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

[Objective] China encourages the construction and development of integrated comprehensive transportation and energy service stations to accommodate the rapid growth and widespread adoption of new-energy vehicles. This study proposes an economic operation method for electric-hydrogen integrated energy stations that incorporates dynamic pricing strategies. [Methods] First， the study analyzes when new-energy vehicle users arrive at energy stations and how they charge their vehicles. Based on this， a charging demand prediction model is developed using the Monte Carlo method. Second， the study considers several factors. These include the power load ratio for charging， the absorption rate of new energy， changes in station storage capacity， and external purchasing costs. Based on these， a dynamic pricing strategy is formulated. The strategy includes both electricity and hydrogen pricing. This strategy encourages new-energy vehicle users to participate in demand response programs. It also helps manage the energy loads of hydrogen buses and sanitation vehicles. Based on this， an economic operation method for electric-hydrogen integrated energy stations is proposed. [Results] Simulation results show that dynamic pricing strategies significantly improve station revenue compared to fixed pricing strategies. Specifically， electricity sales， hydrogen sales， and total energy sales revenue increased by 24.13%， 4.57%， and 14.59%， respectively. Meanwhile， the total operating cost decreased by 10.3%， further boosting net revenue. [Conclusions] The proposed method overcomes the limitations of fixed pricing strategies and enables more economical operation of electric-hydrogen integrated energy stations.

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

[Objective] Given the sudden drop in photovoltaic power generation in highway service-area microgrids and the interruption of interconnection lines with the large power grid caused by extreme events such as snowstorms and heavy rains， a two-stage optimization scheduling method for highway service-area microgrids， considering emergency power-supply vehicles， is proposed to ensure the stable energy supply of microgrids in service areas. [Methods] In the day-ahead stage， the flexibility of the dispatchable energy storage capacity in the integrated energy microgrid in the service area is considered. The optimization goal is to minimize the operation cost of the microgrid. A day-ahead economic scheduling model of the microgrid is established， and the lower limit of the charge state of the energy storage resource is obtained as a parameter of the intraday optimization scheduling stage. In this stage， based on model predictive control theory， an intraday rolling optimization scheduling model is established. Intraday rolling optimization considers the participation of emergency power-supply vehicles in the scheduling of service-area microgrids， and the various energy loads in the service area are graded according to their importance. The lowest operating cost of the emergency power-supply vehicle and lowest load-shedding penalty are taken as objective functions to obtain the final intraday optimization scheduling. [Results] By constructing simulation examples of photovoltaic output drop and grid interruption under extreme scenarios， the proposed method is compared and analyzed with the state of charge （SOC） optimization constraint scheme without energy storage and the scheduling scheme without considering an emergency power-supply vehicle. The simulation results indicate that the proposed method significantly reduces the curtailment of electrical， thermal， and cooling loads while concurrently achieving a reduction in system operational costs. [Conclusions] Case analysis verifies the effectiveness of the proposed two-stage optimization scheduling method for the service-area microgrid in extreme scenarios. The coordinated scheduling mechanism of the energy storage system and the emergency power-supply vehicle not only enhances the external shock resistance of the service-area microgrid but also achieves the dual goals of improving energy-supply stability and optimizing operating costs， ensuring a stable energy supply for important loads in the service-area microgrid.

[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] With the rapid development of cloud computing and "Internet+"， internet data centers （IDCs）， as the core infrastructure underlying cloud computing， are in a rapid expansion phase. However， because both IDC and integrated energy systems （IES） possess underlying user information， data leakage may lead to various risks. Therefore， when designing collaborative optimization solutions for IDCs and IES， it is essential to consider the privacy preservation of both systems. [Methods] First， the flexible regulation characteristics of data centers were analyzed， and a flexible demand response model for data centers based on the graph theory with M/M/1 queuing theory was constructed. Then， a spatial and temporal joint planning model for an IES incorporating IDCs was established. Based on the Karush-Kuhn-Tucker （KKT） conditions of the IDC and IES operational models， the operational models were transformed into additional constraints for the planning model， which were linearized using the big-M method. Considering the privacy preservation requirements between the IDC and IES， an enhanced Benders decomposition algorithm for mixed-integer linear programming subproblems was improved， and a distributed solution framework was designed to solve the spatio-temporal joint planning model. [Results] The results show that under the example scenarios adopted in this study， after the implementation of the IES and IDC demand response models established in this study， the annualized total cost of the system decreased by 26.79%. The enhanced Benders decomposition algorithm shows that its distributed solution speed is 1.11 times faster than the alternating direction method of multipliers. [Conclusions] This study analyzed the flexible regulation methods of IDC and IES， and constructed a feasible distributed optimization scheme for IES containing IDC that considers privacy preservation. The study provides corresponding solutions and methodological references for similar multistakeholder collaborative optimization scenarios.

