Marine pipelines are widely used in offshore engineering and are highly vulnerable to accidental damage caused by underwater structures such as ship anchors and deep-sea submersibles, especially in the dark and unpredictable marine environment. Research on configuration monitoring of marine pipelines is essential to ensure their operational safety. This paper develops a displacement field inversion model for marine pipelines under the influence of three-dimensional background ocean currents, based on the inverse finite element method. The model consists of an input parameter module, a coordinate conversion module, and a displacement reconstruction function module. It takes into account key characteristics such as large curvature, three-dimensional coupling with large displacements, and local flipping behavior. The proposed approach addresses the technical challenges associated with low-order deformation modes and irregular displacement patterns. The impact of the number and layout of monitoring points on the accuracy of displacement field inversion is studied. The results show that the layout with a monitoring point spacing of 100 m and an angle of 30° can meet the engineering accuracy requirements. The findings of this paper can provide valuable insights and methods for the design of marine pipeline health monitoring systems.

Diversified frequency regulation resources are an effective and inevitable approach for addressing frequency safety issues in new power system. Based on a differentiated frequency regulation strategy that coordinates wind power, photovoltaic (PV), thermal power, and energy storage, this paper proposes a source-side battery energy storage system (BESS) optimization method under multiple scenarios by coupling long-term planning with short-term unit commitment. Joint frequency regulation strategies for thermal-storage, wind-storage, and PV-storage systems are developed, refining various functional roles of supporting battery storage to enhance flexibility during frequency regulation. The optimization configuration model aims to minimize both the investment and operational costs of wind-solar-thermal-storage systems. Frequency response capacity available from the power system is set as a security constraint, and high-order multi-machine time-domain simulations are used to verify and iterate frequency security margins in the solution process. The proposed method is validated using an improved IEEE 24-bus system. The results show that battery energy storage can flexibly switch between smoothing fluctuations, reducing renewable energy curtailment, and participating in system frequency regulation.

Parallel gas-electric hybrid systems have broad application prospects in low-carbon ships due to their few emissions and dynamic performance. However, uncertain delays in multiple actuators during mode transition can cause violent fluctuations in the shaft speed along the power drive. In this paper, a cascaded internal mode control (IMC) consisting of filters with explicit nominal delay is proposed to improve speed tracking performance and eliminate the effect of delay. A dynamic model of the marine driveline is developed, and the cascade IMC is designed based on the driveline mechanism with the clutch serving as the separating component. The cascade IMC consists of an anti-saturation compensator, a two-stage tracking controller, and a two-stage anti-interference controller. Finally, the small-gain theorem is derived to ensure robust stability conditions, taking the upper bound of the uncertain delays into consideration. The results of simulation and dynamometer test show that the cascaded IMC has excellent robustness in handling uncertain delays, significantly reduces shaft jerk, and ensures smooth mode transition.

In the context of energy transition and “carbon peak and carbon neutrality” goal, active distribution networks in the new power system can achieve scalable energy conservation and emissions reduction by increasing the penetration of renewable energy and leveraging demand-side management. To this end, a two-stage low-carbon energy management strategy for active distribution networks based on dynamic carbon entropy theory is proposed, using carbon price as a price signal to guide flexible loads in participating in low-carbon demand response. First, the carbon entropy model is analyzed, and a carbon entropy model considering energy storage is established to refine the carbon emission characteristics on the demand side. Then, an evaluation index of node carbon potential is proposed to evaluate the cleanliness of carbon emission of the system. Next, a two-stage low-carbon optimization scheduling model is developed for active distribution networks based on the dynamic carbon entropy. By utilizing the time-of-use electricity prices and node carbon prices as guiding signals, the model promotes the renewable energy consumption, reduces system carbon emissions, and achieves a certain peak-shaving and valley-filling effect. Finally, multiple scenarios are set up in the IEEE 33-node system to validate the effectiveness and superiority of the proposed low-carbon energy management model. The results show that the proposed strategy can achieve low-carbon energy management in active distribution networks.

In low frequency alternating current (AC) transmission systems, offshore wind farm is unable to respond to changes in onshore grid frequency in a timely manner due to frequency decoupling and signal transmission delays between the offshore wind power system and the onshore AC system. To address this issue, a control strategy is proposed to improve the active frequency support capability of offshore wind farms by combining the system inertia. In terms of frequency signaling, an additional frequency sag controller is designed based on the V/f control strategy of the low-frequency-side structure network of modular multilevel matrix converter (M³C), combining with the system inertia. The frequency coupling link between the M³C net side and the low-frequency side is established to realize the real-time transmission of frequency information between the two sides. In terms of frequency support, when the system is disturbed to generate frequency deviation, the offshore wind turbine can adjust the power command value through additional droop control, thereby providing frequency support for the system. Finally, the effectiveness of the proposed coordinated control strategy is verified in MATLAB/Simulink by the simulation of load change and three-phase AC short circuit fault.

