A type of thermal energy storage process for large scale electric applications is referred here as pumped thermal electricity storage (PTES), which based on a high temperature heat pump cycle which transforms electrical energy into thermal energy, followed by a thermal engine cycle which transforms the stored thermal energy back into electrical energy. PTES may be able to make a significant contribution towards future large scale energy storage needs, and without limitations in terms of geographical constraints, PTES may make use of different types of thermodynamic cycles and thermal storages. Due to little work in present literature can be found in-depth research on the dynamic characteristics of PTES, it is difficult to predict the dynamic performance of the system, develop the control strategy technology of operating process, or optimize the system design. A dynamic model of PTES system was built with Simulink and modular modeling approach on the basis of thermodynamic cycle. The dynamic response of working characteristics including compression and expansion ratio, compressor rotating speed, temperature, pressure, mass flow rate, power of PTES system were studied. The feasibility of power control and meeting the requirements of the public grid of energy storage power change were indicated, which providing computational tools and reference of control strategy and design optimization of PTES system.

The randomness and volatility of new energy bring difficulties to grid dispatching. Therefore, large-scale energy storage is introduced at the side of new energy stations, and the impact of the energy storage system on the operation simulation of power systems containing new energy is analyzed. This paper combines the characteristics of different modules with different functions during the operation of the power system, and proposes a simulation framework for the operation of new energy power systems with energy storage. It mainly includes: in the simulation of new energy output, using neural networks to predict the output of new energy; in the economy in terms of dispatching, an economic dispatch model for new energy systems with energy storage is established. Its objective function takes into account the cost of thermal power emissions, and considers the cost of reserve capacity compensation and the penalty cost of negative efficiency operation of wind farms. In terms of energy storage costs, the initial investment cost and operation and maintenance cost; in terms of operation analysis, in the ultra-short period, the two indicators of average energy change rate of new energy and peak inversion probability are used to evaluate the energy storage's ability to suppress power fluctuations and peak shaving; Operation simulation program for environment, suppression of fluctuations and peak shaving ability. Using genetic algorithm to take Xinjiang power grid as an example for simulation verification, the results show that the large-scale application of energy storage on the side of new energy stations can effectively suppress the fluctuation and reverse peaking of new energy output power.

The large-scale application of energy storage technology is an effective way to improve the economic performance and safety of the power grid containing renewable energy. In order to reasonably evaluate the economy of energy storage in the power grid, the life cycle cost method is adopted, according to the energy storage cost and technical characteristics of pumped storage power station, such as compressed air storage, lead-acid battery, sodium sulfur battery, liquid flow battery, lithium ion battery, etc. The investment, annual cost and electricity cost of various kinds of energy storage are calculated, and the economy of various types of energy storage under different utilization hours is compared. The research results show that the minimum cost of electricity storage for pumped storage power station is the lowest, followed by compressed air energy storage, and the highest energy cost of battery energy storage.

Graphene is a two-dimensional material with excellent mechanical properties, electrical properties and thermal conductivity, which can improve energy utilization and is an effective boost for the development of new energy and smart grids. The development status of the graphene industry and its advantages and disadvantages as energy storage materials are introduced. The application of graphene materials in the field of lithium-ion batteries and supercapacitors and the current status of graphene preparation technology are mainly discussed. On this basis, the application prospect of graphene in the field of energy storage is prospected. Graphene's large-scale preparation technology, process equipment and product quality have achieved staged breakthroughs. However, the maturity of graphene's large-scale preparation technology is still relatively low, and there are common problems of different quality and properties of different batches of graphene products. Coal-made graphene has already achieved engineering applications. Different application scenarios have different requirements on the structure and performance of graphene. Targeted research can be done for different requirements to develop graphene materials suitable for specific application scenarios. The research on the electrochemical characteristics of coal-made graphene electrode materials and the coal-made graphene-based lithium-ion batteries and supercapacitors are another important direction for future research.

Energy storage is a key technology to support the development of new generation power system and energy internet. It is of great significance to China's energy transformation and high quality development of power grid. In order to guide the healthy and orderly development of China's electrochemical energy storage industry, the development situation of electrochemical energy storage in the United States is introduced in detail. The industrial policy of electrochemical energy storage development in California and New York is systematically reviewed. On the basis of this, combined with the current development of electrochemical energy storage in China, relevant suggestions on promoting the development of electrochemical energy storage in China are put forward from the aspects of fiscal and taxation policies, electricity market and regulatory mechanism.

