Unit scheduling optimization method, system and equipment of gravity energy storage system and medium

Abstract

The invention relates to the technical field of unit scheduling of a gravity energy storage system, in particular to a unit scheduling optimization method, system and equipment of the gravity energy storage system and a medium, and the method comprises the steps: obtaining historical scheduling demand data of a power grid and real-time capacity parameters of each unit in the gravity energy storage system; based on the historical scheduling demand data and the capacity parameters, solving a unit combination scheme meeting the power grid demand through a combination optimization algorithm; layering the units according to the capacity, selecting the unit combination scheme with the highest proportion of the middle capacity layer as a first target scheduling instruction, and outputting the first target scheduling instruction to the execution end. The method has the advantages that intelligent decision support can be provided for power grid dispatching, the system operation cost is reduced, the dispatching flexibility and reliability are improved, the overall performance of an energy storage system is improved, resource waste is reduced, and the power grid stability is improved.

Description

Technical Field

[0001] This invention relates to the field of unit scheduling technology for gravity energy storage systems, and in particular to a method, system, equipment, and medium for optimizing unit scheduling of gravity energy storage systems. Background Technology

[0002] Gravity energy storage systems, as an emerging energy storage technology, have received widespread attention and achieved certain development results in the fields of renewable energy and grid regulation in recent years. This energy storage method mainly stores potential energy by lifting heavy objects to a high place, and then releasing the objects to convert the energy into electricity when needed. The energy storage media are mainly water and solid materials. Water-based gravity energy storage systems, leveraging the high fluidity and compressibility of water, can utilize natural water resources or artificially constructed water bodies, and achieve energy storage and release through well-sealed pipes, shafts, and other structures. They feature high site selection flexibility and large storage capacity, demonstrating promising application prospects in large-scale energy storage projects.

[0003] Currently, gravity energy storage systems lack precise and efficient methods for optimizing unit combination schemes during dispatch. Existing dispatch methods are often simplistic and crude, typically relying on fixed rules or single indicators for unit start-up and shutdown operations. They fail to fully utilize historical grid dispatch demand data and real-time capacity parameters of each unit in the gravity energy storage system, resulting in an inability to accurately match the dynamic demands of the grid and hindering precise system dispatch. Furthermore, the determination of unit combination schemes does not adequately consider the rational stratification and optimized configuration of unit capacity, lacking an effective stratified dispatch strategy. This prevents the full utilization of the advantages of units with different capacities, resulting in insufficient flexibility and reliability in responding to grid load changes. This limits the deep involvement and efficient application of gravity energy storage systems in grid regulation. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0005] In a first aspect, the present invention provides a method for optimizing the scheduling of units in a gravity energy storage system, including acquiring historical grid scheduling demand data and real-time capacity parameters of each unit in the gravity energy storage system;

[0006] Based on the historical scheduling demand data and the capacity parameters, a unit combination scheme that meets the grid demand is solved by a combinatorial optimization algorithm.

[0007] The generating units are stratified by capacity, and the unit combination scheme with the highest proportion in the middle capacity layer is selected as the first target scheduling instruction and output to the execution end.

[0008] As a preferred embodiment of the unit scheduling optimization method for the gravity energy storage system described in this invention, the step of solving the unit combination scheme that meets the grid demand through a combinatorial optimization algorithm includes:

[0009] Establish an integer programming model with unit load rate as the variable;

[0010] Solve for the unit combination scheme that meets the grid demand, record the number of times each unit is called and store it in the statistics set.

[0011] As a preferred embodiment of the unit scheduling optimization method for the gravity energy storage system described in this invention, the step of stratifying the units according to their capacity includes:

[0012] Based on the maximum single-machine capacity in the system;

[0013] Based on percentages, the zones are divided into low reserve zone, transition zone, base load zone, adjustment zone, and high reserve zone.

[0014] As a preferred embodiment of the unit scheduling optimization method for the gravity energy storage system described in this invention, the step of selecting the unit combination scheme with the highest proportion of intermediate capacity layer as the first target scheduling instruction includes:

[0015] Calculate the total call frequency of the transition zone, base load zone, and adjustment zone, and determine the unit combination scheme with the highest current frequency as the first target dispatch instruction;

[0016] If there are multiple unit combination schemes with the highest current frequency, the unit combination scheme with the lowest low standby area call frequency is selected as the first target scheduling instruction.

