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Optimization Scheduling of Cold-Heat-Electricity Integrated Energy System Under Pumped Storage Gray Start AI Introduction
Abstract：ObjectivesPumped storage units have gray start potential. Integrating this capability with the multi-energy complementary advantages of an integrated energy system (IES) makes it suitable for system recovery under extreme events. To investigate the post-disaster recovery mechanism of IES, this paper proposes an optimization scheduling model for a cold-heat-electricity integrated energy system (CHEIES) under pumped storage gray start.MethodsFirst, stochastic scenario optimization is employed to address the uncertainties in wind, solar, cold, and thermal power. Latin hypercube sampling is used to generate a large number of random wind-solar-cold-heat scenarios, and a probability distance-based rapid reduction method is applied to reduce the number of scenarios. Then, for CHEIES under gray start, pumped storage serves as the gray start power source to provide startup power for the combined heat and power unit. A single-objective optimization scheduling model is established with gray start benefit as the core consideration, incorporating cold-heat-electricity power balance constraints to ensure the stable operation of IES under various load conditions. Finally, simulations are conducted to solve the model, and optimization scheduling strategies and economic benefits under different operation schemes are analyzed.ResultsCHEIES with pumped storage ash start-up shows high flexibility and operation efficiency in response to extreme natural disasters. Compared with the scheme without pumped storage ash start-up, the system operation cost is reduced by 12.14%.ConclusionsThe proposed method fully explores the reliability, economic efficiency, and flexibility of CHEIES under emergency conditions, providing strategic support for the rapid recovery of IES after extreme events.Keywords：integrated energy system (IES);pumped storage;gray start;black start;cold-heat-electricity integrated energy system (CHEIES);optimization scheduling;extreme events63|118|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Optimization Scheduling of Integrated Energy Systems in Communities Based on Demand Response and Stackelberg Game AI Introduction
Abstract：ObjectivesWith the continuous growth of demand-side response resources, traditional energy scheduling models struggle to meet the system requirements of high penetration levels of renewable energy. To achieve the rational allocation of multiple energy sources within a community, this study proposes an energy trading strategy based on demand-side response from users, aiming to optimize energy scheduling in smart community.MethodsFor a residential community with multiple buildings, this study coordinates distributed photovoltaics, energy storage systems, and flexible loads. A two-stage scheduling optimization model is established using the Stackelberg game framework based on pricing interactions between community operators and user load aggregators.ResultsSimulation results show that, compared to the traditional heat-determined power strategy, the proposed model reduces operational costs by 40.22% and increases photovoltaic utilization by 22.57%. Compared to the conventional cost-optimal operation strategy, the proposed model results in a 29.66% reduction in operational costs and a 6.78% increase in photovoltaic utilization.ConclusionsThe proposed strategy demonstrates excellent performance in achieving equitable benefit distribution, mitigating power fluctuations, flexibly meeting peak-load demands, enhancing renewable energy integration, and ensuring grid operational security.Keywords：renewable energy;integrated energy system;demand-side response;Stackelberg game;energy trading;scheduling strategy;smart community;power mutual support109|854|6<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Research on Fault Location in Integrated Energy Systems Based on Improved Binary Particle Swarm Optimization Algorithm AI Introduction
Abstract：ObjectivesThe coverage of power systems continues to expand, and the structure of integrated energy systems is becoming increasingly complex. This trend leads to a significant decline in the accuracy of fault location in the distribution network that is a critical component of the energy system. To address this, a fault location method for distribution network based on an improved binary particle swarm optimization (BPSO) algorithm is proposed.MethodsDuring each iteration of the binary particles, an adaptive mutation operation is first performed on the position of the particle. Furthermore, an adaptive method is introduced into the setting of inertia weight, establishing a BPSO algorithm with dual adaptive characteristics.ResultsIn the standard radial distribution networks and those incorporating distributed generation, the improved BPSO algorithm can accurately pinpoint fault sections.ConclusionsCompared with the traditional BPSO algorithm and genetic algorithm, the improved algorithm demonstrates stronger robustness in convergence ability. It remains unaffected by differences in fault types and has greater reliability. Therefore, it is more suitable for fault location tasks in complex and dynamic distribution network environments.Keywords：integrated energy;distribution network;fault location;distributed generation;binary particle swarm optimization (BPSO) algorithm;dual adaptive88|279|4<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Optimal Capacity Configuration Method for Multi-Microgrid System Utilizing Wind-Solar-Electric-Hydrogen Hybrid Energy Storage AI Introduction
