A new energy field full life cycle benefit evaluation method and device

Abstract

This invention relates to the field of computer technology, and more particularly to a method and apparatus for assessing the profitability of a new energy field throughout its entire lifecycle. The invention acquires short-term electricity price dynamics and historical electricity price data; inputs the short-term electricity price dynamics into a preset short-term electricity price prediction model to obtain the day-ahead electricity price standard deviation and the real-time day-ahead volatility ratio; inputs historical electricity price data into a preset long-term electricity price prediction model to obtain the long-term electricity price prediction result; performs Monte Carlo simulation on the long-term electricity price prediction result to obtain the day-ahead electricity price; uniformly samples the day-ahead electricity price within the PCH trust region to obtain the market electricity price; wherein, the PCH trust region is constructed based on the day-ahead electricity price standard deviation and the real-time day-ahead volatility ratio; based on the market electricity price, the profitability assessment result for the entire lifecycle of the new energy field is determined. Thus, this invention can improve the accuracy of the profitability assessment for the entire lifecycle of a new energy field.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method and apparatus for evaluating the benefits of a new energy field throughout its entire life cycle. Background Technology

[0002] With the full entry of new energy sources into the market, project returns are directly exposed to fluctuations in market electricity prices, making the accuracy of electricity price forecasts a prerequisite for investment return assessment. Short-term electricity price forecasting, as the time-series analysis basis for full life-cycle return assessment, has undergone a continuous evolution in methodology from classical statistics to deep learning.

[0003] In existing technologies, a closed-loop calculation model is constructed that couples the technology, market, and finance at three levels. Although this method involves full-cycle assessment, it is geared towards energy storage systems and lacks explicit modeling of the dual-track structure of electricity prices ("mechanism price + market price difference"), and it is also difficult to handle cross-scale transitions from short-term to long-term time scales.

[0004] Based on this, the present invention proposes a method and apparatus for evaluating the benefits of a new energy field throughout its entire life cycle to address the problem of how to improve the accuracy of the evaluation of the benefits of a new energy field throughout its entire life cycle. Summary of the Invention

[0005] To address the issue of improving the accuracy of revenue assessment throughout the entire life cycle of a new energy plant, this invention provides a method and apparatus for assessing revenue throughout the entire life cycle of a new energy plant.

[0006] In a first aspect, embodiments of the present invention provide a method for evaluating the benefits of a new energy field throughout its entire life cycle, including: Obtain short-term electricity price dynamics and historical electricity price data; Short-term electricity price dynamics are input into a preset short-term electricity price forecasting model to obtain the day-ahead electricity price standard deviation and the real-time day-ahead volatility ratio. Historical electricity price data are input into a preset long-term electricity price forecasting model to obtain the long-term electricity price forecast results. The long-term electricity price forecast results include the annual decay rate, seasonality, and benchmark electricity price. The day-ahead electricity price is obtained by performing a Monte Carlo simulation on the long-term electricity price forecast results; The market electricity price is obtained by uniformly sampling the day-ahead electricity price within the PCH trust region; where the PCH trust region is constructed based on the standard deviation of the day-ahead electricity price and the ratio of the real-time day-ahead volatility. Based on market electricity prices, determine the revenue assessment results for the entire life cycle of new energy plants.

[0007] Secondly, embodiments of the present invention provide a device for evaluating the benefits of a new energy field throughout its entire life cycle, comprising: The data acquisition module is used to acquire short-term electricity price dynamics and historical electricity price data; The first data processing module is used to input short-term electricity price dynamics into a preset short-term electricity price forecasting model to obtain the day-ahead electricity price standard deviation and real-time day-ahead volatility ratio, and to input historical electricity price data into a preset long-term electricity price forecasting model to obtain long-term electricity price forecast results; wherein, the long-term electricity price forecast results include annual decay rate, seasonal amplitude, and benchmark electricity price; The second data processing module is used to perform Monte Carlo simulation on the long-term electricity price forecast to obtain the day-ahead electricity price. The third data processing module is used to uniformly sample the day-ahead electricity price within the PCH trust region to obtain the market electricity price; wherein, the PCH trust region is constructed based on the standard deviation of the day-ahead electricity price and the real-time day-ahead volatility ratio; The fourth data processing module is used to determine the revenue assessment results of the entire life cycle of the new energy plant based on market electricity prices.

