Clean energy consumption capability assessment method and system oriented to cooperation of sending end and receiving end

Abstract

The invention discloses a clean energy consumption capability assessment method and system oriented to sending and receiving end collaboration, and relates to the technical field of consumption capability assessment, and the method comprises the steps: collecting sending and receiving end data in real time, building a clean energy big data platform of sending and receiving end collaboration through the collected data, and carrying out the preprocessing of the collected data, performing feature extraction on the preprocessed data, analyzing the power generation capacity of the transmitting-end clean energy, learning the receiving-end data to predict the load demand, constructing a transmitting-end and receiving-end coordinated clean energy consumption capacity evaluation model by using the power generation capacity of the transmitting-end clean energy and a receiving-end load prediction result, and optimizing model parameters to evaluate the consumption capacity of the transmitting-end and receiving-end coordinated clean energy. And dynamically evaluating the cooperative clean energy consumption capability of the sending end and the receiving end by using the optimized evaluation model, and matching a clean energy scheduling strategy according to an evaluation result. According to the invention, the operation efficiency and economy of a power system are improved, the efficient utilization of clean energy is ensured, and a solid technical support is provided for constructing a sustainable energy system.

Description

Technical Field

[0001] The present invention relates to the technical field of consumption capacity assessment, and in particular to a method and system for assessing clean energy consumption capacity in coordination with transmitters and receivers. Background Art

[0002] With the transformation of the global energy structure and the increasing attention paid to environmental protection, the development and utilization of clean energy has become an important part of the energy strategy of various countries. In recent years, the grid-connected power generation technology of renewable energy such as wind power and solar energy has developed rapidly, but it has also brought uncertainty and complexity to the operation of the power system. In order to improve the clean energy absorption capacity and ensure the stable operation of the power system, the research in related technical fields mainly focuses on data-driven clean energy absorption capacity assessment and power system optimization and dispatching.

[0003] However, the existing technology has certain shortcomings; first, the traditional clean energy absorption capacity assessment method often relies on static models, which cannot reflect the volatility of clean energy and changes in load demand in real time, resulting in low accuracy of the assessment results; second, the existing technology fails to fully consider the diversity and complexity of the data at the sending and receiving ends during data preprocessing and feature extraction, thereby affecting the accuracy of the subsequent assessment model; in addition, the current clean energy scheduling strategy lacks flexibility and adaptability, and it is difficult to dynamically adjust according to changes in real-time absorption capacity, which to a certain extent limits the efficient use of clean energy and the optimized operation of the power system. The clean energy absorption capacity assessment method for the coordination of the sending and receiving ends proposed in the present invention is expected to achieve significant technical progress and beneficial effects in the above aspects through real-time data collection, preprocessing, feature extraction and the construction of a dynamic assessment model. Summary of the invention

[0004] In view of the above-mentioned existing problems, the present invention provides a clean energy consumption capacity assessment method and system for collaboration between sending and receiving ends, so as to solve the problems in the prior art of low accuracy of assessment results, low precision of assessment models, and restrictions on efficient utilization of clean energy and optimized operation of power systems.

[0005] In order to solve the above technical problems, a clean energy consumption capacity evaluation method for the collaboration between the transmitter and the receiver is proposed, including:

[0006] Collect data from the sending and receiving ends in real time, use the collected data to build a clean energy big data platform for the coordination between the sending and receiving ends, and pre-process the collected data; extract features from the pre-processed data, analyze the clean energy power generation capacity of the sending end, and learn the receiving end data to predict load demand; use the sending end clean energy power generation capacity and the receiving end load prediction results to build a clean energy consumption capacity assessment model for the coordination between the sending and receiving ends, and optimize the model parameters; use the optimized assessment model to dynamically assess the clean energy consumption capacity for the coordination between the sending and receiving ends, and match the clean energy scheduling strategy according to the assessment results.

[0007] As a preferred solution of the clean energy consumption capacity assessment method for collaboration between senders and receivers described in the present invention, the construction of a clean energy big data platform for collaboration between senders and receivers includes real-time collection of sender data and receiver data, designing the platform's data layer, service layer and application layer, and designing the data processing flow.

