Frame type gravity energy storage system power granularity refining method

Abstract

The invention discloses a frame type gravity energy storage system power granularity refining method, and belongs to the technical field of energy storage. The method comprises the following steps: collecting power load data in real time through a sensor and carrying out demand analysis; configuring gravity blocks with various mass specifications; constructing a configuration matrix representing the number of the gravity blocks of each floor; with energy output maximization or cost minimization as a target, solving an optimal configuration scheme by using an optimization algorithm under the condition of satisfying floor bearing constraints; and finally, based on real-time data and a working strategy, the release rates of the gravity blocks with different masses are controlled in a grading manner through a dynamic control mechanism, and the granularity refined output of the power is realized. Through collaborative optimization and control of multiple mass gravity blocks, the problems that a traditional gravity energy storage system is coarse in power regulation granularity and slow in response speed are solved, and the flexibility and precision of power grid regulation are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of new energy and energy storage and power system management, and particularly relates to a frame type gravity energy storage system power granularity refinement method. BACKGROUND

[0002] Under the background of current global energy transformation, more and more countries and regions have begun to promote the use of renewable energy on a large scale to address climate change and resource depletion. However, although traditional wind energy, solar energy and other renewable energy sources have the advantages of being clean and renewable, their intermittent and unstable output characteristics pose new challenges to the scheduling and management of power grids. In order to effectively cope with the imbalance between supply and demand, it is particularly important to develop efficient, safe and economical energy storage technology.

[0003] Among many energy storage technologies, gravity energy storage systems are attracting attention due to their relatively low cost, long-term reliability and environmentally friendly characteristics. As a mature and potential physical energy storage technology, gravity energy storage systems use gravitational potential energy for energy storage and release, gradually becoming one of the important solutions to the volatility of renewable energy. This technology stores energy by lifting heavy blocks (such as concrete blocks, water tanks, etc.), and releases energy during peak power demand or when renewable energy is insufficient, providing necessary adjustment support for power grids.

[0004] However, the current gravity energy storage system still has certain limitations in power regulation. Traditional systems usually rely on large block volumes or a single lifting / release mechanism, which results in a coarse granularity of power output, making it difficult to meet rapidly changing power demands. For example, in the case of rapid load fluctuations, the system may not be able to adjust the output power in real time, affecting the stability of the power grid and the quality of electrical energy. In addition, the existing gravity block configuration is often not optimally designed, affecting the overall efficiency of the system, and even causing material waste and improper use of space.

[0005] To solve the above problems, we propose a frame type gravity energy storage system power granularity refinement method, aiming to improve the response speed and power regulation accuracy of the gravity energy storage system through reasonable design and innovative methods, to meet the growing demand for electricity and ensure the effective use of renewable energy. SUMMARY

[0006] The purpose of the present application is to provide a frame type gravity energy storage system power granularity refinement method. By analyzing the impact of different gravity block distributions on energy density and power regulation flexibility, the optimal design not only improves the efficiency of the energy storage system, but also enables more detailed power control under different working conditions.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: a method for refining the power granularity of a frame-type gravity energy storage system, comprising the following steps:

[0008] S1. Data Collection: Real-time collection of power load data and environmental data through sensors, including current sensors and meteorological sensors. The collected data includes load data such as instantaneous load and power consumption trends, as well as environmental data, to ensure that the system can respond to changes in power demand in a timely manner.

[0009] S2. Demand Analysis: Based on the data collected in step S1, conduct demand analysis to determine the required power output and storage capacity, and formulate the working strategy of the energy storage system.

[0010] S3, equipped with gravity blocks of various mass specifications;

[0011] S4. Construct a configuration matrix representing the spatial location and number of gravity blocks on each floor of the vertical frame;

[0012] S5. Apply optimization algorithms to maximize energy output or minimize cost. Under the conditions of satisfying the floor bearing capacity constraint and the non-negative constraint of the number of gravity blocks, iteratively calculate the configuration matrix to solve the optimal gravity block configuration scheme. The gravity blocks are arranged in a way that the lighter gravity blocks are placed on the lower floors and the heavier gravity blocks are placed on the higher floors.

