Rural photovoltaic flexible direct current distribution network system design method and device

Abstract

The application relates to a rural photovoltaic flexible direct-current power distribution network system design method and device, wherein the method comprises the following steps: obtaining a farmer's hourly electric load curve based on the electric appliance types and living habits of the farmer; obtaining an hourly power generation power curve in combination with a diode model and local meteorological parameters; taking economy and smooth interaction with a transformer area as a first objective function, combining a first constraint condition, establishing an optimization equation and solving the optimization equation to obtain the photovoltaic installed capacity of each farmer, the indoor electricity storage capacity and the first operation strategy; taking economy and smooth interaction with a power grid as a second objective function, combining a second constraint condition, establishing an optimization equation and solving the optimization equation to obtain the transformer area centralized electricity storage capacity and the second operation strategy. The rural power distribution network system can be designed as a virtual power plant according to the embodiment of the application, the effective consumption of photovoltaic power of the power system is realized, the renewable energy utilization rate is improved, the smooth interaction with the power grid is strengthened, the safety of the power grid is ensured, and the quality of electric energy is improved.

Description

Technical Field

[0001] This application relates to the fields of renewable energy utilization and microgrid technology, and in particular to a design method and device for a rural photovoltaic flexible DC distribution network system. Background Technology

[0002] Solar energy is an inexhaustible and clean energy source. With the development of the photovoltaic industry, the price of photovoltaic equipment has gradually become more affordable, and photovoltaic power generation has become an alternative to traditional fossil fuel power generation. However, due to the influence of weather conditions, photovoltaic power generation is random and volatile. Direct connection with the power grid will endanger the safety of the power grid and reduce the power quality. How to achieve effective consumption and stable grid connection of photovoltaic power is an important problem.

[0003] Among the relevant technologies, rural areas have vast rooftop areas and great potential for photovoltaic installation. At the same time, rural areas have low electricity consumption, with the annual electricity consumption of the vast majority of farmers being far lower than the potential for photovoltaic installation in rural areas. It is possible to meet their own electricity needs entirely by relying on photovoltaic power, without relying on the power grid. Meanwhile, excess power can be sent to the grid in an orderly and stable manner, turning the rural power distribution network system into a virtual power plant.

[0004] However, in related technologies, photovoltaic power in photovoltaic system design is difficult to absorb, disorderly grid connection endangers grid security, reduces power quality, makes it difficult to reasonably optimize photovoltaic capacity, energy storage capacity and operation strategies, and cannot guarantee the stable operation of the system. Summary of the Invention

[0005] This application provides a design method and apparatus for a rural photovoltaic flexible DC distribution network system to solve problems in related technologies, such as the difficulty in absorbing photovoltaic power, disorderly grid connection endangering grid security, reducing power quality, difficulty in reasonably optimizing photovoltaic capacity, energy storage capacity and operation strategy, and inability to guarantee the stable operation of the system.

[0006] The first aspect of this application provides a design method for a rural photovoltaic flexible DC distribution network system, comprising the following steps: obtaining hourly electricity load curves for farmers based on their appliance types and living habits; obtaining hourly photovoltaic power generation curves for different seasons and weather conditions by combining a diode model and local meteorological parameters; establishing an optimization equation and solving it using a first optimization algorithm, with economic efficiency and stable interaction with the power grid as the first objective function, and node power balance and meeting the electricity demand of farmers at night as the first constraint, to obtain the photovoltaic installed capacity, household energy storage capacity, and first operating strategy for each farmer; and establishing an optimization equation and solving it using a second optimization algorithm, with economic efficiency and stable interaction with the power grid as the second objective function, and node power balance and meeting the electricity demand of the power grid during consecutive cloudy and rainy days as the second constraint, to obtain the centralized energy storage capacity and second operating strategy for the power grid.

[0007] Optionally, in one embodiment of this application, the step of establishing a farmer electricity consumption prediction model based on the farmer's appliance type and living habits to obtain the farmer's hourly electricity load curve includes: calculating the farmer's hourly electricity consumption power by introducing a stochastic process based on the usage time and frequency of various electrical devices and the power of each electrical device, thereby generating the farmer's hourly electricity load curve.

[0008] Optionally, in one embodiment of this application, the step of combining a diode model and local meteorological parameters to obtain the photovoltaic hourly power generation curves for different seasons and weather conditions includes: calculating the photovoltaic hourly power generation based on the hourly total irradiance, hourly direct radiation, and hourly outdoor temperature, combined with a diode model corrected by meteorological parameters and the incremental conductance method, and generating the photovoltaic hourly power generation curves.