[0008] The sending-end data includes environmental data, power generation equipment status data, energy storage status data and power grid interaction data; the receiving-end data includes power consumption data, load characteristic data, power grid operation data, user behavior data and power quality data.

[0009] The environmental data includes wind speed, light intensity, temperature, humidity and air pressure; the power generation equipment status data includes power generation, power generation efficiency, equipment speed, equipment temperature and fault code; the energy storage status data includes energy storage capacity, charging and discharging status, energy storage efficiency and energy storage equipment temperature; the grid interaction data includes grid-connected voltage, grid-connected current, frequency and power factor.

[0010] The power consumption data includes real-time power consumption, cumulative power consumption and peak power consumption; the load characteristic data includes load type, load curve and load forecast; the power grid operation data includes substation load, distribution network voltage, distribution network current and line loss; the user behavior data includes power consumption habits, peak and valley power consumption and demand response; the power quality data includes voltage fluctuations, current harmonics and power supply reliability.

[0011] As a preferred solution of the clean energy consumption capacity assessment method for collaboration between senders and receivers described in the present invention, the preprocessing includes data cleaning, data standardization, and data fusion of the collected sender and receiver data, and feature extraction of the preprocessed data.

[0012] The data cleaning includes using statistical methods to detect outliers and noise in the data, and interpolating and deleting missing data; the data fusion includes using timestamp matching to align time series and using identifiers to associate data.

[0013] As a preferred solution of the clean energy consumption capacity assessment method for collaboration between sending and receiving ends described in the present invention, the feature extraction includes extracting the time domain characteristics and frequency domain characteristics of the preprocessed data to analyze the power generation capacity of the clean energy at the sending end.

[0014] The formula for extracting time domain features is:

[0015]

[0016] Among them, σ is the variance, Y i is the data point, is the average value of the data points, and m is the number of data points.

[0017] The formula for extracting frequency domain features is:

[0018]

[0019] Among them, X(f) is the frequency domain representation after Fourier transform, x(t) is the original time domain signal, f is the frequency, j is the imaginary unit, and t is the time.

[0020] The analysis of the power generation capacity of the clean energy at the sending end includes constructing a graph neural network model, initializing model parameters using a graph attention network, evaluating the difference between the model prediction and the actual power generation capacity using a cross entropy loss, analyzing the power generation capacity of the clean energy at the sending end, and predicting load demand.

[0021] The initialization model parameters include the node feature matrix: Where N is the number of nodes, F

[0022] is the feature dimension, the weight matrix Perform node feature conversion, F is the feature dimension after conversion, and bias

[0023] The cross entropy loss formula is:

[0024]

[0025] Among them, y is the true label, is the probability distribution predicted by the model, N is the number of nodes, is the loss function.

[0026] As a preferred scheme of the clean energy consumption capacity assessment method for collaboration between sending and receiving ends described in the present invention, wherein: the construction of the clean energy consumption capacity assessment model for collaboration between sending and receiving ends includes utilizing the prediction results and feature extraction of the clean energy power generation capacity at the sending end and the load at the receiving end, selecting a time series model, and setting the model structure.

[0027] The time series model includes combining the prediction models of the sending end and the receiving end, determining the model parameters and order, selecting the optimal model order using the information criterion, and performing parameter significance test; the model structure includes a predicted power generation capacity layer and a predicted load demand layer.

[0028] The optimization model parameter formula is:

[0029]

[0030] Among them, θ is the gradient, α is the learning rate, is the gradient of the loss function with respect to the parameter θ.

[0031] As a preferred scheme of the clean energy consumption capacity assessment method for collaboration between sending and receiving ends described in the present invention, the dynamic assessment of the clean energy consumption capacity for collaboration between sending and receiving ends includes dynamically assessing the clean energy consumption capacity for collaboration between sending and receiving ends by outputting the clean energy predicted power generation and the predicted power load demand through the assessment model for clean energy consumption capacity for collaboration between sending and receiving ends.

