Benefit evaluation method, device and equipment of optical storage combined power station and storage medium

Abstract

The invention discloses a benefit evaluation method, device and equipment of a light storage combined power station and a storage medium, and relates to the technical field of new energy. According to the method, data acquisition, training and prediction are carried out on the cost and the income of the optical storage combined power station, a data basis is provided for subsequent benefit prediction, construction cost is calculated through predicted future cost, and hedging with future income is carried out, so that the future benefit of the optical storage combined power station is obtained. Finally, the mutual relation between the hedging ratio and the evaluation index is defined to obtain the final benefit evaluation of the photovoltaic-energy storage combined power station in a matching mode, due to the fact that the prediction function is introduced, the current benefit evaluation of the photovoltaic-energy storage combined power station can be obtained, the future benefit evaluation can also be obtained, and the economic benefit of the photovoltaic-energy storage combined power station is improved. And the benefit evaluation of the photovoltaic-energy storage combined power station is more comprehensive.

Description

Technical Field

[0001] The present application relates to the field of new energy technology, and in particular to a benefit evaluation method, device, equipment and storage medium for a photovoltaic and energy storage combined power station. Background Art

[0002] A photovoltaic power station with energy storage is a power station that is built by configuring a photovoltaic power station with a battery energy storage system. The photovoltaic power station combines photovoltaic power generation and energy storage systems, and uses battery energy storage systems to suppress the volatility of photovoltaic power generation output, improve power quality, and increase the grid's ability to accept large-scale centralized photovoltaic power generation. It also promotes the consumption of photovoltaic energy. When solar power generation is lower than the load demand, the system will use solar energy and the grid to supply power at the same time; conversely, if solar power generation exceeds the load demand, the excess power will be stored in the energy storage device. The construction and operation of photovoltaic power stations with energy storage are of great significance for promoting the optimization and upgrading of the energy structure and increasing the proportion of renewable energy.

[0003] At present, photovoltaic and energy storage combined power generation projects are mainly demonstration projects. How to fully and reasonably utilize the energy storage battery system and explore feasible operation modes under typical scenarios of photovoltaic and energy storage combined power stations are important means to improve the economic benefits of photovoltaic and energy storage combined power stations.

[0004] At present, the benefit evaluation of the photovoltaic and energy storage combined power station is usually calculated through manual reports to obtain benefit data based on natural months and natural years, and then subsequent evaluation operations are carried out through the relationship between input and expenditure. Since manual operations will inevitably introduce subjective experience errors, the benefit analysis results of the photovoltaic and energy storage combined power station are inaccurate, which leads to inaccurate subsequent evaluation results. Summary of the invention

[0005] The main purpose of the present application is to provide a benefit evaluation method, device, equipment and storage medium for a photovoltaic and energy storage combined power station, so as to solve the problem in the prior art that the benefit analysis of the photovoltaic and energy storage combined power station will inevitably introduce subjective experience errors due to manual operation, making the benefit analysis results of the photovoltaic and energy storage combined power station inaccurate, thereby leading to inaccurate subsequent evaluation results.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] A benefit evaluation method for a photovoltaic power plant with energy storage is provided, wherein the benefit evaluation method is applied to a photovoltaic power plant with energy storage that has been built and put into operation, and the benefit evaluation method comprises:

[0008] Step S1, obtaining several historical operation and maintenance costs of the photovoltaic power plant based on several preset time periods, and analyzing all the historical operation and maintenance costs through linear regression to obtain future operation and maintenance costs based on a preset number of prediction steps;

[0009] Step S2, obtaining several historical power generation revenues of the photovoltaic power plant based on several preset time periods, and learning and training all the historical power generation revenues through a machine learning machine to obtain future power generation revenues based on the preset prediction steps;

[0010] Step S3, defining the signs of all future operation and maintenance costs as negative and the signs of all future power generation revenues as positive;

[0011] Step S4, generating a plane rectangular coordinate system with natural time as the horizontal axis and economic benefit as the vertical axis;

[0012] Step S5, converting all future operation and maintenance costs and all future power generation revenues into future operation and maintenance cost coordinate points and future power generation revenue coordinate points of the plane rectangular coordinate system respectively;

[0013] Step S6, linearly fitting all future operation and maintenance cost coordinate points and all future power generation revenue coordinate points respectively to form a cost function and a revenue function;

[0014] Step S7, respectively obtaining the definite integrals of the revenue function and the cost function, and obtaining the ratio of the definite integral of the revenue function to the definite integral of the cost function and the construction cost of the photovoltaic power station;

[0015] Step S8, defining a number of continuous and increasing ratio intervals, and an evaluation index that increases as all ratio intervals increase;

[0016] Step S9, obtaining a ratio interval matching the ratio and a corresponding evaluation index as a future benefit evaluation of the photovoltaic-storage combined power station.

[0017] As a further improvement of the present application, step S1, based on a number of preset time periods, obtains a number of historical operation and maintenance costs of the photovoltaic power station, and analyzes all the historical operation and maintenance costs through linear regression to obtain the future operation and maintenance costs based on a preset number of prediction steps, including:

[0018] Step S11, obtaining several equipment operation and maintenance cost data, several equipment startup durations, several equipment failure rates, several equipment environment temperatures, and several equipment environment humidity of all equipment of the photovoltaic power station based on several preset time periods;

[0019] Step S12, defining the equipment operation and maintenance cost data of the same preset time period as a dependent variable;

[0020] Step S13, defining the device startup time, device failure rate, device environment temperature, and device environment humidity of the same preset time period as a set of independent variables;

[0021] Step S14, defining the linear regression relationship between all dependent variables and all independent variables through a multiple linear regression model;

[0022] Step S15, solving all linear regression coefficients in the linear regression relationship;

[0023] Step S16, substituting all the regression coefficients obtained by solving into the multivariate linear regression model to obtain a future operation and maintenance cost prediction model;

[0024] Step S17: predicting a number of future operation and maintenance costs through the future operation and maintenance cost prediction model based on the preset prediction steps.

