An optimization design method and device for a gravity energy storage system

Abstract

This application provides an optimization design method and device for a gravity energy storage system, relating to the field of gravity energy storage technology. The method includes: obtaining the preset total energy storage capacity, rated power, slope length, and slope angle of the energy storage system; determining the weight block size parameters based on the preset total energy storage capacity and a preset weight block mass range; solving for operating parameters that satisfy preset dynamic constraints based on the rated power, slope angle, and weight block size parameters, including the number of weight blocks operating simultaneously on each channel and the weight block operating speed; verifying the storage area topology layout based on the operating parameters, the preset total energy storage capacity, and weight block size parameters to obtain candidate layout schemes; calculating the total construction cost of each candidate layout scheme; and selecting the scheme with the lowest total construction cost as the optimal design scheme. This application reduces the construction cost of the gravity energy storage system.

Description

Technical Field

[0001] This application relates to the field of gravity energy storage technology, and more specifically, to an optimization design method and device for a gravity energy storage system. Background Technology

[0002] Gravity energy storage, as a mechanical energy storage method with long lifespan, high reliability, and environmental friendliness, is considered one of the important technologies to support the stable operation of future grids with a high proportion of renewable energy. However, its engineering implementation faces a series of severe challenges in system-level optimization design.

[0003] Currently, the engineering design of such systems largely relies on empirical formulas or simplified linear programming methods. These traditional methods have significant limitations: First, key system parameters such as the size of the load, transmission speed, and storage area layout exhibit a highly nonlinear and strongly coupled relationship, making it difficult to effectively perform global optimization in a high-dimensional discrete parameter space. Second, the cost estimation models used are usually static and cannot accurately reflect the dynamic nonlinear impact of changes in the weight of the load on the cost of key equipment such as the transmission chain and gantry crane. More importantly, existing design processes lack an effective verification mechanism for matching the energy flow on the grid side and the material flow on the storage side in the spatiotemporal dimensions, which poses a hidden danger to the reliability and efficiency of the system after its completion. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide an optimization design method and device for a gravity energy storage system to overcome at least one of the above-mentioned defects.

[0005] In a first aspect, embodiments of this application provide an optimization design method for a gravity energy storage system. The method includes: obtaining a preset total energy storage capacity, rated power, slope length, and slope angle of the energy storage system; determining the size parameters of the weight blocks based on the preset total energy storage capacity and a preset range of weight block mass; solving for operating parameters that satisfy preset dynamic constraints based on the rated power, the slope angle, and the weight block size parameters, wherein the operating parameters include the number of weight blocks operating simultaneously on each channel and the operating speed of the weight blocks; verifying the topology layout of the storage area based on the operating parameters, the preset total energy storage capacity, and the weight block size parameters to obtain candidate layout schemes; calculating the total construction cost of each candidate layout scheme according to the candidate layout schemes; and selecting the scheme with the lowest total construction cost as the optimal design scheme.

[0006] In one optional embodiment of this application, the size parameters of the weight block are determined by: discretizing the sampling within the design range of the length, width, and height of the weight block to obtain multiple initial size parameter combinations; calculating the mass of a single weight block based on each initial size parameter combination; comparing the mass of the single weight block with a preset mass range, and selecting size parameter combinations that fall within the mass range as the size parameters of the weight block.

[0007] In one optional embodiment of this application, the operating parameters that satisfy the preset dynamic constraints are solved by: determining the range of the number of channels in the system; for each group of weight block size parameters and the number of channels within the range of the number of channels, obtaining the combination of the number of operating blocks and the operating speed based on the power balance equation; applying speed constraints and length constraints to the combination of the number of operating blocks and the operating speed for screening, and using the selected combination as the operating parameters, wherein the speed constraint requires the operating speed to be within a preset range, and the length constraint requires that the total length of all weight blocks operating simultaneously and their intervals does not exceed the slope length.

[0008] In one optional embodiment of this application, the step of verifying the storage area topology layout based on the operating parameters, the preset total energy storage capacity, and the weight block size parameters to obtain candidate layout schemes includes: enumerating all existing topology layouts in the storage area based on the operating parameters, the preset total energy storage capacity, and the weight block size parameters, where each topology layout includes the number of rows, columns, and layers of the weight block stack; for each topology layout, verifying whether the topology layout meets the verification requirements within the total system operating cycle, wherein the verification requirement is whether the maximum transport distance of the gantry crane is not less than the theoretical distance required to complete the transport task, and the number of gantry cranes is set to be equal to the number of rows; and using the verified topology layouts as the candidate layout schemes.

[0009] In one optional embodiment of this application, the total construction cost of each candidate layout scheme is calculated as follows: For each of the candidate layout schemes, the manufacturing cost of the heavy blocks, the transmission system cost, the buffer cost, and the storage area equipment cost corresponding to that scheme are calculated respectively. The manufacturing cost of the heavy blocks is determined based on the total number of heavy blocks required for the scheme and the mass of a single heavy block. The transmission system cost is determined based on the number of heavy blocks operating simultaneously on each channel in the operating parameters and the mass of a single heavy block, and the unit cost of the transmission system is dynamically corrected using a correction coefficient positively correlated with the maximum load of a single channel. The buffer cost is determined based on the total length of the buffer and the mass of a single heavy block, and the unit cost of the buffer equipment is dynamically corrected using a correction coefficient positively correlated with the mass of a single heavy block. The storage area equipment cost is determined based on the mass of a single heavy block and the topology of the storage area, and the unit cost of the storage area equipment is dynamically corrected using a correction coefficient positively correlated with the mass of a single heavy block. The total construction cost of the scheme is obtained by summing the manufacturing cost of the heavy blocks, the dynamically corrected transmission system cost, the dynamically corrected buffer cost, and the dynamically corrected storage area equipment cost.

[0010] In one optional embodiment of this application, the step of selecting the scheme with the lowest total construction cost as the optimal design scheme output includes: performing a global search on the weight block size parameters with a first set step size to calculate multiple corresponding total construction costs, and determining a first optimal cost scheme and its corresponding weight block size range from among them; performing a local discretization search within the weight block size range corresponding to the first optimal cost scheme with a second set step size smaller than the first set step size to calculate multiple corresponding total construction costs, and selecting the scheme with the lowest total construction cost from among them as the optimal design scheme output.

[0011] In one optional embodiment of this application, the total construction cost further includes a buffer cost, wherein the buffer cost is calculated as follows: the total length of the buffer is determined based on the mass of the individual heavy block, the length in the size parameters of the heavy block, the minimum interval between the heavy blocks, and the number of channels; based on the mass of the individual heavy block, a buffer cost correction coefficient positively correlated with the mass of the individual heavy block is determined, and the buffer cost per unit length is dynamically corrected using the buffer cost correction coefficient to obtain the corrected buffer cost per unit length; the buffer cost is calculated based on the total length of the buffer and the corrected buffer cost per unit length.

