Photovoltaic power station configuration optimization method and system based on economic evaluation

Abstract

The invention relates to the technical field of photovoltaic power generation and energy storage, in particular to a photovoltaic power station configuration optimization method and system based on economic evaluation, and the method comprises the following steps: obtaining the annual energy output of a photovoltaic power station, the annual electrical load of a user and an electricity price parameter; obtaining annual photovoltaic consumption data based on the annual energy production of the photovoltaic power station and the annual electrical load of the user; obtaining a plurality of groups of photovoltaic power generation configurations and photovoltaic energy storage configurations based on the annual photovoltaic consumption data and the electricity price parameters; performing economic evaluation on each group of photovoltaic power generation configuration and photovoltaic energy storage configuration based on the electricity price parameters, thereby obtaining financial internal return rates of each group of photovoltaic power generation configuration and photovoltaic energy storage configuration; and optimizing the photovoltaic power station configuration based on the group of photovoltaic power generation configuration and photovoltaic energy storage configuration of which the financial internal return rate accords with the preset target. According to the method, the configuration of the light storage meeting the target return rate in the project is inversely calculated through economic evaluation, and the method is of great significance to real-time correction of photovoltaic power station configuration parameters and realization of light storage target benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation and energy storage, and in particular to a photovoltaic power station configuration optimization method and system based on economic evaluation. Background Art

[0002] The field of photovoltaic power generation and energy storage technology is a comprehensive technical system with photovoltaic power generation as the core, combining power electronics, information technology and environmental adaptability design. Its core goal is to improve the efficiency of photovoltaic power generation and energy storage, system safety and economic efficiency throughout the entire life cycle.

[0003] Currently, when configuring photovoltaic power station systems, existing methods mostly estimate power generation and energy storage capacity based on empirical formulas. The collaborative operating benefits of photovoltaic modules and energy storage systems lack a quantitative feedback mechanism, making it difficult to correct equipment configuration parameters in real time, affecting the realization of the project's overall economic goals.

[0004] Existing technology requires data for evaluating the economic viability of photovoltaic power plants, primarily based on annual power generation data calculated by designers using specialized wind and solar software. These data are then used to estimate energy storage charge and discharge capacity and green power consumption based on commonly used energy storage charging methods. Finally, technical and economic personnel input this data using a custom Excel tool to calculate the project's economic viability. This inability to refine the calculated data leads to significant discrepancies between the calculated data and actual conditions, resulting in a disconnect between the photovoltaic power plant system capacity configuration plan and the economic benefit assessment, making it impossible to derive the optimal photovoltaic and energy storage equipment size. Summary of the Invention

[0005] The present invention aims to provide a photovoltaic power station configuration optimization method and system based on economic evaluation. By comprehensively considering the multi-dimensional data of the photovoltaic power station, the economic efficiency of the photovoltaic storage configuration scheme is quantitatively evaluated and analyzed, thereby determining the optimal configuration of the photovoltaic power station. This solves the technical problem that the capacity configuration scheme of the photovoltaic power station system is separated from the economic benefit evaluation, and the optimal photovoltaic storage equipment scale cannot be derived.

[0006] To achieve the above objectives, the present invention provides a first aspect of a photovoltaic power station configuration optimization method based on economic evaluation, comprising the following steps:

[0007] Obtain annual power generation of photovoltaic power stations, annual electricity load of users and electricity price parameters;

[0008] Acquiring annual photovoltaic consumption data based on the annual power generation of the photovoltaic power station and the annual electricity load of the user;

[0009] Acquire several groups of photovoltaic power generation configurations and photovoltaic energy storage configurations based on the annual photovoltaic consumption data and the electricity price parameters;

[0010] Performing an economic evaluation on each set of the photovoltaic power generation configuration and the photovoltaic energy storage configuration based on the electricity price parameters, thereby obtaining a financial internal rate of return for each set of the photovoltaic power generation configuration and the photovoltaic energy storage configuration;

[0011] The photovoltaic power station configuration is optimized based on a set of photovoltaic power generation configurations and photovoltaic energy storage configurations whose financial internal rate of return meets a preset target.

[0012] The above-mentioned photovoltaic power station configuration optimization method based on economic evaluation comprehensively considers the multi-dimensional data of the annual power generation of the photovoltaic power station, the annual electricity load of the user and the electricity price parameters to design multiple groups of photovoltaic power generation configurations and photovoltaic energy storage configurations, and then conducts economic evaluation on multiple groups of different photovoltaic storage configuration schemes, so as to quantitatively evaluate the economic benefits of each group of photovoltaic power generation configurations and photovoltaic energy storage configurations according to the financial internal rate of return corresponding to each group of photovoltaic power generation configurations and photovoltaic energy storage configurations. Finally, the photovoltaic power station configuration is optimized based on the group of photovoltaic power generation configurations and photovoltaic energy storage configurations with the highest financial internal rate of return, and the optimal photovoltaic power station photovoltaic storage configuration is obtained. This is of great significance for real-time correction of photovoltaic power station configuration parameters and realization of photovoltaic storage target benefits.

[0013] Preferably, the obtaining of the annual power generation of the photovoltaic power station, the annual power load of users and the electricity price parameters includes:

[0014] Obtain the hourly power generation of the photovoltaic power station and the hourly power load of users every hour of the year;

[0015] The hourly power generation of all the photovoltaic power stations is taken as the annual power generation of the photovoltaic power station;

[0016] The hourly electricity loads of all the users are taken as the annual electricity loads of the users.

[0017] In this implementation, the hourly power generation of the photovoltaic power station and the hourly electricity load of users are recorded for 8,760 hours in 365 days a year, and the photovoltaic absorption data for each hour is analyzed in detail. The fluctuations of user load and electricity price parameters are then incorporated into the photovoltaic power generation configuration and photovoltaic energy storage configuration on an hourly basis, resulting in a photovoltaic power station configuration with a higher financial internal rate of return.

[0018] Preferably, the obtaining of annual photovoltaic consumption data based on the annual power generation of the photovoltaic power station and the annual electricity load of the user includes:

[0019] For any hour in a year, analyze whether the user's hourly electricity load in that hour completely consumes the hourly power generation of the photovoltaic power station, and then use the analysis result as the hourly photovoltaic consumption data corresponding to that hour;

[0020] All the hourly photovoltaic absorption data are used as the annual photovoltaic absorption data.

[0021] In this implementation, analyzing whether the user's hourly electricity load fully consumes the PV station's hourly generated capacity is performed by subtracting the user's hourly electricity load from the PV station's hourly generated capacity. By calculating the difference between the PV station's hourly generated capacity and the user's hourly electricity load, it is possible to determine whether the user's hourly electricity load fully consumes the PV station's hourly generated capacity.

[0022] Specifically, if the user load is greater than the power generation for 8,760 hours, it means that the photovoltaic power generation has been fully absorbed by the users. At this time, there is no excess power generation that needs to be stored, and the photovoltaic energy storage configuration can be zero; if the user load is less than the power generation at a certain point in time, it means that the photovoltaic power generation has not been fully absorbed by the users. At this time, it is necessary to calculate the photovoltaic energy storage configuration to store the unabsorbed power generation.

[0023] Preferably, the obtaining of several groups of photovoltaic power generation configurations and photovoltaic energy storage configurations based on the annual photovoltaic consumption data and the electricity price parameters includes:

[0024] Adjusting the photovoltaic power generation capacity step by step based on all the hourly photovoltaic consumption data, thereby obtaining several photovoltaic power generation configurations;

[0025] The photovoltaic energy storage configuration corresponding to each photovoltaic power generation configuration is obtained based on the electricity price parameter, thereby obtaining several groups of photovoltaic power generation configurations and photovoltaic energy storage configurations.