[0009] Optionally, in one embodiment of this application, the step of establishing an optimization equation and solving it using a first optimization algorithm with economic efficiency and smooth interaction with the transformer substation as the first objective function, and node power balance and meeting the electricity demand of farmers at night as the first constraints, to obtain the photovoltaic installed capacity, indoor energy storage capacity and first operation strategy of each farmer, includes: taking the maximization of system revenue as the first sub-objective function to obtain the indoor energy storage and photovoltaic capacity, and taking the minimum hourly power standard deviation sent to the transformer substation during the day as the second sub-objective function, so as to solve the first operation strategy using the first optimization algorithm.

[0010] Optionally, in one embodiment of this application, the step of establishing an optimization equation and solving it using a second optimization algorithm with economic efficiency and stable interaction with the power grid as the second objective function, and node power balance and meeting the electricity demand of the distribution area during consecutive cloudy and rainy days as the second constraint, to obtain the centralized energy storage capacity and the second operation strategy of the distribution area, includes: determining the lower limit of the centralized energy storage capacity based on the total electricity consumption of the distribution area under the longest consecutive cloudy and rainy days in historical meteorological data, taking the maximization of system benefits as the second sub-objective function, and taking the minimum hourly power standard deviation fed into the power grid during the day as the second sub-objective function, and solving the second operation strategy using the second optimization algorithm.

[0011] The second aspect of this application provides a design device for a rural photovoltaic flexible DC distribution network system, comprising: a first acquisition module for obtaining hourly electricity load curves for farmers based on their appliance types and living habits; a second acquisition module for obtaining hourly photovoltaic power generation curves under different seasons and weather conditions by combining a diode model and local meteorological parameters; a first design module for establishing an optimization equation and solving it using a first optimization algorithm, with economic efficiency and stable interaction with the power grid as the first objective function and node power balance and meeting the electricity demand of farmers at night as the first constraint, to obtain the photovoltaic installed capacity, indoor energy storage capacity, and first operating strategy for each farmer; and a second design module for establishing an optimization equation and solving it using a second optimization algorithm, with economic efficiency and stable interaction with the power grid as the second objective function and node power balance and meeting the electricity demand of the power grid during consecutive rainy days as the second constraint, to obtain the centralized energy storage capacity and second operating strategy of the power grid.

[0012] Optionally, in one embodiment of this application, the first acquisition module includes: a first generation unit, configured to calculate the hourly electricity consumption of farmers by introducing a random process based on the usage time and frequency of various electrical devices and the power of each type of electrical device, and generate the hourly electricity load curve of the farmers.

[0013] Optionally, in one embodiment of this application, the second acquisition module includes: a second generation unit, configured to calculate the hourly power generation of photovoltaics based on hourly total irradiance, hourly direct radiation and hourly outdoor temperature, combined with a diode model corrected by meteorological parameters and the incremental conductance method, and generate the hourly power generation curve of photovoltaics.

[0014] Optionally, in one embodiment of this application, the first design module includes: a first solution unit, configured to take maximizing system revenue as a first sub-objective function to obtain indoor energy storage and photovoltaic capacity, and take minimizing the hourly power standard deviation sent into the transformer area during the day as a second sub-objective function, so as to solve the first operating strategy using the first optimization algorithm.

[0015] Optionally, in one embodiment of this application, the second design module includes: a second solution unit, used to determine the lower limit of the centralized storage capacity based on the total electricity consumption of the transformer area under the longest consecutive cloudy days in historical meteorological data, and to take maximizing the system benefits as the second sub-objective function, and to take minimizing the hourly power standard deviation sent to the grid during the day as the second sub-objective function, so as to solve the second operation strategy using the second optimization algorithm.

[0016] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the rural photovoltaic flexible DC distribution network system design method as described in the above embodiments.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described design method for a rural photovoltaic flexible DC distribution network system.

[0018] This application's embodiments can design a rural power distribution network system as a virtual power plant, enabling effective absorption of photovoltaic power, improving renewable energy utilization, strengthening stable interaction with the power grid, ensuring grid security, and improving power quality. This solves the problems in related technologies, such as the difficulty in absorbing photovoltaic power, disorderly grid connection endangering grid security, reduced power quality, difficulty in rationally optimizing photovoltaic capacity, energy storage capacity, and operating strategies, and the inability to guarantee stable system operation.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0021] Figure 1 This is a flowchart illustrating a design method for a rural photovoltaic flexible DC distribution network system according to an embodiment of this application.