With the gradual promotion of the “unit based” planning method for distribution networks, regional distribution networks have been divided into several relatively independent power supply units. However, there are multiple interconnecting lines within the power supply units, and the complexity of the structure makes the mapping of power supply units more difficult. The heuristic automated mapping methods based on rules and force orientation are inefficient and relies on manual intervention, which cannot adapt to the complex and changing distribution network application scenarios. Therefore, this paper proposes an automatic mapping method for medium-voltage distribution network power supply units based on the mean square error condition generation adversarial network. The layout generator and genetic mutation algorithm in this method can generate and optimize the layout and connection of distribution network nodes at a fine-grained level, achieving automatic mapping at various node scales. Then, it designs an evaluation function for node layout generators, which takes topology visualization performances such as node clustering degree, line crossing, and inflection points as key evaluation indicators. This function can be used to iteratively optimize the layout generator and thereby improve the mapping effect. The experimental results show that the method proposed outperforms other heuristic layout methods in terms of mapping effect.

The parameter uncertainty of LCL type grid-connected inverter can significantly affect the power quality of renewable energy, making it essential to analyze the stability of the inverter under the parameter inception. To solve these problems, this paper establishes a state space model of LCL type single-phase grid-connected inverter and proposes a neural network modeling method based on the parameter model. Through the parameter characterization, a training dataset based on the parameter distribution is obtained. This dataset is then trained in the neural network to produce the stability discrimination results. Finally, the effectiveness of the proposed method is verified by MATLAB/Simulink simulation and the experimental platform.

Large-scale new energy bases in desert, Gobi, and arid regions are key components of new-type power systems in China. Considering factors such as construction cost and carbon emissions, the capacities of thermal power and energy storage in these bases are limited, resulting in constrained flexibility. Consequently, the scheduling and operation of these large bases face significant challenges. This paper proposes a coordinated day-ahead and real-time scheduling method for wind-thermal-storage integrated bases. In the day-ahead stage, the startup/shutdown plans and adjustable output ranges of thermal units are determined based on a rough prediction of wind power. Then, it constructs a wind power accommodation interval based on the adjustable range of thermal power output and the operational constraints of energy storage. In the real-time stage, dispatch strategies are generated using a quantile-based rule according to current wind and solar power output, eliminating the need for high-precision forecasts. It is further demonstrated that the dispatch strategies generated by the quantile rule inherently satisfy system operational constraints. The case study validates the effectiveness of the proposed method for wind-thermal-storage systems. The results demonstrate that the proposed method, which does not rely on point prediction, outperforms rolling optimization methods when the three-step prediction error exceeds 10%. Moreover, the performance of operational scheduling can be improved by enhancing the accuracy of day-ahead or intraday short-term forecasts. The proposed method provides valuable reference for the operation of large-scale new energy bases.

To tackle the challenges of high difficulty and time-consuming micro-site optimization of wind farms in complex terrains, a multi-objective optimization method for micro-site selection is proposed, assisted by proxy model. First, considering the geographical features of complex terrains with significent undulations, the ruggedness index is calculated and the ground flatness is numerically quantified, constraining the points with excessive ruggedness. Then, a mathematical model for three-dimensional windy downward wake superposition calculation of power generation is established, a three-dimensional terrain collector line topology optimization agent model is constructed, and the prediction accuracy of the proxy model is verified, demonstrating the ability to replace numerous calculations in collector line topology optimization and effectively improving the computing efficiency. Finally, taking a real complex terrain wind farm in Xinjiang Uygur Autonomous Region, China as an example, multi-objective micro-site selection of complex terrain wind farm is realized, and the results are compared with those obtained through the single-objective optimization. The simulation results show that the multi-objective discrete state transfer algorithm assisted by the proxy model can reduce the total cable laying length, decrease the construction costs, and provide more feasible layout schemes while optimizing the annual power generation.

In asymmetric weak grid, there exists a complex coupling path between the positive- and negative-sequences of the impedance between a doubly-fed wind turbine (DFIG) and the grid, leading to intricate interactions. Since grid codes require wind turbines to provide positive- and negative-sequence dynamic reactive power support, improper settings of these currents may result in system instability and oscillations. Therefore, this paper first develops a sequence impedance model of the DFIG-grid system using a harmonic linearization method, revealing the effects of positive-sequence active current, positive-sequence reactive current, and negative-sequence reactive current on system stability, and analyzes the stability mechanism induced by inter-sequence coupling. Furthermore, considering stability constraints, grid codes, and converter capacity, it characterizes the stable operating domain of the DFIG. To solve the problem of insufficient stable operating range in weak grid, it proposes an adaptive oscillation suppression phase-locked loop (PLL) method, which adaptively suppresses the oscillatory component in the PLL input, thereby extending the stable operating domain of the DFIG. Finally, simulation results verify the correctness of the theoretical analysis and the effectiveness of the proposed control method.

In recent years, as an effective positioning method, electromagnetic wave time reversal (EMTR) technology has begun to be applied in the field of partial discharge. Compared with traditional ultra high frequency (UHF) positioning methods, EMTR technology requires only a single sensor, offering significant advantages and promising application prospects. However, the commonly used maximum field strength and minimum entropy criteria struggle to accurately determine the focusing time and position of time reversal in complex structures. To address these problems, this paper proposes an EMTR location method based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm leveraging the distinct characteristics of waveforms at signal and non-signal sources, and verify the location of partial discharges in gas-insulated switchgear (GIS) using CST Studio Suite simulation software. To verify the field feasibility of EMTR, it develops a laboratory model to conduct partial discharge experiments. The results show that the average positioning error of the EMTR method is less than 20 cm, which can realize accurate positioning of partial discharge sources in GIS. Compared with traditional methods, the EMTR method reduces the number of sensors and improves the anti-interference performance, which has certain advantages.

