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.

As an efficient hydrogen production technology， alkaline electrolyzer holds significant application prospects in green hydrogen production. For alkaline water electrolysis hydrogen production systems， this paper proposes an accurate and applicable semi-theoretical and semi-empirical alkaline electrolyzer model based on electrochemical principles and thermodynamic analysis. This model treats the electrolyzer’s cell voltage and gas purity as functions of operating pressure， temperature， and current density， and incorporates the influence of Faraday efficiency on hydrogen production rate. Based on literature-reported experimental data of a 50 m³/h alkaline electrolyzer under different operating conditions， this paper determines the model parameters via fitting using a nonlinear regression method. This paper further employs these experimental data to verify the model’s accuracy and analyze its applicability under varying operating conditions. The simulation results show that the proposed model can effectively predict the electrolyzer’s performance parameters， thereby providing a theoretical basis for the optimal design and the control systems of electrolyzers.

Hydrogen production from new energy is the most promising hydrogen production method under the targets of carbon peak and carbon neutrality. Aiming at the problem that the off-grid new energy hybrid hydrogen production system lacks a cooperative control strategy， this paper proposes a multi-level power replacement strategy based on the expected operation interval of the proton exchange membrane （PEM） electrolyzer. When the power command on the hydrogen production changes， the PEM electrolyzer first makes rapid adjustments to respond to the change. Then， a power replacement strategy is formulated in combination with the expected operation interval of the PEM electrolyzer. By utilizing the large-capacity support capability of the alkaline electrolyzer， the power outside the expected range of the PEM electrolyzer is gradually transferred to the alkaline electrolyzer， which fully exploits the multi-type and multi-time-scale response capabilities. Finally， the strategy proposed in this paper is tested on the self-developed integrated simulation platform for new energy hydrogen production. The operation results show that the strategy proposed in this paper can fully exert the multi-time scale response capabilities of the two types of electrolyzers， not only newly improves the response speed of the power command but also ensures the safety of the hydrogen production system. Moreover， it can meet the stability requirements of off-grid hydrogen production using new energy， and thus has high practical value.

The study explores the risk spillover effects among carbon markets， energy markets， and green finance markets， with a particular focus on the impact of extreme events on these markets. The aim is to provide insights for mitigating risks in the carbon market and promoting its healthy development. Based on an in-depth analysis of the risk spillover mechanisms among carbon， energy， and green finance markets， this research employs time-varying parameter vector autoregressive （TVP-VAR）-Diebold-Yilmaz（DY） and TVP-VAR-Baruník-Křehlík（BK） spillover index methods-and integrates them with a spillover network model. Utilizing historical data from 2018 to 2023 pertaining to China’s carbon market， energy market， and green finance market， this paper conducts an empirical investigation into the time-frequency risk spillover effects across these various markets. The results indicate that： （1） The spillover effects among the carbon-energy-green finance markets exhibit time variability and are susceptible to extreme sudden events. （2） Within different temporal domains and frequency cycles， both the green stock market and new energy market serve as primary sources of systemic risk transmission； conversely， while the carbon market acts as a recipient of risk in the short term， it transitions into a source of risk outflow over the long term. （3） The green stock market and new energy market constitute significant centers within the systemic spillover network.

With the rapid advancement of renewable energy， wind power has emerged as a significant clean energy source. Consequently， the accuracy of wind power forecasting is essential for ensuring both the stability and economic efficiency of the power system. To address the challenges of nonlinearity and non-stationarity in wind power forecasting and to enhance prediction accuracy and reliability， this study proposes a novel wind power forecasting model based on variational mode decomposition （VMD）， K-means clustering analysis algorithm， and TimesNet deep learning model. Firstly， VMD is employed to decompose nonlinear and non-stationary time series signals into multiple intrinsic mode functions （IMF）， facilitating the analysis and extraction of trends and periodic components from wind speed and generation data. Secondly， K-means clustering algorithm is utilized to classify the obtained IMFs， thereby identifying characteristic patterns of fluctuations in wind power. This process effectively enhances the model’s ability to capture variations in power under different wind conditions. Finally， the results processed through clustering are inputted into the TimesNet deep learning model for prediction. Comparative experiments with various existing wind power forecasting models demonstrate that the proposed forecasting model significantly reduces errors in predicting wind power output.