[0022] Figure 2 This is a flowchart illustrating a design method for a rural photovoltaic flexible DC distribution network system according to an embodiment of this application;

[0023] Figure 3 This is a schematic diagram of a design device for a rural photovoltaic flexible DC distribution network system according to an embodiment of this application;

[0024] Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0025] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0026] The following description, with reference to the accompanying drawings, illustrates a design method and apparatus for a rural photovoltaic flexible DC distribution network system according to embodiments of this application. Addressing the problems mentioned in the background section regarding photovoltaic system design, such as the difficulty in absorbing photovoltaic power, disorderly grid connection jeopardizing grid security, reduced power quality, difficulty in rationally optimizing photovoltaic capacity, energy storage capacity, and operating strategies, and the inability to guarantee stable system operation, this application provides a design method for a rural photovoltaic flexible DC distribution network system. In this method, the rural distribution network system can be designed as a virtual power plant, achieving effective absorption of photovoltaic power, improving renewable energy utilization, strengthening stable interaction with the grid, ensuring grid security, and improving power quality. Thus, it solves the problems in related technologies regarding photovoltaic system design, such as the difficulty in absorbing photovoltaic power, disorderly grid connection jeopardizing grid security, reduced power quality, difficulty in rationally optimizing photovoltaic capacity, energy storage capacity, and operating strategies, and the inability to guarantee stable system operation.

[0027] Specifically, Figure 1 This is a flowchart illustrating a design method for a rural photovoltaic flexible DC distribution network system provided in an embodiment of this application.

[0028] like Figure 1 As shown, the design method for this rural photovoltaic flexible DC distribution network system includes the following steps:

[0029] In step S101, based on the type of electrical appliances and living habits of farmers, the hourly electricity load curve of farmers is obtained.

[0030] It is understood that the seasons in the embodiments of this application can be winter, summer and transitional seasons. The usage habits of farmers in different seasons can be the time period, frequency and power of electrical appliances. Different electricity consumption characteristics can be high-consumption farmers, ordinary farmers and frugal farmers.

[0031] As one possible approach, embodiments of this application can establish an electricity consumption prediction model based on the types of electrical appliances and living habits of villagers in different seasons, and obtain hourly load curves. Cluster analysis can be used to simplify calculations based on different types of characteristic data, thereby quickly obtaining the hourly electricity load curves of villagers.

[0032] It should be noted that, since farmers' lifestyles are somewhat random, hourly power needs to be calculated in conjunction with stochastic processes.

[0033] The embodiments of this application can obtain the hourly electricity load curve of farmers based on their electrical appliance types and living habits, ensuring that the photovoltaic capacity, energy storage capacity and operation strategy of the system are obtained on the basis of economic efficiency and stable and orderly grid connection, thereby realizing the effective consumption of photovoltaic power in the power system.

[0034] In one embodiment of this application, an electricity consumption prediction model for farmers is established based on the types of electrical appliances and living habits of farmers to obtain the hourly electricity load curve of farmers. This includes: calculating the hourly electricity consumption power of farmers by introducing a stochastic process based on the usage time and frequency of various electrical devices and the power of each type of electrical device, thereby generating the hourly electricity load curve of farmers.

[0035] It is understood that the usage habits of farmers in this application embodiment can be information such as the usage time and frequency of various electrical devices. This application embodiment can combine the power of electrical devices and introduce a random process to calculate the hourly power consumption of farmers.

[0036] In a specific embodiment, based on the usage time and frequency of various electrical devices used by villagers in different seasons, combined with the power of the electrical devices and the randomness of the villagers' living habits, the hourly power can be calculated using a random process to establish an electricity consumption prediction model. By using different types of characteristic data to simplify the calculation, the hourly electricity load curve of the villagers can be obtained quickly.

[0037] The embodiments of this application can calculate the hourly power consumption of farmers based on the usage time and frequency of various electrical devices, combined with the power of each type of electrical device, by introducing a random process, thereby generating the hourly power load curve of farmers, and thus realizing the effective absorption of photovoltaic power in the power system.

[0038] In step S102, the hourly photovoltaic power generation curves for different seasons and weather conditions are obtained by combining the diode model and local meteorological parameters.

[0039] It is understood that the meteorological parameters in this application embodiment can be hourly total irradiance, hourly direct radiation, and hourly outdoor temperature. The hourly total irradiance and hourly direct radiation can be combined with the latitude and longitude of the installation location and the installation tilt angle. This application embodiment can use the coordinate analysis method to calculate the hourly radiation intensity of the photovoltaic panel surface. The coordinate analysis method is to establish a three-dimensional rectangular coordinate system with the direction perpendicular to the ground, due east, and due south respectively, to express the coordinates of the unit normal of the photovoltaic panel and the coordinates of the sun's position, to obtain the angle between the direct ray and the normal of the photovoltaic panel, and finally obtain the hourly irradiance received by the photovoltaic panel surface.

