Clean energy partition power interaction optimization method and system based on load level

Abstract

This invention discloses a method and system for optimizing clean energy zoned power interaction based on load levels, belonging to the field of power system optimization technology. The method includes collecting data from clean energy zones, cleaning and preprocessing the data, calculating dynamic load elasticity coefficients, using models to predict load elasticity trends, dynamically adjusting the boundaries and scale of clean energy zones, facilitating data and information flow, assessing the clean energy generation potential, determining optimization objectives and formulating optimization strategies, real-time monitoring of clean energy power data, evaluating optimization effects, and establishing an information sharing platform for power interaction. This invention, through real-time monitoring and big data analysis, as well as the establishment of an information sharing platform, improves the operational efficiency and decision support capabilities of the power market, and comprehensively enhances the management level and interaction capabilities of the clean energy power system.

Description

Technical Field

[0001] This invention relates to the field of power system optimization technology, specifically to a method and system for optimizing clean energy zoned power interaction based on load levels. Background Technology

[0002] With the transformation of the global energy structure and the widespread application of clean energy, the optimization and dispatch of clean energy power systems has become a research hotspot in the energy field. Traditional power system optimization methods mainly rely on static models and fixed parameters, which are difficult to adapt to the volatility and uncertainty of clean energy. In recent years, researchers have made some progress in areas such as clean energy zoning management, load resilience analysis, and power interaction optimization. By introducing technologies such as big data analysis and machine learning, they have improved the operating efficiency and stability of clean energy power systems.

[0003] However, existing technologies still have many shortcomings in practical applications. First, in terms of data collection and preprocessing, existing technologies often neglect the comprehensive application of multi-source data such as satellite remote sensing data and meteorological data, resulting in poor data cleaning and noise reduction effects, which affect the accuracy of subsequent analysis. Second, in terms of calculating and predicting load elasticity coefficients, existing technologies lack real-time response to dynamic load changes and cannot effectively predict the trend of load elasticity changes, thus making it difficult to achieve dynamic adjustment of clean energy zone boundaries. In addition, existing technologies often lack specificity and real-time performance in assessing the potential of clean energy power generation and monitoring the optimization effect, making it difficult to ensure the maximum utilization of clean energy, power reliability, and minimization of greenhouse gas emissions. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention provides a method and system for optimizing clean energy zoned power interaction based on load levels, in order to solve the problems of lack of specificity and real-time monitoring in the prior art, which makes it difficult to ensure the maximum utilization of clean energy, power reliability and the minimization of greenhouse gas emissions.

[0005] To address the aforementioned technical issues, a clean energy zoned power interaction optimization method based on load levels is proposed, including:

[0006] Collect data from clean energy zones, clean and preprocess the data, and calculate the dynamic load resilience coefficient; use models to predict the changing trend of load resilience, dynamically adjust the boundaries and scale of clean energy zones, and facilitate data and information flow; assess the power generation potential of clean energy, determine optimization objectives and formulate optimization strategies, monitor clean energy power data in real time, evaluate the optimization effect, and establish an information sharing platform for power interaction.

[0007] As a preferred embodiment of the clean energy zone power interaction optimization method based on load level described in this invention, the collection of clean energy zone data includes collecting satellite remote sensing data, meteorological data, and power grid operation data, cleaning the collected data, using BDFP technology for data noise reduction, outlier detection and missing value supplementation, and formatting and standardizing the data.

[0008] The data denoising includes using BDFP technology to smooth the data through a median filter and a Gaussian filter to eliminate noise interference, and setting a noise threshold to replace noise values ​​with background values.

[0009] As a preferred embodiment of the clean energy zoned power interaction optimization method based on load level described in this invention, the calculation of the dynamic load elasticity coefficient includes using a smart load elasticity analysis engine to calculate the dynamic load elasticity coefficient from the preprocessed data.

[0010] As a preferred embodiment of the clean energy zone power interaction optimization method based on load level described in this invention, the method for predicting the changing trend of load elasticity includes: using a preprocessed data input combined with an LSTM model of load change rate and clean energy supply change rate to predict the changing trend of load elasticity, and dynamically adjusting the boundary and scale of the clean energy zone.