[0012] Secondly, embodiments of this application also provide an optimization design apparatus, comprising: a parameter acquisition module for acquiring a preset total energy storage capacity, rated power, ramp length, and ramp angle of an energy storage system; a weight block size parameter determination module for determining weight block size parameters based on the preset total energy storage capacity and a preset weight block mass range; an operation parameter solving module for solving operation parameters that satisfy preset dynamic constraints based on the rated power, ramp angle, and weight block size parameters, wherein the operation parameters include the number of weight blocks operating simultaneously on each channel and the weight block operating speed; a candidate layout scheme obtaining module for verifying the storage area topology layout based on the operation parameters, the preset total energy storage capacity, and weight block size parameters to obtain candidate layout schemes; a total construction cost calculation module for calculating the total construction cost of each candidate layout scheme according to the candidate layout schemes; and an optimal design scheme output module for selecting the scheme with the lowest total construction cost as the optimal design scheme output.

[0013] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the steps of the method described above are performed.

[0014] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the method described above.

[0015] The optimization design method and apparatus for a gravity energy storage system provided in this application obtain the preset total energy storage capacity, rated power, slope length, and slope angle of the energy storage system; based on the preset total energy storage capacity and a preset range of weight block mass, determine the weight block size parameters; based on the rated power, slope angle, and weight block size parameters, solve for the operating parameters that satisfy preset dynamic constraints, including the number of weight blocks operating simultaneously on each channel and the weight block operating speed; based on the operating parameters, the preset total energy storage capacity, and weight block size parameters, verify the topology layout of the storage area to obtain candidate layout schemes; calculate the total construction cost of each candidate layout scheme according to the candidate layout schemes; and select the scheme with the lowest total construction cost as the optimal design scheme. This application saves on the construction cost of the gravity energy storage system.

[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, 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 this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the optimized design method for a gravity energy storage system provided in this application embodiment;

[0019] Figure 2 This is a flowchart illustrating the determination of the weight block size parameters provided in an embodiment of this application;

[0020] Figure 3 A flowchart for solving the operating parameters that satisfy preset dynamic constraints, provided in an embodiment of this application;

[0021] Figure 4 A flowchart illustrating the candidate layout schemes provided in this application embodiment;

[0022] Figure 5 A flowchart for calculating the total construction cost of each candidate layout scheme provided in this application embodiment;

[0023] Figure 6 A flowchart illustrating the optimal design scheme provided in the embodiments of this application;

[0024] Figure 7 A flowchart for calculating buffer cost provided in an embodiment of this application;

[0025] Figure 8 A schematic diagram of the structure of the optimized design device for the gravity energy storage system provided in the embodiments of this application;

[0026] Figure 9 The present application provides a schematic diagram of the structure of an electronic device. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0028] First, the applicable application scenarios of this application will be introduced. This application can be applied to the field of gravity energy storage technology.

[0029] Gravity energy storage, as a novel mechanical energy storage method, is considered a core supporting technology for future high-proportion renewable energy grids due to its long lifespan, high reliability, and environmental friendliness. However, its engineering implementation faces significant system-level optimization challenges.

[0030] Existing engineering designs often rely on empirical formulas or linear programming, which have inherent technical limitations: There are highly nonlinear and strongly coupled relationships between system parameters such as the size of the load, transmission speed, and storage area layout, making it difficult for traditional methods to effectively locate the optimal solution in such a high-dimensional discrete space. Furthermore, equipment cost estimation models are typically static and cannot reflect the dynamic nonlinear impact of changes in the load's mass on the chain strength, gantry crane load, and other unit costs. More importantly, there is a lack of an effective mechanism to ensure precise spatiotemporal matching between the charging and discharging energy flow rate on the grid side and the gantry crane's material handling cycle time on the storage side. These challenges severely restrict the economic efficiency and operational reliability of the design scheme.

[0031] Based on this, this application provides an optimization design method and device for a gravity energy storage system to solve core problems in the prior art, such as optimization difficulties caused by strong parameter coupling, economic evaluation distortion caused by static cost models, and operational reliability risks caused by lack of dynamic verification. This solution constructs a systems engineering optimization framework integrating hierarchical decoupling of parameters, dynamic cost correction, and spatiotemporal matching verification. This ensures the finding of the optimal design scheme with the lowest total construction cost under physical constraints, achieving global optimization across a complex design space and saving construction costs for the gravity energy storage system.

[0032] Please see Figure 1 , Figure 1 This is a flowchart illustrating the optimized design method for a gravity energy storage system provided in an embodiment of this application. Figure 1As shown in the figure, the optimization design method for a gravity energy storage system provided in this application includes:

[0033] S101. Obtain the preset total energy storage capacity, rated power, slope length, and slope angle of the energy storage system.

[0034] All parameters are either inputs from the project's top-level design or inherent site conditions, obtained directly from project planning documents and geological survey data.

[0035] The preset total energy storage capacity is the total energy that the system needs to store, denoted as E (e.g., 100MWh). This is one of the core design goals of the project.

[0036] Rated power is the power the system needs to achieve under charging and discharging conditions, denoted as P_rated. It is determined by grid demand or system functional positioning.

[0037] The ramp length is the total length of the ramp track used for lifting and lowering the heavy block, denoted by L (e.g., 500m). It is determined by the terrain conditions at the site.

[0038] The slope angle is the angle between the slope and the horizontal plane, denoted as θ (e.g., 60°). It is also an inherent parameter determined by the site topography.

[0039] These parameters are the fundamental boundary conditions for all subsequent calculations and constraints, and are directly passed to later steps to drive the construction of the physical model, energy calculations, and constraint setting.

[0040] S102. Determine the size parameters of the heavy block based on the preset total energy storage capacity and the preset mass range of the heavy block.

[0041] First, within the feasible design range of the length, width, and height of the weight blocks, discretization sampling is performed with a set step size to generate a massive number of "initial combinations." Next, based on the dimensions, material density, total system energy storage capacity, and slope parameters of each combination, the mass of a single weight block and the total number required to meet the energy demand are calculated. Finally, combinations meeting the pre-defined reasonable mass range are selected, forming "selected combinations." This process transforms the system's energy demand into multiple physically feasible solutions for "how many weight blocks are needed and what size they are," thereby constructing a feasible domain of initial parameters that satisfies basic constraints, reducing the subsequent search space, and avoiding computational waste on obviously infeasible parameters.

[0042] Specifically, please refer to Figure 2 , Figure 2 The flowchart for determining the size parameters of the weight block provided in the embodiments of this application is as follows: Figure 2 As shown, the dimensions of the weight block are determined in the following way:

[0043] S201. Discretize the length, width and height of the weight block within the design range to obtain multiple combinations of initial size parameters.

[0044] The design range for the length (L_b), width (W), and height (H_b) of the weight block is a pre-defined reasonable range based on engineering experience, manufacturing capabilities, and site constraints (e.g., length, width, and height are all between 1.5 meters and 3.0 meters). Discretization sampling refers to systematically and equally spaced selection of the value of each dimension within this three-dimensional design range at set step sizes (e.g., 0.5 meters), and combining them. For example, the length starts at 1.5 meters, increasing by 0.5 meters each time until it reaches 3.0 meters; the same operation is performed on the width and height. Combining all possible (L_b, W, H_b) generates a massive number of initial dimensional parameter combinations.