[0026] In this implementation, the conditions of each of the 8760 hours in a year are analyzed separately to obtain multiple photovoltaic power generation configurations for subsequent economic evaluation.

[0027] Specifically, if the hourly electricity load of users in each hour is greater than the hourly power generation of the corresponding photovoltaic power station, it means that the photovoltaic power generation is fully absorbed by the users. In this case, the photovoltaic power generation capacity is increased step by step until the hourly electricity load of users in each hour is less than the hourly power generation of the corresponding photovoltaic power station, and each level of photovoltaic power generation capacity is regarded as a photovoltaic power generation configuration.

[0028] If the hourly electricity load of users in each hour is less than the hourly power generation of the corresponding photovoltaic power station, it means that the photovoltaic power generation has not been fully absorbed by the users. In this case, the photovoltaic power generation capacity will be reduced step by step until the hourly electricity load of users in each hour is greater than the hourly power generation of the corresponding photovoltaic power station, and each level of photovoltaic power generation capacity will be regarded as a photovoltaic power generation configuration.

[0029] If the hourly electricity load of users in each hour is partly greater than the hourly power generation of the corresponding photovoltaic power station and partly less than the hourly power generation of the corresponding photovoltaic power station, the photovoltaic power generation capacity should be increased step by step on the one hand, and the photovoltaic power generation capacity should be reduced step by step on the other hand, until the hourly electricity load of users in each hour is equal to the hourly power generation of the corresponding photovoltaic power station, and each level of photovoltaic power generation capacity should be regarded as a photovoltaic power generation configuration.

[0030] Preferably, the obtaining of the photovoltaic energy storage configuration corresponding to each photovoltaic power generation configuration based on the electricity price parameter, thereby obtaining several groups of photovoltaic power generation configurations and photovoltaic energy storage configurations, includes:

[0031] For any of the photovoltaic power generation configurations:

[0032] Obtaining a first energy storage charging and discharging setting based on the photovoltaic power generation configuration and the hourly electricity load of all the users;

[0033] obtaining a second energy storage charge and discharge setting based on the electricity price parameter;

[0034] A photovoltaic energy storage configuration corresponding to the photovoltaic power generation configuration is obtained based on the first energy storage charge and discharge setting and the second energy storage charge and discharge setting.

[0035] In this implementation, the hourly photovoltaic absorption is calculated based on the photovoltaic power generation capacity corresponding to the photovoltaic power generation configuration and the hourly electricity load of all users, thereby obtaining the first energy storage charging and discharging settings. Specifically, if the photovoltaic power generation capacity is greater than the user's hourly electricity load in any hour, it indicates that the photovoltaic absorption is greater than zero in that hour, and there is excess power generation that needs to be stored. Therefore, the photovoltaic energy storage is set to charge in that hour; otherwise, the photovoltaic energy storage is set to discharge in that hour to fill the photovoltaic power generation gap and meet the user's electricity load demand.

[0036] Furthermore, if the electricity price parameter is a time-of-use electricity price parameter, the electricity price valley period, electricity price peak period and electricity price peak period of the day are obtained based on the electricity price parameter; because the electricity price is lower during the electricity price valley period and higher during the electricity price peak and electricity price peak periods, in order to obtain higher economic benefits, charging during the electricity price valley period and discharging during the electricity price peak and electricity price peak periods of the day will be used as the second energy storage charging and discharging setting.

[0037] If the electricity price parameter is a fixed electricity price parameter, there is no electricity price difference for each hour of the day. At this time, there is no electricity price difference during the valley period, peak period and peak period, and peak-valley arbitrage cannot be performed. There is only a load difference between the user's electricity load during the valley period, peak period and peak period. Therefore, at this time, only discharging during the peak period and peak period of the day will be used as the second energy storage charging and discharging setting.

[0038] It should be noted that time-of-use electricity prices: a pricing mechanism that divides each day into different time periods based on changes in grid load and sets differentiated electricity prices. Its core is to adjust the balance of supply and demand through price levers, which usually includes three basic time periods: peak period, flat period, and valley period. Some areas have added peak period as a special pricing range for the period with the highest load. Fixed electricity price: an electricity price model that maintains a constant price within an agreed period, regardless of the electricity consumption period or grid load. Valley period: the electricity price range during the period with the lowest grid load (such as 00:00-06:00), the electricity price is usually the lowest of the day. Peak period: the electricity price range during the period with higher grid load (such as 08:00-12:00, 18:00-22:00), the electricity price is higher than the flat period. Peak period: the super-peak period when the grid load reaches an extreme value (such as 14:00-16:00 in summer), the electricity price is set to the highest of the day.

[0039] Preferably, the acquiring of the photovoltaic energy storage configuration corresponding to the photovoltaic power generation configuration based on the first energy storage charge and discharge setting and the second energy storage charge and discharge setting includes:

[0040] adjusting the photovoltaic energy storage capacity step by step based on the first energy storage charge and discharge setting and the second energy storage charge and discharge setting, thereby obtaining several intermediate photovoltaic energy storage configurations;

[0041] Performing an economic evaluation on each of the photovoltaic energy storage intermediate configurations based on the electricity price parameters, thereby obtaining a financial internal rate of return for each of the photovoltaic energy storage intermediate configurations in each group;

[0042] The photovoltaic energy storage intermediate configuration whose financial internal rate of return meets the preset target is used as the photovoltaic energy storage configuration corresponding to the photovoltaic power generation configuration.

[0043] In this implementation, the charge and discharge capacity of the photovoltaic energy storage is calculated based on the first energy storage charge and discharge setting and the second energy storage charge and discharge setting, and then a variety of possible photovoltaic energy storage capacities are calculated based on the photovoltaic energy storage charge and discharge capacity adaptation. Finally, through economic evaluation calculation, the rationality of each intermediate configuration of the photovoltaic energy storage is analyzed with the financial internal rate of return, and finally the photovoltaic energy storage capacity with the highest financial internal rate of return is given as the corresponding photovoltaic energy storage configuration. Among them, the charge and discharge capacity of the energy storage is calculated in different directions according to the presence or absence of load scenarios and electricity prices. The charge and discharge capacity of the energy storage should be determined by the power of the energy storage configuration and the current demand for charge and discharge. When the power of the energy storage configuration is less than the charge and discharge demand, the charge and discharge capacity of the energy storage is equal to the power of the energy storage configuration, otherwise it is equal to the current demand for charge and discharge.

[0044] Preferably, the economic evaluation of each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration is performed based on the electricity price parameter to obtain the financial internal rate of return of each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration, including:

[0045] For each set of photovoltaic power generation configuration and photovoltaic energy storage configuration:

[0046] Obtaining the number of energy storage charge and discharge cycles based on the photovoltaic energy storage configuration;

[0047] Obtaining a cell replacement cost based on a preset cell cycle life model and the number of energy storage charge and discharge cycles;

[0048] An economic evaluation of the photovoltaic power generation configuration and photovoltaic energy storage configuration of the group is performed based on the battery cell replacement cost and the electricity price parameters.