Abstract：ObjectivesIn order to study the problems of increasing complexity of power balance, and increasing uncertainty of power flow distribution and increasing security and stability requirements of power grid caused by high proportion of renewable energy access, a capacity optimization configuration method of multi-microgrid system including wind power, photovoltaic, battery and hydrogen energy storage is proposed.MethodsA bi-level optimization model is established under the framework of multi-microgrid distribution network. The outer objective of the model is to minimize the life cycle cost, and the inner objective is to minimize the peak-valley difference, network loss and voltage offset of the distribution network. Based on the IEEE 69-bus system, the white shark optimizer (WSO) algorithm and Cplex solver were used to solve the model, and the optimal capacity configuration scheme and planning operation results are obtained. Finally, case analysis is carried out through different energy storage combinations.ResultsWhen the capacity configuration of each component of the system is optimal, the installed ratio of the wind-solar power generation system to the hybrid energy storage system is 1∶0.27. The wind-solar-electric-hydrogen hybrid energy storage system is superior to the wind-solar-single energy storage system in terms of economy and stability.ConclusionsThe proposed method can not only optimize the joint optimal cost of the system, but also effectively reduce the load peak-valley difference, distribution network loss and improve the power quality.Keywords：wind power;photovoltaic;hydrogen energy;energy storage;multi-energy complementary;capacity configuration;power distribution;bi-level optimization model;multi-microgrid136|175|6<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Two-Stage Robust Optimization Scheduling of Park-Level Hydrogen-Electric Coupling Systems With Demand Response and Carbon Trading AI Introduction
Abstract：ObjectivesHydrogen energy, as a clean energy source with high energy density and zero carbon emissions, is an important component of future energy systems. For park-level hydrogen-electric coupling systems (HECS), a two-stage robust optimization scheduling model that considers demand response and tiered carbon trading is proposed.MethodsFirst, a park-level HECS is established, consisting of wind and solar power generation units, backup generation units, energy storage systems, and hydrogen-electric conversion devices. Then, demand response and tiered carbon trading are integrated into the model, aiming to minimize the total costs of system energy procurement, operation and maintenance, and carbon emissions, thereby establishing a deterministic optimization model for the system. Finally, a source-load uncertainty set is incorporated into the deterministic optimization model to mitigate the effect of source-load uncertainty on scheduling results, forming a two-stage robust optimization model. This model is re-established using a master-slave framework and solved using the column-and-constraint generation method.ResultsWith source-load uncertainty coefficients of 12 and 6, demand response loads with an adjustable ratio below 0.5 can reduce the system's operating costs by 1.6%. The introduction of tiered carbon trading can reduce carbon emissions by 604.9 kg.ConclusionsThe proposed model can improve the risk resilience of park-level HECS, and the integration of demand response and tiered carbon trading can ensure the economic and low-carbon operation of HECS.Keywords：hydrogen energy;dual carbon;hydrogen-electric coupling system (HECS);two-stage robust optimization;tiered carbon trading;uncertainty;demand response111|391|3<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Low-Carbon Economic Scheduling of Integrated Energy System Considering Diversified Utilization of Hydrogen Energy AI Introduction