[0011] The prediction formula of the LSTM model is:

[0012]

[0013] Among them, E new E is the original load elasticity coefficient, γ is the weighting coefficient of the load change rate, Δα is the load change amount, α is the load, δ is the weighting coefficient of the clean energy supply change rate, and Δβ is the clean energy supply change amount, β is the clean energy supply quantity.

[0014] As a preferred embodiment of the clean energy zoned power interaction optimization method based on load level described in this invention, the step of data and information flow includes setting a load resilience coefficient threshold, when E new When the threshold is exceeded, the partition boundary adjustment is triggered, and the boundary and scale of the clean energy partition are adjusted according to the short-term and long-term optimization models to facilitate the flow of data and information.

[0015] Short-term optimization models include employing a fast response mechanism when immediate load balancing is involved, when E new When the threshold is exceeded, the partition boundaries are adjusted immediately. The adjustment formula is as follows:

[0016]

[0017] Where, ΔB short φ represents the short-term zone boundary adjustment, φ represents the fast response coefficient, ΔL represents the load change, and L represents the load level.

[0018] The long-term optimization model includes a slow adjustment mechanism when seasonal variations and long-term trends are involved. The adjustment amount is calculated using the following formula:

[0019]

[0020] Where, ΔB long ψ is the long-term partition boundary adjustment amount, t0 is the long-term adjustment coefficient, and t is the start time. f E is the end time. avg E is the average load elasticity coefficient. new To predict the new load resilience coefficient; to integrate short-term and long-term optimization models, adjust the boundaries and scale of clean energy zones, and facilitate data and information flow.

[0021] As a preferred embodiment of the clean energy zoned power interaction optimization method based on load level described in this invention, the evaluation of clean energy power generation potential includes analyzing the efficiency, cost, and applicability of different clean energy technologies, determining the power generation of each technology, and calculating the power generation potential of clean energy.

[0022] The analysis of the efficiency of different clean energy technologies includes analyzing the conversion efficiency, load factor, and system efficiency of different clean energy sources.

[0023] The calculation of the power generation potential of clean energy includes assessing the amount of regional resources through historical data and geographic information, and combining resource assessment and technical parameters to calculate the power generation potential of solar photovoltaic, wind power and hydropower.

[0024] The formula for calculating the potential of solar photovoltaic power generation is:

[0025] P pot =A s ×η s ×G×T×DF

[0026] Among them, P pot Let A be the area of ​​the photovoltaic panel, and η be the solar photovoltaic power generation. s Where is the conversion efficiency of the photovoltaic panel, G is the peak sunshine hours, i.e. the solar radiation energy received per unit area per day, T is the number of days in the year to be calculated, and DF is the loss factor.

[0027] The formula for calculating wind power generation potential is:

[0028]

[0029] Among them, E potFor wind power generation, ρ l For air density, A w Let P be the area swept by the wind turbine blades, v be the wind speed, and P be the area swept by the wind turbine blades. avail CF represents the time proportion of wind resources, and CF is the capacity factor, which is the ratio of the actual operating time of the wind turbine to the theoretical operating time.

[0030] As a preferred embodiment of the clean energy zoned power interaction optimization method based on load level described in this invention, the evaluation of the optimization effect includes: determining the optimization target based on the clean energy power generation potential, formulating specific optimization strategies, monitoring clean energy power data in real time, analyzing the clean energy power data using big data, evaluating the power generation, cost savings, and environmental benefits of clean energy, comparing the data with the data before optimization, evaluating the effect of the optimization strategy, and establishing an information sharing platform for power interaction.

[0031] Another objective of this invention is to provide a clean energy zoned power interaction optimization system based on load levels. This invention establishes an information sharing platform, improves the operational efficiency and decision support capabilities of the power market, and comprehensively enhances the management level of the clean energy power system. The system of this invention calculates and predicts the load elasticity coefficient in real time through an intelligent load elasticity analysis engine and an LSTM model, providing a scientific basis for the dynamic adjustment of clean energy zone boundaries, enhancing the power system's responsiveness to load changes, optimizing the boundaries and scale of clean energy zones, realizing efficient data and information flow, and ensuring the long-term stability of the power system.

