In response to the challenges where large-scale renewable energy integration leads to intricate source-network-load-storage elements and surging complexity in new power systems, rendering traditional balancing architectures and hierarchical analysis methods inadequate, theoretical achievements and research technologies regarding hierarchical and partitioned balance architectures are comprehensively reviewed. The adaptability requirements of new power systems for such architectures are elucidated, followed by a summary and comparative analysis of existing hierarchical control and partitioning strategies. Furthermore, layer-zone fusion mechanisms are explored, and existing technical limitations are analyzed from data and modeling perspectives. The results indicate that while renewable energy control pressures can be alleviated by existing strategies, deficiencies remain in handling massive heterogeneous data fusion and precise modeling of complex systems; moreover, high dynamic balance demands are difficult to be met by current layer-zone coordination mechanisms. Future hierarchical and partitioned balance architectures are identified as a critical direction for supporting the operation of new power systems. Notably, a novel technical pathway for achieving safe and efficient operation under the “carbon neutralization and carbon peaking” goals is offered by the introduction of large models and artificial intelligence technologies.

To address the issues of dispatch failure and economic losses caused by multi-source uncertainties—including the randomness of wind and solar power generation, load fluctuations, and parameter deviations—during the aggregation of distributed energy resources in virtual power plants (VPP), this paper proposes a multi-time scale adaptive dispatching framework embedded with multi-source uncertainty modeling and an online parameter correction mechanism. Based on two-stage robust optimization and an improved quantum genetic algorithm (QGA), a pre-dispatch scheme is generated via robust optimization during the day-ahead stage. During the intraday stage, a state feedback mechanism is introduced to rolling-correct key parameters using the improved QGA, thereby establishing a closed-loop dispatching structure. Simulation results demonstrate that under significant prediction deviations in wind/solar generation and electric/thermal loads, the actual operational revenue of the proposed method increases by approximately 3.2% compared to traditional deterministic dispatching. Furthermore, the online parameter correction strategy significantly reduces the system balancing cost in most periods, with a reduction margin approaching 90%. The proposed method effectively coordinates the robustness, economics, and adaptability of the dispatching scheme, providing a technical pathway for the secure and economic operation of VPP in highly uncertain environments.

Compared with salt caverns and artificial cavities，using pipeline steel as above-ground gas storage chambers offers greater advantages for small-scale distributed compressed air energy storage（CAES）systems. This paper establishes a detailed dynamic simulation model of a 10 MW-class distributed CAES system based on AMESIM software. The research investigates key parameters such as discharge duration，above-ground storage chamber volume，system efficiency，and energy storage density under different energy storage durations and different maximum storage pressures of the above-ground storage chambers. In addition，an economic analysis of the system is also conducted. The results show that heat loss of the thermal storage and exchange system is the main cause of energy loss in the CAES system. As the energy storage duration increases，the volume of above-ground storage chambers increases，while the system efficiency remains unchanged and energy storage density increases，meanwhile，the reduction rate of the static payback period gradually slows down. With an increase of maximum ground chamber pressure，the chamber volume decreases，system efficiency declines and energy storage density increases，while the static payback period first declines and then rises. When the maximum pressure of the above-ground chamber rises from 9 MPa to 14 MPa，the system efficiency drops from 67.59% to 54.37%. The minimum static payback period of 8.29 years is achieved at the optimal pressure of 11.8 MPa.

To address the issue of rational energy storage configuration in off-grid hydrogen production systems, this paper proposes an optimized configuration method for energy storage in such systems. Firstly, the supporting role of grid-forming energy storage in the voltage and frequency of off-grid systems is analyzed, clarifying the grid-connection approach using grid-forming energy storage as the power source for off-grid system. Secondly, based on the configuration of renewable energy off-grid hydrogen production systems, a control strategy for energy storage to support black start of off-grid systems is formulated. Thirdly, an optimized energy storage configuration model considering the unit cost of hydrogen production and the system’s electricity curtailment rate is established, and the particle swarm optimization algorithm is employed to solve the model. Finally, the Datang Duolun Wind and Solar Hydrogen Production Project is selected as the research object. Through economic evaluation and stability analysis of the off-grid hydrogen production system, the effectiveness of the optimized energy storage configuration method for off-grid hydrogen production systems is verified.

To address the challenges posed by time-varying system inertia and the insufficient adaptability of conventional thermal-storage frequency regulation strategies under high renewable penetration, this paper proposes a coordinated thermal-storage frequency control strategy based on online inertia estimation and adaptive deadband optimization. The strategy employs a hierarchical coordination mechanism: under small disturbances, energy storage systems—acting as fast, distributed flexible resources—are prioritized for response through a reduced deadband setting, thereby avoiding frequent cycling and wear of thermal units; under large disturbances, the equivalent system inertia is identified via inversion of the frequency response, enabling adaptive adjustment of the storage’s virtual inertia and droop coefficient to dynamically compensate system damping. Furthermore, a full-lifecycle cost model incorporating cycle-life degradation is established to quantify the economic benefits of the proposed strategy. Simulation results demonstrate that the approach effectively mitigates system oscillations while significantly reducing overall frequency regulation costs, offering a technically and economically viable solution for distributed energy storage participation in grid ancillary services and frequency stability management in low-inertia power systems.