[0017] As a preferred embodiment of the unit scheduling optimization method for the gravity energy storage system described in this invention, the method further includes outputting a second target scheduling instruction;

[0018] The second target scheduling instruction is a unit combination scheme with the second highest proportion of intermediate capacity layer after the first target scheduling instruction and the lowest frequency of low standby area calls.

[0019] As a preferred embodiment of the unit scheduling optimization method for the gravity energy storage system described in this invention, the step of obtaining historical grid scheduling demand data includes collecting scheduling demand values ​​of the grid over multiple past time periods to form a first dataset.

[0020] The process of obtaining the real-time capacity parameters of each unit in the gravity energy storage system includes determining the capacity of each unit in the current gravity energy storage system to form a second dataset.

[0021] As a preferred embodiment of the unit scheduling optimization method for the gravity energy storage system described in this invention, the execution end is a controller and execution mechanism of the gravity energy storage system, used to control the operation of the gravity energy storage system units according to the received first target scheduling instruction or second target scheduling instruction.

[0022] In a second aspect, the present invention provides a unit scheduling optimization system for a gravity energy storage system, comprising: an acquisition module for acquiring historical grid scheduling demand data and real-time capacity parameters of each unit in the gravity energy storage system;

[0023] The calculation module is used to solve the unit combination scheme that meets the grid demand by using a combination optimization algorithm based on the historical scheduling demand data and the capacity parameters.

[0024] The output module is used to stratify the units by capacity, select the unit combination scheme with the highest proportion of the middle capacity layer as the first target scheduling instruction, and output it to the execution end.

[0025] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0026] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0027] Compared with existing technologies, the beneficial effects of this invention are as follows: Based on historical grid dispatch demand data and real-time capacity parameters, an integer programming model is constructed, achieving accurate solutions for unit combination schemes that meet grid demands; by stratifying units by capacity and selecting the scheme with the highest proportion in the middle capacity layer as the dispatch instruction, the economic and reliability optimization path for unit dispatch is determined, enabling rapid response and accurate execution of grid dispatch instructions; dynamic monitoring of unit operating status and real-time adjustment of dispatch schemes provide early warnings of abnormal system state fluctuations. This invention can provide intelligent decision support for grid dispatch, reduce system operating costs, improve dispatch flexibility and reliability, enhance the overall performance of energy storage systems, reduce resource waste, and improve grid stability. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart illustrating the unit scheduling optimization method for gravity energy storage systems. Detailed Implementation

[0030] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0031] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for optimizing the scheduling of units in a gravity energy storage system, including:

[0032] S100: Acquire historical grid dispatch demand data and real-time capacity parameters of each unit in the gravity energy storage system;

[0033] S200: Based on the historical scheduling demand data and the capacity parameters, a unit combination scheme that meets the grid demand is solved by a combinatorial optimization algorithm;

[0034] S300: The units are divided into layers according to capacity, and the unit combination scheme with the highest proportion in the middle capacity layer is selected as the first target scheduling instruction and output to the execution end.

[0035] It should be noted that gravity energy storage systems play a crucial role in grid regulation. During operation, the start-up, shutdown, and power output of the units need to be rapidly adjusted according to the real-time demand of the grid. However, gravity energy storage systems typically comprise multiple units of varying capacities, which differ in operating characteristics, regulation capabilities, and economics. In actual operation, grid dispatch demand fluctuates frequently, placing high demands on the response speed and regulation accuracy of gravity energy storage systems. On the one hand, given the large number and diverse capacities of the units, a key issue is how to rationally combine units of different capacities to achieve efficient system operation while meeting grid demand. On the other hand, traditional dispatching methods are often based on empirical rules or simple algorithms, lacking refined optimization of unit combination schemes. This leads to low system operating efficiency, failing to fully leverage the performance advantages of gravity energy storage systems, and potentially increasing system operating costs and equipment wear.