[0013] S6. Based on real-time monitoring data and the working strategy of step S2, the release rate of gravity blocks of different masses is controlled in stages through a dynamic control mechanism to achieve refined power output.

[0014] The aforementioned method for refining the power granularity of a frame-type gravity energy storage system, wherein the gravity blocks of various mass specifications mentioned in step S3 include at least two different mass specifications, and the mass specifications are selected from at least two of 5t, 10t, and 20t.

[0015] The aforementioned method for refining the power granularity of a frame-type gravity energy storage system involves quickly selecting and combining gravity blocks of different masses for energy output based on real-time load requirements. The maximum energy that each gravity block can provide is calculated using the formula E = mgh, where E is energy, m is mass, g is gravitational acceleration, and h is height.

[0016] In the aforementioned method for refining the power granularity of a frame-type gravity energy storage system, the configuration matrix in step S4 is the gravity block quantity matrix X:

[0017] X = [x1 x2 x3 x4 ... x] i ]

[0018] Where, xi This represents the total number of gravity blocks configured in the i-th layer, which consists of gravity blocks of different mass specifications.

[0019] In the aforementioned method for refining the power granularity of a frame-type gravity energy storage system, the floor bearing capacity constraint in step S5 is as follows:

[0020]

[0021] Where Ci is the maximum load-bearing capacity of the i-th layer, and Wi is the total weight of the gravity blocks configured in the i-th layer;

[0022] The non-negative constraint on the number of gravity blocks is that the number of each type of gravity block configured in each layer cannot be negative.

[0023] The aforementioned method for refining the power granularity of a frame-type gravity energy storage system, wherein the optimization algorithm in step S5 is a linear programming algorithm or an integer programming algorithm.

[0024] The aforementioned method for refining the power granularity of a frame-type gravity energy storage system, wherein the dynamic control mechanism in step S6 includes:

[0025] The monitoring and feedback system monitors power load demand and gravity block status in real time.

[0026] The intelligent control algorithm automatically adjusts the position, quantity combination, and release rate of gravity blocks of different masses based on feedback information from the monitoring and feedback system.

[0027] The aforementioned method for refining the power granularity of a frame-type gravity energy storage system, specifically includes the demand analysis in step S2: using historical load data, establishing and training a short-term load forecasting model through time series analysis or machine learning methods to predict future electricity demand, and dividing a day into peak electricity consumption period, low electricity consumption period, and off-peak electricity consumption period.

[0028] In the aforementioned method for refining the power granularity of a frame-type gravity energy storage system, step S6 involves prioritizing the release of lighter gravity blocks located on lower floors during peak electricity consumption periods; and during off-peak electricity consumption periods, lifting heavier gravity blocks to higher floors to store energy.

[0029] The aforementioned method for refining the power granularity of a frame-type gravity energy storage system further includes a testing and verification step before step S5, including:

[0030] Build a simulation platform or a small-scale prototype system;

[0031] Test the response speed and power regulation accuracy under different gravity block distribution schemes;

[0032] Record system operation data and iteratively optimize configuration matrix X.

[0033] Compared with the prior art, the present invention has at least the following beneficial effects:

[0034] (1) Refined power granularity: This invention achieves very fine power output control by combining gravity blocks of different masses in different layers, which can better adapt to diverse power load requirements.

[0035] (2) High efficiency response: This invention improves the overall response speed of the system by using the fast response characteristics of the lighter gravity block in the lower layer design.

[0036] (3) Maximize energy utilization: The upper layer of this invention adopts the large-scale energy storage capacity of a heavier gravity block, which improves energy utilization efficiency.

[0037] (4) High degree of automation: The present invention adopts an intelligent control system to reduce manual intervention, reduce operational complexity and error rate.

[0038] (5) Safety and stability: This invention ensures the long-term safety and stability of the system. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating the present invention;

[0040] Figure 2 This is a schematic diagram of the overall system architecture of the present invention.

[0041] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Detailed Implementation

[0042] Embodiment 1 of the present invention: A method for refining the power granularity of a frame-type gravity energy storage system, comprising the following steps:

[0043] S1. Data Collection: During each stage of system operation, various high-precision sensors, such as current sensors and meteorological sensors, are used to collect relevant data in real time, including load data such as instantaneous load and power consumption trends, environmental data, etc., to ensure that the system can respond to changes in power demand in a timely manner.

