Potential evaluation method for wide-area gravity energy storage system

Abstract

The invention discloses a potential evaluation method for a wide-area gravity energy storage system, and belongs to the technical field of new energy storage. Comprising the following steps: acquiring multi-source geospatial data of a research area, and preprocessing the multi-source geospatial data; slope analysis is carried out by utilizing the slope raster data, and critical stable slopes under different material combinations are calculated; projecting the terrain according to the minimum running gradient to form an LES mapping structure, and extracting centroid parameters; calculating the energy storage potential of an energy storage unit according to the centroid parameter, and deleting an area with an abnormal gradient in the LES mapping structure; and performing quantitative evaluation and economic evaluation on the carbon emission of the wide-area gravity energy storage system. According to the method, GES suitability analysis and potential measurement and calculation in a large-area range can be efficiently and automatically completed, and systematicness and precision of a traditional method in the aspects of multi-source data processing, deployment feasibility analysis and environmental economic evaluation are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of new energy storage technology, and in particular to a method for assessing the potential of a wide-area gravity energy storage system. Background Technology

[0002] As the global carbon neutrality process accelerates, renewable energy will become the main power source in the coming decades, with its share in the global power generation structure expected to approach 90%. However, the volatility and uncontrollability of solar and wind power pose serious challenges to the stability of the power system, making long-duration and large-scale energy storage (LLDES) technology a key support for the energy transition.

[0003] Current mainstream large-scale energy storage technologies each face significant constraints. Pumped hydro storage is limited by long construction cycles and dependence on topographical features for water resources; compressed air storage relies on specific geological conditions such as salt caverns and abandoned mines, limiting resource distribution; hydrogen storage suffers from low thermodynamic efficiency and poor safety (e.g., flammability); and thermal energy storage is constrained by bottlenecks such as material separation, corrosive reactions, and heat loss from storage tanks. These problems expose the shortcomings of existing technologies in terms of safety, sustainability, and scalability, necessitating new energy storage pathways to achieve a balance between system reliability and low-carbon goals.

[0004] Gravity energy storage (GES), a novel energy storage technology based on potential energy extraction, possesses characteristics such as simple structure, stable operation, and slow system response but long-term continuous output. In recent years, it has been validated in multiple pilot projects, gradually demonstrating strong engineering feasibility and scalability. Based on structural type, GES can be divided into two categories: artificial structure type and terrain-adaptive type. Artificial structure type (such as tower and well type) offers flexible site selection but is generally limited by high construction costs and small single-unit capacity; while terrain-adaptive GES, relying on natural slopes or terrain with elevation differences, can achieve large-scale energy storage at low cost, becoming an important candidate technology route for future LLDES applications.

[0005] Despite the broad prospects of GES in terms of technology, economics, and environment, its evaluation system remains incomplete. Most existing technologies are designed and analyzed around specific scenarios (such as mine-type, cableway-type, or slope-type), lacking an integrated assessment of the feasibility of various GES forms under different geographical and resource contexts. This leads to underestimated potential and suboptimal site selection. Furthermore, economic assessments generally fail to consider key factors such as regional topographical differences and electricity price trends, making it difficult to reflect cost differences and investment risks between regions. From a carbon reduction perspective, while GES facilitates the smooth integration of clean energy sources and the replacement of thermal power, the construction of such systems involves large amounts of high-carbon materials such as steel and cement. Its full life-cycle carbon footprint urgently needs to be quantified through systematic analysis to determine its net carbon benefits.

[0006] Therefore, there is an urgent need to construct a multidimensional assessment framework that integrates geospatial characteristics, economic parameters, and carbon emission factors, and to unify and integrate site selection adaptability analysis, energy potential modeling, economic feasibility calculation, and environmental impact assessment, so as to support the large-scale deployment of GES technology at the regional scale and promote its alignment with global low-carbon development goals. Summary of the Invention

[0007] The purpose of this invention is to propose a method for assessing the potential of a wide-area gravity energy storage system, comprising:

[0008] Acquire multi-source geospatial data of the study area and preprocess the multi-source geospatial data;

[0009] Slope analysis was conducted using slope raster data to calculate the critical stable slope under different material combinations;

[0010] The terrain is projected according to the minimum running slope to form an LES mapping structure, and the centroid parameters are extracted.

[0011] The energy storage potential of the energy storage unit is calculated based on the centroid parameters, and areas with abnormal slopes in the LES mapping structure are removed.