To address voltage violations, frequency fluctuations, and other challenges caused by the high-penetration integration of distributed photovoltaic (PV) generation into distribution networks under the “dual carbon” goals and energy transition, as well as the limitations of conventional grid-following energy storage systems due to their passive response characteristics, this paper proposes a grid-forming energy storage-based solution. A bi-level coordinated optimization model integrating site selection, capacity allocation, and control is developed. Scenario analysis is employed to handle PV output uncertainty, and a hybrid optimization method combining an improved particle swarm optimization algorithm with an interior-point method is adopted to solve the model, achieving a multi-objective balance between economic and technical performance. The proposed grid-forming energy storage effectively mitigates reverse power flow from PV systems and significantly improves PV curtailment reduction. Under fault conditions, it enhances the self-healing capability of the distribution network. By integrating virtual synchronous generator control with a multi-objective coordinated optimization strategy, the approach overcomes the technical bottleneck of passive response inherent in traditional energy storage, offering a systematic solution for the secure and stable operation of distribution networks with high renewable penetration.

Underwater compressed air energy storage（UWCAES）is vital for balancing power supply-demand fluctuations but faces challenges of instantaneous overpressure and pressure oscillations in flexible balloons during deep-sea operation and dynamic charging/discharging. This paper proposes a fuzzy PID（proportional integral derivative）- based method to suppress these pressure fluctuations. First，a dynamic pressure transmission model incorporating underwater environmental parameters is established for the balloon. Then，a fuzzy PID control algorithm is developed，utilizing the pressure error and its rate of change as inputs. This algorithm constructs membership functions and a fuzzy rule base to dynamically adjust PID parameters in real-time，optimizing the valve opening adjustment rate. Finally，case studies confirm algorithm robustness under dynamic conditions like vortex-induced shock. By achieving a 26.7% reduction in pressure standard deviation（to 30.8 kPa ）over PID control，the proposed strategy effectively mitigates overpressure and fluctuations，advancing the deployment of underwater flexible compressed air energy storage.

With the large-scale integration of high-penetration renewable energy into the power grid, there are increasing demands for frequency regulation. To address the issues of high regulation losses and poor economic performance resulting from the frequent ramping of conventional thermal power units, this paper proposes a secondary frequency regulation strategy for a hybrid energy storage system (HESS) that incorporates the response characteristics of both thermal power and compressed air energy storage (CAES). First, the automatic generation control signal is decomposed into high-frequency and low-frequency components using the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and multiscale permutation entropy (MPE) methods. Subsequently, leveraging the similarity between thermal power units and CAES in terms of dynamic response time and regulation inertia, a coordinated control method for a thermal-HESS is developed. This method enables the rational allocation of high- and low-frequency components among different units, thereby enhancing the system’s frequency regulation performance while reducing the output variability of the thermal unit. Finally, a dynamic simulation model is built in Matlab/Simulink to validate the regulation performance and economic benefits of the proposed strategy. Simulation results demonstrate that the proposed strategy can fully leverage the analogous response characteristics between thermal power and CAES during secondary frequency regulation, as well as the complementary advantages of the HESS in terms of fast response and large capacity. This coordinated approach effectively reduces and smoothens the output of the thermal power unit, thereby enhancing the overall frequency regulation performance and economic benefits of the thermal-HESS.

To address the challenges of diversified participants in the electricity market and resolve conflicts of interest among multiple virtual power plants (VPPs), this paper proposes a three-layer hybrid game strategy involving the distribution system operator (DSO), virtual power plant operator (VPPO), and user aggregator (UA) to support collaborative optimization of multi-VPP systems. First, a UA coalition is formed by aggregating various flexible resources, including photovoltaic prosumers, electric vehicle charging stations, and integrated energy loads. Next, a three-layer hybrid game model is constructed, encompassing hierarchical energy transactions among DSO, VPPO, and UA, as well as peer-to-peer energy trading among UA entities, structured as a "Stackelberg game - Stackelberg game - cooperative game" framework. Finally, the proposed model is solved using the bisection method, Karush-Kuhn-Tucker conditions, and the alternating direction multiplier method. Case studies demonstrate that the proposed multi-layer hybrid game strategy reduces the operating costs of the photovoltaic prosumer UA and the electric vehicle charging station UA by 4.5% and 15.3%, respectively. Meanwhile, guided by the dynamic pricing mechanism of the DSO, the total profit of the system operators increases by 2.7%. This strategy can effectively balance the interests of multiple stakeholders, optimizing the energy trading and benefit allocation mechanisms among different operators.

To address the poor operational stability and high unit hydrogen production cost caused by strong power fluctuations of wind and photovoltaic (PV) renewable energy, this study investigates an optimal control strategy for a dual-channel hybrid hydrogen production system under wind-PV coupled application scenarios. An optimal control strategy for a dual-channel electrolytic cell system based on ensemble empirical mode decomposition (EEMD) and Petri net-based start-stop correction is proposed. Wind and PV power signals are decomposed using EEMD, and power components at different frequency bands are allocated to alkaline and proton exchange membrane (PEM) electrolytic cells according to their dynamic response characteristics. Meanwhile, a Petri net model is employed to construct start-stop logic for electrolytic cells, effectively suppressing frequent switching under low-load conditions. Furthermore, a multi-objective optimization model is established with the objectives of maximizing system energy conversion efficiency and minimizing the unit hydrogen production cost, which is solved using a multi-objective particle swarm optimization algorithm. Simulation results based on measured wind-PV power output data from the Zhangbei region indicate that the optimized hybrid hydrogen production system achieves an energy conversion efficiency of 58.64% and a unit hydrogen production cost of 2.3958 USD/kg. Compared with conventional single-type hydrogen production schemes, the proposed method improves efficiency by 15.25% and reduces cost by 1.7384 USD/kg, while significantly decreasing the number of start-stop events of electrolytic cells. The results demonstrate that the proposed control strategy effectively enhances system stability and reduces economic cost, providing a practical and feasible optimization approach for the efficient operation of wind-PV hydrogen production systems.

