To promote the process of carbon emission reduction in the electric power industry and achieve the goal of “carbon peaking and carbon neutrality”, the construction of a unified national power market system is being accelerated. A two-stage market clearing model considering load participation in carbon trading is proposed to reduce carbon emissions and facilitate clean energy substitution in the electricity sector. First, an initial carbon quota allocation method for thermal power units based on zero sum gains-data envelopment analysis is introduced, and the electricity market clearing model considering carbon trading is established. Then, based on the market clearing results from the first stage, the new energy consumption of loads is determined using the power flow tracing theory, and the Chinese certified emission reduction (CCER) is calculated. Following CCER carbon offset rules, the second stage of carbon emission trading is initiated, and the secondary electricity market subject to carbon emission constraints, is cleared based on the carbon trading results. Finally, an analysis using the improved IEEE 30-bus system is conducted to validate the effectiveness of the proposed market model. The results show that the proposed model not only helps reduce the carbon emissions from thermal power units but also increases the market share of new energy consumption and lowers average electricity prices. Additionly, the model provides a viable scheme for the large-scale marketization consumption of new energy.

To address the issues of reduced adaptability of secondary frequency regulation caused by changes in power system parameters, a self-adaptive secondary frequency regulation strategy based on distributed model predictive control (DMPC) is proposed. First, a model of a multi-area interconnected power system is built. Based on the frequency response trajectory, a parameter identification model for each area of the system is established. Then, the recursive least square method is used to solve the parameter identification model and update the parameters of each area online. Additionally, with the objective to minimize the area control error (ACE), DMPC is adopted to optimize the power of generators for secondary frequency regulation. Finally, a case study is conducted to demonstrate the effectiveness of the proposed strategy.

Urban rail transit construction plays an important part in supporting the “dual carbon” goals and accelerating the development of new-type power system. However, stray currents generated during subway operations can lead to soil polarization and corrosion of buried metal pipelines. To analyze the impact of stray currents on buried metal pipelines, a method is proposed to calculate the corrosion current density of buried pipelines based on ground potential distribution. First, a long-line four-layer grounding network reflux model with multiple trains is established based on the actual subway traction power supply system, which enables the real-time dynamic calculation of stray current distribution and rail potential. Next, the rail potential distribution is treated as a line voltage source, which is integrated into the subway line model to calculate ground potential distribution. Then, a soil-pipeline circuit model including anti-corrosion coating is established to realize dynamic calculation of pipeline corrosion current density based on ground potential distribution. Finally, the influencing factors of the pipelines corrosion current density are studied, such as rail direct current (DC) resistance, transition resistance, and soil resistivity. The simulation results show that the pipeline corrosion current density is linearly related to rail DC resistance. When the transition resistance between the rail and the drainage network increases from 5 Ω·km to 50 Ω·km, the corrosion current density of the two buried pipelines reduces by 65.94% and 67.45%, respectively. Additionly, a higher soil resistivity has a certain inhibitory effect on stray current propagation, providing a degree of mitigation against corrosion.

The inertia and frequency regulation resources in power systems with a high proportion of renewable energy are scarce, resulting in prominent problems of frequency stability. To address these problems, this paper incorporates the potential frequency support capability of wind farms into the frequency control measures of the power grid, and proposes an optimization method for wind farm control parameters that considers the security requirements of system inertia. After a credible disturbance, the system inertia security requirement that meets the frequency stability constraint is calculated based on the transient frequency index limit. Then, the primary frequency regulation capability that wind farms can provide under different wind conditions is modeled with the goal of minimizing the adjustment of wind farm virtual inertia and frequency droop parameters under disturbances, and a dynamic optimization model for wind farm frequency control parameters is established. Finally, the frequency control parameters are calculated and numerical tests are conducted on the modified IEEE RTS-79 system. The results show that the proposed parameter optimization method effectively improves the transient frequency response process of wind farms, which helps enhance the frequency stability margin of the system.

