To improve the thermal performance of phase change thermal energy storage systems, this study proposes a novel triplex eccentric helical tube phase change thermal energy storage unit. This innovative configuration integrates a helical tube structure, an eccentric inner tube arrangement, and internal fins to synergistically enhance both heat storage and release performance. Numerical simulations were conducted to investigate the effects of inner tube eccentricity (0–16 mm) on the heat storage and heat release characteristics. The CRITIC-VIKOR method was employed to determine the optimal eccentricity, using the total melting/solidification time and average heat storage/release rates as evaluation metrics. Furthermore, the effcets of the cold fluid inlet temperature on the heat release performance and the optimal eccentricity was also analyzed. Finally, the heat transfer enhancement provided by single/double-tube built-in cross-shaped fins and X-shaped fins (equal-height/unequal-height) was evaluated. The results indicate that increasing eccentricity enhances natural convection during the melting process; at an eccentricity of 16 mm, the melting time was reduced by 33.84% compared to the concentric structure. During solidification, the optimal eccentricity was 7 mm, yielding an 11.41% reduction in solidification time. A comprehensive assessment identified 10 mm as the optimal compromise eccentricity, and reducing the cold fluid inlet temperature does not alter this optimal value. Among all configurations, the double tube built-in unequal-height X-shaped fins exhibited the best overall thermal performance. Compared to the baseline smooth tube, this configuration reduced the total melting/solidification time by 35.78% and increases the average heat storage and release rates by 39.80% and 67.82%, respectively.

As an emerging thermoelectric storage solution, pumped thermal energy storage (PTES), alternatively termed Carnot battery, plays a vital role in bridging the supply-demand gap between intermittent renewable generation and grid consumption, operating through a dual-mode cycle of electrical charging and thermal discharging to enhance overall energy efficiency. Based on a heat pump-organic Rankine cycle, three Rankine-based Carnot battery architectures were constructed in this paper, with innovative incorporation of internally regenerated configuration and reversible design. Parametric analysis reveals three distinct pathways to enhance system thermodynamic efficiency (encompassing both power-to-power efficiency and exergy efficiency): (1) elevating thermal storage temperature, (2) reducing pinch point temperature difference, (3) improving critical component efficiencies. Increased turbine and compressor efficiencies similarly boost system thermodynamic performance, though notably, the LCOS exhibits higher sensitivity to turbine efficiency compared to compressor efficiency. Furthermore, under fixed heat source conditions, a fundamental trade-off emerges between the thermodynamic metric eta p2p and economic indicator LCOS, thus necessitating a systematic multi-objective optimization. The internally regenerated reversible configuration Carnot battery (R-RCCB) emerges as the most thermoeconomically viable configuration in the optimization results, with the eta p2p escalates from 90% to 118% as LCOS increases from $0.33/kWh to $0.43/kWh.

The high-voltage lead exit is installed in a compact turret external to a 1000 kV power transformer tank. Strong shock waves induced by high-energy arc faults in the lead exit pose a serious risk to turret and tank safety. To enhance the arc withstand capability of 1000 kV power transformer tanks, this study proposes a novel embedded high-voltage lead exit design. Compared to traditional configurations, the enlarged embedded lead exit facilitates the dispersion and attenuation of arc-induced pressure waves, thereby reducing the risk of explosion and combustion. A comprehensive evaluation—including electric field distribution, mechanical strength, and explosion resistance —is conducted to validate the structural reliability and arc fault tolerance of the proposed design. Notably, an arc pressure simulation method based on the bubble pulsation approach is introduced, in which actual arc power is injected into a gas volume equivalent to the arc length to model the pressure source. The Johnson–Cook (JC) failure criterion is employed to accurately evaluate material failure. Simulation results confirm that the embedded configuration significantly enhances arc fault resistance under various internal fault scenarios. This novel design offers a practical solution for improving the safety and reliability of ultra-high-voltage (UHV) transformers and provides valuable insights for optimizing high-voltage lead exit structures in critical applications.