Driven by the “carbon neutrality and carbon peaking” goal，gravity energy storage has become an important support technology for new power systems due to its advantages of environmental protection，no self-discharge and flexible layout. Aiming at the power fluctuation problem caused by mass block scheduling in the charging and discharging process of gravity energy storage and the active/reactive power response demand of the grid，this study takes the ramp gravity energy storage as the object，and respectively constructes the simulation model of the gravity energy storage system including two types of motors（electrically excited synchronous motor and doubly-fed motor）and three typical control strategies（vector control，direct power control and sliding mode control）. The corresponding mathematical model and power coordination control strategy are established. The simulation results show that the electrically excited synchronous motor system with sliding mode control has the best dynamic response and steady-state accuracy in terms of active/reactive power regulation performance. The doubly-fed motor combined with sliding mode direct power control strategy also shows good adjustment ability and robustness.

To address voltage over-limit and instability issues caused by high penetration of distributed photovoltaic in distribution grids, this paper proposes a voltage regulation strategy based on distributed photovoltaic cluster partition. Firstly, a dual-criterion modularity function considering net load and equivalent electrical distance is constructed. A community detection algorithm based on modularity (i.e. fast-unfolding) is applied to dynamically partition photovoltaic clusters. Secondly, differentiated voltage regulation is designed according to the severity of cluster over-limit conditions: intra-cluster reactive power adjustment for mild over-limits, and multi-device hierarchical collaborative control for severe over-limits, with task allocation based on response speed and economic priority. Finally, an optimization model targeting minimization of network losses and voltage deviation is established. The improved multi-organization particle swarm optimization (MPSO) algorithm, combined with niche-based techniques, is employed to determine the optimal regulation sequence and device action levels. Simulation results demonstrate that this method effectively controls voltage fluctuations, reduces network losses, and enhances system stability in both the modified IEEE 33-node system and the IEEE 123-node system.

Hydrogen energy, as a core area in global energy transition and low-carbon development, plays a critical role in supporting industrial innovation and talent cultivation through discipline construction. Universities and research institutions worldwide are actively exploring pathways for establishing a hydrogen energy discipline system. Based on a systematic study of hydrogen energy discipline development, this paper reviews the current status in China and conducts practical explorations focusing on talent cultivation and curriculum system design. From the perspectives of disciplinary layout, curriculum structure, research platforms, and faculty development, and by integrating goal-oriented analysis, pedagogical innovation, and strategic resource allocation, the paper presents achievements in cultivating specialized talent, advancing technological innovation, and serving industrial development. Furthermore, it analyzes existing challenges and proposes targeted optimization strategies, aiming to provide theoretical references and practical insights for the high-quality development of hydrogen energy disciplines in China. The findings indicate that current disciplinary construction faces challenges, including insufficient interdisciplinary integration, a shortage of practical resources, and a need for enhanced internationalization. Accordingly, recommendations for subsequent construction and exploration are proposed to facilitate the high-quality development of the hydrogen energy industry.

To address the interconnected challenges of bus voltage limit violations, reverse power flow overloading, and deteriorated power supply reliability caused by high-penetration distributed renewable energy integration, this paper proposes an energy storage optimal planning method considering generation-storage coordination for local consumption and power supply reliability. An energy storage optimal planning model is established, aiming to minimize the annualized comprehensive cost (including energy storage investment and renewable curtailment penalties) while optimizing voltage fluctuation and net load fluctuation. The non-convex nonlinear model is solved using an improved multi-objective particle swarm optimization algorithm. By incorporating an adaptive inertia weight mechanism and a dynamic crowding distance-based non-dominated solution set update strategy, the algorithm effectively avoids premature convergence and local optima traps. Simulation results based on the IEEE 33-bus distribution network demonstrate that the “storage configuration + reasonable curtailment of renewable energy” scheme increases renewable energy local utilization by 12% and reduces annualized comprehensive cost by 5.6% compared to the “reasonable curtailment of renewable energy” scheme, while achieving a 7.5% cost reduction compared to the “storage configuration” scheme alone.

To address the issues of existing regional distributed photovoltaic (PV) power forecasting, such as heavy reliance on meteorological data, high operation and maintenance costs, poor data quality and insufficient result credibility, a joint credible forecasting method for PV power and energy is proposed. First, power measurements from smart meters and daily frozen energy data are jointly filtered, fused, and normalized to enhance data set quality. Second, a multi-time-scale, high-accuracy sequence-to-sequence (Seq2Seq) forecasting framework is developed, integrating historical and forecast data from centralized regional PV plants; a multi-time-scale loss function that jointly accounts for both power and energy is employed to optimize prediction accuracy. Finally, a model integrity verification scheme based on commit-and-prove succinct non-interactive argument of knowledges (cp-SNARKs) is designed to ensure result credibility while preserving model confidentiality. Experimental validation using real-world data from a city in North China demonstrates that the proposed method significantly reduces forecasting errors for both power and energy, thereby improving PV power prediction accuracy. Requiring no meteorological inputs or system modifications, the approach features high data quality, superior prediction accuracy, low operational cost, and strong verifiability, making it readily extensible to other time-series forecasting tasks such as load forecasting and wind power prediction.