[0036] Therefore, to address the aforementioned issues of unit combination optimization and efficient scheduling, steps S100-S300 are used to first acquire historical grid scheduling demand data and real-time capacity parameters of each unit in the gravity energy storage system, providing data support for subsequent optimized scheduling. Then, based on this data, a combinatorial optimization algorithm is used to solve for unit combination schemes that meet grid demands. This algorithm can find the optimal or near-optimal scheme among numerous possible combinations, improving the scientific and rational nature of scheduling. Finally, the units are stratified by capacity, and the unit combination scheme with the highest proportion in the middle capacity layer is selected as the first target scheduling command and output to the execution end. This allows the system to meet grid demands while fully utilizing the flexibility and economy of the middle capacity layer units, improving the overall operating efficiency and reliability of the system, reducing operating costs, extending equipment lifespan, and achieving precise scheduling and optimized operation of the gravity energy storage system.

[0037] Example 2, refer to Figure 1 As an embodiment of the present invention, based on the above embodiment, a method for optimizing the scheduling of units in a gravity energy storage system is provided.

[0038] In this embodiment of the application, step S100, which involves obtaining historical grid dispatch demand data and real-time capacity parameters of each unit in the gravity energy storage system, includes the following steps A1-A2:

[0039] A1: The acquisition of historical power grid dispatch demand data includes collecting dispatch demand values ​​of the power grid over multiple past time periods to form a first dataset;

[0040] A2: Obtaining the real-time capacity parameters of each unit in the gravity energy storage system includes determining the capacity of each unit in the current gravity energy storage system to form a second dataset.

[0041] Specifically, in step A1, the specific expression for the first dataset DWXQ is:

[0042] DWXQ={dwxq1, dwxq2,...,dwxq n}

[0043] Where: dwxq1, dwxq2, ..., dwxq n These represent n different historical power grid dispatch demand values.

[0044] In step A2, the specific expression for the second dataset DD is:

[0045]

[0046] Where: JZ1, JZ2, ..., JZ m Let rl1, rl2, ..., rl be the m units in the current gravity energy storage system. mThese represent the corresponding capacities of m units in the gravity energy storage system.

[0047] In one optional implementation, the acquisition of historical grid dispatch demand data and real-time capacity parameters of each unit in the gravity energy storage system in step S100 can be based on the smart grid data platform and the online monitoring system. That is, the smart grid data platform is used to extract historical dispatch data, and at the same time, an online monitoring system is deployed in the gravity energy storage system to collect unit capacity parameters in real time, so as to realize automatic data acquisition and updating and ensure that dispatch decisions are based on the latest information.

[0048] In another optional implementation, the acquisition of historical grid dispatch demand data and real-time capacity parameters of each unit in the gravity energy storage system in step S100 can also be based on an offline data collection method that combines manual inspection and periodic testing. That is, by manually collecting grid dispatch reports and inspecting units on-site, combined with periodic performance testing, the data is manually sorted and entered. This method is applied to situations where the technical facilities are not perfect or on-site verification is required, so as to ensure the accuracy and reliability of the data.

[0049] In this embodiment of the application, step S200 involves solving a unit combination scheme that meets the grid demand based on the historical dispatch demand data and the capacity parameters using a combinatorial optimization algorithm, including the following steps B1-B2:

[0050] It is understandable that the combinatorial optimization algorithm is a recursive backtracking method, and a pruning strategy is set in the algorithm to improve computational efficiency. The pruning strategy includes: calculating the maximum possible number of blocks that the remaining units can contribute; if the number of remaining blocks is insufficient to meet the remaining demand, then terminating the current recursive path; and when traversing the possible number of units that can be activated in the current unit, if the cumulative number of blocks exceeds the demand, then skipping the combination of subsequent units with a larger number of units.

[0051] B1: Establish an integer programming model with unit load rate as the variable;

[0052] B2: Solve for the unit combination scheme that meets the grid demand, record the number of times each unit is called and store it in the statistics set.

[0053] Specifically, in step B1, the load rate of the m units is first set, and the specific form is as follows:

[0054] FZ = {fz1, fz2, ..., fz} m}

[0055] Where: fz1, fz2, ..., fz m These represent the load rates corresponding to m units.

[0056] It should be noted that the load factor refers to the ratio of the actual output power of the unit to its maximum capacity. Setting the load factor is to determine the power output level of each unit during operation. Furthermore, the load factor is set according to actual needs and is not limited here.

[0057] For example, when the load rate of a unit is set to 0.8, it means that the unit will operate at 80% of its maximum capacity.

