Accounting method for cost budget cost electricity price of onshore wind power engineering project

Abstract

The invention provides a land wind power engineering project cost rough calculation cost electricity price accounting method comprising the following steps: a back-end algorithm calls a power grid electricity price calculation model to calculate according to input parameters so as to obtain a power grid electricity price; a calculation result is fed back to a front-end interface to be displayed for a user to check; the back-end algorithm recalculates the power grid electricity price calculation model, and recalculates the power grid electricity price according to the corrected parameters; repeatedly calculating the power grid electricity price until the calculation result of the power grid electricity price meets the expectation; inputting the power grid electricity price and the diesel generator price into a comprehensive electricity price calculation model; the back-end algorithm calculates the comprehensive electricity price according to the input parameters; when the power grid electricity price, the diesel generator price and the comprehensive electricity price all meet expectations, a final accounting result is output; and the final accounting result is exported in the format of Excel or PDF and the like. According to the invention, the method achieves the quick and accurate accounting of the cost budget price of the onshore wind power engineering project, and is important in technical effect and practical significance.

Description

Technical Field

[0001] The present invention relates to the technical field of cost calculation, and in particular to a method for calculating the cost and electricity price of an onshore wind power project. Background Art

[0002] In the cost estimate of onshore wind power projects, the calculation of electricity prices is an important component, which not only affects the economic benefit evaluation of the project, but also directly relates to the project's payback period and profitability. Under the existing technical background, electricity price calculation is usually based on a variety of methods such as cost-plus method, market bidding method or government guidance price, and the specific choice depends on national policies, market environment and project characteristics. With the adjustment of renewable energy subsidy policies and technological progress, the electricity price calculation of wind power projects tends to reflect the actual cost and market supply and demand conditions, while considering environmental and social benefits. At present, in order to promote the development of clean energy such as wind power, many countries and regions have adopted measures such as feed-in tariffs (FITs), green certificate trading and direct subsidies to support the construction and operation of wind power projects. In China, with the deepening of power system reform, the wind power price mechanism is gradually shifting towards a more market-oriented direction, through competitive resource allocation and implementation of differentiated subsidies, aiming to improve the overall competitiveness and sustainable development capabilities of the wind power industry.

[0003] In the cost estimate of onshore wind power projects, although the existing technology for electricity price calculation methods has made certain progress, there are still some problems: (1) Strong policy dependence: At present, the electricity price calculation of many wind power projects is highly dependent on the government's subsidy policy. Once the policy changes, such as subsidy reduction or cancellation, it may have a significant impact on the financial feasibility of the project. (2) Imperfect market mechanism: Although some countries and regions have begun to try to determine wind power prices through market competition, due to the particularity of the power market, such as monopoly and regionality, the market price mechanism is not yet fully mature, and the results of market bidding sometimes cannot truly reflect the cost and value of the project. (3) Difficulty in cost estimation: The cost of wind power projects is affected by many factors such as geographical location, climate conditions, and technology selection. It is difficult to accurately estimate the cost; especially in the early stages of the project, due to the lack of detailed engineering design data, the accuracy of cost prediction is often low. (4) Rapid technological updates: The rapid development of wind power technology has led to the continuous emergence of new equipment and technologies, which has put forward higher requirements for electricity price calculation; the existing electricity price calculation method may fail to reflect the latest technological advances in a timely manner, resulting in deviations between the electricity price calculation results and the actual situation.

[0004] Prior art 1, application number: CN202411136540.2 discloses a method for optimizing the configuration of the wind-solar ratio system of a hydrogen production park, including: calculating the output time series of local wind power and photovoltaic power generation, analyzing the output characteristics of wind power and photovoltaic power generation and the wind-solar correlation; establishing equivalent equipment models of hydrogen energy storage units and electrochemical energy storage units, and according to the operation characteristic models of relevant electricity and hydrogen equipment in the park; combining the above objective functions, obtaining the optimal configuration scheme for the system planning of the new energy hydrogen production park and the total cost of the system configuration; further considering the electricity price factors and the constraints of the energy storage operation characteristics, conducting a simulation of the wind-solar hydrogen production time series operation throughout the year, and analyzing the power purchase demand and new energy consumption capacity of the park under this planning optimization configuration scheme. Although the accurate simulation of the operation of energy equipment has been achieved; however, the lack of wind power engineering cost calculation makes it impossible to maximize the benefits of wind power generation.

[0005] Prior art 2, application number: CN 202410324330.X discloses a capacity optimization configuration method for a wind power-photovoltaic-photothermal coupled hydrogen production system, including: collecting data such as wind speed and light intensity in a certain area, collecting output power operating parameters of a photothermal power station, collecting electricity price fluctuation data in the area and hydrogen load data of a refinery; analyzing the output power of wind farms and photovoltaic farms, the volatility of hydrogen load in refineries, simulating wind power-photovoltaic-photothermal coupled hydrogen production systems under multiple scenarios; establishing a wind power-photovoltaic-photothermal coupled hydrogen production system based on renewable energy hydrogen production in multiple scenarios. The minimum total cost objective function is established, and constraints are given to the objective function; the minimum total cost objective function includes investment cost, operation and maintenance cost, and system revenue. Although based on the minimum total cost objective function and constraints, the CPLEX solver is called through the YALMIP platform, and the hydrogen production cost of the system and the optimal hydrogen production plan are determined through MATLAB solution. However, the focus is on the calculation of hydrogen production costs, without involving the cost and electricity price accounting of wind power, resulting in the need to further improve the level of intelligence in the technology of cost and electricity price accounting of wind power projects.

[0006] Prior art three, application number: CN 202311697073.6 discloses a method and system for calculating transmission and distribution prices considering grid-side energy storage, the method comprising: analyzing the cost structure of the grid-side energy storage system, and constructing a cost model of the energy storage system based on the cost structure; constructing a grid-side energy storage revenue model based on the application scenario of the grid-side energy storage system; analyzing the substitution benefits of the grid-side energy storage system for access to the grid, and constructing a grid-side alternative energy storage investment cost model based on the substitution benefits; analyzing the transmission and distribution price recovery measurement of the grid-side energy storage system in different scenarios based on the energy storage system cost model, the grid-side energy storage revenue model, and the grid-side alternative energy storage investment cost model. Although it is possible to convert the grid-side energy storage alternative cost into effective benefits and incorporate it into the transmission and distribution price calculation, thereby clearly considering the return mechanism of grid-side energy storage; however, its solution is relatively complex, requires high accuracy of the model, and has certain limitations during use.

[0007] At present, the existing technologies 1, 2 and 3 lack the cost and electricity price calculation methods for wind power projects, and the degree of intelligence needs to be further improved. Therefore, the present invention provides a method for calculating the cost and electricity price of onshore wind power project cost estimates. Summary of the invention

[0008] In order to solve the above technical problems, the present invention provides a method for calculating the cost and electricity price of an onshore wind power project, comprising the following steps:

[0009] Collect grid electricity price-related data and build a grid electricity price calculation model containing multiple configurable parameters; develop a front-end interface to display and operate the grid electricity price calculation model; input the parameters required for grid electricity price calculation through the front-end interface, and the back-end algorithm calls the grid electricity price calculation model to calculate according to the input parameters to obtain the grid electricity price; feedback the calculation results to the front-end interface for display and user verification;

[0010] The user checks the grid electricity price calculation process and results on the front-end interface. If any deviation is found, the input parameters are adjusted; the back-end algorithm calls the grid electricity price calculation model again and recalculates the grid electricity price based on the revised parameters; the grid electricity price calculation is repeated until the grid electricity price calculation result meets expectations;

[0011] Input the relevant parameters of the diesel generator on the front-end interface, including the purchase price and operation and maintenance costs; the back-end algorithm calculates the price of the diesel generator based on the input parameters and the quota standards; the calculation results are fed back to the front-end interface for users to check; the power grid electricity price and the diesel generator price are input into the comprehensive electricity price calculation model; the back-end algorithm calculates the comprehensive electricity price based on the input parameters; the calculation results are fed back to the front-end interface for users to check; the user checks the comprehensive electricity price calculation process and results on the front-end interface, and adjusts the input parameters if any deviation is found; the back-end algorithm re-calls the comprehensive electricity price calculation model, and recalculates the comprehensive electricity price based on the revised parameters until the comprehensive electricity price calculation result meets expectations;

[0012] When the grid electricity price, diesel generator price and comprehensive electricity price are in line with expectations, the final calculation results are output.

[0013] Optionally, the process of obtaining the grid electricity price includes the following steps:

[0014] Construct a grid electricity price calculation model that includes power generation cost, power generation efficiency, transmission and distribution cost, bookstore loss rate and government subsidy parameters. The back-end algorithm receives the parameters transmitted by the front-end interface, including power generation cost, transmission and distribution cost and government subsidy parameters, and performs preliminary processing on the parameters.

