Research progress on energy storage technologies of China in 2023 is reviewed in this paper. By reviewing and analyzing three aspects in terms of fundamental study, technical research, integration and demonstration, the progress on China's energy storage technologies in 2023 is summarized on the basis of comprehensive analysis, including hydro pumped energy storage, compressed air energy storage, flywheel, lithium-ion battery, lead battery, flow battery, sodium-ion battery, supercapacitor, new technologies, integration technology, firecontrol technology etc. It is found that important achievements in energy storage technologies have been obtained during 2023, and China is now the most active country in the world in energy storage fields on all the three aspects of fundamental study, technical research, integration and application, which maintained the first in terms of SCI paper number, filed patent number and installed capacity. Looking to 2024, energy storage technologies of China will very likely develop rapidly and need a high-quality development.

The high proportion of new energy access to the distribution network and the decline in the proportion of traditional controllable generating units have led to a significant shortage of power system regulation capacity. Demand-side flexibility resources can provide regulation capacity for power systems at multiple scales through certain control means. In this context, this paper first introduces the basic concept, characteristics, and types of demand-side flexibility resources. Then, it summarizes the research hotspots and progress of the spatio-temporal co-optimization and dynamic equilibrium mechanism of new power-system demand-side flexibility resources. It also summarizes the key technologies and achievements of the spatio-temporal co-optimization and dynamic equilibrium mechanism in the areas of morphology and structure decoupling analysis, coordinated allocation of supply-demand equilibrium, aggregated dispatch, and co-optimization and dynamic and equilibrium allocation of cluster benefits. Finally, this paper summarizes the advantages and disadvantages of key technologies and implementation algorithms of demand-side flexibility resource spatio-temporal cooperative optimization and dynamic balancing mechanism in terms of morphological structure decoupling analysis, supply-demand balance coordination allocation, aggregation scheduling and cooperative optimization, and cluster benefit dynamic balancing allocation.

Energy storage is a key supportive technology for the energy revolution. In the context of carbon neutrality， the fast-growing energy storage technology is playing an increasingly important role in energy industry. Existing energy storage technologies and their development statuses are expounded， focusing on their characteristics and differences. And the application scenarios and economy of these technologies are comprehensively compared. The key to battery researches is introducing new energy storage materials to solve its non-traditional electrochemical problems. Thermochemical energy storage is suitable for long-term storage due to its low energy consumption in reversible reactions， but attention should be paid to its cyclic dynamic characteristics， modeling and cost control. Pumped energy storage and compressed air storage technology are mature technologies， which are of high storage capacity and suitable for large-scale energy storage projects. However， the two technologies are limited by siting constraints， high cost of infrastructure construction and difference in operation and maintenance costs. Flywheel energy storage technology is suitable for the scenarios in need of frequent start-up and short energy release time， but how to the reduce energy loss in conversion is the challenge. Applications of hydrogen energy is restricted by difficulties in storage and transportation and its low energy conversion efficiency. The study can provide reference for relevant researches and policy formulation.

Power supply structure has undergone tremendous changes with the increasing proportion of renewable energy in the power system. The high permeability, randomness and uncertainty of the output of renewable energy cause problems increasingly prominent, such as demand of power system peak-shaving resources on both power supply and load side. Firstly, this paper summarizes temporal and spatial distribution characteristics of renewable energy, and explains the peak-shaving problem of power system with high proportion of renewable energy. Then, the equipment, characteristics and development trend of various peak-shaving energy storage types are summarized. The paper also elaborates the peak-shaving modes of the demand side in the power system, including demand response construction, power market and auxiliary peak-shaving service market, and enhancing power transmission capacity. Finally, the future research prospects of peak-shaving of power system with high proportion of renewable energy are discussed and prospected.

Objectives Under the current electricity price mechanism, it is difficult for Xinjiang pumped storage power station to fully recover the cost through the arbitrage of peak-valley price difference and participation in electric auxiliary services. Therefore, the capacity and electricity charge dredging mechanism of pumped storage power station is studied. Methods Based on the calculation of the electricity cost of Xinjiang pumped storage capacity, the problems existing in the cost dredging are analyzed. The dredging path of “market-oriented + transmission and distribution electricity price” is proposed for Xinjiang pumped storage power station. That is, the cost is recovered by transmission and distribution price before the maturity of power market construction, and the cost is recovered by marketization after the maturity of power market construction. Results Compared with the pure transmission and distribution electricity price recovery, the “market-oriented + transmission and distribution electricity price” method has higher feasibility in dredging capacity electricity price and the increase of transmission and distribution electricity price decrease by more than 90%, which is a feasible model to calm the excessive rise of transmission and distribution electricity price. Conclusions As a supplementary scheme for the operation cost recovery of pumped storage power stations, the research results will provide a broad market trading space for the pumped storage power stations.