Fossil fuel plants provide critical grid flexibility in renewable-dominated energy transitions, but their high carbon emissions and deep peak shaving need explore solutions like energy storage coupled with carbon capture to ensure stability and decarbonization. In this work, a novel coal-fired power plant system synergistically integrated with compressed air energy storage and carbon capture (CFPP-CAES-CC) is proposed to achieve these objectives. First through simulation comparative analysis, the optimal coupling scheme is determined. And the system achieves a heat rate of 7149.79 kJ/kWh, steam consumption of 2.732 kg/kWh, coupled efficiency of 49.92% under the optimal configuration. Besides, results demonstrate that this system achieves an energy consumption of COQ capture (EC) of 297.38 kWh/t COQ, which is not only lower than the 354.23 kWh/t COQ reported in the literature for a traditional two-stage water-based carbon capture method under comparable modeling principles but also substantially less than the reported consumption of over 900 kWh/t COQ for industrial-standard amine absorption. However, the round-trip efficiency (RTE) of CAES is only 46.52% due to additional energy consumption for carbon capture. Economically, the system yields a net present value (NPV) of 377.48 million$ with an 9.37 years payback period (PP). Meanwhile, the impacts of key operating parameters on the performance characteristics of both the CAES and carbon capture are further investigated in this study. Furthermore, the optimal configuration was identified through multi-objective optimization with NSGA-II and TOPSIS algorithm. The results demonstrate a balanced performance, achieving an RTE of 52.05%, an NPV of 509.83 million$, and an EC of 305.62 kWh/t COQ, along with an exergy efficiency of 57.73% and an annual COQ capture of 395.16 kt.

To address the challenges posed by high renewable energy penetration in integrated transmission-distribution grids, including complex operational scenarios, difficulties in the unified quantification of peak-shaving and voltage-regulation demands, and the inadequate adaptability of traditional single weighting methods, this paper proposes an evaluation model for coordinated transmission-distribution systems using a game theory-based AHP-CRITIC combined weighting approach. By employing K-means clustering to characterize representative and extreme operational scenarios, this study establishes a comprehensive peak-shaving and voltage-regulation index system. Game theory is utilized to coordinate the trade-off between subjective and objective weights, enabling a scoring model that quantifies operational requirements and ranks potential risks. Results from a 108-node system demonstration indicate that the proposed method effectively reveals risk evolution mechanisms, supporting the strategic planning and stable operation of modern power grids.

Existing carbon emission flow (CEF) and carbon-aware dispatch studies primarily focus on carbon accounting and emission evaluation, with limited attention to converting temporal information contained in user-side carbon-emission-intensity profiles into actionable demand response (DR) signals. To address this gap, this paper develops a CEF-based method for characterizing the temporal features of user-side carbon emission intensity. Four features—average excess carbon intensity, high-carbon share, temporal continuity, and peak carbon intensity—are integrated into a temporal carbon-intensity index. On this basis, a low-carbon dispatch framework for integrated energy systems (IESs) is established. The resulting index is used to adjust the dynamic carbon price, which, together with the time-of-use electricity price, guides flexible-load reconstruction through DR feedback. Simulation results show that, compared with the baseline scheme, the proposed carbon pricing and demand response (CP+DR) scheme reduces the total operating cost, network-side transmission exergy-loss index, and user-side carbon emissions by 8.15%, 6.73%, and 9.64%, respectively. Incorporating the hydrogen subsystem achieves a further 8.88% reduction in user-side carbon emissions relative to the CP+DR scheme, but increases the operating cost and network-side exergy-loss index. This result indicates a trade-off between further carbon-emission reduction and economic and network-efficiency performance. Overall, the proposed framework converts descriptive carbon-accounting results into time-varying operational signals, thereby providing an effective approach to carbon-aware demand-side regulation and low-carbon dispatch of IESs.