Underwater compressed air energy storage (UW-CAES), which utilizes flexible underwater air bags to enable constant-pressure charge and discharge, has emerged as a compelling solution for renewable energy accommodation. However, there remains a distinct lack of research focused on parameter optimization to simultaneously reduce the capital costs of UW-CAES and enhance the operational economics of the plant. To address this critical gap, this paper proposes an optimal configuration method for UW-CAES based on distributionally robust chance constraints (DRCC). First, a comprehensive UW-CAES system model is established, explicitly accounting for the impact of pipeline pressure losses on system dynamics. Subsequently, an optimal configuration framework incorporating these pressure losses is formulated to optimize key system parameters, with the dual objectives of minimizing investment costs and maximizing operational revenues. Furthermore, the DRCC approach is employed to reformulate the stochastic chance constraints into tractable linear constraints. This mathematical transformation not only ensures computational efficiency but also facilitates a flexible trade-off between economic optimality and robustness. Case studies demonstrate the efficacy of the proposed methodology: the optimized system maintains a rated discharge power of 60 MW while reducing the required rated charge power to 53.2 MW − an 8.75% decrease compared to the original baseline − thereby significantly improving overall system efficiency. Finally, sensitivity analyses reveal that systematically calibrating the confidence level and Wasserstein radius within the DRCC framework effectively navigates the equilibrium between economic performance and system conservatism.

To address the challenges of power fluctuations and ramping demands faced by regional integrated energy systems under high penetration of renewable energy, this paper focuses on the ramping support capability of advanced adiabatic compressed air energy storage (AA-CAES). A multi-timescale optimization dispatch model for regional integrated energy systems incorporating AA-CAES ramping capability is established. First, an operational model of AA-CAES is established to analyze its support capability for thermal power ramping. Second, a multi-timescale optimization dispatch strategy for regional integrated energy systems incorporating AA-CAES ramping capability is proposed. Long-timescale optimization minimizes operational costs while ensuring system power balance, and short-timescale dynamic power correction is achieved using model predictive control. Simulation results demonstrate that multi-timescale scheduling, incorporating AA-CAES ramping capability, effectively enhances the system’s resilience to renewable energy fluctuations, reduces thermal power dispatch requirements, lowers operational costs, and improves the integration of renewable energy. This approach provides theoretical guidance for the economic and stable operation of regional integrated energy systems.

To address the prominent issues of insufficient utilization of user-side flexibility resources and the low degree of energy coupling in park-level electricity-heat-hydrogen integrated energy systems，this paper proposes a low-carbon scheduling strategy incorporating the concept of equivalent energy storage. First，user-side adjustable resources are considered，and the dispersed regulation capabilities among multiple user-side entities are aggregated，thereby introducing the concept of equivalent energy storage（EES）. Second，a multi-mode coordinated operation framework is established for park-level multi-energy systems，which integrates electrical energy storage，hydrogen energy storage，and hydrogen-blended gas combined heat and power units. This framework characterizes the coupling relationships of electricity-heat-hydrogen energy flows，while a stepwise carbon trading mechanism is introduced. Together with EES，user-side adjustable resources are aggregated to reduce the system’s dependence on high-carbon units. Finally，case studies are conducted to validate the effectiveness of the proposed strategy. The results demonstrate that，compared with the case without EES，the proposed method reduces the total operating cost of the system by 13.04% and achieves a 29.62% reduction in carbon emissions under the constraints

The efficient operation of virtual power plants (VPPs) relies on massive heterogeneous data interactions, facing the fundamental contradiction between data value mining and privacy protection. Addressing the challenges of data silos, single points of failure, and privacy leakage in centralized management, this paper reviews the application status and integration solutions of federated learning and trusted execution environment in VPPs. Through comparative analysis of blockchain and cryptographic collaboration schemes, the advantages of federated learning in privacy protection, collaboration efficiency, and technical adaptability are clarified. The applications of federated learning in VPP load forecasting, distributed energy resource coordination, electricity market trading, and demand response are systematically reviewed, along with an analysis of trusted execution environment mechanisms that ensure computational confidentiality and integrity through hardware isolation. The integration architecture of both technologies is emphasized: trusted execution environment addresses federated learning’s vulnerabilities in parameter leakage and poisoning attack defense by protecting model aggregation processes and sensitive data processing, while federated learning’s distributed characteristics overcome the performance and scalability bottlenecks of trusted hardware. This integrated technology enables “data availability without visibility”, and provides a feasible path for secure and efficient data collaboration in the energy internet. On the basis, this paper further discusses future challenges related to performance optimization, side-channel defense, and standardization.