[0040] Specifically, in this application embodiment, the hourly radiation intensity of the photovoltaic panel surface can be calculated using coordinate analysis based on the hourly total irradiance, hourly direct radiation, latitude and longitude of the installation location, and installation tilt angle, by combining the diode model and local meteorological parameters. The parameters of the diode model can be corrected based on the hourly radiation intensity of the photovoltaic panel surface and the outdoor air temperature. Then, the hourly power generation of the photovoltaic can be calculated using the incremental conductivity method, resulting in hourly power generation curves of the photovoltaic for different seasons and weather conditions.

[0041] The embodiments of this application can combine diode models and local meteorological parameters to obtain hourly photovoltaic power generation curves for different seasons and weather conditions, thereby achieving effective absorption of photovoltaic power in the power system, improving stable interaction with the power grid, and ensuring that power is delivered to the power grid without damaging the normal operation of the power grid.

[0042] In one embodiment of this application, photovoltaic hourly power generation curves for different seasons and weather conditions are obtained by combining a diode model and local meteorological parameters. This includes: calculating the hourly power generation of photovoltaics based on hourly total irradiance, hourly direct radiation, and hourly outdoor temperature, combined with a diode model modified by meteorological parameters and the incremental conductance method, and generating the photovoltaic hourly power generation curves.

[0043] It is understood that the meteorological parameters in this application embodiment can be hourly total irradiance, hourly direct radiation, and hourly outdoor temperature. The hourly total irradiance and hourly direct radiation can be combined with the latitude and longitude of the installation location and the installation tilt angle. This application embodiment can use the coordinate analysis method to calculate the hourly radiation intensity of the photovoltaic panel surface. The coordinate analysis method is to establish a three-dimensional rectangular coordinate system with the direction perpendicular to the ground, due east, and due south respectively, to express the coordinates of the unit normal of the photovoltaic panel and the coordinates of the sun's position, to obtain the angle between the direct ray and the normal of the photovoltaic panel, and finally obtain the hourly irradiance received by the photovoltaic panel surface.

[0044] In some cases, embodiments of this application can calculate the hourly radiation intensity of the photovoltaic panel surface using coordinate analysis by combining hourly total irradiance, hourly direct radiation, latitude and longitude of the installation location, and installation tilt angle. Embodiments of this application can correct the parameters of the diode model based on the hourly radiation intensity of the photovoltaic panel surface and the outdoor air temperature, and calculate the hourly power generation of the photovoltaic system by combining the incremental conductance method, generating an hourly power generation curve of the photovoltaic system. This enables the effective absorption of photovoltaic power in the power system, improves the smooth interaction with the power grid, and ensures that power is delivered to the power grid without damaging the normal operation of the power grid.

[0045] In step S103, with economic efficiency and smooth interaction with the transformer area as the first objective function, and node power balance and meeting the nighttime electricity demand of farmers as the first constraints, an optimization equation is established and solved using the first optimization algorithm to obtain the photovoltaic installed capacity, indoor energy storage capacity and first operation strategy of each farmer.

[0046] It is understood that the operation strategy of the centralized energy storage equipment in the distribution area in this application embodiment can achieve power flow balance based on photovoltaic power generation, indoor energy storage equipment power, power consumption, and grid-connected power. By controlling the charging and discharging power of the centralized energy storage equipment in the distribution area, the standard deviation of the hourly grid-connected power is minimized, and stable interaction with the grid is achieved. The economic indicators of the household can be the revenue from selling electricity plus the revenue from saving electricity minus the investment in equipment and the cost of purchasing electricity from the centralized energy storage in the distribution area on cloudy or rainy days. Stable interaction can be achieved by minimizing the standard deviation of the hourly power supplied to the distribution area. The power supplied to the distribution area can be the photovoltaic power of each household minus the power taken by the household electrical equipment and batteries. The optimization algorithm in this application embodiment can use conventional optimization algorithms, such as simulated degradation algorithm, particle swarm optimization, etc. The indoor energy storage equipment can be used to meet the electricity consumption of farmers at night, and the design of the indoor energy storage capacity should be greater than the electricity consumption of farmers at night.

[0047] As one possible implementation method, this application embodiment can establish an optimization equation and solve it using the economy and stable interaction with the power grid as the first objective function, and the node power balance and meeting the nighttime electricity demand of farmers as the first constraints. The result is the photovoltaic installed capacity, indoor energy storage capacity and first operation strategy of each farmer. The operation strategy of the centralized energy storage equipment in the power grid in this application embodiment can achieve the minimum standard deviation of hourly grid power by controlling the charging and discharging power of the centralized energy storage equipment in the power grid, under the premise of achieving power flow balance of photovoltaic power generation, indoor energy storage equipment power, power consumption and grid connection power, thereby achieving stable interaction with the grid, meeting the nighttime electricity demand of farmers, and meeting the electricity demand of the power grid in the power grid during cloudy and rainy days. This effectively solves the problem of rural domestic electricity use, saves farmers' electricity costs, and can increase farmers' income by selling electricity to the grid.

