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

Abstract

The invention discloses a clean energy partition electric power interaction optimization method and system based on a load level, and relates to the technical field of electric power system optimization, and the method comprises the steps: collecting data of clean energy partitions, carrying out the cleaning and preprocessing of the data, calculating a dynamic load elasticity coefficient, predicting the change trend of load elasticity through a model, and carrying out the optimization of the electric power system. The method comprises the following steps: dynamically adjusting the boundary and scale of a clean energy partition, carrying out data and information circulation, evaluating the power generation potential of clean energy, determining an optimization target, formulating an optimization strategy, monitoring clean energy power data in real time, evaluating an optimization effect, and establishing an information sharing platform for power interaction. Through real-time monitoring and big data analysis and establishment of an information sharing platform, the operation efficiency and decision support capability of the power market are improved, and the management level and interaction capability of the clean energy power system are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization, and in particular to a clean energy partition power interactive optimization method and system based on load levels. Background Art

[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 certain progress in clean energy zoning management, load elasticity analysis, and power interaction optimization. By introducing technologies such as big data analysis and machine learning, the operating efficiency and stability of clean energy power systems have been improved.

[0003] However, the existing relevant technologies still have many shortcomings in practical applications; first, in terms of data collection and preprocessing, the existing technologies often ignore 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, affecting the accuracy of subsequent analysis; secondly, in terms of the calculation and prediction of load elasticity coefficients, the existing technologies lack real-time response to dynamic load changes and cannot effectively predict the changing trend of load elasticity, making it difficult to achieve dynamic adjustment of clean energy zoning boundaries; in addition, the existing technologies often lack pertinence and real-time nature in the evaluation of clean energy power generation potential and optimization effect monitoring, 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 above-mentioned existing problems, the present invention provides a clean energy zoning power interaction optimization method and system based on load level, so as to solve the problem that the monitoring in the prior art lacks pertinence and real-time performance, and it is difficult to ensure the maximum utilization of clean energy, power reliability and minimization of greenhouse gas emissions.

[0005] In order to solve the above technical problems, a clean energy partition power interactive optimization method based on load level is proposed, including:

[0006] Collect data on clean energy zones, clean and preprocess the data, and calculate the dynamic load elasticity coefficient; use the model to predict the changing trend of load elasticity, dynamically adjust the boundaries and scale of clean energy zones, and conduct data and information circulation; evaluate 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.

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

[0008] The data noise reduction 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 a background value.

[0009] As a preferred solution of the clean energy zoned power interactive optimization method based on load level described in the present invention, wherein: the calculation of the dynamic load elasticity coefficient includes calculating the dynamic load elasticity coefficient using the intelligent load elasticity analysis engine on the preprocessed data.

[0010] As a preferred solution of the clean energy zoning power interaction optimization method based on load level described in the present invention, the predicted changing trend of load elasticity includes: the pre-processed data input is combined with the LSTM model of load change rate and clean energy supply change rate to predict the changing trend of load elasticity, and dynamically adjust the boundaries and scales of clean energy zoning.

[0011] The LSTM model prediction formula is:

[0012]

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

[0014] As a preferred solution of the clean energy partition power interactive optimization method based on load level described in the present invention, wherein: the data and information flow includes setting a load elasticity coefficient threshold, when E new When the threshold is exceeded, the zone boundary adjustment is triggered, and the boundaries and scale of the clean energy zone are adjusted according to the short-term and long-term optimization models to facilitate data and information flow.

[0015] The short-term optimization model includes the use of a fast response mechanism when it comes to immediate load balancing. new When the threshold is exceeded, the partition boundary is adjusted immediately. The adjustment formula is expressed as:

[0016]

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

[0018] The long-term optimization model includes a slow adjustment mechanism when seasonal changes and long-term trends are involved. The adjustment amount is calculated as:

[0019]

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

[0021] As a preferred scheme of the clean energy zoning power interaction optimization method based on load level described in the present 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.

[0023] The calculation of the power generation potential of clean energy includes evaluating the regional resource quantity through historical data and geographic information, combining resource evaluation and technical parameters, and calculating the power generation potential of solar photovoltaic, wind power and hydropower.