[0058] Secondly, an equation for the ratio of the number of generating units to their individual capacity is established (this equation can quantitatively describe the power contribution of each unit in the system), specifically in the following form:

[0059] F(dwxq)=rl1fz1+rl2fz2+...+rl m fz m

[0060] In the formula: rl1, rl2, ..., rl m Let fz1, fz2, ..., fz be the capacities of the m units in the gravity energy storage system. m This represents the load rate corresponding to m units.

[0061] For example, if the system has three units with capacities of 10kW, 20kW and 30kW, and load rates of 0.5, 0.6 and 0.7 respectively, then the total power is calculated to be 38kW according to the above formula.

[0062] Finally, the data from the first dataset DWXQ is used in the equations for the number of units and the capacity of a single unit in the gravity energy storage system, and calculations are performed one by one. The calculation results are stored separately. Furthermore, when determining the number and capacity of computer groups, an integer programming algorithm is used to select an appropriate algorithm, while ensuring that the final total number of the equations remains unchanged. The results are then stored in the statistical data set TJ.

[0063] For example, if the grid demand is 100kW, an integer programming algorithm can be used to find all possible combinations of generating units (such as 5 units of 20kW or 4 units of 25kW, etc.), calculate the number of times each unit is called under each combination, and store these results for subsequent analysis and selection of the optimal solution.

[0064] The specific representation of the statistical data set TJ is as follows:

[0065] TJ = [N1 N2 … N] n ]

[0066] Where: N1, N2, ..., N n This indicates the number of times each individual machine group is called during the i-th calculation.

[0067] For example, suppose there are 3 standalone machines, and their call counts are 2, 1 and 3 respectively during the first calculation. Then the corresponding record in TJ is

[213] .

[0068] In an optional implementation, the unit combination scheme that satisfies grid demand in step S200 can be solved using an improved genetic algorithm, namely, a combination of binary and integer encoding. Binary encoding is used to represent the start-up and shutdown status of the units, and integer encoding is used to represent the output power of the units, adapting to the characteristics and constraints of the gravity energy storage system unit scheduling problem, and introducing a tournament selection operator. In each generation of the population, several individuals are randomly selected to compete. Based on the individual's objective function value (such as deviation from grid demand, economic indicators, etc.), the better individuals are selected to proceed to the next step, and adaptive crossover and mutation operators are designed. The crossover rate and mutation rate are dynamically adjusted according to the individual's fitness in the population. For individuals with high fitness, their crossover rate and mutation rate are appropriately reduced to maintain their superior characteristics; for individuals with low fitness, their crossover rate and mutation rate are increased to enhance the global search capability.

[0069] In another optional implementation, the solution for the unit combination scheme that meets the grid demand in step S200 can also be solved using a simulated annealing algorithm combined with local search. Specifically, a greedy algorithm is used to generate an initial solution, and simulated annealing is performed according to a certain annealing rate and cooling schedule. In each iteration, a unit is randomly selected for adjustment of its start-up / shutdown state or output power. The objective function value of the new solution is calculated, and the acceptance of the new solution is determined based on an acceptance criterion. The Metropolis criterion is used: if the objective function value of the new solution is better than the current solution, the new solution is accepted with a certain probability; if the objective function value of the new solution is worse than the current solution, the new solution is accepted with a certain probability, which gradually decreases as the temperature decreases. Local search is performed at certain intervals during the simulated annealing process (e.g., every certain number of iterations). Small-scale adjustments are made within the neighborhood of the current solution, such as swapping the start-up / shutdown states of adjacent units or fine-tuning the output power, to find a better solution. If a better solution is found through local search, the current solution is replaced with this solution, and the simulated annealing process continues.

[0070] In this embodiment of the application, step S300 involves stratifying the units by capacity, selecting the unit combination scheme with the highest proportion in the middle capacity layer as the first target scheduling instruction, and outputting it to the execution end. This includes the following steps C1-C4:

[0071] C1: Based on the largest single unit capacity in the system, it is divided into low reserve zone, transition zone, base load zone, adjustment zone and high reserve zone according to percentage.

[0072] C2: Calculate the total call frequency of the transition zone, base load zone, and adjustment zone, and determine the unit combination scheme with the highest current frequency as the first target dispatch instruction;

[0073] C3: If there are multiple unit combination schemes with the highest current frequency, then the unit combination scheme with the lowest low standby area call frequency is selected as the first target scheduling instruction.

