Multi-element energy storage hybrid modeling and operation optimization method and related equipment

Abstract

The invention relates to the technical field of hybrid energy storage, and discloses a multi-element energy storage hybrid modeling and operation optimization method and related equipment, and the method comprises the steps: decomposing the charging and discharging power based on the total power demand, specifically setting the upper and lower limit constraints of active power, energy and reactive power, constructing a multi-element energy storage hybrid model, and optimizing the operation of the multi-element energy storage hybrid model. The system characteristic description of different energy storage types under a unified framework is realized, and the complementary potential of various types of energy storage is fully released. According to the scheme, through P / Q cooperative constraint design, multiple requirements of peak regulation, frequency modulation and voltage regulation of the power system are effectively joined, the operation stability of the system is guaranteed, and the problem that cooperation is difficult to achieve through separate modeling in the prior art is solved. Meanwhile, by means of the optimal design with the optimal economical efficiency as the target, energy storage resources can be reasonably allocated, the energy storage utilization efficiency can be improved, resource waste can be reduced, the overall operation cost of the system can be reduced, the safety and economical efficiency of energy storage operation are considered, and the comprehensive performance of operation of the electric power system under new energy high permeation is practically improved.

Description

Technical Field

[0001] This invention belongs to the field of hybrid energy storage technology, specifically a method and related equipment for hybrid modeling and operation optimization of multi-element energy storage. Background Technology

[0002] In scenarios with high penetration of new energy sources, the power system needs to simultaneously meet multiple core requirements, including long-term net load balance (peak shaving), short-term power disturbance suppression (frequency regulation), and voltage support (voltage regulation).

[0003] Existing energy storage technologies struggle to balance the complementary characteristics of different energy storage systems and P / Q synergy under unified constraints and objectives, thereby affecting the economic efficiency and stability of power system operation.

[0004] Current energy storage application technologies targeting the diverse needs of power systems primarily involve modeling, optimizing, and scheduling different types of energy storage separately. These approaches focus on the characteristics of a single energy storage type, developing technical strategies to adapt to individual needs such as peak shaving, frequency regulation, or voltage regulation. They concentrate solely on the performance adaptation and local optimization of a single energy storage system, neglecting the synergistic potential inherent in the differences in power energy characteristics, efficiency, and other dimensions of different energy storage systems. Essentially, they address the system's localized needs through the independent operation of a single energy storage system.

[0005] The current separate modeling and optimization approach has significant technical shortcomings: First, it lacks a unified framework, making it impossible to systematically characterize the differences in power energy characteristics, efficiency levels, capacity boundaries, and P / Q regulation capabilities of hybrid energy storage systems (HESS), and failing to tap the complementary potential of various energy storage types; second, it severs the synergistic relationship between different energy storage systems, making it difficult to achieve P / Q coordinated control, resulting in low overall utilization efficiency of energy storage resources; third, the local optimization logic cannot take into account the comprehensive needs of peak shaving, frequency regulation, and voltage regulation, and cannot achieve global optimization at the system level, ultimately affecting the economic efficiency and stability of power system operation, and making it difficult to adapt to the diverse operational requirements under the high penetration of new energy sources. Summary of the Invention

[0006] This invention provides a method and related equipment for hybrid modeling and operation optimization of multi-energy storage, which solves the problem that existing studies often model and optimize different energy storage types separately, making it difficult to reflect the complementary characteristics and P / Q synergy of the two types of energy storage under unified constraints and objectives.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for hybrid modeling and operation optimization of multi-element energy storage includes: Obtain the total power requirement of the energy storage system; The active power of energy storage is decomposed according to the total power demand, and the charging power and discharging power are distinguished. Set upper and lower limits for active power according to the total power demand; The energy constraints of the energy storage system are determined based on the charging power and discharging power, and upper and lower limits of the energy constraints are set. Set upper and lower limits for the reactive power of the energy storage system; A multi-element energy storage hybrid model is established based on the upper and lower limits of active power, energy, and reactive power constraints. The optimization is based on a multi-element energy storage hybrid model with the goal of achieving optimal economic efficiency.