With the construction of new power systems, the stochasticity of high-proportion renewable energy significantly increases the uncertainty in the operation of the power grid, posing severe challenges to its safe, stable, and economically efficient operation. Data-driven artificial intelligence methods, such as deep reinforcement learning, are becoming increasingly important for regulating and assisting decision-making in the power grid in the new power system. However, current online scheduling algorithms based on deep reinforcement learning still face challenges in modeling the high-dimensional decision space and optimizing scheduling strategies, resulting in low model search efficiency and slow convergence. Therefore, a novel online steady-state scheduling method is proposed for the new power system based on hierarchical reinforcement learning, which reduces the decision space by adaptively selecting key nodes for adjustment. In addition, a state context-aware module based on gated recurrent units is introduced to model the high-dimensional environmental state, and a model with the optimization objectives of comprehensive operating costs, energy consumption, and over-limit conditions is constructed considering various operational constraints. The effectiveness of the proposed algorithm is thoroughly validated through experiments on three standard test cases, including IEEE-118, L2RPN-WCCI-2022, and SG-126.

In an islanded DC microgrid, there is a problem of slow state of charge (SOC) equalization between distributed energy storage units (DESUs) with different capacities. To address this issue, a fast SOC equalization strategy for DESUs, which accounts for capacity differences, is proposed. First, the SOC equalizer constructs a relationship between the droop coefficient and SOC using a power function. By selecting appropriate equalization adjustment coefficients, the droop coefficient can be adaptively controlled, thereby accelerating SOC equalization. Then, the virtual droop equalizer is introduced to mitigate the impact of line impedance on current distribution accuracy by simply adjusting the PI controller, which improves the precision of current distribution. Additionly, the selection range of control parameters for this strategy is determined by using a system stability analysis. Finally, a DC microgrid hardware-in-the-loop experimental platform is developed. The experimental results, compared with those from existing literature under various operating conditions, demonstrate that the proposed control strategy improves the speed of SOC equalization and realizes the rapid recovery of bus voltage.

To address the challenges of complex detection background and poor detection performance for small targets, a transmission line channel security detection algorithm based on the fusion of window self-attention network and the YOLOv5 model is proposed. First, the Swin Transformer (S-T) is employed to optimize the backbone network, expanding the perception field of the model and enhancing its ability to extract effective information. Then, the adaptive spatial feature fusion (ASFF) module is improved to enhance the feature fusion ability of the model. Finally, considering the mismatch between the real frame and the predicted frame, the structural similarity intersection over union (SIoU) is introduced to optimize the boundary errors and improve the generalization ability of the model. The experimental results show that the model proposed achieves a multi-target intrusion detection accuracy of 90.2%, and with significant improvements in the detection of small targets. This approach better meets the requirements of foreign object detection in transmission line channels compared to other object detection algorithms.

In the context of large-scale energy storage stations, such as pumped storage, participating in both spot trading and grid scheduling, it is difficult for the grid to directly access the consumption of renewable energy in the spot market. In this regard, the influence of spot electricity trading on the pumped storage scheduling is considered and a multi-agent scheduling method with an embedded market game model is proposed. First, combined with the power spot market clearing model, with the objective of maximizing the benefits of the pumped storage power station in the spot market, a strategy for the pumped storage power station to participate in the spot trading of electric energy is developed. Then, combined with the two-part electricity price policy, the capacity allocation and power dispatching strategy of the grid operator about the pumped storage is formulated to minimize grid operating costs and the amount of renewable energy discarded in the entire grid. To formulate the proposed scheduling strategy, a bi-level optimization problem with an embedded game model is solved: the decision-making problem of the pumped storage power station participating in the electric energy spot market, and the optimization with the embedded marketing game model of capacity allocation and power scheduling strategy about pumped storage. The decision-making problem of pumped storage in the spot market follows a Stackelberg game model, which is integrated into the optimization problem of pumped storage capacity allocation and power scheduling strategy via the strong dual theory. The embedded bi-objective problem is solved by using the NSGA-II algorithm. Finally, based on the data from a pumped storage power station in East China, a simulation model is built to verify the effectiveness of the proposed method. The test results show that the proposed method can effectively coordinate the decision-making of direct grid dispatching and pumped storage participation in the electric energy spot market, enhancing the economic benefits of pumped storage, reducing the operating cost of the grid, and improving the consumption of renewable energy.