With the rapid development of the global digital economy, the problems of high energy consumption and high carbon emissions in data centers have become increasingly prominent. Building green data centers dominated by renewable energy represents a critical measure to achieve energy conservation and emission reduction. Nevertheless, there exists a severe temporal and spatial mismatch between the computing power demand and renewable energy. By integrating cross regional task migration, multi-energy power supply and operation strategy of energy storage system, a multidimensional collaborative optimal scheduling strategy for computing power and energy collaboration among cross regional multi-data centers is proposed in this paper, while considering regional differences in grid electricity prices, renewable energy output, network bandwidth constraints, and service level agreements. Firstly, modeling key energy equipment and task loads based on six real data centers in the eastern and western regions of China to improve the authenticity of the research; Secondly, an optimization scheduling model targeting the minimization of total cost is constructed; Finally, the effectiveness of the proposed strategy is verified through a case study of cross regional computing power scheduling between eastern and western China. The research results indicate that compared with the independent operation scenario without loads migration, the cross regional collaborative optimization scenario reduces the total cost by 25.9%: by investing 19.8% of the migration cost, the grid electricity purchase cost is reduced by 35.1%, the carbon emission cost is reduced by 10.8%, and the service level agreement penalty cost is completely eliminated. In comparison with the intra-regional collaboration plan, the total cost is decreased by 18.7%, with the grid electricity purchase cost and carbon emission cost reduced by 35.3% and 10.9% respectively. Sensitivity analysis shows that grid electricity prices and renewable energy output are the core driving factors affecting system economy. The proposed operation strategy not only achieves the efficient and low-carbon transformation of data centers and significant economic benefits, but also provides a feasible path for the coordinated development of the digital economy and green energy.

Winding damage is one of the most common and highly destructive faults in power transformers. To analyze the winding force and deformation under short-circuit conditions, this paper establishes a three-dimensional simulation model of a 220 kV oil-immersed power transformer. The force distribution of the windings under different short-circuit scenarios is investigated, and the vulnerable locations in different simulation model configurations are identified. The effects of variations in spacer blocks and tie bar quantities, as well as differences in material parameters of each component, on the evolution of weak-force regions are summarized. Finally, the influence of short-circuit cumulative effects on the maximum winding deformation is studied, providing a theoretical basis for transformer condition-based maintenance and fault prediction.

With the access of high proportion of renewable energy and power electronic equipment, the power system shows significant parameter randomness and strong rigidity. The traditional electromagnetic transient (EMT) simulation method based on fixed-step trapezoidal integration is inefficient in dealing with random power fluctuations, and it is easy to generate numerical oscillation when simulating power electronic switch transients. Therefore, this paper proposes a stochastic electromagnetic transient simulation algorithm based on pseudo Wiener process and adaptive discontinuous Galerkin-dimensional precise integration (DG-PIM). Firstly, the stochastic differential algebraic equations (SDAEs) are constructed by pseudo Wiener process, and the random parameter migration of new energy is described by large step approximation. Secondly, the discontinuous Galerkin (DG) discrete framework is used to achieve high-order convergence in the time domain, and the augmented dimensional precise integration method (PIM) is introduced to solve the rigid differential equation in the element. The precise calculation of the exponential matrix avoids the matrix inversion and suppresses the numerical oscillation. Finally, an adaptive step size strategy based on local truncation error is designed. The simulation results show that the proposed method can significantly improve the computational efficiency and numerical stability while ensuring the simulation accuracy of stochastic dynamic processes.

To improve the deep peak-shaving capability of combined heat and power (CHP) units under high-penetration renewable energy integration while maintaining heating security and operational economy, this study investigates a 350 MW supercritical extraction heating unit. An off-design thermodynamic model is established in EBSILON and validated against design data. Five heat–power decoupling schemes, namely low-pressure turbine zero-output renovation, hot-water thermal energy storage tank, electric boiler, molten-salt thermal energy storage, and compressed air energy storage, are constructed and compared under a unified framework in terms of the heat–power feasible operating region, energy utilization efficiency, exergy efficiency, and standard coal consumption rate for power generation. On this basis, the hot-water tank scheme, which features bidirectional regulation, low disturbance to the original thermal system, and relatively low investment, is selected for capacity optimization and economic evaluation. The results show that the hot-water tank shifts heat on the district-heating side, reconstructs the actual heating load undertaken by the CHP unit, and moves operating points toward the low-coal-consumption region. For the typical-day dispatch, the optimal tank capacity is 163.986 MWh, corresponding to a volume of 3513.979 m3. Compared with the original CHP unit, the cumulative upward peak-regulation capability, downward valley-load reduction capability, and total operating-range expansion increase by 263.220, 618.162, and 850.062 MWh, respectively. The total energy utilization efficiency increases from 64.50% to 69.24%, and the typical-day net profit increases from 4.230654×106 CNY/day to 4.305578×106 CNY/day. On an annual basis, the hot-water tank scheme yields an incremental annual net cash flow of 1.1279×10⁷ CNY/a, an incremental net present value of 2.160×108 CNY, and a discounted payback period of 0.225 a. These results indicate that the hot-water tank is a heat–power decoupling option with low investment, a short payback period, and stable operating benefits, and can provide a quantitative reference for flexibility retrofits of existing CHP units and the capacity optimization of district-heating-side hot-water storage systems.

