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- Abstract：ObjectivesIn recent years, organic Rankine cycle-vapor compression cycle (ORC-VCC) systems have made great progress in the fields of low-grade heat energy utilization and cooling and heating supply owing to their compact structure, low-carbon, and energy-saving advantages. Accordingly, focusing on three key aspects of cycle configuration, working fluid selection, and application directions, this study reviews the latest research progress in ORC-VCC systems.MethodsFirstly, five different cycle configurations are outlined, and their principles and characteristics are elucidated to demonstrate the structural diversity and adaptability to operating conditions of ORC-VCC systems. Subsequently, the development history of working fluids is explored in depth, and the principles of working fluid selection are summarized from multiple dimensions such as thermophysical properties, environmental friendliness, and economic efficiency. The economic evaluation results of the systems in different studies are summarized, highlighting the importance of economic analysis. In addition, the potential application fields of the systems are summarized from both the supply and demand sides, and application cases of integration with other systems are presented, with a view to revealing the extensive research and application potential of ORC-VCC systems. Finally, the shortcomings of the current research are pointed out and the future research directions are prospected.ConclusionsORC-VCC systems have broad development prospects and huge development potential. Their application in the energy field will make important contributions to achieving low-carbon, energy-saving, and sustainable development goals.Keywords：renewable energy;multi-energy complementarity;distributed energy;organic Rankine cycle-vapor compression cycle;configuration optimization;working fluid screening;integrated application10|8|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Research on Modeling and Rapid Load Change Control Strategy for High Back- Pressure Extraction-Condensing Combined Heat and Power System AI Introduction
Abstract：ObjectivesRapid load change capability of combined heat and power units can ensure the consumption capacity of the power grid for renewable energy and play an important role in improving the economic efficiency of thermal power units. Therefore, taking a high back-pressure extraction-condensing combined heat and power system in a power plant as the research object, this study focuses on system modeling, dynamic characteristic analysis, and rapid load change control strategy, leveraging the characteristics of its high back-pressure exhaust steam and heating extraction steam to achieve cascade heating of the return water in the heating network.MethodsA dynamic characteristic model of the system is established based on the APROS simulation platform, and the accuracy of the model is verified using on-site operation data. The dynamic response characteristics of the thermal and electrical regulation process of the system under varying operating conditions are investigated, and the relationship between the thermal and electrical loads of the unit and the steam extraction valve (EV) under different operating conditions is established. A rapid load change control strategy coupling the heating steam extraction regulation of the unit is proposed. By implementing feedforward compensation at the EV valve, rapid changes of the unit output are realized, improving its load change response speed.ResultsCompared to coordinated thermal-electrical control, the rapid load change strategy reduces the response time to automatic generation control (AGC) commands by 150 seconds and shortens the time to reach steady state by 300 seconds.ConclusionsThe rapid load change control strategy for the dual-unit combined heat and power system significantly enhances the response speed of the system to AGC commands, with reduced oscillations during the transition process and a shorter time to reach steady state.Keywords：combined heat and power system;new energy consumption;steam extraction valve control;rapid load change;coordinated control system;high back-pressure;steam extraction regulation butterfly valve6|7|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Exergoeconomic Research on Low-Carbon-Emission Combined Cooling, Heating, and Power System Based on Heat Pump Technology AI Introduction