Abstract：ObjectivesIn the context of “dual-carbon” goals, achieving low-carbon economic operation of integrated energy system (IES) is of paramount importance. However, existing IES models incorporating power-to-gas (P2G) technology often overlook energy losses during hydrogen production. To address this issue, this study proposes a day-ahead low-carbon economic scheduling model for an IES that considers diversified utilization of hydrogen energy.MethodsFirst, a mathematical model of a cross-regional IES is established, with carbon capture power plants and P2G as the main energy coupling technologies. Second, given the clean and efficient nature of hydrogen energy, mathematical models are developed for hydrogen-blended combined heat and power generation, P2G systems, hydrogen fuel cells, and hydrogen storage tanks. To avoid energy waste, a heat recovery device is integrated into the two-stage conversion process of P2G. Third, a mathematical model for demand response and green certificate-carbon trading mechanism is established and incorporated into the system’s low-carbon economic scheduling strategy. Finally, the optimization objective is set to minimize the total cost, including expenses on green certificate trading, carbon trading, coal-fired power generation, electricity procurement, and natural gas purchases.ResultsThe proposed model reduces the total system cost by 55% and achieves the full utilization of wind power.ConclusionsThis scheduling model effectively mitigates energy losses during system operation and significantly enhances the low-carbon economic efficiency of IES.Keywords：dual-carbon;integrated energy system (IES);hydrogen energy;economic dispatch;green certificate-carbon trading;diversified utilization;demand response;carbon capture power plant;power-to-gas (P2G)47|144|7<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Gas Network Aggregate Modeling and Identification Method for Integrated Energy System Operation AI Introduction
Abstract：ObjectivesNatural gas networks are important components of integrated energy systems, which can provide considerable flexibility for the operation of the integrated energy system. Developing an accurate gas network model is the foundation for the operation and control of integrated energy systems. However, existing mechanism models for gas networks show high complexity, with certain parameters being unidentifiable in practical engineering applications. Therefore, this study proposes a gas network aggregate modeling and identification method specifically for integrated energy system operation.MethodsTaking the gas network as the research object, the concept of the gas network aggregate model is proposed, its formula is derived, and a corresponding offline parameter estimation method is designed. Since the derivation of the gas network aggregate model depends on specific operating conditions, the model parameters vary with condition changes, leading to non-negligible errors in the offline estimation model. To address this issue, a rolling calibration method based on forgetting factor recursive least squares is proposed.ResultsUnder small gas flow variations, the offline parameter estimation method demonstrates satisfactory accuracy. However, under rapid flow variations, the offline estimation fails to accurately describe the operation status of the gas network, and rolling calibration can achieve accurate fitting of the model.ConclusionsThe proposed gas network aggregate model integrating offline estimation and online rolling calibration can provide a practical and efficient fluid network modeling solution for the operation analysis and control of integrated energy systems.Keywords：natural gas;integrated energy system;new energy consumption;gas network;aggregate model;parameter estimation;online calibration129|146|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29- Abstract：ObjectivesIn view of the limitations of the traditional distribution network demand response model in regulating the flexible and controllable resources on the load side, especially ignoring the unique and efficient resources such as ice storage air conditioning systems. Therefore, this paper aims to deeply optimize the load-side control object such as ice storage air conditioning, so as to significantly improve the utilization rate of renewable energy in the new distribution system and deeply explore its low carbon operation potential.MethodsIce storage air conditioning with virtual energy storage characteristics is introduced as the control object. A new distribution system multi-time scale optimization strategy is proposed for photovoltaic consumption and low carbon demand response of ice storage air conditioning groups. Firstly, the operating characteristics and low carbon demand response model of ice storage air conditioning are established. Secondly, with the minimum incentive cost for low carbon demand response in power supply companies, the maximum profit for photovoltaic manufacturers, and the minimum electricity consumption cost for air conditioning users as optimization objectives, a multi-objective optimization model for the day ahead is constructed. The non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) is used to efficiently solve the model, and the optimal solution for the multi-objective optimization model is selected based on the linear programming