[Objective] To fully exploit the flexible and adjustable potential of the charging load of a taxi battery swapping station， a charging optimization scheduling strategy is proposed. This strategy aims to ease the conflict between the charging load， peak and valley pressures on the power grid， and new energy consumption. It considers the coupling between the power market and new energy sources. [Methods] The strategy is based on two main goals： providing auxiliary services in the power market and addressing the abandonment and consumption of new energy. A coordinated operation framework is constructed， linking battery swapping stations， power grids， and new energy stations. An optimization mechanism is designed， incorporating peak response， time-of-use tariff matching， and dynamic tracking of battery SOC. Taking 96 time slots as the scheduling granularity， a dual-objective model—maximizing economic benefits and optimizing new energy consumption—was established， and an improved Harris Hawk optimization algorithm was introduced to solve the problem. [Results] Results from a case study show that the proposed strategy increases the economic benefit of the battery swapping station by 25%. It also raises new energy consumption by 16.5%. Additionally， the charging load during grid peak hours is significantly reduced. This helps achieve peak shaving and valley filling. [Conclusions] By dynamically matching new energy abandonment with time-of-use tariffs， the proposed strategy enhances both economic efficiency and the station's ability to consume new energy. It also reduces grid pressure during peak periods. The proposed market-new energy synergy framework offers a new approach for battery swapping stations to participate in power system regulation.

[Objective] The integration of a high proportion of renewable energy generation has reduced the amplitude of fault currents and changes in their directionality in power grids. Traditional backup protection that relies on offline settings struggles to adapt to the complex conditions of looped networks. Additionally， the low-inertia and low-voltage ride-through （LVRT） control of renewable energy sources exacerbates the changes in the characteristics of positive- and negative-sequence networks， making it difficult to identify faulty components and often resulting in protection mismatch or excessive delay. This study addresses the dynamic adaptability of backup protection in power grids with renewable energy， overcoming the bottlenecks of looped network deadlocks and rigid setting values. [Methods] A dual-criteria approach based on wide-area measurements is proposed. For asymmetrical faults， negative-sequence voltage/current ranking is used to identify fault-associated buses and branches， enabling rapid identification through a regional centralized architecture. For symmetrical faults， a single traveling wave monitoring device at the substation， combined with the global traveling wave arrival time difference and a double-ended ranging algorithm， is utilized to achieve microsecond-level fault location identification. The backup-protection logic is further optimized by dynamically setting only the remote backup protection associated with the fault line， adjusting the impedance circle range， and fixing the action delay to two time intervals， thereby avoiding the cumulative delays of traditional step-by-step coordination. [Results] The PSCAD simulation results indicate that the accuracy rate of the negative-sequence criterion for asymmetrical faults was 100%， with reliable identification possible with a transition resistance of 30 Ω. For symmetrical faults， the traveling wave ranging error is less than 100 m， and the location time is reduced by 90% compared with traditional methods. After optimization， the remote backup-action delay was reduced from 4-7 intervals to 2 intervals， while the setting coverage increased by 18.4%， effectively avoiding misoperations owing to load intrusion. [Conclusions] The proposed method achieved rapid and dynamic identification of multiple types of fault components in renewable energy grids through the complementary use of negative sequence ranking and traveling wave time-difference criteria， overcoming the limitations of looped network deadlocks. The dynamic setting strategy significantly shortens the action delay of remote backup protection， thus considerably enhancing sensitivity and speed. Moreover， this strategy does not rely on high-sampling equipment or complex communication architectures， thus providing an efficient and reliable engineering solution for online backup protection in power grids with a high proportion of renewable energy.

