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Energy storage technologies and their applications and development XUE Fu, MA Xiaoming, YOU Yanjun Integrated Intelligent Energy    2023, 45 (9): 48-58.   DOI: 10.3969/j.issn.2097-0706.2023.09.007 Abstract （1159）   HTML （49）    PDF （929KB）（3220）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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Application and research progress of molten salt heat storage technology ZHANG Zhongping, LIU Heng, XIE Yurong, ZHAO Dazhou, MOU Min, CHEN Qiao Integrated Intelligent Energy    2023, 45 (9): 40-47.   DOI: 10.3969/j.issn.2097-0706.2023.09.006 Abstract （974）   HTML （42）    PDF （2330KB）（1367）       Knowledge map    Molten salt heat storage is a key technology for constructing future neo power systems.Since molten salt，an ideal heat storage medium，is of low viscosity，low steam pressure，high stability，high heat storage density，molten salt heat storage technology can be widely used in solar thermal power generation， thermal power peak and frequency regulation，heating，and waste heat recovery and utilization.However，current researches about this technology mainly focus on its employment in solar photothermal power generation，while its applications in other scenarios are insufficient in study.Under different application scenarios，the working temperature range，heating mode，selection of key components and design of working flow of a molten salt heat storage system are different.The advantages，characteristics and key technologies of molten salt heat storage technology are expounded，the research progress and the latest demonstration projects under different scenarios are listed，and the key problems subjected to further research are analyzed.Finally，future development trend and target of this technology are proposed. Table and Figures | Reference | Related Articles | Metrics

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Research progress and prospect of compressed air energy storage technology WAN Mingzhong, WANG Yuanyuan, LI Jun, LU Yuanwei, ZHAO Tian, WU Yuting Integrated Intelligent Energy    2023, 45 (9): 26-31.   DOI: 10.3969/j.issn.2097-0706.2023.09.004 Abstract （880）   HTML （28）    PDF （1004KB）（1343）       Knowledge map    Energy storage is the key technology to achieve the initiative of "reaching carbon peak in 2030 and carbon neutrality in 2060".Since compressed air energy storage has the advantages of large energy storage capacity， high system efficiency， and long operating life，it is a technology suitable for promotion in large-scale electric energy storage projects， and also an important means of large-scale renewable energy consumption on grid side. The development process， working principles， research statuses and challenges of compressed air energy storage systems in different forms are comprehensively expounded， and the development trend of compressed air energy storage technology is analysed from the perspective of compressed heat storage， providing references for the design for the future systems. The research results show that with the development of high-temperature heat storage technologies， high temperature adiabatic compressed air energy storage technology has become a research hotspot in this field because of its extraordinary working efficiency. Taking the molten salt with low melting point as the heat storage medium of a compressed air energy storage system to store the heat from the high-temperature compressor， can reduce the storage temperature of compressed water and the initial investment cost of the compressed air energy storage system significantly. Table and Figures | Reference | Related Articles | Metrics

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Analysis on development and key technologies of integrated intelligent energy in the context of carbon neutrality TENG Jialun, LI Hongzhong Integrated Intelligent Energy    2023, 45 (8): 53-63.   DOI: 10.3969/j.issn.2097-0706.2023.08.007 Abstract （575）   HTML （28）    PDF （1233KB）（1154）       Knowledge map    Integrated intelligent energy is an energy system that can achieve intelligent, efficient, green and safe energy production, transmission, storage, consumption and management by integrating information and communication technology, energy, intelligent manufacturing and other technical means. It is not only a technological revolution，but also a revolution in energy industry，transforming traditional energy systems into intelligent，integrated and green systems.Relying on energy data collection，transmission and processing，an integrated intelligent energy system can optimized energy allocation and manage energy precisely，thereby achieving sustainable development.In the analysis on the key technologies and development direction of integrated intelligent energy under the background of carbon neutrality，the development status of integrated intelligent energy at home and abroad is introduced，then its connotations and technical architecture are elaborated.The key technologies can be sorted into six groups：energy production，energy transmission，energy storage，energy consumption，intelligent energy and multi-energy synergistic optimization.According to the core issues of each technology pointed out in the analysis，four suggestions are put forward：promoting the construction of intelligent power market，enhancing energy data management capabilities，boosting the power supply business on user end，and developing core technologies and equipment independently in China. Table and Figures | Reference | Related Articles | Metrics

