Distributed photovoltaic planning method based on user characteristics and readable storage medium

Abstract

The invention discloses a distributed photovoltaic planning method based on user characteristics and a readable storage medium, and the method comprises the steps: collecting a user daily load curve of a statistical period, and determining a plurality of target positions in a radius R region around a position where a user is located; according to the sunshine condition of each target position and the distance between each target position and the position where the user is located, determining a distributed photovoltaic optimal establishment position; according to the user daily power consumption curve and the distributed photovoltaic daily power generation curve corresponding to the optimal distributed photovoltaic establishment position, determining the electricity quantity which needs to be purchased from the power grid by the user, calculating the electricity purchasing cost of the user according to the electricity quantity which needs to be purchased from the power grid by the user, and calculating the power generation cost of the distributed photovoltaic; and determining distributed photovoltaic planning information according to the power purchase cost of the user and the power generation cost of the distributed photovoltaic. The problems of insufficient distributed photovoltaic planning precision and low economical efficiency under human subjective factors are avoided.

Description

Technical Field

[0001] The present application relates to the field of distributed photovoltaic technology, and in particular to a distributed photovoltaic planning method based on user characteristics and a readable storage medium. Background Art

[0002] Distributed photovoltaic power generation is a technology that converts solar energy into electricity. It is typically installed on building rooftops, walls, or other locations close to power demand. It adheres to the principle of "nearest generation, nearest grid connection, nearest conversion, and nearest use," effectively reducing power losses during long-distance transportation. This system boasts relatively low output power, minimal pollution, and significant environmental benefits. Distributed photovoltaic power generation not only reduces dependence on the traditional power grid but also brings economic benefits to users through self-generation and grid access, with surplus power available.

[0003] For users, the "self-generation, self-consumption" model of distributed photovoltaics can significantly reduce electricity costs. By installing a photovoltaic system on a building's rooftop, users can use solar energy to meet their own electricity needs, reducing the amount of electricity purchased from the grid and thus saving on electricity bills. However, there are still some shortcomings in the decision-making and location selection of distributed photovoltaic systems. Currently, there is a lack of quantitative data to refer to, and relying solely on human experience can lead to low accuracy and unsatisfactory economic benefits. For example, the selection of a construction location requires comprehensive consideration of factors such as sunlight conditions, roof area, and grid access, but these factors often lack precise quantitative assessment. Therefore, in practical applications, more scientific analysis and data support are needed to optimize the construction plan of distributed photovoltaic systems and improve their economic benefits and operational efficiency. Summary of the Invention

[0004] In view of this, embodiments of the present application provide a distributed photovoltaic planning method based on user characteristics and a readable storage medium.

[0005] According to one aspect of the present application, a distributed photovoltaic planning method based on user characteristics is provided, comprising:

[0006] Collect the user's daily load curve during the statistical period and determine multiple target locations within a radius R around the user's location;

[0007] Determine the optimal location for distributed photovoltaic installation based on the sunshine conditions of each target location and the distance between each target location and the user's location;

[0008] Based on the user's daily power consumption curve and the distributed photovoltaic daily power generation curve corresponding to the optimal distributed photovoltaic installation location, determine the amount of electricity that the user needs to purchase from the grid, calculate the user's electricity purchase cost based on the amount of electricity that the user needs to purchase from the grid, and calculate the distributed photovoltaic power generation cost;

[0009] Determine the distributed photovoltaic planning information based on the user's electricity purchase cost and the distributed photovoltaic power generation cost.

[0010] According to another aspect of the present application, a storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the distributed photovoltaic planning method based on user characteristics is implemented.

[0011] According to another aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the program, the above-mentioned distributed photovoltaic planning method based on user characteristics is implemented.

[0012] By means of the above technical solution, the embodiment of the present application provides a distributed photovoltaic planning method based on user characteristics and a readable storage medium. By quantitatively analyzing factors such as user electricity consumption characteristics, sunshine conditions, and distance, the optimal location for establishing distributed photovoltaics is scientifically determined, and its economic benefits are evaluated, thereby optimizing the construction plan of distributed photovoltaics and improving its economic benefits and operating efficiency. The method first collects the user's daily load curve and determines multiple target locations within a radius R area around the user; then, considering the sunshine conditions of each target location and the distance from the user location, the optimal location is determined through a location selection function; then, the daily power generation curve of the optimal location is obtained and compared with the user's daily load curve to analyze the benefits and costs of constructing distributed photovoltaics; finally, based on the benefit-cost analysis results, planning information is generated on whether it is recommended to construct distributed photovoltaics. The technical solution of the present application can significantly improve the scientificity and accuracy of distributed photovoltaic planning, reduce construction and operation costs, improve the economic efficiency and clean energy utilization rate of the project, and promote the widespread application and sustainable development of distributed photovoltaics.