[0045] The dimensional design scope originates from the engineering feasibility study conducted in the early stages of the project. Through the systematic operation of discretization sampling, the continuous parameter space is transformed into a finite, traversable set of discrete points, i.e., a "feature library." Each "initial dimensional parameter combination" contains a set of three-dimensional geometric descriptions of the weight block.

[0046] This step transforms the design problem into a high-dimensional discrete data set that can be traversed and processed by a computer program, making subsequent automated screening and optimization possible.

[0047] S202. Calculate the mass of a single weight block based on each initial combination of size parameters.

[0048] For each initial size parameter combination (L_b, W, H_b) obtained in the previous step, calculate the mass (m_block) of a single heavy block based on the material density (ρ) of the heavy block.

[0049] The calculation formula is: m_block = ρ × L_b × W × H_b.

[0050] Wherein, the material density ρ is a preset physical constant determined by the selected building material (such as concrete, steel or a combination thereof), L_b is the length of the weight block, W is the width of the weight block, and H_b is the height of the weight block.

[0051] L_b, W, and H_b come from the previous step (S201). ρ is used as a known physical property parameter input, and the calculated m_block is the core variable for evaluating the feasibility of the heavy block and affecting the design of all subsequent systems (such as transmission and support).

[0052] S203. Compare the mass of a single weight block with a preset mass range, and select the combination of size parameters that falls within the mass range as the size parameters of the weight block.

[0053] This step performs the first round of physical feasibility screening. The preset mass range (e.g., 25 to 50 tons) is set based on comprehensive engineering judgment, taking into account the feasibility of manufacturing, transporting, and installing the heavy block, as well as the reasonableness of the load it imposes on the support and transmission systems. Each m_block calculated in S202 is compared with this mass range.

[0054] The preset quality range is a set of constraints predefined based on engineering experience and techno-economic analysis. The screening operation is a simple logical judgment: if 25t ≤ m_block ≤ 50t, then retain the original dimensional parameter combination (L_b, W, H_b) corresponding to m_block; otherwise, discard it. All retained dimensional combinations constitute the physical feasible region of the weight block's dimensional parameters, also known as the "screening combination".

[0055] S103. Based on the rated power, slope angle, and weight block size parameters, solve for the operating parameters that satisfy the preset dynamic constraints.

[0056] The operating parameters include the number of weights running simultaneously on each channel and the speed at which the weights run.

[0057] In this step, firstly, the range of the number of parallel channels is determined based on the system's rated power and the power capacity of a single channel. Then, for each combination of weight block parameters and the number of channels selected in the previous stage, the single-channel power balance equation is solved in reverse to determine the number of operating blocks and the combination of operating speeds that meet the power requirements. Subsequently, strict speed and length constraints are imposed on this operating combination (ensuring it meets the engineering-allowed speed range and ramp space limitations). Finally, the operating parameters that have passed all constraints are combined with the corresponding weight block and channel parameters to form a "feasible combination." This process achieves a precise match between the system's power requirements and the transmission operation scheme.

[0058] Specifically, please refer to Figure 3 , Figure 3 The flowchart provided in this application embodiment for solving the operating parameters that satisfy the preset dynamic constraints is as follows: Figure 3 As shown, the operating parameters that satisfy the preset dynamic constraints are solved in the following way:

[0059] S301. Determine the range of the number of channels in the system.

[0060] Here, the core inputs are the system rated power (P_rated) and the preset single-channel power range (e.g., 1MW to 5MW). The number of channels (N_channel) refers to the number of parallel tracks capable of independently operating the lifting and lowering of the heavy block. Its range is determined by allocating the total power requirement to individual channels: minimum number of channels = P_rated ÷ maximum power per channel (e.g., 5MW), maximum number of channels = P_rated ÷ minimum power per channel (e.g., 1MW). For example, if P_rated is 25MW, then the possible range for N_channel is 5 to 25.

[0061] S302. For each group of weight block size parameters and the number of channels within the range of channel quantity, the combination of the number of running blocks and the running speed is obtained by inverse solution based on the power balance equation.

[0062] For each set of "weight block size parameters" (including L_b, W, H_b, m_block) selected above and each N_channel value determined by S301, the following calculations are performed:

[0063] Calculate the power required for a single channel: P_channel = P_rated ÷ N_channel.

[0064] Based on the power balance equation: P_channel=M×m_block×g×sin(θ)×v.

[0065] Where M is the number of weight blocks running simultaneously on each channel (number of running blocks), m_block is the mass of a single weight block, v is the speed of the weight block running on the channel, g is the acceleration due to gravity, and θ is the slope angle.

[0066] Inverse solution: In the equation, P_channel, m_block, g, and sin(θ) are known quantities. The goal is to find all positive combinations of (M, v) that satisfy the equation.

[0067] This typically requires iterating through possible values ​​of M (e.g., starting from 1 and incrementing) and then solving for the corresponding v = P_channel ÷ (M × m_block × g × sin(θ)).

[0068] Energy demand (power) and physical properties (mass, ramp) were transformed into operable operating parameters, generating a large number of (M,v) candidate pairs.

[0069] S303. Apply speed constraints and length constraints to the combination of running blocks and running speed for screening. Use the selected combination as the running parameter. The speed constraint requires the running speed to be within the preset range, and the length constraint requires that the total length of all running heavy blocks and their intervals does not exceed the length of the slope.

[0070] Speed ​​constraint: The operating speed v must be within a preset reasonable range (e.g., 0.5 m / s ≤ v ≤ 2.0 m / s). The lower limit usually considers system stability and control accuracy, while the upper limit considers safety, wear and tear, and equipment capacity.

[0071] Length constraint: The total length of all simultaneously running weights on a single channel and their minimum interval L_gap (e.g., 0.15m) must not exceed the total length L of the ramp. This satisfies the inequality: M×(L_b+L_gap)≤L, where L_b is the length of the current weight.

[0072] The (M,v) combination that simultaneously satisfies the above two constraints is bound to its corresponding N_channel, weight block parameters, etc., and output as feasible running parameters to form a "feasible combination".

[0073] Preferably, L and L_gap are preset engineering constants.

[0074] S104. Based on the operating parameters, preset total energy storage capacity and weight block size parameters, the topology layout of the storage area is verified to obtain candidate layout schemes.

[0075] First, for each "feasible combination," all possible topologies of the storage area (including the number of layers, rows, and columns) are enumerated. Then, a core spatiotemporal cycle time closed-loop verification is performed—by calculating the system energy flow cycle time, total operating cycle, maximum gantry crane handling capacity, and required handling workload, it is determined whether the gantry crane capacity meets the task requirements, thereby selecting layouts feasible in terms of dynamic logistics. Finally, the verified layouts are merged with the feasible combinations to form a complete "candidate layout scheme." This process, by introducing a dynamic time dimension, fundamentally eliminates the risk of logistics congestion, ensuring the reliability of the design scheme at both static and dynamic operational levels.