[0025] As a further improvement of the present application, step S2, based on a number of preset time periods, obtains a number of historical power generation revenues of the photovoltaic power plant, and learns and trains all the historical power generation revenues through a machine learning machine to obtain future power generation revenues based on the preset number of prediction steps, including:

[0026] Step S21, integrating the historical power generation revenues of all preset time periods into a power generation revenue data set;

[0027] Step S22, performing standard normalization processing on the power generation revenue data set to obtain a normalized data set;

[0028] Step S23, dividing the normalized data set into a training set and a validation set according to a preset ratio;

[0029] Step S24, defining a neural network model in which the input layer, the hidden layer, and the output layer are sequentially connected;

[0030] Step S25, inputting the training set into the input layer, and performing several trainings through the neural network model;

[0031] Step S26, obtaining a root mean square error between the verification set and the current training result based on each training;

[0032] Step S27, obtaining a minimum error value among all root mean square errors, and obtaining a training result corresponding to the minimum error value as a future power generation revenue prediction model;

[0033] Step S28, predicting a number of future power generation revenues through the future power generation revenue prediction model based on the preset prediction step number.

[0034] As a further improvement of the present application, step S8 defines a number of continuous and increasing ratio intervals, and an evaluation index that increases with the increase of all ratio intervals, including:

[0035] Step S81, define all ratio intervals as (0,1], (1,1.1], (1.1,1.2], (1.2,1.3], (1.3,1.4], (1.4,+∞) in sequence;

[0036] Step S82, defining each evaluation index as failing on a 100-point scale, 60 points on a 100-point scale, 70 points on a 100-point scale, 80 points on a 100-point scale, 90 points on a 100-point scale, in sequence according to the increasing relationship of all ratio intervals.

[0037] As a further improvement of the present application, step S9, obtaining a ratio interval matching the ratio and a corresponding evaluation index as a future benefit evaluation of the photovoltaic power plant, then includes:

[0038] Step S10, outputting the plane rectangular coordinate system, the cost function, and the benefit function to an external visual monitoring terminal;

[0039] Step S20, marking the definite integral region of the cost function in red;

[0040] Step S30, marking the definite integral region of the profit function in green;

[0041] Step S40: output the ratio and the benefit index to the external visual monitoring terminal and adjacent to the cost function and the benefit function.

[0042] In order to achieve the above objectives, this application also provides the following technical solutions:

[0043] A benefit evaluation device for a photovoltaic power plant is provided. The benefit evaluation device is applied to the benefit evaluation method as described above. The benefit evaluation device comprises:

[0044] A future operation and maintenance cost acquisition module is used to acquire several historical operation and maintenance costs of the photovoltaic power plant based on several preset time periods, and to obtain future operation and maintenance costs based on a preset number of prediction steps by analyzing all the historical operation and maintenance costs through linear regression;

[0045] A future power generation revenue acquisition module is used to acquire several historical power generation revenues of the photovoltaic power plant based on several preset time periods, and learn and train all the historical power generation revenues through a machine learning machine to obtain future power generation revenues based on the preset prediction steps;

[0046] The operation and maintenance cost and power generation income sign definition module is used to define the signs of all future operation and maintenance costs as negative and the signs of all future power generation income as positive;

[0047] A plane rectangular coordinate system generation module is used to generate a plane rectangular coordinate system with natural time as the horizontal axis and economic benefits as the vertical axis;

[0048] A coordinate point conversion module, used to convert all future operation and maintenance costs and all future power generation revenues into future operation and maintenance cost coordinate points and future power generation revenue coordinate points of the plane rectangular coordinate system respectively;

[0049] The cost function and benefit function fitting module is used to linearly fit all future operation and maintenance cost coordinate points and all future power generation benefit coordinate points to form a cost function and a benefit function;

[0050] A function definite integral acquisition module, used to respectively obtain the definite integrals of the revenue function and the cost function, and to obtain the ratio of the definite integral of the revenue function to the definite integral of the cost function and the construction cost of the photovoltaic power station;

[0051] The ratio interval and evaluation index definition module is used to define a number of continuous and increasing ratio intervals, and an evaluation index that increases with the increase of all ratio intervals;

[0052] The future benefit evaluation acquisition module is used to obtain a ratio interval matching the ratio and a corresponding evaluation index as a future benefit evaluation of the photovoltaic energy storage combined power station.

[0053] In order to achieve the above objectives, this application also provides the following technical solutions:

[0054] An electronic device includes a processor and a memory coupled to the processor, wherein the memory stores program instructions executable by the processor; when the processor executes the program instructions stored in the memory, the benefit evaluation method as described above is implemented.

[0055] In order to achieve the above objectives, this application also provides the following technical solutions:

[0056] A storage medium stores program instructions, which can implement the above-mentioned benefit evaluation method when executed by a processor.

[0057] The present application obtains several historical operation and maintenance costs of the photovoltaic and energy storage combined power station based on several preset time periods, and obtains future operation and maintenance costs based on a preset number of prediction steps by analyzing all historical operation and maintenance costs through linear regression; obtains several historical power generation benefits of the photovoltaic and energy storage combined power station based on several preset time periods, and obtains future power generation benefits based on a preset number of prediction steps by learning and training all historical power generation benefits through a machine learning machine; defines the signs of all future operation and maintenance costs as negative and the signs of all future power generation benefits as positive; generates a plane rectangular coordinate system with natural time as the horizontal axis and economic benefits as the vertical axis; and transforms all future operation and maintenance costs, all The future power generation income is converted into the future operation and maintenance cost coordinate points and the future power generation income coordinate points of the plane rectangular coordinate system; all future operation and maintenance cost coordinate points and all future power generation income coordinate points are linearly fitted to form a cost function and a benefit function; the definite integrals of the benefit function and the cost function are obtained respectively, and the ratio of the definite integral of the benefit function to the definite integral of the cost function plus the construction cost of the photovoltaic and energy storage combined power station is obtained; several continuous and increasing ratio intervals and evaluation indicators that increase with the increase of all ratio intervals are defined; the ratio intervals matching the ratios and the corresponding evaluation indicators are obtained as the future benefit evaluation of the photovoltaic and energy storage combined power station. This application obtains, trains, and predicts data on the costs and benefits of the photovoltaic and storage power station, providing a data basis for subsequent benefit predictions, and then hedges the predicted future costs with future benefits after adding the construction costs to obtain the future benefits of the photovoltaic and storage power station. Finally, the relationship between the hedging ratio and the evaluation index is defined to match and obtain the final benefit evaluation of the photovoltaic and storage power station. Since this application introduces a prediction function, this application can not only obtain the current benefit evaluation of the photovoltaic and storage power station, but also the future benefit evaluation, making the benefit evaluation of the photovoltaic and storage power station more comprehensive. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of the process steps of an embodiment of a method for evaluating the benefits of a photovoltaic power plant combined with energy storage in this application;

[0059] Figure 2 This is a functional module diagram of an embodiment of a benefit evaluation device for a photovoltaic power plant combined with energy storage in the present application;

[0060] Figure 3 This is a schematic diagram of the structure of an embodiment of the electronic device of the present application;

[0061] Figure 4 This is a schematic diagram of the structure of an embodiment of the storage medium of the present application. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] The terms "first", "second" and "third" in this application are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of the present application (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or equipment.