Against the booming expansion of the global digital economy, data centers are confronted with severe challenges including excessive energy consumption and massive carbon emissions. Constructing renewable energy-oriented green data centers has become an essential approach to facilitate energy conservation and carbon abatement. However, pronounced spatio-temporal mismatches persist between computing power demand and renewable energy generation. This paper establishes a multi-dimensional collaborative optimal scheduling framework for cross-regional multi-data centers from the perspective of computing-energy synergy, incorporating cross-region load migration, multi-energy complementary power supply and energy storage system scheduling. Practical constraints such as regional electricity price disparities, renewable energy fluctuation, network bandwidth limitation and user service satisfaction are fully taken into account. First, key energy devices and computing task loads are modeled based on actual operational data of six east-west China data centers to enhance research practicality. Then, a total operation cost minimization oriented optimal scheduling model is formulated. Finally, numerical examples concerning east-west cross-regional computing power allocation verify the superiority of the proposed strategy. Simulation results reveal that compared with the isolated operation mode without load migration, the proposed collaborative scheme cuts total system costs by 25.9%. Specifically, it increases migration expenditure by 19.8%, yet reduces grid power purchase costs by 35.1% and carbon emission costs by 10.8%, and completely eliminates service satisfaction penalty expenses. In contrast to intra-regional collaborative strategies, the total cost is lowered by 18.7%, with grid power purchase and carbon emission costs declining by 35.3% and 10.9% respectively. Sensitivity analysis confirms that electricity price and renewable energy output are dominant factors governing system economic performance. The devised scheduling strategy realizes low-carbon and high-efficiency operational upgrading of data centers while achieving prominent economic gains, and further provides a reliable reference for the integrated development of digital economy and green low-carbon energy systems.

Traditional eccentric straight tube heat exchangers fail to optimize heat storage and release performance simultaneously. This limitation significantly reduces the efficiency of latent heat thermal energy storage systems. To overcome this limitation, this study proposes a novel eccentric helical shell and tube heat exchanger (EHSTHE) designed to enhance both performance. This study evaluates the thermal efficiency of the EHSTHE and traditional heat exchangers through FLUENT simulations, and thoroughly investigates the influence of eccentricity on natural convection. The impacts of geometric/operational parameters, phase change materials, and heat loss on the phase change process are examined. The results demonstrate the superior heat transfer ability of the EHSTHE. For the EHSTHE with 13 mm eccentricity, the melting time is reduced by up to 58.78 %, and the solidification time by 43.18 %. Increased eccentricity promotes vortex formation, intensifying natural convection. The average Nusselt number for the EHSTHE with an eccentricity of 13 mm surpasses those with smaller eccentricities, reaching values of 15.05 and 5.19 during melting and solidification, respectively. Moreover, larger coil diameters and smaller coil pitches reduce phase change duration. Higher inlet temperatures significantly decrease melting time, while lower temperatures accelerate solidification. Compared to inlet temperatures of 348 K (melting) and 308.15 K (solidification), using 358 K and 298.15 K reduces the times by 30.42 % and 27.84 %, respectively. In contrast, the inlet flow rate has a minimal impact. The alteration of the phase change material and the consideration of heat loss do not alter the conclusions of this study.