[0076] Specifically, please refer to Figure 4 , Figure 4 A flowchart illustrating the candidate layout schemes provided in this application embodiment is shown below. Figure 4 As shown, candidate layout schemes are obtained in the following way:

[0077] S401. Based on the operating parameters, preset total energy storage capacity, and weight block size parameters, enumerate all the topological layouts existing in the storage area.

[0078] Each topology layout includes the number of rows, columns, and layers of the stacked heavy blocks.

[0079] This step aims to plan the physical organization of the storage area for each system configuration (specific stack blocks and operating parameters) determined by the preceding steps. The storage area topology layout is defined by three key parameters: the number of rows (N_rows, the number of stack blocks stacked along the Y-axis), the number of columns (N_columns, the number of stack blocks stacked along the X-axis), and the number of layers (N_layer, the number of stack blocks stacked along the Z-axis).

[0080] Enumeration refers to systematically listing all possible positive integer combinations of (N_rows, N_columns, N_layer) such that N_rows × N_columns × N_layer ≥ N_total, provided that the total energy storage capacity requirement (i.e., it must be able to accommodate N_total heavy blocks) and engineering site constraints (such as total length and width limitations) are met. N_total (the total number of heavy blocks) is calculated from the preset total energy storage capacity E, the mass of a single block m_block, and the slope parameters.

[0081] Total number of weight blocks (the total number of all weight blocks in the entire gravity energy storage system).

[0082] Ntotal=E÷(m_block×g×sin(θ)×L).

[0083] Where N_total is the total number of all weight blocks in the entire gravity energy storage system, E is the preset total energy storage capacity, that is, the total energy that the system needs to store, m_block is the mass of a single weight block, g is the gravitational acceleration, θ is the slope angle, that is, the angle between the slope and the horizontal plane, and L is the slope length.

[0084] The enumeration process is a combinatorial traversal algorithm that takes into account engineering rationality (such as layer height limit, row and column length limit) to generate all stacking schemes that can accommodate a given number of heavy blocks and have regular shapes.

[0085] S402. For each topology layout, verify whether the topology layout meets the verification requirements within the total system operating cycle.

[0086] The verification requirement is whether the maximum transport distance of the gantry crane is not less than the theoretical distance required to complete the transport task, and the number of gantry cranes is set to be equal to the number of rows.

[0087] For each enumerated layout (N_rows, N_columns, N_layer), perform the following calculations and comparisons:

[0088] Calculate the energy flow cycle time and total operating cycle of the system:

[0089] Energy flow beat T_rhythm = L ÷ (M × v).

[0090] Where L is the ramp length, and M and v are operating parameters, representing the number of blocks and speed per channel, respectively. The cycle time represents the equivalent time interval for the system to "spit out" or "swallow" a heavy block.

[0091] The total system operating cycle T_total = T_rhythm × (N_total ÷ N_channel). This represents the total time required to complete one full charge-discharge cycle.

[0092] Where T_total is the total operating cycle of the system, T_rhythm is the energy flow cycle, N_total is the total number of heavy blocks, and N_channel is the number of channels corresponding to the parallel tracks that can independently operate the lifting and lowering of heavy blocks.

[0093] Calculate the overhead crane's handling capacity and requirements:

[0094] Maximum lifting distance (capacity) of the gantry crane: D_single = T_total × v_crane.

[0095] Where v_crane is the preset gantry crane operating speed (e.g., 0.5 m / s). This represents the maximum distance that a single gantry crane can cover continuously within the total system operating cycle T_total.

[0096] Required transport distance for overhead crane (demand):

[0097] D_required=N_columns×(N_columns+1)×N_layer×(L_b+L_gap).

[0098] This formula simulates the theoretical total distance that a gantry crane needs to move to pick up and place all heavy blocks under the most unfavorable working conditions (requiring it to traverse all layers of the entire column it is responsible for).

[0099] Where D_required is the required transport distance of the gantry crane, N_columns is the number of columns (the number of rows of heavy blocks stacked along the X-axis), N_layer is the number of layers (the number of layers of heavy blocks stacked along the Z-axis), L_b is the length of the heavy block, and L_gap is the minimum interval between heavy blocks.

[0100] Execution verification: Perform a key judgment: whether D_single≥D_required is satisfied, and set the number of cranes to be equal to the number of rows (N_crane=N_rows), that is, configure one crane for each row to be responsible for all columns of that row.

[0101] S403. Select the topology layout that passes the verification as a candidate layout scheme.

[0102] For each enumerated topology layout, it will only be retained if it passes the strict check of S402 (i.e., satisfies D_single≥D_required). This is a logical filtering process, and the output is a set of feasible layout schemes that have passed the spatiotemporal beat matching verification.

[0103] S105. Calculate the total construction cost of each candidate layout scheme based on the candidate layout schemes.

[0104] Here, for each candidate layout scheme, a dynamic load-sensitive cost model is applied to calculate the total construction cost by dynamically adjusting the unit cost of key equipment. The manufacturing cost of the heavy blocks is linearly related to the total mass, while the cost of the transmission system is based on the maximum load of a single channel, and the costs of the storage area and buffer zone equipment are based on the mass of a single block. The unit costs of these components are dynamically adjusted using corresponding correction coefficients. This model ensures that cost estimation accurately reflects the engineering reality of "heavy loads leading to non-linear price increases," thus providing an objective function that closely matches the actual cost. This ensures that subsequent optimization can pinpoint the true global economic optimum, overcoming the distortion defects of traditional static cost models.

[0105] For example, the model can be a DLS cost model (the model involves cost calculation and dynamic correction mechanisms).

[0106] Specifically, please refer to Figure 5 , Figure 5 A flowchart for calculating the total construction cost of each candidate layout scheme provided in this application embodiment is shown below. Figure 5 As shown, the total construction cost for each candidate layout scheme is calculated in the following way:

[0107] S501. For each of the candidate layout schemes, calculate the manufacturing cost of the heavy block, the transmission system cost, the buffer cost, and the storage area equipment cost corresponding to that scheme.

[0108] Here, this application adopts an innovative cost calculation model, the key feature of which is that the unit cost of some key equipment changes dynamically with its design load, rather than being a fixed value.

[0109] The manufacturing cost of the heavy blocks is determined based on the total number of heavy blocks required by the scheme and the mass of each heavy block. The cost of the transmission system is determined based on the number of heavy blocks operating simultaneously on each channel and the mass of each heavy block in the operating parameters. The unit cost of the transmission system is dynamically adjusted using a correction coefficient that is positively correlated with the maximum load of a single channel. The cost of the buffer zone is determined based on the total length of the buffer zone and the mass of each heavy block. The unit cost of the buffer zone equipment is dynamically adjusted using a correction coefficient that is positively correlated with the mass of each heavy block. The cost of the storage area equipment is determined based on the mass of each heavy block and the topology of the storage area. The unit cost of the storage area equipment is dynamically adjusted using a correction coefficient that is positively correlated with the mass of each heavy block.