[0048] In one embodiment of this application, an optimization equation is established with economic efficiency and smooth interaction with the transformer substation as the first objective function, and node power balance and meeting the nighttime electricity demand of farmers as the first constraints. The equation is then solved using a first optimization algorithm to obtain the photovoltaic installed capacity, indoor energy storage capacity, and first operating strategy for each farmer. This includes: maximizing system revenue as the first sub-objective function to obtain the indoor energy storage and photovoltaic capacity, and minimizing the hourly power standard deviation sent to the transformer substation during the day as the second sub-objective function, and using the first optimization algorithm to solve the first operating strategy.

[0049] It is understood that the energy storage equipment operation strategy in this application embodiment can be to meet the electricity demand of farmers at night with indoor energy storage equipment, meet the electricity demand of the distribution area on rainy days with centralized energy storage equipment in the distribution area, and set the lower limit of the capacity of indoor energy storage equipment as the electricity consumption of farmers at night, and set the lower limit of the capacity of centralized energy storage equipment in the distribution area as the electricity consumption of the distribution area during the longest consecutive rainy days.

[0050] Specifically, in this application embodiment, maximizing system benefits can be used as the first sub-objective function to obtain the indoor energy storage and photovoltaic capacity, and minimizing the hourly power standard deviation fed into the grid during the day can be used as the second sub-objective function. Conventional optimization algorithms, such as simulated degradation algorithm and particle swarm optimization algorithm, can be used to solve the optimization equation to obtain the centralized energy storage capacity of the distribution area and the operation strategy, thereby effectively solving the problem of rural domestic electricity use, saving farmers' electricity costs, and increasing farmers' income by selling electricity to the grid.

[0051] In step S104, with economic efficiency and stable interaction with the power grid as the second objective function, and node power balance and meeting the electricity demand of the distribution area during consecutive rainy days as the second constraint, an optimization equation is established and solved using the second optimization algorithm to obtain the centralized energy storage capacity of the distribution area and the second operation strategy.

[0052] It is understood that the energy storage equipment operation strategy in this application embodiment can be to meet the electricity demand of farmers at night with indoor energy storage equipment, and meet the electricity demand of the distribution area on cloudy and rainy days with centralized energy storage equipment in the distribution area. The lower limit of the capacity of indoor energy storage equipment is the electricity consumption of farmers at night, and the lower limit of the capacity of centralized energy storage equipment in the distribution area can be the electricity consumption of the distribution area during the longest consecutive cloudy and rainy days. The economic indicator of the optimization method of the centralized energy storage capacity and operation strategy in this application embodiment is the electricity sales revenue minus the investment cost. The electricity sales revenue includes not only the revenue from selling electricity to the grid on sunny days, but also the revenue from selling electricity to farmers in the distribution area on cloudy and rainy days. In the optimization model of photovoltaic capacity and indoor energy storage equipment capacity, the economic indicator is the electricity sales revenue plus the revenue from saving electricity minus the investment in equipment and the cost of purchasing electricity from centralized energy storage in the distribution area on cloudy and rainy days.

[0053] Furthermore, the optimization algorithm in this application embodiment can be a conventional optimization algorithm, such as simulated degradation algorithm, particle swarm optimization algorithm, etc. The power fed into the grid in this application embodiment is the power sent by farmers to the transformer substation minus the charging power of the energy storage equipment in the transformer substation. The power consumption of the transformer substation in this application embodiment can be calculated based on the farmer power consumption model combined with the Monte Carlo stochastic method to calculate the power consumption and electricity consumption of the entire transformer substation. In this application embodiment, the total power generation of the transformer substation can be calculated based on the photovoltaic power generation model combined with the Monte Carlo stochastic method.

[0054] As one possible implementation method, the embodiments of this application can establish an optimization equation with economic efficiency and stable interaction with the power grid as the second objective function, and with node power balance and meeting the electricity demand of the distribution area during consecutive cloudy and rainy days as the second constraint. The second optimization algorithm can be used to solve the equation. The lower limit of the centralized energy storage capacity can be determined based on the total electricity consumption of the distribution area under the longest consecutive cloudy days in historical meteorological data. At the same time, the system revenue can be maximized as the first sub-objective function, and the hourly power standard deviation fed into the power grid during the daytime can be minimized as the second sub-objective function. Solving the optimization equation yields the centralized energy storage operation strategy of the distribution area, and the centralized energy storage capacity and the second operation strategy of the distribution area are obtained.