[0044] S2. Demand Analysis: Based on the data acquisition results, demand analysis is conducted. Load data preprocessing is performed using Python, including missing value imputation and feature selection. The trend of power load change is assessed, and the required power output and storage capacity are determined. Historical data is analyzed using time series analysis or machine learning methods (random forest and long short-term memory network LSTM, etc.). A short-term load forecasting model is established and trained, its accuracy is verified, and parameters are tuned to predict the power demand and matching power generation capacity in the future. The demand analysis results are used to formulate the working strategy of the energy storage system and provide data support for the subsequent gravity block configuration design.

[0045] S3. Configure gravity blocks of various mass specifications: Design gravity blocks of various mass specifications such as 5t, 10t, and 20t, and flexibly combine them according to the actual needs obtained from the analysis.

[0046] S4. Construct a configuration matrix representing the spatial location and number of gravity blocks on each floor of the vertical frame: In the process of responding to the demand, a matrix algorithm is used to optimize the configuration of gravity blocks. A matrix containing the locations of all gravity blocks is constructed to represent the layout of the entire system. The advantages and disadvantages of different configurations are evaluated by defining objective functions such as energy output and uniform load distribution.

[0047] S5. Apply optimization algorithms to maximize energy output or minimize cost. Under the conditions of satisfying floor load-bearing capacity constraints and non-negative constraints on the number of gravity blocks, iteratively calculate the arrangement of gravity blocks using matrix operation techniques to find the optimal solution. For example, by swapping elements in the matrix, different combinations of gravity blocks can be configured to maximize the efficiency of energy storage and release. Furthermore, this matrix model should integrate various constraints and objective factors, and the design should be simulated using finite element analysis tools to ensure the safety and stability of the structure and analyze the mechanical performance under each configuration.

[0048] S6. Based on the real-time monitoring system, intelligent adjustment algorithm, and granular adjustment strategy of step S2, the power output is divided into multiple levels. By controlling the release rate of the gravity block in stages, the system can flexibly cope with instantaneous load fluctuations.

[0049] S7. System Testing and Iterative Optimization: A simulation platform for small-scale gravity block distribution was built using Python and Matlab. Comprehensive functional and performance tests were conducted, including response speed, power regulation accuracy, and overall energy efficiency. Test data was collected, and problems were identified and optimized accordingly, such as adjusting algorithm parameters or gravity block configuration, to ensure the long-term effectiveness and stability of the system.

[0050] Example 2: A method for refining the power granularity of a frame-type gravity energy storage system, comprising the following steps:

[0051] S1. Data Collection: Real-time collection of power load data and environmental data through sensors, including current sensors and meteorological sensors. The collected data includes load data such as instantaneous load and power consumption trends, as well as environmental data, to ensure that the system can respond to changes in power demand in a timely manner.

[0052] S2. Demand Analysis: Based on the data collected in step S1, demand analysis is conducted to determine the required power output and storage capacity, and to formulate the operating strategy of the energy storage system. This can begin by using Python to preprocess the load data collected in step S1, including missing value imputation and feature selection, to assess the trend of power load changes and determine the required power output and storage capacity. Then, time series analysis or machine learning methods (such as random forest and Long Short-Term Memory networks, LSTM, etc.) are used to analyze historical data, establish and train a short-term load forecasting model, verify its accuracy, and fine-tune the parameters to predict future power demand and matching generation capacity. For example, a day can be divided into peak, neutral, and off-peak periods. The results of the demand analysis are then used to formulate the operating strategy of the energy storage system, providing data support for subsequent gravity block configuration design.

[0053] S3. Configure gravity blocks with multiple mass specifications, including at least two different mass specifications, with the mass specifications selected from at least two of 5t, 10t, and 20t.

[0054] S4. Construct a configuration matrix representing the spatial location and number of gravity blocks on each floor of the vertical frame. The configuration is flexibly combined according to the actual needs obtained from the analysis. In the process of responding to the needs, a matrix algorithm is used to optimize the configuration of gravity blocks. Construct a matrix containing the locations of all gravity blocks to represent the layout of the entire system. The advantages and disadvantages of different configurations are evaluated by defining objective functions (such as energy output, uniform load distribution, etc.).