method with multi-dimensional preference analysis. Subsequently, in order to eliminate the impact of prediction errors on the model results, a daily rolling correction optimization model is further constructed. Finally, the effectiveness of the proposed model is analyzed using test cases.ResultsThe optimal solution obtained after intra-day rolling correction results in a cost reduction of approximately 20% for both power supply companies and air conditioning users, as well as an increase in profit margins of about 3% for photovoltaic producers.ConclusionsThe multi-objective optimization and control method based on multiple time scales not only ensures the interests of all parties, but also eliminates the impact of prediction errors on model results, providing important technical support for low carbon operation of new distribution systems.Keywords：new distribution system;photovoltaic power generation;distribution network;renewable energy;ice storage air conditioning;low carbon demand response;multi-time scale;multi-objective optimization115|434|4<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Modeling\, Simulation and Optimal Operation of Integrated Energy System Based on Swarm Intelligence
Study on Properties of Hydrophilic Modified Polypropylene Regular Filler by Melt Blending in Carbon Dioxide Capture Tower AI Introduction
Abstract：ObjectivesAt present, most amine based CO2 capture systems for flue gas use stainless steel filled towers. In order to significantly reduce the cost of filled towers, this paper proposes the use of polypropylene structured filler instead of high cost stainless steel structured filler, thereby reducing the investment cost of absorption towers in CO2 capture systems after combustion.MethodsUsing plastic fillers instead of stainless steel fillers, the selected polypropylene material and 30% mass fraction of polypropylene grafted maleic anhydride (PP-g-MAH) particles are mixed, and then they are melt blended to form regular fillers. On the constructed testing platform for fluid dynamics and mass transfer performance of fillers, different filler properties are tested.ResultsThe dry tower pressure drop of regular fillers of 410Y and 350Y modified by melt blending of polypropylene and polypropylene grafted maleic anhydride is similar to that of stainless steel 500Y. The effective specific surface area of modified polypropylene filler 350Y is close to that of stainless steel filler 350Y with the same nominal specific surface area. Under liquid-phase loading conditions in the CO2 amine reaction absorption tower, the effective specific surface area of polypropylene filler of 350Y is about 90% of that of stainless steel filler of 350Y.ConclusionsThe modified polypropylene filler has similar performance to stainless steel filler and can replace stainless steel filler in practical applications, significantly reducing the investment cost of absorption towers in CO2 capture systems.Keywords：carbon capture;chemical absorption;polypropylene filler;polypropylene grafted maleic anhydride (PP-g-MAH);filler performance;stainless steel filler;effective specific surface area67|295|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Experimental and Application Research of Novel Polyamine-Based CO2 Absorbents in Gas-Fired Power Plants AI Introduction
Abstract：ObjectivesCurrently, chemical absorption CO2 capture technology used in post-combustion is rarely applied in large-scale decarbonization projects in gas-fired power plants due to its high operational energy consumption and total costs. In order to reduce the energy consumption, particularly the regeneration energy consumption, it is necessary to carry out experimental research to improve the performance of chemical absorbents.MethodsTwo novel polyamine-based absorbents are developed through composition design, laboratory testing with small-scale experimental setups, and engineering validation.ResultsCompared to monoethanolamine (MEA), the two polyamine absorbents—19% diethylaminoethanol + 9% piperazine + 2% MEA (DT01-5) and 20% 1,4-butanediamine + 5% methyldie-thanolamine+5% 2-amino-2-methyl-1-propanol (DT02-3)—significantly improve absorption loading, absorption rate, desorption rate, and cyclic capacity. Their physicochemical properties are close to those of the 30% MEA commonly used in industrial plants. Through testing on a 2 m3/h small-scale experimental setup, the energy consumption of the two absorbents is reduced by 15.84% and 9.32%, respectively, compared to that of MEA. The 3 000 m3/h industrial test results show that the regeneration heat consumption of the two absorbents decreases by 32.89% and 39.52%, respectively, compared to MEA, while the capture power consumption decreases by 9.83% and 16.14%, respectively. Additionally, other performance indicators improve to varying degrees, resulting in total operating cost reductions of 25.95% and 34.14%, respectively.ConclusionsThe two novel polyamine absorbents demonstrate strong potential for commercial applications.Keywords：gas-fired power plants;carbon capture, utilization and storage (CCUS);heat consumption;power consumption;absorbents;polyamines;chemical absorption50|188|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Carbon Neutrality