[0015] The backend algorithm calls the power grid electricity price calculation model, which receives the power generation cost, transmission and distribution cost and government subsidy parameters input by the user and performs specific calculations based on the input parameters;

[0016] Call the calculation result and feed the calculation result back to the front-end interface.

[0017] Optionally, the process of performing specific calculations based on input parameters includes the following steps:

[0018] The backend algorithm calls the grid electricity price calculation model to calculate the actual power generation cost of each power generation method, and performs weighted summation according to their weights to obtain the total power generation cost; the transmission and distribution costs are added to the total power generation cost to obtain the basic electricity price;

[0019] Considering the transmission loss rate, multiply the basic electricity price by the transmission loss rate to get the electricity price after considering the loss; considering the market competition coefficient, multiply the electricity price after considering the loss by the market competition coefficient to get the electricity price after considering the market competition;

[0020] Subtract government subsidies from the electricity price after considering market competition to obtain the preliminary grid electricity price; consider the impact of different demand responses, and perform weighted summation based on their coefficients, add them to the preliminary grid electricity price, and obtain the final grid electricity price.

[0021] Optionally, the process of the backend algorithm calling the grid electricity price calculation model includes the following steps:

[0022] Automatically identify the wrong format in the user input parameters through the preset rule set and pattern matching algorithm; automatically convert the identified wrong format into the standard format based on the preset correction rule library; after correcting the parameters, automatically generate a correction log to record the data comparison before and after the correction, and provide real-time feedback to the user;

[0023] Automatically identify different data types in user input data through preset data type classification rules; automatically call conversion algorithms based on the identified data types to convert data in different formats into a unified standard format;

[0024] Through the preset rationality rule base, it automatically learns and identifies the rationality range of user input data, automatically detects abnormal values ​​in user input data, and generates anomaly detection reports; when an anomaly is detected, it automatically generates prompt information to inform users of possible input errors and provide correction suggestions.

[0025] Optionally, the process of the user checking the grid electricity price calculation process and results on the front-end interface includes the following steps:

[0026] Through the custom time period, you can select the historical electricity price data of a certain time period for comparison;

[0027] Generate corresponding charts to display historical electricity price data. The line chart in the chart shows the trend of electricity price changes over time, the bar chart compares the high and low electricity prices at different time points, and the area chart shows the fluctuation range of electricity prices;

[0028] Select the currently calculated electricity price for comparison with the historical data, calculate the percentage difference between the current electricity price and the historical electricity price, mark the significant difference points, and list all the time points of significant differences.

[0029] Optionally, the process of calculating the comprehensive electricity price includes the following steps:

[0030] The back-end algorithm calculates the total price of the diesel generator based on the input purchase price and operation and maintenance costs combined with the quota standards; the calculation results are fed back to the front-end interface for users to check;

[0031] Build a comprehensive electricity price calculation model, and the back-end algorithm calls the comprehensive electricity price calculation model; input the power grid electricity price and diesel generator price into the comprehensive electricity price calculation model to calculate the comprehensive electricity price;

[0032] Check the comprehensive electricity price calculation process and results on the front-end interface, find deviations, and adjust the input parameters; re-call the comprehensive electricity price calculation model, and recalculate the comprehensive electricity price based on the revised parameters until the comprehensive electricity price calculation results meet expectations.

[0033] Optionally, the process of constructing a comprehensive electricity price calculation model includes the following steps:

[0034] Preset fuzzy rules to generate a fuzzy inference system, which can output a preliminary comprehensive electricity price based on the input power grid electricity price and diesel generator price; the fuzzy inference system receives the power grid electricity price and diesel generator price and outputs a preliminary comprehensive electricity price;

[0035] The preliminary comprehensive electricity price is input into the multi-layer perceptron neural network. The input layer contains two input nodes: the grid electricity price and the diesel generator price. The hidden layer contains multiple hidden nodes to capture the complex relationship between the input comprehensive electricity prices. The output layer outputs the final comprehensive electricity price.

[0036] The grid electricity price and diesel generator price are input into the fuzzy logic module to obtain a preliminary comprehensive electricity price; the preliminary comprehensive electricity price is used as input and further optimized through the neural network to obtain the final comprehensive electricity price; the back-end algorithm calls the comprehensive electricity price calculation model, and feeds back the results of the comprehensive electricity price calculation model to the front-end interface for user verification; the user adjusts the input parameters according to the actual situation and re-calls the model for calculation until the comprehensive electricity price calculation result meets expectations.

[0037] Optionally, the process of obtaining the final comprehensive electricity price includes the following steps:

[0038] Based on historical data and real-time data, the membership function of the fuzzy set is dynamically generated, and its shape and range are automatically adjusted according to the changes in the power grid electricity price and the diesel generator price;

[0039] The input layer of the neural network includes the power grid price, diesel generator price and the preliminary comprehensive power price output by the fuzzy inference layer; the hidden layer adopts a multi-layer structure to capture the complex relationship and nonlinear characteristics between the input data;

[0040] The back-end algorithm feeds back the final comprehensive electricity price output by the neural network to the front-end interface in real time, and provides explanations and suggestions through analysis; through the visual interface, the composition and influencing factors of the comprehensive electricity price can be understood.

[0041] Optionally, the process in which the back-end algorithm feeds back the final comprehensive electricity price output by the neural network to the front-end interface in real time includes the following steps:

[0042] The front-end interface receives the data packet pushed by the back-end through the WebSocket connection. The front-end uses JavaScript to parse the received data packet and convert the received JSON string into a JavaScript object; the comprehensive electricity price and related information, such as the electricity price value, timestamp, and input variables, are extracted from the parsed object;

[0043] Display the comprehensive electricity price value in the text box, set a timer, regularly request the backend to obtain the latest data and update the interface; connect through WebSocket, listen to the data update events pushed by the backend, and update the interface in real time;

[0044] Select the chart library, configure the basic parameters of the chart, and update the data source of the chart and refresh the chart each time new comprehensive electricity price data is received; append the new electricity price data to the data set of the chart, call the update method provided by the chart library, and refresh the chart.

[0045] Optionally, the process of exporting the calculation results includes the following steps:

[0046] Extract all relevant data from the grid electricity price calculation model, diesel generator price calculation model and comprehensive electricity price calculation model, including power generation cost, transmission and distribution cost, government subsidies, diesel generator purchase price and operation and maintenance cost; organize the collected data into a hierarchical structure;

[0047] Check the data for duplicates and generate the final accounting results, which include the key indicators of power grid electricity price, diesel generator price and comprehensive electricity price; format the generated accounting results;

[0048] Export the formatted accounting results to the selected file format; verify the generated export file and deliver the verified export file to the user.

[0049] The present invention collects grid electricity price-related data, constructs a grid electricity price calculation model containing multiple configurable parameters, and realizes the automatic calculation of grid electricity price; develops a front-end interface for displaying and operating the grid electricity price calculation model, which improves the convenience and intuitiveness of user operation; inputs parameters through the front-end interface, and the back-end algorithm calls the model to calculate the grid electricity price, realizing the separation of the front and back ends, and improving the maintainability and scalability of the system; the calculation results are fed back to the front-end interface for display, which is convenient for users to check and improves the accuracy and credibility of the calculation results. The user checks the grid electricity price calculation process and results on the front-end interface, and can adjust the input parameters after finding deviations, realizing interactive correction of grid electricity price calculation; the back-end algorithm recalculates the grid electricity price according to the corrected parameters, and repeatedly executes this process until the result meets expectations, ensuring the accuracy of grid electricity price calculation. Input the relevant parameters of the diesel generator in the front-end interface, and the back-end algorithm calculates the price of the diesel generator in combination with the quota standard, thus realizing the automatic accounting of the diesel generator price; input the power grid electricity price and the diesel generator price into the comprehensive electricity price calculation model, calculate the comprehensive electricity price, thus realizing the automatic accounting of the comprehensive electricity price; the user checks the comprehensive electricity price calculation process and results in the front-end interface, and can adjust the input parameters after finding deviations, thus realizing the interactive correction of the comprehensive electricity price calculation; the back-end algorithm recalculates the comprehensive electricity price according to the corrected parameters, and repeats this process until the result meets expectations, thus ensuring the accuracy of the comprehensive electricity price calculation. When the power grid electricity price, the diesel generator price and the comprehensive electricity price all meet expectations, the final accounting results are output, thus realizing the automatic output of the project cost estimate; the final accounting results can be exported to Excel or PDF and other formats, which is convenient for users to use and save, thus improving the practicality and ease of use of the system.