[0032] The clean energy predicted power generation formula is:

[0033] A t =f(B t ,B t-1 ,...,B t-P )+ε t

[0034] Among them, A t is the predicted clean energy power generation at time t, B t is the clean energy feature vector input at time t, p is the impact value of clean energy data on prediction, ε t is the error term.

[0035] The power load forecast demand formula is:

[0036]

[0037] Among them, E t is the predicted power load demand at time t, H t is the load characteristic vector input at time t, q is the impact value of load data on the prediction, is the error term.

[0038] The formula for calculating the absorption capacity is:

[0039] ΔS t =A t -E t

[0040] Among them, A tis the predicted clean energy power generation at time t, E t is the predicted power load demand at time t, ΔS t is the clean energy absorption capacity at time t.

[0041] As a preferred solution of the clean energy absorption capacity assessment method for collaboration between transmitters and receivers described in the present invention, the matching of clean energy scheduling strategies includes setting a clean energy absorption capacity threshold and matching clean energy scheduling strategies according to different absorption capacity intervals.

[0042] When ΔS t When it is greater than or equal to the set threshold, it is necessary to increase the clean energy generation, start the backup energy, and adjust the user's electricity consumption behavior by adjusting the electricity price to reduce the load during peak hours; when ΔS t When it is less than the set threshold, the clean energy generation is reduced, the use of energy storage systems is increased, and energy is stored when there is excess generation, energy is released when demand peaks, and the operation mode of the power grid is optimized.

[0043] Another object of the present invention is to provide a clean energy absorption capacity assessment system for collaboration between sending and receiving ends. The present invention optimizes the operating efficiency of the power system and improves the absorption capacity of clean energy. The system of the present invention promotes the optimized operation of the power system through real-time data collection, preprocessing, feature extraction and construction of a dynamic evaluation model, improves the absorption capacity of clean energy, reduces energy waste, enhances the reliability and economy of the power system, and provides strong support for building a cleaner, more efficient and intelligent power system.

[0044] As a preferred solution of the clean energy consumption capacity assessment system for collaboration between transmitters and receivers described in the present invention, it is characterized by including a data acquisition and preprocessing module, a feature extraction and analysis module, a consumption capacity dynamic assessment module and a clean energy scheduling strategy matching module.

[0045] The data acquisition and preprocessing module is used to collect data from the sending and receiving ends in real time through sensors and smart meters, and to clean, standardize and fuse the collected data, laying the foundation for subsequent analysis.

[0046] The feature extraction and analysis module is used to extract time domain features and frequency domain features from the preprocessed data, and use the extracted features to construct a graph neural network model to evaluate the power generation capacity of clean energy at the sending end, analyze the receiving end data, predict the power load demand, and provide a basis for the evaluation of the absorption capacity.

[0047] The dynamic evaluation module for absorption capacity is used to combine the clean energy power generation capacity at the sending end and the load forecast results at the receiving end to construct an evaluation model, adjust the model parameters through a gradient descent optimization algorithm, and use the optimized evaluation model to output the predicted power generation of clean energy and the predicted demand for power load in real time, and calculate the absorption capacity of clean energy by comparing the predicted clean energy power generation and power load demand.

[0048] The clean energy scheduling strategy matching module is used to set a threshold of clean energy consumption capacity according to the consumption capacity evaluation result, and match the corresponding clean energy scheduling strategy according to the comparison result between the consumption capacity and the threshold.

[0049] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of a method described in a method for evaluating clean energy consumption capacity for collaboration between senders and receivers are implemented.

[0050] 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 a method described in a method for evaluating clean energy consumption capacity for collaboration between senders and receivers are implemented.