[0055] This application embodiment uses economic efficiency and stable grid interaction as the second objective function, and node power balance and meeting the electricity demand of the distribution area during consecutive cloudy and rainy days as the second constraint. An optimization equation is established and solved using a second optimization algorithm to obtain the centralized energy storage capacity and second operation strategy of the distribution area. This ensures that the photovoltaic capacity, energy storage capacity, and operation strategy of the system are obtained while maintaining economic efficiency and stable grid connection. It guarantees that the photovoltaic power generated in the distribution area is preferentially consumed by farmers and electrical equipment in the area, eliminating the need for the distribution area to draw power from the grid. Furthermore, excess power is flexibly and orderly fed back into the grid, transforming the rural distribution network system from a consumer of electricity into a virtual power plant. This ensures that the entire distribution system relies entirely on photovoltaic power supply, sending excess power to the municipal grid on sunny days and utilizing energy storage to meet electricity demand during cloudy and rainy days and at night. The system does not need to draw power from the grid throughout the year, transforming rural areas from electricity consumers into electricity producers. This ensures economic efficiency and stable grid interaction, feeding power into the grid without disrupting its normal operation, and generating no carbon emissions during system operation.

[0056] In one embodiment of this application, an optimization equation is established with economic efficiency and stable interaction with the power grid as the second objective function, and node power balance and meeting the electricity demand of the distribution area during consecutive cloudy and rainy days as the second constraint. The equation is then solved using a second optimization algorithm to obtain the centralized energy storage capacity of the distribution area and a second operating strategy. This includes: determining the lower limit of the centralized energy storage capacity based on the total electricity consumption of the distribution area under the longest consecutive cloudy and rainy days in historical meteorological data, and taking the maximization of system benefits as the second sub-objective function, and taking the minimization of the hourly power standard deviation fed into the power grid during the day as the second sub-objective function. The second operating strategy is then solved using the second optimization algorithm. The energy storage equipment operating strategy in this embodiment of the application can be that the indoor energy storage equipment meets the electricity demand of farmers at night, and the centralized energy storage equipment in the distribution area meets the electricity demand of the distribution area during cloudy and rainy days. Therefore, the lower limit of the capacity of the indoor energy storage equipment is the electricity consumption of farmers at night, and the lower limit of the capacity of the centralized energy storage equipment in the distribution area is the electricity consumption of the distribution area during the longest consecutive cloudy and rainy days.

[0057] It is understood that the electricity consumption of the distribution area in this application embodiment can be calculated based on the electricity consumption model of farmers combined with the Monte Carlo stochastic method to calculate the power and electricity consumption of the entire distribution area. The total power generation of the distribution area in this application embodiment can be calculated based on the photovoltaic power generation combined with the Monte Carlo stochastic method. The economic indicator of the optimization method of the centralized energy storage capacity and operation strategy of the distribution area in this application embodiment is the electricity sales revenue minus the investment cost. The electricity sales revenue is not only the revenue from selling electricity to the grid on sunny days, but also the revenue from selling electricity to farmers in the distribution area on cloudy and rainy days. In the optimization model of photovoltaic capacity and indoor energy storage equipment capacity, the economic indicator is the electricity sales revenue plus the revenue from saving electricity minus the investment in equipment and the cost of purchasing electricity from the centralized energy storage in the distribution area on cloudy and rainy days.

[0058] Specifically, in this embodiment, the lower limit of the centralized energy storage capacity can be determined based on the total electricity consumption of the transformer area under the longest consecutive cloudy days in historical meteorological data. Maximizing system benefits is taken as the second sub-objective function, and minimizing the hourly power standard deviation fed into the grid during the day is taken as the second sub-objective function. Thus, the second optimization algorithm is used to solve the second operating strategy. The energy storage equipment operating strategy in this embodiment can be that the indoor energy storage equipment meets the electricity demand of farmers at night, and the centralized energy storage equipment in the transformer area meets the electricity demand of the transformer area on cloudy and rainy days. Therefore, the lower limit of the capacity of the indoor energy storage equipment is the electricity consumption of farmers at night, and the lower limit of the capacity of the centralized energy storage equipment in the transformer area is the electricity consumption of the transformer area during the longest consecutive cloudy and rainy days.

[0059] This application embodiment can further realize that the photovoltaic power generation in the distribution area is prioritized for consumption by farmers and electrical equipment in the distribution area. The distribution area does not need to obtain power support from the grid. The excess power is then sent back to the grid in an orderly manner through flexible regulation, turning the rural distribution network system from a consumer of electricity into a virtual power plant. This further ensures that the entire distribution system relies entirely on photovoltaic power supply. On sunny days, excess power is sent to the municipal grid, and on rainy days and at night, electricity storage is used to meet the electricity demand. There is no need to draw power from the grid throughout the year, turning rural areas from electricity consumers into electricity producers.