Abstract：ObjectivesUnder the background of the “dual-carbon” strategy and the global energy structure transition, achieving high-efficiency and low-carbon operation of conventional energy systems is a pressing challenge that needs to be overcome. To address this, an low-carbon-emission combined cooling, heating, and power (CCHP) system coupled with heat pump (HP) technology is proposed.MethodsA multi-stage waste heat recovery and temperature-zone coordinated matching mechanism is employed to optimize the energy cascade utilization pathway. A multi-dimensional evaluation model, covering thermodynamics, environmental friendliness, economic efficiency, and exergoeconomic performance, is established and compared with the performance of conventional systems.ResultsThe results of economic analysis show that the heat-pump integration increases the unit power generation cost from 1.24 to 1.43 yuan/(kW⋅h), but reduces the unit capacity cost from 0.24 to 0.22 yuan/(kW⋅h), resulting in an overall improvement in energy efficiency. The sensitivity analysis results reveal that operating duration and interest rate have the greatest impact on costs. Exergoeconomic analysis indicates that the turbine, carbon capture and storage (CCS) system, and compressor contribute 67.019%, 17.107%, and 8.169% of the exergy loss, respectively, representing the core directions for future optimization.ConclusionsThese research findings provide a theoretical basis for the design and promotion of low-carbon-emission CCHP systems.Keywords：combined cooling, heating, and power (CCHP) system;waste heat recovery;heat pump (HP) technology;carbon capture and storage (CCS);carbon emissions;thermodynamic analysis;environmental friendliness;exergoeconomic analysis10|11|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Distributed Energy
- Abstract：ObjectivesRenewable energy generation primarily using wind, solar, and hydro power is one of the practical approaches to achieving the dual carbon goals. Therefore, it is essential to review the research on the increasingly mature and gradually systematized wind-solar-hydro complementary power generation systems.MethodsFirstly, the research background and working principle of the wind-solar-hydro complementary power generation systems are outlined. Subsequently, the research progress on the systems is reviewed, including wind-solar-hydro multi-energy power prediction, configuration ratio evaluation, integrated scheduling studies, and research on wind-solar-hydro complementarity combined with energy storage technologies.ResultsWind-solar-hydro forecasting faces temporal scale synchronization challenges, with short-term accuracy inferior to hydropower forecasting. The comparison optimization method and the direct optimization method are mainly used to evaluate the capacity ratio. Integrated scheduling predominantly utilizes uncertainty modeling approaches, with intelligent optimization algorithms demonstrating superiority over conventional algorithms in solving nonlinear problems. Hybrid configurations of pumped storage and emerging energy storage technologies exhibit the most promising development prospects.ConclusionsIn the future, breakthroughs are required in four key areas: enhancing multi-scale coordinated forecasting accuracy, strengthening flexible regulation capabilities of hydropower and energy storage, constructing intelligent scheduling platforms, and optimizing modeling and solution algorithms, thereby supporting China’s 14th Five-Year Plan for clean energy system construction.Keywords：renewable energy;new energy;wind-solar-hydro power system;complementary power generation;power prediction;configuration ratios;optimal scheduling;energy storage18|14|8<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
- Abstract：ObjectivesThe Guangdong-Hong Kong-Macao Greater Bay Area is an important strategic area for developing China’s hydrogen energy industry and a key platform for realizing the “dual carbon” goals. In order to promote high-level development of the hydrogen industry in Greater Bay Area and accelerate the establishment of a comprehensive hydrogen industry assessment system, this study investigates the major factors influencing the development of the hydrogen energy industry in Greater Bay Area and proposes comprehensive recommendations to promote its robust growth.MethodsThe current development status of the hydrogen energy industry in Greater Bay Area is thoroughly analyzed. The SWOT analysis framework and analytic hierarchy process (AHP) are used to quantitatively assess and investigate the influencing factors of the distribution of the hydrogen energy industry in Greater Bay Area.ResultsThe three most important indicators influencing the development of the hydrogen energy industry in Greater Bay Area are technological strength, market demand growth, and technological innovation and cooperation opportunities. Among internal factors, technological strength is the most prominent advantage of the hydrogen energy industry in Greater Bay Area, with a weight value of 0.48, while an incomplete industrial chain and insufficient technological level are the primary disadvantages, both with a weight value of 0.36. Among external factors, the growth in market demand provides opportunities for the hydrogen energy industry development in Greater Bay Area, but the diversity of alternative energy sources has become the biggest obstacle threatening the industry’s development.ConclusionsKey measures to effectively promote the sustainable development of the hydrogen energy industry in Greater Bay Area include advancing technological innovation, enhancing industry collaboration, expanding the hydrogen energy application market, and strengthening policy support.Keywords：dual carbon;carbon neutrality;hydrogen energy;energy transition;Guangdong-Hong Kong-Macao Greater Bay Area;analytic hierarchy process (AHP);SWOT analysis;policy recommendations16|11|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Power Prediction Method for Offshore Wind Farms Based on Temporal Convolutional Network-Transformer AI Introduction