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Key technologies and construction practices of virtual power plants LIU Jian, LIU Yuxin, ZHUANG Hanyu Integrated Intelligent Energy    2023, 45 (6): 59-65.   DOI: 10.3969/j.issn.2097-0706.2023.06.008 Abstract （526）   HTML （18）    PDF （1216KB）（983）       Knowledge map    In the context of pursuing dual carbon target and constructing a new power system with new energy as the main body， the virtual power plant has become an important component of smart grid and Energy Internet due to its flexibility and effectiveness in managing distributed energy resources. The virtual power plant integrates distributed energy resources （including adjustable load，interruptible load and energy storage）through controlling，metering，communication and optimization technologies，and realizes smooth interactions and optimized operation of source，network，load and storage，which is conducive to the rational and optimal allocation and utilization of resources. The structure，network topology，control strategy and key technologies of the virtual power plant accessed to the power dispatching system are introduced，and the problems in protection configuration，information communication and intelligent terminals are discussed.The improvement measures put forward provide a reference for similar projects. Table and Figures | Reference | Related Articles | Metrics

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Diverse modeling methods for energy hubs in integrated energy systems and their typical applications LI Yizhe, WANG Dan, JIA Hongjie, ZHOU Tianshuo, CAO Yitao, ZHANG Shuai, LIU Jiawei Integrated Intelligent Energy    2023, 45 (7): 22-29.   DOI: 10.3969/j.issn.2097-0706.2023.07.003 Abstract （270）   HTML （3）    PDF （1429KB）（980）       Knowledge map    Under the background of "dual carbon"， the planning and operation of an integrated energy system （IES） is faced with new requirements on various evaluation indicators， such as effective energy utilization rate， energy unavailability and carbon emission level. In this context， selecting appropriate analytical elements and establishing their mechanism models has become an important task in IES research. An energy hub plays a key role in energy transmission and conversion in an IES， determining the distribution of energy and profoundly affecting the energy supply of the system from the perspective of multidimensional evaluation. Therefore， how to construct a multi-element model of an energy hub has become a key issue in IES analysis. The modeling methods of energy hubs with four modeling elements are analyzed， and their mechanisms and the applicability to match the development of energy systems are studied. The study provides references for following theoretical researches and practical applications. Table and Figures | Reference | Related Articles | Metrics

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Anomalous data detection methods for new power systems WANG Liang, DENG Song Integrated Intelligent Energy    2024, 46 (5): 12-19.   DOI: 10.3969/j.issn.2097-0706.2024.05.002 Abstract （289）   HTML （10）    PDF （1719KB）（923）       Knowledge map    With the rapid development of new power systems， massive data with various types are generated from the power systems. The complicated data conditions bring new challenges to anomaly data detection for power systems. In a summary on commonly used methods for anomalous power data in detecting， traditional technology-based， machine learning-based and deep learning-based detection methods are introduces. The working principle， characteristics and shortcomings of the three types of detecting methods are analysed. In the end， the challenges and development trends of anomalous data detection in new power systems are looked forward. Table and Figures | Reference | Related Articles | Metrics

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Research on security and privacy protection of electric power data sharing XING Huidi, GONG Gangjun, ZHAI Mingyue, LIU Xuesong, WANG Haomiao, YANG Shuang Integrated Intelligent Energy    2024, 46 (5): 30-40.   DOI: 10.3969/j.issn.2097-0706.2024.05.004 Abstract （346）   HTML （10）    PDF （1839KB）（861）       Knowledge map    Electric power data can reflect the development status of society and is promising in the opening and integrated applications. In order to create and release the value of power data， power companies need to build a data sharing service system for all industries. However， there are security issues such as privacy leakage， data tampering and shortage of data security aggregation method in the process of power data sharing. At present， there are three main technologies that can deal with the security problems above， blockchain， privacy computing and desensitization. Based on reviews on relevant literatures， a demand model for and privacy data protection in power data sharing is proposed. The three security and privacy protection technologies for electric power data sharing are summarized，and their integration and applications in different scenarios are comparatively analyzed. Different security and privacy protection schemes provide references for promoting electric power data opening and sharing. Table and Figures | Reference | Related Articles | Metrics