[0049] This implementation further considers the lifespan loss of photovoltaic energy storage cells during long-term charging and discharging, and incorporates the cost of replacing cells as a variable in the economic evaluation. Specifically, based on the first energy storage charge and discharge setting, the second energy storage charge and discharge setting, and the energy storage capacity in the photovoltaic energy storage configuration, the annual charge and discharge volume of the photovoltaic energy storage and the number of photovoltaic energy storage charge and discharge cycles can be obtained. Furthermore, based on a preset cell cycle life model and the number of energy storage charge and discharge cycles, the number of times the energy storage cell needs to be replaced after its lifespan has expired can be determined, thereby deriving the cell replacement cost within a year. This further increases the consideration dimension of the economic evaluation and improves the performance of photovoltaic power station configuration optimization.

[0050] Preferably, the economic evaluation of each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration is performed based on the electricity price parameter to obtain the financial internal rate of return of each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration, including:

[0051] Predicting the power generation of the photovoltaic power station in the nth year based on the annual power generation of the photovoltaic power station and a preset annual photovoltaic attenuation model, and predicting the user's power load in the nth year based on the user's annual power load and a preset annual load growth model;

[0052] An economic evaluation is performed on each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration based on the power generation of the photovoltaic power station in the nth year, the user's electricity load in the nth year, and the electricity price parameters, so as to obtain the internal rate of return of each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration in the nth year, and the internal rate of return in the nth year is used as the financial internal rate of return.

[0053] In this implementation method, the annual changes in the financial internal rate of return of each group of photovoltaic power generation configurations and photovoltaic energy storage configurations are further predicted, and the changes in user electricity load in the time dimension, the impact of photovoltaic power generation attenuation and energy storage battery attenuation on the economic efficiency of photovoltaic power station configuration are comprehensively considered. In this way, the photovoltaic power generation configuration and photovoltaic energy storage configuration that maintain a high level of financial internal rate of return in the next few years are obtained to optimize the photovoltaic power station configuration, which is of great significance for achieving long-term photovoltaic and energy storage target benefits.

[0054] A second aspect of the present invention provides a photovoltaic power station configuration optimization system based on economic evaluation, which includes a data acquisition module, a data preprocessing module, an economic evaluation module and a photovoltaic power station configuration optimization module, wherein:

[0055] The data acquisition module is used to obtain the annual power generation of the photovoltaic power station, the annual power load of users and the electricity price parameters;

[0056] The data preprocessing module is used to obtain annual photovoltaic consumption data based on the annual power generation of the photovoltaic power station and the annual electricity load of the user, and then obtain several groups of photovoltaic power generation configurations and photovoltaic energy storage configurations based on the annual photovoltaic consumption data and the electricity price parameters;

[0057] The economic evaluation module is used to perform an economic evaluation on each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration based on the electricity price parameter, thereby obtaining a financial internal rate of return of each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration;

[0058] The photovoltaic power station configuration optimization module is used to optimize the photovoltaic power station configuration based on a group of photovoltaic power generation configurations and photovoltaic energy storage configurations whose financial internal rate of return meets a preset target.

[0059] The above-mentioned photovoltaic power station configuration optimization system based on economic evaluation comprehensively considers the multi-dimensional data of the annual power generation of the photovoltaic power station, the annual electricity load of the user and the electricity price parameters to design multiple groups of photovoltaic power generation configurations and photovoltaic energy storage configurations, and then conducts economic evaluation on multiple groups of different photovoltaic storage configuration schemes, so as to quantitatively evaluate the economic benefits of each group of photovoltaic power generation configurations and photovoltaic energy storage configurations according to the financial internal rate of return corresponding to each group of photovoltaic power generation configurations and photovoltaic energy storage configurations. Finally, the photovoltaic power station configuration is optimized based on the group of photovoltaic power generation configurations and photovoltaic energy storage configurations with the highest financial internal rate of return, and the optimal photovoltaic power station photovoltaic storage configuration is obtained. This is of great significance for real-time correction of photovoltaic power station configuration parameters and realization of photovoltaic storage target benefits.

[0060] Preferably, in the data acquisition module, the acquisition of the annual power generation of the photovoltaic power station, the annual power load of the user and the electricity price parameters includes:

[0061] Obtain the hourly power generation of the photovoltaic power station and the hourly power load of users every hour of the year;

[0062] The hourly power generation of all the photovoltaic power stations is taken as the annual power generation of the photovoltaic power station;

[0063] The hourly electricity loads of all the users are taken as the annual electricity loads of the users.

[0064] In this implementation, the hourly power generation of the photovoltaic power station and the hourly electricity load of users are recorded for 8,760 hours in 365 days a year, and the photovoltaic absorption data for each hour is analyzed in detail. The fluctuations of user load and electricity price parameters are then incorporated into the photovoltaic power generation configuration and photovoltaic energy storage configuration on an hourly basis, resulting in a photovoltaic power station configuration with a higher financial internal rate of return. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a flow chart of a photovoltaic power station configuration optimization method based on economic evaluation provided by an embodiment of the present invention;

[0066] Figure 2 This is a schematic diagram of a process for obtaining several groups of photovoltaic power generation configurations based on the annual photovoltaic consumption data and the electricity price parameters, provided by an embodiment of the present invention;

[0067] Figure 3 This is a structural diagram of a photovoltaic power station configuration optimization system based on economic evaluation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. It should be noted that the following detailed descriptions are all exemplary descriptions and are intended to provide further detailed descriptions of the present invention. Unless otherwise defined, all technical and scientific terms used herein have the same meanings as those generally understood by those skilled in the art to which this application belongs; the terms used herein in the specification of the application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the description of the above-mentioned drawings, as well as any variations thereof, are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order.

[0069] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0070] The field of photovoltaic power generation and energy storage technology is a comprehensive technical system with photovoltaic power generation as the core, combining power electronics, information technology and environmental adaptability design. Its core goal is to improve the efficiency of photovoltaic power generation and energy storage, system safety and economic efficiency throughout the entire life cycle.

[0071] Currently, when configuring photovoltaic power station systems, existing methods mostly estimate power generation and energy storage capacity based on empirical formulas. The collaborative operating benefits of photovoltaic modules and energy storage systems lack a quantitative feedback mechanism, making it difficult to correct equipment configuration parameters in real time, affecting the realization of the project's overall economic goals.

[0072] Existing technology requires data for evaluating the economic viability of photovoltaic power plants, primarily based on annual power generation data calculated by designers using specialized wind and solar software. These data are then used to estimate energy storage charge and discharge capacity and green power consumption based on commonly used energy storage charging methods. Finally, technical and economic personnel input this data using a custom Excel tool to calculate the project's economic viability. This inability to refine the calculated data leads to significant discrepancies between the calculated data and actual conditions, resulting in a disconnect between the photovoltaic power plant system capacity configuration plan and the economic benefit assessment, making it impossible to derive the optimal photovoltaic and energy storage equipment size.

[0073] Reference Figure 1 In order to solve the above technical problems, the first aspect of the present invention provides a photovoltaic power station configuration optimization method based on economic evaluation, comprising the following steps:

[0074] S1. Obtain the annual power generation of the photovoltaic power station, the annual power load of users and the electricity price parameters;

[0075] S2. Obtaining annual photovoltaic consumption data based on the annual power generation of the photovoltaic power station and the annual electricity load of the user;

[0076] S3. Acquire several sets of photovoltaic power generation configurations and photovoltaic energy storage configurations based on the annual photovoltaic consumption data and the electricity price parameters;

[0077] S4. Performing an economic evaluation on each set of the photovoltaic power generation configuration and the photovoltaic energy storage configuration based on the electricity price parameters, thereby obtaining a financial internal rate of return for each set of the photovoltaic power generation configuration and the photovoltaic energy storage configuration;

[0078] S5. Optimizing the photovoltaic power station configuration based on a set of photovoltaic power generation configurations and photovoltaic energy storage configurations whose financial internal rate of return meets a preset target.