[0013] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0015] Figure 1 A flow chart of a distributed photovoltaic planning method based on user characteristics provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0016] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0017] In this embodiment, a distributed photovoltaic planning method based on user characteristics is provided, such as Figure 1 As shown, the method includes:

[0018] Step 101: Collect the user's daily load curve in a statistical period, and determine multiple target locations within a radius R around the user's location.

[0019] This embodiment of the present application provides a distributed photovoltaic planning method based on user characteristics, aiming to address the current problems in distributed photovoltaic construction decision-making and location selection, which lack quantitative data references and rely solely on empirical judgment, resulting in poor accuracy and economic benefits. Through data analysis, construction plans are optimized, improving economic benefits and operational efficiency. First, the user's daily load curve within the statistical period is obtained. The user's daily load curve refers to the curve that shows the time-varying user electricity demand during the statistical period. Multiple target locations are determined within an area with a radius R around the user's location. Specifically, an area with a radius of R is defined around the user's location, and multiple potential locations for distributed photovoltaic system construction within this area are selected as candidate locations, i.e., target locations. By collecting user daily load curves, users' abstract electricity usage habits are converted into quantifiable data, providing a data foundation for subsequent planning. This overcomes the shortcomings of relying solely on empirical judgment of user electricity usage and enables more accurate planning. Determining the area with a radius of R around the user defines the geographical range for distributed photovoltaic system construction, making planning more targeted and operational, and avoiding blind site selection.

[0020] In this embodiment, the statistical period is set to one year, that is, in subsequent calculations and judgments, the data used is the data from one year before the calculation time. It should be noted that in actual applications, the setting of the statistical period can be changed according to the calculation efficiency or accuracy requirements. Due to different seasons or holidays, the user's daily load curve will show significant differences. Regardless of the season or holiday conditions, they will repeat within a one-year cycle, including the sunlight conditions, which can also repeat regularly on an annual basis. Therefore, obtaining the daily load conditions or daily power generation conditions within the statistical period of one year is sufficient to obtain a comprehensive consideration of the user load and sunlight conditions, providing a comprehensive data basis for subsequent calculations and judgments, making the judgment results more accurate.

[0021] In an embodiment of the present application, optionally, collecting the user's daily load curve for the statistical period includes: obtaining the user's daily load curve for the statistical period through the daily load curve of the users during the statistical period published by each regional power grid or the daily load curve data set of the users during the statistical period provided by the power company; if the current user's load curve is not published through the above channels, collecting the user's real-time electricity consumption data during the statistical period through the user's electricity meter, determining the user's daily electricity consumption based on the real-time electricity consumption data, and plotting the daily electricity consumption and the corresponding time points point by point to form the user's daily load curve.

[0022] In this embodiment, two approaches to obtaining a user's daily load curve are provided to accommodate different data availability scenarios: Approach 1: Directly obtain data from the power grid or power company (when data is publicly available). Specifically, this approach uses data published by regional power grids or user daily load curve datasets provided by power companies. The acquired data is the user's daily load curve for the statistical period. This means that the power grid or power company has already compiled and analyzed the user's electricity usage data and directly provides the user with a visually intuitive data format, the daily load curve. Approach 2: Self-collect data through the electricity meter (when data is not directly available). When the user's load curve is not published through the aforementioned channels (power grid or power company), that is, when ready-made daily load curve data cannot be directly obtained externally, real-time electricity usage data is collected through the user's electricity meter. The user's electricity usage values ​​measured by the meter at different time points during the statistical period are obtained to determine the daily electricity consumption. With time as the horizontal axis and electricity consumption as the vertical axis, daily electricity consumption is plotted against the corresponding time points point by point to form the user's daily load curve.

[0023] Step 102: Determine the optimal location for establishing distributed photovoltaic systems based on the sunshine conditions of each target location and the distance between each target location and the user's location.