With the increasing penetration of new energy sources and the development of new power systems, grid-following converter (GFL) plays a crucial role in maintaining the stability of power systems. However, existing transient stability analyses of GFLs assume that the direct current (DC) side behaves as a constant-voltage source, neglecting the effects of DC-bus voltage control. This paper aims to investigate the transient instability mechanism of GFL considering DC-bus voltage control. First, a transient synchronous stability model considering DC voltage control is established, followed by an analysis of the transient synchronous stability of GFL under DC-bus voltage control. The findings indicate that DC voltage control increases the active current reference value and decreases the equivalent damping of the GFL, which in turn reduces its transient synchronous stability of GFL. By increasing the proportional coefficient or reducing the integral coefficient of DC-bus voltage control, transient synchronous stability can be appropriately improved. Finally, the theoretical analysis is validated through MATLAB/Simulink simulations.

A hybrid energy storage system consisting of batteries and supercapacitors can be integrated into the microgrid, to mitigate the power fluctuations on both sides of the source and load sides, which is a common issue in AC/DC hybrid microgrids. This paper proposes a power coordination strategy for hybrid energy storage in AC/DC microgrids based on virtual impedance and fuzzy algorithm optimization. First, the initial power allocation for hybrid energy storage is autonomously determined using composite virtual impedance. Then, a comprehensive analysis is conducted to assess the influence of composite virtual impedance parameters and filtering time constant on power distribution. Based on this, a power distribution adjustment method is proposed, and fuzzy control rules are derived. The fuzzy algorithm is used to adaptively optimize the output power of hybrid energy storage based on the state of charge and the power demond on the supercapacitor. Finally, the power in the AC/DC interlinking converter is adaptively adjusted to enhance the regulation ability of hybrid energy storage. The simulation results in MATLAB/Simulink experiments demonstrate that the proposed strategy can effectively suppress power fluctuations on both the source and load sides, eliminate DC bus voltage deviation, and prevent the issues such as over-charging and over-discharging of supercapacitors, thereby extending the service life of the equipment.

With the launch of the national carbon market, electricity industry is expected to play an important role in the carbon market. However, the impact mechanism of carbon market on low-carbon scheduling in power systems remains underexplored and warrants further study. This paper investigates the impact of the annual clearing mechanism in the carbon market on the day-ahead dispatch of power systems. A non-parametric statistical model is developed to annual represent the carbon market price. Considering the uncertainty in carbon price, this paper proposes several low-carbon economic dispatch methods for power systems with different risk preferences, including conditional value at risk (CVaR) optimization, expected value optimization, and robust optimization. The impact of carbon price uncertainty on day-ahead dispatch is analyzed through a mathematical optimization framework. The results show that, compared with traditional economic dispatch models, the low-carbon economic dispatch model can reduce carbon emissions by 8.41%. Additionally, the more risk-averse the approach to carbon pricing, the lower the carbon emissions. The smaller carbon quota coefficient further promotes low-carbon dispatch through the carbon market. Finally, this paper identifies conditions under which carbon capture systems become profitable, specifically, when the carbon price in the market exceeds the product of the unit generation cost and the energy consumption coefficient of carbon capture.

Although the adaptive bistable wave energy generation device solves the problem that the bistable system may be difficult to cross the barrier when the amplitude of the incident wave is small, its efficiency can still be improved. Previous studies have proved that the change of the parameters of the device will have a great impact on its performance, and the optimal device parameters are closely related to the spectral peak frequency at a given time. Therefore, in the control study of the device, a control scheme is designed and the device parameters are adjusted accordingly in order to improve efficiency assuming that the peak frequency within a period of time is predictable. In this study, three control parameters are selected, and the optimal device parameter library with different spectral peak frequencies is determined by simulation calculation. The control module is then added to the simulation program to control the parameters by interpolation. The results show that the device with variable parameter control improves energy capture efficiency.

The constant power load (CPL) in a DC microgrid can reduce the effective damping of the system, resulting in high frequency voltage oscillations on the DC bus, which threatens the safe and stable operation of the system. To address this issue, this paper proposes a DC-bus voltage oscillation suppressor based on an active capacitor and its control method. The oscillation suppressor is connected in parallel to the DC bus, enabling direct interaction with the DC bus. The energy storage capacitor in the oscillator suppressor effectively stores the transient energy generated by voltage oscillations, thereby reducing the amplitude of voltage oscillation and improving the voltage stability of the bus. The voltage of the power supply in the oscillation suppressor adapts to the voltage of the DC bus, allowing for stable operation in the face of load changes in the system. The design offers advantages such as plug-and-play functionality, strong applicability, and flexible control. In addition, by analyzing the operating mode and mechanism of the oscillation suppressor, a small signal model is established, and the influence of controller parameters on the stability and dynamic performance of the suppressor is analyzed, based on which the controller parameter optimization scheme is proposed. Finally, the effectiveness of the oscillatory suppressor is validated through the experimental results.