In order to reduce greenhouse gas emissions and solve the energy problem, the South Korean government has formulated a sustainable development strategy, increasing policy support and capital investment, focusing on supporting the energy storage system (ESS) industry. At present, the production capacity and market scale of Korean ESS equipment are growing, and Korean enterprises occupy a pivotal position in the global market. In the field of electrochemical energy storage systems, the research on lithium-ion batteries is mainly focused on ideal nanostructures and nanomaterials with large specific surface area; the cycle life of sodium-ion batteries developed by Korean enterprises is as long as 15 years, which is 3 times higher than that of traditional batteries, and the research on redox flow batteries and supercapacitors is still in its infancy.

In order to solve the problem that the energy density of supercapacitors is small and the state of charge(SOC) is easy to exceed the limit during operation, this paper improves the traditional low-pass filtering method and proposes a power allocation strategy considering the SOC of supercapacitors. The method divides five different working areas according to the supercapacitor SOC, and takes the supercapacitor SOC as the variable, establishes the corresponding functional relationship with the filter time constant in the different working areas, and then dynamically adjusts the filter time constant according to the SOC change, so as to realize the reasonable distribution of power between the battery and the supercapacitor, and ensure that the supercapacitor SOC is maintained in a reasonable range. Finally, the relevant model is built in Matlab/Simulink and the correctness and effectiveness of the proposed scheme is verified by simulation. The simulation results show that, compared with the traditional low-pass filtering method, this method can reasonably allocate the power demand of the supercapacitor and the battery according to the supercapacitor SOC while stabilizing the power fluctuation, so that the supercapacitor SOC can recover itself, prevent its overcharge and over discharge, and improve the economy and stability of the DC microgrid system operation.

As one of new long-term energy storage technology, the vanadium redox flow battery (VRFB) is suitable for peak-shaving, frequency modulation and new energy support, etc. However, there are issues such as complex power to capacity ratio, larger occupied area, and lower energy efficiency. Based on solving the problem of abandoning of wind, solar, and power limit in new energy power generation, this paper forwards a theoretical method for the optimal design of energy storage capacity configuration by establishing a mathematical model and optimizing the objective function. By optimizing the topology design of energy storage station (ESS), system integration and reliability of power access are improved. By establishing an energy recovery mathematical model, a method for improving system efficiency through energy recovery based thermal management design is proposed. By establishing a multi-source data mix digital twinning model, the idea of VRFB energy storage operation and maintenance technology based on digital twinning is proposed. Finally, the simulation analysis verifies the feasibility of the optimized design. The peak shaving rate can be reached to 16%, and the power limit is reduced from 45% to 14%. Meanwhile, the occupied area can be saved by 15%, and the efficiency can be increased from 70% to about 90%. It provides a systematic theoretical method and feasible solution for the large-scale VRFB ESS optimization design for new energy.

Frame gravity energy storage system is not limited by geographical conditions, easy to scale expansion and application, is an effective way to achieve large-scale commercial applications of gravity energy storage in the future, and gradually received people's attention. Based on the structural composition analysis and cost calculation of the frame gravity energy storage system, the economy of the frame gravity energy storage system is analyzed, and the investment cost and the quasi energy storage power cost of the frame gravity energy storage system with different system capacities are obtained, which can provide references for the construction and operation of the frame gravity energy storage system.

Energy storage is an efficient resource to improve the flexibility of power system and stabilize the power and load's random fluctuation. However, the current clear high demand for energy storage is in sharp contrast with the ambiguity of the energy storage business model. Based on this, the concept of business model is first given, and then the energy storage business model is elaborated in combination with energy storage application scenarios. On this basis, based on the six-element model, the energy storage business model is reconstructed from overall to detailed analysis. And it is reconstructed from six aspects, including positioning, business system, profit model, key resource capabilities, cash flow structure, realized value and so on. The important factors in each key link of energy storage business model are sorted out in this paper. An integrated analysis model throughout the full life cycle is proposed to grasp the starting point of energy storage business model design, identify the electricity market business that energy storage can participate in, discover the internal logical relationship of energy storage business model construction, and promote the sustainable development and improvement of energy storage business model.