With the large-scale integration of distributed energy resources such as small thermal power units， energy storage systems， wind turbines， photovoltaic systems， and electric vehicle charging stations， it is essential to optimize the system operating costs and transmission loss in distribution networks. Based on the output of multiple distributed energy resources and the load demand at different time periods within distribution networks， a multi-objective optimization model was established to analyze different line planning schemes for distribution networks. An improved multi-objective grey wolf optimizer integrated with the forward/backward sweep algorithm was used for optimization to optimize voltage and output of distributed energy resources at each node. Planning route scenarios with different typical characteristics were developed to analyze the effect of electric vehicle access at different nodes on line loss and system operating costs， thereby obtaining the optimal line planning scheme. Simulation calculations were conducted using the IEEE 33-bus system. The results showed that compared to traditional single-source power supply methods， the integration of distributed energy resources reduced transmission loss by 52.39%. Additionally， the hybrid system combining renewable energy with electric vehicle charging stations could effectively reduce system operating costs， providing a reference for large-scale integration of charging stations into distribution networks in the future.

The optimal scheduling of integrated energy systems （IES） is affected by fluctuations in uncertain factors such as renewable energy and load. Failure to accurately describe and process these uncertain parameters will constrain system reliability， and the lack of refined modeling and optimization methods makes uncertainty analysis more complex. To comprehensively and systematically analyze uncertainty modeling and optimization methods， the structure of IES， sources of uncertainty， and modeling approaches are reviewed. Monte Carlo simulation， information gap decision theory， interval methods， robust optimization， and data-driven methods are summarized， along with their applications and studies in uncertainty optimization. Research findings indicate that there is no single best optimization method. The complementarity of multiple methods can maximize the economic and environmental benefits of IES. Based on current research challenges and hotspots， future directions for uncertainty optimization are outlined.

Currently， the global energy structure is undergoing a profound transformation. Renewable energy sources represented by wind and solar power are increasingly becoming key components of power supply due to their cleanliness and sustainability. However， the inherent intermittency and volatility of these energy resources pose significant challenges to the frequency， voltage， and overall stability of power systems， threatening the security and reliability of power supply. To address these challenges effectively， it is crucial to integrate advanced technologies and methods to ensure reliable and efficient operation of power systems. The roles of simulation technologies， frequency regulation strategies， and artificial intelligence in new-type power systems are systematically analyzed. The evolution of hybrid simulation， from traditional serial methods to high-performance parallel and intelligent approaches， is analyzed in depth. The key roles of virtual synchronous generators and multi-type energy storage in mitigating inertia reduction and frequency regulation are examined. Additionally， recent advances and potential of artificial intelligence in renewable energy power generation prediction， load prediction， and smart microgrid scheduling are comprehensively evaluated. It is emphasized that the supporting roles of energy storage technologies and artificial intelligence should be fully leveraged， and more flexible market mechanisms and resource allocation systems should be established， thereby laying a foundation for the stable operation of new-type power systems.

As global demand for renewable energy continues to surge， efficiently and intelligently managing and forecasting renewable energy generation has become a pivotal research objective in energy sector. Applications of artificial intelligence （AI） technologies in the multi-dimensional data processing and intelligent forecasting of renewable energy generation are explored， focusing on its role in handling complex and highly variable data. First，the role of multi-dimensional feature mining techniques in processing wind and solar energy generation data from the perspective of meteorological conditions and spatiotemporal features is studied. Subsequently， a systematic analysis on intelligent forecasting techniques applied across different spatiotemporal scales and scenarios is offered， with particular emphasis on its usage in machine learning and deep learning models. These models have gained significant attention for their outstanding performance in dealing with nonlinear and high-dimensional data. Thorough reviews on the latest research findings demonstrate the substantial benefits of these AI technologies in enhancing the accuracy and efficiency of wind and solar energy generation forecasts. Additionally， it delves into the strengths and limitations of existing technologies and their development directions， particularly emphasizing the importance of improving the robustness， real-time processing capabilities， and adaptability of intelligent forecasting models in various scenarios. This study provides theoretical insights and practical guidance for advancing the development of renewable energy.