Against the risk of frequency instability arising from reduced system inertia due to the integration of high-proportion new energy into the power grid, this paper proposes a power system safety forewarning and auxiliary decision-making method considering minimum inertia constraints. Firstly, a dual-constraint critical inertia evaluation model is established, which calculates the critical inertia by integrating the rate of change of frequency constraint and the minimum frequency constraint, thereby improving the accuracy of the inertia safety boundary. Secondly, an equivalent inertia calculation framework for the source-grid-load-storage system is constructed to accurately calculate the total system inertia level and quantify the specific contributions of virtual inertia from conventional units and new energy sources, load inertia, and dynamic inertia from energy storage. Once the actual system inertia falls below the critical inertia threshold, a forewarning is activated and the inertia deficit is quantified; meanwhile, to minimize the system operating cost, a multi-resource optimal dispatch model incorporating inertia security constraints is developed. By coordinately adjusting the output of conventional units, the frequency regulation strategies of new energy sources, and the charging-discharging strategies of energy storage, the proposed method achieves inertia safety forewarning and auxiliary decision-making for power systems. Simulation results demonstrate that: the proposed dual-constraint inertia safety model effectively avoids the risk of missed judgment inherent in traditional single-constraint models; the proposed forewarning mechanism enables advance identification of inertia shortages and quantifies the deficit; the proposed auxiliary decision-making scheme significantly reduces system operating costs while ensuring frequency security.

To investigate the effects of blade icing and anti-icing modifications on the vibration characteristics of 5 MW wind turbines in the winter icing environment of the Yunnan-Guizhou Plateau, and to evaluate their operational safety under complex climatic conditions, this study utilizes three anti-icing test turbines retrofitted with an active aerothermal method. Field vibration monitoring data spanning four months were collected. Nine vibration state variables at critical locations, such as the nacelle, were selected. By calculating characteristic parameters and applying threshold criteria, longitudinal and comparative analyses were conducted to comprehensively evaluate the vibration status and evolution trends of the turbines. The results indicate that the overall vibration severity of the three test turbines remains at a low level with negligible individual variations, and no abnormal fluctuations were observed. The mass imbalance and aerodynamic profile alterations induced by blade icing, as well as the load variations caused by the newly installed anti-icing equipment, did not lead to a significant exacerbation of turbine vibrations. It is concluded that, under icing conditions, neither blade icing nor the anti-icing modifications have a significant impact on the overall vibration levels of the 5 MW wind turbines. The structural dynamic performance of the turbines remains stable, demonstrating the capability for long-term safe operation.

The large-scale integration of distributed photovoltaics and emerging loads has made limited distribution network hosting capacity a key physical bottleneck to the development of new-type power systems. Existing research on virtual power plants (VPPs) mostly focuses on commercial aggregation and optimal dispatch, lacking a systematic exploration of active support mechanisms for the underlying operational constraints of distribution networks. Stepping out of the single resource aggregation perspective, this paper proposes a four-stage evolutionary analysis framework for VPPs oriented towards enhancing distribution network hosting capacity, and systematically reviews international frontier practices from VPP 1.0 (aggregation arbitrage) to VPP 4.0 (hierarchical autonomy). Specifically, VPP 2.0 relies on the local autonomous control of smart inverters to mitigate voltage violations, effectively enhancing the static hosting capacity of distribution networks; VPP 3.0 introduces the dynamic operating envelope mechanism to decouple physical constraints from market optimization, deeply unlocking the spatiotemporal dynamic flexibility of the system; VPP 4.0 utilizes cellular energy systems and distributed collaborative algorithms to address the scalability bottleneck under the integration of hundreds of millions of nodes. On this basis, considering China’s practical national conditions—specifically the management system based on secondary substation areas (transformer service areas) and the massive scale of legacy non-smart assets, this paper proposes a three-stage localized evolution pathway encompassing physical capability upgrades, physical-market decoupling, and cellular autonomy at the secondary substation level. This research provides theoretical references and engineering guidelines for the transformation of VPPs in China from pure commercial aggregators to technical aggregators equipped with physical support capabilities for distribution networks.

Under the background of the “carbon peak and carbon neutrality” goals, the contradiction between the high proportion of renewable energy grid connection and the reliance on fossil energy has become increasingly prominent. To coordinate low-carbon constraints with energy security, this paper proposes an optimized scheduling model for virtual power plants (VPPs) based on the collaboration of carbon capture and storage (CCS), power-to-gas (P2G), and electric vehicles (EVs). This model builds an integrated framework of “emission reduction - conversion - benefit” by aggregating distributed resources such as gas turbine units, combined heat and power (CHP), wind power, photovoltaic power and EVs: Firstly, CCS is used to capture CO₂; Secondly, through CCS-P2G, CO₂ is converted into methane by utilizing the abandoned wind and photovoltaic energy, and the captured CO₂ is consumed to form a carbon cycle. Finally, aggregated EVs participate in carbon market transactions and increase their profits by using the China certified emission reductions (CCERs) they generate. The case analysis based on Matlab/Cplex shows that compared with the traditional gas-CHP system model, the model proposed in this paper can reduce carbon emissions by 91.3% (from 2,466.9 tons to 214.34 tons), lower the cost of wind and solar power curtailage by 71,457 yuan, and increase the consumption rate of renewable energy. And by selling CCER, the net cost of VPP was reduced by 8,208 yuan. Ultimately, the overall net cost of VPP was reduced by 96,611 yuan. The research verified the effectiveness of multi-technology collaboration in enhancing the economic and environmental benefits of VPP, providing theoretical support and practical paths for the low-carbon transformation of the new power system.