Abstract：ObjectivesOffshore wind power is of great significance for achieving carbon peaking and carbon neutrality goals, yet its power prediction remains challenging. To address this issue, this study proposes a novel offshore wind power prediction method integrating temporal convolutional network (TCN) with the Transformer framework.MethodsFirstly, discrete cosine transform (DCT) technology is incorporated to effectively extract frequency-domain features from time-series data, optimizing feature weight allocation by fully exploiting frequency-domain characteristics. Based on this, a hybrid TCN-Transformer model is constructed, which is capable of comprehensively integrating both time-domain and DCT-processed frequency-domain information, enabling the deep learning and accurate prediction of the data.ResultsComparative experiments are conducted using two sets of actual operational data from an offshore wind farm. The results show that the proposed method not only performs well in feature extraction but also significantly improves the accuracy of power prediction, fully verifying its effectiveness and application value.ConclusionsThe TCN-Transformer model integrated with DCT technology demonstrates exceptional prediction performance in offshore wind power prediction. Compared to other benchmark models, it achieves a significant improvement in prediction accuracy. This approach provides robust support for the efficient operation and optimal management of offshore wind farms.Keywords：offshore wind power;power prediction;discrete cosine transform;temporal convolutional network;machine learning;deep learning;renewable energy16|14|4<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
New Energy
Bi-Level Planning Model for Distributed Energy Storage Based on Accommodation-Supply Security Game AI Introduction
Abstract：ObjectivesTo address the conflicting objectives between “maximizing renewable energy accommodation” and “ensuring power supply reliability” in distributed energy storage systems, a coordinated planning method that balances economic efficiency and resilience is proposed.MethodsA dynamic Stackelberg game-driven bi-level planning model is established, integrating Stackelberg game theory with robust optimization to address the cross-level coupling problem between planning and operation. The model coordinates the interests of planning, operation, and market stakeholders to optimize energy storage capacity allocation and charging/discharging strategies, thereby enhancing power supply reliability. Through bi-level optimization, coordination between planning and operation is achieved, and the trade-off between accommodation and supply security is quantified, with Pareto-optimal solutions derived to achieve the lowest investment costs and highest renewable energy accommodation rates.ResultsA rapid solution algorithm for a bi-level model incorporating multi-objective optimization is developed, and a “dynamic pricing-capacity subsidy” linkage mechanism is proposed. This mechanism effectively balances the conflicting interests among multiple stakeholders in planning and operation. By integrating Stackelberg game theory with robust optimization, the cross-level coupling problem between planning and operation is solved. The simulation cases demonstrate that the proposed bi-level game model significantly outperforms conventional planning methods, validating the long-term economic efficiency of the dynamic game mechanism. Furthermore, a nonlinear trade-off relationship between renewable energy accommodation and power supply security is revealed.ConclusionsThe bi-level planning model based on accommodation-supply security game coordination can significantly enhance the system’s renewable energy accommodation rate and power supply reliability, while reducing energy storage system investment costs and user electricity costs.Keywords：distributed energy storage planning;accommodation-supply security game;bi-level optimization model;renewable energy accommodation;power supply reliability14|8|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Optimization Configuration of Hybrid Energy Storage Capacity for Wind Power Fluctuation Smoothing Based on Variational Mode Decomposition AI Introduction