[0008] Preferably, the active power of energy storage is decomposed according to the total power demand, specifically distinguishing between charging power and discharging power as follows:

[0009] in, The total power of the energy storage system. The charging power for the energy storage system, This represents the discharge power of the energy storage system.

[0010] Preferably, the upper and lower limits of active power are set according to the total power demand as follows:

[0011]

[0012] in, The power capacity ratio of energy storage The power capacity ratio of power-type energy storage For energy storage capacity, For power-type energy storage capacity, The power of energy storage. The power of power-type energy storage.

[0013] Preferably, the energy constraint of the energy storage system is determined based on the charging power and discharging power as follows:

[0014] in, x This represents the type of energy storage, either power-type or energy-type. Let t be the energy state of the stored energy. The energy state of the stored energy at time t-1. For the round-trip efficiency of this energy storage, To charge the energy storage power, This is the energy storage discharge power. Set a value for the length of each scheduling time period.

[0015] Preferably, the upper and lower limits of the energy constraint are set as follows:

[0016] in, To constrain the overall energy state of energy storage This is the minimum energy state constraint for the overall energy storage system. This refers to the overall energy storage capacity.

[0017] Preferably, the optimization based on the multi-element energy storage hybrid model with the goal of achieving optimal economic efficiency is specifically as follows:

[0018] For optimal economic efficiency, for t Time-of-use pricing; x This represents the type of energy storage, either power-type or energy-type. The operation and maintenance cost of this energy storage unit, This represents the real-time power of the energy storage.

[0019] A multi-element energy storage hybrid modeling and operation optimization system includes: Data acquisition module: used to acquire the total power demand of the energy storage system; Decomposition module: Used to decompose the active power of energy storage according to the total power demand, and distinguish between charging power and discharging power; First constraint module: used to set upper and lower limits of active power constraints according to the total power demand; The second constraint module is used to determine the energy constraints of the energy storage system based on the charging power and discharging power, and to set the upper and lower limits of the energy constraints. The third constraint module is used to set the upper and lower limits of reactive power constraints for the energy storage system. Model building module: used to build a multi-element energy storage hybrid model based on active power upper and lower limit constraints, energy constraints upper and lower limit constraints, and reactive power upper and lower limit constraints; Optimization module: Used to optimize based on a multi-element energy storage hybrid model with the goal of achieving optimal economic efficiency.

[0020] A computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of a multi-energy storage hybrid modeling and operation optimization method.

[0021] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of a multi-element energy storage hybrid modeling and operation optimization method.

[0022] A computer program product includes a computer program that, when executed by a processor, implements the steps of a multi-energy storage hybrid modeling and operation optimization method. Compared with existing technologies, this invention has the following advantages: This invention provides a multi-energy storage hybrid modeling and operation optimization method that decomposes charging and discharging power based on total power demand and sets targeted upper and lower limits for active power, energy, and reactive power to construct a multi-energy storage hybrid model. This achieves system characteristic characterization of different energy storage types under a unified framework, fully releasing the complementary potential of various energy storage types. This scheme, through P / Q collaborative constraint design, effectively connects the multiple needs of power system peak shaving, frequency regulation, and voltage regulation, ensuring system operational stability and solving the pain point of existing technologies where separate modeling makes collaborative achievement difficult. Simultaneously, the optimization design aimed at optimal economic efficiency can rationally allocate energy storage resources, improve energy storage utilization efficiency, reduce resource waste, lower the overall system operating cost, and balance the safety and economy of energy storage operation, effectively improving the comprehensive performance of power system operation under high penetration of new energy sources. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a multi-element energy storage hybrid modeling and operation optimization method according to an embodiment of the present invention; Figure 2 This is a diagram showing the optimized scheduling output of the system according to an embodiment of the present invention. Figure 3 This invention provides an analysis of photovoltaic power generation utilization in an embodiment of the invention. Figure 4 Analysis of wind power generation utilization in embodiments of the present invention; Figure 5 The table shows the SOC variation curves of the energy storage system in this embodiment of the invention, where a is the SOC of battery energy storage, b is the SOC of flywheel energy storage, c is the SOC of flow battery, and d is a comparison of the SOC of all energy storage systems. Figure 6 This is a block diagram of a multi-element energy storage hybrid modeling and operation optimization system according to the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0026] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0027] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0028] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0029] like Figure 1 As shown, this invention provides a method for hybrid modeling and operation optimization of multi-element energy storage, including: S1: Obtain the total power requirement of the energy storage system; S2: Decompose the active power of energy storage according to the total power demand, and distinguish between charging power and discharging power. S3: Set upper and lower limits for active power according to the total power demand; S4: Determine the energy constraints of the energy storage system based on the charging power and discharging power, and set the upper and lower limits of the energy constraints; S5: Set upper and lower limits for reactive power of the energy storage system; S6: Establish a multi-element energy storage hybrid model based on the upper and lower limits of active power, energy constraints, and reactive power constraints; S7: Optimize based on a multi-element energy storage hybrid model with the goal of achieving optimal economic efficiency.