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 weak infrastructure， poor voltage stability， and low renewable-energy utilization in rural areas， this paper proposes a siting-and-sizing model for distributed generation （DG） that simultaneously optimizes voltage quality and economic performance. One objective aims to minimize voltage deviations caused by DG integration， thereby enhancing distribution-network power quality； the other seeks to minimize the levelized cost of energy （LCOE） over the full life cycle of the DG portfolio， accounting for investment， operation and maintenance expenses， and energy yield. The model is solved with a double deep Q-network （DDQN）， yielding a configuration that balances voltage stability and cost. Simulation on a modified IEEE 33-bus rural feeder shows that the DDQN-based scheme markedly improves voltage profiles while reducing upgrade costs. Furthermore， comparative analyses with the deep Q-network （DQN）， non-dominated sorting genetic algorithm II （NSGA-II）， and multi-objective particle swarm optimization （MOPSO） methods verify the superiority of the proposed approach， highlighting the efficiency， adaptability， and robustness of reinforcement learning for complex energy-system optimization.

The gigawatt-hour level large-capacity energy storage station provides critical support for the absorption of renewable energy， as well as enhancing system safety and operational control capabilities. However， with the significant increase in the number of energy storage units and individual battery cells within gigawatt-hour level battery energy storage stations， the risk of battery inconsistency has also intensified. This poses a serious threat to the safe and efficient operation of these storage facilities. To address this issue， an analysis is conducted on how the consistency of individual batteries and energy storage units affects the charging and discharging capabilities of energy storage stations. A method for calculating and evaluating these capabilities is proposed. A two-tier power distribution model for energy storage stations is developed， taking into account both station-level and virtual subsystem-level considerations. At the station level， based on the state of health （SOH） of each battery unit， the energy storage station is divided into several virtual subsystems. The overall charging and discharging strategy for each subsystem is designed according to their average state of charge （SOC） consistency， allowing for accurate calculation of total power demand across subsystems. At the virtual subsystem level， a power distribution strategy is established with an optimization goal aimed at minimizing SOC variance among stored units. The proposed power distribution method can delay lifespan degradation in low SOH energy storage units while accelerating SOC consistency convergence among them， thereby improving overall utilization rates within the energy storage station. Through simulation experiments conducted alongside analyses from Qinghai’s Togruoge 270 MW/1.080 GW·h battery energy storage project， we evaluated our proposed power distribution and management strategies. Results indicate that this approach demonstrates considerable effectiveness in achieving battery consistency management.

To address the issue of low prediction accuracy in photovoltaic power generation caused by the intermittency and volatility resulting from weather changes， this paper proposes a multi-level short-term photovoltaic power forecasting method. The method is based on collaborative clustering using self-organizing map and K-means algorithm （S-Kmeans）， and an improved artificial lemming algorithm （IALA）-optimized variational mode decomposition （VMD）， combined with a temporal convolutional network （TCN） and bidirectional gated recurrent unit （BiGRU）. First， key meteorological factors are selected through correlation analysis， and photovoltaic data is classified into three typical weather conditions - sunny， cloudy， and rainy by using the S-Kmeans co-clustering method. Then， the IALA is employed to adaptively optimize the VMD parameters， enabling optimal decomposition of the photovoltaic power series and capturing local signal features more effectively. Finally， a TCN-BiGRU model is constructed for each subsequence， and the prediction result is obtained through component forecasting and global reconstruction， thereby improving prediction accuracy. Experimental results show that the proposed model outperforms the comparison models across all performance metrics under various weather conditions， validating its effectiveness in short-term photovoltaic power forecasting.