Research on Doubly-Fed Wind Turbine Shaft System With Virtual Inertia for Torsional Vibration Analysis AI Introduction
Abstract：ObjectivesShafting mathematical model of wind turbine is the basis of torsional vibration characteristics analysis. Shafting model is very important for the reliability of analysis results. In order to study the influence of virtual inertia on the torsional vibration characteristics of transmission chain more accurately, the modeling of doubly-fed wind turbine shaft system with virtual inertia control is studied.MethodsAfter the simulation model is determined, a damping controller is designed, which can suppress the torsional vibration of the shafting caused by virtual inertia control. Firstly, by establishing the mathematical model of various shafting, the dynamic model of fan system based on virtual inertia control is established, and the key state variables affecting the torsional vibration characteristics of the shafting are determined by modal analysis method. Secondly, the applicability of different shafting models of wind turbine is studied from two aspects : torsional vibration characteristics of wind turbine with virtual inertia control and dynamic interaction between wind turbine shafting and synchronous machine. Finally, the effectiveness of the conclusion and the damping controller is verified by simulation analysis.ResultsThe use of virtual inertia control can effectively improve the system inertia attenuation caused by wind power grid connection, but it will also reduce the damping ratio of the system. The two-mass model has stronger observability of torsional vibration mode, and the two-mass shafting model is more sensitive to the change of virtual inertia parameters. The torsional vibration of the shafting of the three-mass model changes more than that of the two-mass model.ConclusionsThe two-mass shaft system model with virtual inertia control is more suitable for torsional vibration analysis of wind turbine shaft systems.Keywords：doubly-fed wind turbine;wind power grid connection;torsional vibration characteristics;virtual inertia control;damping controller;low-frequency oscillation;band-pass filter;shorted fault;wind turbine shaft system;power grid support51|435|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Short-Term Wind Power Prediction Method Considering Wind Turbine Operation Status Clustering Under Low-Temperature Conditions AI Introduction
Abstract：ObjectivesLow-temperature weather poses challenges to the operation of power systems with a high proportion of new energy, such as wind power. Improving the accuracy of short-term wind power prediction under low-temperature conditions will provide effective decision-making information for power system scheduling and operation. To address this, a wind power prediction method considering the clustering of unit operation status under low-temperature conditions is proposed.MethodsThe fuzzy C-means (FCM) clustering algorithm is used to cluster wind turbines based on their operation status and protection control information. Then, a prediction method based on support vector machine is proposed to predict whether the wind turbines are in normal operation status. The LightGBM algorithm in ensemble learning is employed to predict the power output of wind turbines under normal operation. Based on the prediction results of both operation status and power values, the overall wind power output of the wind farm is determined. Finally, a case study of a wind farm in northern Hebei is conducted to validate the effectiveness of the proposed method.ResultsBy fully utilizing the characteristics of wind turbine protection control behaviors under low temperatures, the proposed method accurately predicts the critical shutdown time of wind turbines and provides the shutdown capacity. It effectively fits the variation patterns of wind power curves,which improves the prediction accuracy of the wind power to more than 90%.ConclusionThe proposed method can provide reliable prediction information for power scheduling and control. Additionally, it can provide a reference for short-term wind power prediction under other extreme weather conditions, such as strong winds.Keywords：new energy;power system;wind power;power prediction;unit operation;fuzzy C-means (FCM)clustering;support vector machine;power scheduling and control77|432|2<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Numerical Simulation Study on Effect of Deflectors on Aerodynamic Characteristics of Horizontal Axis Wind Turbines AI Introduction