[0008] Thirdly, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method described in any embodiment of the present invention.

[0009] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods described in any embodiment of the present invention.

[0010] This invention provides a method and apparatus for assessing the profitability of a renewable energy plant throughout its entire lifecycle. First, short-term and historical electricity price data are collected. The short-term electricity price data is input into a pre-defined short-term electricity price prediction model to obtain the day-ahead price standard deviation and the real-time day-ahead volatility ratio. Historical electricity price data is input into a pre-defined long-term electricity price prediction model to obtain long-term electricity price prediction results. These long-term prediction results include the annual decay rate, seasonality, and benchmark electricity price. Based on these results, Monte Carlo simulations are performed using large-scale random sampling to eliminate prediction randomness and obtain the day-ahead electricity price. Based on the day-ahead price standard deviation and real-time day-ahead volatility ratio obtained from the short-term predictions, a PCH (Plan-Do-Check-Act) trust region is constructed to adapt to the market electricity price volatility characteristics. Day-ahead electricity prices are uniformly sampled within this trust region to effectively match the random volatility characteristics of electricity market prices, ultimately obtaining a market electricity price that reflects actual market conditions. Finally, based on the market electricity price, the profitability assessment results for the renewable energy plant throughout its entire lifecycle are determined. Thus, by using tiered prediction of electricity prices over long and short cycles, simulation correction, and regional sampling optimization, this invention effectively avoids the problems of one-sidedness and large errors in traditional electricity price prediction, and improves the accuracy and reliability of the full life cycle benefit assessment of new energy fields. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a method for evaluating the benefits of a new energy field throughout its entire life cycle, according to one embodiment, is shown. Figure 2 This is a hardware architecture diagram of an electronic device provided in an embodiment of the present invention; Figure 3 A structural diagram of a new energy field life-cycle benefit assessment device according to one embodiment is shown; Figure 4 A schematic diagram comparing day-ahead and real-time electricity prices according to one embodiment is shown. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] Please refer to Figure 1 This invention provides a method for evaluating the benefits of a new energy field throughout its entire life cycle, including: Step 100: Obtain short-term electricity price dynamics and historical electricity price data; Step 102: Input the short-term electricity price dynamics into the preset short-term electricity price forecast model to obtain the day-ahead electricity price standard deviation and the real-time day-ahead volatility ratio. Input the historical electricity price data into the preset long-term electricity price forecast model to obtain the long-term electricity price forecast results. The long-term electricity price forecast results include the annual decay rate, seasonality, and benchmark electricity price. Step 104: Perform Monte Carlo simulation on the long-term electricity price forecast to obtain the day-ahead electricity price; Step 106: Sample the day-ahead electricity price uniformly within the PCH trust region to obtain the market electricity price; wherein, the PCH trust region is constructed based on the standard deviation of the day-ahead electricity price and the ratio of the real-time day-ahead volatility. Step 108: Determine the revenue assessment results for the entire life cycle of the new energy plant based on market electricity prices.