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

[Objective] With the trend of energy consumption diversification， multiload forecasting plays an increasingly important role in optimizing the scheduling and operation planning of integrated energy systems（IES）. [Methods] To address the problem in which the coupling relationship between multiple loads is often ignored in current integrated energy system load forecasting research， a multiple load joint forecasting method is proposed in this study for integrated energy systems based on the multi-energy demand response and improved bidirectional long short-term memory （BiLSTM）. First， by integrating user demand response behavior， the input feature variables of the multi-energy demand response is constructed， and together with multiload forecasting， strong correlation features selected by the maximum information coefficient form the input feature set of the prediction model. Second， the crested porcupine optimizer is improved based on the chaotic mapping theory and elite reverse learning strategy to optimize the model parameters of the BiLSTM neural network. Finally， based on the multihead self-attention mechanism， the input feature weight is adaptively adjusted. The simulation results show that the prediction accuracy of the proposed multiload joint forecasting method is significantly improved compared with the single-load forecasting method. [Results] Compared with the multiload forecasting method without considering the demand response， the mean absolute percentage error of the electricity， heat， and cooling loads was reduced by 6.59%， 13.04%， and 24.86%， respectively. In addition， compared with other forecasting models， the model proposed in this study is more effective in improving the prediction accuracy and can achieve more accurate multi-element load forecasting. [Conclusions] The proposed load forecasting method was combined with integrated energy system dispatching to analyze the economic benefits of load forecasting. Compared with ordinary dispatching， the total operating cost of the system using the proposed load forecasting method was reduced by 16.49%， which can improve the comprehensive benefits of integrated energy systems.

[Objective] With large-scale access to distributed power sources， new energy storage， charging facilities， etc.， the physical， digital， and commercial forms of distribution networks have undergone profound changes. The traditional planning method based on manual decision-making hinders distribution network optimization due to massive factors， complex structures， and numerous pieces of equipment. Artificial intelligence technology provides a feasible solution for overcoming the technical bottlenecks of distribution network planning. [Methods] In this context， this study analyzes the challenges faced by the distribution network planning process under new circumstances， including the precise spatiotemporal prediction of source-load， probabilistic balance of power and energy， coordinated planning of source-grid-load-storage， and empowerment of digitalization and intelligence. It elaborates on the current research status of artificial intelligence-based distribution network planning， focusing on key aspects such as knowledge graph construction， source-load scenario generation， power-energy balance， planning demand reduction， and intelligent network planning. [Results] This study summarizes and analyzes the issues in artificial intelligence-based distribution network planning technologies， including difficulties in processing unstructured and semi-structured data， limited scenario applicability， low accuracy in demand deduction， lack of interpretability， and high-dimensional solution spaces for planning schemes. It proposes potential solutions in technical research， such as graph learning， transfer learning， multimodal fusion， enhanced interpretability， and human-machine hybrid intelligence enhancement. [Conclusions] Compared with traditional distribution network planning methods， artificial intelligence-based distribution network planning demonstrates significant advantages of strong generalization， applicability， and scalability. However， it still faces critical issues， such as insufficient model accuracy and poor quality of generated solutions. In future work， we will continue to investigate artificial intelligence-based distribution network planning methods based on technical prospects， aiming to address the key challenges involved. This will provide references and insights for the development and digital-intelligent transformation of distribution network planning technology systems under the new power system framework.

[Objective] In this study， we aim to address the submodule capacitor voltage frequency reduction effects and the resulting deterioration in system switching losses and harmonic performance in modular multilevel converter （MMC）-based unified power flow controllers （UPFCs） under low switching frequencies， frequency reduction suppression， and capacitor voltage balancing optimization strategy based on profiling tag technology. [Methods] The proposed strategy first involves constructing a tag system based on the multidimensional data resources of the MMC-UPFC and determining the optimal frequency ratio tag value by combining evaluation tags and optimized parameters. Subsequently， we propose a capacitor voltage-balancing optimization strategy based on tag technology feedback regulation， which selectively activates the voltage-balancing controller when the capacitor voltage imbalance degree exceeds a certain threshold， as determined by the clustering range of the imbalance degree tags. [Results] Simulation results indicate the following： 1） when the frequency ratio is set to 2+1/N， the capacitor voltage imbalance degree stabilizes around 1.6%， whereas it exceeds 3% and continues to rise when the frequency ratio is 2.5； 2） in the capacitor voltage balancing optimization strategy based on tag technology feedback regulation， the bridge arm capacitor voltage imbalance degree can stabilize at 0.7% when the voltage balancing controller is selectively activated， significantly outperforming traditional full-time voltage balancing strategies； 3） at a frequency ratio of R_F=2.4， the non-full-time voltage balancing strategy effectively suppresses capacitor voltage fluctuations， reducing the total harmonic distortion （THD） of the AC current from 12.61% under the full-time voltage balancing strategy to 0.11%. [Conclusions] The proposed frequency reduction suppression and capacitor voltage balancing optimization strategy based on profiling tag technology demonstrates excellent performance， addresses the issues of harmonic suppression and capacitor voltage balancing in MMC-UPFC systems under low frequency ratios， and provides theoretical support for the optimized operation of MMC-UPFC systems.