[0079] The above-mentioned photovoltaic power station configuration optimization method based on economic evaluation comprehensively considers the multi-dimensional data of the annual power generation of the photovoltaic power station, the annual electricity load of the user and the electricity price parameters to design multiple groups of photovoltaic power generation configurations and photovoltaic energy storage configurations, and then conducts economic evaluation on multiple groups of different photovoltaic storage configuration schemes, so as to quantitatively evaluate the economic benefits of each group of photovoltaic power generation configurations and photovoltaic energy storage configurations according to the financial internal rate of return corresponding to each group of photovoltaic power generation configurations and photovoltaic energy storage configurations. Finally, the photovoltaic power station configuration is optimized based on the group of photovoltaic power generation configurations and photovoltaic energy storage configurations with the highest financial internal rate of return, and the optimal photovoltaic power station photovoltaic storage configuration is obtained. This is of great significance for real-time correction of photovoltaic power station configuration parameters and realization of photovoltaic storage target benefits.

[0080] Preferably, in a possible embodiment, after obtaining the financial internal rate of return of each group of photovoltaic power generation configurations and photovoltaic energy storage configurations, a curve table corresponding to different scales is generated based on the financial internal rate of return of each group of photovoltaic power generation configurations and photovoltaic energy storage configurations (financial internal rate of return result curves corresponding to different photovoltaic and energy storage scales are given), and the curve table can show the rate of return under different photovoltaic power generation configurations and photovoltaic energy storage configurations.

[0081] Preferably, the obtaining of the annual power generation of the photovoltaic power station, the annual power load of users and the electricity price parameters includes:

[0082] Obtain the hourly power generation of the photovoltaic power station and the hourly power load of users every hour of the year;

[0083] The hourly power generation of all the photovoltaic power stations is taken as the annual power generation of the photovoltaic power station;

[0084] The hourly electricity loads of all the users are taken as the annual electricity loads of the users.

[0085] In this embodiment, by recording the hourly power generation of the photovoltaic power station and the hourly electricity load of users for 8760 hours in 365 days a year, the photovoltaic absorption data for each hour is analyzed in detail, so that the fluctuations of user load and electricity price parameters are incorporated into the photovoltaic power generation configuration and photovoltaic energy storage configuration on an hourly basis, and a photovoltaic power station configuration with a higher financial internal rate of return is obtained.

[0086] Preferably, the obtaining of annual photovoltaic consumption data based on the annual power generation of the photovoltaic power station and the annual electricity load of the user includes:

[0087] For any hour in a year, analyze whether the user's hourly electricity load in that hour completely consumes the hourly power generation of the photovoltaic power station, and then use the analysis result as the hourly photovoltaic consumption data corresponding to that hour;

[0088] All the hourly photovoltaic absorption data are used as the annual photovoltaic absorption data.

[0089] In this embodiment, analyzing whether the user's hourly electricity load completely consumes the PV station's hourly generated capacity is performed by subtracting the user's hourly electricity load from the PV station's hourly generated capacity. By calculating the difference between the PV station's hourly generated capacity and the user's hourly electricity load, it is possible to determine whether the user's hourly electricity load completely consumes the PV station's hourly generated capacity.

[0090] Specifically, if the user load is greater than the power generation for 8,760 hours, it means that the photovoltaic power generation has been fully absorbed by the users. At this time, there is no excess power generation that needs to be stored, and the photovoltaic energy storage configuration can be zero; if the user load is less than the power generation at a certain point in time, it means that the photovoltaic power generation has not been fully absorbed by the users. At this time, it is necessary to calculate the photovoltaic energy storage configuration to store the unabsorbed power generation.

[0091] In a possible embodiment, if the target photovoltaic power station to be optimized is not equipped with an energy storage device, the photovoltaic energy storage configuration can be set to zero, and the economic evaluation of each group of the photovoltaic power generation configurations is performed based only on the electricity price parameters to obtain the financial internal rate of return of each group of the photovoltaic power generation configurations, thereby guiding the optimization configuration of the target photovoltaic power station.

[0092] Preferably, the obtaining of several groups of photovoltaic power generation configurations and photovoltaic energy storage configurations based on the annual photovoltaic consumption data and the electricity price parameters includes:

[0093] Adjusting the photovoltaic power generation capacity step by step based on all the hourly photovoltaic consumption data, thereby obtaining several photovoltaic power generation configurations;

[0094] The photovoltaic energy storage configuration corresponding to each photovoltaic power generation configuration is obtained based on the electricity price parameter, thereby obtaining several groups of photovoltaic power generation configurations and photovoltaic energy storage configurations.

[0095] In this embodiment, the conditions of each of the 8760 hours in a year are analyzed separately to obtain multiple photovoltaic power generation configurations for subsequent economic evaluation.

[0096] Specifically, refer to Figure 2 If the hourly electricity load of users in each hour is greater than the hourly power generation of the corresponding photovoltaic power station, it means that the photovoltaic power generation has been fully absorbed by the users and the users still have residual load demand. In this case, it is necessary to calculate upwards and increase the photovoltaic power generation capacity step by step until the hourly electricity load of users in each hour is less than the hourly power generation of the corresponding photovoltaic power station, and each level of photovoltaic power generation capacity is regarded as a photovoltaic power generation configuration.

[0097] If the hourly electricity load of users in each hour is less than the hourly power generation of the corresponding photovoltaic power station, it means that the photovoltaic power generation has not been fully absorbed by the users and the users have no residual load demand. In this case, it is necessary to perform downward calculations and reduce the photovoltaic power generation capacity step by step until the hourly electricity load of users in each hour is greater than the hourly power generation of the corresponding photovoltaic power station, and each level of photovoltaic power generation capacity is regarded as a photovoltaic power generation configuration.

[0098] If the hourly electricity load of users in each hour is partially greater than the hourly power generation of the corresponding photovoltaic power station, and partially less than the hourly power generation of the corresponding photovoltaic power station, it means that the photovoltaic power generation is partially absorbed by the users. In this case, upward and downward calculations are performed simultaneously. On the one hand, the photovoltaic power generation capacity is increased step by step, and on the other hand, the photovoltaic power generation capacity is reduced step by step until the hourly electricity load of users in each hour is equal to the hourly power generation of the corresponding photovoltaic power station, and each level of photovoltaic power generation capacity is regarded as a photovoltaic power generation configuration.

[0099] Preferably, the obtaining of the photovoltaic energy storage configuration corresponding to each photovoltaic power generation configuration based on the electricity price parameter, thereby obtaining several groups of photovoltaic power generation configurations and photovoltaic energy storage configurations, includes:

[0100] For any of the photovoltaic power generation configurations:

[0101] Obtaining a first energy storage charging and discharging setting based on the photovoltaic power generation configuration and the hourly electricity load of all the users;

[0102] obtaining a second energy storage charge and discharge setting based on the electricity price parameter;

[0103] A photovoltaic energy storage configuration corresponding to the photovoltaic power generation configuration is obtained based on the first energy storage charge and discharge setting and the second energy storage charge and discharge setting.