[0015] In this embodiment, firstly, short-term electricity price dynamics and historical electricity price data are collected. The short-term electricity price dynamics are input into a pre-set short-term electricity price prediction model to obtain the day-ahead electricity price standard deviation and the real-time day-ahead volatility ratio. Historical electricity price data are input into a pre-set long-term electricity price prediction model to obtain long-term electricity price prediction results. These long-term prediction results include the annual decay rate, seasonality, and benchmark electricity price. Based on these results, Monte Carlo simulations are conducted using large-scale random sampling to eliminate prediction randomness and obtain the day-ahead electricity price. Based on the day-ahead electricity price standard deviation and real-time day-ahead volatility ratio obtained from the short-term predictions, a PCH trust region adapted to the market electricity price volatility characteristics is constructed. Day-ahead electricity prices are uniformly sampled within this trust region, effectively reflecting the random volatility characteristics of electricity market prices, ultimately yielding a market electricity price that reflects actual market conditions. Finally, based on the market electricity price, the full life-cycle revenue assessment results of the new energy plant are determined. Thus, by using tiered prediction of electricity prices over long and short cycles, simulation correction, and regional sampling optimization, this invention effectively avoids the problems of one-sidedness and large errors in traditional electricity price prediction, and improves the accuracy and reliability of the full life cycle benefit assessment of new energy fields.

[0016] In one embodiment of the present invention, the PCH trust region is constructed using the following formula: In the formula, The benchmark electricity price, This represents the standard deviation of the day-ahead electricity price. This is the real-time day-ahead volatility ratio. This is the upper bound confidence coefficient. This is the lower bound confidence coefficient. As an anchor point for the marginal cost of coal-fired power, For expansion rate, It is a contractile factor. For the trust area center, For the radius of the trust region, This is the upper bound of the PCH trust region. Let t be the lower bound of the PCH trust region. To monitor electricity prices in practice, For the PCH projection operator, These are the predicted values ​​for the points to be projected.

[0017] In this embodiment, when the statistical lower bound is lower than the marginal cost of coal-fired power, physical constraints replace statistical constraints as the effective lower bound. When the benchmark electricity price is lower than the anchor point of the marginal cost of coal-fired power, The value of does not affect the effective lower bound, regardless of For any positive value, the statistical lower bound is always... Truncation. This means within the parameter range. It is in a redundant state. In Monte Carlo simulations, market electricity prices are directly sampled uniformly from the trust region, and the projection operation is reflected in the physical truncation of extreme sampled values. The PCH trust region, by constructing an adaptive convex hull trust region, constrains the electricity price prediction within a physically reasonable range, effectively eliminating extreme value interference, while dynamically expanding and shrinking the boundary to track market fluctuations, significantly improving the robustness and accuracy of new energy revenue assessment.

[0018] In one embodiment of the present invention, the day-ahead electricity price is determined by the following formula: In the formula, The current day electricity price, The benchmark electricity price; This represents the annual decay rate. This represents the seasonal range; It is 15-minute level random noise; d represents the interannual perturbation, and d represents the date.

[0019] In this embodiment, the 15-minute electricity price path generated by Monte Carlo simulation is perfectly aligned with the power output path in terms of time and granularity, and the day-ahead electricity price of a single simulation path is generated according to the above formula.

[0020] In one embodiment of the present invention, the market electricity price is determined by the following formula: In the formula, For market electricity prices, To ensure uniform distribution, y represents the annual decay rate, and y represents the year number.

[0021] and These represent the lower and upper bounds of the PCH trust region, respectively; y represents the year sequence. The PCH boundary decays over time to reflect the downward trend of the long-term electricity price center. Mechanized electricity is evenly distributed throughout the day, with the mechanized electricity for each 15-minute period being: In the formula, The energy consumption of the mechanism during the i-th time period on the d-th day for the s-th path; The annual electricity consumption of the mechanism in year y; Let y be the number of days in year y.

[0022] In one embodiment of the present invention, the revenue assessment result of the entire life cycle of a new energy field is determined by the following formula: In the formula, For the project number In the simulated path, the first The day of Power generation in a given period The power consumption mechanism is matched to the project for the corresponding time period. The market-based electricity price is matched to the project for the corresponding time period. This is the electricity trading capacity coefficient. For the project number In the simulated path, the first Total daily revenue For the project number In the simulated path, the first The mechanism reward for the i-th time period of the day, The results of the benefit assessment for the entire life cycle of a new energy field. Let y be the set of dates in year y. For the project number The energy consumption of the mechanism in the i-th time period of the day. The price is determined by the mechanism.