[0024] Secondly, sunshine data for each target location during the statistical period, including specific data such as sunshine duration and solar radiation intensity, is collected to assess the location's light resource abundance. The straight-line distance or actual path distance between each target location and the user's location is measured. This comprehensively considers sunshine conditions and distance factors to select an optimal location for the distributed photovoltaic system, ensuring high power generation while minimizing transmission costs. By selecting a location with good sunshine conditions and close proximity to users, distributed photovoltaic systems can maximize power generation, increase the proportion of self-generation and self-consumption, reduce power purchases from the grid, and lower electricity costs. Furthermore, transmission distances can be shortened, minimizing power losses during transmission and reducing transmission line construction and maintenance costs, further improving the project's economic viability. This avoids the inaccuracy associated with selecting a location based solely on experience. By quantitatively assessing sunshine and distance factors, a scientifically and rationally optimal location is selected, laying the foundation for the efficient operation of the distributed photovoltaic system.

[0025] In an embodiment of the present application, optionally, the optimal location for establishing distributed photovoltaics is determined based on the sunshine conditions of each target location and the distance between each target location and the user's location, including: determining the installed capacity and light intensity of the distributed photovoltaics at each target location based on the sunshine conditions of each target location, and determining the total power generation of the distributed photovoltaics at each target location during a statistical period based on the installed capacity and light intensity of each target location; determining the line length between the distributed photovoltaics corresponding to each target location and the user based on the distance between each target location and the user's location; obtaining a pre-constructed location selection function, solving the location selection function based on the total power generation and line length corresponding to each target location, and determining the optimal location for establishing distributed photovoltaics based on the solution of the location selection function corresponding to each target location, wherein the location selection function is expressed as follows:

[0026]

[0027] Where Z j is the position selection function, j = 1, 2, 3, ..., m, m is the number of target positions, R d is the electricity price, Q(P j ) indicates that the target location j establishes an installed capacity of P j The total power generation of distributed photovoltaic during the statistical period, L ij is the length of the i-th line connecting the distributed photovoltaic power station at the target location j and the user, C ij is the statistical cycle cost of the i-th distributed photovoltaic line at the target location j.

[0028] In this embodiment, the two key factors of sunshine conditions and distance from the user location are comprehensively considered, and the location most suitable for building a distributed photovoltaic system is selected from multiple candidate locations through a pre-built location selection function. In this way, a location that can get more sunlight (good sunshine) and is close to the user (short line, low cost) is found. Specifically, for each candidate target location, its sunshine conditions need to be evaluated. This includes the sunshine duration of the location, the solar radiation intensity, etc. Based on the sunshine conditions, the installed capacity P of the distributed photovoltaic system to be built at the location is determined. j And the light intensity at that location. Further, the total power generation Q (P j ). The total power generation is an important indicator to measure the power generation capacity of a location. At the same time, the actual distance between each target location and the user's location is measured or estimated. Based on this distance, the length L of each transmission line between the distributed photovoltaic system and the user can be determined. ij, line length is an important factor affecting transmission costs. The location selection function is used to determine the optimal location for distributed photovoltaic installations. The pre-built location selection function is: Among them, Z j is the position selection function, which indicates the quality of the j-th target position, Z j The larger the value, the better the position. j is the index of the target position, j = 1, 2, 3, ..., m, m is the number of target positions. R d is the electricity price, that is, the price per kilowatt-hour. j ) is at the jth target location, and the installed capacity is P j The total power generation of the distributed photovoltaic system during the statistical period. ij C is the length of the i-th transmission line connecting the distributed photovoltaic system at the j-th target location and the user. ij is the cost of the i-th transmission line of the distributed photovoltaic system at the j-th target location during the statistical period, which is determined based on factors such as line length, line material, and maintenance costs. j ), L ij 、R d and C ij Substitute into the position selection function and calculate the Z of each target position j Compare the Z values ​​of all target positions j Value, select Z j The target location corresponding to the maximum value serves as the optimal location for the distributed photovoltaic system, achieving the best balance between power generation efficiency and line costs, thereby maximizing overall economic benefits. By optimizing the construction location of the distributed photovoltaic system and selecting the optimal location using scientific methods, such as locations with good sunlight and close proximity to users, the distributed photovoltaic system's power generation capacity can be increased, thereby increasing self-generated electricity consumption, reducing electricity purchased from the grid, lowering electricity costs, shortening transmission lines, and reducing line construction and maintenance costs and transmission losses, further reducing the overall cost of the project. By comprehensively considering power generation efficiency and line costs, the location with the best overall economic benefits is selected, maximizing the return on investment of the distributed photovoltaic system project.