[0110] Specifically, the transmission system cost C_transmission is positively correlated with the single-channel maximum load correction coefficient ζtran; the storage area cost Cstorage is positively correlated with the single heavy block mass correction coefficient ζstore. This innovative model can accurately capture the engineering reality of "heavy loads leading to non-linear increases in unit cost," making the optimization results highly consistent with actual engineering costs.

[0111] In this embodiment of the application, the calculation method is as follows:

[0112] 1) Calculation of manufacturing cost C_blocks for heavy blocks:

[0113] This cost mainly consists of the cost of raw materials (such as concrete and steel) and the cost of basic molding and processing.

[0114] Calculation method: The cost is determined based on the total number of weight blocks required for the scheme (N_total) and the mass of a single weight block (m_block), which usually exhibit a linear relationship, as shown in the formula:

[0115] C_blocks=N_total×m_block×C_block

[0116] Where C_blocks is the total manufacturing cost of the weight blocks, N_total is the total number of weight blocks required by the system, m_block is the mass of a single weight block (calculated from length L_b, width W, height H_b and material density), and C_block is the unit price of the weight block.

[0117] For example, C_block can be 0.06 million yuan / ton, and its unit cost (yuan / ton) is relatively stable and does not undergo dynamic adjustment.

[0118] 2) Calculation of transmission system cost C_transmission:

[0119] This cost mainly includes the purchase and installation costs of equipment such as chains, drive systems, pallets, and wheel sets.

[0120] Calculation method: The cost is determined based on the total system channel length (L×N_channel) and the dynamically adjusted unit length cost (Cslope_per_m), using the following formula:

[0121] C_transmission=L×N_channel×Cslope_per_m

[0122] Where C_transmission is the cost of the transmission system, L is the total length of the ramp, N_channel is the number of channels, and Cslope_per_m is the cost per unit length.

[0123] Cslope_per_m=(Cchain,double×ζtran)+Csupport,plate+(Cdrive,perm×ζtran)+Cwheel,perm

[0124] Where Cslope_per_m is the unit length cost, Cchain,double is the price per meter of the double-reciprocating chain (the benchmark unit price of the chain), ζtran is the single-channel maximum load correction factor, Csupport,plate is the price per meter of the chain support plate, Cdrive,perm is the price per meter of the drive system (the benchmark unit price of the drive system), and Cwheel,perm is the price per meter of the wheelset (the benchmark unit price of the wheelset).

[0125] The calculation formula is ζtran=M×m_block÷250 (base mass 250 tons), which is used to dynamically adjust the unit cost of the chain and drive system.

[0126] For example, the price per meter of a double-sided chain (baseline): Cchain,double = 36,000 yuan / m; the price per meter of the chain support plate: Csupport,plate = 2,000 yuan / m; the price per meter of the drive system: Cdrive,per m = 12,000 yuan / m; the price per meter of the wheel set: Cwheel,per m = (m_block÷25)×0.48×1÷L_b 10,000 yuan / m.

[0127] This cost is through ζ tran enables dynamic correction, reflecting the nonlinear impact of heavy loads on the specifications and price of transmission system equipment.

[0128] Price per meter of the revised double reciprocating chain drive system:

[0129] Cchainweighted_transmission=Cchain,double×ζtran

[0130] Price per meter of the revised drivetrain / drive system:

[0131] Cdriveweighted_transmission=Cdrive,perm×ζtran

[0132] Correction function: As M×m_block increases, the price per meter of the corrected transmission system double reciprocating chain and the price per meter of the corrected transmission system drive system also increase, reflecting the impact of heavy load on equipment specifications and price.

[0133] Where M is the number of weight blocks running simultaneously on each channel (number of running blocks), and m_block is the mass of a single weight block.

[0134] 3) Calculation of buffer cost C_buffer:

[0135] This cost primarily includes the equipment costs within the buffer zone, such as the chain, drive system, pallet, and wheel assembly. Calculation method: The cost is determined based on the total length of the buffer zone (L_buffer) and the dynamically adjusted cost per unit length (Cbufferperm), using the following formula:

[0136] C_buffer=L_buffer×Cbufferperm

[0137] Here, C_buffer is the buffer cost, L_buffer is the total length of the buffer, and Cbufferperm is the dynamically adjusted cost per unit length.

[0138] L_buffer=2×N_channel×(4×L_b+3×L_gap)

[0139] Where L_buffer is the total length of the buffer, N_channel is the number of channels, L_b is the length of the weight block, and L_gap is the minimum gap between weight blocks.

[0140] Cbufferperm=(Cchain,double×ζstore)+Csupport,plate+(Cdrive,perm×ζstore)+Cwheel,perm

[0141] Where Cbufferperm is the dynamically corrected unit length cost, Cchain,double is the price per meter of the double-reciprocating chain (the base unit price of the chain), ζstore is the mass correction coefficient for a single heavy block, Csupport,plate is the price per meter of the chain support plate, Cdrive,perm is the price per meter of the drive system (the base unit price of the drive system), and Cwheel,perm is the price per meter of the wheel set (the base unit price of the wheel set).

[0142] The calculation formula is ζstore=m_block÷25 (base mass 25 tons), which is used to dynamically adjust the unit cost of the buffer device.

[0143] This cost is dynamically adjusted via ζstore, reflecting the impact of the mass of a single heavy block on the cost of the buffer-supporting equipment.

[0144] Price per meter of double-sided chain in the buffer zone after correction:

[0145] Cchain_weighted_bufer = Cchain, double × ζstore (baseline Cchain, double = 36,000 yuan / m)

[0146] Price per meter of drive system per meter after correction:

[0147] Cdrive_weighted_buffer = Cdrive,perm × ζstore (Baseline Cdrive,perm = 12,000 yuan / m)

[0148] Correction function: The device in the buffer needs to support the mass m_block of a single heavy block. Therefore, the price per meter of the double-return chain in the buffer and the price per meter of the drive system in the buffer increase with the increase of m_block.

[0149] 4) Calculation of storage area equipment cost C_storage:

[0150] This cost mainly includes the purchase and installation costs of the overhead crane equipment and supporting structure.

[0151] Calculation method: The cost is determined based on the number of gantry cranes (N_crane) and the length of the support structure (Lsupport,y), along with the dynamically adjusted unit cost. The formula is as follows:

[0152] C_storage=N_crane×(Ccrane×ζstore)+Lsupport,x×(Csupport,x×ζstore)+Lsupport,y×(Csupport,y×ζstore)

[0153] Where C_storage is the cost of storage equipment, N_crane is the number of gantry cranes, Ccrane is the base unit price of the gantry crane, ζstore is the mass correction factor for a single heavy block, Lsupport,x is the base unit price of the X-axis support frame, and Lsupport,y is the base unit price of the Y-axis support frame.