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Application and prospect of multimodal knowledge graph in electric power operation inspection LIN Jiajun, YAN Weidan, HU Junhua, ZHENG Yiming, SHAO Xianjun, GUO Bingyan Integrated Intelligent Energy    2024, 46 (1): 65-74.   DOI: 10.3969/j.issn.2097-0706.2024.01.008 Abstract （288）   HTML （18）    PDF （3326KB）（848）       Knowledge map    In the context of building a new power system with new energy as the main body，knowledge graph（KG），a large-scale visual semantic network，is expanding its applications rapidly in power operation and inspection. The applications of KG in power operation and inspection mainly focus on semantic information processing. However，a large amount of heterogeneous data will be generated in power grid operation，being able to uphold the construction of multimodal knowledge graph（MMKG） which provides data support for various downstream tasks. In view of functional requirements on electric power inspection， MMKG is introduced to support the intelligent query answering system and fault handling. Expounding the construction technology of MMKG for power inspection data，the scenarios of power operation and inspection that MMKG can give full play in are summarized，and the development direction is forecasted. Finally，the challenges that will be faced by MMKG is analyzed comprehensively，which provides a reference for the development of intelligent power operation and inspection. Table and Figures | Reference | Related Articles | Metrics

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Review on intelligent planning and decision-making technology for the new active distribution network WU Xueqiong, XIA Dong Integrated Intelligent Energy    2023, 45 (11): 20-26.   DOI: 10.3969/j.issn.2097-0706.2023.11.003 Abstract （307）   HTML （9）    PDF （921KB）（777）       Knowledge map    With the accelerating construction of new power system and popularity of active renewable distributed energy，passive distribution networks are moving towards active distribution networks quickly.However，renewable power is intermittent and uncontrollable，and the penetration of high-proportion renewable energy has brought serious threats to the safe and reliable operation of networks.Active distribution network is an effective solution for large-scale distributed energy grid connection and distribution network optimal operation.To maintain the optimal operation state of the power grid， scholars have conducted extensive researches about active distribution network management.The hot issues in this field include active distribution network planning，active distribution network intelligent decision-making，active distribution network power supply restoration and active distribution network load management. The progress made in these key technologies and the status quos of active distribution networks at home and abroad are analysed. And the analysis results show that the robust planning for active distribution networks taking uncertainties and temporal and spatial correlations into consideration is the development direction for the following studies. Table and Figures | Reference | Related Articles | Metrics

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Optimal scheduling of the data center integrated energy system considering load response characteristics YU Wenchang, CHEN Yonggang, CAO Junbo, ZUO Luyuan, ZHANG Xiangyin, YANG Xiu Integrated Intelligent Energy    2023, 45 (10): 25-34.   DOI: 10.3969/j.issn.2097-0706.2023.10.004 Abstract （250）   HTML （10）    PDF （1418KB）（753）       Knowledge map    In the collaboration of "computing power + electric power" under the goal of carbon peaking and carbon neutrality，the data center，whose power consumption is substantive and growing rapidly，has great potential in carbon emission reduction and load regulation. To give full play to the flexibility of data center loads，an optimal scheduling strategy of the data center integrated energy system considering the load response characteristics is proposed. Firstly，an optimized scheduling framework for the data center integrated energy system is established. The load response characteristics and equipment energy consumption of the data center are modelled，to study the cold，heat and electric loads of the data center. Then，the objective function and constraint conditions are determined，and the optimization model is established to minimize the operation cost of the data center energy system. Finally，the simulation analysis is made on a data center. Comparing the performances of the data center energy system under different scenarios， the optimal strategy with the optimal allocation of workloads is figured out which can effectively reduce the system cost and energy consumption. Table and Figures | Reference | Related Articles | Metrics