[0029] Step 103: Determine the amount of electricity that the user needs to purchase from the grid based on the user's daily electricity consumption curve and the distributed photovoltaic daily power generation curve corresponding to the optimal distributed photovoltaic establishment location, calculate the user's electricity purchase cost based on the amount of electricity that the user needs to purchase from the grid, and calculate the distributed photovoltaic power generation cost.

[0030] Next, determine the distributed photovoltaic daily power generation curve corresponding to the optimal distributed photovoltaic installation location. After determining the optimal distributed photovoltaic installation location, it is necessary to predict or simulate the distributed photovoltaic system's daily power generation during the statistical period to generate a daily power generation curve. Power generation curves are affected by factors such as sunlight intensity and temperature. By comparing the user's daily power consumption curve with the distributed photovoltaic daily power generation curve, it is possible to calculate the amount of power consumed by the user that exceeds the photovoltaic power generation during the statistical period. This amount of power must be purchased from the grid. Furthermore, based on the amount of power the user needs to purchase from the grid and the grid's electricity price, the electricity fee the user must pay to the grid, i.e., the power purchase cost, can be calculated. The power generation cost of the distributed photovoltaic system can also be calculated. Based on the user's daily power consumption curve and the photovoltaic daily power generation curve, the amount of power the user needs to purchase from the grid and the corresponding power purchase cost can be accurately calculated, avoiding estimation errors and providing accurate data for economic evaluation. Clarifying the power generation cost of the distributed photovoltaic system provides a key basis for project investment decisions.

[0031] In the embodiment of the present application, optionally, before establishing the distributed photovoltaic daily power generation curve corresponding to the user's daily power consumption curve and the distributed photovoltaic optimal establishment location, the method further includes:

[0032] Obtain the light intensity data per minute during the statistical period and calculate the power generation per minute using the following method:

[0033]

[0034] Where, P min Indicates the power generated per minute; P ra Represents the installed capacity of the photovoltaic system; G min represents the light intensity per minute; η represents the photoelectric conversion efficiency of the photovoltaic module; α is the temperature coefficient of the photovoltaic module; W represents the surface temperature of the photovoltaic module per minute;

[0035] The daily power generation is determined based on the power generation per minute. The time is used as the horizontal axis value and the daily power generation is used as the vertical axis value. The daily power generation is connected to draw a daily power generation curve.

[0036] In this embodiment, the daily power generation curve is obtained by:

[0037] Get the light intensity data per minute of the day (unit: W / m 2This data can be obtained from weather stations, professional meteorological software (such as METEOINFO), or field measurement equipment. Based on experimental data or empirical experience, an empirical formula is established to link the surface temperature of PV modules with parameters such as ambient temperature and light intensity. By obtaining the ambient temperature and light intensity every minute and using the linear relationship between these parameters and the surface temperature of the PV modules, the surface temperature of the PV modules can be estimated by inputting the values ​​of these parameters.

[0038] The power generated per minute is calculated as follows:

[0039]

[0040] Where, P min Indicates the power generated per minute, in kilowatts (kW); P ra Indicates the installed capacity of the photovoltaic system in kilowatts (kW), which is the power output under standard test conditions (usually 1000W / m 2 The maximum power that a photovoltaic system can achieve (light intensity and temperature of 25°C); G min Indicates the light intensity per minute in watts per square meter (W / m 2 );η represents the photovoltaic conversion efficiency of the photovoltaic module, which is a dimensionless percentage value. It represents the efficiency of the photovoltaic module in converting sunlight into electrical energy. The 1000 in the above formula is a conversion factor used to convert the light intensity from watts per square meter (W / m 2 ) is converted to kilowatts per square meter (kW / m 2 ), because the installed capacity Pra is measured in kilowatts (kW); α is the temperature coefficient of the PV module, expressed in the rate of change of efficiency per degree Celsius (1 / °C), indicating the rate at which the efficiency of the PV module changes with temperature. Typically, the temperature coefficient is negative, meaning that efficiency decreases as the temperature rises; W is the surface temperature of the PV module at a specific minute, expressed in degrees Celsius (°C); 25 in the above formula is the temperature under standard test conditions, expressed in degrees Celsius (°C), because the efficiency of PV modules is typically standardized and measured at 25°C.