[0032] As a preferred embodiment of the clean energy zoned power interaction optimization system based on load level described in this invention, it is characterized by including a data collection and preprocessing module, a load elasticity change trend prediction module, a power interaction optimization module, and a power generation potential assessment and optimization module.

[0033] The data collection and preprocessing module is used to collect satellite remote sensing data, meteorological data and power grid operation data of the clean energy zone, and to perform data cleaning, noise reduction, outlier detection, missing value supplementation, formatting and standardization.

[0034] The module for predicting load elasticity change trends is used to calculate the dynamic load elasticity coefficient using preprocessed data and predict the change trend of load elasticity using an LSTM model.

[0035] The power interaction optimization module is used to set the load elasticity coefficient threshold, adjust the boundary and scale of clean energy zones according to short-term and long-term optimization models, and realize the flow of data and information.

[0036] The power generation potential assessment and optimization module is used to analyze the efficiency, cost and applicability of different clean energy technologies, calculate the power generation potential of clean energy, formulate optimization strategies, monitor clean energy power data in real time, evaluate the optimization effect, and conduct power interaction through an information sharing platform.

[0037] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of a method for optimizing clean energy zoned power interaction based on load levels.

[0038] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the method for optimizing clean energy zoned power interaction based on load levels.

[0039] The beneficial effects of this invention are as follows: By utilizing models to predict the changing trends of load elasticity and dynamically adjusting the zoning boundaries and scale, this invention achieves real-time response to load changes and flexible adjustment of the power system, improving the system's adaptability to load changes and ensuring the stability and reliability of power supply. By calculating the dynamic load elasticity coefficient, the sensitivity of load to changes in clean energy supply is quantified, providing a scientific basis for the dynamic adjustment of zoning boundaries, optimizing resource allocation, and improving operational efficiency. By setting a load elasticity coefficient threshold and adjusting zoning boundaries and scale according to short-term and long-term optimization models, efficient data and information flow is achieved, ensuring the power system's rapid response to load changes while maintaining long-term system stability and optimal operating conditions. By assessing the clean energy generation potential and determining optimization objectives and strategies, the invention maximizes the utilization of clean energy and enhances the environmental friendliness of the power system, promoting the rational development and efficient utilization of clean energy, reducing the environmental footprint, and improving economic efficiency. By real-time monitoring and analysis of clean energy power data, the invention achieves accurate evaluation and continuous improvement of optimization effects, verifies the effectiveness of optimization strategies, and enhances the scientific nature of decision-making. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0041] Figure 1 The overall flowchart of a clean energy zoned power interaction optimization method based on load level is provided in one embodiment of the present invention.

[0042] Figure 2A system scheme flowchart of a clean energy zoned power interaction optimization system based on load level provided in one embodiment of the present invention. Detailed Implementation

[0043] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0044] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive, either alone or selectively, with other embodiments.

[0046] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0047] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0048] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0049] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a clean energy zoned power interaction optimization method based on load levels, including:

[0050] S1: Collect data from clean energy zones, clean and preprocess the data, and calculate the dynamic load resilience coefficient;

[0051] The collection of clean energy zone data includes collecting satellite remote sensing data, meteorological data, and power grid operation data. The collected data is cleaned, and BDFP technology is used for data noise reduction, outlier detection, and missing value supplementation. The data is also formatted and standardized.

[0052] It should be noted that the data denoising includes using BDFP technology to smooth the data through a median filter and a Gaussian filter to eliminate noise interference, and setting a noise threshold to replace the noise value with the background value.

[0053] The calculation of the dynamic load elasticity coefficient includes using an intelligent load elasticity analysis engine to calculate the dynamic load elasticity coefficient from the preprocessed data.