[0050] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0051] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0053] Figure 1 This is a flow chart of a method for calculating the cost and electricity price of an onshore wind power project in Embodiment 1 of the present invention;

[0054] Figure 2 A process diagram for obtaining the power grid electricity price in Embodiment 2 of the present invention;

[0055] Figure 3 A process diagram of performing specific calculations according to input parameters in Embodiment 3 of the present invention;

[0056] Figure 4 This is a process diagram of the back-end algorithm calling the grid electricity price calculation model in Embodiment 4 of the present invention;

[0057] Figure 5 A process diagram of a user checking the calculation process and results of the power grid electricity price on the front-end interface in Embodiment 5 of the present invention;

[0058] Figure 6 A process diagram for calculating the comprehensive electricity price in Embodiment 6 of the present invention;

[0059] Figure 7 This is a process diagram of constructing a comprehensive electricity price calculation model in Example 7 of the present invention;

[0060] Figure 8 This is a process diagram for obtaining the final comprehensive electricity price in Example 8 of the present invention;

[0061] Fig. 9 This is a process diagram of the back-end algorithm in Example 9 of the present invention feeding back the final comprehensive electricity price output by the neural network to the front-end interface in real time;

[0062] Fig.10 This is a process diagram for deriving the calculation results in Example 10 of the present invention. DETAILED DESCRIPTION

[0063] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0064] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms of "a", "said" and "the" used in the embodiments of the present application are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items.

[0065] When the following description relates to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are only examples of devices and methods consistent with some aspects of the present application. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and do not have to be used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0066] Example 1: Figure 1 As shown, an embodiment of the present invention provides a method for calculating the cost and electricity price of an onshore wind power project, comprising the following steps:

[0067] S100: Collect grid electricity price-related data and build a grid electricity price calculation model containing multiple configurable parameters; develop a front-end interface to display and operate the grid electricity price calculation model; input the parameters required for grid electricity price calculation through the front-end interface, and the back-end algorithm calls the grid electricity price calculation model to calculate according to the input parameters to obtain the grid electricity price; feedback the calculation results to the front-end interface for display and user verification;

[0068] The relevant data include power generation costs, transmission and distribution costs, and government subsidies; the grid price calculation model includes functions such as parameter input, calculation process display, and result output;

[0069] S200: The user checks the grid electricity price calculation process and results on the front-end interface, and adjusts the input parameters if any deviation is found; the back-end algorithm re-calls the grid electricity price calculation model, and recalculates the grid electricity price according to the revised parameters; the grid electricity price calculation is repeated until the grid electricity price calculation result meets expectations;

[0070] S300: Input diesel generator related parameters, including purchase price and operation and maintenance cost, on the front-end interface; the back-end algorithm calculates the diesel generator price based on the input parameters and the quota standard; the calculation result is fed back to the front-end interface for user verification; the power grid price and diesel generator price are input into the comprehensive power price calculation model; the back-end algorithm calculates the comprehensive power price based on the input parameters; the calculation result is fed back to the front-end interface for user verification; the user verifies the comprehensive power price calculation process and results on the front-end interface, and adjusts the input parameters if any deviation is found; the back-end algorithm re-calls the comprehensive power price calculation model, and recalculates the comprehensive power price based on the revised parameters until the comprehensive power price calculation result meets expectations;

[0071] S400: When the grid electricity price, diesel generator price and comprehensive electricity price are in line with expectations, the final calculation results are output; the final calculation results are exported to Excel or PDF format.

[0072] The working principle and beneficial effects of the above technical solution are as follows: this embodiment realizes the collection of grid electricity price related data, constructs a grid electricity price calculation model containing multiple configurable parameters; develops a front-end interface to display and operate the grid electricity price calculation model; inputs the parameters required for grid electricity price calculation through the front-end interface, and the back-end algorithm calls the grid electricity price calculation model to calculate according to the input parameters to obtain the grid electricity price; the calculation results are fed back to the front-end interface for display for users to check; the relevant data include power generation costs, transmission and distribution costs and government subsidies, etc.; the grid electricity price calculation model includes functions such as parameter input, calculation process display and result output; secondly, the user checks the grid electricity price calculation process and results on the front-end interface, and if deviations are found, adjusts the input parameters; the back-end algorithm calls the grid electricity price calculation model again, and recalculates the grid electricity price according to the revised parameters; the calculation of the grid electricity price is repeated until the grid electricity price is The price calculation result meets expectations; then the relevant parameters of the diesel generator are entered in the front-end interface, including the purchase price and operation and maintenance costs; the back-end algorithm calculates the price of the diesel generator based on the input parameters and the quota standards; the calculation results are fed back to the front-end interface for display for users to check; the power grid electricity price and the diesel generator price are entered into the comprehensive electricity price calculation model; the back-end algorithm calculates the comprehensive electricity price based on the input parameters; the calculation results are fed back to the front-end interface for display; the user checks the comprehensive electricity price calculation process and results in the front-end interface, and adjusts the input parameters if any deviation is found; the back-end algorithm re-calls the comprehensive electricity price calculation model, and recalculates the comprehensive electricity price based on the revised parameters until the comprehensive electricity price calculation result meets expectations; finally, when the power grid electricity price, the diesel generator price and the comprehensive electricity price all meet expectations, the final accounting results are output; the final accounting results are exported to formats such as Excel or PDF. In the above scheme, step S100 collects grid electricity price related data, builds a grid electricity price calculation model containing multiple configurable parameters, and realizes the automatic calculation of grid electricity price; develops a front-end interface for displaying and operating the grid electricity price calculation model, which improves the convenience and intuitiveness of user operation; inputs parameters through the front-end interface, and the back-end algorithm calls the model to calculate the grid electricity price, realizing the separation of front and back ends, and improving the maintainability and scalability of the system; the calculation results are fed back to the front-end interface for display, which is convenient for users to check and improves the accuracy and credibility of the calculation results. Significance: The fast and accurate calculation of grid electricity price is realized, laying the foundation for the calculation of comprehensive electricity price; through parameter configuration and adjustment, it can flexibly respond to the calculation requirements of different grid electricity prices, and improve the adaptability and versatility of the system; the design of separation of front and back ends facilitates the development, maintenance and upgrading of the system, which is conducive to the long-term stable operation of the system. In step S200, the user checks the grid electricity price calculation process and results on the front-end interface, and can adjust the input parameters after finding deviations, realizing the interactive correction of grid electricity price calculation; the back-end algorithm recalculates the grid electricity price according to the corrected parameters, and repeatedly executes this process until the result meets expectations, ensuring the accuracy of grid electricity price calculation.Significance: Through user participation and feedback, the calculation results of the power grid electricity price are continuously optimized, and the accuracy and reliability of the calculation are improved; the interactive correction process enables users to deeply understand the calculation logic, and enhances the user's trust in the system and the use experience. Step S300 Input the relevant parameters of the diesel generator in the front-end interface, and the back-end algorithm calculates the price of the diesel generator in combination with the quota standard, realizing the automatic accounting of the diesel generator price; input the power grid electricity price and the diesel generator price into the comprehensive electricity price calculation model, calculate the comprehensive electricity price, and realize the automatic accounting of the comprehensive electricity price; the user checks the comprehensive electricity price calculation process and results in the front-end interface, and can adjust the input parameters after finding the deviation, realizing the interactive correction of the comprehensive electricity price calculation; the back-end algorithm recalculates the comprehensive electricity price according to the corrected parameters, and repeatedly executes this process until the result meets the expectation, ensuring the accuracy of the comprehensive electricity price calculation. Significance: The fast and accurate calculation of the diesel generator price and the comprehensive electricity price is realized, providing a reliable basis for the project cost estimate; through parameter configuration and adjustment, it can flexibly respond to the needs of different projects, improving the adaptability and versatility of the system; the interactive correction process enables users to deeply understand the calculation logic, and enhances the user's trust in the system and the use experience. Step S400: When the power grid price, diesel generator price and comprehensive power price are in line with expectations, the final accounting results are output, realizing the automatic output of the project cost estimate; the final accounting results can be exported to Excel or PDF and other formats, which is convenient for users to use and save, and improves the practicality and usability of the system. Significance: The automatic output of the project cost estimate is realized, which improves work efficiency and reduces labor costs; the diversification of export formats meets the different usage needs of users, enhances the practicality and user experience of the system; the output of the final accounting results provides a reliable basis for project decision-making and has important practical application value.

[0073] In summary, this embodiment achieves rapid and accurate calculation of the cost and electricity price of the onshore wind power project cost estimate through technical means such as automated calculation, parameter configuration, interactive correction and result output, which has important technical effects and practical significance.

[0074] Example 2: Figure 2 As shown, based on Example 1, the process of obtaining the grid electricity price provided by the embodiment of the present invention includes the following steps:

[0075] S101: Build a grid electricity price calculation model that includes parameters such as power generation cost, power generation efficiency, power transmission and distribution cost, bookstore loss rate, and government subsidies. The back-end algorithm receives the parameters transmitted by the front-end interface, including power generation cost, power transmission and distribution cost, and government subsidies, and performs preliminary processing on the parameters.