Abstract：ObjectivesTo address the issues of variability and intermittency of wind power generation, a configuration scheme of hybrid energy storage system for wind power smoothing is proposed. The system integrates lithium-ion batteries and supercapacitors into the proposed wind power system to reduce its instability.MethodsFirstly, filtering techniques of exponential smoothing and recursive weighted average are used to dynamically adjust their weights, determining both the grid-connected wind power and the reference power for the energy storage system. Then, particle swarm optimization algorithm is applied to adjust the parameters of variational mode decomposition, decomposing the reference power of the energy storage system into high-frequency and low-frequency components, which are handled by supercapacitors and lithium-ion batteries, respectively. Finally, a lifecycle optimization configuration model for the hybrid energy storage system is developed to minimize the configuration costs of storage facilities.ResultsThe proposed solution can make the output power better meet the grid-connected standard, reduce the grid-connected power output time, and realize the functional complementarity between lithium-ion batteries and supercapacitors.ConclusionsThe proposed solution effectively addresses the variability and intermittency of wind power generation, achieving optimal energy storage configuration and striking a balance between economic efficiency and stabilize fluctuation performance.Keywords：wind power fluctuation smoothing;wind power system;weighted algorithm;hybrid energy storage;variational mode decomposition;particle swarm;capacity configuration;cost effectiveness7|6|2<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29- Abstract：ObjectivesLithium-ion batteries, as an important power source for new energy vehicles, require accurate state of health (SOH) estimation to design safe and reliable battery management systems. Traditional methods often overlook issues such as capacity recovery and insufficient feature effectiveness, which significantly affects estimation accuracy. To address these issues, a novel SOH estimation method for lithium-ion batteries that considers battery capacity recovery is proposed.MethodsBy combining the median absolute deviation with the Savitzky-Golay filter in the data preprocessing stage, the model effectively removes outliers and noise to improve the effectiveness of the features. Subsequently, feature decomposition is performed to remove redundant information and alleviate the computational load of the model. Highly correlated features are then selected as inputs for the temporal convolutional network model, reducing data dimensionality and simplifying the computational complexity of the neural network. Furthermore, an improved self-adaptive honey badger algorithm is proposed to optimize the hyperparameters of the network, accelerating model convergence and enhancing network performance.ResultsThe proposed method has a high level of accuracy, with both the root mean squared error and the mean absolute error being lower than 0.007.ConclusionsThe proposed method exhibits high robustness, enabling effective SOH estimation of on-board lithium-ion batteries and meeting requirements of practical application.Keywords：energy storage;new energy;electric vehicles;lithium-ion battery;state of health estimation;improved honey badger algorithm;temporal convolutional network;data-driven;state of charge4|6|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Hydrated Salt Composite Phase Change Materials for Passive Thermal Management and Safety Protection of Lithium-Ion Batteries AI Introduction
Abstract：ObjectivesWith the development of high power and high energy density batteries, thermal safety has become the key to limit the large-scale application of lithium-ion batteries (LIB), and efficient thermal safety protection technology has become an inevitable development. Therefore, a hydrated salt composite phase change material (CPCM) with high latent heat, high thermal conductivity and two-stage temperature control function is prepared for LIB thermal management and thermal safety protection.MethodsThe thermal physical properties and heat storage process of CPCM are determined by scanning electron microscope test, differential scanning calorimetry analysis and thermal constant analysis. Then, the temperature control effect is studied by combining the thermal performance test, and the best ratio of CPCM is selected.ResultsThe CPCM with an expanded graphite incorporation mass fraction of 10% has a latent heat of 183.7 J/g and a thermal conductivity of 3.926 W/(m⋅K). The latent heat of phase change can absorb heat and quickly transfer to the outside under the condition of high rate discharge of LIB, and the phase change platform lasts for 20-40 min. The CPCM thermochemical heat storage can extend the time for the cell surface to reach 130 ℃ from 50 s to 180 s under the thermal