[0030] By decomposing charging and discharging power based on total power demand and setting targeted upper and lower limits for active power, energy, and reactive power, a multi-element energy storage hybrid model is constructed. This model enables the characterization of system characteristics for different energy storage types within a unified framework, fully releasing the complementary potential of various energy storage types. Through P / Q collaborative constraint design, this scheme effectively connects the multiple needs of power system peak shaving, frequency regulation, and voltage regulation, ensuring system operational stability and addressing the pain point of existing technologies where separate modeling makes collaborative operation difficult. Simultaneously, the optimized design, aiming for optimal economic efficiency, can rationally allocate energy storage resources, improve energy storage utilization efficiency, reduce resource waste, and lower the overall system operating cost. It balances the safety and economy of energy storage operation, effectively improving the comprehensive performance of power system operation under high renewable energy penetration.

[0031] The detailed steps are as follows: Hybrid Energy Storage System (HESS) organically combines energy-type energy storage (such as lithium-ion battery energy storage system, BESS) and power-type energy storage (such as flywheel energy storage system, SESS) to achieve synergistic complementarity of energy and power under a unified control and coordinated optimization framework. This system can undertake peak shaving and frequency regulation tasks for the power grid (active power level), and can also achieve voltage support and regulation functions through flexible reactive power adjustment (reactive power level), thus balancing both economic and safety requirements.

[0032] In model building, the total power demand of the energy storage system is obtained, and the active power of energy storage is decomposed to distinguish charging power. With discharge power ,Right now:

[0033] in, The total power of the energy storage system. The charging power for the energy storage system, This represents the discharge power of the energy storage system.

[0034] This separation approach allows for the inclusion of operation and maintenance costs corresponding to charging and discharging in the objective function, thereby more accurately reflecting the lifespan loss and operational economy of the energy storage unit.

[0035] Energy storage based on energy source (BESS) and power storage based on energy source (SESS) are each subject to independent upper and lower power limits.

[0036] in This reflects the power-to-capacity ratio of the energy storage system. The power capacity ratio of energy storage The power capacity ratio of power-type energy storage For energy storage capacity, For power-type energy storage capacity, The power of energy storage. For power-type energy storage, BESS has a smaller value, which is suitable for long-term energy balance and peak shaving and valley filling; SESS has a larger value, has a high power ratio, and can quickly respond to frequency deviations with a larger short-term power level, and undertake frequency regulation tasks on a short time scale.

[0037] Energy constraints are represented in the form of State of Energy (SOE), and their changes are determined by both charging and discharging power and efficiency.

[0038] in, x This represents the type of energy storage, either power-type or energy-type. Let t be the energy state of the stored energy. The energy state of the stored energy at time t-1. For the round-trip efficiency of this energy storage, To charge the energy storage power, This is the energy storage discharge power. Set a value for the length of each scheduling time period.