[0055] S5. Applying an optimization algorithm, with the objective of maximizing energy output or minimizing cost, and under the conditions of satisfying the floor load-bearing capacity constraint and the non-negativity constraint of the number of gravity blocks, iteratively calculate the configuration matrix to solve for the optimal gravity block configuration scheme. The gravity blocks are arranged in a manner where lighter gravity blocks are placed on lower floors and heavier gravity blocks are placed on higher floors. The optimization algorithm is a linear programming algorithm or an integer programming algorithm, wherein:

[0056] The floor load-bearing capacity constraint is:

[0057]

[0058] Ci is the maximum load-bearing capacity of the i-th layer, and Wi is the total weight of the gravity blocks configured in the i-th layer.

[0059] The non-negative constraint on the number of gravity blocks is: the number of each type of gravity block configured in each layer cannot be negative;

[0060] S6. Based on real-time monitoring data and the working strategy of step S2, the release rate of gravity blocks of different masses is controlled hierarchically through a dynamic control mechanism to achieve refined power output. The dynamic control mechanism includes:

[0061] The monitoring and feedback system monitors power load demand and gravity block status in real time.

[0062] The intelligent control algorithm automatically adjusts the position, quantity combination, and release rate of gravity blocks of different masses based on feedback information from the monitoring and feedback system.

[0063] Specifically, gravity blocks of different masses can be quickly selected and combined for energy output based on real-time load requirements, wherein:

[0064] The maximum energy that each gravity block can provide is calculated using the formula E = mgh, where E is energy, m is mass, g is gravitational acceleration, and h is height.

[0065] When selecting and combining gravity blocks of different masses for energy output, a combination model can be constructed, allowing users to quickly select and combine gravity blocks of different masses according to real-time load requirements. For example, if 30kW of power needs to be released at a certain moment, three 10t gravity blocks, or six 5t gravity blocks, or even a 20t block combined with two 5t blocks can be combined.

[0066] Specifically, the configuration matrix mentioned in step S4 is the gravity block quantity matrix X:

[0067] X = [x1 x2 x3 x4 ... x] i ]

[0068] Where, x i This represents the total number of gravity blocks configured in the i-th layer. This layer consists of gravity blocks of different mass specifications, for example, using gravity blocks with mass specifications of 5t, 10t, and 20t.

[0069]

[0070] Therefore, the power output matrix P that can be provided by gravity blocks of different masses is:

[0071]

[0072] Therefore, at a specific point in time, the total output power P needs to be... total This can be described by a system of linear equations:

[0073] a1x 5t +a2x 10t +a3x 20t =P total

[0074] Where a1, a2, and a3 are the power coefficients corresponding to gravity blocks of different masses (depending on height and gravitational acceleration, etc.).

[0075] Specifically, in step S6, during peak electricity consumption periods, lighter gravity blocks located on lower floors are released first; during off-peak electricity consumption periods, heavier gravity blocks are moved to higher floors to store energy.

[0076] Specifically, before step S5 is executed, a testing and verification process is also included, including: building a simulation platform or a small-scale prototype system; using simulation software to test the performance of various combination schemes under different working conditions to ensure that the required power output is achieved; testing the response speed and power adjustment accuracy under different gravity block distribution schemes; recording system operation data and iteratively optimizing the configuration matrix X; and establishing physical or mathematical models to simulate the impact of different floor distribution schemes on the overall performance and conducting mechanical analysis to ensure structural safety and stability.

[0077] Example 3: A method for refining the power granularity of a frame-type gravity energy storage system, comprising the following steps:

[0078] S1. Data Collection: Real-time collection of power load data and environmental data through sensors, including current sensors and meteorological sensors, to collect historical power load data, including daily, seasonal and special events (such as holidays).

[0079] S2. Demand Analysis: Based on the data collected in step S1, demand analysis is performed. Machine learning algorithms are used to process and analyze the data, extract electricity consumption patterns, and generate a load forecasting model. The predicted load demand is compared with the existing power supply to identify potential periods of supply and demand imbalance. Based on this, the day is divided (e.g., peak, flat, and off-peak). Each period has different power output requirements. Through sensor devices, the operating data of all energy storage facilities are collected in real time and sent to the central processing system through a data transmission network to ensure that the gravity energy storage system can respond to grid demand in a timely manner.