runaway state of LIB, delaying the triggering and spreading of thermal runaway characteristic temperature.ConclusionsThe CPCM can simultaneously absorb heat in the thermal management range of 20-40 ℃ and the thermal runaway range above 100 ℃, and play an efficient two-stage temperature control role. The research results can provide new ideas for the development of battery thermal management system.Keywords：energy storage;composite phase change material (CPCM);hydrated salt;lithium-ion batteries (LIB);thermal management;thermal runaway11|7|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29- Abstract：ObjectivesThe application of thermal charging supercapacitors in energy storage is constrained by the ionic characteristics of the electrolyte. This study aims to explore the impact of electrolyte ionic characteristics on the capacitance performance and power density of the device. Based on the principle of cyclic voltammetry, a numerical model for the thermal charging supercapacitors is developed using multi-physics field numerical simulation methods.MethodsThe effects of ion size ratio and ion type on capacitance performance and power density are investigated separately, and the variation pattern of cation concentration at the electrode solid-liquid interface under a given temperature difference is summarized.ResultsThe current density and specific capacitance of thermal charging supercapacitors decrease as ion size ratio increases at low scan rates. At this point, the power density is mainly affected by the ion size ratio, and it decreases as the ion size ratio increases. At high scan rates, power density is mainly affected by the ion type.ConclusionsThis study proposes an optimization strategy for electrolyte ionic characteristics, which is important for numerical modeling and theoretical analysis of thermal charging supercapacitors.Keywords：waste heat power generation technology;supercapacitors;cyclic voltammetry;numerical model;ionic characteristic;current density;power density;capacitive performance3|7|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Research on Heat Storage and Release Characteristics of Solid Heat Storage Materials in Low-Pressure Environments AI Introduction
Abstract：ObjectivesWhen solid heat storage devices are applied in high-altitude areas, the thin air leads to significant variations in their heat storage and release performance. To address this, a method for testing the heat storage and release characteristics of solid heat storage materials in low-pressure environments in high-altitude areas is proposed.MethodsA test bench for low-pressure environment simulation is constructed, consisting of an air loop unit, a water loop unit, and a data acquisition unit. Heat storage and release tests are conducted on a heat storage unit composed of two heat storage bricks under pressures ranging from 40 kPa to 101 kPa. By recording the inlet air temperature and the temperature variations of the heat storage bricks, the heat storage and release rates of the heat storage materials in low-pressure environments are analyzed, and the heat transfer performance between the air and heat storage bricks under different pressures is compared.ResultsCompared to the 101 kPa condition, the heat storage time increases significantly under both 80 kPa and 40 kPa conditions, with lower air pressure requiring longer heat storage time. Additionally, the average heat storage rates of the heat storage bricks at 80 kPa and 40 kPa decrease by approximately 15% and 55%, respectively. During heat release, the heat release time increases by 26.1% at 80 kPa and 135.4% at 40 kPa.ConclusionsAir pressure significantly affects the heat storage and release rates of heat storage bricks, the experimental results can provide guidance for the design of solid heat storage devices applied in high-altitude areas.Keywords：renewable energy;energy storage;heat storage materials;heat storage characteristics;heat release characteristics;temperature distribution;heat storage technology8|7|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Study on Heat Storage Performance of Various Mixture Combinations of Stones, Sand, and Thermal Oil AI Introduction
Abstract：ObjectivesTraditional concrete thermal energy storage systems are prone to cracking during cyclic heat storage processes, leading to a sharp decline in overall heat storage capacity and power, which severely affects system lifespan and increases heat storage costs. To mitigate these issues, a multi-component mixed heat storage method incorporating stones, sand, and thermal oil is proposed.MethodsUtilizing computational fluid dynamics (CFD) simulation technology, the heat storage processes of different heat storage media are simulated and investigated.ResultsCracked concrete experiences a 23.1% reduction in heat storage capacity compared to uncracked concrete. Under the same heat storage conditions, coarse sand demonstrates better heat storage