[0039] Simultaneously set upper and lower limit constraints

[0040] in, To constrain the overall energy state of energy storage This is the minimum energy state constraint for the overall energy storage system. This refers to the overall energy storage capacity.

[0041] This is to prevent overcharging and discharging from causing performance degradation or safety hazards to the energy storage unit. Since power loss costs have already been factored into the operational targets, there is no need to set additional constraints to prevent simultaneous charging and discharging.

[0042] At the system control level, the functions undertaken by HESS can be divided into two categories: (1) Active power response function: Peak shaving and frequency regulation at different time scales are achieved through the energy balance of BESS and the power rapid response of SESS. BESS mainly participates in peak shaving and valley filling at the hour to day scale, smoothing load fluctuations and reducing the power impact of the upstream grid; SESS rapidly adjusts at the second to minute scale to offset system frequency disturbances and achieve frequency stability control. The synergistic effect of the two significantly improves the system's adaptability to multi-temporal and spatial disturbances.

[0043] (2) Reactive power response function: In order to improve voltage stability, HESS has reactive power support capability while regulating active power, and its reactive power output This can be achieved through virtual synchronous generator (VSG) control or a separate reactive power regulation module. Its reactive power regulation follows these constraints:

[0044] When the voltage deviates, the system provides voltage support by quickly adjusting the reactive power output, maintaining the bus voltage within the allowable range, thereby meeting the dual stability requirements of system frequency and voltage.

[0045] In summary, the hybrid model of the multi-element energy storage system comprehensively considers power-energy characteristic differences, charging and discharging efficiency, capacity boundary constraints, and active / reactive power coordinated regulation mechanisms within a unified optimization framework. This results in a comprehensive energy storage operation model capable of participating in primary and secondary frequency regulation of the power grid, as well as undertaking peak shaving and voltage regulation tasks. This model provides fundamental mathematical support for subsequent multi-scenario production simulation, operation optimization, and system support capability assessment, and lays the theoretical foundation for the design of control strategies for multi-element energy storage systems at the park level and even regional level.

[0046] The objective function for optimizing the operation of multi-energy storage for peak shaving, frequency regulation, and voltage regulation is set to achieve optimal economic efficiency, with the decision variables being the allocation of active and reactive power among the energy storage units. Specifically, active power is used to respond to and execute grid peak shaving and frequency regulation commands, while reactive power is used to respond to grid voltage regulation demands.

[0047]

[0048] in, for t Time-of-use pricing; x This represents the type of energy storage, either power-type or energy-type. The operation and maintenance cost of this energy storage unit, This represents the real-time power of the energy storage.

[0049] In-depth operational analysis based on the optimization model shows that this integrated energy system exhibits complex and insightful characteristics in several aspects.

[0050] As shown in Figure 1, the system optimization scheduling output clearly demonstrates the coordinated operation of each component in 96 time periods. The grid interaction power, various energy storage devices (battery energy storage, flywheel energy storage, flow battery energy storage) and thermal power units work together to maintain the real-time balance of the system.

[0051] From the load curve and the output characteristics of thermal power units Figure 2The adjustable load curve shown has relatively small overall fluctuations, exhibiting a relatively stable operating trend. This is mainly due to the characteristics of industrial loads and the synergistic adjustment effect of the energy storage system. Thermal power units maintained around 6000MW with minimal fluctuations, effectively complementing renewable energy. Specifically, around 7:00 AM, thermal power units experienced a brief power drop, related to the power decrease in industrial loads; while during the midday period (approximately 12:00-15:00), the output of thermal power units showed a significant initial decrease followed by an increase, dropping by approximately 3000MW from full output for 2.5 hours before recovering to full output. This dynamic adjustment process is consistent with... Figure 2 The peak periods of photovoltaic (PV) output highly coincide with those of thermal power units. Because renewable energy has relatively low operating costs and priority in grid connection, the system fully utilizes PV power generation during this period, correspondingly reducing the operating load of thermal power units, reflecting the economic considerations of system operation. Furthermore, in the evening (approximately 6:00 PM to 8:00 PM), when PV output gradually diminishes while the load remains high, such as... Figure 2 The data clearly shows that thermal power units maintained high-output operation, while due to the increased wind power output, battery storage and flow batteries participated almost entirely in discharge, further demonstrating the system's priority on renewable energy. This coordinated operation across multiple time scales and technological approaches fully showcases the technological advantages and development potential of integrated energy systems.