With the rapid development of the digital economy, the energy consumption and carbon emissions of data centers (DCs) have significantly increased. In recent years, the construction of data center integrated energy systems (DC-IES) has emerged as one of the critical trends in energy conservation and emission reduction for DCs under the global net-zero emission initiative. To support the planning and construction of low-carbon DC-IES, this paper proposes a multi-objective optimization model for capacity allocation and operational planning of DC-IES, integrating energy and economic considerations with a focus on low-carbon performance. Based on the “quality” analysis method of exergy from the second law of thermodynamics, the model proposed comprehensively accounts for the dynamic exergy efficient characteristics of energy conversion devices under varying load conditions, revealing the energy flow distribution characteristics of DC-IES under different objectives. The computational results indicate that compared with the optimization scheme assuming constant equipment efficiency, the scheme considering dynamic equipment efficiency reduces energy loss rate, economic cost, and carbon emissions by 2.6%, 1.9%, and 4.8%, respectively, demonstrating clear advantages. Moreover, compared with the economically optimal scheme, the multi-objective optimization scheme significantly reduces carbon emissions and energy loss rate of the DC-IES by 22.72% and 20.73%, respectively. Furthermore, compared to the scheme scenarios with the minimum exergy loss rate and lowest carbon emissions, the multi-objective optimization scheme reduces economic costs by 54.54% and 60.78%, respectively. Compared with the scheme relying solely on grid electricity supply, the multi-objective optimization scheme that regards the DC as an integrated energy system can reduce carbon emissions by 40.97%.

A power allocation strategy based on variational modal decomposition-multifuzzy control for wind power hybrid storage system is proposed to address the poor grid-connected power quality caused by the uncertainty of wind power output and the poor power allocation of hybrid storage systems. First, Latin hypercubic sampling and Euclidean distance method are used to generate a typical scenario of wind power considering the uncertainty of wind power output. Then, the variational modal decomposition optimized by the positive cosine algorithm is used for the initial allocation of wind power to obtain the grid-connected power of wind power which is lower than the upper limit of the grid-connected fluctuation and the levelling power of the hybrid energy storage system. Finally, the load state of the hybrid energy storage system is partitioned, and a power redistribution strategy under multi-fuzzy control is proposed to achieve the correction of the power of the hybrid energy storage system by considering the system load state and the characteristics of the hybrid energy storage. Simulation results show that the proposed strategy can suppress wind power fluctuations and obtain stable grid-connected power. Additionly, it can effectively solve the overcharging and over-discharging problems of the hybrid energy storage system.

New power system achitecture will greatly increase the difficulty and vulnerability of the operation and control of power systems. The high integration of information and communication technology (ICT) promotes comprehensive information sharing, but it also highlights the urgency of establishing modeling and analysis methods for ICT-based power secondary systems. In this paper, simulation modeling methods for power secondary systems are proposed for the first time to achieve information sharing under interconnectivity and interoperability criteria. A smart substation secondary system with complex functional descriptions is taken as the research object. First, a structural model of intelligent electronic devices (IEDs) is proposed which meets the interconnectivity requirements. Then, a functional model of IEDs with built-in algorithms for secondary business in power systems is proposed, as well as power communication protocol models which meet the interoperability requirements under IEC 61850 standard. Furthermore, the IED function in the node domain and data exchange between IEDs in the network domain are achieved, through the state design in the process domain. Finally, taking a typical 220 kV substation line current protection as an example, the entire process of protection setting modification and protection actions after a fault occurs are simulated by correlating the operating status of the power primary system, verifying the correctness of the proposed simulation models.

To address the challenge of effectively filtering constraints in the unit commitment problem constrained by large-scale line transmission networks, this paper reviews the operating principles of line constraints in both transient and steady states. An effective filtering method based on load similarity mining is proposed to eliminate redundant transmission constraints and reduce the complexity of the problem. Distance functions are developed to measure the similarity of historical load data according to the influence of different nodes on line flows. Based on the similarity analysis, typical power load scenarios are clustered, and effective line constraints are identified according to their operational significance. In addition, a pre-filtering strategy is applied to system states in which line statuses remain unchanged over time, thereby reducing the computational burden during the mining process. Simulations conducted on the IEEE 118 and Case2746wop systems validate the effectiveness of the proposed method, showing that the proposed method efficiently eliminates 99% of ineffective line constraints, and reduces solving time by over 80% compared to existing approaches.