[0060] Specifically, it can be combined with Figure 2 As shown, the working principle of the rural photovoltaic flexible DC distribution network system design method of this application is explained in detail with a specific embodiment.

[0061] like Figure 2 As shown, embodiments of this application may include the following steps:

[0062] Step S201: Establish an electricity consumption prediction model and generate hourly load curves. In this embodiment, based on the usage time and frequency of various electrical devices used by villagers in different seasons, combined with the power of the electrical devices and the randomness of villagers' living habits, hourly power is calculated using a stochastic process to establish an electricity consumption prediction model. This model utilizes different types of characteristic data to simplify calculations, thereby quickly obtaining the hourly electricity load curves for villagers.

[0063] Step S202: Correct the parameters of the diode model and obtain the hourly power generation of the photovoltaic system using the incremental conductance method. In this embodiment, the hourly radiation intensity of the photovoltaic panel surface can be calculated using coordinate analysis based on the hourly total irradiance, hourly direct radiation, latitude and longitude of the installation location, and installation tilt angle, combined with the diode model and local meteorological parameters. The parameters of the diode model can be corrected based on the hourly radiation intensity of the photovoltaic panel surface and the outdoor air temperature. The hourly power generation of the photovoltaic system is then calculated using the incremental conductance method, resulting in hourly power generation curves for different seasons and weather conditions.

[0064] Step S203: Considering economic efficiency and stable interaction with the transformer substation, the first optimization algorithm is used to solve for the photovoltaic installed capacity, indoor energy storage capacity, and first operating strategy of each farmer. In this embodiment, economic efficiency and stable interaction with the transformer substation are used as the first objective function, and nodal power balance and meeting the nighttime electricity demand of farmers are used as the first constraints. An optimization equation is established and solved using the first optimization algorithm to obtain the photovoltaic installed capacity, indoor energy storage capacity, and first operating strategy of each farmer.

[0065] Step S204: Considering economic efficiency and stable interaction with the power grid, the second optimization algorithm is used to solve for the centralized energy storage capacity of the distribution area and the second operating strategy. In this embodiment, conventional optimization algorithms such as simulated degradation algorithms and particle swarm optimization algorithms can be used to solve for the centralized energy storage capacity of the distribution area and the second operating strategy.

[0066] The rural photovoltaic flexible DC distribution network system design method proposed in this application can design the rural distribution network system as a virtual power plant, realizing the effective absorption of photovoltaic power, improving the utilization rate of renewable energy, strengthening the stable interaction with the power grid, ensuring grid security, and improving power quality. This solves the problems in related technologies regarding photovoltaic system design: difficulty in absorbing photovoltaic power, disorderly grid connection endangering grid security, reduced power quality, difficulty in rationally optimizing photovoltaic capacity, energy storage capacity, and operating strategies, and inability to guarantee stable system operation.

[0067] Next, referring to the accompanying drawings, a design device for a rural photovoltaic flexible DC distribution network system according to an embodiment of this application is described.

[0068] Figure 3This is a block diagram of a rural photovoltaic flexible DC distribution network system design device according to an embodiment of this application.

[0069] like Figure 3 As shown, the rural photovoltaic flexible DC distribution network system design device 10 includes: a first acquisition module 100, a second acquisition module 200, a first design module 300, and a second design module 400.

[0070] Specifically, the first acquisition module 100 is used to obtain the hourly electricity load curve of farmers based on the types of electrical appliances and living habits of farmers.

[0071] The second acquisition module 200 is used to combine the diode model and local meteorological parameters to obtain the hourly photovoltaic power generation curves for different seasons and weather conditions.

[0072] The first design module 300 is used to establish an optimization equation and solve it using a first optimization algorithm, with the first objective function being economy and stable interaction with the transformer area, and the first constraint being node power balance and meeting the electricity demand of farmers at night. This yields the photovoltaic installed capacity, indoor energy storage capacity and first operation strategy for each farmer.

[0073] The second design module 400 is used to establish an optimization equation and solve it using a second optimization algorithm, with the second objective function being economy and stable interaction with the power grid, and the second constraint being node power balance and meeting the electricity demand of the distribution area during consecutive rainy days, to obtain the centralized energy storage capacity of the distribution area and the second operation strategy.

[0074] Optionally, in one embodiment of this application, the first acquisition module 100 includes: a first generation unit.