[0154] Preferably, N_crane = N_rows (the number of cranes equals the number of rows in the storage area).

[0155] Lsupport,x=(N_rows+1)×Ly, Lsupport,y=(N_columns+1)×Lx.

[0156] Where N_rows is the number of rows in the storage area, Ly is the maximum length of the storage area in the Y direction, N_columns is the number of columns in the storage area, and Lx is the maximum length of the storage area in the X direction.

[0157] This cost is dynamically adjusted through ζstore to ensure that the load-bearing capacity of the gantry crane and supporting structure matches the mass of the heavy object, reflecting the actual load requirements on the structural strength.

[0158] Corrected cost per unit of overhead crane in the storage area:

[0159] Ccrane_weighted_storage = Ccrane × ζstore (Base Ccrane = 450,000 yuan / unit)

[0160] Among them, the cost of a single gantry crane is Ccrane = 450,000 yuan / unit, and the mass correction coefficient for a single heavy object is ζstore.

[0161] Cost per meter of the revised X-axis support frame for the storage area:

[0162] Csupport,xweighted_storage=Csupport,x×ζstore(Baseline Csupport,x=1.0 million yuan / m)

[0163] Cost per meter of the revised Y-axis support frame for the storage area:

[0164] Csupport,yweighted_storage=Csupport,y×ζstore(Baseline Csupport,y=1.0 million yuan / m)

[0165] Overhead crane correction: After correction, the cost of a single overhead crane in the storage area increases linearly with the mass of a single block, m_block.

[0166] Support frame correction: The cost per meter of the X-direction support frame and the cost per meter of the Y-direction support frame in the storage area after correction, Csupport,xweighted_storage,Csupport,yweighted_storage, increase linearly with m_block to reflect the structural strength requirements of heavy loads.

[0167] S502. Based on the manufacturing cost of the heavy block, the dynamically corrected transmission system cost, the dynamically corrected buffer cost, and the dynamically corrected storage area equipment cost, the total construction cost of the scheme is obtained by summing them up.

[0168] Ctotal0=C_blocks+C_transmission+C_buffer+C_storage

[0169] Where Ctotal0 is the total construction cost, C_blocks is the manufacturing cost of the heavy blocks, C_transmission is the dynamically corrected transmission system cost, C_buffer is the dynamically corrected buffer cost, and C_storage is the dynamically corrected storage area device cost.

[0170] By aggregating the dynamically adjusted costs, the system can generate a comprehensive, accurate, and realistic overall cost assessment, providing a reliable economic basis for the subsequent selection of the global optimal solution and ensuring that the final design scheme minimizes construction costs while meeting all physical and operational constraints.

[0171] S106. Select the scheme with the lowest total construction cost as the optimal design scheme output.

[0172] Here, firstly, a global coarse search is performed on the dimensions of the heavy block with a relatively large step size. This complete process quickly scans the parameter space to identify the region with the lowest cost advantage. Then, within this region, a high-density local refinement search is performed with an extremely small step size, and the entire calculation process is executed again. Finally, the solution with the lowest total cost from the refined results is selected as the optimal design solution. This strategy, by first exploring a wide area in a vast high-dimensional space and then cultivating local areas in depth, quickly converges to the globally cost-optimal solution, effectively avoiding the "curse of dimensionality" and local optima, thus improving the efficiency and reliability of design decisions.

[0173] The optimal design scheme is a complete set of design parameters, specifically including:

[0174] 1) Design parameters of the weight block: the optimal length, width, and height determined by the size parameters of the weight block, and the corresponding mass of a single weight block;

[0175] 2) System operating parameters: The optimal number of channels, the number of weights running simultaneously on each channel, and the running speed of the weights are determined by the operating parameters;

[0176] 3) Storage area layout parameters: the optimal number of rows, columns, and layers determined by the candidate layout schemes;

[0177] 4) Economic indicators: the lowest total construction cost.

[0178] This scheme ensures that the gravity energy storage system minimizes construction costs while meeting all physical, dynamic, and operational constraints.

[0179] For further details, please refer to Figure 6 , Figure 6 A flowchart illustrating the optimal design solution provided in the embodiments of this application is shown below. Figure 6 As shown, the optimal design scheme is obtained through the following method:

[0180] S601. Perform a global search on the weight block size parameters with a first set step size, calculate the corresponding total construction costs, and determine the first optimal cost scheme and its corresponding weight block size range.

[0181] The initial step size (e.g., 0.3 meters) is a relatively large discretization granularity, determined based on the processing and design constraints of the heavy block. The operation is not isolated, but rather involves re-discretizing and sampling the length, width, and height of the heavy block at this step size, and then performing the entire process described in S102 to S105 (physical screening, dynamic solution, layout verification, and cost calculation) for these newly generated size combinations.

[0182] For each combination of dimensions defined by the coarse step size, a total construction cost (C_total) will be calculated after passing through layers of constraints. Among all the solutions that successfully complete the calculation, the one that minimizes C_total is called the first optimal cost solution.

[0183] Record the specific values ​​of the weight block dimensions (L_b, W, H_b) corresponding to the "first optimal cost solution". Using these values ​​as the center, and based on engineering experience or algorithm settings (e.g., expanding by a first set step size before and after), define a smaller range of weight block size parameters as the area for the second stage of fine-tuning search.

[0184] "First set step size" refers to the preset algorithm parameters. This step essentially involves quickly executing the entire optimization process globally with lower precision. Its core principle is to sacrifice local precision to gain a global perspective.

[0185] This step uses a large step size to quickly scan the entire vast design space, avoiding unnecessary fine calculations in non-optimal areas, overcoming the "curse of dimensionality" problem in high-dimensional optimization, and improving optimization efficiency. Based on a rough but comprehensive cost assessment, it can reliably locate the parameter subspace where the global cost minimum point may be located, providing a target for the next stage of precise targeting.

[0186] Here, the total construction cost also includes buffer zone costs; please refer to [link / reference]. Figure 7 , Figure 7 A flowchart for calculating the buffer cost provided in the embodiments of this application is shown below. Figure 7 As shown, the buffer cost is calculated in the following way:

[0187] S701. Determine the total length of the buffer zone based on the mass of a single weight block, the length in the weight block size parameters, the minimum interval between weight blocks, and the number of channels.

[0188] Buffer zones are typically located at the top and bottom of a slope and are used for accelerating, decelerating, and temporarily storing heavy objects.

[0189] The total buffer length (L_buffer) is calculated based on:

[0190] The length of a single channel on one side is 4 × L_b + 3 × L_gap (where L_b is the length of the weight block and L_gap is the minimum gap). Therefore, the total length L_buffer = 2 × N_channel × (4 × L_b + 3 × L_gap). N_channel is the number of channels.

[0191] S702. Based on the mass of a single heavy block, determine the buffer cost correction coefficient that is positively correlated with the mass of the single heavy block, and use the buffer cost correction coefficient to dynamically correct the unit length cost of the buffer to obtain the corrected unit length cost of the buffer.