[0052] From the perspective of renewable energy structure, the configuration capacity ratio of photovoltaic (PV) to wind turbines in this system is approximately 3:1, a ratio with clear engineering practice basis. This capacity ratio references the actual installed capacity structure of new energy in regions such as Yulin-Hengshui and Shuofang in Shaanxi Province. It considers both the abundant solar energy resources and the moderate to high wind energy resources of the local area, as well as the grid absorption capacity and system regulation needs, reflecting the characteristics of regional resource endowment and energy structure, and has certain regional representativeness and engineering practice value. Furthermore, the operational performance of renewable energy reflects the system's ability to absorb clean energy, such as... Figure 3 and Figure 4 The curtailment analysis chart clearly demonstrates the system's ability to absorb solar and wind power resources. Both the solar curtailment rate and the wind curtailment rate are approximately 0%, indicating that the system utilizes almost all renewable energy sources available that day. This performance reflects the renewable energy absorption capacity of the diversified energy storage within this integrated energy system, and also showcases the technological advantages of the integrated energy system in coordinating multiple time scales and technological approaches.

[0053] In terms of frequency regulation services, the system participates in frequency regulation services through power capacity reserves. Combined with... Figure 2 and Figure 5 Analysis: Figure 2The frequency modulation power curve in the system exhibits rapid, small-amplitude bidirectional fluctuations near zero, which reflects the system's ability to respond to higher-level frequency modulation commands and participate in the initial adjustment of the system frequency. Figure 5 The flywheel energy storage system exhibits extremely frequent charge-discharge transitions in its SOC curve between 10:00 and 15:00, consistent with its millisecond-level rapid response characteristics. This indicates that it primarily handles short-term power regulation at the second to minute level, effectively smoothing out instantaneous system fluctuations. In contrast, the power curves of battery energy storage and flow battery energy storage are relatively flat, with slower SOC changes, suggesting that they focus on providing long-term energy support at the hour level. The flywheel, battery, and flow battery work together to form a complete energy storage regulation system.

[0054] In terms of peak-shaving services, the system participates in peak shaving by controlling the peak value of grid interaction power, thereby reducing the impact on the external grid. Specifically, at midday, when photovoltaic power reaches its peak, the system prioritizes the consumption of local renewable energy, resulting in a significant reduction in peak-shaving power, and may even send power in reverse. During the evening peak load period, when load demand is high and photovoltaic output is zero, thermal power output remains at a high level. Wind power generation fills the gap left by photovoltaic power generation, working together with thermal power units to support internal power consumption. At this time, the peak-shaving power curve fluctuates, and flywheel energy storage operates, continuously charging and discharging to eventually flatten the peak-shaving curve.

[0055] From the perspective of actual operational efficiency of energy storage systems, both battery energy storage and flywheel energy storage achieved a cycle efficiency of 85.74%, demonstrating good energy conversion characteristics. Figure 5 The SOC curves show that both battery energy storage and flywheel energy storage achieve a depth of charge / discharge of 80%, demonstrating high utilization. In contrast, the flow battery's cycle efficiency of 61.41% and depth of discharge of 70.46% are relatively low, which is closely related to its technical characteristics and its position in the system.

[0056] A computer device is provided according to an embodiment of the present invention. This computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the various method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above.

[0057] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0058] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device may include, but is not limited to, a processor and memory.