To achieve China’s “dual carbon” goal, integrating large-scale renewable energy into power grids has become an irreversible trend. With the continuous increase in the use of renewable energy, the wind and solar power integration poses critical challenges to the stable operation of the power system. With the perfect dynamic response of active and reactive power, energy storage system can smooth power fluctuations caused by intermittent and uncertain renewable energy, which is conducive to promoting the access of large-scale new energy, realizing the smooth load regulation, and improving the interactive friendliness of the power grid. First, starting from the development of energy storage technology, this paper introduces the domestic and foreign research status of energy storage participating in the auxiliary service market of power peak regulation and frequency modulation. Then, it conducts a comprehensive review on the optimization configuration of energy storage systems taking into account peak shaving and frequency regulation requirements, analyzing from two perspectives: single-type setup and hybrid energy storage. Additionally, it summarizes the solving algorithms for the optimal configuration of energy storage systems. Afterwards, it proposes a grid-friendly new power system based on energy storage participation, and elaborates on collaborative scheduling methods and control strategies in multiple time scales and multiple regions from the perspective of collaborative operation. Finally, it provides an outlook on the future research direction of energy storage from four aspects, which are shared cloud energy storage, numerical intelligent aggregation modeling,intelligent and adaptive control technology, and improving multi-regional cooperation and standardization policy mechanism.

To address the difficulties in adjusting new energy resources along electrified railways, uneven load distribution, and waste of potential energy in loading and unloading operations after trains arrive at stations, a design optimization strategy is proposed for the energy storage traction system of cross-regional freight railways. A time-sharing zoning electricity price model and an energy storage traction system capacity optimization allocation model are developed to guide load time shifting to achieve peak shaving and valley filling in different time domains and geographical spans by analyzing the spatiotemporal characteristics of mobile energy storage charging and discharging. Then, an energy management strategy is proposed aiming at maximizing the daily operating efficiency. The calculation example verifies that the designed energy storage traction system and its operation strategy can effectively improve the economic benefits during the operation cycle of the train, promote energy consumption along the line, and have important reference value for the low-carbon operation of electrified railways.

To address the issues of large current ripple, stagnant current gradient update, and sampling noise interference in the model-free predictive current control method for grid-connected inverters based on look-up table, a three-vector model-free predictive current control method with sampling noise compensation is proposed. First, the output current of each control period is predicted by using the current gradient corresponding to the three-voltage vectors, and the action time of each vector is determined by a cost function to reduce the current ripple. Then, based on the overlapping relationship between the coordinate components of the three vector and the basic vector, a two-step updating method is used to eliminate the stagnation phenomenon. Finally, according to the impact of sampling noise on current gradient updating, a second-order generalized integrator is employed to estimate and compensate the gradient error, thereby improving the accuracy of current gradient. The feasibility and effectiveness of the method proposed is verified by simulation and experiment.

As one of the most promising carbon capture technologies for coal-fired power plants, oxy-fuel combustion provides a new solution for improving the flexibility of the integrated energy system (IES) and reducing carbon emissions. In this paper, a two-stage optimal dispatch strategy for the integrated energy system with oxy-fuel combustion units considering the constraints of multi-energy flexibility is proposed based on the intergration of oxy-fuel combustion technology and the optimal operation of the integrated energy system. First, a model of integrated energy system with oxy-fuel combustion (Oxy-IES) is established. Then, a matrix model of multi-energy flexibility constraints for Oxy-IES is proposed to reveal the supply and demand relationship of flexibility within the system. Finally, a two-stage optimization dispatch strategy for Oxy-IES is constructed, in which the output of each unit is optimized to minimize the daily operating cost of carbon trading in the day-ahead stage, while the rapid variable load capacity of the oxy-fuel combustion unit improves the flexibility of the system in the intraday stage. The simulation results of Oxy-IES show that the proposed strategy can improve the flexibility and economy performance of the IES while reducing carbon emissions.

Under the new situation, there is an urgent need to model and simulate the power secondary system which highly shares information and implements real-time decision-making, in line with the modeling and simulation requirements of new power systems. In this paper, design methods are proposed for the first time to achieve simulation of power secondary systems by correlating the operating status of the power primary system. The smart substation secondary system with complex functional descriptions is taken as the research object. First, an interrelated simulation method for power primary and secondary systems is proposed, and its simulation implementation framework, data interaction method, and data synchronization management are explained, which enables the actual electrical quantity data of the primary system to be transmitted to the secondary side, solving the problem of data source in the secondary system simulation. Then, a simulation design method for the power secondary system is proposed, incorporating system-level interaction design, component-level class design, and module-level state design based on the object-oriented unified modeling language (UML). Thus, the entire process of transmission, interaction, processing, and conversion of electrical quantity data in the secondary system can be analyzed. Finally, to validate the effectiveness of the proposed method, a case study is conducted using a short-circuit fault scenario at the 110 kV side outlet of the 220/110/10 kV main transformer bay, in conjunction with a differential protection scheme.

To develop a maneuvering model for a typical unconventional ship, a decoupled modeling approach was adopted as the foundation to construct a numerical calculation based maneuvering model framework. To determine the hydrodynamic derivatives for the maneuvering model, static oblique towing test (OTT) and dynamic circular motion test (CMT) were performed by using numerical simulations to obtain the forces on ship body. To improve the accuracy of nonlinear hydrodynamic derivatives, a hybrid method combining cubic spline interpolation with least squares fitting was proposed. The zigzag and rotation tests of the scaled ship model verified the accuracy of the maneuvering model. The results show that the maneuvering motion model established by numerical calculation and the hybrid method has good accuracy and can be used for modeling unconventional ship maneuvering motion.