[0076] Among them, the expression of the grid electricity price calculation model is:

[0077]

[0078] Where P represents the grid electricity price, represents the power generation cost of the i-th power generation method, represents the power generation efficiency of the i-th power generation mode, α i represents the weight of the i-th power generation mode, C t represents the transmission and distribution cost, λ t represents the transmission loss rate, λ m represents the market competition coefficient, S represents government subsidies, β j represents the coefficient of the j-th demand response, (D_j) represents the impact of the j-th demand response;

[0079] S102: The backend algorithm calls the power grid electricity price calculation model, which receives the power generation cost, transmission and distribution cost, government subsidy and other parameters input by the user, and performs specific calculations according to the input parameters;

[0080] S103: Call the calculation result and feed the calculation result back to the front-end interface.

[0081] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first constructs a grid electricity price calculation model including parameters such as power generation cost, power generation efficiency, power transmission and distribution cost, bookstore loss rate and government subsidy. The back-end algorithm receives the parameters transmitted by the front-end interface, including parameters such as power generation cost, power transmission and distribution cost and government subsidy, and performs preliminary processing on the parameters; secondly, the back-end algorithm calls the grid electricity price calculation model, and the grid electricity price calculation model receives the parameters such as power generation cost, power transmission and distribution cost and government subsidy input by the user, and performs specific calculations according to the input parameters; finally, the calculation result is called and the calculation result is fed back to the front-end interface. Step S101 of the above scheme constructs a grid electricity price calculation model and performs preliminary processing, constructs a calculation model including multiple parameters (such as power generation cost, power generation efficiency, power transmission and distribution cost, bookstore loss rate and government subsidy, etc.), and ensures the comprehensiveness and scientificity of the electricity price calculation; the back-end algorithm receives the parameters transmitted by the front-end interface, and performs preliminary processing on these parameters to ensure the accuracy and consistency of the data. Significance: By clearly listing the various parameters that affect the electricity price, the transparency of the electricity price calculation is increased, so that users can understand the composition of the electricity price; using scientific models and algorithms to ensure that the calculation of the electricity price is based on actual costs and efficiency, and avoid interference from human factors. Step S102 calls the power grid electricity price calculation model for specific calculations, and the back-end algorithm calls the power grid electricity price calculation model to ensure the automation and efficiency of the calculation process; the model receives the parameters such as power generation cost, transmission and distribution cost and government subsidies input by the user, and performs specific calculations based on these parameters to ensure the accuracy of the calculation results. Significance: Through automated calculations, human errors are reduced and calculation efficiency is improved; calculations are performed based on actual input parameters to ensure the accuracy of electricity prices and avoid electricity price deviations caused by parameter errors. Step S103 calls the calculation results and feeds them back to the front-end interface, calls the calculation results to ensure the timeliness and availability of the results; feeds the calculation results back to the front-end interface so that users can view the electricity price calculation results in real time. Significance: By feeding back the calculation results in real time, users can understand the electricity price situation in a timely manner and improve the user experience; users can directly view the electricity price calculation process and results, and increase users' trust and sense of participation in electricity price calculation.

[0082] In summary, this embodiment ensures the accuracy and transparency of power grid electricity prices through scientific modeling, automated calculations, and real-time feedback, improves user trust and sense of participation, and also provides a scientific basis for power grid management.

[0083] Example 3: Figure 3 As shown, based on Example 2, the process of performing specific calculation according to the input parameters provided in the embodiment of the present invention includes the following steps:

[0084] S1021: The backend algorithm calls the grid electricity price calculation model to calculate the actual power generation cost of each power generation method, performs weighted summation according to their weights, and obtains the total power generation cost; the transmission and distribution cost is added to the total power generation cost to obtain the basic electricity price;

[0085] S1022: Considering the transmission loss rate, multiplying the basic electricity price by the transmission loss rate to obtain the electricity price after considering the loss; considering the market competition coefficient, multiplying the electricity price after considering the loss by the market competition coefficient to obtain the electricity price after considering the market competition;

[0086] S1023: Subtract government subsidies from the electricity price after considering market competition to obtain a preliminary grid electricity price; consider the impact of different demand responses, and perform weighted summation based on their coefficients, add them to the preliminary grid electricity price, and obtain the final grid electricity price.

[0087] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the back-end algorithm first calls the grid electricity price calculation model to calculate the actual power generation cost of each power generation mode, and performs weighted summation according to its weight to obtain the total power generation cost; the transmission and distribution cost is added to the total power generation cost to obtain the basic electricity price; secondly, the transmission loss rate is considered, and the basic electricity price is multiplied by the transmission loss rate to obtain the electricity price after considering the loss; considering the market competition coefficient, the electricity price after considering the loss is multiplied by the market competition coefficient to obtain the electricity price after considering the market competition; finally, the government subsidy is subtracted from the electricity price after considering the market competition to obtain the preliminary grid electricity price; considering the impact of different demand responses, and performing weighted summation according to its coefficient, adding it to the preliminary grid electricity price to obtain the final grid electricity price. Step S1021 of the above scheme can accurately reflect the contribution of different power generation modes to the total power generation cost by calculating the actual power generation cost of each power generation mode and performing weighted summation according to its weight; adding the transmission and distribution cost to the total power generation cost to obtain the basic electricity price, which is the basis for electricity price calculation. Significance: By calculating the cost of each power generation method in detail, it is ensured that the calculation of the electricity price is based on the actual cost, which improves the transparency of the electricity price; the calculation of the basic electricity price takes into account the transmission and distribution costs, ensures the fairness of the electricity price, and avoids the impact of the excessive or low cost of a single power generation method on the electricity price. Step S1022 adjusts the basic electricity price by considering the transmission loss rate to ensure that the electricity price reflects the actual power transmission cost; adjusts the electricity price by the market competition coefficient to reflect the impact of market supply and demand on the electricity price. Significance: The consideration of the loss rate makes the electricity price more realistically reflect the actual cost of electricity from power generation to the user end; the introduction of the market competition coefficient enables the electricity price to be dynamically adjusted according to the market supply and demand relationship, enhancing the flexibility and efficiency of the market. Step S1023 subtracts the government subsidy from the electricity price after considering market competition to ensure that the electricity price is not excessively affected by the government subsidy; considers the impact of different demand responses, and adjusts the electricity price by weighted summation according to its coefficient, reflecting the impact of user demand on the electricity price. Significance: By deducting government subsidies, we ensure that electricity prices are more market-oriented and avoid excessive intervention of government subsidies in electricity prices; demand response considerations enable electricity prices to be adjusted according to users' actual needs, enhancing the rationality of electricity prices and user satisfaction.

[0088] In summary, the calculation of the electricity price in this embodiment not only takes into account the actual cost of power generation and transmission and distribution, but also reflects the market supply and demand relationship and user needs, ensuring the fairness, transparency and rationality of the electricity price.

[0089] Example 4: Figure 4 As shown, based on Example 3, the process of the back-end algorithm provided in the embodiment of the present invention calling the grid electricity price calculation model includes the following steps:

[0090] S10211: Automatically identify incorrect formats in user input parameters, such as spelling errors, unit errors, etc., through a preset rule set and pattern matching algorithm; automatically convert the identified incorrect formats into standard formats based on a preset correction rule library; after correcting the parameters, automatically generate a correction log to record the data comparison before and after the correction, and provide real-time feedback to the user;

[0091] S10212: automatically identifying different data types in the user input data, such as text, numbers, dates, etc., through preset data type classification rules; automatically calling a conversion algorithm based on the identified data types to convert data in different formats into a unified standard format;

[0092] S10213: Through the preset rationality rule base, automatically learn and identify the rationality range of user input data, automatically detect abnormal values ​​in user input data, and generate anomaly detection reports; when an anomaly is detected, automatically generate prompt information to inform users of possible input errors and provide correction suggestions.