[0059] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0060] The memory can be used to store the computer program and / or module, and the processor implements various functions of the computer device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory.

[0061] If the modules / units integrated into the computer device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory, random access memory, electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0062] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0063] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A multi-energy storage hybrid modeling and operation optimization method, characterized in that, The method comprises the following steps: acquiring a total power demand of an energy storage system; decomposing active power of the energy storage according to the total power demand to distinguish between charging power and discharging power; setting upper and lower limit constraints of the active power according to the total power demand; determining energy constraints of the energy storage system according to the charging power and the discharging power, and setting upper and lower limit constraints of the energy constraints; setting upper and lower limit constraints of reactive power of the energy storage system; establishing a multi-energy storage hybrid model according to the upper and lower limit constraints of the active power, the upper and lower limit constraints of the energy constraints, and the upper and lower limit constraints of the reactive power; optimizing based on the multi-energy storage hybrid model with the optimal economy as the target.

2. The multi-energy storage hybrid modeling and operation optimization method of claim 1, wherein, The decomposing of the active power of the energy storage according to the total power demand to distinguish between the charging power and the discharging power specifically comprises: wherein, Ptotai is the total power of the energy storage system, Pcharge is the charging power of the energy storage system, Pdischarge is the discharging power of the energy storage system.

3. The multi-energy storage hybrid modeling and operation optimization method of claim 1, wherein, The setting of the upper and lower limit constraints of the active power according to the total power demand specifically comprises: wherein, is the power capacity ratio for energy-type storage, is the power capacity ratio for power-type storage, is the capacity for energy-type storage, is the capacity for power-type storage, is the power for energy-type storage, is the power for power-type storage.

4. The multi-energy storage hybrid modeling and operation optimization method of claim 1, wherein, The determining of the energy constraints of the energy storage system according to the charging power and the discharging power specifically comprises: wherein, x represents the type of energy storage, either power or energy, is the state of energy of the energy storage at time t, is the state of energy of the energy storage at time t-1, is the round trip efficiency of the energy storage, is the charging power of the energy storage, is the discharging power of the energy storage, is a set quantity for the length of each dispatch period.

5. The multi-energy storage hybrid modeling and operation optimization method of claim 1, wherein, The setting of the upper and lower limit constraints of the energy constraints specifically comprises: wherein, is a maximum energy state constraint for the energy storage as a whole, is a minimum energy state constraint for the energy storage as a whole, is a capacity of the energy storage as a whole.

6. The multi-energy storage hybrid modeling and operation optimization method of claim 1, wherein, The optimizing based on the multi-energy storage hybrid model with the optimal economy as the target specifically comprises: for optimal economy, for t time-of-use electricity price; x representing the type of energy storage, either power or energy, operational cost of the energy storage unit, real-time power of the energy storage.

7. A multi-energy storage hybrid modeling and operation optimization system, characterized in that, The method comprises the following steps: a data acquisition module for acquiring a total power demand of an energy storage system; a decomposition module for decomposing active power of the energy storage according to the total power demand to distinguish between charging power and discharging power; a first constraint module for setting upper and lower limit constraints of the active power according to the total power demand; a second constraint module for determining energy constraints of the energy storage system according to the charging power and the discharging power, and setting upper and lower limit constraints of the energy constraints; a third constraint module for setting upper and lower limit constraints of reactive power of the energy storage system; a model establishment module for establishing a multi-energy storage hybrid model according to the upper and lower limit constraints of the active power, the upper and lower limit constraints of the energy constraints, and the upper and lower limit constraints of the reactive power; an optimization module for optimizing based on the multi-energy storage hybrid model with the optimal economy as the target.

8. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-7. The processor executes the computer program to implement the steps of the multi-energy storage hybrid modeling and operation optimization method according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the multi-energy storage hybrid modeling and operation optimization method according to any one of claims 1-6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the multi-energy storage hybrid modeling and operation optimization method according to any one of claims 1-6.

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