To alleviate the adverse effect of large-scale electric vehicles (EVs) random charging, an EV hierarchical charging method considering multiple modes coordination is proposed in this paper, which avoids the large-scale charging load centralized in a certain period by coordinating diverse charging modes. Considering the load characteristics such as the output of renewable power generation, the power load curves of an area in Inner Mongolia Autonomous Region are clustered into five typical power load curves based on the K-means clustering algorithm and elbow method. According to the characteristics of EV charging and battery swapping (BS) modes, the charging station models and EV hierarchical power exchange model are established. At the upper level, the users’ requirements and charging costs are considered, and EVs are matched with the charging mode based on the particle swarm optimization algorithm. At the lower level, the electric price and charging station operation conditions are considered, and the charging schemes in the charging/BS station are optimized based on the optimization toolbox in the MATLAB software platform. Extensive case studies are conducted to validate the effectiveness of the proposed method, where a large number of EVs charge continuously with cost efficiency. However, relying solely on electricity price as the control signal for EV charging load may exacerbate the valley-to-peak disparity and instability of the local power load curve.

Due to the much faster response time of permanent magnet synchronous motor (PMSM) compared to mechanical dynamics, a tracking differentiator (TD)-based dual-time-scale sliding mode control (SMC) method is proposed. First, the mathematical model is established in a two-phase synchronous rotating orthogonal reference coordinate system, and the fast and slow subsystems are then derived based on the quasi-steady-state theory. To address the conflict between reaching velocity and chattering phenomenon existing in the traditional exponential reaching law, a novel reaching law is introduced, allowing for a comparison and analysis of the reaching-time and switching-band. Next, the SMC laws are separately designed within a dual-time scale, thus resulting in the eventual TD-based composite non-cascade sliding mode controller. Finally, the advantages and effectiveness of the proposed methods are demonstrated through the simulation comparisons and experimental results. The results illustrate that the proposed control strategy can realize tracking without any overshoot, ensuring a fast dynamic response procedure in the servo system. The control system has the perfect dynamic performance when the PMSM operates in the reverse direction, and possesses strong robustness against the external load disturbances.

As China accelerates the construction of power systems with renewable energy as the mainstay, the large-scale integration of renewables has led to prominent issues such as wind and light curtailment. To improve the utilization of new energy consumption in power systems, this paper proposes a novel renewable energy consumption method based on load alignment and deep reinforcement learning. First, it proposes a node load line formation model based on linearized power flow calculations, which can guide adjustable loads to shift the electricity consumption period, thereby promoting the improvement of new energy consumption. Unlike the direct current (DC) power flow model, the proposed alternating current (AC) model accounts for voltage constraints and other related constraints of the power system. Compared with other AC power flow models, this model linearizes all nonlinear constraints and has lower computational costs. Then, this paper constructs a market framework for load alignment mechanism. The framework involves three main entities: independent system operators, regional power grid sellers, and electric vehicle adjustable load aggregators. It also explores the solution for load alignment incentive prices using electric vehicle clusters as adjustable loads. As the solution of the load benchmark incentive price involves a master-slave game between three entities, conventional mathematical analysis methods face high complexity. Therefore, it employs deep reinforcement learning algorithm to solve the problem. The deep reinforcement learning algorithm takes the marginal electricity price of each node as state space, the load benchmark incentive price as action space, and the cost of regional power grid sellers as feedback. The agent can find the load line incentive price that maximizes the benefits of regional power grid sellers after continuous training. Finally, the example analysis shows that the load alignment mechanism not only effectively promotes the improvement of new energy consumption level, but also enhances the interests of independent system operators, regional power grid sellers, and electric vehicle aggregators. The results further confirm that the deep reinforcement learning algorithm maximizes the benefits of regional power grid sellers.

It is difficult for the existing junction temperature algorithms of insulated gate bipolar transistor (IGBT) to evaluate the impact on the thermal diffusion angle of the IGBT module under varying output power and heat dissipation conditions of optical storage unit interface converters in optical storage microgrids, which results in limited accuracy of junction temperature algorithm and poses a huge challenge to system thermal management. To address the above issues, a junction temperature algorithm of IGBT in interface converters in optical storage microgrid systems is proposed considering three-dimensional transverse heat conduction (3-D THC). First, a physical thermal model of power devices is established considering the thermal coupling between multiple chips in the optical storage microgrid system. Then, a junction temperature algorithm considering 3-D THC is further proposed based on the established physical model, and a thermal network model considering 3-D THC is established, which effectively improves the calculation accuracy of current state thermal parameters and power module thermal diffusion angle. Finally, the accuracy of the proposed model is verified using finite element analysis in the PinFin heat sink structure. The simulation results show that compared with various junction temperature algorithms, the proposed algorithm has the smallest error in junction temperature calculation under steady-state and sudden power change conditions, with approximately 3.11% and 3.65% respectively, which increases accuracy by 11.53% and 61.93% respectively compared with the algorithm not considering thermal diffusion angle (α=0). The proposed algorithm also has the highest junction temperature accuracy and the smallest error under different heat dissipation conditions.