[0074] C4: The execution end is the controller and actuator of the gravity energy storage system, used to control the operation of the gravity energy storage system unit according to the received first target scheduling instruction.

[0075] Specifically, in step C1, the low reserve zone corresponds to the 0-20% capacity range, the transition zone corresponds to the 20-40% capacity range, the base load zone corresponds to the 40-60% capacity range, the adjustment zone corresponds to the 60-80% capacity range, and the high reserve zone corresponds to the 80-100% capacity range.

[0076] In step C3, when there are two identical schemes for the unit combination scheme with the highest current frequency, the unit combination scheme with the lowest low standby area call frequency is determined as the first target scheduling instruction, and the other is determined as the second target scheduling instruction. The second target scheduling instruction is the backup solution. When a unit fails while the first target scheduling instruction is being executed, the execution terminal switches to the second target scheduling instruction.

[0077] For example, suppose there are four different unit combination schemes, and the call frequency of each scheme in the transition zone, base load zone, adjustment zone, and low reserve zone is as follows:

[0078] Table 1 Example Table

[0079] scheme number Total call frequency (transition zone + base load zone + adjustment zone) Low standby area call frequency Option 1 35 8 Option 2 38 10 Option 3 38 7 Option 4 33 5

[0080] Step C2: First, compare the total call frequency (transition zone + base load zone + adjustment zone) of the four schemes. Schemes 2 and 3 have the highest total call frequency, both at 38 times, which is greater than Scheme 1's 35 times and Scheme 4's 33 times. Therefore, Schemes 2 and 3 are selected as the schemes with the highest current frequency.

[0081] Step C3: Since there are schemes with the same total call frequency (Scheme 2 and Scheme 3), it is necessary to further compare the low backup area call frequency of these two schemes. Scheme 2 has a low backup area call frequency of 10 times, while Scheme 3 has a low backup area call frequency of 7 times. Scheme 3 has a lower low backup area call frequency.

[0082] Therefore, Scheme 3 was ultimately determined as the first target scheduling instruction, and Scheme 2 was determined as the second target scheduling instruction.

[0083] In this embodiment of the application, the method further includes step S400 of outputting a second target scheduling instruction, specifically including the following steps D1-D2:

[0084] D1: The second target scheduling instruction is a unit combination scheme with the second highest proportion of intermediate capacity layer after the first target scheduling instruction and the lowest frequency of low standby area call;

[0085] It should be noted that the determination of the second target scheduling instruction in this step does not include the case where there are multiple unit combination schemes with the highest current frequency. That is, only when there is only one unit combination scheme with the highest current frequency will the unit combination scheme with the second highest current frequency capacity be determined as the second target scheduling instruction.

[0086] D2: The execution end is also used to control the operation of the gravity energy storage system unit according to the received second target scheduling instruction.

[0087] It should be noted that the second target scheduling instruction determined in this step does not include the second target scheduling instruction already determined by executing step C3. That is, when the second target scheduling instruction has been determined in step C3, the entire step S400 will not be executed. Instead, the first target scheduling instruction and the second target scheduling instruction determined in steps C2 and C3 will be directly output for the execution end to execute.

[0088] It should be further explained that when executing step D1, if there are multiple intermediate capacity layers that are second only to the first target scheduling instruction, the unit combination scheme with the lowest low standby area call frequency is selected as the second target scheduling instruction.

[0089] In summary, this invention constructs an integer programming model based on historical grid dispatch demand data and real-time capacity parameters, achieving accurate solutions for unit combination schemes that meet grid demands. By stratifying units by capacity and selecting the scheme with the highest proportion in the middle capacity layer as the dispatch instruction, it determines the economic and reliability optimization path for unit dispatch, realizing rapid response and accurate execution of grid dispatch instructions. It also dynamically monitors the operating status of units, adjusts the dispatch scheme in real time, and provides early warning for abnormal fluctuations in system status.

[0090] Example 3 illustrates a schematic scheme for a unit scheduling optimization method for a gravity energy storage system. It should be noted that the technical solution of this gravity energy storage system unit scheduling optimization system is based on the same concept as the technical solution of the aforementioned gravity energy storage system unit scheduling optimization method. Details not described in detail in this example can be found in the description of the technical solution of the aforementioned gravity energy storage system unit scheduling optimization method.