To achieve efficient collaborative operation of a virtual power plant (VPP) under low-carbon and economic goals, A multi-energy coordinated scheduling framework for VPP is proposed. A Stackelberg game model is established with the VPP operator (VPPO) as the leader and the energy supplier operator and user aggregator (UA) as followers. The main contributions include: an electric vehicle (EV) charging/discharging strategy was designed, which ensures its practicality through managed fixed charging/discharging windows and state of charge constraints; concurrently, integrated demand response (IDR) coordinated with Vehicle-to-Grid (V2G) technology via a real-time pricing mechanism to optimize controllable device outputs and shape user consumption. A tiered carbon trading mechanism is further introduced to analyze the game decision-making behavior of each entity under carbon constraints. A VPP in a certain park incorporating renewable energy and combined cooling, heating, and power units is selected as the research case to verify the proposed method. Case simulation results demonstrate that VPPO revenue increases by 17.15%, UA consumer surplus rises by 8.75%, system carbon emissions reduce by 7.75%, and the load peak-valley difference decreases by 57.13%. The proportion of electric demand response load and the number of EVs participating in V2G operations will impact the revenues of different entities and load stability. Studies indicate that the strong constraints of the tiered carbon trading mechanism on major carbon emitters, combined with the collaborative optimization capability of V2G and IDR, can effectively balance low-carbon economic goals and multi-stakeholder interests, providing theoretical support for the low-carbon transformation of complex energy systems.

As the penetration of new energy sources increases, joint operation among microgrids has become an important approach to improving the accommodation level of new energy. To accurately quantify the complementary potential among microgrids within a cluster, this paper proposes a comprehensive evaluation method. It establishes an indicator system covering three dimensions: power supply shortage mitigation potential, supply-demand coordination potential, and grid interaction friendliness potential. The analytic hierarchy process (AHP) and the improved Criteria Importance Through Intercriteria Correlation (CRITIC) method, based on the distance correlation coefficient, are employed for combined weighting, and the fuzzy comprehensive evaluation method is used to quantify the complementary potential among microgrids within a cluster. Case studies show that the proposed method can provide a scientific and effective framework for evaluating the complementary potential among microgrids within a cluster, and has practical value for the planning, design, and operation scheduling of microgrid clusters.

Advanced adiabatic compressed air energy storage (AA-CAES) technology offers a flexible approach in the energy storage field, providing integrated multi-energy storage and supply capabilities to support diverse energy systems. This study introduces an improved AA-CAES system incorporating an electric heater, thermal energy allocation management, and cold energy recovery from exhaust compressed air. The electric heater enhances the temperature of the storage medium, thereby boosting the expander's output performance. Additionally, a thermal energy allocation ratio is introduced to improve operational flexibility in distributing thermal energy between heating consumers and the compressed air system. Energy, exergy, economic, and environmental (4E) models are established, and a detailed comparison between the conventional and improved AA-CAES systems is performed. The findings reveal that the improved system achieves higher net present value (NPV), exergy density (ED), and carbon emission reductions (CER), despite a minor decline in system coupling efficiency ((cyc) and exergy efficiency ((E). Parametric sensitivity analysis is conducted to evaluate the impact of six key design parameters on the overall performance of both systems. Moreover, a multi-objective optimization approach is employed to optimize system performance by maximizing thermodynamic performance and economic benefits while minimizing environmental impact. The results show that the maximum (cycfor the improved and conventional systems is 42.4 % and 54.6 %, respectively, with corresponding optimal NPV values of 161.9 million and 95.27 million dollars, and optimal CER values of 198.4 tons and 54.41 tons, respectively.

Since 2009, ultra-high voltage (UHV) transmission technology has been promoted and applied in China. Over the years, with the accumulation of experience in the construction and operation of UHV projects and the continuous deepening of scientific and technological innovation, UHV technology and key equipment have made great progress. This paper introduces the main achievements of UHV technology innovation from various perspectives including electromagnetic environment, overvoltage and insulation coordination, external insulation and major equipment such as converter transformers, converter valves and gas-insulated transmission lines (GIL).