[0064] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0065] Preferably, this embodiment provides an embodiment of a benefit evaluation method for a photovoltaic and energy storage combined power station. In this embodiment, the benefit evaluation method is applied to a photovoltaic and energy storage combined power station that has been built and put into operation.

[0066] Preferably, the photovoltaic-storage combined power station mainly includes three parts: photovoltaic power generation system, energy storage system and charging system.

[0067] Among them, the photovoltaic power generation system is the core component of the photovoltaic and storage combined power station, which is composed of solar photovoltaic panels, solar inverters and distribution systems. Solar photovoltaic panels convert solar radiation into DC electricity, and solar inverters convert DC electricity into AC electricity, which is then supplied to charging facilities and energy storage systems through the distribution system; the energy storage system is an important part of the photovoltaic and storage combined power station. It stores the electricity generated by the photovoltaic power generation system through energy storage devices (such as lithium-ion batteries, sodium-sulfur batteries and supercapacitors) so that it can continue to supply electricity at night or under low light conditions. The energy storage system also includes energy storage inverters and energy management systems for controlling the storage and release of electricity, as well as managing and monitoring energy storage devices; the charging system is used to provide power to electric vehicles or other equipment, including charging piles, charging controllers and other equipment.

[0068] From the above, we can see that the benefits of the combined photovoltaic and storage power station mainly come from multiple aspects such as energy utilization and environmental protection, economic benefits, grid stability and social influence.

[0069] Among them, energy utilization and environmental protection: the photovoltaic and energy storage combined power station uses solar energy to generate electricity, which can reduce dependence on fossil fuels and reduce greenhouse gas emissions, thereby achieving energy conservation and environmental protection benefits. At the same time, through the combination of photovoltaic power generation and energy storage systems, the photovoltaic and energy storage combined power station can reduce the transmission loss of the power grid and improve energy utilization efficiency.

[0070] Economic benefits: The photovoltaic power station can store electricity when the sun is sufficient, and release it when the electricity consumption is at its peak or the electricity price is high, thus reducing the peak load and filling the valley, and reducing the electricity cost. For electric car users, using the electricity generated by the photovoltaic power station for charging can also save charging costs. In addition, the excess electricity of the photovoltaic power station can also be sold, further increasing the economic benefits.

[0071] Grid stability: The photovoltaic and energy storage combined power station can relieve grid pressure and improve grid stability through the regulation of the energy storage system. In particular, the photovoltaic and energy storage combined power station can play a greater role in scenarios with large power fluctuations and obvious peak-to-valley differences.

[0072] Social impact: As an innovative energy solution, the promotion and application of the photovoltaic and energy storage combined power station can drive the development of related industries, improve the society's awareness and acceptance of renewable energy, and thus generate positive social influence. In addition, the construction of the photovoltaic and energy storage combined power station can also enhance the city's image and promote sustainable urban development.

[0073] Specifically, the benefit evaluation method includes the following steps:

[0074] Step S1, obtaining several historical operation and maintenance costs of the photovoltaic power station based on several preset time periods, and analyzing all historical operation and maintenance costs through linear regression to obtain future operation and maintenance costs based on a preset number of prediction steps.

[0075] Preferably, the linear regression in step S1 is preferably LASSO linear regression.

[0076] Step S2, obtaining several historical power generation revenues of the photovoltaic and energy storage combined power station based on several preset time periods, and learning and training all historical power generation revenues through a machine learning machine to obtain future power generation revenues based on a preset number of prediction steps.

[0077] Preferably, the machine learning machine of step S2 is preferably a bp neural network.

[0078] Preferably, the preset time period and the preset number of prediction steps in this embodiment can be set to the same step size, such as one natural day, one natural hour, etc.

[0079] Step S3, defining the signs of all future operation and maintenance costs as negative and the signs of all future power generation revenues as positive.

[0080] Step S4, generating a plane rectangular coordinate system with natural time as the horizontal axis and economic benefit as the vertical axis.

[0081] Step S5, converting all future operation and maintenance costs and all future power generation revenues into future operation and maintenance cost coordinate points and future power generation revenue coordinate points of a plane rectangular coordinate system respectively.

[0082] Step S6, linearly fitting all future operation and maintenance cost coordinate points and all future power generation revenue coordinate points respectively to form a cost function and a revenue function.

[0083] Step S7, respectively obtain the definite integrals of the revenue function and the cost function, and obtain the ratio of the definite integral of the revenue function to the definite integral of the cost function and the construction cost of the photovoltaic power station combined with energy storage.

[0084] Step S8, defining a number of continuous and increasing ratio intervals, and an evaluation index that increases as all ratio intervals increase.

[0085] Step S9, obtaining a ratio interval that matches the ratio and a corresponding evaluation index as a future benefit evaluation of the photovoltaic energy storage combined power station.

[0086] Preferably, the benefit evaluation of a solar-storage combined power station is a multi-dimensional process, and the suitability of the benefit depends on multiple factors, including the investment cost, operating cost, power generation efficiency, performance of the energy storage system, market demand, and policy environment of the power station. The benefit of a solar-storage combined power station can be evaluated from one of the following aspects:

[0087] Economic benefits: A combined photovoltaic and energy storage power station should be able to recover its costs within a reasonable payback period and generate sustained profits. This can be judged comprehensively by analyzing the power station's power generation income, the benefits of the energy storage system (such as benefits from participating in grid peak and frequency regulation, delaying grid expansion, etc.), electricity savings, and possible government subsidies. The profit rate of a combined photovoltaic and energy storage power station is relatively moderate at between 10% and 20%. This range is mainly based on the rate of return when the photovoltaic power generation system and the energy storage system operate together, and is affected by multiple factors, including the capacity of the photovoltaic power generation system, the capacity of the energy storage system, the electricity market price, and the cost of the energy storage system.

[0088] Environmental benefits: As a clean energy project, the solar-storage combined power station should be able to significantly reduce greenhouse gas emissions and other environmental pollutants, thereby having a positive impact on the environment. This is one of the important indicators for evaluating whether its benefits are appropriate.

[0089] System stability and reliability: The photovoltaic power station should be able to operate stably and provide reliable power supply. This includes the ability of the energy storage system to replenish power in time in the event of insufficient sunlight or grid failure, ensuring the continuous power supply of the power station. The stability and reliability of the system are crucial to improving user experience and meeting market demand.