To address the problems in the profit model and transaction pricing of distributed energy storage providing multiple customized power services for sensitive customers, a multi-grade pricing strategy for distributed energy storage to provide various customized power services is proposed, including reactive power compensation, voltage sag control, and harmonic control. First, based on the four-quadrant operation characteristics of energy storage converter, a multi-grade evaluation indicator system of customized power services is established considering the differentiated user demands for power quality. Then, a cost-to-capacity model is developed for energy storage to provide customized power services in different power quality standards. Next, by taking economic loss of power quality into account, a user customized power utility function is established with individual rational constraint. Afterwards, considering power quality default risk and investment cost constraints, a customized power revenue model of distributed energy storage is constructed. Furthermore, a multi-grade trading framework for distributed energy storage to provide differentiated customized power services and its multi-grade pricing optimization strategy are proposed. Finally, in order to obtain the optimal additional tariff and user purchase package for premium power, the nonlinear multi-grade pricing model is transformed into a mixed integer linear programming model for optimization by using the big M method and transforming the user utility function into a constraint. The comparative analysis of the algorithm demonstrates that the proposed strategy can reduce the annual cost of customized power services for users while simultaneously enhancing the energy storage revenue.

The power sector is the largest single source of carbon dioxide emissions in China, and its low-carbon transition is a pivotal lever for achieving the dual carbon goals. However, there remains a lack of focused studies on the low-carbon transition of power generation enterprises in the existing literature. To address this gap, this paper constructs a corporate low-carbon transition planning model integrating multidimensional factors including technological, economic, and environmental considerations. It analyzes the decarbonization pathways for power generation enterprises to achieve carbon neutrality by 2060 under the current policy. Furthermore, it conducts comparative simulations of several low-carbon transition scenarios for future power companies. The findings indicate that, for power generation enterprises, advancing carbon neutrality moderately ahead of schedule can yield certain benefits, but overly aggressive timelines may lead to steep cost escalations. Additionally, future policies are likely to drive increased energy storage deployment, necessitating preparatory technological and resource investments in relevant enterprises. Finally, the paper proposes solutions for decommissioned coal-fired power units, recommending their retrofitting or the integration of carbon capture and storage (CCS) technologies, which could be assigned roles in grid peak-shaving and emergency backup.

In a composite energy storage system, coordinating the operation of different types of energy storage is an important approach to enhancing frequency regulation performance. To fully tap the potential of energy storage for frequency modulation, this paper proposes a secondary frequency modulation strategy based on a hybrid system combining battery energy storage and pumped hydro storage. To address the limitation of traditional first-order low-pass filter with fixed cutoff frequencies, it proposes a dynamic adjustment method for the filter time constant based on frequency variation, enabling flexible allocation of modulation commands in the composite storage system. During frequency modulation, it designs a dual fuzzy control strategy to coordinate battery energy storage and pumped hydro storage, taking into account the state of charge (SOC) constraint of the battery. During non-frequency modulation, it constructs the SOC self-recovery curve of the battery using the logistic function, and utilizes the remaining capacity of the pumped hydro storage to restore the battery SOC. Simulation analyses under two typical working conditions show that the proposed strategy has advantages in improving frequency modulation performance and maintaining the SOC of the battery energy storage system.

Electric vehicles (EVs) can provide significant support for the flexible operation of power systems, in which vehicle-to-grid (V2G) mode is an important way for EVs to participate in the frequency and voltage regulation of power grids. However, the commercialization of V2G has experienced slow progress to date, and the lack of an effective market operation mechanism makes it difficult for large-scale EVs to participate in the ancillary services of the grid. Therefore, a novel evolutionary game model is proposed with the participation of the electricity regulatory commission, power grid company, and EVs and the impact of the strategic choices of the three parties on the operation of the V2G market is explored to identify the subsidy and pricing mechanisms for the government to facilitate the long-term evolution of the V2G. First, replicator dynamic equations for the game are established to investigate the stability of multiple strategy equilibrium points in the three-party evolutionary game. Then, the Lyapunov stability theory is employed to analyze the stability of these equilibrium points and to determine the subsidy amount to promote V2G development. Next, a simulation analysis is conducted on the actual electricity price data from Shanghai in China, which quantitatively identified the government subsidy coefficient range and electricity price range to incentivize EV participation in the V2G model. The simulation results provide theoretical support for the electricity regulatory commission and power grid company in formulating subsidy and pricing strategies.

The high proportion of new energy and dynamic load brings significant bidirectional uncertainty of source and load to power system. The strong uncertainty makes scheduling planning face high dimensional decision space and increases planning risk. Therefore, an information gap decision theory (IGDT)-spectral clustering typical scenario generation method considering the uncertainty of source load is proposed to provide a more accurate planning scenario for the determination of the operation mode of multi-source joint systems. First, the source load uncertainty can be quantified effectively by the IGDT theory without considering the uncertain quantitative probability distribution. The source load fluctuation range is described using the IGDT robust model and the IGDT chance model, and the original scenario representing each uncertainty situation is generated using the Latin hypercube sampling method to ensure the adequacy and accuracy of sample space. Then, to address the huge scale of the original scenario caused by the uncertainty of the source load, a spectral clustering method considering the adjustment ability of the system is introduced to mine the feature vectors of different source load fluctuations which have an important impact on the scheduling decision to reduce the complex original scenario set to a representative typical scenario of the source load. Finally, the simulation analysis of the actual system and operation data of a provincial power grid shows that compared with the traditional spectral clustering method, the proposed method generates four more typical scenarios after considering the bidirectional uncertainty of source load, the comprehensive average Pearson correlation coefficient is increased by 8.76%, the comprehensive average Euclidian distance is reduced by 43.48%, and the clustering scenario is more similar to the actual scenario.