[0104] In this embodiment, the photovoltaic power consumption per hour is calculated based on the photovoltaic power generation capacity corresponding to the photovoltaic power generation configuration and the hourly electricity load of all users, thereby obtaining a first energy storage charging and discharging setting. Specifically, if the photovoltaic power generation capacity is greater than the user's hourly electricity load in any hour, it indicates that the photovoltaic power consumption in that hour is greater than zero, and there is excess power generation that needs to be stored. Therefore, the photovoltaic energy storage is set to charge in that hour. Otherwise, the photovoltaic energy storage is set to discharge in that hour to fill the photovoltaic power generation gap and meet the user's electricity load demand.

[0105] Furthermore, if the electricity price parameter is a time-of-use electricity price parameter, the electricity price valley period, electricity price peak period and electricity price peak period of the day are obtained based on the electricity price parameter; because the electricity price is lower during the electricity price valley period and higher during the electricity price peak and electricity price peak periods, in order to obtain higher economic benefits, charging during the electricity price valley period and discharging during the electricity price peak and electricity price peak periods of the day will be used as the second energy storage charging and discharging setting.

[0106] If the electricity price parameter is a fixed electricity price parameter, there is no electricity price difference for each hour of the day. At this time, there is no electricity price difference during the valley period, peak period and peak period, and peak-valley arbitrage cannot be performed. There is only a load difference between the user's electricity load during the valley period, peak period and peak period. Therefore, at this time, only discharging during the peak period and peak period of the day will be used as the second energy storage charging and discharging setting.

[0107] In another possible embodiment, if the user load for 8760 hours is greater than the power generation, the photovoltaic power generation is fully absorbed by users, and the electricity price parameter is a fixed electricity price parameter, it means that it is meaningless to configure photovoltaic energy storage in the target photovoltaic power station, and photovoltaic energy storage configuration can be ignored. The reasons are: first, the photovoltaic power generation is fully absorbed by users, indicating that there is no excess power generation that needs to be stored; second, the electricity price parameter is a fixed electricity price parameter, which cannot be used for peak-valley arbitrage, and there is no need to charge and store energy during the valley period.

[0108] It should be noted that time-of-use electricity prices: a pricing mechanism that divides each day into different time periods based on changes in grid load and sets differentiated electricity prices. Its core is to adjust the balance of supply and demand through price levers, which usually includes three basic time periods: peak period, flat period, and valley period. Some areas have added peak period as a special pricing range for the period with the highest load. Fixed electricity price: an electricity price model that maintains a constant price within an agreed period, regardless of the electricity consumption period or grid load. Valley period: the electricity price range during the period with the lowest grid load (such as 00:00-06:00), the electricity price is usually the lowest of the day. Peak period: the electricity price range during the period with higher grid load (such as 08:00-12:00, 18:00-22:00), the electricity price is higher than the flat period. Peak period: the super-peak period when the grid load reaches an extreme value (such as 14:00-16:00 in summer), the electricity price is set to the highest of the day.

[0109] In one possible embodiment, the system cannot collect user load data. In other words, in this embodiment, the system has no load data input, but in this scenario, user load actually exists. In this embodiment, because actual load data cannot be collected, an intelligent empirical estimate of the ratio of power consumption to photovoltaic grid access is required. In this case, if the electricity price parameter is a time-of-use price parameter, since there is no load input, photovoltaic grid access cannot be calculated. Therefore, the energy storage charge and discharge logic is to charge to full charge during the daily off-peak period and discharge at full load during the daily peak and peak periods. This logic is used to perform peak-valley arbitrage, serving as the second energy storage charge and discharge configuration. If the electricity price parameter is a fixed price parameter, since there is no photovoltaic grid access data and no price difference, peak-valley arbitrage cannot be performed. Therefore, the energy storage charge and discharge logic is to calculate the photovoltaic grid access ratio in the photovoltaic input. This means that the current photovoltaic grid access data is used as the data when the photovoltaic power generation capacity is greater than the user's hourly electricity load (photovoltaic grid access is greater than zero). Charging is calculated, and then full load discharge is performed during the daily peak and peak periods.

[0110] In one possible embodiment, the "charging and discharging logic of energy storage is calculated based on the proportion of photovoltaic grid-connected power in the photovoltaic input" includes: if the input photovoltaic grid-connected power ratio is 60%, then 40% of the photovoltaic power should be directly absorbed by the user's load, and 60% cannot be absorbed. This 60% of the energy storage is included in the calculation, and the remaining photovoltaic power is finally connected to the grid.

[0111] Preferably, the acquiring of the photovoltaic energy storage configuration corresponding to the photovoltaic power generation configuration based on the first energy storage charge and discharge setting and the second energy storage charge and discharge setting includes:

[0112] adjusting the photovoltaic energy storage capacity step by step based on the first energy storage charge and discharge setting and the second energy storage charge and discharge setting, thereby obtaining several intermediate photovoltaic energy storage configurations;

[0113] Performing an economic evaluation on each of the photovoltaic energy storage intermediate configurations based on the electricity price parameters, thereby obtaining a financial internal rate of return for each of the photovoltaic energy storage intermediate configurations in each group;

[0114] The photovoltaic energy storage intermediate configuration whose financial internal rate of return meets the preset target is used as the photovoltaic energy storage configuration corresponding to the photovoltaic power generation configuration.

[0115] In this embodiment, the charge and discharge capacity of the photovoltaic energy storage is calculated based on the first energy storage charge and discharge setting and the second energy storage charge and discharge setting, and then a plurality of possible photovoltaic energy storage capacities are calculated based on the photovoltaic energy storage charge and discharge capacity adaptation. Finally, through economic evaluation calculation, the rationality of each intermediate configuration of the photovoltaic energy storage is analyzed with the financial internal rate of return, and finally the photovoltaic energy storage capacity with the highest financial internal rate of return is given as the corresponding photovoltaic energy storage configuration. Among them, the charge and discharge capacity of the energy storage is calculated in different directions according to the presence or absence of load scenarios and electricity prices. The charge and discharge capacity of the energy storage should be determined by the power of the energy storage configuration and the current demand for charge and discharge. When the power of the energy storage configuration is less than the charge and discharge demand, the charge and discharge capacity of the energy storage is equal to the power of the energy storage configuration, otherwise it is equal to the current demand for charge and discharge.

[0116] In a possible embodiment, after obtaining several groups of photovoltaic power generation configurations based on the annual photovoltaic absorption data and the electricity price parameters, an economic evaluation is first performed on each group of the photovoltaic power generation configurations based on the electricity price parameters to obtain the financial internal rate of return of each group of the photovoltaic power generation configurations, and then the photovoltaic power generation configuration with the highest financial internal rate of return is selected to obtain the corresponding photovoltaic energy storage configuration; then, an economic evaluation is performed on each of the photovoltaic energy storage intermediate configurations based on the electricity price parameters to obtain the financial internal rate of return of each of the photovoltaic energy storage intermediate configurations in each group; and one of the photovoltaic energy storage intermediate configurations whose financial internal rate of return meets the preset target is used as the photovoltaic energy storage configuration corresponding to the photovoltaic power generation configuration.

[0117] In the above embodiment, a set of photovoltaic power generation configurations and photovoltaic energy storage configurations with the highest financial internal rate of return can also be obtained to optimize the photovoltaic power station configuration.

[0118] Preferably, the economic evaluation of each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration is performed based on the electricity price parameter to obtain the financial internal rate of return of each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration, including:

[0119] For each set of photovoltaic power generation configuration and photovoltaic energy storage configuration:

[0120] Obtaining the number of energy storage charge and discharge cycles based on the photovoltaic energy storage configuration;

[0121] Obtaining a cell replacement cost based on a preset cell cycle life model and the number of energy storage charge and discharge cycles;

[0122] An economic evaluation of the photovoltaic power generation configuration and photovoltaic energy storage configuration of the group is performed based on the battery cell replacement cost and the electricity price parameters.