[0090] Policy compliance and market adaptability: The construction and operation of combined photovoltaic and energy storage power stations should comply with the requirements of relevant national policies and regulations, and at the same time be able to adapt to changes in market demand. Policy support and the growth of market demand are important factors in promoting the development of combined photovoltaic and energy storage power stations.

[0091] Further, step S1, based on a number of preset time periods, obtains several historical operation and maintenance costs of the photovoltaic power station, and analyzes all the historical operation and maintenance costs through linear regression to obtain the future operation and maintenance costs based on a preset number of prediction steps, which specifically includes the following steps:

[0092] Step S11, based on several preset time periods, obtain several equipment operation and maintenance cost data, several equipment startup time, several equipment failure rates, several equipment ambient temperatures, and several equipment ambient humidity of all equipment in the photovoltaic and energy storage combined power station.

[0093] Step S12, defining the equipment operation and maintenance cost data of the same preset time period as a dependent variable.

[0094] Step S13, defining the device startup time, device failure rate, device environment temperature, and device environment humidity in the same preset time period as a set of independent variables.

[0095] Step S14, defining the linear regression relationship between all dependent variables and all independent variables through a multiple linear regression model.

[0096] Preferably, the multiple linear regression model is as follows:

[0097]

[0098] Among them, y i is the dependent variable of the i-th preset time period, n is the total number of all preset time periods, β 0 is the intercept of the linear regression relationship, β j is the linear regression coefficient of the jth independent variable, m is the total number of independent variables in a group of independent variables, x j,i is the jth independent variable of the ith preset time period, and δ is the random error of the linear regression relationship.

[0099] Step S15, solving all linear regression coefficients in the linear regression relationship.

[0100] Preferably, all linear regression coefficients of the multivariate linear regression model may be solved by the least square method.

[0101] The least squares method is as follows:

[0102]

[0103] in, β j The estimated value of , j = 1, 2, ..., m, X is the matrix of all independent variables, X T is the transposed matrix of matrix X.

[0104] It should be noted that the above formulas and formula symbols are only used to illustrate the principles, and their meanings are not interchangeable with those in other locations.

[0105] Step S16, substituting all the obtained regression coefficients into the multivariate linear regression model to obtain a future operation and maintenance cost prediction model.

[0106] Step S17, predicting a number of future operation and maintenance costs through a future operation and maintenance cost prediction model based on a preset number of prediction steps.

[0107] Preferably, the residual square of linear regression can be used to judge the fitting effect of the model by comparing its size. The residual sum of squares (RSS) is the sum of squares of the difference between the actual observed value and the value predicted by the regression equation, which is used to quantify the difference between the model predicted value and the actual value.

[0108] Preferably, the judgment criterion of the residual square is that the smaller the better, that is, the smaller the residual square sum is, the closer the model's predicted value is to the actual observed value, and the better the model fitting effect is; conversely, if the residual square sum is large, it indicates that there is a large deviation between the model's predicted value and the actual observed value, and the model fitting effect is poor.

[0109] Preferably, Multiple Linear Regression is a statistical method used to study the linear relationship between a dependent variable and multiple independent variables.

[0110] The basic principle and basic calculation process of multiple linear regression are similar to those of univariate linear regression, but due to the large number of independent variables, the calculation is relatively complex and usually requires the help of statistical software. In practical applications, the multiple linear regression model can help us understand which independent variables have a significant impact on the dependent variable, as well as the size and direction of these impacts.

[0111] When building a multiple linear regression model, you need to pay attention to the following points:

[0112] Selection of independent variables: The independent variables must have a significant impact on the dependent variable and be closely linearly correlated. At the same time, the independent variables should have a certain degree of mutual exclusivity, that is, the correlation between the independent variables should not be higher than the correlation between the independent variables and the dependent variable.

[0113] Model testing and evaluation: After building the model, it is necessary to conduct a significance test on the model as a whole to determine whether the model is effective. Furthermore, it is also necessary to conduct a significance test on the regression coefficients of each variable and use indicators such as R-square or adjusted R-square to evaluate the goodness of fit of the model.

[0114] Preferably, the significance test of the regression equation usually uses the F test to evaluate whether the entire regression model is significant, that is, whether at least one independent variable has a significant effect on the dependent variable. The null hypothesis of the F test is that the regression coefficients of all independent variables in the regression equation are 0. If the p value of the F test is less than 0.05, the null hypothesis is rejected and the model is considered significant, that is, at least one independent variable has a statistically significant effect on the dependent variable; conversely, if the p value is greater than 0.05, the null hypothesis is not rejected and the model is considered insignificant.

[0115] Preferably, if you want to further determine which independent variables' regression coefficients are significant based on the overall significance of the regression equation, you need to perform a t-test. The t-test is used to test whether a single regression coefficient is significantly different from 0, that is, whether the independent variable has a significant effect on the dependent variable. If the t-test p value of the regression coefficient is less than a certain significance level (such as 0.05), the regression coefficient is considered significant.

[0116] In addition, the significance test of multiple linear regression may also include the evaluation of the goodness of fit of the regression model, which is usually measured by the coefficient of determination R 2 or the adjusted R 2 To evaluate. 2 If it is close to 1, it means that the regression model has a good fit and can better explain the variation of the dependent variable; R 2 If it is close to 0, it means that the regression model has poor goodness of fit and its explanatory power for the dependent variable is weak.

[0117] Further, step S2, based on a number of preset time periods, obtains a number of historical power generation benefits of the photovoltaic power plant, and learns and trains all the historical power generation benefits through a machine learning machine to obtain future power generation benefits based on a preset number of prediction steps, specifically including the following steps:

[0118] Step S21, integrating the historical power generation revenues of all preset time periods into a power generation revenue data set.

[0119] Step S22, performing standard normalization processing on the power generation revenue data set to obtain a normalized data set.

[0120] Preferably, this embodiment prefers the normalization method of zero-mean normalization (Z-score normalization), which gives the mean and standard deviation of the original data to standardize the data, and the processed data conforms to the standard normal distribution, that is, the mean is 0 and the standard deviation is 1. For the normalization method, this embodiment can also use batch normalization. Compared with simple normalization, in the previous neural network training, only the input layer data is normalized, but not in the middle layer. Although the data set of the input node is normalized, the data distribution of the input data after matrix multiplication is likely to change greatly, and as the number of hidden layer network layers continues to deepen, the change in data distribution will become greater and greater. Therefore, the normalization processing performed by batch normalization in the middle layer of the neural network makes the training effect better.