[0093] The working principle and beneficial effects of the above technical solution are as follows: This embodiment firstly automatically identifies the incorrect format in the user input parameters, such as spelling errors, unit errors, etc., through a preset rule set and pattern matching algorithm; based on a preset correction rule library, automatically converts the identified incorrect format into a standard format; after correcting the parameters, automatically generates a correction log to record the data comparison before and after the correction, and provides real-time feedback to the user; secondly, through the preset data type classification rules, automatically identifies different data types in the user input data, such as text, numbers, dates, etc.; according to the identified data type, automatically calls the conversion algorithm to convert data of different formats into a unified standard format; finally, through the preset rationality rule library, automatically learns and identifies the rationality range of the user input data, automatically detects abnormal values ​​in the user input data, and generates an abnormality detection report; when an abnormality is detected, automatically generates a prompt message to inform the user of possible input errors and provide correction suggestions. Step S10211 of the above scheme is data cleaning and correction. Through the preset rule set and pattern matching algorithm, it can quickly and accurately identify the wrong format in the user input parameters, such as spelling errors, unit errors, etc.; based on the preset correction rule base, the system can automatically convert the identified wrong format into a standard format to ensure the accuracy and consistency of the data; after correcting the parameters, it automatically generates a correction log to record the data comparison before and after the correction, and provides real-time feedback to the user to ensure that the user understands the data correction. Significance: By automatically identifying and correcting the wrong format, the accuracy of the user input data can be significantly improved and human errors can be reduced; real-time feedback of the correction log enables users to understand the data correction in a timely manner and enhance the user's trust in the system; data of different formats are unified into a standard format to ensure the consistency of subsequent data processing and analysis. Step S10212 data type identification and format conversion. Through the preset data type classification rules, it can automatically identify different data types in the user input data, such as text, numbers, dates, etc.; according to the identified data type, the corresponding conversion algorithm is automatically called to convert data of different formats into a unified standard format. Significance: Automatically identify and convert data types, simplify the data processing process, and reduce the need for manual intervention; by automatically calling the conversion algorithm, the system can quickly convert data in different formats into a standard format to improve data processing efficiency; unify data in different formats into a standard format to ensure the consistency of subsequent data processing and analysis. Step S10213 Data rationality check and anomaly detection, through the preset rationality rule library, the system can automatically learn and identify the rationality range of user input data; based on the learned rationality rules, the system can automatically detect abnormal values ​​in user input data and generate anomaly detection reports; when an anomaly is detected, a prompt message is automatically generated to inform the user of possible input errors and provide correction suggestions.Significance: By automatically detecting outliers, the system can improve the reliability of user input data and reduce the impact of abnormal data on subsequent analysis; generate prompt information and correction suggestions so that users can understand input errors in a timely manner and make corrections, thereby enhancing the interactivity between users and the system; through rationality verification, ensure the rationality of user input data, guarantee data quality, and provide reliable data support for subsequent analysis and decision-making.

[0094] In summary, this embodiment can automatically identify, correct, convert and verify user input data to ensure the accuracy, consistency and reliability of the data. It not only improves the efficiency and quality of data processing, but also enhances user experience and system interactivity, provides high-quality data input for the power grid electricity price calculation model, and ensures the accuracy and reliability of the calculation results.

[0095] Example 5: Figure 5 As shown, based on Example 1, the process of the user checking the grid electricity price calculation process and results on the front-end interface provided by the embodiment of the present invention includes the following steps:

[0096] S201: You can select the historical electricity price data of a certain time period for comparison by customizing the time period;

[0097] S202: Generate corresponding charts to display historical electricity price data. The line chart in the chart displays the changing trend of electricity price over time, the bar chart compares the high and low electricity prices at different time points, and the area chart displays the fluctuation range of electricity price;

[0098] S203: Select the currently calculated electricity price for comparison with the historical data, calculate the difference percentage between the current electricity price and the historical electricity price, mark the significant difference points, and list all the time points of significant differences.

[0099] The working principle and beneficial effects of the above technical solution are as follows: First, the embodiment provides you with a custom time period, and selects the historical electricity price data of a certain time period for comparison; secondly, generates a corresponding chart to display the historical electricity price data, the line chart in the chart shows the trend of electricity price changes over time, the bar chart compares the high and low electricity prices at different time points, and the area chart shows the fluctuation range of electricity prices; finally, the currently calculated electricity price is compared with the historical data, the difference percentage between the current electricity price and the historical electricity price is calculated, and the significant difference points are marked, and all the time points of significant differences are listed. Step S201 of the above scheme allows users to select the historical electricity price data of a certain time period for comparison through the selection of a custom time period. Users can select data of any time period according to their needs, rather than being limited to the preset time range; it can quickly respond to user requests and extract data of the specified time period from the database, reducing unnecessary waiting time. The significance achieved: users can select a suitable time period for analysis according to their business needs or specific events, so as to obtain more targeted data support; by selecting data of different time periods, users can have a more comprehensive understanding of the long-term trend and short-term fluctuation of electricity prices, and provide richer references for decision-making. Step S202 generates corresponding charts to display historical electricity price data. The data is displayed in the form of charts, making the changing trend and fluctuation of electricity prices more intuitive and easy to understand; line charts, bar charts and area charts display data from different angles, helping users to fully understand all aspects of electricity prices. Significance achieved: Charts can help users quickly understand complex electricity price data, reducing the difficulty of interpreting data; through charts, users can more easily discover abnormal fluctuations and trends in electricity prices, so as to make more informed decisions. Step S203 can automatically calculate the percentage difference between the current electricity price and the historical electricity price, reducing the errors and time costs of manual calculations; by marking significant difference points, it helps users quickly locate abnormal fluctuations in electricity prices and improves the efficiency of data analysis. Significance achieved: Through comparative analysis, users can promptly discover abnormal fluctuations in electricity prices, so as to take corresponding measures to deal with potential risks; by understanding the difference between the current electricity price and the historical electricity price, users can better adjust strategies, optimize resource allocation and reduce costs.

[0100] In summary, this embodiment not only provides flexible data selection and multi-dimensional data display, but also realizes automated comparative analysis and anomaly detection, thereby helping users to understand electricity price data more comprehensively and intuitively, optimize the decision-making process, and improve business efficiency.

[0101] Example 6: Figure 6 As shown, based on Example 1, the process of calculating the comprehensive electricity price provided by the embodiment of the present invention includes the following steps:

[0102] S301: The back-end algorithm calculates the total price of the diesel generator based on the input purchase price and operation and maintenance cost combined with the quota standard; the calculation result is fed back to the front-end interface for display and verification by the user;

[0103] Among them, the calculation expression of the total price of the diesel generator is:

[0104]

[0105] In the formula, Total P rice represents the total price of the diesel generator, Purchase P Rice represents the purchase price of diesel generators, Maintenance C ost i Represents the maintenance cost of item i, Maintenance F requency i Indicates the frequency of maintenance of item i, Operational C ost j Operational F requency j represents the frequency of the jth operation, Standard A Adjustment indicates the adjustment made according to the quota standard, n indicates the total number of maintenance cost items, and m indicates the total number of operating cost items. The various costs of diesel generators are comprehensively considered and adjusted according to the actual situation to ensure the accuracy and reliability of the calculation results. Users can check and adjust according to the results displayed on the front-end interface to ensure that the final comprehensive electricity price calculation results meet expectations;

[0106] S302: construct a comprehensive electricity price calculation model, and the back-end algorithm calls the comprehensive electricity price calculation model; input the power grid electricity price and the diesel generator price into the comprehensive electricity price calculation model to calculate the comprehensive electricity price;

[0107] S303: Check the comprehensive electricity price calculation process and results on the front-end interface, find deviations, and adjust the input parameters; re-call the comprehensive electricity price calculation model, and recalculate the comprehensive electricity price according to the revised parameters until the comprehensive electricity price calculation results meet expectations.

[0108] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, first, the back-end algorithm calculates the total price of the diesel generator according to the input purchase price and operation and maintenance cost, combined with the quota standard; the calculation result is fed back to the front-end interface for display for users to check; secondly, a comprehensive electricity price calculation model is constructed, and the back-end algorithm calls the comprehensive electricity price calculation model; the power grid electricity price and the diesel generator price are input into the comprehensive electricity price calculation model to calculate the comprehensive electricity price; finally, the comprehensive electricity price calculation process and results are checked on the front-end interface, deviations are found, and input parameters are adjusted; the comprehensive electricity price calculation model is re-called, and the comprehensive electricity price is recalculated according to the corrected parameters until the comprehensive electricity price calculation result meets expectations. In the above scheme, step S301 is used to calculate the total price of the diesel generator. The back-end algorithm automatically calculates the total price of the diesel generator according to the input purchase price and operation and maintenance cost, combined with the quota standard; the calculation result is fed back to the front-end interface in real time for users to check, ensuring the accuracy and transparency of the data. Significance: Automated calculation reduces the errors and time costs of manual calculations and improves work efficiency; through real-time feedback, users can check data in time to ensure the accuracy of the calculation results, and provide reliable basic data for comprehensive electricity price calculation. Step S302: Construction and call of comprehensive electricity price calculation model. The comprehensive electricity price calculation model is constructed to integrate the power grid electricity price and the diesel generator price for comprehensive calculation. The back-end algorithm calls the comprehensive electricity price calculation model to automatically calculate the comprehensive electricity price according to the input parameters. Significance: By constructing and calling the comprehensive electricity price calculation model, the systematic calculation of electricity price is realized to ensure the standardization and consistency of the calculation process. The power grid electricity price and the diesel generator price are integrated to calculate the comprehensive electricity price, which provides comprehensive data support for project decision-making. Step S303: Verification and adjustment of comprehensive electricity price calculation results. The user verifies the comprehensive electricity price calculation process and results on the front-end interface. If deviations are found, the input parameters can be adjusted. The back-end algorithm re-calls the comprehensive electricity price calculation model according to the corrected parameters and automatically recalculates the comprehensive electricity price until the result meets expectations. Significance: Through user verification and adjustment, the user's sense of participation and control in the calculation process is enhanced, ensuring the accuracy of the final result and user satisfaction. It supports dynamic adjustment of input parameters, and the system automatically recalculates, which improves the flexibility and adaptability of the calculation and ensures that the final result meets actual needs.