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Optimized scheduling on large-scale hydrogen production system for off-grid renewable energy based on DDPG algorithm ZHENG Qingming, JING Yanwei, LIANG Tao, CHAI Lulu, LYU Liangnian Integrated Intelligent Energy    2024, 46 (6): 35-43.   DOI: 10.3969/j.issn.2097-0706.2024.06.005 Abstract （193）   HTML （3）    PDF （3212KB）（750）       Knowledge map    To improve the renewable energy consumption， reduce the investment on rectifiers and grid connection equipment， cut down the cost of water electrolysis for hydrogen production through powering hydrogen production by renewable energy， an islanded renewable energy large-scale hydrogen production system is constructed. An intelligent energy management platform can improve the economy and safety of the system. Firstly， a simulation model of the renewable energy large-scale hydrogen production system is established and its control strategy is formulated. Secondly， an energy optimization scheduling strategy based on deep deterministic policy gradient （DDPG） algorithm is proposed. Through long-term trainings， the agent obtained from the DDPG algorithm can achieve intelligent dynamic optimized scheduling on energy. Comparing the performances of the proposed strategy with deep Q network （DQN）， Particle Swarm Optimization （PSO） and traditional control methods in terms of economy and safety， it is shown that applying the DDPG algorithm in energy optimization and management can get higher economic returns and utilization rates of renewable resources， and ensure the safe operation of the system. Table and Figures | Reference | Related Articles | Metrics

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Prediction on the regional carbon emission factor for power generation based on multi-dimensional data and deep learning LI Fangyi, LI Nan, ZHOU Yan, XIE Wu Integrated Intelligent Energy    2023, 45 (8): 11-17.   DOI: 10.3969/j.issn.2097-0706.2023.08.002 Abstract （202）   HTML （7）    PDF （1277KB）（744）       Knowledge map    With the support of carbon trading policy， the real-time， accurate and comprehensive measurement on power enterprises' carbon emissions is the basis for structure adjustment， technological innovation， supply and demand side interaction and carbon trading of power generation industry. The calculation and prediction on dynamic carbon emission factors is still limited by the data collection and transmission system. By taking deep learning， a prediction model， called GRU-Attention model， was built by combining dual attention mechanism with traditional Gate Recurrent Unit （GRU） neural network. Then， a GRU model， a Long Short-Term Memory （LSTM） model， a LSTM model based on dual attention mechanism（LSTM-Attention） and a GRU-Attention model were constructed and trained by the power data of Hefei in 2022 and average meteorological data of Hefei， to achieve hourly prediction on carbon emission factor. Comparing the prediction results made by the four models above， it is found that the prediction made by the GRU-Attention model is more accurate than that of the other three models， which can advance the mid- and long-term prediction on carbon emission factor. Table and Figures | Reference | Related Articles | Metrics

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Optimized control method for flexible load of a building complex based on MADDPG reinforcement learning BAO Yixin, XU Luoyun, YANG Qiang Integrated Intelligent Energy    2023, 45 (7): 61-69.   DOI: 10.3969/j.issn.2097-0706.2023.07.007 Abstract （227）   HTML （4）    PDF （1734KB）（666）       Knowledge map    The power grid dispatch environment and information organization environment have become more complex， and the difficulty of power grid regulation has gradually increased. Since deep reinforcement learning technology is of effective perception on complex system operation statuses，strong adaptability and good scalability，a distribution network optimization scheduling method based on deep reinforcement learning is proposed. Based on the simulated source-network-load-storage integrated distribution network model of a building complex，Multi-Agent Deep Deterministic Policy Gradient（MADDPG）algorithm was statically optimized from its principle.The model and real data were input into a multi-agent reinforcement learning framework suitable for grid-level objectives，and the optimized algorithm was tried to regulate the voltage of the distribution network system. The results show that the algorithm basically eliminates the abnormal peak voltages and reduces the overall voltage deviation.The optimized multi-objective oriented algorithm reduces the load-generated power difference while levelling the voltage off at a low level. The optimized control method for building complex flexible load based on reinforcement learning is proven to be effective. Table and Figures | Reference | Related Articles | Metrics