[0123] This embodiment further considers the lifespan loss of photovoltaic energy storage cells during long-term charging and discharging, and incorporates the cost of cell replacement as a variable in the economic evaluation. Specifically, based on the first energy storage charge and discharge setting, the second energy storage charge and discharge setting, and the energy storage capacity in the photovoltaic energy storage configuration, the annual photovoltaic energy storage charge and discharge volume and the annual number of photovoltaic energy storage charge and discharge cycles can be obtained. Furthermore, based on a preset cell cycle life model and the number of energy storage charge and discharge cycles, the number of times the energy storage cell needs to be replaced due to exhaustion can be determined, thereby deriving the annual cell replacement cost. This further increases the consideration dimension of the economic evaluation and improves the performance of photovoltaic power station configuration optimization.

[0124] Preferably, the economic evaluation of each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration is performed based on the electricity price parameter to obtain the financial internal rate of return of each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration, including:

[0125] Predicting the power generation of the photovoltaic power station in the nth year based on the annual power generation of the photovoltaic power station and a preset annual photovoltaic attenuation model, and predicting the user's power load in the nth year based on the user's annual power load and a preset annual load growth model;

[0126] An economic evaluation is performed on each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration based on the power generation of the photovoltaic power station in the nth year, the user's electricity load in the nth year, and the electricity price parameters, so as to obtain the internal rate of return of each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration in the nth year, and the internal rate of return in the nth year is used as the financial internal rate of return.

[0127] In this embodiment, the power generation of the photovoltaic power station in the nth year and the user's electricity load in the nth year are further predicted, and then the annual financial internal rate of return changes of each group of photovoltaic power generation configurations and photovoltaic energy storage configurations are predicted. The changes in user electricity load in the time dimension, the impact of photovoltaic power generation attenuation and energy storage battery attenuation on the economic efficiency of the photovoltaic power station configuration are comprehensively considered, so as to obtain photovoltaic power generation configurations and photovoltaic energy storage configurations that maintain a high level of financial internal rate of return in the next few years to optimize the photovoltaic power station configuration, which is of great significance for achieving long-term photovoltaic and energy storage target benefits.

[0128] In another possible embodiment, after the step S of optimizing the photovoltaic power station configuration based on a set of photovoltaic power generation configurations and photovoltaic energy storage configurations that meet a preset target for the financial internal rate of return, the following steps are further included:

[0129] The financial internal rate of return obtained in step S5 is used as the financial internal rate of return of the photovoltaic power generation configuration and photovoltaic energy storage configuration in the first year;

[0130] By predicting the annual load growth and annual photovoltaic attenuation, the annual power generation of the photovoltaic power station and the annual electricity load of users in the second year are simulated;

[0131] Based on the annual power generation of the photovoltaic power station and the annual power load of users in the second year, repeat steps S100 to S500 to obtain the financial internal rate of return of the photovoltaic power generation configuration and photovoltaic energy storage configuration in the second year;

[0132] Repeat the above steps to finally obtain the financial internal rate of return of the photovoltaic power generation configuration and photovoltaic energy storage configuration in each year of the photovoltaic power station project cycle.

[0133] Among them, if the life of the energy storage battery cell is taken into account, the cost of replacing the battery cell needs to be introduced to calculate the financial internal rate of return; based on the preset battery cell cycle life model and the number of energy storage charge and discharge cycles, the number of times the energy storage battery cell needs to be replaced when its life is exhausted is obtained, thereby obtaining the battery cell replacement cost within one year, further increasing the consideration dimension of economic evaluation and improving the performance of photovoltaic power station configuration optimization.

[0134] In one possible embodiment, if at the end of a certain year a photovoltaic energy storage cell has undergone a certain number of charge and discharge cycles but its lifespan has not yet reached its cycle life limit, an economic evaluation can be conducted based on two options: retaining the photovoltaic energy storage cell or replacing the photovoltaic energy storage cell.

[0135] Specifically, the process of performing economic evaluation to obtain the financial internal rate of return includes:

[0136] 1) Read the photovoltaic power generation, energy storage discharge and electricity price, and output the photovoltaic power generation income equal to the power generation multiplied by the electricity price, the energy storage discharge income equal to the energy storage discharge multiplied by the electricity price, and the output tax-inclusive operating income equal to the sum of all income.

[0137] 2) Read the construction investment, read the fixed asset formation ratio, and output the value-added tax to be deducted, which is equal to the construction investment multiplied by 1 minus the fixed asset ratio.

[0138] 3) Read the energy storage cell cost and energy storage scale, and output the energy storage overhaul cost equal to the energy storage cell cost multiplied by the energy storage scale.

[0139] 4) Read the taxable operating income and VAT rate, and output the VAT output tax, which is equal to the taxable operating income divided by 1 plus the corresponding VAT rate multiplied by the corresponding VAT rate. Output the non-taxable operating income, which is equal to the taxable operating income minus the VAT output tax.

[0140] 5) Read the operation and maintenance parameters, read the construction investment, installed capacity, and fixed asset formation ratio, and output the annual operation and maintenance fee equal to the installed capacity multiplied by the operation and maintenance parameters, the annual operation and maintenance fee equal to the corresponding part of the construction investment multiplied by the fixed asset formation ratio multiplied by the operation and maintenance fee rate, the material fee equal to the corresponding part of the construction investment multiplied by the fixed asset formation ratio multiplied by the material fee rate, other fees equal to the corresponding part of the construction investment multiplied by the fixed asset formation ratio multiplied by other fees, the insurance premium equal to the construction investment multiplied by the fixed asset formation ratio multiplied by the insurance premium rate, the employee salary equal to the number of people multiplied by the average salary and benefits, the overhaul cost equal to the overhaul parameter multiplied by the installed capacity, the site rental fee, and the output tax-inclusive operating cost is the sum of all the above.

[0141] 6) Read the annual operation and maintenance fees, material costs, other fees, insurance premiums, employee salaries, overhaul costs, and site rental fees, read the corresponding VAT rate, and output the VAT input tax, which is equal to the various taxable operating costs divided by 1 plus the corresponding VAT rate multiplied by the corresponding VAT rate. Output the non-taxable operating costs, which is equal to the taxable operating costs minus the VAT input tax. Output the VAT payable, which is equal to the VAT output tax minus the VAT input tax. If there is VAT to be deducted, the VAT to be deducted shall be deducted first.

[0142] 7) Read the additional tax rate, read the value-added tax payable, and output the additional tax payable, which is equal to the value-added tax payable multiplied by the additional tax rate. Output the value-added tax and additional tax, which is equal to the value-added tax payable plus the additional tax payable.

[0143] 8) Read the depreciation / amortization years and the residual value rates for each item, and output the annual depreciation and amortization equal to the various construction investment / overhaul costs multiplied by the fixed asset formation ratio plus the portion of the construction period interest included in the depreciation, and the final residual value equal to the various construction investment / overhaul costs multiplied by the fixed asset formation ratio plus the portion of the construction period interest included in the depreciation multiplied by the residual value rate.

[0144] 9) Read policy subsidies and output other tax-free income equal to the amount of all policy subsidies.