With the integration of large-scale， distributed， and diverse distributed resources， virtual power plant （VPP） technology has become a vital tool for effectively managing and optimizing demand-side resources. To better align VPPs with the development needs of China’s new-type power system， this paper proposes an optimal scheduling model for VPPs participating in a green certificate-carbon joint trading mechanism， taking into account uncertainty risks. First， an optimal operation model for a VPP is constructed， consisting of wind turbines， photovoltaic units， gas turbine units， energy storage systems， and flexible load resources on the user side. The objective is to minimize the VPP’s operating cost， considering electricity markets， the green certificate-carbon joint trading mechanism， and incentive-based demand response. Second， multiple uncertainty factors within the VPP， such as generation sources， loads， and demand response， are comprehensively considered， and the conditional value-at-risk （CVaR） theory is applied to quantify the risks associated with these uncertainties. Finally， a case study is introduced to verify the economic and environmental benefits of the proposed model. The inclusion of CVaR also provides a robust decision-making basis for balancing VPP profits and risks.

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.

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.

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

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.

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.

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.

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.

In response to the supply-demand imbalance faced by intelligent buildings under the dual uncertainties of photovoltaic output on the source side and electricity demand on the load side， this study aims to reduce energy storage investment and electricity costs while enhancing the economic viability and robustness of shared energy storage systems. To achieve this goal， we develop a bi-level optimization model for shared energy storage based on hybrid game theory. In this model， energy storage operators and building users form a leader-follower game relationship； operators act as leaders setting internal transaction prices while users respond as followers through demand response strategies. Additionally， cooperative game theory is employed among buildings to fairly allocate costs using bilateral Shapley value methods.The uncertainty in source-load dynamics is characterized by constructing fuzzy sets for photovoltaic output using Wasserstein distance and incorporating conditional value at risk （CVaR） to depict investment risks arising from load fluctuations. The Karush-Kuhn-Tucker （KKT） conditions are utilized to transform the bi-level model into a single-layer mixed-integer linear programming problem for solution. Simulation results based on an intelligent building cluster in Jiangsu demonstrate that the proposed strategy effectively reduces redundant energy storage capacity by 12.3% and lowers average electricity costs across buildings by 8.7%， while simultaneously increasing operator profits and shortening payback periods for investments. Compared with traditional robust optimization methods and deterministic approaches， our method significantly enhances economic performance without compromising system robustness. The proposed hybrid game optimization strategy can collaboratively address dual uncertainties in sources and loads， facilitating efficient utilization of shared energy storage resources while achieving mutual benefits for all parties involved. This approach provides an effective pathway toward low-carbon operational efficiency for clusters of intelligent buildings.

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.

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.

Accurately predicting the low output of wind power is the key to ensuring the power supply security of a high-proportion new energy power system. To this end， a parallel prediction method for wind power low output based on gate recurrent unit -denoising auto encoder（GRU-DAE）-DLinear is proposed. The unsupervised learning method is adopted to characterize the typical fluctuation characteristics of low output， and the prediction accuracy is improved through targeted modeling. Firstly， a low-output event classification method based on GRU-DAE is proposed， and the sequential data denoising， induction and reconstruction capabilities of temporal neural networks are utilized to identify typical low-output events. Then， a parallel prediction model for low-output events based on DLinear is established， independently modeling the timing characteristics of different types of low-output events， thereby improving the overall prediction accuracy. Finally， the effectiveness of the proposed method is verified based on the actual operation data of a wind farm in northern China.

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.

With the continuous advancement in the construction of new power systems， the issues related to insufficient new energy consumption capacity in Northwest China and electricity supply shortages in eastern regions have become increasingly prominent. Therefore， transporting surplus new energy from Northwest China to developed eastern regions has emerged as an effective means to achieve inter-regional energy balance. In response to the demand for long-distance and large-capacity new energy delivery， this paper proposes a collaborative approach utilizing both electricity and hydrogen for new energy transmission. First， we establish a framework for a new energy delivery system that encompasses both electric power transmission and hydrogen transport channels. Next， considering cost and loss factors associated with equipment at the source end， transmission end， and load end during the new energy delivery process， while imposing constraints on supply-demand balance， losses incurred during transportation capacity limits as well as delivery time， we formulate an optimization model aimed at minimizing total costs and losses within this collaborative planning context. Finally， we conduct simulation analyses using four cases from Gansu Province’s new energy delivery projects. The results indicate that implementing this collaborative planning scheme reduces overall system costs by approximately 5.72% to 7.74%， thereby validating the effectiveness and rationality of the proposed model.