performance than fine sand. The mixture of coarse sand and thermal oil exhibits a 7.2% higher heat storage capacity than that of cracked concrete. Furthermore, the mixture of sand, stones, and thermal oil increases heat storage capacity by 20.5% relative to cracked concrete used as heat storage medium. The released heat is slightly lower than the stored heat. The heat released by cracked concrete is 21.7% lower than that of uncracked concrete and 18.86% lower than that of the mixture of sand, stones, and thermal oil used as the heat storage medium. Cost calculations further reveal that the thermal energy storage system using the mixture of stones, sand, and thermal oil as the storage medium achieves higher heat storage capacity while resulting in lower fixed and maintenance costs.ConclusionsThis multi-component mixed heat storage method can effectively address concrete cracking issues, enhance heat storage efficiency, and reduce costs, which is of great significance for advancing the development and application of heat storage technologies.Keywords：energy storage;packed bed;thermal energy storage technology;heat storage medium;multi-component mixing;computational fluid dynamics;heat storage capacity;fixed cost12|8|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Energy Storage
Study on Peak Shaving Characteristics of Nuclear Power Unit Based on Molten Salt Heat Storage AI Introduction
Abstract：ObjectivesNuclear power units have great potential for peak-shaving. However, the peak-valley fluctuations in terminal load and the characteristics of nuclear reactors to maintain stable output seriously affect the safe and efficient operation of nuclear power peak-shaving process. Heat storage technology, which enables the storage of thermal energy, is an important way to solve the imbalance between nuclear power supply and demand. Therefore, the peak-shaving characteristics of nuclear power units based on molten salt heat storage are studied.MethodsThis study takes an actual nuclear power unit as the research object. Based on the principle of energy cascade utilization, three nuclear power peak-shaving system schemes coupled with molten salt heat storage are proposed: main steam heating molten salt, high-pressure cylinder exhaust steam heating molten salt, and electric heating molten salt. A simulation model of the coupled system is established and supplemented with targeted experimental validation. The system thermodynamic performance under five peak-shaving amplitudes of 5%, 10%, 15%, 20%, and 25% is systematically studied.ResultsThe nuclear power peak-shaving system coupled with molten salt heat storage improves the peak-shaving performance of the nuclear power unit while meeting different heating demands. The high-pressure cylinder exhaust steam heating molten salt coupled system scheme exhibits the best thermodynamic performance, followed by the main steam heating molten salt coupled system scheme, and the electric heating molten salt coupled system scheme performs the worst. Under a 25% peak-shaving amplitude, the energy efficiencies of the high-pressure cylinder exhaust steam heating molten salt coupled system scheme and the electric heating molten salt coupled system scheme are 63.76% and 37.84%, respectively, and the exergy efficiencies are 59.41% and 23.04%, respectively.ConclusionsThe proposed molten salt heat storage schemes for nuclear power units can be applied to scenarios such as municipal heating supply, providing guidance for the safe and efficient operation of nuclear power units in grid peak shaving.Keywords：nuclear power unit;peak-shaving;molten salt heat storage;integrated system;energy cascade utilization;thermodynamic analysis10|7|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Superheated Steam Temperature Prediction Based on Long Short-Term Memory Neural Network Optimized by Novel Whale Optimization Algorithm AI Introduction
Abstract：ObjectivesIn the process of participating in flexible peak shaving, thermal power units frequently increase and decrease load, leading to significant fluctuations in superheated steam temperature, which affects the operational safety and economic performance of the units. Accurately predicting the change trend of superheated steam temperature is crucial. Therefore, a superheated steam temperature prediction model based on long short-term memory (LSTM) neural network optimized by the novel whale optimization algorithm (NWOA) is proposed.MethodsPrincipal component analysis (PCA) is used for variable selection to eliminate redundant variables in the initial data. The whale optimization algorithm (WOA) is improved by nonlinear convergence factor adjustment strategy, adaptive weighting strategy, and dynamic spiral update strategy to enhance the accuracy and global search ability of the algorithm. An LSTM model for superheated steam temperature prediction is established for a direct current boiler in a power plant in Xinjiang. The