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

[0025] P pot =A s ×η s ×G×T×DF

[0026] Among them, P pot is the solar photovoltaic power generation, A is the area of ​​the photovoltaic panel, η s is the conversion efficiency of the photovoltaic panel, G is the peak sunshine hours, that is, the solar radiation energy received by a unit area in one 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 potis the wind power generation, ρ l is the air density, A w is the area swept by the wind turbine blades, v is the wind speed, P avail is 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 scheme of the clean energy zoning power interaction optimization method based on load level described in the present invention, wherein: the evaluation of the optimization effect includes determining the optimization target according to the clean energy power generation potential, and formulating a specific optimization strategy, real-time monitoring of clean energy power data, and using big data to analyze the clean energy power data, evaluate the clean energy power generation, cost savings and environmental benefits, and compare with the data before optimization, evaluate the optimization strategy effect, and establish an information sharing platform for power interaction.

[0031] Another object of the present invention is to provide a clean energy zoning power interactive optimization system based on load levels. The present invention establishes an information sharing platform, improves the operational efficiency and decision-making support capabilities of the power market, and comprehensively improves the management level of the clean energy power system; the system of the present invention uses an intelligent load elasticity analysis engine and LSTM model to calculate and predict the load elasticity coefficient in real time, providing a scientific basis for the dynamic adjustment of the boundaries of clean energy zoning, enhancing the power system's ability to respond to load changes, optimizing the boundaries and scale of clean energy zoning, achieving efficient flow of data and information, and ensuring the long-term stability of the power system.

[0032] As a preferred solution of the clean energy partition power interactive optimization system based on load level described in the present invention, it is characterized by comprising a data collection and preprocessing module, a load elasticity change trend prediction module, a power interactive optimization module and a power generation potential evaluation 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 clean energy zones, and perform data cleaning, noise reduction, outlier detection, missing value supplementation, formatting and standardization.

[0034] The load elasticity change trend prediction module is used to calculate the dynamic load elasticity coefficient using the preprocessed data and predict the load elasticity change trend through the LSTM model.

[0035] The electric power interactive optimization module is used to set the load elasticity coefficient threshold, adjust the boundaries and scales of clean energy partitions according to short-term and long-term optimization models, and realize data and information circulation.

[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 optimization effects, and conduct power interaction through an information sharing platform.

[0037] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein when the processor executes the computer program, the steps of the method described in the load level-based clean energy partition power interactive optimization are implemented.

[0038] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method described in the load level-based clean energy partition power interaction optimization are implemented.

[0039] Beneficial effects of the present invention: The present invention predicts the changing trend of load elasticity by using a model, and dynamically adjusts the partition boundaries and scale, so as to achieve real-time response to load changes and flexible adjustment of the power system, improve the system's adaptability to load changes, and ensure the stability and reliability of power supply; by calculating the dynamic load elasticity coefficient, the sensitivity of the load to changes in clean energy supply is quantified, a scientific basis is provided for the dynamic adjustment of the partition boundaries, resource allocation is optimized, and operation efficiency is improved; by setting the load elasticity coefficient threshold, and adjusting the partition boundaries and scale according to the short-term and long-term optimization models, efficient flow of data and information is achieved, ensuring the rapid response of the power system to load changes, while maintaining the long-term stability and optimal operation state of the system; by evaluating the clean energy power generation potential and determining the optimization goals and strategies, the maximum utilization of clean energy and the environmental friendliness of the power system are achieved, the rational development and efficient utilization of clean energy are promoted, the environmental footprint is reduced, and the economy is improved; by real-time monitoring and analysis of clean energy power data, accurate evaluation and continuous improvement of optimization effects are achieved, the effectiveness of the optimization strategy is verified, and the scientific nature of decision-making is enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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 work, among which:

[0041] Figure 1 An overall flow chart of a clean energy zone power interactive optimization method based on load levels provided for one embodiment of the present invention.

[0042] Figure 2A system solution flow chart of a clean energy zoned power interactive optimization system based on load levels provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0044] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "one 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 term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0046] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0047] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0048] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0049] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a clean energy partition power interactive optimization method based on load level, comprising:

[0050] S1: Collect data of clean energy partitions, clean and preprocess the data, and calculate the dynamic load elasticity coefficient;

[0051] The data collection of clean energy zones includes collecting satellite remote sensing data, meteorological data, and power grid operation data, and cleaning the collected data, using BDFP technology to perform data noise reduction, outlier detection and missing value supplementation, and formatting and standardizing the data.