[0054] It should also be noted that the formula for calculating the dynamic load elasticity coefficient is as follows:

[0055]

[0056] Where E is the load elasticity coefficient, ΔL is the load change, L is the load level, ΔP is the change in clean energy supply, and P is the clean energy supply level.

[0057] S2: Utilize models to predict the changing trends of load resilience, dynamically adjust the boundaries and scale of clean energy zones, and facilitate data and information flow.

[0058] Furthermore, the prediction of load resilience trends includes using a preprocessed data input combined with an LSTM model of load change rate and clean energy supply change rate to predict load resilience trends, and dynamically adjusting the boundaries and scale of clean energy zones.

[0059] The prediction formula of the LSTM model is:

[0060]

[0061] Among them, E new E is the original load elasticity coefficient, γ is the weighting coefficient of the load change rate, Δα is the load change amount, α is the load, δ is the weighting coefficient of the clean energy supply change rate, and Δβ is the clean energy supply change amount, β is the clean energy supply quantity.

[0062] Furthermore, the data and information flow includes setting a load resilience coefficient threshold, when E new When the threshold is exceeded, the partition boundary adjustment is triggered, and the boundary and scale of the clean energy partition are adjusted according to the short-term and long-term optimization models to facilitate the flow of data and information.

[0063] Short-term optimization models include employing a fast response mechanism when immediate load balancing is involved, when E new When the threshold is exceeded, the partition boundaries are adjusted immediately. The adjustment formula is as follows:

[0064]

[0065] Where, ΔB short φ represents the short-term zone boundary adjustment, φ represents the fast response coefficient, ΔL represents the load change, and L represents the load level.

[0066] It should be noted that the long-term optimization model includes a slow adjustment mechanism when seasonal changes and long-term trends are involved. The adjustment amount is calculated using the following formula:

[0067]

[0068] Where, ΔB long ψ is the long-term partition boundary adjustment amount, t0 is the long-term adjustment coefficient, and t is the start time. f E is the end time. avg E is the average load elasticity coefficient. new To predict the new load resilience coefficient; to integrate short-term and long-term optimization models, adjust the boundaries and scale of clean energy zones, and facilitate data and information flow.

[0069] S3: Assess the potential of clean energy power generation, determine optimization goals and formulate optimization strategies, monitor clean energy power data in real time, evaluate the optimization effect, and establish an information sharing platform for power interaction.

[0070] Furthermore, the assessment of clean energy power generation potential includes analyzing the efficiency, cost, and applicability of different clean energy technologies, determining the power generation capacity of each technology, and calculating the power generation potential of clean energy.

[0071] The analysis of the efficiency of different clean energy technologies includes the analysis of the conversion efficiency, load factor and system efficiency of different clean energy sources; the conversion efficiency of photovoltaic cells is usually between 15% and 22%, the efficiency of wind turbines is usually between 30% and 50%, the efficiency of water turbines is usually between 80% and 90%, and the overall efficiency of biomass to electricity conversion is about 20% to 30%.

[0072] The calculation of the power generation potential of clean energy includes assessing the amount of regional resources through historical data and geographic information, and combining resource assessment and technical parameters to calculate the power generation potential of solar photovoltaic, wind power and hydropower.

[0073] The formula for calculating the potential of solar photovoltaic power generation is:

[0074] P pot =A s ×η s ×G×T×DF

[0075] Among them, P pot Let A be the area of ​​the photovoltaic panel, and η be the solar photovoltaic power generation. s Where is the conversion efficiency of the photovoltaic panel, G is the peak sunshine hours, i.e. the solar radiation energy received per unit area per day, T is the number of days in the year to be calculated, and DF is the loss factor.

[0076] The formula for calculating wind power generation potential is:

[0077]

[0078] Among them, E pot For wind power generation, ρ l For air density, A w Let P be the area swept by the wind turbine blades, v be the wind speed, and P be the area swept by the wind turbine blades. avail CF represents the time proportion of wind resources, and CF is the capacity factor, which is the ratio of the actual operating time of the wind turbine to the theoretical operating time.