[0012] A quantitative assessment and economic evaluation of the carbon emissions of wide-area gravity energy storage systems were conducted.

[0013] Furthermore, multi-source geospatial data includes digital elevation models, climate data, population density layers, water body boundaries, and land use layers.

[0014] Furthermore, the centroid parameters include the centroid mass and the centroid distance.

[0015] Furthermore, the formula for calculating the minimum operating gradient is:

[0016]

[0017] Where, θ min For the minimum operating gradient, ΔH ijThe elevation difference between two flat areas is ΔL. ij This represents the horizontal distance.

[0018] Furthermore, the formula for calculating the energy storage potential of an energy storage unit based on the centroid parameters is as follows:

[0019] E=ρVτgλ

[0020] Where E is the gravitational potential energy of the energy storage unit, ρ is the density of the weight, V is the total volume of the weight, λ is the centroid path, τ is the density coefficient of the weight on the stacking platform, and g is the gravitational acceleration constant.

[0021] Furthermore, the LES mapping structure satisfies the following conditions:

[0022] Each mapping unit consists of a rectangular grid with an area that increases with the level; all levels are aligned with the same spatial index; adjacent levels maintain a geometric connection with an average slope greater than or equal to the minimum running slope; the geometric center of each height level slice is on the same straight line as the center of the lowest level candidate unit; the collinear axis is perpendicular to the plane where all slices are located, forming a continuous stack of inverted cones in three-dimensional space.

[0023] Furthermore, the LES mapping structure includes the following during its formation:

[0024] The geometric center point of each slice is expanded into a surface region basic unit of equal area; the surface region unit covers the entire slice layer range, carrying multi-dimensional information such as elevation, area and spatial coordinates; the surface region unit replaces the point-level unit to perform potential calculation and structural mapping, improving the efficiency of large-scale calculation and the integrity of spatial coverage.

[0025] Furthermore, it also includes centroid path modeling, specifically:

[0026] Within a multi-layer LES mapping structure, combinations of flat land slices that satisfy geometric constraints are identified; the geometric centers of each combined slice are used as nodes and connected vertically to construct gravitational potential energy conversion paths; based on the path elevation difference and the distance between nodes, combined with the mass parameter of the heavy object, the gravitational potential energy capacity corresponding to each path is calculated.

[0027] Furthermore, the formula for quantitatively assessing the carbon emissions of wide-area gravity energy storage systems is as follows:

[0028]

[0029] Among them, R carbon It is the carbon emission reduction of the energy storage system; E generation (t) represents the total power generation of the energy storage system in year t; F fossil (t) is the carbon emission factor of thermal power in year t.

[0030] The beneficial effects of this invention are as follows:

[0031] The proposed method for assessing the potential of wide-area gravity energy storage systems can efficiently and automatically complete the suitability analysis and potential calculation of GES over a large area. It significantly improves the systematicness and accuracy of traditional methods in multi-source data processing, deployment feasibility analysis and environmental economic assessment, and provides theoretical tools and engineering support for promoting the large-scale and low-carbon deployment of gravity energy storage systems. Attached Figure Description

[0032] Figure 1 This is a flowchart of the GES potential assessment according to an embodiment of the present invention;

[0033] Figure 2 This is a critical slope diagram between different common GES media according to embodiments of the present invention;

[0034] Figure 3(a) is a front view of the surface mapping structure (using rectangles as basic units) according to an embodiment of the present invention;

[0035] Figure 3(b) is a side view of a surface mapping structure (using rectangles as basic units) according to an embodiment of the present invention;

[0036] Figure 3(c) is a top view of the surface mapping structure (using rectangles as basic units) according to an embodiment of the present invention;

[0037] Figure 3(d) is a slice diagram of the rectangular surface mapping structure (using rectangles as basic units) according to an embodiment of the present invention.

[0038] Figure 4(a) is a schematic diagram of the M-GES power station in full-charge state according to an embodiment of the present invention;

[0039] Figure 4(b) is a schematic diagram of the fully discharged state of the M-GES power station according to an embodiment of the present invention;

[0040] Figure 5 This is a boundary diagram of a PLCA system for a gravity energy storage power station according to an embodiment of the present invention;

[0041] Figure 6 This is a learning curve of the main carbon emission factors from 2024 to 2060 according to an embodiment of the present invention. Detailed Implementation

[0042] This invention proposes a method for assessing the potential of a wide-area gravity energy storage system. The invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0043] This invention provides an evaluation method for gravity energy storage systems (GES) designed for wide-area deployment. The aim is to systematically, scientifically, and efficiently explore the spatial resources and potential suitable for GES deployment in different geographical regions. It should be noted that the steps shown in the flowcharts can be executed in a computer system such as a set of executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be performed in a different order than that presented here.