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Performance analysis on high temperature air source heat pump coupling cycle based on industrial waste heat SUN Jian, QIN Yu, HAO Junhong, YANG Yongping Integrated Intelligent Energy    2023, 45 (7): 40-47.   DOI: 10.3969/j.issn.2097-0706.2023.07.005 Abstract （187）   HTML （4）    PDF （1319KB）（641）       Knowledge map    Industrial waste heat，with a wide range of temperature，can be hardly utilized by conventional ways. Heat pumps can recovery medium and low temperature waste heat effectively， safely and environmental-friendly with low energy consumption. However， traditional absorption heat pumps and compressive heat pumps can only work in a narrow temperature range due to the limitations of thermodynamic cycle， thermodynamic properties of their working mediums and temperature and pressure resistance of their compressors， which cannot meet the requirements of "high heating temperature" and " wide temperature range heat transfer" of industrial waste heat recovery. To solve the problems above， an ultra-high temperature air source heat pump unit based on absorption and compression coupling cycle is proposed. The unit can recover heat from industrial waste steam（120 ℃） and air to produce 160 ℃ hot water（vapor）. The proposed coupling cycle is modelled and simulated by Engineering Equation Solver（EES）. The results show that the COP of the heat pump unit peaks at 1.600 under the optimal working condition under which hot water temperature is 130 ℃ and outdoor temperature is 30 ℃. When the hot water temperature rises to 160 ℃， the COP of the heat pump unit will be 1.400. The coupling cycle greatly broadens the working temperature range of heat pumps and improves their heating temperature. The study is of certain reference value for heat pumps in industrial waste heat recovery， and can significantly improve the utilization rate of primary energy in industrial field. Table and Figures | Reference | Related Articles | Metrics

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Optimal scheduling of HVAC systems based on predicted loads SUN Jian, ZHANG Yunfan, CAI Xiaolong, LIU Dingqun Integrated Intelligent Energy    2024, 46 (3): 12-19.   DOI: 10.3969/j.issn.2097-0706.2024.03.002 Abstract （250）   HTML （6）    PDF （969KB）（620）       Knowledge map    With the proposal of "dual-carbon" goal in China，the decarbonization of energy consumption in public buildings has become a key research area，in which optimizing the scheduling strategy for energy supply systems based on the prediction results of hot and cold loads is an effective technological means to achieve the "on-demand energy supply". A hot and cold load prediction model for public buildings is constructed based on the thermal resistance method. According to the load prediction results，control optimization is performed on the parameters of an energy supply system by improved particle swarm algorithm（PSO）， with the objectives of minimizing operating costs，reducing environmental costs and prolonging the service life of units. After iterative optimization on the parameters including the load of the combined cold-heat-power supply system， temperatures and flow rates of supply and return water， openings of valves and number of operating pump units， an optimal operation strategy under all operation conditions is proposed. A public building taking the proposed optimal operation strategy to update its heating system can reduce the power consumption of pump units by 10.66% and cut the operating cost by 21.52%， while meeting the heating demand of the building， balancing the hydraulic conditions and extending the operating life of the units. The consistency of the test results and the theoretical data proves the feasibility and effectiveness of the method proposed，providing an effective reference for on-demand heat supply and energy-saving operation of public buildings. Table and Figures | Reference | Related Articles | Metrics

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Pricing strategy in district-level integrated energy market based on deep reinforcement learning HU Ze, ZHU Ziqing, BU Siqi, CHAN Jiarong, WEI Xiang Integrated Intelligent Energy    2023, 45 (7): 87-96.   DOI: 10.3969/j.issn.2097-0706.2023.07.010 Abstract （190）   HTML （7）    PDF （1378KB）（604）       Knowledge map    Integrated energy market （IEM）， being able to integrate multiple forms of energy transactions and promote the efficient use of energy， is growing and gradually taking place the traditional energy markets. District integrated energy market （DIEM）， which serves as a link between the supply and demand side， is crucial for energy transaction and pricing， and affects the operation of integrated energy systems. Given this context， a DIEM transaction structure is constructed to optimize the pricing strategy for Energy Service Providers （IESPs） and the demand response mechanism for Integrated Energy Consumers （IECs）. The double-layer decision-making optimization takes into account the elasticity of the energy demand， the uncertainty of the output of renewable energy sources， and privacy protection comprehensively. The optimal pricing of the IESP can be obtained by Deep Deterministic Policy Gradient （DDPG），which is compared with the pricing strategy made by Deep-Q-Learning（DQN） in a simulation case. The simulation analyzes the coupling relationship of energy prices in DIEM and the interaction between integrated energy pricing strategy and demand elasticity， showing that the revenue of the Integrated energy system obtained by DDPG is 6.8% higher than that made based on DQN. Table and Figures | Reference | Related Articles | Metrics