[0041] Taking each minute of the day as the time unit, the daily power generation is calculated based on the power generation power per minute, and this is used as the vertical axis value. The daily power generation is connected to form a smooth curve that reflects the power generation situation of the day, which is used as the daily power generation curve.

[0042] Step 104: Determine distributed photovoltaic planning information based on the user's electricity purchase cost and the distributed photovoltaic power generation cost.

[0043] Finally, based on the user's electricity purchase cost and the distributed PV generation cost, the distributed PV planning information is determined. This distributed PV planning information refers to the distributed PV system construction plan and related parameter information, ultimately determined based on the calculations and analysis in the previous steps. This information may include whether to construct a distributed PV system, the construction location, and the expected economic benefits (such as electricity cost savings and payback period). Subsequently, a comprehensive analysis and comparison is conducted based on the user's electricity purchase cost and the distributed PV generation cost, weighing the project's economic viability and feasibility. The final decision on whether to construct a distributed PV system and the specific construction plan is made. Based on the quantified user electricity consumption characteristics, optimal construction location, electricity purchase cost, and construction cost from the previous steps, a scientific analysis and decision-making process is conducted to determine whether to construct a distributed PV system and the optimal construction plan, thus avoiding blind investment and waste of resources. By optimizing the planning and selecting the most economically efficient construction plan, the economic benefits of the distributed PV system can be maximized, improving the return on investment and generating better economic benefits for users.

[0044] In an embodiment of the present application, optionally, a daily power generation curve of the optimal location for establishing distributed photovoltaics is obtained and compared with the corresponding user daily load curve to obtain an interval V1 in which the daily power generation is greater than or equal to the daily load and an interval V2 in which the daily power generation is less than the daily load; based on the daily power generation and daily load of intervals V1 and V2, it is judged whether the benefit is greater than the cost; and distributed photovoltaic planning information is generated based on the judgment result. If the benefit is greater than the cost, distributed photovoltaic planning information is generated indicating that it is recommended to build distributed photovoltaics at the optimal location for establishing distributed photovoltaics; otherwise, distributed photovoltaic planning information is generated indicating that it is not recommended to build distributed photovoltaics. In interval V1, daily power generation ≥ demand, in which case the benefit is the electricity price multiplied by the demand, and the cost is the power generation cost; in interval V2, daily power generation < demand, in which case the benefit is the electricity price multiplied by the power generation, and the cost is the power generation cost and the cost of purchasing the required amount from the power grid, where the required amount = demand - power generation.

[0045] In this embodiment, by comparing the daily power generation curve of the distributed photovoltaic system at the optimal installation location with the user's daily load curve, the economic benefits of building a distributed photovoltaic system are accurately calculated and compared with the construction cost, thereby making a scientific decision on whether to build a distributed photovoltaic system at that location. This emphasizes data-based, quantitative assessment of the economic feasibility of a project to avoid blind construction. Specifically, based on the optimal installation location of the distributed photovoltaic system, the daily power generation curve of the distributed photovoltaic system at that location is further obtained or predicted. The daily power generation curve is compared with the corresponding user's daily load curve to obtain intervals V1 and V2. Interval V1 (daily power generation greater than or equal to daily load) refers to the period when the power generation of the distributed photovoltaic system is greater than or equal to the user's electricity consumption. During these periods, the photovoltaic system can not only fully meet the user's electricity demand, but may also have surplus electricity. Interval V2 (daily power generation less than daily load) refers to the period when the power generation of the distributed photovoltaic system is less than the user's electricity consumption. During these periods, the power generation of the photovoltaic system is insufficient to meet the user's electricity demand, and the user needs to purchase additional electricity from the grid to make up the shortfall. Furthermore, based on the daily power generation and daily load of intervals V1 and V2, it is determined whether the benefits outweigh the costs. Specifically, the economic benefits and costs for intervals V1 and V2 are calculated separately, then summed to obtain the total benefits and costs for the entire day. Finally, the total benefits and costs are compared. Within interval V1, the power generated by distributed PV systems fully meets user demand, and may even have a surplus. In this case, the benefit is the electricity price multiplied by the user's daily load (i.e., the actual amount of electricity used by the user) during that interval. By using electricity generated by their own PV system, the user saves the cost of purchasing electricity from the grid at the price of the electricity price, which is the benefit. Within interval V1, the cost is the power generation cost of the distributed PV system. Within interval V2, the power generated by distributed PV systems only partially meets user demand. In this case, the benefit is the electricity price multiplied by the daily power generation of the distributed PV system during that interval. By using this portion of electricity generated by the PV system, the user also saves the cost of purchasing electricity from the grid, generating a benefit. Within interval V2, the cost consists of two components: the power generation cost of the distributed PV system and the cost of purchasing electricity from the grid to meet demand. Due to insufficient PV power generation, the user needs to purchase additional electricity from the grid to meet demand, and the cost of this additional electricity needs must also be included in the total cost. The amount of demand to be met is equal to the user's daily load in that interval minus the daily power generation of the distributed PV system. The total daily benefit (Benefit of Interval V1 + Benefit of Interval V2) and total daily cost (Cost of Interval V1 + Cost of Interval V2) are calculated for each day within the statistical period. The total benefit is then compared to determine whether it exceeds the total cost. If the total benefit exceeds the total cost, constructing a distributed PV system is economically feasible and can generate net profits. Distributed PV planning information is then generated, recommending the optimal location for the system.If the total benefit within the statistical period is less than or equal to the total cost, constructing a distributed PV system is economically unfeasible, resulting in no net profit and potentially even a loss. In this case, distributed PV planning information is generated, indicating a recommendation against establishing distributed PV. This quantitative analysis of daily power generation and load curves accurately calculates economic benefits and costs, providing a data-driven decision-making process for whether to construct a distributed PV system. This avoids subjective assumptions and blind decisions, improving the scientific nature and accuracy of the decision-making. This economic benefit analysis can identify truly economically viable distributed PV construction projects, avoiding resource waste, optimizing resource allocation, and directing limited resources toward more profitable projects. This method, based on a user's specific daily load curve, provides personalized distributed PV construction recommendations that better meet their actual electricity needs and economic goals.