Abstract：ObjectivesDeflectors can modify flow distribution within wind farms, and their integration into existing wind farms is an effective method for improving wind energy capture by wind turbines. To quantify the effect of deflectors, various operating conditions of deflectors are established to obtain the velocity distribution within the wind field and the wind turbine output power under different conditions.MethodsA three-level orthogonal combination of three factors of deflector inclination angle, length, and distance between the deflector and the wind turbine is conducted. Numerical simulations based on the Reynolds averaged Navier-Stokes (RANS) method are performed to maximize the power output of the wind turbine.ResultsDifferent deflector inclination angles, lengths, and distances between the deflector and the wind turbine have varying degrees of effect on inflow wind velocity and wind turbine output power. Among these influencing factors, the inclination angle has the most significant effect on the inflow wind velocity and output power of the wind turbine, followed by the deflector length, and finally the distance between the deflector and the wind turbine.ConclusionsThe findings provide valuable guidance for improving wind turbine power output and optimizing the design and application of deflectors in wind energy utilization.Keywords：renewable energy;wind power;wind resource;wind turbine;deflector;aerodynamic characteristics;numerical simulation40|190|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
New Energy
Research on Auto-Coupling PID Control Method of Firing Rate to Feed Water Ratio System of Supercritical Unit AI Introduction
Abstract：ObjectivesAiming at the problems of strong coupling, large load, and weak disturbance resistance in the firing rate to feed water ratio (FR/FW) control process of supercritical units, a new type of auto-coupling PI (ACPI) control method is proposed based on the theory of auto-coupling PID control.MethodsThe multivariable FR/FW system is equivalent to multiple single-variable closed-loop systems, and all complex factors such as the coupling, known or unknown internal dynamics, and external disturbances of the FR/FW system are defined as total disturbances. Then, the FR/FW control system is equivalently mapped to a linear disturbance system, and a controlled error system under total disturbance excitation is constructed. Based on this, a speed factor based ACPI controller is designed for each single variable system, and the robust stability and disturbance resistance of the control system are analyzed in the complex frequency domain theory.ResultsCompared with PI and active disturbance rejection control methods, the ACPI control method has fast response speed, dynamic quality without overshoot, and strong steady-state performance with strong disturbance resistance.ConclusionsIt is of great significance to improve the control accuracy and robustness of the FR/FW system in supercritical units.Keywords：thermal power generation;supercritical unit;firing rate to feed water ratio (FR/FW);auto-coupling PI (ACPI);velocity factor;robustness against disturbance56|305|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Screening Experiments and Static Scale Inhibition Performance Evaluation of Scale Inhibitors for Synchronous Condenser Circulating Cooling Water AI Introduction
Abstract：ObjectivesDuring the operation of the circulating cooling water of the regulating camera, the concentration effect leads to the scaling of the heat exchanger and the cooling water packing, which affects the safe and stable operation of the regulating camera.MethodsIn order to solve the scaling problem of circulating cooling water, it is necessary to add various scale inhibitors to alleviate the scaling trend. In order to explore the scale inhibition effect of different formulations of scale inhibitors under different working conditions, the scale inhibition performance of PBTCA, HEDP, LQ-8531, TQ low phosphine scale inhibitors and TQ scale inhibitors was evaluated by limiting carbonate deposition method and limiting carbonate concentration ratio method. The effects of temperature, hardness and dosage on the scale inhibition performance of five kinds of scale inhibitors were studied by using self-made scale inhibition test bench.ResultsThe comprehensive evaluation shows that LQ-8531 has the best scale inhibition performance among the five scale inhibitors, and it can maintain high scale inhibition efficiency in high hardness and high temperature environment.ConclusionsThe research results provide a valuable reference for the selection of scale inhibitor for circulating cooling water of heat exchange equipment in high temperature and high salt area.Keywords：synchronous condenser;converter station;circulating water scale inhibition;static screening experiments;static scale inhibition simulation;performance evaluation112|400|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Reconstruction of Temperature Distribution in Acoustic Tomography Based on Robust Regularized Extreme Learning Machine AI Introduction