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

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

[Objective] A bidirectional converter integrates traction， feedback， and reactive power compensation and is the primary choice for upgrading the main equipment of an urban rail DC traction power supply system. In the field of low-voltage DC traction power supply， converters have the characteristics of low voltage and high current. The T-type three-level multi-unit parallel connection combined with carrier phase-shifting technology can meet the special requirements of low cost， low harmonic， high power density， high efficiency， and wide DC voltage operation range of urban rail DC traction power supply systems for the main equipment. However， this approach has problems such as excessive zero-sequence circulating currents and unbalanced midpoint potentials. [Methods] First， a zero-sequence circulating current model is established to address the circulating current problem in bidirectional converters. The circulating current is classified and analyzed， and a transformer low-voltage winding splitting， shared neutral line， L-shaped filter， and software control algorithm are proposed to suppress the zero-sequence circulating current. Second， in response to the problem of midpoint potential imbalance and based on the implementation of the equivalent space vector pulse width modulation， the relation of the midpoint potential to the phase voltage， phase current polarity， and space vector type is established by analyzing the T-shaped three-level single-phase topology structure. Accordingly， a feedback-compensation-based midpoint potential balance control method is proposed. [Results] Based on PSCAD/EMTDC simulation testing， it was found that various zero-sequence circulating currents could be effectively suppressed， achieving a midpoint potential balance in three-level converters during four-quadrant operation. [Conclusions] The proposed circulating current suppression method can not only suppress various zero sequence circulating currents， but also help reduce the cost of measuring the midpoint potential of three-level systems. The proposed midpoint potential control method does not require complex space vector modulation processes， has low computational complexity， is easy to implement， and can effectively control the midpoint potential.

[Objective] As the penetration rate of photovoltaics （PVs） increases， their volatility and randomness lead to intensified peak and valley fluctuations in the user net load， resulting in an increase in electricity demand. Energy storage can reduce the demand for electricity by utilizing the characteristics of peak shaving and valley filling； however， the high initial investment in energy storage limits its large-scale application on the user side. [Methods] A photovoltaic park energy storage optimal configuration method based on Stackelberg game pricing and information gap decision theory （IGDT） with electric vehicle （EV） demand response is proposed. First， considering the uncertainty of the grid， time-of-use， demand， and purchase and sale electricity price of EVs and PV output， an energy storage configuration model based on IGDT and an optimized operation model for EV clusters were constructed. Second， with the park as the leader and EVs as followers， a Stackelberg game model is constructed to minimize the costs of the park and EVs. Then， the Stackelberg game model is transformed into a mixed-integer linear programming problem for a solution using Karush-Kuhn-Tucker （KKT） conditions and the dual theorem of linear programming. Finally， we analyzed a PV park in a certain region as the research object. [Results] The results show that the proposed strategy reduced the annual comprehensive cost of the park by 12.06% and the charging and discharging costs of EV users by 54.88%. Owing to the participation of EVs in park scheduling， the storage configuration capacity and power were reduced by 62.80%， and the on-grid power by 1.32%， which improved the local consumption rate of PV. Compared with the IGDT model proposed in this study， the park cost of the robust optimization model is 1.97% higher， which proves that the IGDT model is more economical. [Conclusions] The comparison shows that the strategy proposed in this study meets the charging demand of EVs while reducing the comprehensive cost of the park， reduces the charging cost of the owners， and realizes a mutual benefit and win-win situation on both sides of the game.

[Objective] A robust multi-stage planning model is established for a park-level integrated energy system （PIES） to minimize the total life-cycle present-value cost while considering source-load uncertainties， optimal construction sequencing， and tiered carbon trading to enhance economic efficiency and low-carbon benefits. [Methods] First， a box-type uncertainty set is used to model the source-load uncertainty and uncertainty adjustment parameters are introduced to reduce the conservatism of PIES planning and obtain a two-stage robust optimization （TSRO） model. Both discrete and continuous variables are included in the second-stage decision variables of the TSRO model， facilitating directly solving the second-stage problem using Lagrangian duality theory. Instead， an additional nested layer is required to solve the model. Therefore， this paper employs the nested column-and-constraint generation （NC&CG） algorithm to solve the model. [Results] Simulation results show that the robust programming model can improve the robustness of the system by sacrificing the economic and low-carbon benefits of the system to a certain extent and can flexibly adjust the robustness of the PIES planning scheme by changing the value of the uncertainty parameters. [Conclusions] The proposed multi-stage planning approach accounts for load growth and source-load uncertainties from a long-term system development perspective， thereby improving the flexibility and adaptability of the planning scheme.