[0109] In summary, this embodiment reduces manual intervention and errors and improves calculation efficiency through automated calculation and real-time feedback; users can check and adjust data in real time to ensure the accuracy of calculation results and provide reliable data support for project decision-making; users check and adjust data on the front-end interface, which enhances the user's sense of participation and control over the calculation process and improves user satisfaction; by building and calling a comprehensive electricity price calculation model, the systematic calculation of electricity prices is realized to ensure the standardization and consistency of the calculation process. The ability to efficiently and accurately complete the calculation of comprehensive electricity prices provides strong support for the cost estimate of onshore wind power engineering projects and ensures the scientificity and rationality of project decisions.

[0110] Example 7: Figure 7 As shown, based on Example 6, the process of constructing a comprehensive electricity price calculation model provided by the embodiment of the present invention includes the following steps:

[0111] S3021: presetting fuzzy rules to generate a fuzzy inference system, which can output a preliminary comprehensive electricity price based on the input power grid electricity price and diesel generator price; the fuzzy inference system receives the power grid electricity price and diesel generator price and outputs a preliminary comprehensive electricity price;

[0112] The content of the fuzzy rules is:

[0113] Rule 1: If the grid electricity price is high and the diesel generator price is low, the comprehensive electricity price will be biased towards the diesel generator price;

[0114] Rule 2: If the grid electricity price is low and the diesel generator price is high, the comprehensive electricity price will tend to the grid electricity price;

[0115] S3022: The preliminary comprehensive electricity price is input into a multi-layer perceptron neural network, the input layer includes two input nodes, namely, the grid electricity price and the diesel generator price, the hidden layer includes multiple hidden nodes for capturing the complex relationship between the input comprehensive electricity prices, and the output layer outputs the final comprehensive electricity price;

[0116] S3023: Input the grid electricity price and diesel generator price into the fuzzy logic module to obtain a preliminary comprehensive electricity price; use the preliminary comprehensive electricity price as input and further optimize it through the neural network to obtain the final comprehensive electricity price; the back-end algorithm calls the comprehensive electricity price calculation model, and feeds back the results of the comprehensive electricity price calculation model to the front-end interface for user verification; the user adjusts the input parameters according to the actual situation and re-calls the model for calculation until the comprehensive electricity price calculation result meets expectations.

[0117] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, fuzzy rules are first pre-set to generate a fuzzy inference system, which can output a preliminary comprehensive electricity price according to the input grid electricity price and diesel generator price; the fuzzy inference system receives the grid electricity price and diesel generator price and outputs a preliminary comprehensive electricity price; wherein the content of the fuzzy rules is as follows: Rule 1: If the grid electricity price is high and the diesel generator price is low, the comprehensive electricity price is biased towards the diesel generator price; Rule 2: If the grid electricity price is low and the diesel generator price is high, the comprehensive electricity price is biased towards the grid electricity price; secondly, the preliminary comprehensive electricity price is input into a multi-layer perceptron neural network, the input layer includes two input nodes of the grid electricity price and the diesel generator price, the hidden layer includes multiple hidden nodes, which are used to capture the complex relationship between the input comprehensive electricity prices, and the output layer outputs the final comprehensive electricity price; finally, the grid electricity price and the diesel generator price are input into the fuzzy logic module to obtain a preliminary comprehensive electricity price; the preliminary comprehensive electricity price is used as input, and further optimized through the neural network to obtain the final comprehensive electricity price; the result of the comprehensive electricity price calculation model is fed back to the front-end interface for user verification. The user can adjust the input parameters according to the actual situation and call the model again for calculation until the comprehensive electricity price calculation result meets the expectations. Step S3021 of the above scheme pre-sets fuzzy rules and generates a fuzzy reasoning system, which can handle uncertainty and ambiguity. Through the preset fuzzy rules, a preliminary comprehensive electricity price can be output according to the input grid electricity price and diesel generator price; the setting of fuzzy rules enables the system to flexibly adjust the output results according to different input conditions, thereby enhancing the adaptability and robustness of the model. Significance achieved: The preliminary comprehensive electricity price provides basic data for optimization and ensures the coherence and logic of the calculation process; the use of the fuzzy reasoning system enables the model to better handle complex situations in practical applications and improves the accuracy and reliability of the calculation results. Step S3022 The preliminary comprehensive electricity price is input into the multilayer perceptron neural network. The multilayer perceptron neural network can capture the complex relationship between the input data. Through multiple nodes of the hidden layer, the model can learn deeper features and patterns; the optimization of the neural network further refines and improves the preliminary comprehensive electricity price, and the output final comprehensive electricity price is closer to the actual value. Significance achieved: The optimization of the neural network enhances the prediction ability and accuracy of the model, making the final comprehensive electricity price more in line with actual needs; the use of multi-layer perceptrons enables the model to handle complex nonlinear relationships, improving the generalization ability and adaptability of the model. Step S3023: The integration of the fuzzy logic module and the neural network module, the integration of fuzzy logic and neural network, forms a hybrid model that combines the advantages of both, can handle fuzziness and uncertainty, and can capture and optimize complex relationships; model integration makes the calculation process more efficient and accurate, and the output comprehensive electricity price is more reliable.Significance achieved: The construction of the hybrid model makes the comprehensive electricity price calculation process more scientific and systematic, ensuring the accuracy and reliability of the calculation results; the front-end interface display and user core enhance the user experience. Users can adjust the input parameters according to actual conditions and re-call the model for calculation to ensure that the final comprehensive electricity price calculation results meet expectations.

[0118] In summary, this embodiment constructs a hybrid model based on fuzzy logic and neural network for calculating the comprehensive electricity price. Each step ensures the accuracy and reliability of the model; ultimately, the construction of the model not only improves the accuracy of the calculation results, but also enhances the user experience, making the comprehensive electricity price calculation process more scientific, systematic and user-friendly.

[0119] Example 8: Figure 8 As shown, based on Example 7, the process of obtaining the final comprehensive electricity price provided by the embodiment of the present invention includes the following steps:

[0120] S30231: Dynamically generate membership functions of fuzzy sets based on historical and real-time data, and automatically adjust their shapes and ranges according to changes in grid electricity prices and diesel generator prices;

[0121] Among them, the expression of membership function is:

[0122]

[0123] In the formula, μ A (x) represents the membership function, which indicates the degree to which the element x belongs to the fuzzy set A; its value range is [0,1], where 0 indicates that it does not belong at all and 1 indicates that it belongs completely; x represents the input variable, which indicates the element or data point to be evaluated; c represents the center point, which indicates the position where the membership function reaches the maximum value (i.e., 1); w represents the width, which indicates the width or range of the membership function; and p represents the power exponent, which is used to adjust the shape of the membership function;

[0124] S30232: The input layer of the neural network contains the power grid price, diesel generator price and the preliminary comprehensive power price output by the fuzzy inference layer; the hidden layer adopts a multi-layer structure to capture the complex relationship and nonlinear characteristics between the input data;

[0125] S30233: The back-end algorithm feeds back the final comprehensive electricity price output by the neural network to the front-end interface in real time, and provides explanations and suggestions through analysis; through the visual interface, the composition and influencing factors of the comprehensive electricity price are understood.