[0080] S3, equipped with gravity blocks of various mass specifications;

[0081] S4. Construct a configuration matrix representing the spatial location and number of gravity blocks on each floor of the vertical frame. Based on the real-time power load demand expected by the model at different time periods, use a matrix algorithm to optimize the configuration of gravity blocks, ultimately reflecting the layout of the entire floor.

[0082] S5. Apply optimization algorithms to maximize energy output or minimize cost. Under the conditions of satisfying the floor load-bearing capacity constraint and the non-negative constraint of the number of gravity blocks, continuously simulate the exchange of elements, i.e., change the combination to find the optimal solution. Use intelligent algorithms such as genetic algorithms or particle swarm optimization to calculate the optimal gravity block configuration matrix X. The gravity blocks are arranged in a way that the lighter gravity blocks are placed on the lower floors and the heavier gravity blocks are placed on the higher floors. Considering the constraints of operation portability, actual space limitations, floor load-bearing capacity, etc., and based on economy, timeliness, etc., the algorithm is continuously adjusted to obtain a better objective function. At the same time, the safe accessibility of gravity blocks is ensured during the optimization process to facilitate subsequent maintenance and adjustment.

[0083] S6. Based on real-time monitoring data and the working strategy of step S2, a dynamic control mechanism is used to hierarchically control the release rate of gravity blocks of different masses, achieving refined power output granularity. The system is designed with dynamic adjustment capabilities. Combined with real-time monitoring data, the system can continuously update and adjust the gravity block configuration matrix in response to changes in grid demand. That is, during peak electricity consumption periods, lower-level light gravity blocks are prioritized, while upper-level gravity blocks are used for energy storage during low load periods. Intelligent control algorithms are used to calculate the optimal gravity block combination under different load conditions in real time and automatically adjust it to improve the system's responsiveness. This matrix approach allows for flexible combination of gravity blocks at each level, thereby meeting instantaneous power load demands and improving the overall system efficiency.

[0084] S7. Testing and Iteration: Before application, build a simulation platform or a small-scale prototype system to conduct field tests, observe the performance under different gravity block distributions, record system operation data, compare the efficiency of different configurations, and further optimize the floor distribution design.

[0085] The working principle of one embodiment of the present invention is as follows: The working principle of the present invention is based on hierarchical gravitational potential energy regulation and dynamic matrix optimization technology, and achieves power granularity refinement through the following core mechanisms:

[0086] Data-driven load response mechanism: High-precision sensors collect grid load data and environmental parameters in real time and transmit them to the central processing system; the load prediction model based on LSTM neural network divides the electricity consumption cycle into peak, flat and low periods and generates power demand timing instructions.

[0087] Multi-mass gravity block collaborative layout mechanism: Configure three standard mass gravity blocks of 5t / 10t / 20t, distributed according to the principle of "lighter at the bottom and heavier at the top": 5t light blocks are deployed on lower floors, using their low inertia characteristics to achieve millisecond-level response and meet the needs of instantaneous power fluctuations; 10t / 20t heavy blocks are deployed on higher floors, storing basic load energy through high potential energy to support continuous high power output.

[0088] Matrix-based optimization configuration mechanism: Construct a gravity block quantity matrix function, use the maximum floor bearing capacity and non-negative constraints as boundary conditions, and use the particle swarm algorithm to solve the objective function to finally output the optimal spatial distribution scheme.

[0089] Staged power release mechanism: During peak periods, lower-level light blocks are released first, and the output power is precisely controlled by adjusting the descent speed. During off-peak periods, higher-level heavy blocks are used to store energy, and the energy loss is reduced by utilizing the high potential energy conversion efficiency.

[0090] Closed-loop testing and iteration mechanism: Build a Python / Matlab simulation platform, inject historical load data to test different distribution schemes, record indicators such as response speed and power tracking error, verify structural safety through finite element analysis, iteratively update the layout matrix, and achieve continuous system optimization.