With the integration of a high proportion of renewable energy into the grid, the volatility and uncertainty of wind and solar output will pose more severe challenges to the flexible operation of the system. Reasonable utilization of flexible resources and improvement of the flexible ramping ability of power systems will become an important approach to addressing the challenges of new energy. This article analyzes the differences in system flexibility ramping requirements under different clearing intervals, taking into account the uncertainty of renewable energy. It establishes a two-stage electricity energy and flexible ramping market joint clearing model based on a stochastic programming model. By utilizing flexible resource rich regulation methods such as wind/photovoltaic/energy storage power station, while alleviating real-time market uncertainty, it provides flexible ramping products to improve the flexible adjustment ability of the system. Finally, it conducts a case study to analyze the impact of the clearing model under different clearing methods and prediction errors. The results indicate that a two-stage market joint clearance considering the participation of wind/photovoltaic/energy storage power station in flexible ramping can improve the real-time operational flexibility and overall economic efficiency of the system.

The rapid development of data centers makes it possible for them to participate in power system dispatching as demand response. By dispatching the computing resources in data centers between regions, it is possible to achieve energy saving, emission reduction, and cost saving. However, considering the demand response of data center computing resources in power system dispatching faces the problem of insufficient computing speed. To address this issue, a neural branching power-computation network fast optimization method considering computing demand response is proposed. First, a unit commitment double-layer model considering computing power resource demand response is established. Then, the graph convolutional neural network and the branch and bound method are combined and applied to the double-layer model. Through historical data training, the neural branching power-computation network fast optimization method considering computing demand response has the ability to quickly determine the order of branch and bound variables and minimize the number of iterations, which significantly improves the solution speed and realizes the fast solution of unit combination demand response considering data center computing resources. The performance of the proposed method is verified in the simulation scenario of “east data, west computing” project. The proposed method achieves an average solution time reduction of 39.1% compared to the pseudo-cost branching algorithm, 38.1% compared to the commercial solver CPLEX, and 13.5% compared to the machine learning-based optimization acceleration algorithm Extratrees, the average solution time is reduced by 13.5%. In addition, the system coordinated dispatching frequency is increased from 1 time/h to 4 times/h, and the maximum potential reduction in wind curtailment of the total wind power generation during 24 h accounts for 17.42%.

To utilize the operation data of photovoltaic(PV) panels for identifying key parameters of PV cells and diagnosing their operation status, the second-order Bézier function is combined with the adaptive war strategy optimization algorithm to obtain the optimal solutions for the five unknown parameters of silicon-based PV cell single diode topology: photogenerated current, diode reverse saturation current, diode ideal factor, series resistance and parallel resistance. The method includes theoretical and simulation analyses of four common faults, namely, shadowing, aging, short-circuit and open-circuit. By comparing the changes of the parameters in the identification results and the types of faults based on experimental validation, it is concluded that there is a certain correspondence between the five parameters of the PV single diode model and the four typical fault types. Additionly, the output behavior of PV modules in different fault types is characterized, which provides a reference for the identification of PV cell faults and the judgment of battery performance.

The discrimination and recognition of underwater targets has been the focus of active sonar detection. The structural characteristics of the target echo arrival in the bottom bounce area are analyzed when the target depth is greater than the transmit-receive depth. A 9-path arrival structure model is established. The relationship between the time delay of the bottom reflected/ surface-bottom reflected path and the target depth is analyzed. A method is proposed to estimate the time delay by searching for target arrival structure in bottom bounce area, and to estimate the target depth by using the relationship between the time delay and the target depth. The algorithm is verified based on explosion sound source data. The estimated depth of the underwater target obtained by a single hydrophone is in good agreement with the real target depth. The depth estimation of 20 targets within the range of 2.0—25.6 km is conducted, among which the depth estimation error of 17 batches is less than 10 m, and the average depth estimation error of 20 batches is approximately 7 m.

In the context of the new power system, traditional energy consumers are transforming into prosumers, entities that both consume and produce electricity. The emergence of a large number of prosumers has made the purchase and sale decisions of electricity retailers more complicated. At the same time, electricity retailers will also face more uncertainties, resulting in greater market risks. To address these challenges, this paper proposes an optimization method of power purchase and sales strategy for electricity retailers facing prosumers under uncertain factors. First, it establishes a two-tier optimization model for the purchase and sale of electricity by electricity retailers under uncertain conditions. The upper model focuses on the comfort and cost of prosumers, incorporates the uncertainty of photovoltaic output, and develops a robust energy optimization model for prosumers. For the uncertainty of electricity purchase and sale prices in the spot market, the lower model establishes a robust comprehensive decision-making model of electricity sales companies based on information gap decision theory (IGDT). Then, it uses the Karush-Kuhn-Tucker (KKT) condition to transform the two-tier optimization problem proposed in into a single-layer nonlinear programming problem. Finally, the simulation analysis demonstrates the economy of the retail electricity price obtained and the effectiveness of the proposed model.