[0126] The working principle and beneficial effects of the above technical solution are as follows: first, this embodiment dynamically generates the membership function of the fuzzy set according to historical data and real-time data, and automatically adjusts its shape and range according to the changes in the power grid electricity price and the diesel generator price; secondly, the input layer of the neural network includes the power grid electricity price, the diesel generator price and the preliminary comprehensive electricity price output by the fuzzy reasoning layer; the hidden layer adopts a multi-layer structure to capture the complex relationship and nonlinear characteristics between the input data; finally, the back-end algorithm feeds back the final comprehensive electricity price output by the neural network to the front-end interface in real time, and provides explanations and suggestions through analysis; through the visual interface, the composition and influencing factors of the comprehensive electricity price are understood. Step S30231 of the above scheme dynamically generates the membership function of the fuzzy set, and can dynamically adjust the membership function of the fuzzy set according to historical data and real-time data, so that it can more accurately reflect the current market conditions; the shape and range of the fuzzy set can be automatically adjusted according to the changes in the power grid electricity price and the diesel generator price, which enhances the flexibility and adaptability of the system; by dynamically adjusting the fuzzy set, the uncertainty and complexity of the input data can be more accurately processed, and the accuracy of the preliminary comprehensive electricity price can be improved. Significance: It can respond to market changes in real time to ensure that the output preliminary comprehensive electricity price is always consistent with the actual market situation; through data-driven adaptive adjustment, it can better utilize historical data and real-time data to improve the model's predictive ability and practicality; the dynamically adjusted fuzzy set makes the system more transparent and easy to understand, and users can more intuitively understand the composition and influencing factors of the comprehensive electricity price. Step S30232 The input layer and hidden layer design of the neural network. The input layer of the neural network not only includes the power grid electricity price and the diesel generator price, but also introduces the preliminary comprehensive electricity price output by the fuzzy reasoning layer, making the input data more comprehensive and rich; the hidden layer adopts a multi-layer structure, and through the deep learning algorithm, it can capture the complex relationship and nonlinear characteristics between the input data, improve the model's expression ability and prediction accuracy; through the nonlinear mapping of the multi-layer neural network, the preliminary comprehensive electricity price can be further optimized, and a more accurate and reliable final comprehensive electricity price can be output. Significance: Through the complex relationship capture and nonlinear mapping of multi-layer neural networks, high-precision prediction of comprehensive electricity prices can be achieved to meet users' needs for accuracy; multi-layer structures and deep learning algorithms improve the generalization ability of the model, enabling it to better adapt to different market environments and user needs; through multi-dimensional input and complex relationship capture, it can better integrate data from different sources and improve the comprehensive analysis and decision-making support capabilities of the model. Step S30233 Result feedback and adjustment: The back-end algorithm feeds back the final comprehensive electricity price output by the neural network to the front-end interface in real time to ensure that users can obtain the latest calculation results in a timely manner; through the intelligent analysis module, detailed explanations and suggestions can be provided to help users better understand the composition and influencing factors of the comprehensive electricity price; through the visual interface, users can intuitively understand the composition and influencing factors of the comprehensive electricity price, improving the transparency and user-friendliness of the system.Significance: Real-time feedback and intelligent analysis enable users to actively participate in the calculation and adjustment of the comprehensive electricity price, thereby improving user satisfaction and trust; through detailed explanations and suggestions, it can provide users with strong decision-making support and help them make more scientific and reasonable decisions; visual display and user feedback mechanism enable the system to be continuously optimized and improved to ensure that the output comprehensive electricity price always meets user expectations and actual market needs.

[0127] In summary, the comprehensive electricity price calculation model of this embodiment can not only realize the accurate calculation and optimization of the power grid electricity price and the diesel generator price, but also dynamically adjust and optimize the model according to real-time data and user needs to ensure that the output comprehensive electricity price always meets the user's expectations and the actual needs of the market. The characteristics of adaptability, high-precision prediction, user participation and continuous optimization make this model of great value and significance in practical applications.

[0128] Example 9: Fig. 9 As shown, based on Example 8, the process of the back-end algorithm provided in the embodiment of the present invention feeding back the final comprehensive electricity price output by the neural network to the front-end interface in real time includes the following steps:

[0129] S302331: The front-end interface receives the data packet pushed by the back-end through the WebSocket connection. The front-end uses JavaScript to parse the received data packet and convert the received JSON string into a JavaScript object; the comprehensive electricity price and related information, such as the electricity price value, timestamp, and input variables, are extracted from the parsed object;

[0130] S302332: Display the comprehensive electricity price value in the text box, set a timer, regularly request the backend to obtain the latest data and update the interface; connect through WebSocket, listen to the data update events pushed by the backend, and update the interface in real time;

[0131] S302333: Select a chart library, configure the basic parameters of the chart, and update the data source of the chart and refresh the chart each time new comprehensive electricity price data is received; append the new electricity price data to the data set of the chart, call the update method provided by the chart library, and refresh the chart.

[0132] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, first, the front-end interface receives the data packet pushed by the back-end through the WebSocket connection, and the front-end uses JavaScript to parse the received data packet and convert the received JSON string into a JavaScript object; extract the comprehensive electricity price and related information from the parsed object, such as the electricity price value, timestamp and input variables; secondly, display the comprehensive electricity price value in a text box, set a timer, and regularly request the back-end to obtain the latest data and update the interface; through the WebSocket connection, listen to the data update events pushed by the back-end, and update the interface in real time; finally, select the chart library, configure the basic parameters of the chart, and update the data source of the chart and refresh the chart each time new comprehensive electricity price data is received; append the new electricity price data to the data set of the chart, call the update method provided by the chart library, and refresh the chart. Step S302331 of the above scheme receives and parses data, uses WebSocket technology to realize real-time two-way communication between the front-end and the back-end, and ensures that data can be transmitted quickly and efficiently; parses the received JSON string through JavaScript and converts it into a JavaScript object for easy processing and operation. Significance achieved: Through WebSocket connection, the front end can receive data pushed by the back end in real time to ensure timely update of electricity price information; after converting the JSON string into a JavaScript object, the data can be easily extracted and manipulated, providing a basis for subsequent data display and chart updates. Step S302332 Data display and update, display the comprehensive electricity price value directly in the text box, so that users can intuitively see the current electricity price information; set a timer to request back-end data regularly, and listen to data update events through WebSocket to ensure that the interface can be updated in real time. Significance achieved: Users can see changes in electricity prices in real time without manually refreshing the page, which improves the user experience; through the dual mechanism of timer and WebSocket, ensure that the front-end interface always displays the latest electricity price information, avoiding the problem of data lag. Step S302333 Chart update and display, select a suitable chart library (such as ECharts, D3.js, etc.), configure the basic parameters of the chart, and ensure that the chart can display data correctly; after receiving new comprehensive electricity price data each time, update the data source of the chart, and call the update method of the chart library to refresh the chart. Significance achieved: Displaying electricity price data in the form of charts allows users to more intuitively understand the changing trends and fluctuations of electricity prices; charts can be updated in real time as new data arrives, helping users better track real-time changes in electricity prices and provide a basis for decision-making.

[0133] In summary, this embodiment realizes the real-time transmission, analysis, display and update of comprehensive electricity price information through technical means such as WebSocket, JavaScript parsing, timer, WebSocket monitoring, chart library, etc. It not only improves the user experience and ensures the real-time and consistency of data, but also enhances the visualization of data in the form of charts, helping users to better understand and use electricity price information.

[0134] Example 10: Fig.10 As shown, based on Example 1, the process of exporting the calculation results provided by the embodiment of the present invention includes the following steps:

[0135] S401: extract all relevant data from the power grid price calculation model, the diesel generator price calculation model and the comprehensive price calculation model, including power generation cost, transmission and distribution cost, government subsidies, diesel generator purchase price and operation and maintenance cost, etc.; organize the collected data according to the hierarchical structure;

[0136] S402: Check the data for duplicates after sorting, and generate the final accounting results, which include key indicators such as power grid electricity price, diesel generator price and comprehensive electricity price; format the generated accounting results;

[0137] S403: Export the formatted accounting results to the selected file format; verify the generated export file, and deliver the verified export file to the user.

[0138] The working principle and beneficial effects of the above technical solution are as follows: the present embodiment first extracts all relevant data from the power grid electricity price calculation model, the diesel generator price calculation model and the comprehensive electricity price calculation model, including power generation cost, transmission and distribution cost, government subsidy, diesel generator purchase price and operation and maintenance cost, etc.; the collected data is sorted according to the hierarchical structure; secondly, the sorted data is checked for duplicates to generate the final accounting results, which include key indicators such as power grid electricity price, diesel generator price and comprehensive electricity price; the generated accounting results are formatted; finally, the formatted accounting results are exported to the selected file format; the generated export file is verified, and the verified export file is delivered to the user. Step S401 of the above scheme extracts and organizes data, extracts relevant data from multiple calculation models, and ensures the comprehensiveness and accuracy of the data; the extracted data is sorted according to the hierarchical structure to facilitate duplicate checking, verification and export. The significance achieved: by comprehensively extracting and organizing data, the integrity of the accounting results is ensured to avoid missing key information; the hierarchical data structure makes the data easier to understand and analyze, laying the foundation for duplicate checking and formatting. Step S402: Data duplication check and result generation. Through the duplication check algorithm, the uniqueness and accuracy of the data are ensured to avoid the impact of duplicate data on the accounting results. According to the data after duplication check, the final accounting results are generated, including key indicators such as power grid electricity price, diesel generator price and comprehensive electricity price. The generated accounting results are formatted to ensure the readability and usability of the data. The significance achieved: Through duplication check processing, the accuracy of the accounting results is ensured to avoid errors caused by duplicate data; formatting processing makes the accounting results easier to understand and use, which is convenient for users to check and further analyze. Step S403: File export and verification. The formatted accounting results are exported to the selected file format (such as Excel or PDF) for user use and sharing; the generated export file is verified to ensure that the file content is consistent with the accounting results and the file format meets expectations. The significance achieved: Through file export, the accounting results can be delivered to users in a standardized format, which is convenient for them to use in different scenarios; through verification, the accuracy and completeness of the exported files are ensured to avoid data loss or format errors, and enhance the user's trust in the accounting results.

[0139] In summary, this embodiment ensures the integrity, accuracy and readability of the accounting results. Each step together builds an efficient and reliable accounting result export process. It not only improves the efficiency of data processing, but also enhances the user's trust and satisfaction with the accounting results.

[0140] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of equivalent technologies of the present invention, the present invention is also intended to include these modifications and variations.