[0046] In the embodiment of the present application, optionally, the specific calculation of the daily power generation cost is as follows:

[0047]

[0048] Among them, P dynamic_cost_g is the dynamic investment cost of distributed photovoltaics, T O&M_g is the operating time of distributed photovoltaics, D deprectation_g is the depreciation of distributed photovoltaic fixed assets, P O&M_g is the distributed photovoltaic operation and maintenance cost, R tax_g is the income tax rate for distributed photovoltaics, R discount is the discount rate, V restdualvaiue_g is the residual value of distributed photovoltaic fixed assets, E annual_g is the total power generation of distributed photovoltaics during the statistical period, Q 发电量 is the daily power generation.

[0049] In this embodiment, P dynamic_cost_g The dynamic investment cost of distributed photovoltaics refers to the dynamic investment cost of distributed photovoltaic systems throughout their entire life cycle. This includes the initial construction cost, and can also include dynamic investments generated during the operating cycle due to factors such as technology updates, equipment upgrades, and capacity expansion. O&M_g D is the operating time of distributed photovoltaic system, which indicates the expected operating time of distributed photovoltaic system, usually in years. It is used to calculate the cost and benefits in the whole operating cycle. deprectation_g The depreciation of distributed photovoltaic fixed assets refers to the annual depreciation of fixed assets in distributed photovoltaic systems (such as photovoltaic modules, inverters, brackets, etc.). Depreciation is a way of allocating costs and reflects the decrease in the value of fixed assets over time. O&M_gR is the distributed photovoltaic operation and maintenance cost, which represents the cost incurred during the annual operation and maintenance of the distributed photovoltaic system, including daily inspections, equipment repairs, component replacements, cleaning and maintenance, etc. tax_g R is the income tax rate for distributed photovoltaic projects, which indicates the income tax rate applicable to distributed photovoltaic projects. discount The discount rate is used to convert future costs and benefits to current values. Since money has time value, 1 yuan in the future will be worth less than 1 yuan today. The discount rate is the ratio used to reflect this time value. restdualvaiue_g E is the residual value of distributed photovoltaic fixed assets, which means the residual value of its fixed assets at the end of the distributed photovoltaic system operation cycle, that is, the value that can be recovered after the equipment is scrapped. annual_g The total power generation during the distributed photovoltaic statistical period refers to the total power generation of the distributed photovoltaic system in one year, which is used to measure the power generation capacity of the system. 发电量 Daily power generation refers to the power generation of the distributed photovoltaic system in one day.