[0192] The buffer devices (chains, drives, etc.) need to support the mass m_block of a single heavy block. Therefore, the same single heavy block mass correction factor ζ_store = m_block / reference mass (e.g., 25 tons) is used as the storage area devices.

[0193] The unit length cost of the buffer (including the base unit price of chains, drives, etc.) is dynamically adjusted: the adjusted unit length cost of the buffer = base unit length cost × ζ_store.

[0194] S703. The buffer cost is calculated based on the total length of the buffer and the corrected cost per unit length of the buffer.

[0195] The total cost of the buffer, C_buffer, is equal to L_buffer multiplied by the cost per unit length of the corrected buffer.

[0196] L_buffer is the total length of the buffer. The above steps incorporate this important component, the buffer, into the economic considerations, making the estimation of the total construction cost C_total more complete and accurate. The buffer cost also follows the dynamic adjustment principle of "the greater the load, the higher the unit cost", ensuring the consistency of the internal logic of the entire cost model and truly reflecting the actual project.

[0197] S602. Using a second set step size smaller than the first set step size, perform a local discretization search within the size range of the heavy block corresponding to the first optimal cost scheme, calculate the corresponding multiple total construction costs, and select the scheme with the lowest total construction cost as the optimal design scheme output.

[0198] This step involves fine-tuning within the advantageous area locked by S601. The second set step size (e.g., 0.1 meters) is much smaller than the first set step size, and is determined based on the minimum machining accuracy of the weight block.

[0199] Within the range of the weight block size parameters defined in S601, high-density discretization sampling is performed with a second set step size. Similarly, for these dense size points, the entire process from S102 to S105 (including dynamic cost calculation and buffer cost) is executed again.

[0200] Determining the global optimum: Among this series of high-precision solutions, compare their calculated total construction cost C_total, and select the solution with the absolute lowest total construction cost as the final optimal design solution output. This solution includes a complete set of design parameters such as the optimal size of the weight block, operating parameters (M, v, N_channel), and optimal layout of the storage area.

[0201] The "second set step size" is a preset accuracy parameter. This step involves a high-resolution, fine-grained search and confirmation within the most promising parameter subspace.

[0202] The small step size of this application ensures that better solutions located between coarse grids are not missed, enabling the algorithm to converge stably and accurately to the theoretically optimal global cost point or its nearest neighbor. The two-stage strategy (coarse first, then fine) balances computational efficiency (avoiding global fine calculation) and result accuracy (local fine confirmation), which is an effective strategy for solving complex engineering optimization problems. The final output is a reliable design scheme that is both highly economical and engineering feasible.

[0203] Compared with existing design methods that rely on empirical formulas and static models, the gravity energy storage system optimization design method and device provided in this application solves three core problems by constructing a system engineering optimization framework that includes hierarchical decoupling parameters, dynamic cost correction, and closed-loop verification cycle time. This addresses the difficulties in finding the optimal solution caused by strong parameter coupling, the economic distortion caused by static cost, and the low operational reliability caused by material mismatch. The method achieves the technical effect of significantly reducing construction costs and greatly improving the degree of design automation and decision-making efficiency while ensuring the reliability of system operation.

[0204] This application constructs a coupled optimization model for key design parameters (weight block size, operating parameters, and storage layout) of a gravity energy storage system. It introduces a dynamic load cost correction and spatiotemporal logistics verification mechanism, which enables the system to automatically and efficiently find the globally optimal or near-optimal design scheme while satisfying all physical and operational constraints. This fundamentally improves the scientific and economic efficiency of the design and ultimately reduces construction costs.

[0205] The gravity energy storage system optimization design method provided in this application has the following beneficial effects:

[0206] 1) Significant Cost Optimization Results: By combining a dynamic load-sensitive cost model with global optimization, precise optimization of construction costs is achieved. The model introduces a load correction coefficient to dynamically adjust the unit cost of equipment, accurately reflecting the nonlinear cost growth caused by heavy loads. This method can significantly reduce construction investment, potentially saving more than 50% of costs compared to traditional designs, and improving system economics.

[0207] 2) Reliable system operation: Through the closed-loop verification of the spatiotemporal rhythm of energy flow and material flow, the dynamic matching of charging and discharging and handling rhythm is ensured, preventing material flow blockage and operational imbalance from the design source, and ensuring long-term stable operation of the system.

[0208] 3) High design efficiency: The three-layer decoupled architecture and adaptive two-stage optimization effectively avoid the "curse of dimensionality" in high-dimensional optimization, achieve fast and stable convergence, and greatly improve the efficiency of design decision-making.

[0209] 4) Complete theoretical system: A complete mathematical mapping and optimization framework from physical parameters to economic indicators has been established, which has clear engineering applicability and scalability, and provides a reusable theoretical and methodological foundation for the standardized design of gravity energy storage.

[0210] Based on the same inventive concept, this application also provides an optimization design device for a gravity energy storage system corresponding to the optimization design method for a gravity energy storage system. Since the principle of the device in this application is similar to the optimization design method for a gravity energy storage system described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0211] Please see Figure 8 , Figure 8 This is a schematic diagram of the optimized design device for a gravity energy storage system provided in an embodiment of this application. Figure 8 As shown, the optimization design device 800 for the gravity energy storage system includes:

[0212] The parameter acquisition module 801 is used to acquire the preset total energy storage capacity, rated power, slope length and slope angle of the energy storage system.

[0213] The weight block size parameter determination module 802 is used to determine the weight block size parameters based on the preset total energy storage capacity and the preset weight block mass range;

[0214] The operating parameter solving module 803 is used to solve the operating parameters that satisfy the preset dynamic constraints based on the rated power, the slope angle and the size parameters of the weight block. The operating parameters include the number of weight blocks running simultaneously on each channel and the running speed of the weight blocks.

[0215] The candidate layout scheme acquisition module 804 is used to verify the topology layout of the storage area based on the operating parameters, the preset total energy storage capacity and the weight block size parameters, and to obtain candidate layout schemes.

[0216] The total construction cost calculation module 805 is used to calculate the total construction cost of each candidate layout scheme based on the candidate layout schemes.

[0217] The optimal design scheme output module 806 is used to select the scheme with the lowest total construction cost as the optimal design scheme output.

[0218] Please see Figure 9 , Figure 9 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. Figure 9 As shown, the electronic device 500 includes a processor 510, a memory 520, and a bus 530.