[0091] This embodiment also provides a unit scheduling optimization system for a gravity energy storage system, including:

[0092] The acquisition module is used to acquire historical grid dispatch demand data and real-time capacity parameters of each unit in the gravity energy storage system;

[0093] The calculation module is used to solve the unit combination scheme that meets the grid demand by using a combination optimization algorithm based on the historical scheduling demand data and the capacity parameters.

[0094] The output module is used to stratify the units by capacity, select the unit combination scheme with the highest proportion of the middle capacity layer as the first target scheduling instruction, and output it to the execution end.

[0095] This embodiment also provides an electronic device applicable to the unit scheduling optimization of a gravity energy storage system, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the unit scheduling optimization method for a gravity energy storage system as proposed in the above embodiment.

[0096] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the unit scheduling optimization method for the gravity energy storage system proposed in the above embodiments.

[0097] The storage medium proposed in this embodiment and the unit scheduling optimization method for implementing gravity energy storage system proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0098] Based on the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0099] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing the scheduling of units in a gravity energy storage system, characterized in that: include, Acquire historical power grid dispatch demand data and real-time capacity parameters of each unit in the gravity energy storage system; Based on the historical scheduling demand data and the capacity parameters, a unit combination scheme that meets the grid demand is solved by a combinatorial optimization algorithm. The generating units are stratified by capacity, and the unit combination scheme with the highest proportion in the middle capacity layer is selected as the first target scheduling instruction and output to the execution end.

2. The unit scheduling optimization method for a gravity energy storage system as described in claim 1, characterized in that: The method of solving the unit combination scheme that meets the grid demand through combinatorial optimization algorithm includes: Establish an integer programming model with unit load rate as the variable; Solve for the unit combination scheme that meets the grid demand, record the number of times each unit is called and store it in the statistics set.

3. The unit scheduling optimization method for a gravity energy storage system as described in claim 2, characterized in that: The unit is stratified by capacity. include, Based on the maximum single-machine capacity in the system; Based on percentages, the zones are divided into low reserve zone, transition zone, base load zone, adjustment zone, and high reserve zone.

4. The unit scheduling optimization method for a gravity energy storage system as described in claim 3, characterized in that: The unit combination scheme with the highest proportion of intermediate capacity layer is selected as the first target scheduling instruction. include, Calculate the total call frequency of the transition zone, base load zone, and adjustment zone, and determine the unit combination scheme with the highest current frequency as the first target dispatch instruction; If there are multiple unit combination schemes with the highest current frequency, the unit combination scheme with the lowest low standby area call frequency is selected as the first target scheduling instruction.

5. The unit scheduling optimization method for a gravity energy storage system as described in claim 4, characterized in that: The method further includes outputting a second target scheduling instruction; The second target scheduling instruction is a unit combination scheme with the second highest proportion of intermediate capacity layer after the first target scheduling instruction and the lowest frequency of low standby area calls.

6. The unit scheduling optimization method for a gravity energy storage system as described in claim 5, characterized in that: The acquisition of historical power grid dispatch demand data includes collecting dispatch demand values ​​from the power grid over multiple past time periods to form a first dataset; The process of obtaining the real-time capacity parameters of each unit in the gravity energy storage system includes determining the capacity of each unit in the current gravity energy storage system to form a second dataset.

7. The unit scheduling optimization method for a gravity energy storage system as described in claim 6, characterized in that: The execution end is the controller and actuator of the gravity energy storage system, used to control the operation of the gravity energy storage system unit according to the received first target scheduling instruction or second target scheduling instruction.

8. A unit scheduling optimization system for a gravity energy storage system, using the method described in any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire historical grid dispatch demand data and real-time capacity parameters of each unit in the gravity energy storage system; The calculation module is used to solve the unit combination scheme that meets the grid demand by using a combination optimization algorithm based on the historical scheduling demand data and the capacity parameters. The output module is used to stratify the units by capacity, select the unit combination scheme with the highest proportion of the middle capacity layer as the first target scheduling instruction, and output it to the execution end.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. 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 the method according to any one of claims 1 to 7.

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