[0121] Step S23, dividing the normalized data set into a training set and a validation set according to a preset ratio.

[0122] Preferably, the preset ratio can be set to 8:2, so as to divide the normalized data set into a training set and a sample set in a ratio of 8:2.

[0123] Step S24, defining a neural network model in which the input layer, the hidden layer, and the output layer are sequentially signal-connected.

[0124] Preferably, the neural network model is characterized by the following formula:

[0125]

[0126] Among them, y is the neural network model; x n is the nth input node of the input layer, each input node corresponds to a data in the training set, is the weight from the mth input node of the input layer to the nth input node of the hidden layer; is the bias of the nth input node connected to the hidden layer; is the bias of the output layer; tansig(·) is the activation function; the numbers in the brackets of the symbol subscripts are the number of layers, the subscript (1) is the first layer, that is, the input layer, and the subscript (1, 2) is the first layer to the second layer, that is, the input layer to the hidden layer.

[0127] It should be noted that the above formulas and formula symbols are only used to illustrate the principles, and their meanings are not interchangeable with those in other locations.

[0128] Step S25, input the training set to the input layer, and perform several trainings through the neural network model.

[0129] Step S26, based on each training, obtain the root mean square error between the validation set and the current training result.

[0130] Step S27, obtaining the minimum error value among all root mean square errors, and obtaining the training result corresponding to the minimum error value as a future power generation revenue prediction model.

[0131] Step S28, predicting a number of future power generation revenues through a future power generation revenue prediction model based on a preset number of prediction steps.

[0132] Preferably, the training model training a neural network usually requires providing a large amount of data, namely a data set; the data set is generally divided into three categories, namely the above-mentioned training set (training set), validation set (validation set) and test set (test set).

[0133] Among them, one epoch is the process of training once with all the samples in the training set. The so-called training once refers to one forward pass and one back pass. When the number of samples in an epoch (i.e., training set) is too large, training once may consume too much time, and it is not necessary to use all the data in the training set for each training. In this case, the entire training set needs to be divided into multiple small blocks, that is, divided into multiple batches for training. An epoch consists of one or more batches, where a batch is a part of the training set. Each training process only uses a part of the data, i.e., a batch. The process of training a batch is an iteration.

[0134] Preferably, the neural network training specifically includes a perceptron, which is composed of two layers of neurons. The input layer receives external input signals and transmits them to the output layer. The output layer is MP neurons, and the step function is y j =f(∑ i w i ·x i -θ i ), the step function here is for principle explanation and is not interchangeable with other symbols.

[0135] Preferably, given a training data set, the weight w i (i=1,2,...,n) and training bias θ i It can be obtained through learning, θ i It can be understood as a weight w corresponding to a fixed value of -1,0. i+1 .

[0136] Preferably, in this embodiment, the number of neural network training times can be set to 10,000 times.

[0137] Preferably, the learning rate from the 1st to the 5000th epoch can be set to 0.01, the learning rate from the 5001st to the 7500th epoch can be set to 0.001, and the learning rate from the 7501st to the 10000th epoch can be set to 0.0001.

[0138] It can be understood that the neural network training of this embodiment mainly includes the following ideas:

[0139] ① Initialize the weights and bias items in the network.

[0140] Initializing parameter values ​​(output unit weights, bias terms and hidden unit weights, bias terms are all model parameters) is to activate forward propagation, obtain the output value of each layer element, and then obtain the value of the loss function.

[0141] ②Activate forward propagation to obtain the output value of each layer and the expected value of the loss function of each layer.

[0142] ③According to the loss function, calculate the error term of the output unit and the error term of the hidden unit.

[0143] Calculate various errors, calculate the gradient of the parameters with respect to the loss function, or calculate partial derivatives according to the chain rule of calculus. For partial derivatives of vectors or matrices in a composite function, the partial derivative of the function inside the composite function is always multiplied on the left; for partial derivatives of scalars in a composite function, the partial derivative of the function inside the composite function can be multiplied on the left or on the right.

[0144] ④Update the weights and bias items in the neural network.

[0145] ⑤ Repeat ② to ④ until the loss function is less than the preset bias or the number of iterations is used up, and the parameters output at this time are the current optimal parameters.

[0146] Furthermore, step S8, defining a plurality of continuous and increasing ratio intervals, and an evaluation index that increases with the increase of all ratio intervals, specifically includes the following steps:

[0147] Step S81, define all ratio intervals as (0,1], (1,1.1], (1.1,1.2], (1.2,1.3], (1.3,1.4], (1.4,+∞) in sequence.

[0148] Preferably, the ratio interval definition of step S81 is based on the above additional content that the profit rate of the photovoltaic and energy storage combined power station is relatively moderate between 10% and 20% "The profit rate of the photovoltaic and energy storage combined power station is relatively moderate between 10% and 20%", so step S81 defines the ratio interval with a span of 10%.

[0149] Step S82, defining each evaluation index as failing on a 100-point scale, 60 points on a 100-point scale, 70 points on a 100-point scale, 80 points on a 100-point scale, 90 points on a 100-point scale, in sequence according to the increasing relationship of all ratio intervals.

[0150] Preferably, step S82 only provides one scoring mechanism, and other scoring mechanisms with the same principle may also be used.

[0151] Further, step S9, obtaining a ratio interval matching the ratio and a corresponding evaluation index as a future benefit evaluation of the photovoltaic energy storage combined power station, and then including:

[0152] Step S10, outputting the plane rectangular coordinate system, cost function, and benefit function to an external visual monitoring terminal.

[0153] Step S20, marking the definite integral region of the cost function in red.

[0154] Step S30, marking the definite integral region of the profit function in green.

[0155] Preferably, the red and green in step S20 and step S30 may be replaced by other two colors with striking differences.

[0156] Step S40: output the ratio and the benefit index to an external visual monitoring terminal and adjacent to the cost function and the benefit function.