Ships sailing in shallow water areas with uneven seabed often suffer from shallow water effects due to channel limitations. At the same time, due to the uneven seabed, the hydrodynamic performance will undergo more drastic changes, which will have a negative impact on the safety and maneuverability of the ship. Therefore, based on STAR-CCM+, analyze and calculate the influence of step bank and shallow water depth on the hydrodynamic performance and flow field of ships. The results indicate that the hull undergoes obvious heave and longitudinal tilting motions after experiencing step bank, and the stern roll of the hull occurs when the hull is sailing on the step bank, and then gradually changes from stern tilt to bow tilt. The hull resistance reaches its maximum when the stern reaches the step bank, in which the change in total drag is mainly due to the residual drag.  During the shallow water area, the bow wave gradually decreases, and the transverse wave and scattered wave in the stern wave area have a tendency to merge.

The recognition of engagement state aids landing signal officers in formulating command decisions promptly and precisely, which is crucial for guiding carrier-based aircraft landings. A method is proposed for recognizing the engagement state, leveraging adaptive feature enhancement and fusion, which includes an attention mechanism-based feature enhancement module and a multi-scale feature fusion module. The front module enhances visual representation by segmenting feature maps and concatenating spatial and channel domains, and the back module merges high-resolution shallow features with semantically rich deep features to fully utilize contextual information. A prototype system is developed to recognize landing engagement states based on the wearable augmented reality devices. To evaluate the performance of the method proposed, hybrid datasets of landing operations are constructed. The results show that the proposed algorithm outperforms baseline algorithms and meets the application requirements of engagement state recognition.

The introduction of modern power supply service system has raised higher requirements for the service quality of electricity customer service. Accurate power supply service traffic prediction not only improves the quality of power customer service, but also effectively reduces the cost of customer service personnel. Therefore, this paper proposes a short-term traffic prediction method for power grid based on Adaboost and convolutional neural network (Adaboost-CNN) and a value-added service correction method. First, the isolated forest algorithm is used to identify the abnormal data, and the Lagrange interpolation function is applied to repair the abnormal data or missing data. Next, the analytic hierarchy process is employed to quantify user information, meteorological data, and power outage details. The grey correlation method is then used to analyze the influence factors of traffic volume, and these factors are incorporated as inputs to the traffic volume prediction model. An Adaboost algorithm is applied to integrate multiple CNN models, resulting in an Adaboost-CNN traffic prediction model. Finally, considering the value-added services within the power supply service system, the prediction results of the model are corrected to obtain the final traffic prediction value. The case analysis shows that the proposed forecasting model reduces prediction error by an average of 11.05 percentage points compared to a single forecasting model and by 5.32 percentage points compared to a combined forecasting model, demonstrating better forecasting accuracy.

In order to meet the challenges of operational reliability and economy in the low-carbon park caused by the uncertainty of wind power output, a multi-timescale robustly coordinated operation scheme is proposed to minimize the integrated electricity-carbon operation cost in the low-carbon park, where the diverse regulation capabilities of the flexible resources, such as hydrogen, natural gas, and electrochemical energy storages are fully utilized. The first stage of the proposed operation scheme is the day-ahead decision stage, which considers the multi-timescale fluctuation of wind power output and load demand, and formulates the robust scheduling commands for hydrogen, natural gas, and electrochemical energy storage. The second stage is the intra-day decision stage, which combines the short-term forecast results of wind power output and load demand to dynamically adjust the wind turbine power scheduling commands. The day-ahead decision is an adaptive robust optimization problem, which is solved by the column-and-constraint generation (C&CG) algorithm, while the intra-day decision is a deterministic optimization problem, which is solved by the linear programming algorithm. Finally, this paper proposes an operation simulation model to validate the effectiveness of the operation scheme by combining the hourly and daily operation data of wind power and the load demand in a practical low-carbon park. The simulation results show that the proposed method can effectively improve the operational economy and reliability of the low carbon park with a high penetration of wind power.