[0091] This invention deeply integrates physical potential energy storage, mathematical optimization algorithms, and real-time control strategies. Through reasonable design and innovative methods, it improves the response speed and power regulation accuracy of gravity energy storage systems to meet the ever-increasing electricity demand and ensure the effective utilization of renewable energy. The successful application of this technology will lay a solid foundation for realizing a more efficient and sustainable energy management system.

Claims

1. A method for refining the power granularity of a frame-type gravity energy storage system, characterized in that, Includes the following steps: S1. Data Collection: Real-time collection of power load data and environmental data through sensors; S2. Demand Analysis: Based on the data collected in step S1, conduct demand analysis to determine the required power output and storage capacity, and formulate the working strategy of the energy storage system. S3, equipped with gravity blocks of various mass specifications; S4. Construct a configuration matrix representing the spatial location and number of gravity blocks on each floor of the vertical frame; S5. Apply optimization algorithms to maximize energy output or minimize cost. Under the conditions of satisfying the floor bearing capacity constraint and the non-negative constraint of the number of gravity blocks, iteratively calculate the configuration matrix to obtain the optimal gravity block configuration scheme. S6. Based on real-time monitoring data and the working strategy of step S2, the release rate of gravity blocks of different masses is controlled in stages through a dynamic control mechanism to achieve refined power output.

2. The method for refining the power granularity of a frame-type gravity energy storage system according to claim 1, characterized in that, The gravity blocks with multiple mass specifications mentioned in step S3 include at least two different mass specifications, and the mass specifications are selected from at least two of 5t, 10t, and 20t.

3. A method for refining the power granularity of a frame-type gravity energy storage system according to claim 1 or 2, characterized in that, Based on real-time load requirements, gravity blocks of different masses can be quickly selected and combined for energy output. The maximum energy that each gravity block can provide is calculated using the formula E = mgh, where E is energy, m is mass, g is gravitational acceleration, and h is height.

4. A method for refining the power granularity of a frame-type gravity energy storage system according to claim 1 or 2, characterized in that, The configuration matrix mentioned in step S4 is the gravity block quantity matrix X: X=[x1x2x3x4......x i ] Where, x i This represents the total number of gravity blocks configured in the i-th layer, which consists of gravity blocks of different mass specifications.

5. The method for refining the power granularity of a frame-type gravity energy storage system according to claim 4, characterized in that, The floor bearing capacity constraint mentioned in step S5 is: Among them, is C i The maximum load-bearing capacity of the i-th layer, W i It is the total weight of the gravity blocks configured in the i-th layer; The non-negative constraint on the number of gravity blocks is that the number of each type of gravity block configured in each layer cannot be negative.

6. The method for refining the power granularity of a frame-type gravity energy storage system according to claim 1, characterized in that, The optimization algorithm mentioned in step S5 is a linear programming algorithm or an integer programming algorithm.

7. The method for refining the power granularity of a frame-type gravity energy storage system according to claim 1, characterized in that, The dynamic control mechanism described in step S6 includes: The monitoring and feedback system monitors power load demand and gravity block status in real time. The intelligent control algorithm automatically adjusts the position, quantity combination, and release rate of gravity blocks of different masses based on feedback information from the monitoring and feedback system.

8. The method for refining the power granularity of a frame-type gravity energy storage system according to claim 1, characterized in that, The demand analysis described in step S2 specifically includes: using historical load data, establishing and training a short-term load forecasting model through time series analysis or machine learning methods to predict future electricity demand, and dividing a day into peak electricity consumption period, low electricity consumption period, and low electricity consumption period.

9. The method for refining the power granularity of a frame-type gravity energy storage system according to claim 1, characterized in that, In step S6, during peak electricity consumption periods, lighter gravity blocks located on lower floors are released first; during off-peak electricity consumption periods, heavier gravity blocks are moved to higher floors to store energy.

10. The method for refining the power granularity of a frame-type gravity energy storage system according to claim 1, characterized in that, The test and verification process also includes a step S5 before execution, including: Build a simulation platform or a small-scale prototype system; Test the response speed and power regulation accuracy under different gravity block distribution schemes; Record system operation data and iteratively optimize configuration matrix X.

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