[0052] It should be noted that the data noise reduction 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 a background value.

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

[0054] It should be further explained that the formula for calculating the dynamic load elastic coefficient is:

[0055]

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

[0057] S2: Use the model to predict the changing trend of load elasticity, dynamically adjust the boundaries and scale of clean energy zones, and conduct data and information circulation.

[0058] Furthermore, the prediction of the changing trend of load elasticity includes: using the preprocessed data input combined with the LSTM model of the load change rate and the clean energy supply change rate to predict the changing trend of load elasticity, and dynamically adjusting the boundaries and scales of the clean energy partitions.

[0059] The LSTM model prediction formula is:

[0060]

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

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

[0063] The short-term optimization model includes the use of a fast response mechanism when it comes to immediate load balancing. new When the threshold is exceeded, the partition boundary is adjusted immediately. The adjustment formula is expressed as:

[0064]

[0065] Where, ΔB short is the short-term partition boundary adjustment, φ is the fast response coefficient, ΔL is the load change, and L is 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 as follows:

[0067]

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

[0069] S3: Evaluate the clean energy power generation potential, determine optimization goals and develop 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 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 clean energy power generation potential.

[0071] 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 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 conversion into electrical energy is approximately 20% to 30%.

[0072] The calculation of the power generation potential of clean energy includes evaluating the regional resource quantity through historical data and geographic information, combining resource evaluation and technical parameters, and calculating the power generation potential of solar photovoltaic, wind power and hydropower.

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

[0074] P pot =A s ×η s ×G×T×DF

[0075] Among them, P pot is the solar photovoltaic power generation, A is the area of ​​the photovoltaic panel, η s is the conversion efficiency of the photovoltaic panel, G is the peak sunshine hours, that is, the solar radiation energy received by a unit area in one 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 is the wind power generation, ρ l is the air density, A w is the area swept by the wind turbine blades, v is the wind speed, P avail is 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 hydroelectric power generation potential is:

[0080] P pot =ρ w ×g×H×Q×η w

[0081] Among them, P pot is the hydroelectric power generation, ρ w is the density of water, g is the acceleration due to gravity, H is the height difference of the water flow, Q is the water flow through the turbine, η w is the efficiency of the 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, real-time monitoring of clean energy power data, and using big data to analyze the clean energy power data, evaluating the clean energy power generation, cost savings and environmental benefits, and comparing 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 at its maximum utilization limit, 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 are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0085] Example 2, reference Figure 2 , which is the second embodiment of the present invention, and provides a clean energy partition power interactive optimization system based on load level, including a data collection and preprocessing module M101, a load elasticity change trend prediction module M201, a power interactive optimization module M301, and a power generation potential evaluation 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 clean energy zones, and perform data cleaning, noise reduction, outlier detection, missing value supplementation, formatting and standardization.

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

[0088] The electric power interactive optimization module M301 is used to set the load elasticity coefficient threshold, adjust the boundaries and scales of clean energy partitions according to short-term and long-term optimization models, and realize data and information circulation.

[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 optimization effects, and conduct power interaction through an 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 are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0091] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0092] If the functions are implemented in the form of 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 the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

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

[0094] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0095] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A clean energy partition power interactive optimization method based on load level, characterized by: include, Collect data on clean energy zones, clean and pre-process the data, and calculate dynamic load elasticity coefficients; Use the model to predict the changing trend of load elasticity, dynamically adjust the boundaries and scale of clean energy zones, and conduct data and information circulation; Assess the potential for clean energy power generation, determine optimization goals and develop optimization strategies, monitor clean energy power data in real time, evaluate the optimization effects, and establish an information sharing platform for power interaction.

2. The clean energy zone power interactive optimization method based on load level according to claim 1 is characterized by: The data collection of clean energy zones includes collecting satellite remote sensing data, meteorological data, and power grid operation data, and cleaning the collected data, using BDFP technology to perform data noise reduction, outlier detection and missing value supplementation, and formatting and standardizing the data; The data noise reduction 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 a background value.