[0044] The GES potential assessment system process provided in this embodiment of the invention is as follows: Figure 1 As shown, the process includes the following steps:

[0045] Step S101, model initialization. Specifically includes:

[0046] Step S1011: Acquire multi-source geospatial data for the study area, including digital elevation models (such as ASTERGDEM), climate data (such as WorldClim), population density layers (such as WorldPop), water boundary and land use layers, etc. All data are sourced from globally available databases, supporting the broad applicability and model transferability of this invention across different regions and spatial scales.

[0047] Step S1012, data preprocessing.

[0048] This embodiment uses GDAL to read, convert, and crop GeoTIFF format raster data, and uses Geopandas and Fiona to parse and spatially crop vector boundary data (SHP) to ensure the consistency of data from different sources in coordinate system, resolution, and data format, thus meeting the spatial requirements of subsequent terrain identification and site selection.

[0049] Step S102: Terrain Identification and Classification. After completing the standardized data processing, slope analysis is performed using slope raster data, and combined with the friction coefficient μ, based on the following formula...

[0050] θ f_max =arctan(μ)

[0051] Calculate the critical stability slope under different material combinations. Figure 2 This is a critical slope diagram between different common GES media according to an embodiment of the present invention. Typical friction coefficients are approximately 0.1-0.2 for steel rails and steel wheels, 0.6-0.85 for rubber and concrete surfaces, and 0.6-0.7 for iron blocks and rock surfaces. To ensure the safe operation of the stacking system and provide stable support, this embodiment uses 5° as the upper limit for slope screening, that is, the area with a slope ≤ 5° is defined as a candidate area that can be used to construct a flat-ground stacking structure.

[0052] In addition, to achieve slope adaptation and structure type matching, this embodiment further divides the terrain into: flat land (0-5°), gentle slope (5-10°), sloping land (10-25°), steep slope (25-50°), steep cliff (50-80°) and precipitous cliff (80-90°) to support the differentiated deployment of different GES structures in the region.

[0053] Step S103, construct the LES mapping structure. This step involves mapping the terrain according to the minimum running slope θ. min The inverted cone-shaped mapping body (LES structure) formed by projection searches for elevation slices at higher levels in the three-dimensional terrain to identify combinations of upper and lower flat areas that meet the conditions of elevation difference.

[0054] In the LES mapping structure, each elevation slice is geometrically collinear with the bottom starting point, satisfying the projection condition:

[0055]

[0056] Where, θ min For the minimum operating gradient, ΔH ij The elevation difference between two flat areas is ΔL. ij This represents the horizontal distance.

[0057] The specific operation for constructing an Extended Layered Structure (LES) is as follows: "Using a specific starting point of the lowest height layer as a reference, it expands upward layer by layer, with each layer corresponding to a slice area," forming an inverted cone-shaped mapping structure. The geometric centers of all high-level slices in this structure are collinear and perpendicular to the plane of each layer. The angle between this collinearity and the LES envelope plane is the "minimum operating slope."

[0058] The LES mapping structure satisfies the following conditions and geometric constraints:

[0059] (1) Each mapping unit consists of a rectangular grid with an area that increases with the level, adapting to stacking layouts with different height differences;

[0060] (2) All levels are aligned with the same spatial index to avoid inter-level overlap and computational redundancy;

[0061] (3) Maintain an average slope ≥ θ between adjacent levels min Geometric connections are used to ensure the feasibility of power transmission.

[0062] (4) The geometric center of each height level slice is on the same straight line as the center of the lowest level candidate unit;

[0063] (5) The collinear axes are perpendicular to the plane where all slices are located, forming a continuous stack of inverted cones in three-dimensional space;

[0064] (6) Based on the inverted cone structure, calculate the average slope between adjacent level flat slices, and determine whether the slope threshold required for continuous operation path is met, so as to assist in the judgment of terrain adaptability.