[0157] This embodiment obtains several historical operation and maintenance costs of the photovoltaic and energy storage combined power station based on several preset time periods, and obtains future operation and maintenance costs based on a preset number of prediction steps by analyzing all historical operation and maintenance costs through linear regression; obtains several historical power generation benefits of the photovoltaic and energy storage combined power station based on several preset time periods, and obtains future power generation benefits based on a preset number of prediction steps by learning and training all historical power generation benefits through a machine learning machine; defines the signs of all future operation and maintenance costs as negative and the signs of all future power generation benefits as positive; generates a plane rectangular coordinate system with natural time as the horizontal axis and economic benefits as the vertical axis; and transforms all future operation and maintenance costs, all The future power generation income is converted into the future operation and maintenance cost coordinate points and the future power generation income coordinate points of the plane rectangular coordinate system; all future operation and maintenance cost coordinate points and all future power generation income coordinate points are linearly fitted to form a cost function and a benefit function; the definite integrals of the benefit function and the cost function are obtained respectively, and the ratio of the definite integral of the benefit function to the definite integral of the cost function plus the construction cost of the photovoltaic and energy storage combined power station is obtained; several continuous and increasing ratio intervals and evaluation indicators that increase with the increase of all ratio intervals are defined; the ratio intervals matching the ratios and the corresponding evaluation indicators are obtained as the future benefit evaluation of the photovoltaic and energy storage combined power station. This embodiment acquires, trains, and predicts the data on the costs and benefits of the photovoltaic and storage power station, respectively, to provide a data basis for subsequent benefit prediction, and then hedges the predicted future costs with the future benefits after adding the construction costs, thereby obtaining the future benefits of the photovoltaic and storage power station. Finally, the relationship between the hedging ratio and the evaluation index is defined to match and obtain the final benefit evaluation of the photovoltaic and storage power station. Since this embodiment introduces a prediction function, this embodiment can not only obtain the current benefit evaluation of the photovoltaic and storage power station, but also the future benefit evaluation, making the benefit evaluation of the photovoltaic and storage power station more comprehensive.

[0158] like Figure 2 As shown, this embodiment provides an embodiment of a benefit evaluation device for a photovoltaic and energy-storage combined power station. In this embodiment, the benefit evaluation device is applied to the benefit evaluation method in the above embodiment.

[0159] Specifically, the benefit evaluation device includes a future operation and maintenance cost acquisition module 1, a future power generation income acquisition module 2, an operation and maintenance cost and power generation income symbol definition module 3, a plane rectangular coordinate system generation module 4, a coordinate point conversion module 5, a cost function and benefit function fitting module 6, a function definite integral acquisition module 7, a ratio interval and evaluation index definition module 8, and a future benefit evaluation acquisition module 9, which are electrically connected in sequence.

[0160] Among them, the future operation and maintenance cost acquisition module 1 is used to obtain several historical operation and maintenance costs of the photovoltaic and energy storage combined power station based on several preset time periods, and analyze all historical operation and maintenance costs through linear regression to obtain future operation and maintenance costs based on a preset number of prediction steps; the future power generation income acquisition module 2 is used to obtain several historical power generation income of the photovoltaic and energy storage combined power station based on several preset time periods, and learn and train all historical power generation income through a machine learning machine to obtain future power generation income based on a preset number of prediction steps; the operation and maintenance cost and power generation income sign definition module 3 is used to define the signs of all future operation and maintenance costs as negative and the signs of all future power generation income as positive; the plane rectangular coordinate system generation module 4 is used to generate a plane rectangular coordinate system with natural time as the horizontal axis and economic benefits as the vertical axis; the coordinate point conversion module 5 is used to convert all future operation and maintenance costs into a negative sign and a positive sign. The cost and all future power generation benefits are respectively converted into future operation and maintenance cost coordinate points and future power generation benefit coordinate points in a plane rectangular coordinate system; the cost function and benefit function fitting module 6 is used to linearly fit all future operation and maintenance cost coordinate points and all future power generation benefit coordinate points respectively to form a cost function and a benefit function; the function definite integral acquisition module 7 is used to obtain the definite integrals of the benefit function and the cost function respectively, and obtain the ratio of the definite integral of the benefit function to the definite integral of the cost function and the construction cost of the photovoltaic and energy storage combined power station; the ratio interval and evaluation index definition module 8 is used to define a number of continuous and increasing ratio intervals, and an evaluation index that increases with the increase of all ratio intervals; the future benefit evaluation acquisition module 9 is used to obtain a ratio interval that matches the ratio, and a corresponding evaluation index as a future benefit evaluation of the photovoltaic and energy storage combined power station.

[0161] Furthermore, the future operation and maintenance cost acquisition module 1 specifically includes a first future operation and maintenance cost acquisition submodule, a second future operation and maintenance cost acquisition submodule, a third future operation and maintenance cost acquisition submodule, a fourth future operation and maintenance cost acquisition submodule, a fifth future operation and maintenance cost acquisition submodule, a sixth future operation and maintenance cost acquisition submodule, and a seventh future operation and maintenance cost acquisition submodule, which are electrically connected in sequence; the seventh future operation and maintenance cost acquisition submodule is electrically connected to the future power generation income acquisition module 2.

[0162] Among them, the first future operation and maintenance cost acquisition submodule is used to obtain several equipment operation and maintenance cost data, several equipment startup time, several equipment failure rates, several equipment ambient temperatures, and several equipment ambient humidity of all equipment in the photovoltaic and energy storage combined power station based on several preset time periods; the second future operation and maintenance cost acquisition submodule is used to define the equipment operation and maintenance cost data of the same preset time period as a dependent variable; the third future operation and maintenance cost acquisition submodule is used to define the equipment startup time, equipment failure rate, equipment ambient temperature, and equipment ambient humidity of the same preset time period as a group of independent variables; the fourth future operation and maintenance cost acquisition submodule is used to define the linear regression relationship between all dependent variables and all independent variables through a multivariate linear regression model; the fifth future operation and maintenance cost acquisition submodule is used to solve all linear regression coefficients in the linear regression relationship; the sixth future operation and maintenance cost acquisition submodule is used to substitute all the regression coefficients obtained by the solution into the multivariate linear regression model to obtain a future operation and maintenance cost prediction model; the seventh future operation and maintenance cost acquisition submodule is used to predict several future operation and maintenance costs through the future operation and maintenance cost prediction model based on a preset number of prediction steps.

[0163] Furthermore, the future power generation income acquisition module 2 specifically includes a first future power generation income acquisition submodule, a second future power generation income acquisition submodule, a third future power generation income acquisition submodule, a fourth future power generation income acquisition submodule, a fifth future power generation income acquisition submodule, a sixth future power generation income acquisition submodule, a seventh future power generation income acquisition submodule, and an eighth future power generation income acquisition submodule, which are electrically connected in sequence; the first future power generation income acquisition submodule is electrically connected to the seventh future operation and maintenance cost acquisition submodule, and the eighth future power generation income acquisition submodule is electrically connected to the operation and maintenance cost and power generation income symbol definition module 3.