The re-entrainment of droplets in the inlet filtration components of marine gas turbines severely affects the gas intake quality and the safe operation of gas turbines. To address this issue, the formation and influence mechanisms of droplet re-entrainment in the wire mesh filter were studied, and a comparative analysis was conducted on the effects of different inlet air velocities and droplet diameters on the liquid film thickness on the surface of the mesh and the mass of re-entrainment. The results show that incoming droplets tend to deposit upstream of the mesh segment and form a liquid film due to velocity gradients and inertia effects. Liquid film stripping is the main form of re-entrainment under marine operating conditions, occurring at a critical inlet air velocity between 4 and 4.5 m/s. The compact arrangement of adjacent wire mesh layers accelerates the airflow, and intensifies shear effect on the liquid film, which in turn increases the stripping film mass. This should be avoided during mesh fabrication. With the increase of the inlet air velocity, the overall thickness of the liquid film decreases, while the mass of stripping film increases. The increase in droplet diameter leads to easier blockage of the mesh pores and the local increase in film thickness, eventually leading to the overall decrease in film thickness but the increase in stripping film mass, which seriously affects the filtration efficiency. At a droplet diameter of 20 μm, the filter fails.

Integrated energy systems in buildings are an effective means to achieve low-carbon buildings. To further tap into their demand-side flexibility adjustable potential and carbon reduction potential, and reasonably allocate the interests of various entities in the building integrated energy system, a bi-level optimization scheduling strategy for building integrated energy system considering virtual energy storage in buildings under Stackelberg game framework is proposed. First, the thermal inertia of the cooling and heating system inside the building and the flexibility of the cooling and heating load are considered to leverage the virtual energy storage function of the building and improve system flexibility in the game model. Then, the genetic algorithm is used to solve the upper-level pricing model of energy operators, updating the purchase and sale electricity prices set by upper-level leaders, while the CPLEX solver is used to solve the lower-level problem, optimizing equipment output, demand response, and electricity trading plans. Finally, the proposed model is verified by case studies that it can effectively improve the economic performance and low-carbon characteristics of building integrated energy systems.

As the installed capacity of wind power continues to increase, the frequency security of new power system becomes increasingly significant. To guarantee the frequency security of the system, improve the frequency regulation capability of the system, and determine an optimal wind power installed capacity, a wind power installed capacity optimization model considering dynamic frequency constraints as well as load-side inertia is proposed. First, the dynamic frequency response model with load-side inertia is derived. Then, fuzzy opportunity constraints are introduced considering the uncertainty of wind power, load, and load-side inertia. Taking into account the dynamic frequency constraints, the model incorporates multiple uncertainty fuzzy opportunity constraints, in which the uncertainty constraints are clearly converted into equivalence classes. Finally, to address the dynamic frequency-constrained nonlinear characteristic, the optimization problem is partitioned into a main problem and sub-problems for solution. The validity and feasibility of the proposed model are validated by using an improved 10-machine system.

Multiple regional power grids in China have adopted the control performance standard (CPS) to assess the performance of inter-provincial tie-line operations, which has played a significant role in the reduction of both power deviation on inter-provincial tie lines and frequency deviation across the grid. However, in recent years, during peak electricity consumption months, the provinces in recipient regions often face a collective shortage of frequency regulation resources, while inter-provincial electricity spot market prices remain high. The current CPS assessment mechanism, which calculates CPS penalties based on a lower fixed price, has become ineffective as a safe guard for short-term power and frequency balance. Therefore, a master-slave game model between the regional dispatch center and provincial dispatch centers is proposed to determine the CPS assessment price that ensures sufficient reservation of automatic generation control (AGC) adjustable capacity. In this model, the regional dispatch center sets the CPS assessment price, and the provincial dispatch center responds by formulating day-ahead dispatch plan based on this price. The regional dispatch center adjusts the assessment price based on the dispatch optimization results of the provincial center until the adjustable capacity or frequency deviation of the province meets certain requirements. Case study results show that when the assessment price is set at eight times the highest value of various market prices, the assessment mechanism effectively incentivizes provincial dispatch centers to purchase inter-provincial spot electricity and demand response resources in face of generation shortage. This approach enables the system to meet the maximum predicted load and potential forecast errors, thereby maintaining the frequency deviation of the interconnected power grid within acceptable limits.

For oceanic island clusters with special geographical conditions, this paper proposes a rescue vessel scheduling strategy that integrates island transportation and energy supply to address resource heterogeneity among oceanic island clusters and ensure vessel safety, thereby improving the disaster resilience of rescue vessels under optimized scheduling. Based on both energy dispatch and transportation, a two-stage optimization method is adopted. In the first stage, day-ahead scheduling is performed according to the geographical relationship of the islands to determine preliminary sailing routes. In the second stage, both safe and emergency modes are considered. In the safe mode, based on the optimized routing information from the first stage, further optimal scheduling of power generation is performed to improve operational efficiency and economic performance under environmental constraints. In the emergency mode, considering generator fault, a coordinated optimization strategy between load-side management and power generation is implemented to support critical rigid loads and guarantee the normal operation of rescue vessels. Finally, simulation results verify the effectiveness of the proposed strategy, providing a theoretical reference for the sustainable development and construction of oceanic island clusters.