[0065] The LES structure construction phase employs a point-to-surface unit approximation modeling strategy, including:

[0066] (1) Expand the geometric center point of each slice into a basic unit of equal area surface region;

[0067] (2) The entire slice layer is covered by area units, carrying multi-dimensional information such as elevation, area and spatial coordinates;

[0068] (3) Replace point-level units with area-level units to perform potential calculations and structure mapping, thereby improving large-scale calculation efficiency and spatial coverage integrity.

[0069] The basic unit of the region adopts a regular rectangular grid structure: spatial grid alignment and index matching with DEM and other raster layers; support for batch raster clipping and parallel processing with uniform resolution; and use of rectangular grids to avoid unit overlap and computational redundancy in multi-layer LES mapping, thereby improving spatial resolution.

[0070] The centroid path modeling is introduced in the LES structural potential assessment, and the implementation steps are as follows:

[0071] (1) Identify flat-ground slice combinations that satisfy geometric constraints within a multi-layered LES structure;

[0072] (2) Connect the geometric centers of each combined slice as nodes in the vertical direction to construct a gravitational potential energy conversion path;

[0073] (3) Based on the path elevation difference and the distance between nodes, combined with the mass parameters of the heavy object, calculate the gravitational potential energy capacity corresponding to each path.

[0074] Centroid path modeling further includes path validity determination:

[0075] (1) Calculate the elevation difference ΔH and horizontal distance ΔL between the geometric centers of any adjacent slices;

[0076] (2) Calculate the average slope value arctan(ΔH / ΔL);

[0077] (3) When the average slope value is ≥ θ min If the path is deemed a valid connection unit, it is included in the gravitational potential energy capacity estimation; otherwise, it is discarded.

[0078] In this embodiment, to improve computational feasibility, an approximation strategy from point to surface is introduced, as shown in Figures 3(a), 3(b), 3(c), and 3(d). This strategy expands the basic analysis unit from points to surfaces, performing GES site evaluation on a surface-by-surface basis. In this embodiment, rectangles are used as the basic unit.

[0079] Step S104: Extract the centroid parameters.

[0080] This step first calculates the total mass of all heavy objects within the entire area and determines their weighted average height in space, namely the "center-of-mass mass" and "center-of-mass height." These two satisfy the following relationship:

[0081]

[0082] This formula ensures that the mass of heavy objects at different levels is reasonably weighted, so that the calculated energy storage capacity accurately reflects the actual situation.

[0083] In this embodiment, the centroid path can be calculated using the following formula:

[0084] λ=(H max -H min )

[0085] Among them, H max With H min Figure 4(a) shows the centroid height under fully charged and uncharged states, respectively. The distribution of GES energy storage cells under fully charged and uncharged states is shown in Figure 4(b).

[0086] Step S105, Energy Storage Potential Calculation. This step estimates the gravitational potential energy of the corresponding energy storage unit based on the center-of-mass path and weight parameters:

[0087] E=ρVτgλ

[0088] Where ρ is the density of the weight; V is the total volume of the weight; λ is the centroid path, representing the difference in centroid height between the fully charged and uncharged states; τ is the density coefficient of the weights on the stacking platform, used to describe the tightness of the weights on the stacking platform; and g is the gravitational acceleration constant.

[0089] Step S106, Technological Potential Correction and Constraint Screening. Areas constrained by technology and policy are removed from the initial potential map. If an area corresponding to a LES structure has an abnormal slope (e.g., the slope between distant flat areas is less than the slope), that area will be removed.

[0090] Step S107, carbon emissions and carbon reduction assessment. Figure 5This is a PLCA system boundary diagram of a gravity energy storage power station according to an embodiment of the present invention. This embodiment uses the Prospective Life Cycle Assessment (PLCA) method to quantitatively assess the carbon emissions of the gravity energy storage system during the construction, operation, and decommissioning phases. Specifically, it includes:

[0091] Construction Phase: Carbon emissions primarily originate from the manufacturing and transportation of materials and equipment such as steel, cement, motors, rails, cables, lifting equipment, and power infrastructure. Carbon emission factors are based on the Ecoinvent 3 database or literature averages, and are dynamically adjusted to account for technological advancements. For example, steel emissions are projected to gradually decrease from approximately 1.07 tons of CO2 per ton in 2024 to 0.17 tons of CO2 per ton in 2060.

[0092] Operation and maintenance phase: Since the GES system mainly relies on heavy potential energy, its energy consumption during operation is extremely low. Only the power consumption of auxiliary system operation and maintenance is considered and assumed to be negligible. The energy output substitution benefit is dynamically calculated annually for its carbon emission reduction value.