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Control strategy of virtual synchronous generators based on adaptive control parameter setting DING Leyan, KE Song, YANG Jun, SHI Xingye Integrated Intelligent Energy    2024, 46 (3): 35-44.   DOI: 10.3969/j.issn.2097-0706.2024.03.005 Abstract （153）   HTML （0）    PDF （1093KB）（592）       Knowledge map    The new power system with new energy as the main body presents the characteristics of "low inertia and low damping". Since the virtual synchronous generator （VSG） control strategy can simulate the mechanical motion equation and electromagnetic characteristics of the synchronous generator， the strategy can enhance the stability of the power system by making the distributed inverters possess the inertia and damping characteristics of the synchronous generator. However， the strategy will compromise their dynamic regulation performances. Thus， a VSG control strategy based on adaptive control parameter setting is proposed. Firstly，the effects of inertia moment and damping coefficient on system frequency and output in transient process are analyzed by establishing a small signal model of VSGs. Then，the correlation between the adaptive moment of inertia， angular velocity variation ratio and angular velocity offset，and the correlation between the adaptive damping coefficient and the angular velocity offset are analyzed by the power angle-frequency oscillation curve of a synchronous generator. The selection principle for the inertia moment and damping coefficient in different intervals is obtained，and their calculation formula and the trigger thresholds are designed. Furthermore，by adding adaptive droop coefficient considering the upper and lower limits of frequency modulation power，a control strategy of VSGs based on adaptive control parameter setting is proposed. Finally， the simulation analysis on the off-grid VSG model is built by Matlab/Simulink，and the simulation results verify that the proposed control strategy can reduce the frequency offset， and improve the frequency stability and dynamic regulation ability of the system. Table and Figures | Reference | Related Articles | Metrics

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Optimal configuration for shared energy storage based on improved whale optimization algorithm LI Qinggen, SUN Na, DONG Haiying Integrated Intelligent Energy    2023, 45 (9): 65-76.   DOI: 10.3969/j.issn.2097-0706.2023.09.009 Abstract （168）   HTML （3）    PDF （1727KB）（588）       Knowledge map    Since energy storage resources always be left unused and lack proper business modes in the renewable energy consumption，an optimal allocation method of shared energy storage based on improved whale optimization algorithm（WOA） is proposed to match the power capacity of energy storage systems to new energy consumption target. Taking sharing mode for energy storage on power generation side，this method considers the investment， operation and maintenance costs and benefits of the energy storage system assisting renewable energy consumption，under the constraints of its operation and grid-connected power. Combined the renewable energy output with grid-connected power，an optimal configuration model aiming at maximizing the revenue of the shared energy storage system is established，and solved by improved WOA. The contrastive analysis on examples shows that energy storage sharing can improve the consumption rate of new energy and the return on investment of energy storage systems. Compared with independent energy storage， energy storage sharing mode improves the utilization rate and economy of energy storage devices，and adjustment ability on new energy consumption，which can effectively make up for the grid-connected power shortage caused by new energy generation. Large-scale investment in shared energy storage systems is promising. Table and Figures | Reference | Related Articles | Metrics

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Research on user-side energy storage coordinated and optimized scheduling mechanism under cloud energy storage mode CUI Jindong, WANG Yuqing Integrated Intelligent Energy    2023, 45 (9): 18-25.   DOI: 10.3969/j.issn.2097-0706.2023.09.003 Abstract （165）   HTML （2）    PDF （1287KB）（561）       Knowledge map    With the continuous advancement of the "dual carbon" target， energy storage application scenarios are emerging endlessly. Small energy storage units on the user side have advantages of small size， convenient deployment and flexible application. However， the overly random scheduling mode also brings hidden dangers to the operation of the power grid. Based on the concept of cloud energy storage， the interconnection and interoperability of small energy storage devices on the user side can be realized， and the architecture and operation mode of cloud energy storage system are proposed. Then， a cluster scheduling strategy for small energy storage devices under cloud energy storage mode is designed， whose feasibility is verified by simulation examples. The simulation results show that the proposed operation mode and optimized scheduling scheme are feasible， easy to implement， and effective， which can facilitate the application of energy storage units in new scenarios. Table and Figures | Reference | Related Articles | Metrics