This study investigates the ash deposition behavior of anthracite during combustion, which significantly affects boiler safety and efficiency due to slagging tendencies. A pilot-scale one-dimensional settling furnace system was employed to conduct combustion experiments under varied operating conditions, including different loads, primary/secondary air ratios, and excess air coefficients. Ash samples were analyzed by fusion tests, scanning electron microscope(SEM), X-ray diffraction(XRD), X-ray fluorescence spectroscop(XRF), and laser sizing. Results show ash composition (C, O, Si, Al) and crystalline phases (quartz, mullite, hematite) remain stable. Increasing the load from 0.2 MW to 0.3 MW raises the ash deformation temperature from 1259 ℃ to 1312 ℃, while the slagging index increases from 1 268 to 1 319, indicating a significantly enhanced slagging tendency. When the mass ratio of primary air to secondary air is increased from 2/8 to 4/6, the unburned carbon content in ash increases from approximately 37.5% to 40%, and the median particle size enlarges from 22 μm to about 28 μm, resulting in a pronounced promotion of ash deposition. Excess air coefficient had limited impact on fusibility and slagging. Ash exhibited a bimodal size distribution: fine particles form an adhesive layer, while coarse particles deposit by impaction, jointly accelerating slagging. The results demonstrate that boiler load dominates slagging behavior, with air distribution affecting burnout and particle characteristics. This study provides pilot-scale experimental data and mechanistic insights for slagging prediction and combustion optimization of anthracite-fired boilers under wide-load operation.

Against the backdrop of global energy transition and the rapid development of the new energy vehicle industry, as critical refueling infrastructure, the optimal layout of battery swap stations is essential for enhancing both service efficiency and power infrastructure effectiveness. This study focuses on the operational passenger vehicle battery swap market in City B. Operational data of battery-swap taxis are obtained through market research. A hybrid queueing model is introduced to establish a saturation prediction model based on dynamic dilution effects. Additionally, a fusion algorithm coupling the Voronoi diagram with the particle swarm optimization algorithm is proposed. Based on the aforementioned methods, a “prediction-optimization-layout” collaborative planning framework is constructed, quantifying policy sensitivity and supply-demand interactions. The reliability of the prediction of 23 theoretically new battery swap stations by 2025 is further verified through Monte Carlo simulation. Through the coordinated allocation of battery swap stations and charging guns (65 stations + 4 charging guns), the average user waiting time is controlled within 10 min, and the actual station construction demand is optimized to 13 stations. By integrating dynamic spatial partitioning with global optimization, the challenge of site optimization in high-density urban areas is addressed. The research findings provide an implementable solution for battery swap network planning that balances service efficiency and investment costs and also offer valuable insights for optimizing distributed power infrastructure.

Aiming at the problem of control interference and equipment loss caused by high frequency power electronic switching action when reconfigurable battery energy storage system participates in the frequency modulation process of power grid， a frequency modulation control strategy based on coordinated topology structure is proposed. Firstly， the operation control method of the reconfigurable battery energy storage system is designed to improve the cycle service life， flexibility and security of the battery energy storage system. Secondly， the virtual synchronous generator control is used to provide frequency modulation service. In order to reduce the influence of high-frequency power electronic switching， a reconfigurable battery energy storage system is proposed to participate in frequency modulation control strategy to ensure frequency stability. Finally， the effectiveness of the proposed control strategy is verified by simulation modeling.

To address the increased load volatility and insufficient interaction stability with the main grid caused by large-scale integration of electric vehicles (EVs) into microgrids, a two-stage optimal scheduling strategy for a PV-storage-EV charging microgrid is proposed, incorporating flexible EV charging and discharging. First, in Stage 1, a piecewise logistic regression model is employed to accurately quantify users’ willingness to participate in vehicle-to-grid (V2G) services. A bi-objective optimization model is formulated to minimize both load fluctuations and user charging costs. The zero-sum game strategy is adopted to determine the weighting coefficients of the multiple objectives, thereby fully exploiting the flexible regulation potential of EVs to reduce user costs while smoothing the load profile. Subsequently, based on the results from Stage 1, Stage 2 constructs a model that minimizes both microgrid operating cost and tie-line power standard deviation, optimizing the power dispatch of internal generation units and power exchange with the upstream grid. This stage also investigates microgrid scheduling responses under low EV penetration scenarios. Finally, the mixed-integer programming problem in Stage 1 is solved using Cplex, while the multi-objectivegrey wolf optimizer − enhanced with an improved Tent chaotic map and a state-driven adaptive iterative strategy − is applied to solve the models in both stages. Simulation results demonstrate that, under various EV participation scenarios, the proposed approach enables the microgrid to simultaneously achieve economic benefits for both end-users and the microgrid operator, as well as enhanced grid stability.

To effectively identify and eliminate abnormal data in the measured data of wind turbines, an anomaly detection algorithm based on manifold learning is proposed through the analysis of high-dimensional measured data from wind turbines. Firstly, the k-nearest neighbor mutual information algorithm is employed to select feature variables for the wind turbine. Subsequently, an optimized t-distributed stochastic neighbor embedding (t-SNE) algorithm is utilized. This optimized algorithm replaces the sample distance metric with a weighted sum of the Euclidean distance and the local principal component analysis (LPCA) difference, enabling the extraction of low-dimensional features with inherent patterns from the high-dimensional manifold data. This facilitates the distinct separation of data with different distribution characteristics in a visualized two-dimensional space. Furthermore, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is applied to cluster the data within this two-dimensional space. The results demonstrate that, compared to the principal component analysis (PCA) algorithm, locally linear embedding (LLE) algorithm, and the original t-SNE algorithm, the proposed method can effectively achieve visual separation and clustering for data under various complex operating conditions, successfully identifying and eliminating abnormal data.