The uncertainty of new energy results in power prediction errors, causing new energy producers to bear high wind curtailment losses and deviation penalties due to bidding deviations. To address these issues, this paper proposes a feature-constrained multi-layer perception (MLP) power prediction algorithm, combined with storage-based bilateral transactions, to provide power support and reduce bidding deviations. First, the MLP model is enhanced by improving the relevancy of the hidden layers through adaptive learning, which strengthens its ability to capture nonlinear rules in input data and improves power prediction accuracy. Then, the algorithm allows for bilateral transactions between new energy producers and storage enterprise before entering the day-ahead market, helping mitigate the penalties associated with prediction errors including deviation and curtailment costs. Finally, the case study demonstrates that the feature-constrained MLP effectively improves the power prediction accuracy. Additionally, engaging in bilateral transactions with storage enterprise significantly reduces the costs incurred by new energy producers due to bid deviations.

In the regional power spot market, inter-provincial transmission power costs are typically calculated by multiplying the transmission price of the physical tie-line channel by the transmission power. However, this method fails to effectively account for the inter-provincial power transactions at different transmission prices. To address this issue, this paper first analyzes the impact of inter-provincial power trading on regional power spot clearing. It then proposes an optinization mechanism for inter-provincial power trading network loss handling, transmission cost processing, tie-line channel flow matching, and point-to-network power trading alignment. Based on this, it proposes a regional power grid spot clearing and pricing model, which incorporates inter-provincial power trading transmission costs into a standardized regional power spot clearing model. It derives the mathematical relationship between the system marginal prices of different provincial power grids. The proposed clearing and pricing model aims to achieve optimal allocation of resources, while effectively stimulating market players to bid reasonably. Finally, it validates the correctness and effectiveness of the proposed model through specific numerical examples.

To address the power quality problems caused by power electronic loads, a two-leg topology-based transformerless unified power quality conditioner (TLTT-UPQC) is proposed, which can overcome the volume, weight, and magnetic saturation of power frequency transformer in the existing UPQCs, and can improve the power quality of the distribution network in a lightweight and highly flexible form to meet the high-quality electricity demand of loads. First, the working principle of TLTT-UPQC is analyzed from the perspective of circuit topology. Then, combined with theoretical analysis, the operating mechanisms of various power quality management functions are studied in scenarios such as grid voltage drop, rise and distortion, as well as resistor-inductance and nonlinear loads, based on which, the system control strategy is designed. Finally, a MATLAB-based simulation model is developed to verify the multifunctional operation performances of the TLTT-UPQC through simulation results.

Aimed at the small-signal synchronization instability of grid-following (GFL) and grid-forming (GFM) converter system, a synchronization perspective frequency-domain modeling and analysis method is proposed, which can intuitively reveal mechanism and accurately judge multi-machine stability. Specifically, a node admittance matrix considering GFL, GFM converters, and the transmission network is established. Then, the frequency domain modal analysis (FMA) method is adopted to evaluate system instability characteristics. Afterwards, synchronization forward and feedback paths are partitioned at the oscillation source to formulate a synchronization perspective stability model incorporating dynamics of each converter and transmission network. Finally, the proposed method is validated by using a typical two-machine GFL-GFM system. With such method, the stability judgment failure caused by the feedback path aggregation is addressed, and the interaction mechanism between GFL and GFM synchronization dynamics as well as their parameter influences are revealed.

Due to its load time shifting and power regulation capabilities, virtual power plants (VPPs) have the potential to participate in the electricity market and provide flexible ramping products (FRPs). However, it is hard for VPPs to make accurate bidding in the market, due to the uncertainty of their dispatching capability and system requirements. Therefore, a cloud-edge collaborated market architecture supporting VPPs participation in the electricity market and providing FRPs services is proposed, and the corresponding distributed optimization trading model is established. The market clearing process is completed through the collaborative interaction between the independent system operator and VPPs, which can accurately guide VPPs to optimize the electricity consumption and provide flexible climbing services. The distributed optimization model is iteratively solved using the analytical target cascading (ATC) method, and heuristic constraints are introduced to improve the convergence of the algorithm. Finally, the proposed method is evaluated by the simulation results of typical cases featuring the “duck-curve” net load, which demonstrate that the cloud-edge collaborated market can effectively reduce operating costs and promote the consumption of renewable energy.