[0050] The numerator in the formula can be expressed as the discounted value of the total cost. dynamic_cost_g is the dynamic investment cost, which serves as the initial investment cost. This part calculates the present value of tax deductions brought by depreciation. Depreciation can reduce taxable income, thereby reducing the tax burden. This reduction in tax burden is equivalent to a benefit, so it is reflected as a negative value in the total cost. The annual depreciation amount D in the formula is deprectation_g Multiply by the tax rate R tax_g Get the tax deduction, and then use the discount rate (1+R discount ) n Discount it to present value and sum it over all years in the operating cycle. This part calculates the present value of operation and maintenance costs. Operation and maintenance costs are the actual expenditures during the project operation process. The formula for annual operation and maintenance costs P O&M_g Multiply by (1-R tax_g ) to obtain the after-tax operation and maintenance expenses, which are then discounted to the current value using the discount rate and summed over all years in the operating cycle. This part calculates the present value of the residual value. At the end of the project operation cycle, the fixed assets still have residual value. Through the discount rate Discounting it to present value, the denominator can represent the present value of the total electricity generated. This part calculates the present value of the total power generation. It converts the annual power generation E annual_g Discount it to the present value using the discount rate and sum it over all years. This reflects the change in the value of electricity generated over time. Finally, multiply it by Q 发电量The cost calculation is specific to each day to obtain the cost value related to the daily power generation.

[0051] By applying the technical solution of this embodiment, by quantitatively analyzing factors such as user electricity consumption characteristics, sunshine conditions, and distance, the optimal location for establishing distributed photovoltaics is scientifically determined, and its economic benefits are evaluated, thereby optimizing the construction plan of distributed photovoltaics and improving its economic benefits and operating efficiency. The method first collects the user's daily load curve and determines multiple target locations within a radius R around the user; then, considering the sunshine conditions of each target location and the distance from the user's location, the optimal location is determined through a location selection function; then, the daily power generation curve of the optimal location is obtained and compared with the user's daily load curve to analyze the benefits and costs of constructing distributed photovoltaics; finally, based on the benefit-cost analysis results, planning information is generated on whether it is recommended to construct distributed photovoltaics. The technical solution of this application can significantly improve the scientificity and accuracy of distributed photovoltaic planning, reduce construction and operation costs, improve the economic efficiency and clean energy utilization rate of the project, and promote the widespread application and sustainable development of distributed photovoltaics.

[0052] The embodiment of the present application also provides a computer device, which can be specifically a personal computer, a server, a network device, etc. The computer device includes a bus, a processor, a memory and a communication interface, and may also include an input and output interface and a display device. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store location information. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps in each method embodiment are implemented.

[0053] Those skilled in the art will understand that the structure of the above-mentioned computer device is only a partial structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components, or combine certain components, or have a different component arrangement.

[0054] In one embodiment, a computer-readable storage medium is provided. The computer-readable storage medium may be non-volatile or volatile, and stores a computer program thereon. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0055] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0056] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0057] 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 graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, and the like.

[0058] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the 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.

[0059] 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 various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A distributed photovoltaic planning method based on user characteristics, characterized in that: include: Collect the user's daily load curve during the statistical period and determine multiple target locations within a radius R around the user's location; Determine the optimal location for distributed photovoltaic installation based on the sunshine conditions of each target location and the distance between each target location and the user's location; Based on the user's daily power consumption curve and the distributed photovoltaic daily power generation curve corresponding to the optimal distributed photovoltaic installation location, determine the amount of electricity that the user needs to purchase from the grid, calculate the user's electricity purchase cost based on the amount of electricity that the user needs to purchase from the grid, and calculate the distributed photovoltaic power generation cost; Determine the distributed photovoltaic planning information based on the user's electricity purchase cost and the distributed photovoltaic power generation cost.

2. A distributed photovoltaic planning method based on user characteristics according to claim 1, characterized in that: Collect the user daily load curve during the statistical period, including: The user's daily load curve for the statistical period is obtained through the daily load curve of the users during the statistical period published by the regional power grid or the daily load curve data set of the users during the statistical period provided by the power company; if the current user's load curve is not published through the above channels, the user's real-time electricity consumption data during the statistical period is collected through the user's power meter, and the power meter readings during the statistical period and the corresponding time points are plotted point by point to form the user's daily load curve, where the real-time electricity consumption data includes the power meter readings during the statistical period and the corresponding time points.