As clean new energy generation accounts for an increasing proportion of power grid, cogeneration units are required to further improve their operational flexibility. In this paper, a coupled system that combining a 350 MW cogeneration unit with 30 MW compressed air energy storage (CAES) system was established. The heat generated during the CAES system compression process is remitted to the boiler feedwater. Through simulation analysis, the optimal coupling scheme is determined. The influences of key operating parameters such as compressor efficiency, ambient temperature and expander efficiency on the thermodynamic performance of the coupled system were investigated. The results show that the improvement of compressor efficiency and expander efficiency is helpful to improve the thermal efficiency of the coupled system. After that, the effect of external electrical load demand on the operational performance of the coupled system during the storage and release stages was analyzed, as the external electrical load demand decreases from 340 to 320 MW during the storage stage, the round-trip efficiency (RTE) of the CAES system increases from 72.43 % to 78.35 %, and the heat rate of the cogeneration unit decreases from 7220.07 to 7158.57 kJ & sdot;(kWh)- 1. In addition, a technical economic analysis of the coupled system was conducted. The results show that the technical economy of the coupled system is very sensitive to the energy storage power and annual operation times of the CAES system. The increase of the energy storage power and annual operation times of the CAES system is conducive to reducing the dynamic investment payback period of the combined system and improving the investment. These regulations and conclusions can provide theoretical guidance and direction for the coupling of cogeneration units with CAES systems.

This paper presents a discrete-time solution algorithm fora constrained multi-agent optimization problem with inequality constraints. Its aim is to seek a solution to minimize the sum of all the agents' objective functions while satisfy each agent's local set constraint and nonlinear inequality constraints. Assume that agents' local constraints are heterogeneous and all the objective functions are convex and continuous, but they may not be differentiable. Similar to the distributed alternating direction method of multipliers (ADMM) algorithm, the designed algorithm can solve the multi-agent optimization problem in a distributed manner and has a fast O(1/k) convergence rate. Moreover, it can deal with the nonlinear constraints, which cannot be handled by distributed ADMM algorithm. Finally, the proposed algorithm is applied to solve a robust linear regression problem, a lasso problem and a decentralized joint flow and power control problem with inequality constraints, respectively and thus the effectiveness of the proposed algorithm is verified.

The VSC-HVDC engineering is gradually being enhanced in the power grid. The electromagnetic transient model of the VSC-HVDC engineering faces contradictions between model accuracy and computational efficiency due to the numerous power electronic switching devices in the primary system, the complexity of control logic, and the strong internal coupling of the system. This paper is not target a specific VSC-HVDC engineering but proposes a generalized modeling method suitable for efficient parallel simulation of VSC-HVDC engineering from three aspects: the primary system, control and protection system, and signal interaction. The VSC-HVDC engineering model constructed based on this method supports automatic decoupling and parallel simulation and meets the needs for bulk fault calculation. In order to verify the executability, accuracy, and effectiveness of the method, this paper utilizes the electromagnetic transient simulation software ADPSS to construct an electromagnetic transient model of the Zhangbei VSC-MTDC engineering that accurately reflects the characteristics of the actual engineering. By comparing the digital simulation results with the results of the hardware-in-the-loop simulation, the accuracy of the model is validated. The paper conducts parallel simulation of the mode, and analyzes the acceleration effect of the model based on the supercomputing center of the State Grid Simulation Center of the China Electric Power Research Institute. The paper carries out batch fault simulation, ensuring the safe and stable operation of the Zhangbei VSC-MTDC engineering.

The community integrated energy system (CIES) can coordinate the use of multi-type energy equipment to achieve the mutual assistance of different energy, and provide reliable and economical energy supplies to users. However, with respect to the use of different energy equipment, existing research mostly arranges them on the basis of overall optimization results, and rarely fully considers the inherent characteristics of the equipment to efficiently utilize them. In this paper, a variable time scale optimal operation mode for CIES that coordinates multiple types of energy equipment is proposed, and a detailed analysis is conducted from the perspectives of equipment characteristics and the coordination of equipment and net load trend to improve the optimal operation effect. The off-design operation characteristics of energy equipment are analyzed, and an adaptive piecewise linearization method based on K-means is proposed. Dispersion entropy is used to measure the complexity of the net load sequence, and various equipment with different regulation abilities are matched with operation periods with different complexities. A certain CIES is used to verify the effectiveness of the proposed strategy. The case study shows that the strategy can improve the accuracy of the optimal operation scheme, coordinate various energy equipment characteristics and load requirements, and use multi-type equipment more effectively at the cost of less economic loss. Compared with the same type of strategies, the strategy in this paper can reduce the energy deviation by more than 39%, and reduce the adjustment frequency of large inertia equipment by more than 38% at the cost of increasing costs by 0.09%.