To address the systematic limitations of traditional optimization methods for coal-fired boilers and the inability to accurately perceive in-furnace combustion states, this paper proposes a comprehensive boiler optimization system integrating intelligent sensing and control. First, an infrared temperature measurement array is deployed, combined with a gradient positioning algorithm, to achieve online reconstruction and visualization of the three-dimensional temperature field within the furnace. Second, a dynamic model of the boiler combustion process is established based on a continuous-time Bayesian network. Finally, a multi-objective particle swarm optimization algorithm with dynamically adjusted inertia weights is employed for online optimization, thereby constructing a real-time closed-loop adaptive intelligent combustion control system. Engineering application results demonstrate that the proposed system can effectively perceive the combustion state and accurately identify and provide early warnings for abnormal conditions, such as slagging and uneven combustion. After the system was put into operation, the boiler efficiency increased by no less than 0.3%, and NOx emissions were reduced by no less than 12%. In conclusion, this system provides robust technical support for resolving operational optimization challenges in utility boilers and achieving the synergistic development of safety, economic efficiency, and environmental protection.

To address the issue that distributed energy storage is difficult to meet the online real-time dispatching requirements of aggregators due to its large quantity，geographical dispersion，and strong uncertainty in responding to the demands of multiple user entities，a clustering-based aggregation optimization dispatching strategy for distributed energy storage is proposed. Firstly，the holographic state model of energy storage is established by considering the physical state parameters and electrical location information of energy storage，based on which the energy storage is clustered by K-Means++ algorithm. Secondly，the frequency modulation，peak regulation，distributed energy trading and voltage regulation demands of multi-user subjects are constructed，and the corresponding service consolidated indicators of energy storage clusters are designed to determine the collection of energy storage with excellent performance to be optimised for each demand. Subsequently，taking into account the revenue of the aggregator participating in demand response of multi-user subjects and the cost of leasing energy storage，the energy storage to be optimised is optimally scheduled with the objective of optimal economic benefit of the aggregator. Finally，the feasibility of this paper’s optimal scheduling strategy for aggregation is verified through a simulation experiment.

High-penetration renewable energy systems exhibit pronounced uncertainty. As an emerging long-duration physical energy storage technology，advanced adiabatic compressed air energy storage（AA-CAES）provides valuable support for enhancing system flexibility and regulation capability. However，conventional robust planning typically adopts conservative configurations across all scenarios，making it difficult to accurately characterize the risk of power and energy limit violations in storage operation. To address this gap，this study proposes an AA-CAES capacity optimization method that incorporates wind-photovoltaic uncertainty and achieves an effective trade-off between economic performance and operational risk through chance constraints. First，a chance-constrained model is developed to bound the violation probabilities of AA-CAES charging/discharging power and energy capacity at prescribed confidence levels，and binary variables combined with a big-M linearization strategy are employed to reformulate the problem as a mixed-integer linear program（MILP）. Second，a multi-scenario stochastic planning framework is constructed to represent the temporal variability of renewable resources. Finally，simulation studies and confidence-level sensitivity analyses are conducted. The results demonstrate that，compared with stochastic planning without chance constraints，the proposed method effectively controls violation risk while maintaining superior system cost performance，thereby enhancing both reliability and economic efficiency.

As an extension of the heat exchanger network, the array-type heat exchangers can effectively enhance the operational capability of advanced adiabatic compressed air energy storage (AA-CAES). However, the complexity of the variable-configuration array-type heat exchanger network exerts a significant influence on the operational capability of the AA-CAES system. To address this gap, this paper proposes a wide-range operational strategy for AA-CAES systems that incorporates array-type heat exchangers. First, a model of the array-type heat exchangers array for AA-CAES is established based on the thermal-electrical analogy theory. Subsequently, a wide-range operation method for AA-CAES is proposed, leveraging the operational characteristics of the array-type heat exchangers. This method determines the number of heat exchanger units participating in power regulation according to the required power output, followed by a multi-objective optimization of the array-type heat exchangers using power deviation and residual thermal energy of the thermal oil as objective functions. Finally, a case study based on the parameters of a commercially operational AA-CAES station is conducted to validate the effectiveness of the proposed method. The results demonstrate that, compared to traditional heat exchangers, the modular heat exchanger array can effectively expand the feasible operating region of the AA-CAES discharging system, reduce power tracking deviation, and increase the utilization rate of thermal energy in the thermal oil. The research will provide the theoretical foundation and technical support for flexible regulation of AA-CAES.

In response to the challenge of early warning for abnormal oil sump temperatures in wind turbine gearboxes, a fault warning method based on supervisory control and data acquisition (SCADA) data is proposed to enhance the operational reliability of the turbines. Firstly, by integrating wind speed-power distribution characteristics, an outlier detection approach utilizing the interquartile range and longitudinal filtering based on data dispersion is employed to eliminate anomalous power points. Subsequently, key input features influencing oil sump temperature are identified using a random forest algorithm, leading to the development of a temperature prediction model based on categorical boosting (CatBoost). The hyperparameters of this model are optimized using tree-structured parzen estimator (TPE). Finally, dynamic warning thresholds are established through statistical process control based on residual distributions. In a practical case study from a specific wind farm, this model issued effective warnings approximately 5 hours prior to gearbox failure; notably, the time points at which residuals exceeded control limits closely aligned with the progression of faults.The proposed method demonstrates significant efficacy in identifying abnormal conditions related to oil sump temperatures and possesses strong early warning capabilities along with substantial engineering application value.