[0051] Beneficial effects of the invention: The invention forms a closed-loop clean energy absorption capacity assessment and dispatching system through real-time data collection, feature extraction, dynamic assessment model construction and optimization, and intelligent dispatching strategy matching, which effectively improves the operating efficiency and economy of the power system, ensures the efficient use of clean energy and the stable operation of the power system, and provides solid technical support for building a sustainable energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] 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:

[0053] Figure 1 An overall flow chart of a method for evaluating clean energy consumption capacity for collaboration between transmitters and receivers provided for one embodiment of the present invention.

[0054] Figure 2 A system solution flow chart of a clean energy consumption capacity assessment system for collaboration between senders and receivers provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a clean energy consumption capacity evaluation method for collaboration between transmitters and receivers, including:

[0062] S1: Collect data from the sender and receiver in real time, use the collected data to build a clean energy big data platform that collaborates with the sender and receiver, and pre-process the collected data.

[0063] The construction of a clean energy big data platform for collaboration between senders and receivers includes real-time collection of sender data and receiver data, designing the platform's data layer, service layer and application layer, and designing a data processing flow.

[0064] The sending-end data includes environmental data, power generation equipment status data, energy storage status data and power grid interaction data; the receiving-end data includes power consumption data, load characteristic data, power grid operation data, user behavior data and power quality data.

[0065] The environmental data includes wind speed, light intensity, temperature, humidity and air pressure; the power generation equipment status data includes power generation, power generation efficiency, equipment speed, equipment temperature and fault code; the energy storage status data includes energy storage capacity, charging and discharging status, energy storage efficiency and energy storage equipment temperature; the grid interaction data includes grid-connected voltage, grid-connected current, frequency and power factor.

[0066] The power consumption data includes real-time power consumption, cumulative power consumption and peak power consumption; the load characteristic data includes load type, load curve and load forecast; the power grid operation data includes substation load, distribution network voltage, distribution network current and line loss; the user behavior data includes power consumption habits, peak and valley power consumption and demand response; the power quality data includes voltage fluctuations, current harmonics and power supply reliability.

[0067] It should be noted that the preprocessing includes data cleaning, data standardization, and data fusion of the collected data from the sender and receiver, and feature extraction of the preprocessed data.

[0068] The data cleaning includes using statistical methods to detect outliers and noise in the data, and interpolating and deleting missing data; the data fusion includes using timestamp matching to align time series and using identifiers to associate data.

[0069] It should be further explained that the statistical method formula is:

[0070]

[0071] Where X is the original value, μ is the mean, τ is the standard deviation, and Z is the Z score.

[0072] The interpolation formula is:

[0073]

[0074] in, is the interpolation value for missing values, X i is the non-missing observation, n is the number of non-missing observations, and i is the variable index.

[0075] The standardized formula is:

[0076]

[0077] Among them, X norm is the standardized value, X is the original value, and X min is the minimum value, X max is the maximum value.

[0078] S2: Extract features from the preprocessed data, analyze the power generation capacity of clean energy at the sending end, and learn and predict load demand from the receiving end data.

[0079] Furthermore, the feature extraction includes extracting time domain features and frequency domain features of the preprocessed data to analyze the power generation capacity of the clean energy at the sending end.

[0080] The formula for extracting time domain features is:

[0081]

[0082] Among them, σ is the variance, Y i is the data point, is the average value of the data points, and m is the number of data points.

[0083] The formula for extracting frequency domain features is:

[0084]

[0085] Among them, X(f) is the frequency domain representation after Fourier transform, x(t) is the original time domain signal, f is the frequency, j is the imaginary unit, and t is the time.

[0086] Furthermore, the analysis of the power generation capacity of the clean energy at the sending end includes constructing a graph neural network model, initializing the model parameters using a graph attention network, evaluating the difference between the model prediction and the actual power generation capacity using a cross entropy loss, analyzing the power generation capacity of the clean energy at the sending end, and predicting the load demand.

[0087] The initialization model parameters include the node feature matrix: Where N is the number of nodes, F

[0088] is the feature dimension, the weight matrix Perform node feature conversion, F is the feature dimension after conversion, and bias

[0089] The cross entropy loss formula is:

[0090]

[0091] Among them, y is the true label, is the probability distribution predicted by the model, N is the number of nodes, is the loss function.