[Objective] The synchronization of information in intelligent substations is of great significance for the intelligent diagnosis and protection of power system faults， of which the satellite clock synchronization method is widely used. However， the satellite clock synchronization method is dependent on dedicated communication channels or satellite synchronization clocks， which can easily lead to the loss of synchronization signals. In addition， several synchronization signal transmission channels must be installed， and damage to transmission channels will cause synchronization errors between the master and slave clocks. Information synchronization technology based on the mutation detection algorithm that does not rely on clocks and communication channels， and is not limited by synchronous communication， can effectively avoid the problem of information synchronization caused by communication. [Methods] A multiplant information synchronization scheme based on the mutation detection algorithm is proposed. It compares the performance of typical outlier detection algorithms， such as the wavelet transform and singular value decomposition， in terms of pulse width， boundary effects， and phase-shift effects. [Results] The results show that the Hankel matrix singular value decomposition algorithm has the advantages of a narrow pulse width， no influence of boundary effects， zero phase shift， and has significant advantages in fast signal singularity detection compared with wavelet transform. Accordingly， a signal synchronization scheme based on the Hankel matrix singular value decomposition algorithm was proposed. The feasibility and advantages of the proposed information synchronization scheme were verified through an analysis using a large amount of on-site recorded data. [Conclusions] The results indicate that the proposed information synchronization scheme can achieve the precise synchronization of information between various protection devices and recorders within and between plant stations.

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

[Objective] Reducing carbon emissions is a key measure in addressing the global challenge of climate change. While carbon emissions are generated directly on the energy supply side， demand drives carbon emissions on the supply side， thus making it particularly important to regulate demand-side flexible resources from a demand-side perspective to achieve green and low-carbon energy use. During optimal low-carbon operation of the new power system， accurate measurement of carbon emissions from various devices is a prerequisite for regulatory benefit calculations. Accurate modeling of the stable aggregation of high-uncertainty resources with marginal carbon reduction benefits is crucial for low-carbon optimization. Understanding the change mechanism in a region where the two goals of the economy and carbon emission reduction are consistent is an objective requirement for low-carbon optimization. The reasonable design of the market operation mechanism， user behavior model， and price formation mechanism motivates massive demand-side flexibility resources to actively participate in low-carbon optimization. [Methods] This study explores flexible regulation capabilities on the demand side， focusing on key technologies for utilizing demand-side resources， and reviews existing research from four perspectives： 1） carbon emission measurement of flexible demand-side resources， 2） aggregation and adjustable potential assessment of these resources， 3） low-carbon optimization of new power systems incorporating flexible resources， and 4） participation of flexible resources in electricity-carbon coupled markets. Finally， this study identifies current research gaps and outlines potential future research directions to address these deficiencies. [Conclusions] This study provides readers with a concise guide to quickly grasp the key concepts and latest achievements in this research field， thereby driving innovation in areas such as carbon emission quantification of demand-side flexibility resources， resource aggregation， adjustable-potential assessment， optimization strategies， and market mechanisms.

[Objective] As the energy consumption of data centers continues to grow and renewable electricity rapidly penetrates the market， the synergy between computing power， electricity， and heat can break the siloed development of these sectors， thereby promoting the high-quality and sustainable development of data centers and novel energy systems. However， the construction of a coordinated system and the formulation of standards for computing power， electricity， and heat lack regulation and guidance， which hinders the enhancement of energy utilization efficiency and coordinated optimization levels in data centers. [Methods] This study analyzes the current research and development status of computing power-electricity-heat collaborative systems in data centers. It summarizes the deficiencies of existing terminologies and provides key terminology examples related to the synergy of computing power， electricity， and heat from aspects such as general terms， integrated optimization， and evaluation terminology. [Results] The results indicate that the coordination among computing power， electricity， and heat in data centers is still in its preliminary exploratory stage， and normative work on cross-industry collaborative terminologies should be strengthened. [Conclusions] This study establishes a standard system for the coordination of computing power， electricity， and heat， which primarily includes basic， business， and service standards， with the aim of effectively guiding the deep collaboration between data centers， power grids， and district heating networks， promoting the diversified and intensive utilization of energy， and offering references and suggestions for advancing the standardization efforts in this field.