In order to achieve the strategic goals of "carbon peak" and "carbon neutral", China's power grid will gradually be built into a green smart grid with new energy as the main power source and multiple types of power sources coexisting. However, the limited peak regulation capacity of traditional conventional power sources is difficult to meet the peak regulation demand of the future power system after accessing high proportion of new energy, which restricts the absorption capacity of new energy and reduces the safety and economy of system operation. Therefore, it is necessary to build multiple types of energy storage models, such as pumped storage, electrochemical energy storage, and electric vehicle virtual energy storage. Combined with four typical scenarios and extreme scenarios of a provincial power system, an optimal peak regulation efficiency model from the perspective of dispatching agency is proposed based on the existing energy storage peak regulation auxiliary service compensation mechanism. Through simulation verification using historical data from a provincial power grid, it has been demonstrated that this model plays a positive role in reducing frequent start-stop cycles for thermal power units and improving economic efficiency in peak regulation. This promotes both economic viability and safe operation for future advanced electricity systems.

Energy storage plays an important role in establishing a modern energy system with clean energy as the core. In the new power system, the energy storage technology is mainly applied to promote the consumption of new energy, participate in the auxiliary service of the power market and support the construction of grid-load storage system. Firstly, the structure and classification of energy storage are summarized, and the application characteristics of energy storage are introduced according to power side, grid side and user side respectively. Then, it discusses the way of energy storage promoting new energy consumption on the grid side, and introduces the technology of "electric hydrogen production" to promote new energy consumption. Secondly, the construction of grid-load storage and its typical projects are introduced. The diversification of source grid load storage subjects greatly promotes the development of new energy and realizes the maximization of energy utilization. Finally, the development of power auxiliary services at home and abroad and typical energy storage cases are summarized, and the future development trend of energy storage is prospected.

For photovoltaic-storage charging stations, the optimal configuration of photovoltaic (PV) systems, energy storage, and charging facilities is a crucial factor affecting the economic viability of the charging stations. First, a simulation of the electric vehicle charging situation at the stations over a day is conducted to obtain the daily charging curve. Then, based on the characteristics of PV output and considering time-of-use electricity pricing, considering both investor and user-side factors, a optimal configuration model for photovoltaic-storage charging stations is proposed. This model aims to minimize total social costs while constraining device utilization rates and queuing times. The quantum particle swarm algorithm is employed to solve this model, determining the configuration of additional PV and energy storage capacity for the charging stations and deriving various operational indicators based on queuing theory. Finally, the rationality of the configuration results is validated from multiple perspectives, including trends in costs as capacity change. Calculations and case analyses show that the proposed method can achieve a reasonable allocation of PV, energy storage, and charging facility capacity, thereby effectively reducing the amount of electricity purchased from the grid during peak periods and improving the economic efficiency of the charging stations.

New energy storage is pivotal for smoothing fluctuations in renewable energy generation and adapting to dynamic changes in load, and it is a crucial support for future power systems. With policies allowing new energy storage as an independent participant in the power market, studying its technical and economic feasible region during market transactions becomes crucial to influence the widespread application and realization of commercial value in power systems. This paper establishes a trading model for new energy storage participating in electricity markets, outlines economic calculation methods for its participation in the electricity energy and peak shaving auxiliary service markets. Focusing on trading power, it characterizes the technical and economic feasible region of new energy storage in the electricity energy and auxiliary service markets, and studies the impact of factors like charge-discharge power and electricity prices. Through Matlab simulation analysis, the proposed method can effectively enhance situational awareness for new energy storage transactions, and provide decision-making support for market investors.

In the field of power grid energy storage, the key to obtain the rational load power distribution scheme of energy storage system is optimization algorithm. If the algorithm selection is not reasonable or the algorithm itself has defects, it will lead to premature solution process, and only the local optimal solution is obtained instead of the global optimal solution. To solve this problem, a dynamic load power distribution method of microgrid distributed energy storage system is proposed. The method firstly collects the operation data of microgrid distributed energy storage system. Then, considering the average load power loss rate and state of charge (SOC) balance coefficient, a multi-objective distribution function of distributed energy storage system is set up. Finally, under the three constraints of SOC, charging and discharging quantity, and charging and discharging current, the optimal solution of multiple objective functions is obtained by using the improved sparrow search algorithm, and the optimal dynamic load power distribution scheme of microgrid distributed energy storage system is obtained. The results show that with the proposed method, the average load power loss is relatively lower, SOC balance coefficient is relatively higher, and multi-objective function value is relatively smaller. It shows that the distribution scheme is more scientific and reasonable, and can ensure the stable operation of the energy storage system and realize the high quality power supply of the system.