[0092] S3: Using the clean energy power generation capacity at the sending end and the load forecast results at the receiving end, a clean energy consumption capacity assessment model for the coordinated sending and receiving ends is constructed, and the model parameters are optimized.

[0093] Furthermore, the construction of a clean energy consumption capacity assessment model for coordination between the sending and receiving ends includes utilizing the clean energy generation capacity at the sending end and the load forecast results and extracting features at the receiving end, selecting a time series model, and setting a model structure.

[0094] The time series model includes combining the prediction models of the sending end and the receiving end, determining the model parameters and order, selecting the optimal model order using the information criterion, and performing parameter significance test; the model structure includes a predicted power generation capacity layer and a predicted load demand layer.

[0095] Furthermore, the optimization model parameter formula is:

[0096]

[0097] Among them, θ is the gradient, α is the learning rate, is the gradient of the loss function with respect to the parameter θ.

[0098] S4: Use the optimized evaluation model to dynamically evaluate the clean energy absorption capacity of the sending and receiving ends, and match the clean energy scheduling strategy according to the evaluation results.

[0099] It should be noted that the dynamic evaluation of the clean energy consumption capacity of the sending and receiving ends includes dynamically evaluating the clean energy consumption capacity of the sending and receiving ends through the evaluation model of the clean energy consumption capacity of the sending and receiving ends, the output of the predicted clean energy power generation and the predicted power load demand.

[0100] The clean energy predicted power generation formula is:

[0101] A t =f(B t ,B t-1 ,...,B t-P )+ε t

[0102] Among them, A t is the predicted clean energy power generation at time t, B tis the clean energy feature vector input at time t, p is the impact value of clean energy data on prediction, ε t is the error term.

[0103] The power load forecast demand formula is:

[0104]

[0105] Among them, E t is the predicted power load demand at time t, H t is the load characteristic vector input at time t, q is the impact value of load data on the prediction, is the error term.

[0106] The formula for calculating the absorption capacity is:

[0107] ΔS t =A t -E t

[0108] Among them, A t is the predicted clean energy power generation at time t, E t is the predicted power load demand at time t, ΔS t is the clean energy absorption capacity at time t.

[0109] It should be further explained that the matching clean energy scheduling strategy includes setting a clean energy absorption capacity threshold and matching the clean energy scheduling strategy according to different absorption capacity intervals.

[0110] When ΔS t When it is greater than or equal to the set threshold, it is necessary to increase the clean energy generation, start the backup energy, and adjust the user's electricity consumption behavior by adjusting the electricity price to reduce the load during peak hours; when ΔS t When it is less than the set threshold, the clean energy generation is reduced, the use of energy storage systems is increased, and energy is stored when there is excess generation, energy is released when demand peaks, and the operation mode of the power grid is optimized.

[0111] 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.

[0112] Example 2, reference Figure 2, which is the second embodiment of the present invention, provides a clean energy consumption capacity assessment system for collaboration between transmitters and receivers, including a data acquisition and preprocessing module M100, a feature extraction and analysis module M200, a consumption capacity dynamic assessment module M300, and a clean energy scheduling strategy matching module M400.

[0113] The data acquisition and preprocessing module M100 is used to collect data from the sending and receiving ends in real time through sensors and smart meters, and to clean, standardize and fuse the collected data, laying the foundation for subsequent analysis.

[0114] The feature extraction and analysis module M200 is used to extract time domain features and frequency domain features from the preprocessed data, and use the extracted features to construct a graph neural network model to evaluate the power generation capacity of clean energy at the sending end, analyze the receiving end data, predict the power load demand, and provide a basis for the evaluation of the absorption capacity.

[0115] The dynamic evaluation module M300 for absorption capacity is used to construct an evaluation model by combining the clean energy power generation capacity at the sending end and the load forecast results at the receiving end, adjust the model parameters through a gradient descent optimization algorithm, and use the optimized evaluation model to output the predicted power generation of clean energy and the predicted demand for power load in real time, and calculate the absorption capacity of clean energy by comparing the predicted clean energy power generation and power load demand.