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

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

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

[Objective] This study addresses the problems of the impact of the high frequency and high-power charging of electric trucks on the stable operation of the power system and the insufficient number of charging stations. [Methods] First， the power consumption characteristics of electric trucks are modeled by considering the weather temperature， traffic flow， loaded cargo volume， terrain， and other factors. Second， a path planning model is constructed by using the charging cost of electric trucks and minimum cost of battery loss as the objective function， and a genetic algorithm is used to solve the path planning model according to the logistic order information to obtain dynamic paths. Finally， the Monte Carlo method is used to sample electric trucks randomly and obtain the spatial distribution of electric trucks， judge the charging strategy according to the time window and remaining state of charge of the point of arrival at the customer， and add up the electric truck charging loads in the region to determine the spatial and temporal distributions of the charging loads. The actual traffic network in Tangshan City was used to carry out the simulation validation. [Results] The results showed that， compared with the shortest path algorithm， the peak charging load of electric trucks decreased by 6%， the overall charging load decreased by 2%， and the travel cost decreased by 20，410 yuan overall after adopting the proposed path planning method， which reduced the impact on the grid and the user driving cost. In addition， the charging load was affected by seasonal temperatures， and the peak charging load in winter was 6.3% higher than that in summer. [Conclusion] The proposed load forecasting method has a certain degree of authenticity and rationality， and aligns with the real distribution paths of electric trucks.

[Objective] A day-ahead， real-time optimal scheduling approach for customer directrix load （CDL）-based demand response is proposed to address the problem of the strong uncertainty of the battery swapping demand and inability to define the baseline load of heavy-duty truck battery swapping stations （HTBSSs）， which makes it difficult to characterize their regulation contribution quantitatively and hinders flexibility. [Methods] First， an operation model is constructed based on the classification of the state of charge and considering the number of trucks waiting for switching， which solves the problems of an excessively large strategy space and the difficulty in describing the uncertainty faced by directly controlling the power of each battery. Second， a day-ahead optimization model is proposed for the participation of HTBSSs in CDL-based demand response based on the constructed model， and a real-time rolling optimization method is presented to deal with the uncertainty of the swapping demand. [Results] Examples show that the proposed model is applicable to different swapping demand scenarios， and that the day-ahead and real-time optimization approach can effectively track the CDL and reduce the number of swapping waits. After participating in the CDL-based demand response， the HTBSS and grid operator can reduce the cost by 47.66% and 65.52%， respectively， and the regional renewable energy power abandonment can be reduced by 90.93%. [Conclusions] The proposed method can effectively guide HTBSSs to participate in CDL-based demand response and alleviate the impact of uncertainty of the battery swapping demand in the operation process. The participation of HTBSSs in CDL-based demand response can not only promote the consumption of distributed renewable energy， but also reduce their own operating costs， resulting in a win-win situation for the grid and load.

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

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

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

[Objective] 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] With the development of power systems and continuous expansion of energy systems， massive load power data have been generated. However， missing data are inevitable in the collection and transmission of power data， which greatly restricts the development of system-coordination optimization and advanced data applications. [Methods] To this end， this paper proposes a new power load missing data completion model based on an integrated graph convolutional variational transformer （IGCVT） network. The IGCVT model aggregates an improved graph convolutional network （GCN） and Transformer model using the variational auto-encoder （VAE） architecture. The raw data are processed by the GCN to learn spatial features and deeply mine spatial dependencies； the hidden layer data are reconstructed by the VAE to more effectively restore data distribution characteristics； and the temporal autocorrelation information of the sequence is mined based on the Transformer model. In addition， an improved whale optimization algorithm （WOA） is introduced to optimize the network model hyperparameters and improve the completion accuracy and applicability of the model. Simultaneously， to solve the problem of large errors in the completion of extreme change points of power load data， a two-way data completion method is adopted to make full use of the data information before and after the missing points. [Results] Experimental results show that， compared with the baseline model， the RMSE index is improved by 24.3%， 44.0%， and 47.9%， which verifies the superiority of the proposed method. [Conclusions] The results show that the proposed method provides a feasible solution to the problem of missing power load data and is expected to further expand the application scope of the model.