[0164] Among them, the first future power generation revenue acquisition submodule is used to integrate the historical power generation revenue of all preset time periods into a power generation revenue data set; the second future power generation revenue acquisition submodule is used to perform standard normalization processing on the power generation revenue data set to obtain a normalized data set; the third future power generation revenue acquisition submodule is used to divide the normalized data set into a training set and a verification set according to a preset ratio; the fourth future power generation revenue acquisition submodule is used to define a neural network model in which the input layer, the hidden layer, and the output layer are connected in sequence; the fifth future power generation revenue acquisition submodule is used to input the training set into the input layer and perform several trainings through the neural network model; the sixth future power generation revenue acquisition submodule is used to obtain the root mean square error between the verification set and the current training result based on each training; the seventh future power generation revenue acquisition submodule is used to obtain the minimum error among all root mean square errors, and obtain the training result corresponding to the minimum error as the future power generation revenue prediction model; the eighth future power generation revenue acquisition submodule is used to predict several future power generation revenues through the future power generation revenue prediction model based on a preset prediction step number.

[0165] Furthermore, the ratio interval and evaluation index definition module 8 specifically includes a first ratio interval and evaluation index definition submodule and a second ratio interval and evaluation index definition submodule which are electrically connected in sequence; the first ratio interval and evaluation index definition submodule is electrically connected to the function definite integral acquisition module 7, and the second ratio interval and evaluation index definition submodule is electrically connected to the future benefit evaluation acquisition module 9.

[0166] Among them, the first ratio interval and evaluation index definition submodule is used to define all ratio intervals as (0,1], (1,1.1], (1.1,1.2], (1.2,1.3], (1.3,1.4], (1.4,+∞); the second ratio interval and evaluation index definition submodule is used to define each evaluation index as failing the percentage system, 60 points the percentage system, 70 points the percentage system, 80 points the percentage system, 90 points the percentage system, and 100 points the percentage system according to the increasing relationship of all ratio intervals.

[0167] Furthermore, the benefit evaluation device also includes a function visualization output module, a cost function marking module, a benefit function marking module, and a ratio and benefit index output module which are electrically connected in sequence; the function visualization output module is electrically connected to the future benefit evaluation acquisition module 9.

[0168] Among them, the function visualization output module is used to output the plane rectangular coordinate system, cost function, and benefit function to the external visualization monitoring terminal; the cost function marking module is used to mark the definite integral area of ​​the cost function in red; the benefit function marking module is used to mark the definite integral area of ​​the benefit function in green; the ratio and benefit index output module is used to output both the ratio and the benefit index to the external visualization monitoring terminal and adjacent to the cost function and the benefit function.

[0169] It should be noted that this embodiment is a functional module embodiment based on the above method embodiment. The optimization, expansion, limitation, example, and principle description of this embodiment can be referred to the above embodiment, and this embodiment will not be repeated.

[0170] This embodiment obtains several historical operation and maintenance costs of the photovoltaic and energy storage combined power station based on several preset time periods, and obtains future operation and maintenance costs based on a preset number of prediction steps by analyzing all historical operation and maintenance costs through linear regression; obtains several historical power generation benefits of the photovoltaic and energy storage combined power station based on several preset time periods, and obtains future power generation benefits based on a preset number of prediction steps by learning and training all historical power generation benefits through a machine learning machine; defines the signs of all future operation and maintenance costs as negative and the signs of all future power generation benefits as positive; generates a plane rectangular coordinate system with natural time as the horizontal axis and economic benefits as the vertical axis; and transforms all future operation and maintenance costs, all The future power generation income is converted into the future operation and maintenance cost coordinate points and the future power generation income coordinate points of the plane rectangular coordinate system; all future operation and maintenance cost coordinate points and all future power generation income coordinate points are linearly fitted to form a cost function and a benefit function; the definite integrals of the benefit function and the cost function are obtained respectively, and the ratio of the definite integral of the benefit function to the definite integral of the cost function plus the construction cost of the photovoltaic and energy storage combined power station is obtained; several continuous and increasing ratio intervals and evaluation indicators that increase with the increase of all ratio intervals are defined; the ratio intervals matching the ratios and the corresponding evaluation indicators are obtained as the future benefit evaluation of the photovoltaic and energy storage combined power station. This embodiment acquires, trains, and predicts the data on the costs and benefits of the photovoltaic and storage power station, respectively, to provide a data basis for subsequent benefit prediction, and then hedges the predicted future costs with the future benefits after adding the construction costs, thereby obtaining the future benefits of the photovoltaic and storage power station. Finally, the relationship between the hedging ratio and the evaluation index is defined to match and obtain the final benefit evaluation of the photovoltaic and storage power station. Since this embodiment introduces a prediction function, this embodiment can not only obtain the current benefit evaluation of the photovoltaic and storage power station, but also the future benefit evaluation, making the benefit evaluation of the photovoltaic and storage power station more comprehensive.

[0171] Figure 3 An embodiment of the electronic device of the present application is shown, see Figure 3 The electronic device 10 includes a processor 101 and a memory 102 coupled to the processor 101 .

[0172] The memory 102 stores program instructions for implementing the benefit evaluation method of the photovoltaic power plant combined with energy storage according to any of the above embodiments.

[0173] The processor 101 is used to execute program instructions stored in the memory 102 to evaluate the benefits of the photovoltaic power plant.

[0174] The processor 101 may also be referred to as a CPU (Central Processing Unit). The processor 101 may be an integrated circuit chip having signal processing capabilities. The processor 101 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0175] Further, Figure 4 This is a schematic diagram of the structure of a storage medium according to an embodiment of the present application. Figure 4 The storage medium 11 of the embodiment of the present application stores program instructions 111 that can implement all the above methods, wherein the program instructions 111 can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0176] In the several embodiments provided in the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0177] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

[0178] The specific implementation methods of the present application are described in detail above, but they are only examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application, and therefore, the equalization, modification, and improvement made without departing from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A method for evaluating the benefits of a photovoltaic power plant with energy storage, the method being applied to a photovoltaic power plant that has been built and put into operation, characterized in that: The benefit evaluation method includes: Step S1, obtaining several historical operation and maintenance costs of the photovoltaic power plant based on several preset time periods, and analyzing all the historical operation and maintenance costs through linear regression to obtain future operation and maintenance costs based on a preset number of prediction steps; Step S2, obtaining several historical power generation revenues of the photovoltaic power plant based on several preset time periods, and learning and training all the historical power generation revenues through a machine learning machine to obtain future power generation revenues based on the preset prediction steps; Step S3, defining the signs of all future operation and maintenance costs as negative and the signs of all future power generation revenues as positive; Step S4, generating a plane rectangular coordinate system with natural time as the horizontal axis and economic benefit as the vertical axis; Step S5, converting all future operation and maintenance costs and all future power generation revenues into future operation and maintenance cost coordinate points and future power generation revenue coordinate points of the plane rectangular coordinate system respectively; Step S6, linearly fitting all future operation and maintenance cost coordinate points and all future power generation revenue coordinate points respectively to form a cost function and a revenue function; Step S7, respectively obtaining the definite integrals of the revenue function and the cost function, and obtaining the ratio of the definite integral of the revenue function to the definite integral of the cost function and the construction cost of the photovoltaic power station; Step S8, defining a number of continuous and increasing ratio intervals, and an evaluation index that increases as all ratio intervals increase; Step S9, obtaining a ratio interval matching the ratio and a corresponding evaluation index as a future benefit evaluation of the photovoltaic power plant.