[0079] The formula for calculating hydropower potential is:

[0080] P pot =ρ w ×g×H×Q×η w

[0081] Among them, P pot For hydroelectric power generation, ρ w Let ρ be the density of water, g be the acceleration due to gravity, H be the height difference of the water flow, Q be the water flow rate through the turbine, and η be the velocity of water. w The efficiency of the water turbine.

[0082] It should also be noted that the evaluation of the optimization effect includes determining the optimization target based on the clean energy power generation potential, formulating specific optimization strategies, monitoring clean energy power data in real time, analyzing the clean energy power data using big data, evaluating the power generation, cost savings and environmental benefits of clean energy, comparing the data with the data before optimization, evaluating the effectiveness of the optimization strategy, and establishing an information sharing platform for power interaction.

[0083] The optimization objectives include ensuring that clean energy is used to its maximum extent, ensuring the reliability of electricity, and ensuring that greenhouse gas emissions are minimized.

[0084] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0085] Example 2, refer to Figure 2 This is the second embodiment of the present invention. This embodiment provides a clean energy zoned power interaction optimization system based on load level, including a data collection and preprocessing module M101, a load elasticity change trend prediction module M201, a power interaction optimization module M301, and a power generation potential assessment and optimization module M401.

[0086] The data collection and preprocessing module M101 is used to collect satellite remote sensing data, meteorological data and power grid operation data of the clean energy zone, and to perform data cleaning, noise reduction, outlier detection, missing value supplementation, formatting and standardization.

[0087] The module M201 for predicting load elasticity change trend is used to calculate the dynamic load elasticity coefficient using preprocessed data and predict the change trend of load elasticity using an LSTM model.

[0088] The power interaction optimization module M301 is used to set the load elasticity coefficient threshold, adjust the boundary and scale of the clean energy zone according to the short-term and long-term optimization models, and realize the flow of data and information.

[0089] The power generation potential assessment and optimization module M401 is used to analyze the efficiency, cost and applicability of different clean energy technologies, calculate the power generation potential of clean energy, formulate optimization strategies, monitor clean energy power data in real time, evaluate the optimization effect, and conduct power interaction through the information sharing platform.

[0090] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0091] Example 3, the third embodiment of the present invention, differs from the previous two embodiments in that:

[0092] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part 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 the present invention. 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.

[0093] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0094] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0095] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