Advanced adiabatic compressed air energy storage (AA-CAES) can improve the rate of new energy consumption, and it is a key technology for new power systems. Since the compressor of the AA-CAES system adopts a centrifugal compressor, there is a phenomenon of surge and blockage during the operation, which seriously affects the safe operation of the system. In this paper, the safety control strategy of the compression side of the AA-CAES system is investigated. Firstly, a simple judgement method of the surge and blockage phenomena based on the slope of the compressor mass flow rate is proposed, and the range of the compressor's allowable mass flow rate of air flowing through the compressor at a given rotational speed is determined. Then, the anti-surge and blockage control strategy of the compression subsystem is designed to limit the range of compressor air mass flow rate by controlling the angle of the inlet guide vane of the compressor using the variable flow method. Finally, simulations are carried out under the start-stop condition and grid-connected operation condition to verify the effectiveness of the control strategy.

Shared energy storage is an effective measure to improve the efficiency of energy storage and play the synergistic advantages of micro-energy grid clusters, however, most of the existing studies have simplified the energy transmission channel between shared energy storage and micro-energy network clusters into a bus structure, which has problems such as 'fixed capacity’ but not 'siting’, insufficient utilisation of shared energy storage after configuration, and so on. There are problems such as only 'capacity-setting’ but not 'siting’ and insufficient utilisation of shared energy storage after configuration. In this context, a two-layer optimal configuration model of shared energy storage for micro-energy grid clusters is established, taking into account the carrying capacity of the power grid. Firstly, a two-layer configuration model of shared energy storage for micro-energy grid clusters is established: the upper model takes the minimisation of the operating cost of the power grid and the shared energy storage planning as the goal, and optimises the decision-making of the location and capacity of the shared energy storage by taking into account the carrying capacity of the power grid; the lower model takes the minimisation of the operating cost of the micro-energy grid as the goal, and optimises the solution to the micro-energy grid operation problem. Secondly, the lower-layer model is converted into the constraints of the upper-layer model based on the KKT condition, and the two-layer optimal allocation model is converted into a single-layer optimisation problem. Finally, a comparative analysis is conducted using a test case containing three micro-energy grids. The results show that the proposed model can fully consider the carrying capacity of the grid and obtain a shared energy storage siting and capacity-setting scheme with better economic benefits and shorter payback period.

In order to realize the dynamic assessment of battery state under the whole life cycle of energy storage batteries, and to improve the adaptability of the lithium-ion battery model and the accuracy of state estimation under complex working conditions, a joint estimation method of battery state of charge (SOC) and state of health (SOH) based on the improved technique for order preference by similarity to an ideal solution (TOPSIS)-fuzzy Bayesian network is proposed. The equivalent circuit model of the battery is constructed by applying the multi-order resistor-capacitance circuit (RC) model and the node-branching framework, and the parallel loop in the equivalent circuit model of the second-order RC battery is characterized by Kirchhoff's law and Ohm's law to construct the spatial equations of state and the equivalent output equations. The constructed equations of state are discretized, and the discretized state-space equation of the battery model is analyzed by defining the discretized zero-input response and zero-state response of the parallel independent loop. The expert scoring method is introduced into the TOPSIS algorithm for the quantitative estimation of battery SOC, and combined with the Bayesian network that integrates into the fuzzy scale, the corresponding SOC values in the observed samples of the batteries are calculated from the battery SOH values under the same time distribution scale, so as to realize the joint estimation of battery SOH and SOC. The experimental results show that the proposed method can effectively estimate the results of battery SOC and SOH in different discrete spatial scales, and the estimation method has good accuracy and high precision.

The continuous increase in the proportion of new energy grid connection has reduced the inertia of the power system, leading to a decrease in system frequency regulation capability. In response to this issue, research on the participation of energy storage in frequency regulation has been carried out. The participation of energy storage in frequency regulation is influenced by factors such as unit quantity, state of charge (SOC), and charge-discharge strategy. A coordinated control strategy for energy storage units considering frequency deadband is proposed. Energy storage units are grouped based on SOC consistency, and different groups independently execute droop control or virtual inertia control to simplify output control commands. Control command switching is based on system frequency deviation or frequency rate of change, with frequency deviation and rate-of-change deadbands set to reduce disturbances causing frequent charging and discharging of energy storage systems. Simulation verification using Matlab/Simulink shows that the proposed control strategy is simple to implement, can quickly respond to frequency regulation requirements, and can ensure the lifespan of energy storage batteries.