[0093] Retirement phase: Considering carbon emissions from equipment dismantling, transportation and resource recycling, the main sources include emissions from the treatment of scrapped equipment and emissions from waste transportation. For example, the carbon factor from the treatment of scrapped equipment can be reduced from 59 tons of CO2 / ton in the early stage to about 13 tons of CO2 / ton in 2060.

[0094] The contribution to carbon emission reduction is calculated using the following formula:

[0095]

[0096] Among them, R carbon It is the carbon emission reduction of the energy storage system; E generation (t) represents the total power generation of the energy storage system in year t; F fossil (t) is the carbon emission factor of thermal power in year t. Figure 6 This is a learning curve of the main carbon emission factors from 2024 to 2060 according to an embodiment of the present invention.

[0097] Step S108, Economic Feasibility Assessment. This step evaluates the economic feasibility of each GES unit based on the LCOE (Levelized Cost of Electricity) and NPV (Net Present Value) models.

[0098] The economic feasibility of the project is calculated based on the Net Present Value (NPV) model, taking into account the following cash flows:

[0099] (1) Initial investment cost, covering energy storage equipment, transmission system, power electronic modules and construction and infrastructure construction costs;

[0100] (2) Operating and maintenance costs, including daily operating energy consumption, operating personnel and maintenance and replacement costs, and dispatch and communication overhead;

[0101] (3) Decommissioning costs, which include system dismantling, transportation and resource recycling or disposal costs;

[0102] (4) Carbon-related costs / benefits, covering the carbon emission costs of the system life cycle and the carbon emission reduction benefits achieved by replacing high-carbon power sources, are all measured in carbon price;

[0103] (5) Energy arbitrage income, which includes income from taking advantage of peak-valley differences in electricity prices or providing ancillary services;

[0104] (6) Policy incentive benefits, including government or market incentives such as capacity subsidies, electricity price subsidies and carbon trading;

[0105] (7) Discount the above cash flows to the benchmark time point at the discount rate r, calculate the net present value of the project, and assess its economic feasibility.

[0106] The NPV model comprehensively considers the impact of uncertainties in the following key parameters:

[0107] (1) Discount rate, used to discount future cash flows;

[0108] (2) System lifespan and annual operating cycle are used to allocate cost and benefit timelines;

[0109] (3) The difference between carbon price and electricity price is used to determine the carbon emission reduction benefits and the energy arbitrage range;

[0110] (4) The installed capacity and power capacity of the energy storage system are used to adjust the scale of investment and returns;

[0111] (5) The probability distribution of the above parameters is set by Monte Carlo simulation, and the confidence interval of the NPV results is estimated to evaluate the robustness and risk of the model.

[0112] NPV calculation considers direct revenue, indirect revenue, direct costs, and indirect costs, and performs a discounted calculation based on the time value of money. Its mathematical expression is as follows:

[0113]

[0114] Among them, R total R represents the total direct revenue. carbon For carbon emission reduction benefits; C O&M For maintenance costs; C initial R is the initial investment cost; r is the discount rate, taking into account inflation and the cost of capital; C carbon C decommission This refers to the disposal cost when the system is decommissioned.

[0115] When NPV > 0, it indicates that the GES project is economically feasible and has investment value; when NPV < 0, it indicates that the project cannot generate profits and investment is not recommended.

[0116] This step systematically quantifies the impact of revenue, cost, and carbon market mechanisms on the economic viability of gravity energy storage projects, providing a mathematical basis for optimizing investment decisions for these projects.

[0117] At the same time, the Monte Carlo method is used to conduct sensitivity analysis to address the uncertainty of policy or technical parameter changes.

[0118] Step S109, Site Priority Ranking and Site Selection Optimization. This step integrates "technological potential + economic benefits + carbon emission reduction contribution" to construct a comprehensive benefit index, guiding the optimal layout and development sequence of GES (Geological Emission Reduction System), applicable to regional, national, or continental deployment strategies. Specific steps include:

[0119] (1) Obtain and standardize multi-source datasets, including information such as topographic elevation, climate resources, population distribution and economic parameters;

[0120] (2) Construct energy storage potential model, carbon emission reduction model and NPV model for candidate sites, and quantitatively calculate each indicator respectively;

[0121] (3) Input the values ​​of each indicator into the weighted comprehensive scoring function. The weights can be configured according to policies or user needs.