Claims

1. A method for optimizing clean energy zonal power interaction based on load level, characterized in that: include, Collect data from clean energy zones, clean and preprocess the data, and calculate the dynamic load resilience coefficient; The model is used to predict the changing trend of load elasticity, dynamically adjust the boundaries and scale of clean energy zones, and facilitate the flow of data and information. Assess the potential of clean energy power generation, determine optimization targets and formulate optimization strategies, monitor clean energy power data in real time, evaluate the optimization effect, and establish an information sharing platform for power interaction. The collection of clean energy zone data includes collecting satellite remote sensing data, meteorological data, and power grid operation data. The collected data is cleaned, and BDFP technology is used for data noise reduction, outlier detection and missing value supplementation. The data is also formatted and standardized. The data denoising includes using BDFP technology to smooth the data through a median filter and a Gaussian filter to eliminate noise interference, and setting a noise threshold to replace noise values ​​with background values. The calculation of the dynamic load elasticity coefficient includes using a smart load elasticity analysis engine to calculate the dynamic load elasticity coefficient from the preprocessed data. The predicted trend of load resilience includes using a preprocessed data input combined with an LSTM model of load change rate and clean energy supply change rate to predict the trend of load resilience, and dynamically adjusting the boundaries and scale of clean energy zones. The formula for calculating the dynamic load elasticity coefficient is: Wherein, E is the load elasticity coefficient, is the load change, L is the load level, is the clean energy supply change, P is the clean energy supply level; The prediction formula of the LSTM model is: in, Here, E represents the predicted new load elasticity coefficient, and E represents the original load elasticity coefficient. The weighting coefficient for the load change rate. Load variation For load, The weighting coefficient for the rate of change in clean energy supply. For changes in clean energy supply, For clean energy supply; The process of data and information flow includes setting a load resilience coefficient threshold, when... When the threshold is exceeded, the partition boundary adjustment is triggered, and the boundary and scale of the clean energy partition are adjusted according to the short-term and long-term optimization models to facilitate data and information flow. Short term optimization model includes employing fast response mechanism when it comes to instant load balancing, when Adjusting partition boundaries immediately when threshold is crossed, adjustment formula is represented as: wherein, is a short-term partition boundary adjustment amount, is a fast response coefficient, is a load change amount, L is a load level; The long-term optimization model includes a slow adjustment mechanism when seasonal variations and long-term trends are involved. The adjustment amount is calculated using the following formula: in, This is the amount of long-term partition boundary adjustment. This is a long-term adjustment factor. The start time, End time, The average load elasticity coefficient, To predict the new load resilience coefficient; to integrate short-term and long-term optimization models, adjust the boundaries and scale of clean energy zones, and facilitate data and information flow; The assessment of clean energy power generation potential includes analyzing the efficiency, cost, and applicability of different clean energy technologies, determining the power generation capacity of each technology, and calculating the power generation potential of clean energy. The analysis of the efficiency of different clean energy technologies includes analyzing the conversion efficiency, load factor, and system efficiency of different clean energy sources; The calculation of the power generation potential of clean energy includes assessing the amount of regional resources through historical data and geographic information, and calculating the power generation potential of solar photovoltaic, wind power and hydropower by combining resource assessment and technical parameters. The formula for calculating the potential of solar photovoltaic power generation is: in, Let A represent the solar photovoltaic power generation, and A be the area of ​​the photovoltaic panel. For the conversion efficiency of photovoltaic panels, Peak sunshine hours, which is the solar radiation energy received per unit area per day. To calculate the number of days in a year, As the loss factor; The formula for calculating wind power generation potential is: in, For wind power generation, air density, Let v be the area swept by the wind turbine blades, and v be the wind speed. For the time proportion of wind resources, The capacity factor is the ratio of the actual operating time to the theoretical operating time of a wind turbine. The formula for calculating hydropower potential is: in, For hydroelectric power generation, The density of water, Let H be the acceleration due to gravity, H be the height difference of the water flow, and Q be the water flow rate through the turbine. The efficiency of the water turbine. 2.The method of claim 1, wherein: The evaluation of the optimization effect includes determining the optimization target based on the clean energy power generation potential, formulating specific optimization strategies, monitoring clean energy power data in real time, analyzing the clean energy power data using big data, evaluating the power generation, cost savings and environmental benefits of clean energy, comparing the data with the data before optimization, evaluating the effectiveness of the optimization strategy, and establishing an information sharing platform for power interaction.

3. A system employing the load level based clean energy zoned power interaction optimization method according to any one of claims 1-2, characterized in that: It includes a data collection and preprocessing module, a load elasticity trend prediction module, a power interaction optimization module, and a power generation potential assessment and optimization module; The data collection and preprocessing module is used to collect satellite remote sensing data, meteorological data and power grid operation data of the clean energy zone, and to perform data cleaning, noise reduction, outlier detection, missing value supplementation, formatting and standardization. The module for predicting the trend of load elasticity is used to calculate the dynamic load elasticity coefficient using preprocessed data and to predict the trend of load elasticity using an LSTM model. The power interaction optimization module is used to set the load elasticity coefficient threshold, adjust the boundary and scale of the clean energy zone according to the short-term and long-term optimization models, and realize the flow of data and information. The power generation potential assessment and optimization module is used to analyze the efficiency, cost and applicability of different clean energy technologies, calculate the power generation potential of clean energy, formulate optimization strategies, monitor clean energy power data in real time, evaluate the optimization effect, and conduct power interaction through an information sharing platform.

4. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the clean energy zoned power interaction optimization method based on load level as described in any one of claims 1 to 2.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the clean energy zoned power interaction optimization method based on load level as described in any one of claims 1 to 2.

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