[0023] In this embodiment, during the mechanism execution period ( Market returns for each 15-minute timeframe for: After the mechanism expires ( ): in Representative Project No. In the simulated path, the first The day of Market-based returns for each period and These represent the allocated electricity volume and market-based electricity price for the corresponding time period. Outside of the mechanism's execution period, the power station's revenue is only related to the actual electricity generated and the market price. and electricity trading capacity coefficient related.

[0024] In one embodiment of the present invention, taking a wind farm as an example, the mechanism-based electricity price is determined by the following formula: In the formula, As the lower limit of the mechanism electricity price, The upper limit of the mechanism electricity price, Price sensitivity coefficient To ensure sufficient bidding space, The declared power generation capacity of wind turbine units in the region for year t. The wind turbine generator capacity of the region in year t. Let t be the scale of the region's electricity generation mechanism in year t. This represents the proportion of electricity generated by wind turbines.

[0025] In this embodiment, the calculation formula for the mechanism-based electricity price incorporates factors such as price sensitivity coefficient, bidding adequacy, the declared capacity of wind turbine capacity, and the capacity of wind turbine capacity. Therefore, this... More accurate calculations will improve the accuracy of revenue assessments throughout the entire life cycle of new energy plants.

[0026] In one embodiment of the present invention, the scale of the regional mechanical electricity generation in year t is determined by the following formula: In the formula, To represent the total electricity consumption of the region in year t, To represent the regional non-hydropower consumption responsibility weight in year t, This represents the GDP growth rate.

[0027] In this embodiment, the electricity price data covers 304 days (July 1, 2025 to April 30, 2026), spanning summer, autumn, and winter. The training set (243 days) includes summer, autumn, and winter, while the test set (30 days) covers April 2026 (spring). Because the training set does not cover the spring pattern, the test set exhibits a seasonal distribution bias, with the spring average price deviating from the seasonal average range of the training set by approximately 2 standard deviations. The Informer test set prediction error should be interpreted as performance under cross-seasonal extrapolation scenarios. A comparison of day-ahead and real-time electricity prices is provided. Figure 4 As shown.

[0028] like Figure 2 , Figure 3 As shown, this invention provides a device for evaluating the benefits of a new energy field throughout its entire life cycle. The device can be implemented through software, hardware, or a combination of both. From a hardware perspective, as... Figure 2The diagram shown is a hardware architecture diagram of an electronic device for evaluating the full life cycle benefits of a new energy field, provided in an embodiment of the present invention. (Except for...) Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 3 As shown, a device in a logical sense is formed by the CPU of the electronic device in which it is located reading the corresponding computer program from the non-volatile memory into the memory for execution.

[0029] like Figure 3 As shown in the figure, this embodiment provides a device for evaluating the benefits of a new energy field throughout its entire life cycle, including: The data acquisition module 300 is used to acquire short-term electricity price dynamics and historical electricity price data; The first data processing module 302 is used to input short-term electricity price dynamics into a preset short-term electricity price forecasting model to obtain the day-ahead electricity price standard deviation and the real-time day-ahead volatility ratio, and to input historical electricity price data into a preset long-term electricity price forecasting model to obtain long-term electricity price forecasting results; wherein, the long-term electricity price forecasting results include annual decay rate, seasonal amplitude, and benchmark electricity price; The second data processing module 304 is used to perform Monte Carlo simulation on the long-term electricity price forecast results to obtain the day-ahead electricity price. The third data processing module 306 is used to uniformly sample the day-ahead electricity price within the PCH trust region to obtain the market electricity price; wherein, the PCH trust region is constructed based on the day-ahead electricity price standard deviation and the real-time day-ahead volatility ratio; The fourth data processing module 308 is used to determine the revenue assessment results of the entire life cycle of the new energy field based on market electricity prices.