improved whale algorithm is used to optimize the parameters of this model, addressing the challenge of selecting the optimal hyperparameter combination. The prediction simulation is carried out using the power plant data.ResultsThe improved algorithm demonstrates higher prediction accuracy. It can better predict the change trends of superheated steam temperature during periods of load increase and decrease. The mean absolute error and root mean square error of the model are reduced by 7.02% and 6.22%, respectively, compared to the model before improvement.ConclusionsThe improved algorithm effectively enhances the accuracy of the superheated steam temperature prediction model, providing a basis for the optimal design of the superheated steam temperature control system.Keywords：carbon peak;carbon neutrality;peak shaving;superheated steam temperature prediction;long short-term memory neural network;novel whale optimization algorithm;principal component analysis6|9|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Parameter Optimization of Geothermal Organic Rankine Cycle Power Generation System Based on Ton of Water Generation AI Introduction
Abstract：ObjectivesTaking the net power generation per ton of water in the system as the analysis objective, study the variation trend of system performance parameters under different fluid temperatures of geothermal sources.MethodsA 1 MW geothermal organic Rankine cycle model is established using EES software. The optimization objective is to study the thermodynamic performance parameters of four organic working fluids, R245fa, R1233zd (E), R134a, and R152a, at a heat source temperature of 90-150 ℃.ResultsUnder the same heat source temperature, as the evaporation temperature of the working fluid increases, the net power generation per ton of water increases first to a maximum and then decreases for the system using different organic working fluids. Among them, the net power generation per ton of water of the working fluid R134a is the highest. When the heat source temperature is 110 ℃, the net power generation per ton of water is 3.06 kW⋅h/t. When the heat source temperature is between 90~150 ℃, the maximum net power generation per ton of water, working fluid evaporation temperature, system net efficiency, and expansion ratio of the power generation system increase with the increase of the ground heat source temperature. However, the mass flow rate of the working fluid decreases with the increase of the ground heat source temperature.ConclusionsThe research results can provide a basis for the thermodynamic parameter selection of a 1 MW geothermal dual cycle power generation system.Keywords：new energy;dual circulation system;geothermal power generation;organic working fluid;system optimization;organic Rankine cycle6|9|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Power Generation and Environmental Protection
Fault Ride-Through Control Strategy of Virtual Synchronous Generator Based on Joint Control of Power Loop and Current Loop AI Introduction
Abstract：ObjectivesThe virtual synchronous generator (VSG) will suffer from power angle instability and cannot suppress the fault over-current when voltage drop faults in the power grid, posing a threat to the safe and stable operation of system. In order to solve these problems, based on the output characteristics of VSG under power grid faults, a VSG fault ride-through strategy based on joint control of power loop and current loop was proposed.MethodsDuring the period of voltage drop in the power grid, the proposed strategy stabilizes transient power angle by adjusting the active power command value, suppresses fault current by freezing the reactive power loop and adding virtual resistor. An improved reactive current control is adopted, which can provide necessary reactive power support for the power grid by outputting reactive compensation current during grid faults, and suppresses reactive power oscillations by outputting reactive recovery current during grid recovery period. Finally, the proposed strategy is simulated and verified on the MATLAB/Simulink simulation platform.ResultsAfter adopting the proposed strategy, the transient impulse current of VSG is effectively suppressed, the steady-state fault current is within the safety threshold, and the appropriate reactive power is output.ConclusionsThis control strategy effectively solves the problems of fault overcurrent and reactive power exceeding the limit of VSG during voltage drop in the power grid, which is beneficial for the safe operation of the power grid during faults and the grid connected operation of VSG after fault recovery.Keywords：power grid;distributed generation;new energy;virtual synchronous generator (VSG);power angle stability;fault current suppression;reactive power support9|9|3<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Consensus Algorithm for Economic Scheduling of Microgrids Based on Improved Dynamic Event-Triggered Mechanism AI Introduction