3. The distributed photovoltaic planning method based on user characteristics according to claim 1, characterized in that: Determine the optimal location for distributed photovoltaic installation based on the sunshine conditions at each target location and the distance between each target location and the user's location, including: Determine the installed capacity and sunlight intensity of distributed photovoltaics at each target location based on the sunshine conditions at each target location, and determine the total power generation of distributed photovoltaics at each target location during the statistical period based on the installed capacity and sunlight intensity at each target location; Determine the line length between the distributed photovoltaic power generation unit and the user corresponding to each target location based on the distance between each target location and the user's location; Obtain a pre-built location selection function, and solve the location selection function according to the total power generation and line length corresponding to each target location. Determine the optimal location for distributed photovoltaic installation based on the solution of the location selection function corresponding to each target location. The location selection function is expressed as follows: Where Z j is the position selection function, j = 1, 2, 3, ..., m, m is the number of target positions, R d is the electricity price, Q(P j ) indicates that the target location j establishes an installed capacity of P j The total power generation of distributed photovoltaic during the statistical period, L ij is the length of the i-th line connecting the distributed photovoltaic power station at the target location j and the user, C ij is the statistical cycle cost of the i-th distributed photovoltaic line at the target location j.

4. The distributed photovoltaic planning method based on user characteristics according to claim 1, characterized in that: Before the distributed photovoltaic daily power generation curve corresponding to the user's daily power consumption curve and the optimal distributed photovoltaic establishment location, it also includes: Obtain the light intensity data per minute during the statistical period and calculate the power generation per minute using the following method: Where, P min Indicates the power generated per minute; P ra Represents the installed capacity of the photovoltaic system; G min represents the light intensity per minute; η represents the photoelectric conversion efficiency of the photovoltaic module; α is the temperature coefficient of the photovoltaic module; W represents the surface temperature of the photovoltaic module per minute; The daily power generation is determined based on the power generation per minute. The time is used as the horizontal axis value and the daily power generation is used as the vertical axis value. The daily power generation is connected to draw a daily power generation curve.

5. The distributed photovoltaic planning method based on user characteristics according to claim 1, characterized in that: Based on the user's electricity purchase cost and the distributed photovoltaic power generation cost, determine the distributed photovoltaic planning information, including: Obtain the daily power generation curve of the optimal location for distributed photovoltaic installation, compare it with the corresponding user daily load curve, and obtain the interval V1 where the daily power generation is greater than or equal to the daily load and the interval V2 where the daily power generation is less than the daily load; Based on the daily power generation and daily load of intervals V1 and V2, determine whether the benefits outweigh the costs. The costs include the user's electricity purchase cost and the distributed photovoltaic power generation cost. Generate distributed photovoltaic planning information based on the judgment results.

6. A distributed photovoltaic planning method based on user characteristics according to claim 5, characterized in that: Generate distributed photovoltaic planning information based on the judgment results, including: If the benefit is greater than the cost, distributed photovoltaic planning information is generated, indicating that it is recommended to construct distributed photovoltaics at the optimal location for establishing distributed photovoltaics; Otherwise, distributed photovoltaic planning information is generated, indicating that establishment of distributed photovoltaic is not recommended.

7. The distributed photovoltaic planning method based on user characteristics according to claim 5, characterized in that: In interval V1, daily power generation ≥ demand, in which case the benefit is the electricity price multiplied by the demand, and the cost is the power generation cost; In interval V2, daily power generation is less than demand. At this time, the benefit is the electricity price multiplied by the power generation, and the cost is the power generation cost and the user's purchase cost of electricity from the power grid to supplement the demand. Supplementing demand = demand - power generation.

8. A distributed photovoltaic planning method based on user characteristics according to claim 7, characterized in that: The specific calculation of daily power generation cost is as follows: Among them, P dynamic_cost_g is the dynamic investment cost of distributed photovoltaics, T O&M_g is the operating time of distributed photovoltaics, D deprectation_g is the depreciation of distributed photovoltaic fixed assets, P O&M_g is the distributed photovoltaic operation and maintenance cost, R tax_g is the income tax rate for distributed photovoltaics, R discount is the discount rate, V restdualvaiue_g is the residual value of distributed photovoltaic fixed assets, E annual_g is the total power generation of distributed photovoltaics during the statistical period, Q 发电量 is the daily power generation.

9. The distributed photovoltaic planning method based on user characteristics according to claim 1, characterized in that: The statistical period is one year.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the distributed photovoltaic planning method based on user characteristics according to any one of claims 1 to 9.

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