Claims

1. A method for calculating the cost and electricity price of an onshore wind power project, characterized in that: The following steps are involved: Collect grid electricity price-related data and build a grid electricity price calculation model containing multiple configurable parameters; develop a front-end interface to display and operate the grid electricity price calculation model; input the parameters required for grid electricity price calculation through the front-end interface, and the back-end algorithm calls the grid electricity price calculation model to calculate according to the input parameters to obtain the grid electricity price; feedback the calculation results to the front-end interface for display and user verification; The user checks the grid electricity price calculation process and results on the front-end interface and adjusts the input parameters if any deviation is found; The back-end algorithm calls the grid electricity price calculation model again and recalculates the grid electricity price based on the revised parameters. The grid electricity price calculation is repeated until the grid electricity price calculation result meets expectations. Input the relevant parameters of the diesel generator in the front-end interface, including the purchase price and operation and maintenance cost; the back-end algorithm calculates the price of the diesel generator based on the input parameters and the quota standard; the calculation results are fed back to the front-end interface for users to check; the power grid electricity price and the diesel generator price are input into the comprehensive electricity price calculation model; the back-end algorithm calculates the comprehensive electricity price based on the input parameters; the calculation results are fed back to the front-end interface for display; The user checks the comprehensive electricity price calculation process and results on the front-end interface, finds deviations, and adjusts the input parameters; The back-end algorithm re-calls the comprehensive electricity price calculation model and recalculates the comprehensive electricity price based on the revised parameters until the comprehensive electricity price calculation result meets expectations; When the grid electricity price, diesel generator price and comprehensive electricity price are in line with expectations, the final calculation results are output.

2. The method for calculating the cost and electricity price of an onshore wind power project according to claim 1, characterized in that: The process of obtaining the grid electricity price includes the following steps: Construct a grid electricity price calculation model that includes power generation cost, power generation efficiency, transmission and distribution cost, bookstore loss rate and government subsidy parameters. The back-end algorithm receives the parameters transmitted by the front-end interface, including power generation cost, transmission and distribution cost and government subsidy parameters, and performs preliminary processing on the parameters. The backend algorithm calls the power grid electricity price calculation model, which receives the power generation cost, transmission and distribution cost and government subsidy parameters input by the user and performs specific calculations based on the input parameters; Call the calculation result and feed the calculation result back to the front-end interface.

3. The method for calculating the cost and electricity price of an onshore wind power project as claimed in claim 2, characterized in that: The process of performing specific calculations based on input parameters includes the following steps: The backend algorithm calls the grid electricity price calculation model to calculate the actual power generation cost of each power generation method, and performs weighted summation according to their weights to obtain the total power generation cost; the transmission and distribution costs are added to the total power generation cost to obtain the basic electricity price; Considering the transmission loss rate, multiply the basic electricity price by the transmission loss rate to get the electricity price after considering the loss; considering the market competition coefficient, multiply the electricity price after considering the loss by the market competition coefficient to get the electricity price after considering the market competition; Subtract government subsidies from the electricity price after considering market competition to obtain the preliminary grid electricity price; consider the impact of different demand responses, and perform weighted summation based on their coefficients, add them to the preliminary grid electricity price, and obtain the final grid electricity price.

4. The method for calculating the cost and electricity price of an onshore wind power project as claimed in claim 3, characterized in that: The process of the backend algorithm calling the grid electricity price calculation model includes the following steps: Automatically identify incorrect formats in user input parameters through preset rule sets and pattern matching algorithms; Automatically convert the identified erroneous formats into standard formats based on the preset correction rule library; After the parameters are corrected, a correction log is automatically generated to record the data comparison before and after the correction, and provide real-time feedback to the user; Automatically identify different data types in user input data through preset data type classification rules; automatically call conversion algorithms based on the identified data types to convert data in different formats into a unified standard format; Through the preset rationality rule base, it automatically learns and identifies the rationality range of user input data, automatically detects abnormal values ​​in user input data, and generates anomaly detection reports; when an anomaly is detected, it automatically generates prompt information to inform users of possible input errors and provide correction suggestions.

5. The method for calculating the cost and electricity price of an onshore wind power project according to claim 1, characterized in that: The process of the user checking the grid electricity price calculation process and results on the front-end interface includes the following steps: Through the custom time period, you can select the historical electricity price data of a certain time period for comparison; Generate corresponding charts to display historical electricity price data. The line chart in the chart shows the trend of electricity price changes over time, the bar chart compares the high and low electricity prices at different time points, and the area chart shows the fluctuation range of electricity prices; Select the currently calculated electricity price for comparison with the historical data, calculate the percentage difference between the current electricity price and the historical electricity price, mark the significant difference points, and list all the time points of significant differences.

6. The method for calculating the cost and electricity price of an onshore wind power project according to claim 1, characterized in that: The process of calculating the comprehensive electricity price includes the following steps: The back-end algorithm calculates the total price of the diesel generator based on the input purchase price and operation and maintenance costs combined with the quota standards; the calculation results are fed back to the front-end interface for users to check; Build a comprehensive electricity price calculation model, and the back-end algorithm calls the comprehensive electricity price calculation model; input the power grid electricity price and diesel generator price into the comprehensive electricity price calculation model to calculate the comprehensive electricity price; Check the comprehensive electricity price calculation process and results on the front-end interface, find deviations, and adjust input parameters; Re-call the comprehensive electricity price calculation model and recalculate the comprehensive electricity price based on the revised parameters until the comprehensive electricity price calculation result meets expectations.

7. The method for calculating the cost and electricity price of an onshore wind power project as claimed in claim 6, characterized in that: The process of building a comprehensive electricity price calculation model includes the following steps: Preset fuzzy rules to generate a fuzzy inference system, which can output a preliminary comprehensive electricity price based on the input power grid electricity price and diesel generator price; the fuzzy inference system receives the power grid electricity price and diesel generator price and outputs a preliminary comprehensive electricity price; The preliminary comprehensive electricity price is input into the multi-layer perceptron neural network. The input layer contains two input nodes: the grid electricity price and the diesel generator price. The hidden layer contains multiple hidden nodes to capture the complex relationship between the input comprehensive electricity prices. The output layer outputs the final comprehensive electricity price. The grid electricity price and diesel generator price are input into the fuzzy logic module to obtain a preliminary comprehensive electricity price; The preliminary comprehensive electricity price is used as input and further optimized through the neural network to obtain the final comprehensive electricity price; The back-end algorithm calls the comprehensive electricity price calculation model and feeds back the results of the comprehensive electricity price calculation model to the front-end interface for users to check; users adjust the input parameters according to actual conditions and re-call the model for calculation until the comprehensive electricity price calculation results meet expectations.

8. The method for calculating the cost and electricity price of an onshore wind power project as claimed in claim 7, characterized in that: The process of obtaining the final comprehensive electricity price includes the following steps: Based on historical data and real-time data, the membership function of the fuzzy set is dynamically generated, and its shape and range are automatically adjusted according to the changes in the power grid electricity price and the diesel generator price; The input layer of the neural network includes the power grid price, diesel generator price and the preliminary comprehensive power price output by the fuzzy inference layer; the hidden layer adopts a multi-layer structure to capture the complex relationship and nonlinear characteristics between the input data; The back-end algorithm feeds back the final comprehensive electricity price output by the neural network to the front-end interface in real time, and provides explanations and suggestions through analysis; through the visual interface, the composition and influencing factors of the comprehensive electricity price can be understood.

9. The method for calculating the cost and electricity price of an onshore wind power project as claimed in claim 8, characterized in that: The process of the back-end algorithm feeding back the final comprehensive electricity price output by the neural network to the front-end interface in real time includes the following steps: The front-end interface receives the data packet pushed by the back-end through the WebSocket connection. The front-end uses JavaScript to parse the received data packet and convert the received JSON string into a JavaScript object; the comprehensive electricity price and related information, such as the electricity price value, timestamp, and input variables, are extracted from the parsed object; Display the comprehensive electricity price value in the text box, set a timer, regularly request the backend to obtain the latest data and update the interface; connect through WebSocket, listen to the data update events pushed by the backend, and update the interface in real time; Select the chart library, configure the basic parameters of the chart, update the data source of the chart and refresh the chart every time new comprehensive electricity price data is received; Append the new electricity price data to the chart's data set, call the update method provided by the chart library, and refresh the chart.

10. The method for calculating the cost and electricity price of an onshore wind power project according to claim 1, characterized in that: The process of exporting accounting results includes the following steps: Extract all relevant data from the grid electricity price calculation model, diesel generator price calculation model and comprehensive electricity price calculation model, including power generation cost, transmission and distribution cost, government subsidies, diesel generator purchase price and operation and maintenance cost; organize the collected data into a hierarchical structure; Check the data for duplicates and generate the final accounting results, which include the key indicators of power grid electricity price, diesel generator price and comprehensive electricity price; format the generated accounting results; Export the formatted accounting results to the selected file format; verify the generated export file and deliver the verified export file to the user.

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