[0116] The clean energy scheduling strategy matching module M400 is used to set a threshold of clean energy consumption capacity according to the consumption capacity evaluation result, and match the corresponding clean energy scheduling strategy according to the comparison result between the consumption capacity and the threshold.

[0117] 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.

[0118] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 consumption capacity assessment method for collaboration between transmitters and receivers, characterized by: include, Collect data from the sender and receiver in real time, use the collected data to build a clean energy big data platform that collaborates with the sender and receiver, and pre-process the collected data; Extract features from preprocessed data, analyze the power generation capacity of clean energy at the sending end, and learn and predict load demand from the receiving end data; Using the clean energy generation capacity at the sending end and the load forecast results at the receiving end, a clean energy consumption capacity assessment model for the coordinated sending and receiving ends is constructed, and the model parameters are optimized; The optimized evaluation model is used to dynamically evaluate the clean energy consumption capacity of the sending and receiving ends, and the clean energy scheduling strategy is matched according to the evaluation results.

2. The clean energy consumption capacity assessment method for collaboration between transmitters and receivers as claimed in claim 1, characterized in that: The construction of the clean energy big data platform for collaboration between the sender and the receiver includes real-time collection of sender data and receiver data, designing the data layer, service layer and application layer of the platform, and designing the data processing flow; The sending-end data includes environmental data, power generation equipment status data, energy storage status data and power grid interaction data; the receiving-end data includes power consumption data, load characteristic data, power grid operation data, user behavior data and power quality data; The environmental data includes wind speed, light intensity, temperature, humidity and air pressure; the power generation equipment status data includes power generation, power generation efficiency, equipment speed, equipment temperature and fault code; the energy storage status data includes energy storage capacity, charge and discharge status, energy storage efficiency and energy storage equipment temperature; the grid interaction data includes grid voltage, grid current, frequency and power factor; The power consumption data includes real-time power consumption, cumulative power consumption and peak power consumption; the load characteristic data includes load type, load curve and load forecast; the power grid operation data includes substation load, distribution network voltage, distribution network current and line loss; the user behavior data includes power consumption habits, peak and valley power consumption and demand response; the power quality data includes voltage fluctuations, current harmonics and power supply reliability.

3. The clean energy consumption capacity assessment method for collaboration between transmitters and receivers as claimed in claim 2 is characterized by: The preprocessing includes data cleaning, data standardization, and data fusion of the collected data from the sending and receiving ends, and feature extraction of the preprocessed data; The data cleaning includes using statistical methods to detect outliers and noise in the data, and interpolating and deleting missing data; the data fusion includes using timestamp matching to align time series and using identifiers to associate data.

4. The clean energy consumption capacity assessment method for collaboration between transmitters and receivers as claimed in claim 3 is characterized by: The feature extraction Including, extracting the time domain characteristics and frequency domain characteristics of the pre-processed data, and analyzing the power generation capacity of the clean energy at the sending end; The formula for extracting time domain features is: Among them, σ is the variance, Y i is the data point, is the average value of data points, and m is the number of data points; The formula for extracting frequency domain features is: Where X(f) is the frequency domain representation after Fourier transform, x9t) is the original time domain signal, f is the frequency, j is the imaginary unit, and t is the time; The analysis of the power generation capacity of the clean energy at the sending end includes constructing a graph neural network model, initializing model parameters using a graph attention network, evaluating the difference between the model prediction and the actual power generation capacity using a cross entropy loss, analyzing the power generation capacity of the clean energy at the sending end, and predicting load demand; The initialization model parameters include the node feature matrix: Where N is the number of nodes, F is the feature dimension, and the weight matrix Perform node feature conversion, F′ is the feature dimension after conversion, and bias The cross entropy loss formula is: Among them, y is the true label, is the probability distribution predicted by the model, N is the number of nodes, is the loss function.