[0030] In one embodiment of the present invention, the PCH trust region is constructed using the following formula: In the formula, The benchmark electricity price, This represents the standard deviation of the day-ahead electricity price. This is the real-time day-ahead volatility ratio. This is the upper bound confidence coefficient. This is the lower bound confidence coefficient. As an anchor point for the marginal cost of coal-fired power, For expansion rate, It is a contractile factor. For the trust area center, For the radius of the trust region, This is the upper bound of the PCH trust region. Let t be the lower bound of the PCH trust region. To monitor electricity prices in practice, For the PCH projection operator, These are the predicted values ​​for the points to be projected.

[0031] In one embodiment of the present invention, the day-ahead electricity price is determined by the following formula: In the formula, The current day electricity price, The benchmark electricity price; This represents the annual decay rate. This represents the seasonal range; It is 15-minute level random noise; d represents the interannual perturbation, and d represents the date.

[0032] In one embodiment of the present invention, the market electricity price is determined by the following formula: In the formula, For market electricity prices, To ensure uniform distribution, y represents the annual decay rate, and y represents the year number.

[0033] In one embodiment of the present invention, the revenue assessment result of the new energy field throughout its entire life cycle is determined by the following formula: In the formula, For the project number In the simulated path, the first The day of Power generation in a given period The power consumption mechanism is matched to the project for the corresponding time period. The market-based electricity price is matched to the project for the corresponding time period. This is the electricity trading capacity coefficient. For the project number In the simulated path, the first Total daily revenue For the project number In the simulated path, the first The mechanism reward for the i-th time period of the day, This represents the revenue assessment results for the entire life cycle of the aforementioned new energy field. Let y be the set of dates in year y. For the project number The energy consumption of the mechanism in the i-th time period of the day. The price is determined by the mechanism.

[0034] In one embodiment of the present invention, the mechanism-based electricity price is determined by the following formula: In the formula, As the lower limit of the mechanism electricity price, The upper limit of the mechanism electricity price, Price sensitivity coefficient To ensure sufficient bidding space, The declared power generation capacity of wind turbine units in the region for year t. The wind turbine generator capacity of the region in year t. Let t be the scale of the region's electricity generation mechanism in year t. This represents the proportion of electricity generated by wind turbines.

[0035] In one embodiment of the present invention, the scale of the regional mechanical electricity generation in year t is determined by the following formula: In the formula, To represent the total electricity consumption of the region in year t, To represent the regional non-hydropower consumption responsibility weight in year t, This represents the GDP growth rate.

[0036] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on a device for evaluating the benefits of a new energy field throughout its entire life cycle. In other embodiments of the present invention, a device for evaluating the benefits of a new energy field throughout its entire life cycle may include more or fewer components than illustrated, or combine some components, or split some components, or arrange the components differently. The components illustrated may be implemented in hardware, software, or a combination of software and hardware.

[0037] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0038] This invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a method for evaluating the full life cycle benefits of a new energy field according to any embodiment of this invention.

[0039] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program causes the processor to perform a method for evaluating the full life-cycle benefits of a new energy field according to any embodiment of this invention.

[0040] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or Mpu) of the system or apparatus may read and execute the program code stored in the storage medium.

[0041] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0042] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as Cd-ROM, Cd-R, Cd-Rw, DVD-ROM, DVD-Ram, DVD-Rw, DVD+Rw), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0043] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0044] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other device installed on the expansion board or expansion module executes some and all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0045] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0046] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the benefits of a new energy field throughout its entire life cycle, characterized in that, include: Obtain short-term electricity price dynamics and historical electricity price data; Short-term electricity price dynamics are input into a preset short-term electricity price forecasting model to obtain the day-ahead electricity price standard deviation and the real-time day-ahead volatility ratio. Historical electricity price data are input into a preset long-term electricity price forecasting model to obtain the long-term electricity price forecast results. The long-term electricity price forecast results include the annual decay rate, seasonality, and benchmark electricity price. The day-ahead electricity price is obtained by performing a Monte Carlo simulation on the long-term electricity price forecast results; The market electricity price is obtained by uniformly sampling the day-ahead electricity price within the PCH trust region; where the PCH trust region is constructed based on the standard deviation of the day-ahead electricity price and the ratio of the real-time day-ahead volatility. Based on market electricity prices, determine the revenue assessment results for the entire life cycle of new energy plants.