With the increase in the number and penetration rate of electric vehicles year by year, the uneven layout and insufficient number of existing charging facilities in rural areas have become the main reason restricting the development of new energy vehicles in rural areas. Based on the master-slave game pricing strategy, this paper establishes a charging and storage plant optimisation model for rural power grids with a high percentage of photovoltaic penetration. First of all, based on the master-slave game theory to establish the charging and storage power station optimization model for the high proportion of PV penetration of rural power grid: the upper leader is the charging and storage power station investor, taking into account the construction and operation conditions of the rural distribution network and charging and storage power station, to optimize the decision-making charging and storage power station construction location, the supporting capacity of the storage equipment and the tariff strategy; the lower follower is the electric vehicle owner, according to the tariff strategy formulated in the upper leader model, to optimize the decision-making charging strategy for the electric vehicle. The lower level followers are EV owners, who make optimal decisions on EV charging strategies based on the tariff strategies developed by the upper level leader model. Secondly, the Karush Kuhn Tucker (KKT) condition and the dyadic principle are used to transform the model to obtain an easy-to-solve mixed-integer linear programming model. Finally, the algorithm is validated using a typical IEEE 33-node arithmetic system containing two charging regions. The example analysis shows that the proposed model can coordinate the interests of charging and storage plant investors and EV owners, and obtain a charging and storage plant configuration and operation policy with good economic benefits and strong PV consumption capacity.

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.

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.

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.

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.

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.

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.

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.

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 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

Shared energy storage can effectively address the issues of low utilization and high costs caused by individual energy storage configurations by regulating resources across multiple regions. To further exploit the potential of shared energy storage in demand-side resources，this paper introduces electric vehicles and ice storage air conditioning，both with flexible energy storage characteristics，to construct a generalized shared energy storage model for the coordinated optimization of energy usage in smart building clusters. In response to the uncertainty of photovoltaic（PV）output on the energy input side，a time generative adversarial networks（TimeGAN）is employed to simulate a large number of PV output scenarios. By combining daily irradiance data，the static and dynamic features of these scenarios are mined，and typical scenarios are identified using K-medoids clustering. Additionally，a tiered carbon trading mechanism is introduced to limit the carbon emissions of the energy system. An optimization scheduling model for smart buildings is established，considering operational costs，carbon emissions，and user comfort，and is solved using CPLEX. Case studies demonstrate that the proposed method can generate high-quality PV output scenarios，improve regional PV consumption rates，and effectively balance user comfort and costs.

The coupling of power and heating systems can promote renewable energy integration and improve the comprehensive efficiency of the energy system. Advanced adiabatic compressed air energy storage（AA-CAES）is a large-scale clean energy storage technology with the potential for multi-energy co-storage and supply，which can serve as an energy hub integrating power and heating systems. However，the current bidding mechanism for AA-CAES participating in electricity and heating markets as an independent entity remains unclear，and traditional modeling mostly adopts battery-like energy storage models，leading to difficulties in accurately measuring economic benefits. To address this，this paper proposes a leader-follower game-based bidding strategy for AA-CAES considering combined heat and power supply. Firstly，a combined heat and power mathematical model of AA-CAES is established by accounting for the operational characteristics of each component. Secondly，a single-leader-dual-followers leader-follower game framework is constructed，where the upper layer optimizes bidding parameters with the goal of maximizing AA-CAES’s profit，and the lower layer achieves market clearing with the objective of maximizing social welfare. To solve the challenge of solving the bi-level nonlinear model，the Karush-Kuhn-Tucker（KKT）optimality conditions and binary expansion linearization method are adopted to convert it into a single-level mixed-integer programming problem. Finally，case simulations show that AA-CAES’s profit from participating in both markets increases by 30.6% compared with participating only in the electricity market. The parameters of its own components have a significant impact on profits—especially a 10% improvement in the isentropic efficiency of the turbine can increase total profits by 28%. This study provides key references for the market operation and parameter optimization of AA-CAES.

To optimize the operation of shared energy storage，this study investigates the non-cooperative game problem in transactions between shared energy storage and multi-prosumer. First，a bi-level optimization model is established to characterize the non-cooperative game relationship among the participants，aiming to optimize the trading strategies of the shared energy storage operator and the prosumers. The upper-level model maximizes the operator’s profit by optimizing its operational schedule and pricing strategy to provide charging and discharging services. The lower-level model responds to these prices by minimizing each prosumer’s operational cost through optimizing their electricity trading and storage schedules. This approach helps the operator optimize trading strategies and enhance both market competitiveness and profitability. Next，the Karush-Kuhn-Tucker（KKT）conditions are applied to transform the bi-level problem into a single-level model. The reformulated model is linearized using the big-M method and then solved numerically. Finally，simulation results demonstrate that the proposed method effectively balances the interests of both the shared energy storage operator and the prosumers，achieving a mutually beneficial outcome.