Abstract：ObjectivesAcoustic tomography, as a non-invasive temperature detection technology, holds significant value in industrial process monitoring. However, it is constrained by insufficient spatial resolution caused by ill-posed inversion and sensitivity to noise. To address these issues, a acoustic tomography temperature distribution based on robust regularized extreme learning machine (ELM) is proposed.MethodsA two-stage reconstruction framework is established. In the first stage, the network is trained using acoustic time of flight data and low-resolution temperature data to obtain a low-resolution temperature distribution on a coarse grid. In the second stage, the network is further trained using low-resolution and high-resolution temperature distribution, enabling high-resolution temperature reconstruction on a fine grid. Numerical simulations are conducted on typical temperature field models, and the proposed method is compared with traditional algorithms, including Tikhonov regularization, Landweber algorithm, algebraic reconstruction technique (ART), and ELM algorithm.ResultsThe robust regularized ELM algorithm achieves an average relative error of 0.28% and a root mean square error of 0.38%, significantly outperforming the other algorithms in reconstruction quality.ConclusionsThe acoustic tomography temperature distribution based on robust regularized ELM balances computational efficiency and reconstruction accuracy, providing a new solution for high-resolution temperature monitoring in power plant boilers and similar equipment. It demonstrates significant engineering application value, particularly under harsh conditions such as high temperature and strong interference.Keywords：power plant;power station boiler;acoustic tomography;temperature distribution;high-resolution reconstruction;robust regularized;extreme learning machine;numerical simulation;reconstruction algorithm53|151|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Power Generation and Environmental Protection
Review of Energy Management Systems for Islanded Microgrids AI Introduction
Abstract：ObjectivesAgainst the backdrop of energy resource scarcity and increasing environmental demands, distributed energy supply technologies, particularly renewable energy technologies, have attracted significant attention due to their high energy efficiency, cost-effectiveness and flexible installation. For remote areas such as plateaus, border regions and islands, which are beyond the coverage of public power grids, islanded microgrids have become the key technology to address electricity access challenges. However, optimizing energy management efficiency remains a major challenge. This paper reviews the two core objectives—economic efficiency and stability—of electric energy in islanded microgrids.MethodsThe current development status and research priorities of islanded microgrids, both domestically and internationally, are analyzed. The basic functions and management objectives of islanded microgrids are discussed. Additionally, the technical approaches for microgrid energy management and three types of control strategies are introduced. Finally, the paper provides a discussion of future research directions for islanded microgrid energy management systems, based on existing technological limitations and the ongoing development of new technologies.ConclusionsThe research results can effectively deal with the volatility and uncertainty of renewable energy and ensure the stable operation of the system. It can effectively reduce the cost of energy storage, improve the efficiency of energy utilization, and achieve higher economic benefits. It can also provide references and insights for the future integration of islanded microgrids with new technologies.Keywords：distributed energy supply;islanded operation;microgrid;energy management;energy storage;optimization algorithms;control strategies;artificial intelligence185|1822|5<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Review of Grid-Forming Energy Storage and Its Applications AI Introduction
Abstract：ObjectivesThe characteristics of low inertia and low damping in “double-high” (high renewable energy penetration and high power electronics application) power system pose significant challenges to grid stability, particularly in terms of frequency and voltage. Grid-forming energy storage (GFM-ES), which has the capability of frequency regulation and voltage control, is reviewed in terms of its characteristics, application scenarios, and research outlook.MethodsFirstly, the main characteristics of GFM-ES are described from the aspects of the differences between GFM-ES and grid-following energy storage, as well as the control methods. Then, the main application scenarios of GFM-ES, including frequency support, voltage support, and black start, are elaborated. Finally, the research outlook is presented, focusing on the stability, optimal configuration, and practical engineering applications of GFM-ES.ConclusionsThe stability of GFM converters has an important impact