5. The clean energy consumption capacity assessment method for collaboration between transmitters and receivers as claimed in claim 4, characterized in that: The construction of the clean energy consumption capacity assessment model for the coordination between the sending and receiving ends includes using the forecast results and feature extraction of the clean energy generation capacity at the sending end and the load at the receiving end, selecting a time series model, and setting a model structure; The time series model includes combining the prediction models of the sending end and the receiving end, determining the model parameters and order, selecting the best model order by using the information criterion, and performing parameter significance test; the model structure includes a prediction power generation capacity layer and a prediction load demand layer; The optimization model parameter formula is: Among them, θ is the gradient, α is the learning rate, is the gradient of the loss function with respect to the parameter θ.

6. A clean energy consumption capacity assessment method for collaboration between transmitters and receivers as claimed in claim 5, characterized in that: The dynamic evaluation of the clean energy consumption capacity of the transmission and reception ends includes dynamically evaluating the clean energy consumption capacity of the transmission and reception ends through the clean energy consumption capacity evaluation model of the transmission and reception ends, the output of the clean energy predicted power generation and the power load predicted demand; The clean energy predicted power generation formula is: A t =f(B t ,B t-1 ,...,B t-P )+ε t Among them, A t is the predicted clean energy power generation at time t, B t is the clean energy feature vector input at time t, p is the impact value of clean energy data on prediction, ε t is the error term; The power load forecast demand formula is: Among them, E t is the predicted power load demand at time t, H t is the load characteristic vector input at time t, q is the impact value of load data on the prediction, is the error term; The formula for calculating the absorption capacity is: ΔS t =A t -Yes t Among them, A t is the predicted clean energy power generation at time t, E t is the predicted power load demand at time t, ΔS t is the clean energy absorption capacity at time t.

7. The clean energy consumption capacity assessment method for collaboration between transmitters and receivers as claimed in claim 6, characterized in that: The matching of clean energy dispatching strategies includes setting a clean energy consumption capacity threshold and matching clean energy dispatching strategies according to different consumption capacity intervals; When ΔS t When it is greater than or equal to the set threshold, it is necessary to increase the clean energy generation, start the backup energy, and adjust the user's electricity consumption behavior by adjusting the electricity price to reduce the load during peak hours; when ΔS t When it is less than the set threshold, the clean energy generation is reduced, the use of energy storage systems is increased, and energy is stored when there is excess generation, energy is released when demand peaks, and the operation mode of the power grid is optimized.

8. A system using a method for evaluating clean energy consumption capacity for collaboration between transmitters and receivers as described in any one of claims 1 to 7, characterized in that: It includes data collection and preprocessing module, feature extraction and analysis module, dynamic evaluation module of consumption capacity and clean energy scheduling strategy matching module; The data acquisition and preprocessing module is used to collect data from the sending and receiving ends in real time through sensors and smart meters, and clean, standardize and integrate the collected data to lay the foundation for subsequent analysis; The feature extraction and analysis module is used to extract time domain features and frequency domain features from the preprocessed data, and use the extracted features to build a graph neural network model to evaluate the power generation capacity of the sending-end clean energy, analyze the receiving-end data, predict the power load demand, and provide a basis for the consumption capacity evaluation; The dynamic evaluation module for absorbing capacity is used to combine the clean energy generation capacity at the sending end and the load forecast results at the receiving end to construct an evaluation model, adjust the model parameters through a gradient descent optimization algorithm, and use the optimized evaluation model to output the predicted clean energy generation and the predicted power load demand in real time, and calculate the clean energy absorption capacity by comparing the predicted clean energy generation and power load demand; The clean energy scheduling strategy matching module is used to set a threshold of clean energy consumption capacity according to the consumption capacity evaluation result, and match the corresponding clean energy scheduling strategy according to the comparison result between the consumption capacity and the threshold.

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 a method for evaluating clean energy absorption capacity for collaboration between transmitters and receivers as 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 a method for evaluating clean energy absorption capacity for collaboration between transmitters and receivers described in any one of claims 1 to 7 are implemented.

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