2. The benefit evaluation method according to claim 1, characterized in that: Step S1, based on a number of preset time periods, obtains several historical operation and maintenance costs of the photovoltaic power plant, and analyzes all the historical operation and maintenance costs through linear regression to obtain future operation and maintenance costs based on a preset number of prediction steps, including: Step S11, obtaining several equipment operation and maintenance cost data, several equipment startup durations, several equipment failure rates, several equipment environment temperatures, and several equipment environment humidity of all equipment of the photovoltaic power station based on several preset time periods; Step S12, defining the equipment operation and maintenance cost data of the same preset time period as a dependent variable; Step S13, defining the device startup time, device failure rate, device environment temperature, and device environment humidity of the same preset time period as a set of independent variables; Step S14, defining the linear regression relationship between all dependent variables and all independent variables through a multiple linear regression model; Step S15, solving all linear regression coefficients in the linear regression relationship; Step S16, substituting all the regression coefficients obtained by solving into the multivariate linear regression model to obtain a future operation and maintenance cost prediction model; Step S17: predicting a number of future operation and maintenance costs through the future operation and maintenance cost prediction model based on the preset prediction steps.

3. The benefit evaluation method according to claim 1, characterized in that: Step S2, based on a number of preset time periods, obtains a number of historical power generation revenues of the photovoltaic power plant, and learns and trains all the historical power generation revenues through a machine learning machine to obtain future power generation revenues based on the preset prediction steps, including: Step S21, integrating the historical power generation revenues of all preset time periods into a power generation revenue data set; Step S22, performing standard normalization processing on the power generation revenue data set to obtain a normalized data set; Step S23, dividing the normalized data set into a training set and a validation set according to a preset ratio; Step S24, defining a neural network model in which the input layer, the hidden layer, and the output layer are sequentially connected; Step S25, inputting the training set into the input layer, and performing several trainings through the neural network model; Step S26, obtaining a root mean square error between the verification set and the current training result based on each training; Step S27, obtaining a minimum error value among all root mean square errors, and obtaining a training result corresponding to the minimum error value as a future power generation revenue prediction model; Step S28, predicting a number of future power generation revenues through the future power generation revenue prediction model based on the preset prediction step number.

4. The benefit evaluation method according to claim 1, characterized in that: Step S8, defining a number of continuous and increasing ratio intervals, and an evaluation index that increases with the increase of all ratio intervals, including: Step S81, define all ratio intervals as (0,1], (1,1.1], (1.1,1.2], (1.2,1.3], (1.3,1.4], (1.4,+∞) in sequence; Step S82, defining each evaluation index as failing on a 100-point scale, 60 points on a 100-point scale, 70 points on a 100-point scale, 80 points on a 100-point scale, 90 points on a 100-point scale, in sequence according to the increasing relationship of all ratio intervals.

5. The benefit evaluation method according to claim 1, characterized in that: Step S9, obtaining a ratio interval matching the ratio and a corresponding evaluation index as a future benefit evaluation of the photovoltaic power plant, and then comprising: Step S10, outputting the plane rectangular coordinate system, the cost function, and the benefit function to an external visual monitoring terminal; Step S20, marking the definite integral region of the cost function in red; Step S30, marking the definite integral region of the profit function in green; Step S40: output the ratio and the benefit index to the external visual monitoring terminal and adjacent to the cost function and the benefit function.

6. A benefit evaluation device for a photovoltaic power plant, wherein the benefit evaluation device is applied to the benefit evaluation method according to any one of claims 1 to 5, characterized in that: The benefit evaluation device comprises: A future operation and maintenance cost acquisition module is used to acquire several historical operation and maintenance costs of the photovoltaic power plant based on several preset time periods, and to obtain future operation and maintenance costs based on a preset number of prediction steps by analyzing all the historical operation and maintenance costs through linear regression; A future power generation revenue acquisition module is used to acquire several historical power generation revenues of the photovoltaic power plant based on several preset time periods, and learn and train all the historical power generation revenues through a machine learning machine to obtain future power generation revenues based on the preset prediction steps; The module for defining the signs of operation and maintenance costs and power generation benefits is used to define the signs of all future operation and maintenance costs as negative and the signs of all future power generation benefits as positive; A plane rectangular coordinate system generation module is used to generate a plane rectangular coordinate system with natural time as the horizontal axis and economic benefits as the vertical axis; A coordinate point conversion module, used to convert all future operation and maintenance costs and all future power generation revenues into future operation and maintenance cost coordinate points and future power generation revenue coordinate points of the plane rectangular coordinate system respectively; The cost function and benefit function fitting module is used to linearly fit all future operation and maintenance cost coordinate points and all future power generation benefit coordinate points to form a cost function and a benefit function; A function definite integral acquisition module, used to respectively obtain the definite integrals of the revenue function and the cost function, and to obtain the ratio of the definite integral of the revenue function to the definite integral of the cost function and the construction cost of the photovoltaic power station; The ratio interval and evaluation index definition module is used to define a number of continuous and increasing ratio intervals, and an evaluation index that increases with the increase of all ratio intervals; The future benefit evaluation acquisition module is used to obtain a ratio interval matching the ratio and a corresponding evaluation index as a future benefit evaluation of the photovoltaic energy storage combined power station.

7. An electronic device, characterized in that: It includes a processor and a memory coupled to the processor, wherein the memory stores program instructions that can be executed by the processor; when the processor executes the program instructions stored in the memory, the benefit evaluation method as described in any one of claims 1 to 5 is implemented.

8. A storage medium, characterized in that: The storage medium stores program instructions, and when the program instructions are executed by the processor, the benefit evaluation method according to any one of claims 1 to 5 can be implemented.

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