Abstract：ObjectivesTo address the instability issues in traditional consensus algorithms for microgrid optimization scheduling, caused by frequent information exchanges among distributed units and changes in system topology. An improved dynamic event-triggered mechanism consensus algorithm is proposed.MethodsThe threshold function in the triggering condition of the improved dynamic event-triggering mechanism is modified to enable dynamic adjustment based on the states of distributed units. This ensures that information exchange occurs only when the triggering condition is satisfied.ResultsThe proposed algorithm ensures system stability during changes in microgrid topology and plug-and-play operation of distributed units. It achieves state convergence and consistency among distributed units while significantly reducing communication frequency.ConclusionsThe application of the improved dynamic event-triggered mechanism consensus algorithm in microgrid economic scheduling enhances system stability and reliability, significantly saves communication resources, and meets the economic requirements of microgrids, making it highly practical.Keywords：distributed energy;microgrid;economic scheduling;consensus algorithm;dynamic event-triggered mechanism;communication frequency;topology change;communication resources;economic requirements5|8|2<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Coordinated Trading Mode of Green Electricity and Green Certificates in Regional Common Markets AI Introduction
Abstract：ObjectivesTo promote the green electricity development path of Xinjiang’s power grid and facilitate a smooth transition of the electricity market towards a unified regional market, this study proposes a coordinated trading mode for green electricity and green certificates in a regional common market.MethodsIn electricity trading, the provincial markets are retained, where provincial grid companies organize electricity trading and use a non-cooperative game in the regional market to sell surplus electricity or purchase electricity from other provinces. In green certificate trading, the regional common market employs generalized Nash bargaining game to allocate the environmental value of green electricity.ResultsCase comparison and validation show that the proposed coordinated trading mode can reduce thermal power generation at both sending and receiving ends by an average of 38.33% and increase the renewable energy consumption rate by 83.2%.ConclusionsThe findings provide theoretical support for advancing the green electricity development path of Xinjiang’s power grid.Keywords：electricity market;renewable energy;green electricity;green certificate;synergy;transaction mode7|6|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Research on Capacity Allocation Optimization Model of Thermal Power Unit Participating in Electricity Market Considering Carbon Trading AI Introduction
Abstract：ObjectivesDriven by the “double carbon” goal, the large-scale integration of clean energy leads to a decrease in the demand for electricity in the power grid and an increase in the demand for electricity regulation. On this background, traditional thermal power units need to actively participate in the peak shaving market and find new profit points. In order to achieve the goal of maximizing the comprehensive benefits of the unit, an optimization model is established for the capacity allocation of thermal power units after increasing the carbon market and auxiliary service market.MethodsFirstly, the operation characteristics of thermal power units are analyzed in view of the participation of thermal power units in various power markets. Secondly, considering the costs and benefits of electric energy, deep peak regulation and carbon market, the profit curve of thermal power units participating in these three markets is drew, and the method of optimal capacity allocation is proposed. Finally, taking a thermal power unit in Northeast China as an example, the optimization analysis is carried out in the context of the electricity market to verify the effectiveness of the model.ResultsThe optimization model can find out a better capacity allocation of spot market and peak regulation market, and the maximum profit calculated by the allocation result is higher than that of the traditional model.ConclusionsThe optimization model considers the cost and profit that the traditional model does not calculate, which makes the calculation of the income of thermal power units participating in the electricity market more comprehensive and accurate. It is of great significance for thermal power units to realize their own value under the background of “double carbon”.Keywords：thermal power unit;operating cost;peaking service;carbon market;electricity market;capacity allocation;optimization model7|10|2<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
New Power System