In order to improve the reliability and convenience of the voltage-reduction arc suppression device, a zero-sequence voltage flexible control method based on line voltage series modulation damping grounding is proposed, and its application in arc suppression and feeder protection is studied. First, based on the advantage that the line voltage of the distribution network is not affected by single-phase grounding fault, an active grounding device replacement scheme using isolation transformer, arc suppression coil, and other traditional passive devices is proposed, which has the advantage of flexibly regulating the amplitude and phase of zero sequence voltage. Then, the impact of the asymmetric distribution parameters on the active arc suppression is demonstrated, and an arc suppression control method based on full compensation of fundamental current is proposed. Furthermore, a fault phase voltage active lifting method with line voltage access is proposed to effectively improve the detection sensitivity of high resistance faults. Finally, the optimal selection scheme of the accessed line voltage and modulation damping is analyzed, and the integrated control scheme of fault arc suppression and feeder protection in distribution network is given, whose effectiveness is validated by simulation experiment. In practical application, the equipment cost can be reduced only by connecting the selected line voltage to the neutral point through the isolation transformer and cooperating with the resistive modulation damping, which improves the reliability and control convenience of equipment, and provides a new scheme for the grounding fault feeder protection and zero residual current fault arc elimination of the distribution network.

Under the construction of a new power system, the integrated energy system with electricity-thermal-hydrogen interconnection will become one of the important development directions, but its whole life cycle operation economy and energy supply reliability are affected by the initial equipment capacity and daily operation scheme of the system. Therefore, a multi-objective two-stage robust optimization method is proposed, taking into account the source-load uncertainty. A grid-connected electric-thermal-hydrogen operation model including fuel cells and electrolytic cells is developed, and the source-load uncertainty is added by using the hierarchical Latin hypercube sampling and Euclidean distance scenario reduction methods, and solved by a two-stage robust optimization algorithm. The results show that the proposed method can effectively mitigate the impact of source-load uncertainty on the configuration and operation planning of the integrated energy system, which is expected to provide new ideas for the construction and operation of integrated energy systems in the future.

Modular multilevel matrix converter (M³C) is the core device of the fractional frequency transmission system. Due to the lack of direct current link, the electrical quantities of different frequencies are directly coupled inside the M³C, resulting in a complex harmonic condition, which makes it difficult to balance the capacitor voltage of the sub-module and affects the stable operation of the M³C. In this paper, a closed-loop control strategy based on quasi-proportional resonance (PR) controller is proposed to suppress the internal circulation of the M³C. First, the ripple voltage of the sub-module capacitor in steady state is studied. The mechanism of harmonic current generated by the coupling of sub-module ripple voltage and switching function is analyzed in detail. The analytical formula of harmonic circulating current is derived, and the necessity of suppressing the circulating current is pointed out. Then, a parallel quasi-PR circulation suppressor is designed for the harmonic circulation of four frequency components, which reduces the distortion of the bridge arm current while suppressing the circulating current, ensures the capacitor voltage balance between the bridge arms with a good dynamic performance. Finally, a simulation model is built in MATLAB / Simulink and the effectiveness of the control strategy is validated by simulation.

To address the issue that most current automatic fabric defect detection methods still require manual selection of training sets and cannot achieve unsupervised learning, an automatic unsupervised defect detection method using the median robust extended local binary pattern (MRELBP) feature for flawless image screening and an under-complete dictionary reconstruction method for defect point detection are proposed. An adaptive dictionary size search algorithm is also proposed to automatically select a suitable dictionary size. First, the algorithm selects the flawless images from fabric images. Then, K-SVD is applied to obtain an under-complete dictionary from the normal image blocks as the training set. Finally, the reconstruction-base scheme is used to identify defects with the structural similarity index measure (SSIM) threshold. Experimental results of 334 plain fabrics with warp, weft, and block defects show that compared to the K-SVD method that uses residual segmentation for defect detection, the proposed method increases the correct detection rate up to 21.81%, reduces the false detection rate up to 0.72%, and a 50% increase in detection speed per image on average. The proposed algorithm achieved an average correct detection rate of 83.29% on the AITEX dataset, demonstrating its effectiveness.