[0145] 10) Read construction investment / overhaul cost, required investment ratio, and loan data, and output capital equal to construction investment multiplied by required investment ratio, long-term loan equal to construction investment minus capital, construction period interest equal to long-term loan multiplied by long-term loan interest rate multiplied by construction period divided by 2, annual principal repayment equal to long-term loan plus construction period interest (or overhaul cost) divided by loan term (or battery service life), and annual interest repayment equal to loan balance at the beginning of the period multiplied by long-term loan interest rate.

[0146] 11) Read operating income, other tax-exempt income, value-added tax, operating costs, surcharges, depreciation and amortization, interest repayments, and income tax rates, and output pre-tax profit equal to operating income minus value-added tax minus operating costs minus surcharges minus depreciation and amortization minus interest repayments. Output income tax equals pre-tax profit multiplied by the income tax rate. Adjusted income tax equals pre-tax profit plus interest repayments multiplied by the income tax rate.

[0147] 12) Read operating income, residual value, other tax-exempt income, capital, operating costs, value-added tax and surcharges, income tax, annual principal repayment, and annual interest repayment, and output the net cash flow from capital before income tax, which is equal to operating income plus residual value plus other tax-exempt income minus capital minus operating costs minus value-added tax and surcharges minus annual principal repayment minus annual interest repayment. The net cash flow from capital after income tax is equal to the net cash flow from capital before tax minus income tax. The cumulative net cash flow from capital is equal to the cumulative net cash flow from capital of the previous period plus the net cash flow from capital of the current period.

[0148] 13) Read the net cash flow from capital before income tax and the net cash flow from capital after income tax, and output the internal rate of return on capital before tax, which is equal to the discount rate that makes the present value of the net cash flow from capital before income tax zero. The internal rate of return on capital after tax is equal to the discount rate that makes the present value of the net cash flow from capital after income tax zero. The payback period of capital investment is equal to the time point when the cumulative net cash flow from capital is first positive.

[0149] 14) Read operating income, residual value, other tax-exempt income, construction investment, operating costs, value-added tax and surcharges, and adjusted income tax, and output the project net cash flow before income tax, which is equal to the operating income plus residual value plus other tax-exempt income minus construction investment minus operating costs minus value-added tax and surcharges; the project net cash flow after income tax, which is the project net cash flow before tax minus adjusted income tax; the cumulative project net cash flow before income tax is equal to the cumulative project net cash flow before income tax of the previous period plus the project net cash flow before income tax of the current period; the cumulative project net cash flow after income tax is equal to the cumulative project net cash flow after income tax of the previous period plus the project net cash flow after income tax of the current period.

[0150] 15) Read the project net cash flow before income tax, the cumulative project net cash flow before income tax, the project net cash flow after income tax, and the cumulative project net cash flow after income tax, and output the project internal rate of return before tax, which is equal to the discount rate that makes the present value of the project net cash flow before income tax zero, the project internal rate of return after tax, which is equal to the discount rate that makes the present value of the project net cash flow after income tax zero, the project payback period before tax, which is equal to the time point when the first cumulative project net cash flow before income tax is positive, and the project payback period after tax, which is equal to the time point when the first cumulative project net cash flow after income tax is positive.

[0151] refer to Figure 3 In a second aspect, the present invention provides a photovoltaic power station configuration optimization system based on economic evaluation, which includes a data acquisition module 100, a data preprocessing module 200, an economic evaluation module 300 and a photovoltaic power station configuration optimization module 400, wherein:

[0152] The data acquisition module 100 is used to obtain the annual power generation of the photovoltaic power station, the annual power load of users and the electricity price parameters;

[0153] The data preprocessing module 200 is used to obtain annual photovoltaic consumption data based on the annual power generation of the photovoltaic power station and the annual electricity load of the user, and then obtain several groups of photovoltaic power generation configurations and photovoltaic energy storage configurations based on the annual photovoltaic consumption data and the electricity price parameters;

[0154] The economic evaluation module 300 is used to perform an economic evaluation on each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration based on the electricity price parameter, thereby obtaining a financial internal rate of return of each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration;

[0155] The photovoltaic power station configuration optimization module 400 is used to optimize the photovoltaic power station configuration based on a set of photovoltaic power generation configurations and photovoltaic energy storage configurations whose financial internal rate of return meets a preset target.

[0156] The above-mentioned photovoltaic power station configuration optimization system based on economic evaluation comprehensively considers the multi-dimensional data of the annual power generation of the photovoltaic power station, the annual electricity load of the user and the electricity price parameters to design multiple groups of photovoltaic power generation configurations and photovoltaic energy storage configurations, and then conducts economic evaluation on multiple groups of different photovoltaic storage configuration schemes, so as to quantitatively evaluate the economic benefits of each group of photovoltaic power generation configurations and photovoltaic energy storage configurations according to the financial internal rate of return corresponding to each group of photovoltaic power generation configurations and photovoltaic energy storage configurations. Finally, the photovoltaic power station configuration is optimized based on the group of photovoltaic power generation configurations and photovoltaic energy storage configurations with the highest financial internal rate of return, and the optimal photovoltaic power station photovoltaic storage configuration is obtained. This is of great significance for real-time correction of photovoltaic power station configuration parameters and realization of photovoltaic storage target benefits.

[0157] Preferably, in the data acquisition module 100, the acquisition of the annual power generation of the photovoltaic power station, the annual power load of the user and the electricity price parameters includes:

[0158] Obtain the hourly power generation of the photovoltaic power station and the hourly power load of users every hour of the year;

[0159] The hourly power generation of all the photovoltaic power stations is taken as the annual power generation of the photovoltaic power station;

[0160] The hourly electricity loads of all the users are taken as the annual electricity loads of the users.

[0161] In this embodiment, by recording the hourly power generation of the photovoltaic power station and the hourly electricity load of users for 8760 hours in 365 days a year, the photovoltaic absorption data for each hour is analyzed in detail, so that the fluctuations of user load and electricity price parameters are incorporated into the photovoltaic power generation configuration and photovoltaic energy storage configuration on an hourly basis, and a photovoltaic power station configuration with a higher financial internal rate of return is obtained.

[0162] The photovoltaic power station configuration optimization method and system based on economic evaluation provided by the present invention have at least the following advantages over the existing technology:

[0163] 1) By subtracting the recorded annual 8,760-point load and 8,760-point PV data, we can calculate the shortfall and the amount of electricity that cannot be absorbed by PV. Combined with the currently recorded time-of-use electricity price, we can estimate the amount of PV electricity that energy storage needs to absorb during PV generation, as well as the peak and peak discharge requirements. Combined with these data, we can calculate the amount of energy storage required to charge from the grid during off-peak periods. This algorithm can be used to calculate detailed data such as annual power generation, energy storage charge and discharge, and grid purchases.

[0164] 2) Given the project's time-of-use electricity price and time period, the energy storage's hourly charge and discharge capacity is calculated in detail over the project's lifecycle. Furthermore, using detailed charge and discharge data and combining it with a cell cycle life model, the number of cycles (one full charge and discharge counts as one cycle) is calculated based on the charge and discharge data. The lifecycle of the energy storage is then determined based on the set data to determine whether the energy storage has reached the predetermined number of cycles. The final economic evaluation indicators are provided by separately calculating the energy storage replacement and non-replacement options.

[0165] 3) Based on the actual situation of the photovoltaic power station, the optimal scale of photovoltaic and energy storage can be automatically calculated, and a curve table corresponding to different scales can be generated (the IRR result curve corresponding to different photovoltaic and energy storage scales is given). The curve table can show the benefits under different photovoltaic and energy storage scales.