2. The method according to claim 1, characterized in that, The PCH trust region is constructed using the following formula: In the formula, The benchmark electricity price, This represents the standard deviation of the day-ahead electricity price. This is the real-time day-ahead volatility ratio. This is the upper bound confidence coefficient. This is the lower bound confidence coefficient. As an anchor point for the marginal cost of coal-fired power, For expansion rate, It is a contractile factor. For the trust area center, For the radius of the trust region, This is the upper bound of the PCH trust region. Let t be the lower bound of the PCH trust region. To monitor electricity prices in practice, For the PCH projection operator, These are the predicted values ​​for the points to be projected.

3. The method according to claim 2, characterized in that, The day-ahead electricity price is determined using the following formula: In the formula, The current day electricity price, The benchmark electricity price; This represents the annual decay rate. This represents the seasonal range; It is 15-minute level random noise; d represents the interannual perturbation, and d represents the date.

4. The method according to claim 3, characterized in that, The market electricity price is determined using the following formula: In the formula, For market electricity prices, To ensure uniform distribution, y represents the annual decay rate, and y represents the year number.

5. The method according to claim 4, characterized in that, The revenue assessment result of the new energy field throughout its entire life cycle is determined by the following formula: In the formula, For the project number In the simulated path, the first The day of Power generation in a given period The power consumption mechanism is matched to the project for the corresponding time period. The market-based electricity price is matched to the project for the corresponding time period. This is the electricity trading capacity coefficient. For the project number In the simulated path, the first Total daily revenue For the project number In the simulated path, the first The mechanism reward for the i-th time period of the day. This represents the revenue assessment results for the entire life cycle of the aforementioned new energy field. Let y be the set of dates in year y. For the project number The energy consumption of the mechanism in the i-th time period of the day. The price is determined by the mechanism.

6. The method according to claim 5, characterized in that, The aforementioned electricity price is determined using the following formula: In the formula, As the lower limit of the mechanism electricity price, The upper limit of the mechanism electricity price, Price sensitivity coefficient To ensure sufficient bidding space, The declared power generation capacity of wind turbine units in the region for year t. The wind turbine generator capacity of the region in year t. Let t be the scale of the region's electricity generation mechanism in year t. This represents the proportion of electricity generated by wind turbines.

7. The method according to claim 6, characterized in that, The scale of the region's mechanical electricity generation in year t is determined by the following formula: In the formula, To represent the total electricity consumption of the region in year t, To represent the regional non-hydropower consumption responsibility weight in year t, This represents the GDP growth rate.

8. A device for evaluating the benefits of a new energy field throughout its entire life cycle, characterized in that, include: The data acquisition module is used to acquire short-term electricity price dynamics and historical electricity price data; The first data processing module is used to input short-term electricity price dynamics into a preset short-term electricity price forecasting model to obtain the day-ahead electricity price standard deviation and real-time day-ahead volatility ratio, and to input historical electricity price data into a preset long-term electricity price forecasting model to obtain long-term electricity price forecast results; wherein, the long-term electricity price forecast results include annual decay rate, seasonal amplitude, and benchmark electricity price; The second data processing module is used to perform Monte Carlo simulation on the long-term electricity price forecast to obtain the day-ahead electricity price. The third data processing module is used to uniformly sample the day-ahead electricity price within the PCH trust region to obtain the market electricity price; wherein, the PCH trust region is constructed based on the standard deviation of the day-ahead electricity price and the real-time day-ahead volatility ratio; The fourth data processing module is used to determine the revenue assessment results of the entire life cycle of the new energy plant based on market electricity prices.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-7.

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