[0219] The memory 520 stores machine-readable instructions executable by the processor 510. When the electronic device 500 is running, the processor 510 and the memory 520 communicate via the bus 530. When the machine-readable instructions are executed by the processor 510, they can perform the operations described above. Figure 1 The steps of the optimization design method for the gravity energy storage system in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0220] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 The steps of the optimization design method for the gravity energy storage system in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0221] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0222] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0223] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0224] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0225] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0226] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for the optimal design of a gravitational energy storage system, characterized in that, The method comprises the following steps: acquiring preset total energy storage capacity, rated power, ramp length and ramp angle of an energy storage system; determining weight block size parameters based on the preset total energy storage capacity and preset weight block mass range; solving operation parameters meeting preset dynamic constraints based on the rated power, the ramp angle and the weight block size parameters, the operation parameters comprising the number of weight blocks simultaneously operating on each channel and weight block operating speed; verifying storage area topology layout based on the operation parameters, the preset total energy storage capacity and the weight block size parameters to obtain a candidate layout scheme; calculating total construction cost of each candidate layout scheme according to the candidate layout scheme, wherein the total construction cost of each candidate layout scheme is calculated in the following manner: calculating weight block manufacturing cost, transmission system cost, buffer area cost and storage area equipment cost corresponding to each of the candidate layout schemes respectively, wherein the weight block manufacturing cost is determined based on the total number of weight blocks required by the scheme and the mass of a single weight block, the transmission system cost is determined based on the number of weight blocks simultaneously operating on each channel in the operation parameters and the mass of a single weight block, and the unit cost of the transmission system is dynamically corrected by a correction coefficient positively correlated with the maximum load of a single channel, the buffer area cost is determined based on the total length of the buffer area and the mass of a single weight block, and the unit cost of the buffer area equipment is dynamically corrected by a correction coefficient positively correlated with the mass of a single weight block, and the storage area equipment cost is determined based on the mass of a single weight block and the topology layout of the storage area, and the unit cost of the storage area equipment is dynamically corrected by a correction coefficient positively correlated with the mass of a single weight block; summing the weight block manufacturing cost, the dynamically corrected transmission system cost, the dynamically corrected buffer area cost and the dynamically corrected storage area equipment cost to obtain the total construction cost of the scheme; selecting the scheme with the lowest total construction cost as the optimal design scheme and outputting the optimal design scheme.

2. The method of claim 1, wherein, The weight block size parameters are determined in the following manner: discretely sampling within the design range of the length, width and height of the weight block to obtain a plurality of initial size parameter combinations; calculating the mass of a single weight block based on each initial size parameter combination; comparing the mass of a single weight block with the preset mass range to filter out the size parameter combinations falling within the mass range as the weight block size parameters.

3. The method of claim 1, wherein, The operation parameters meeting the preset dynamic constraints are solved in the following manner: determining the range of the number of channels of the system; for each set of weight block size parameters and each number of channels within the range of the number of channels, obtaining a combination of the number of operating blocks and operating speed based on the power balance equation; applying speed constraints and length constraints to the combination of the number of operating blocks and operating speed to filter out the combination that passes the filtering as the operation parameters, wherein the speed constraints require the operating speed to be within a preset range, and the length constraints require the total length of all weight blocks simultaneously operating and the interval therebetween to be no more than the ramp length.

4. The method of claim 1, wherein, The candidate layout scheme is obtained by checking the topology layout of the storage area based on the operation parameter, the preset total energy storage capacity and the weight block size parameter. All topology layouts existing in the storage area are enumerated based on the operation parameter, the preset total energy storage capacity and the weight block size parameter, and each topology layout includes the number of rows, the number of columns and the number of layers of the weight block stack. For each topology layout, it is checked whether the topology layout meets the verification requirement in the total operation cycle of the system, and the verification requirement is whether the maximum carrying distance of the row crane is not less than the theoretical distance required to complete the carrying task, and the number of row cranes is set to be equal to the number of rows. The topology layout that passes the check is taken as the candidate layout scheme.

5. The method of claim 1, wherein, The scheme with the lowest total construction cost is selected as the optimal design scheme, which includes: A global search is performed on the weight block size parameter with a first set step, a plurality of total construction costs are calculated, and a first optimal cost scheme and a corresponding weight block size range are determined; A local discrete search is performed within the weight block size range corresponding to the first optimal cost scheme with a second set step smaller than the first set step, a plurality of total construction costs are calculated, and the scheme with the lowest total construction cost is selected as the optimal design scheme.

6. The method of claim 1, wherein, The total construction cost also includes a buffer zone cost, The buffer zone cost is calculated by the following method: The total length of the buffer zone is determined based on the mass of the single weight block, the length in the weight block size parameter, the minimum interval of the weight block and the number of channels; A buffer zone cost correction coefficient is determined based on the mass of the single weight block, which is positively correlated with the mass of the single weight block, and a dynamic correction is performed on the buffer zone unit length cost by using the buffer zone cost correction coefficient to obtain a corrected buffer zone unit length cost; The buffer zone cost is calculated based on the total length of the buffer zone and the corrected buffer zone unit length cost.

7. An apparatus for the optimal design of a gravitational energy storage system, characterized by It includes: A parameter acquisition module is configured to acquire a preset total energy storage capacity, a rated power, a ramp length and a ramp angle of an energy storage system. A weight block size parameter determination module is configured to determine a weight block size parameter based on the preset total energy storage capacity and a preset weight block mass range. An operation parameter solving module is configured to solve an operation parameter that meets a preset dynamic constraint based on the rated power, the ramp angle and the weight block size parameter, the operation parameter including the number of weight blocks simultaneously running on each channel and the running speed of the weight blocks. A candidate layout scheme obtaining module is configured to obtain a candidate layout scheme by checking a topology layout of a storage area based on the operation parameter, the preset total energy storage capacity and the weight block size parameter. The total construction cost calculation module is configured to calculate the total construction cost of each candidate layout scheme according to the candidate layout scheme, and the total construction cost of each candidate layout scheme is calculated by calculating the heavy block manufacturing cost, the transmission system cost, the buffer zone cost and the storage area equipment cost corresponding to each candidate layout scheme, respectively, wherein the heavy block manufacturing cost is determined based on the total number of heavy blocks required by the candidate layout scheme and the mass of a single heavy block, the transmission system cost is determined based on the number of heavy blocks running simultaneously on each channel in the operation parameters and the mass of a single heavy block, and the unit cost of the transmission system is dynamically corrected by a correction coefficient positively correlated with the single-channel maximum load, the buffer zone cost is determined based on the total length of the buffer zone and the mass of a single heavy block, and the unit cost of the buffer zone equipment is dynamically corrected by a correction coefficient positively correlated with the mass of a single heavy block, and the storage area equipment cost is determined based on the mass of a single heavy block and the topological layout of the storage area, and the unit cost of the storage area equipment is dynamically corrected by a correction coefficient positively correlated with the mass of a single heavy block; and the total construction cost of the candidate layout scheme is obtained by summing the heavy block manufacturing cost, the dynamically corrected transmission system cost, the dynamically corrected buffer zone cost and the dynamically corrected storage area equipment cost. The optimal design scheme output module is configured to select the scheme with the lowest total construction cost as the optimal design scheme output.

8. An electronic device, comprising: The processor, the memory and the bus, the memory stores machine readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, the processor executes the machine readable instructions to execute the steps of the method as claimed in any one of claims 1 to 6. The computer readable storage medium stores a computer program, and the computer program is executed by the processor to execute the steps of the method as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, ​

Images