[0122] (4) Sort the candidate sites according to the comprehensive score, output the deployment priority list, and form a regional development recommendation sequence.

[0123] Step S1010: Results Statistics and Parameter Analysis. Output maps include: GES energy storage potential distribution map, economic assessment map, carbon emission trend map, site priority map, etc. Export formats support TIF, SHP, PDF, and tables, supporting use by policy departments, investors, and research institutions.

[0124] In summary, the potential assessment method for wide-area gravity energy storage systems proposed in this invention can efficiently and automatically complete the suitability analysis and potential calculation of GES over a large area, significantly improving the systematicness and accuracy of traditional methods in multi-source data processing, deployment feasibility analysis and environmental economic assessment, and providing theoretical tools and engineering support for promoting the large-scale and low-carbon deployment of gravity energy storage systems.

Claims

1. A method for assessing the potential of a wide-area gravity energy storage system, characterized in that, include: Acquire multi-source geospatial data of the study area and preprocess the multi-source geospatial data; Slope analysis was conducted using slope raster data to calculate the critical stable slope under different material combinations; The terrain is projected according to the minimum running slope to form an LES mapping structure, and the centroid parameters are extracted. The energy storage potential of the energy storage unit is calculated based on the centroid parameters, and areas with abnormal slopes in the LES mapping structure are removed. A quantitative assessment and economic evaluation of the carbon emissions of wide-area gravity energy storage systems were conducted.

2. The potential assessment method for a wide-area gravity energy storage system according to claim 1, characterized in that, The multi-source geospatial data includes digital elevation models, climate data, population density layers, water body boundaries, and land use layers.

3. The potential assessment method for a wide-area gravity energy storage system according to claim 1 or 2, characterized in that, The centroid parameters include centroid mass and centroid distance.

4. The potential assessment method for a wide-area gravity energy storage system according to claim 1, characterized in that, The formula for calculating the minimum operating slope is: Where, θ min For the minimum operating gradient, ΔH ij The elevation difference between two flat areas is ΔL. ij This represents the horizontal distance.

5. The potential assessment method for a wide-area gravity energy storage system according to claim 3, characterized in that, The formula for calculating the energy storage potential of an energy storage unit based on the centroid parameter is as follows: E=ρVτgλ Where E is the gravitational potential energy of the energy storage unit, ρ is the density of the weight, V is the total volume of the weight, λ is the centroid path, τ is the density coefficient of the weight on the stacking platform, and g is the gravitational acceleration constant.

6. The potential assessment method for a wide-area gravity energy storage system according to claim 1, characterized in that, The LES mapping structure satisfies the following conditions: Each mapping unit consists of a rectangular grid with an area that increases with the level; all levels are aligned with the same spatial index; adjacent levels maintain a geometric connection with an average slope greater than or equal to the minimum running slope; the geometric center of each height level slice is on the same straight line as the center of the lowest level candidate unit; the collinear axis is perpendicular to the plane where all slices are located, forming a continuous stack of inverted cones in three-dimensional space.

7. The potential assessment method for a wide-area gravity energy storage system according to claim 6, characterized in that, The formation process of the LES mapping structure includes: The geometric center point of each slice is expanded into a surface region basic unit of equal area; the surface region unit covers the entire slice layer range, carrying multi-dimensional information such as elevation, area and spatial coordinates; the surface region unit replaces the point-level unit to perform potential calculation and structural mapping, improving the efficiency of large-scale calculation and the integrity of spatial coverage.

8. The method for assessing the potential of a wide-area gravity energy storage system according to claim 6 or 7, characterized in that, This also includes centroid path modeling, specifically: Within a multi-layer LES mapping structure, combinations of flat land slices that satisfy geometric constraints are identified; the geometric centers of each combined slice are used as nodes and connected vertically to construct gravitational potential energy conversion paths; based on the path elevation difference and the distance between nodes, combined with the mass parameter of the heavy object, the gravitational potential energy capacity corresponding to each path is calculated.

9. The potential assessment method for a wide-area gravity energy storage system according to claim 1, characterized in that, The formula for quantitatively assessing the carbon emissions of wide-area gravity energy storage systems is as follows: Among them, R carbon It is the carbon emission reduction of the energy storage system; E generation (t) represents the total power generation of the energy storage system in year t; F fossil (t) is the carbon emission factor of thermal power in year t.

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