The default behavior of electric vehicle (EV) users to end charging in advance is easy to cause default losses to load aggregators and users themselves. Therefore,it is crucial to dispatch charging behavior rationally considering default behavior. This paper quantifies EV user credit based on Wu’s three-dimensional credit theory, and revises EV charging and discharging plan in the dimensions of discharge depth and charging priority. In addition, it establishes a multi-objective optimization model considering the load fluctuation of regional distribution network and the user cost. Using a park as an example for simulation, it compares and analyzes the effects of different user default rates and EV permeability on the proposed multi-dimensional modified charging and discharging strategy. The results show that the charging and discharging scheduling mechanism based on the user credit index has advantages in calming the load fluctuation of the distribution network in the park and improving the charging experience of users. Compared with the orderly charging and discharging strategy which does not consider the charging and discharging execution of users, the proposed mechanism can better adapt to the scenario of increasing penetration rate of EV and expanding default scale.

Regarding the active power transfer limit issues for new energy units in weak power grids, current research lacks analytical methods considering multiple electrical constraints of new energy systems, including constraints such as maximum steady-state operating current and the voltage variation range at the grid connection point. To address this, the static voltage feasible region under multiple electrical constraints is characterized through the counter lines of electrical quantities, and the analytical expressions for the active power transfer limit under multiple electrical constraints are derived accordingly. Next, the mechanism of active power transfer limit for new energy converters under weak power grids is revealed, where the influence of parameters and the Q-U characteristics is studied. Following this, to satisfy the requirement of operate in weak gird condition within the rated generation range, the minimal parameter requirement is investigated, and the optimal Q-U droop coefficient range is derived to ensure the constrains are always satisfied  Finally, the correctness of the power transfer limit function and the effectiveness of the proposed improvement method is verified through the simulations on PSCAD/EMTDC platform in an extremely weak power grid with a short-circuit ratio of 1.05.

Distribution network lines heavy overloads, voltage instability and other problems occur easily due to large-scale distributed photovoltaic access. The existing economy-centered distribution network photovoltaic hosting capacity optimization assessment method is difficult to meet the distribution network voltage stability need. This paper proposes a quantification method for photovoltaic hosting capacity of distributed network based on static voltage stability margin. First, the analytical equation of static voltage stability index characterized by photovoltaic output power is derived, which reveals the quadratic function relationship between distributed photovoltaic output power and static voltage stability index. Then, with the maximum capacity of distributed photovoltaic access as the optimization objective, a quantitative model for distributed photovoltaic hosting capacity of distribution network is established by considering the constraints of static voltage stability margin, distribution network line loss rate, voltage magnitude, and reactive power compensation device, and an improved adaptive genetic algorithm is used to solve the nonlinear model. Finally, case studies on an IEEE 33-node system validate effectiveness of proposed method in guiding distributed photovoltaic construction in distribution network.

Underwater storage facilities， leveraging oil-water displacement technology， serve as independent storage units that provide diversified media storage at sea and are essential for the development and utilization of marine energy. This paper analyzes five potential application scenarios for future underwater storage facilities， including the development of marginal oil fields in shallow waters， underwater energy storage technology， underwater methanol and ammonia storage technology， underwater carbon dioxide storage technology， and the storage and injection technology of chemical reagents on the seabed. In marginal oil fields， a combined approach using an integrated processing platform， underwater storage facilities， and shuttle tankers can enhance cost adaptability and the flexibility of storage capacity customization. Underwater energy storage technology (underwater compressed air energy storage and underwater hydrogen storage) is provided for offshore wind platforms to ensure the stability of power transmission and reduce the limitations of storage space on the platform. Offshore wind power can also be converted into methanol and ammonia， by adopting underwater storage solutions. The storage units on the platform can be canceled， thereby reducing overall investment. By installing LCO2 storage facilities underwater instead of storing it on the injection platform， the efficiency of LCO2 storage and its environmental adaptability can be improved. The use of seabed chemical reagent storage and injection technology significantly enhances the safety， cost-effectiveness， and injection accuracy of the chemical reagent system.