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.

In response to the urgent demand for clean heating in rural areas， a heat pump heating system utilizing indirect photovoltaic/thermal （PV/T） components has been proposed. This study focuses on a single household building （64 m²） located in a village in the Lanzhou region. A simulation model was constructed using the TRNSYS dynamic system simulation software platform， analyzing the operational characteristics of the system across three time scales： hourly， daily and during the heating period. The research investigates how variations in heat pump rated thermal power and thermal storage tank volume affect system performance. The results indicate that when the heat pump’s rated thermal power is set at 2.50 kW and the thermal storage tank volume is 0.9 m³， the system effectively reduces the average temperature of the thermal storage tank. Consequently， total electricity consumption during peak periods is lowered to 536.2 kW·h. The average electrical efficiency of PV/T components reaches 12.3%， while their average thermal efficiency stands at 35.35%. Additionally， the solar energy guarantee rate for this system is recorded at 77%， with an overall system efficiency of 49%. This optimized parameter combination demonstrates significant advantages for PV/T heat pump heating systems applied in rural clean heating contexts； it effectively enhances energy utilization efficiency and reduces operating costs， providing a viable solution for clean heating technologies in rural areas.

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.

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.

With the gradual complexity of the active power-frequency coupling characteristics of the new power system, the traditional single energy storage grid-connected frequency regulation strategy has brought huge pressure to the primary frequency regulation of the power system. The participation mode of multi-type energy storage in primary frequency regulation and the frequency characteristics of the power grid under the coordinated control of energy storage need to be studied urgently. This paper studies the frequency response mechanism of electrochemical energy storage based on droop control and flywheel energy storage based on virtual synchronous machine control respectively. When multi-type energy storage participates in the primary frequency regulation of the power system, the low-pass filter link is used to process the frequency change rate signal to achieve the coordinated control effect of energy storage. Then, the coordinated control model of multi-type energy storage is combined with the power system containing new energy and traditional thermal power, and the frequency response model of the power system is established. The model is used to quantitatively analyze the influence of energy storage-related parameters on the system frequency change rate and steady-state frequency deviation, and the parameter sensitivity analysis is carried out. Finally, the model is built on Matlab/ Simulink to verify the influence of multi-type energy storage-related frequency regulation parameters on the system frequency characteristics. The research proves that considering the coordinated control of multi-type energy storage in the frequency regulation unit of the new power system can improve the frequency stability characteristics of the power system.

With the rapid development of new energy generation technology， renewable energy sources such as wind energy and photovoltaic not only serve as important active power sources， but their reactive power regulation potentials are also receiving increasing attention. In this paper， an innovative optimization strategy based on the improved Genghis Khan shark optimization （GKSO） algorithm is proposed to address the shortage of virtual power plant （VPP） reactive power sources and the model solving difficulties under high percentage of new energy access. First， a reactive power co-regulation model containing multiple distributed power sources such as wind power， photovoltaic， energy storage and gas turbine is constructed， and the key influencing factors of the uncertainty of new energy reactive power output are revealed through parameter sensitivity analysis. In order to accurately characterize the uncertainty， Latin hypercube sampling （LHS） combined with the scenario generation and reduction technique of Kantorovich distance is innovatively adopted to establish a typical set of scenarios of wind and solar power output. On this basis， a multi-objective optimization model of VPP considering the uncertainty of new energy reactive power is established and efficiently solved using the improved GKSO algorithm. The simulation results show that compared with the particle swarm optimization （PSO） algorithm and seagull optimization algorithm （SOA）， the optimized GKSO algorithm has a significant advantage in solving the VPP reactive power optimization problem， and it is necessary to take the new energy reactive power uncertainty into account in order to reduce the operational risk for large new energy stations with large installed capacity.

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.

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.

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.

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.