on the operational characteristics of energy storage units, and further attention needs to be paid to the induced causes of the stability problem, parameter tuning, and switching of control and current limiting strategies. The GFM-ES configuration requires trade-offs in terms of functionality, complexity, and cost, and the hybrid configuration of grid-forming and grid-following energy storage needs to be further investigated. Coordination and interoperability between GFM-ES units should be strengthened, and technical test specifications and standards should be improved to promote their application in hybrid AC-DC grids and high-voltage transmission grids.Keywords：grid-forming energy storage;frequency support;voltage support;optimal configuration812|1354|21<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Evaluation of Comprehensive Benefits of Electric Energy Substitution in Regional Power Grids Based on Improved Cloud Model AI Introduction
Abstract：ObjectivesTo address issues such as insufficient multi-dimensional coordination, incomplete quantification of environmental benefits, and the lack of dynamic feedback in evaluating the benefits of electric energy substitution in regional power grids, a dynamic evaluation method based on multi-source data fusion is proposed to enhance the decision-making accuracy of electric energy substitution projects.MethodsA three-dimensional evaluation index system covering “supply-side optimization, demand-side transformation, and environmental emission reduction synergy” is established, identifying 21 indicators including cross-regional consumption capacity, electric substitution penetration rate, and pollutant reduction, thereby forming a multi-level quantitative evaluation framework. A coupling weighting strategy based on the entropy weight method and an improved rank correlation method is developed to balance subjective and objective weight deviations through linear weighting. A Bayesian dynamic cloud model is proposed, optimizing the hyper-entropy parameters of the cloud model by incorporating a Bayesian feedback mechanism to achieve dynamic correction of evaluation results.ResultsThe proposed model can effectively quantify the comprehensive benefits of electric energy substitution, accurately identify shortcomings in cross-regional consumption and terminal substitution, and improve evaluation accuracy through the dynamic feedback mechanism, enhancing alignment with practical engineering.ConclusionsThis evaluation method overcomes the bottlenecks in traditional benefit evaluation, such as single-dimensional indicators, inaccurate weight allocation, and significant static deviations, providing a reference for improving the accuracy of electric energy substitution benefit evaluation.Keywords：new power system;new energy;low-carbon transition;carbon neutrality;regional power grid;electric energy substitution;Bayesian dynamic cloud model;comprehensive benefit evaluation;carbon emission58|390|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29LCL Grid-Connected Inverters Control Strategy Based on Improved Fuzzy Linear Active Disturbance Rejection Control AI Introduction
Abstract：ObjectivesIn complex grid environments, traditional control methods for LCL grid-connected inverter systems suffer from poor grid current quality, inadequate dynamic performance, and inherent resonance peaks. To address these issues, a Fuzzy linear active disturbance rejection control (Fuzzy-LADRC) strategy is proposed.MethodsThe disturbance rejection capability is enhanced by modifying the error equation of the traditional linear extended state observer. The modified error equation is then combined with fuzzy logic control to achieve adaptive control of the controller parameters. The improved Fuzzy-LADRC strategy is applied to both the phase-locked loop and the current loop of the LCL control system. The system’s stability and disturbance rejection performance are verified by using Lyapunov stability analysis and frequency-domain analysis. Finally, MATLAB/Simulink simulations are conducted to validate the effectiveness of the proposed control strategy.ResultsThe improved Fuzzy-LADRC strategy shows superior performance in terms of grid current harmonic distortion, offset, and dynamic response time under three different operating conditions compared with other control methods. Additionally, this control strategy simplifies the control structure by eliminating the need for additional damping components.ConclusionsThe improved Fuzzy-LADRC strategy effectively improves the system’s disturbance rejection capability and grid current quality while reducing system costs and achieving good dynamic performance. It provides a new approach for the control of LCL grid-connected inverters.Keywords：power grid;active disturbance rejection control;grid-connected inverter;fuzzy control;control strategy;stability;harmonics;distortion rate82|279|4<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
New Power System