Extracting the latent value embedded in electricity load data constitutes one of the key challenges in the power industry. To address the difficulty faced by conventional clustering approaches in capturing the intrinsic features of high-dimensional load data, this paper proposes an optimized clustering method based on a one-dimensional convolutional autoencoder (1D-CAE). First, a 1D-CAE is employed to extract temporal features from daily customer load profiles through nonlinear dimensionality reduction, with the objective of minimizing reconstruction loss. Second, we introduce an improved Cayley orthogonal constraint to enhance the structural information of the clustering space, thereby optimizing the mapping of latent features and improving clustering stability. Third, a generative adversarial network (GAN) is integrated with K-means clustering to refine the cluster centers and fine-tune the encoder. Finally, the effectiveness of the proposed method is evaluated on real-world load datasets using three widely accepted internal validation metrics: the Davies–Bouldin index (DBI), the Calinski–Harabasz index (CHI), and the silhouette coefficient (SC). Experimental results demonstrate that the proposed approach significantly enhances both inter-cluster separability and intra-cluster compactness. The study confirms that the method can effectively identify and extract morphological characteristics of diverse load profiles, offering robust support for demand response and optimal dispatch in virtual power plants.

The increasing penetration of renewable energy and growing electricity demand in islanded microgrids have intensified the uncertainties on both the generation and load sides, posing severe challenges to their secure, stable, and economic operation. Traditional robust optimization methods, which over-emphasize extreme system conditions, often compromise operational economy. This paper employs fuzzy theory to generate stochastic optimization scenarios for the system. Based on the probability of scenario occurrence and the minimum hybrid energy storage system capacity required for each scenario, a scenario reduction process is conducted. A stochastic optimization-based dispatch method for islanded microgrids is proposed. The method involves establishing uncertainty models for renewable energy and load to generate stochastic scenarios, formulating a mathematical model, performing demand response dispatch under each scenario, and finally filtering out extreme scenarios. Based on the proposed method, experimental verification is carried out in an island microgrid case. The proposed method reduces the system operating cost by 20.17% compared to the traditional robust optimization approach. The results verify the effectiveness and superiority of the proposed method.

To address the issues of data noise interference, feature scale discrepancy, and insufficient multiscale meteorological pattern modeling in photovoltaic power forecasting, a prediction method based on dynamic data preprocessing and gated dense multiscale convolutional neural network (GDMS-CNN) is proposed. Firstly, an anomaly detection mechanism based on dynamic sliding-window Z-score is established, and missing values are processed via covariance-weighted multivariate interpolation. Secondly, an adaptive piecewise normalization algorithm is adopted to eliminate feature dimensional differences, and cloud-cover correction factor and atmospheric attenuation factor are constructed to enhance physical feature representation. Finally, a GDMS-CNN is designed, wherein the feature extraction efficiency is optimized by depthwise separable convolution modules, densely connected dilated convolution blocks are constructed to capture multiscale spatiotemporal correlation features, and an asymmetric gated channel attention mechanism is embedded to dynamically recalibrate feature weights. Experimental results demonstrate that the proposed method reduces the root mean square error (RMSE) by 16.4% compared with the optimal baseline model genetic algorithm-variational mode decomposition-echo state network (GA-VMD-ESN), and by 43.4% compared with the traditional random forest. The proposed method provides a novel solution for photovoltaic output forecasting and effectively enhances the reliability of power grid dispatching.

To enhance the operational flexibility of coal-fired power plants in supporting high penetration of renewable energy, this paper focuses on the coordinated planning of regional coal-fired power flexibility upgrades. Existing research typically optimizes deep peak-shaving modifications on the boiler side of condensing units and thermal-electric decoupling technologies separately, with most studies neglecting the impact of grid structural constraints. this paper first systematically analyses the operational characteristics of four technical approaches: deep peak shaving modifications on the boiler side, zero-output modifications for low-pressure cylinders, electric boilers, and thermal storage devices. Subsequently, a mixed-integer linear programming model incorporating grid topology is constructed. This model aims to minimize total system costs, enabling the coordinated configuration and operational optimization of multiple technical pathways. A case study based on an enhanced IEEE 14-node system demonstrates that integrated optimization of these technologies reduces total system costs by 5.98% and curtailment rates for wind and solar power by 15.68%. The results validate that the proposed collaborative planning approach effectively integrates complementary advantages across different technologies, significantly lowering system costs and alleviating pressure on renewable energy integration. It also reveals that the flexibility regulation capacity of coal-fired power plants is influenced by their node position within the grid.

To address the optimization problem of resource aggregation and bidding decision-making for virtual power plant (VPP) participating in power peak shaving, a decision optimization model based on resource response capability and information gap decision theory (IGDT) is proposed. Considering four dimensions: response potential, fluctuation degree, duration, and response speed, an aggregation indicator system for distributed resources is constructed. A multi-objective aggregation optimization model is established, balancing the maximization of expected response revenue and the minimization of deviation penalty risk, to screen the optimal resource portfolio. The market transaction framework and bidding decision mechanism for VPP participating in power peak shaving are designed. The IGDT theory is introduced to characterize the uncertainty of peak shaving compensation prices, and a risk-averse (RA) model is constructed to optimize bidding strategies. The simulation results show that the multi-objective optimization model of VPP aggregation can take into account both economic and risk considerations. It can provide a theoretical method for VPP aggregators to screen resources and reduce the risk of deviation punishment of VPP. The IGDT-based bidding decision optimization model can help avoid the transaction risk caused by the uncertainty of peak compensation price, so that the VPP can obtain reasonable response benefits.