[0166] 4) Combine different PV power generation configurations and PV energy storage configurations according to their scale, calculate the rate of return for each combination, sort the scales in the rate of return from high to low, and finally sort out the optimal rate of return.

[0167] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0168] The "embodiment" mentioned in this document means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0169] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make several improvements and substitutions without departing from the scope of the present application, and such improvements and substitutions should also be considered within the scope of protection of the present invention. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A photovoltaic power station configuration optimization method based on economic evaluation, characterized in that: include: Obtain annual power generation of photovoltaic power stations, annual electricity load of users and electricity price parameters; Acquiring annual photovoltaic consumption data based on the annual power generation of the photovoltaic power station and the annual electricity load of the user; Acquire several groups of photovoltaic power generation configurations and photovoltaic energy storage configurations based on the annual photovoltaic consumption data and the electricity price parameters; Performing an economic evaluation on each set of the photovoltaic power generation configuration and the photovoltaic energy storage configuration based on the electricity price parameters, thereby obtaining a financial internal rate of return for each set of the photovoltaic power generation configuration and the photovoltaic energy storage configuration; The photovoltaic power station configuration is optimized based on a set of photovoltaic power generation configurations and photovoltaic energy storage configurations whose financial internal rate of return meets a preset target.

2. The photovoltaic power station configuration optimization method based on economic evaluation according to claim 1, characterized in that: The acquisition of the annual power generation of the photovoltaic power station, the annual power load of the user and the electricity price parameters includes: Obtain the hourly power generation of the photovoltaic power station and the hourly power load of users every hour of the year; The hourly power generation of all the photovoltaic power stations is taken as the annual power generation of the photovoltaic power station; The hourly electricity loads of all the users are taken as the annual electricity loads of the users.

3. The photovoltaic power station configuration optimization method based on economic evaluation according to claim 2, characterized in that: The obtaining of annual photovoltaic consumption data based on the annual power generation of the photovoltaic power station and the annual electricity load of the user includes: For any hour in a year, analyze whether the user's hourly electricity load in that hour completely consumes the hourly power generation of the photovoltaic power station, and then use the analysis result as the hourly photovoltaic consumption data corresponding to that hour; All the hourly photovoltaic absorption data are used as the annual photovoltaic absorption data.

4. The photovoltaic power station configuration optimization method based on economic evaluation according to claim 3, characterized in that: The obtaining of several groups of photovoltaic power generation configurations and photovoltaic energy storage configurations based on the annual photovoltaic consumption data and the electricity price parameters includes: Adjusting the photovoltaic power generation capacity step by step based on all the hourly photovoltaic consumption data, thereby obtaining several photovoltaic power generation configurations; The photovoltaic energy storage configuration corresponding to each photovoltaic power generation configuration is obtained based on the electricity price parameter, thereby obtaining several groups of photovoltaic power generation configurations and photovoltaic energy storage configurations.

5. The photovoltaic power station configuration optimization method based on economic evaluation according to claim 4, characterized in that: The photovoltaic energy storage configuration corresponding to each photovoltaic power generation configuration is obtained based on the electricity price parameter, thereby obtaining several groups of photovoltaic power generation configurations and photovoltaic energy storage configurations, including: For any of the photovoltaic power generation configurations: Obtaining a first energy storage charging and discharging setting based on the photovoltaic power generation configuration and the hourly electricity load of all the users; obtaining a second energy storage charging and discharging setting based on the electricity price parameter; A photovoltaic energy storage configuration corresponding to the photovoltaic power generation configuration is obtained based on the first energy storage charge and discharge setting and the second energy storage charge and discharge setting.

6. The photovoltaic power station configuration optimization method based on economic evaluation according to claim 5, characterized in that: The obtaining of the photovoltaic energy storage configuration corresponding to the photovoltaic power generation configuration based on the first energy storage charge and discharge setting and the second energy storage charge and discharge setting includes: adjusting the photovoltaic energy storage capacity step by step based on the first energy storage charge and discharge setting and the second energy storage charge and discharge setting, thereby obtaining several intermediate photovoltaic energy storage configurations; Performing an economic evaluation on each of the photovoltaic energy storage intermediate configurations based on the electricity price parameters, thereby obtaining a financial internal rate of return for each of the photovoltaic energy storage intermediate configurations in each group; The photovoltaic energy storage intermediate configuration whose financial internal rate of return meets the preset target is used as the photovoltaic energy storage configuration corresponding to the photovoltaic power generation configuration.

7. The photovoltaic power station configuration optimization method based on economic evaluation according to claim 1, characterized in that: The economic evaluation of each group of photovoltaic power generation configurations and photovoltaic energy storage configurations based on the electricity price parameters, thereby obtaining the financial internal rate of return of each group of photovoltaic power generation configurations and photovoltaic energy storage configurations, includes: For each set of photovoltaic power generation configuration and photovoltaic energy storage configuration: Obtaining the number of energy storage charge and discharge cycles based on the photovoltaic energy storage configuration; Obtaining a cell replacement cost based on a preset cell cycle life model and the number of energy storage charge and discharge cycles; An economic evaluation of the photovoltaic power generation configuration and photovoltaic energy storage configuration of the group is performed based on the battery cell replacement cost and the electricity price parameters.

8. The photovoltaic power station configuration optimization method based on economic evaluation according to claim 1, characterized in that: The economic evaluation of each group of photovoltaic power generation configurations and photovoltaic energy storage configurations based on the electricity price parameters, thereby obtaining the financial internal rate of return of each group of photovoltaic power generation configurations and photovoltaic energy storage configurations, includes: Predicting the power generation of the photovoltaic power station in the nth year based on the annual power generation of the photovoltaic power station and a preset annual photovoltaic attenuation model, and predicting the user's power load in the nth year based on the user's annual power load and a preset annual load growth model; An economic evaluation is performed on each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration based on the power generation of the photovoltaic power station in the nth year, the user's electricity load in the nth year, and the electricity price parameters, so as to obtain the internal rate of return of each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration in the nth year, and the internal rate of return in the nth year is used as the financial internal rate of return.

9. A photovoltaic power station configuration optimization system based on economic evaluation, characterized in that: It includes data acquisition module, data preprocessing module, economic evaluation module and photovoltaic power station configuration optimization module, among which: The data acquisition module is used to obtain the annual power generation of the photovoltaic power station, the annual power load of users and the electricity price parameters; The data preprocessing module is used to obtain annual photovoltaic consumption data based on the annual power generation of the photovoltaic power station and the annual electricity load of the user, and then obtain several groups of photovoltaic power generation configurations and photovoltaic energy storage configurations based on the annual photovoltaic consumption data and the electricity price parameters; The economic evaluation module is used to perform an economic evaluation on each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration based on the electricity price parameter, thereby obtaining a financial internal rate of return of each group of the photovoltaic power generation configuration and the photovoltaic energy storage configuration; The photovoltaic power station configuration optimization module is used to optimize the photovoltaic power station configuration based on a group of photovoltaic power generation configurations and photovoltaic energy storage configurations whose financial internal rate of return meets a preset target.

10. The photovoltaic power station configuration optimization system based on economic evaluation according to claim 9, characterized in that: In the data acquisition module, the acquisition of the annual power generation of the photovoltaic power station, the annual power load of the user and the electricity price parameters includes: Obtain the hourly power generation of the photovoltaic power station and the hourly power load of users every hour of the year; The hourly power generation of all the photovoltaic power stations is taken as the annual power generation of the photovoltaic power station; The hourly electricity loads of all the users are taken as the annual electricity loads of the users.

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