3. The clean energy zone power interactive optimization method based on load level according to claim 2 is characterized by: Calculation of dynamic load elastic coefficient Including using the intelligent load elasticity analysis engine to calculate the dynamic load elasticity coefficient of the pre-processed data.

4. The clean energy zone power interactive optimization method based on load level according to claim 3 is characterized by: The predicting of the change trend of load elasticity includes: inputting the preprocessed data into an LSTM model combining the load change rate and the clean energy supply change rate to predict the change trend of load elasticity, and dynamically adjusting the boundaries and scales of the clean energy partitions; The LSTM model prediction formula is: Among them, E new is the predicted new load elasticity coefficient, E is the original load elasticity coefficient, γ is the weight coefficient of load change rate, Δα is the load change, α is the load, δ is the weight coefficient of clean energy supply change rate, Δβ is the clean energy supply change, and β is the clean energy supply.

5. The clean energy zone power interactive optimization method based on load level according to claim 4 is characterized by: The data and information flow includes setting a load elasticity coefficient threshold. new When the threshold is exceeded, the zone boundary adjustment is triggered, and the boundaries and scale of the clean energy zone are adjusted according to the short-term and long-term optimization models to conduct data and information circulation; The short-term optimization model includes the use of a fast response mechanism when it comes to immediate load balancing. new When the threshold is exceeded, the partition boundary is adjusted immediately. The adjustment formula is expressed as: Where, ΔB short is the short-term partition boundary adjustment, φ is the fast response coefficient, ΔL is the load change, and L is the load level; The long-term optimization model includes a slow adjustment mechanism when seasonal changes and long-term trends are involved. The adjustment amount is calculated as: Where, ΔB long is the long-term partition boundary adjustment, ψ is the long-term adjustment coefficient, t0 is the starting time, t f is the end time, E avg is the average load elastic coefficient, E new To predict new load elasticity coefficients; integrate short-term and long-term optimization models, adjust the boundaries and scale of clean energy zones, and conduct data and information circulation.

6. The clean energy zone power interactive optimization method based on load level according to claim 5 is characterized by: The assessment 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 clean energy power generation potential; The analysis of the efficiency of different clean energy technologies includes analyzing the conversion efficiency, load factor and system efficiency of different clean energy; The calculation of the power generation potential of clean energy includes evaluating the regional resource quantity through historical data and geographic information, and calculating the power generation potential of solar photovoltaic, wind power and hydropower in combination with resource assessment and technical parameters; The formula for calculating the solar photovoltaic power generation potential is: P pot =A s ×η s ×G×T×DF Among them, P pot is the solar photovoltaic power generation, A is the area of ​​the photovoltaic panel, η s is the conversion efficiency of the photovoltaic panel, G is the peak sunshine hours, that is, the solar radiation energy received by the unit area in one day, T is the number of days in the year to be calculated, and DF is the loss factor; The formula for calculating wind power generation potential is: Among them, E pot is the wind power generation, ρ l is the air density, A w is the area swept by the wind turbine blades, v is the wind speed, P avail is 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.

7. The clean energy zone power interactive optimization method based on load level according to claim 6 is characterized by: The evaluation of the optimization effect includes determining the optimization target according to the clean energy power generation potential, formulating specific optimization strategies, monitoring the clean energy power data in real time, and analyzing the clean energy power data using big data, evaluating the clean energy power generation, cost savings and environmental benefits, and comparing with the data before optimization, evaluating the optimization strategy effect, and establishing an information sharing platform for power interaction.

8. A system using the clean energy zoning power interactive optimization method based on load level as described in any one of claims 1 to 7, characterized in that: It includes data collection and preprocessing module, load elasticity change trend prediction module, power interaction optimization module and 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 clean energy zones, and perform data cleaning, noise reduction, outlier detection, missing value supplementation, formatting and standardization processing; The load elasticity change trend prediction module is used to calculate the dynamic load elasticity coefficient using the preprocessed data and predict the load elasticity change trend through the LSTM model; The power interactive optimization module is used to set the load elasticity coefficient threshold, adjust the boundaries and scale of the clean energy partition according to the short-term and long-term optimization models, and realize data and information circulation; 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 optimization effects, and conduct power interaction through an information sharing platform.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the clean energy partition power interactive optimization method based on load level described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the clean energy zone power interactive optimization method based on load levels described in any one of claims 1 to 7 are implemented.

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