[0075] The first generation unit is used to calculate the hourly electricity consumption of farmers by introducing a random process based on the usage time and frequency of various electrical devices and the power of each type of electrical device, and to generate the hourly electricity load curve of farmers.

[0076] Optionally, in one embodiment of this application, the second acquisition module 200 includes a second generation unit.

[0077] The second generation unit is used to calculate the hourly power generation of photovoltaics based on the hourly total irradiance, hourly direct radiation, and hourly outdoor temperature, combined with a diode model corrected by meteorological parameters and the incremental conductivity method, and to generate the hourly power generation curve of photovoltaics.

[0078] Optionally, in one embodiment of this application, the first design module 300 includes: a first solution unit.

[0079] The first solution unit is used to maximize the system revenue as the first sub-objective function to obtain the indoor energy storage and photovoltaic capacity, and to minimize the hourly power standard deviation sent to the transformer area during the day as the second sub-objective function, so as to solve the first operation strategy using the first optimization algorithm.

[0080] Optionally, in one embodiment of this application, the second design module 400 includes a second solving unit.

[0081] The second solution unit is used to determine the lower limit of the centralized storage capacity based on the total electricity consumption of the distribution area under the longest consecutive cloudy days in historical meteorological data, and to take maximizing the system benefits as the second sub-objective function, and to take minimizing the hourly power standard deviation sent to the grid during the day as the second sub-objective function, so as to solve the second operation strategy using the second optimization algorithm.

[0082] It should be noted that the foregoing explanation of the design method embodiment for rural photovoltaic flexible DC distribution network system also applies to the design device for rural photovoltaic flexible DC distribution network system in this embodiment, and will not be repeated here.

[0083] The rural photovoltaic flexible DC distribution network system design device proposed in this application can design the rural distribution network system as a virtual power plant, realizing the effective absorption of photovoltaic power, improving the utilization rate of renewable energy, strengthening the stable interaction with the power grid, ensuring grid security, and improving power quality. This solves the problems in related technologies regarding photovoltaic system design: difficulty in absorbing photovoltaic power, disorderly grid connection endangering grid security, reduced power quality, difficulty in rationally optimizing photovoltaic capacity, energy storage capacity, and operating strategies, and inability to guarantee stable system operation.

[0084] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0085] The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0086] When processor 402 executes the program, it implements the rural photovoltaic flexible DC distribution network system design method provided in the above embodiments.

[0087] Furthermore, electronic devices also include:

[0088] Communication interface 403 is used for communication between memory 401 and processor 402.

[0089] The memory 401 is used to store computer programs that can run on the processor 402.

[0090] The memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0091] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0092] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0093] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0094] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described design method for a rural photovoltaic flexible DC distribution network system.

[0095] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0096] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0097] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0098] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0099] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0100] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0101] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0102] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A design method for a rural photovoltaic flexible DC distribution network system, characterized in that, Includes the following steps: Based on the types of electrical appliances and living habits of farmers, hourly electricity load curves of farmers are obtained; By combining the diode model and local meteorological parameters, hourly photovoltaic power generation curves for different seasons and weather conditions were obtained; With economic efficiency and stable interaction with the transformer substation as the primary objective function, and nodal power balance and meeting the nighttime electricity needs of farmers as the primary constraints, an optimization equation is established and solved using a primary optimization algorithm. This yields the photovoltaic installed capacity, indoor energy storage capacity, and primary operating strategy for each farmer. This includes: Maximizing system revenue is used as the first sub-objective function to obtain the indoor energy storage and photovoltaic capacity. Minimizing the hourly power standard deviation delivered to the distribution area during the day is used as the second sub-objective function. The first optimization algorithm is then used to solve for the first operating strategy. With economic efficiency and stable interaction with the power grid as the second objective function, and nodal power balance and meeting the electricity demand of the distribution area during consecutive rainy days as the second constraint, an optimization equation is established and solved using a second optimization algorithm to obtain the centralized energy storage capacity of the distribution area and the second operating strategy; which includes: The lower limit of the centralized storage capacity is determined based on the total electricity consumption of the distribution area under the longest consecutive cloudy days in historical meteorological data. Maximizing system revenue is used as the first sub-objective function, and minimizing the hourly power standard deviation fed into the grid during the day is used as the second sub-objective function. The second optimization algorithm is then used to solve for the second operating strategy. The lower limit of the capacity of indoor power storage equipment is the electricity consumption of farmers at night; among which, Cluster analysis is used to simplify calculations based on different types of feature data; among them, The electricity consumption of the transformer substation is calculated based on the electricity consumption model of farmers and a Monte Carlo-based stochastic method. The total power generation of the transformer substation is calculated based on the photovoltaic power generation and a Monte Carlo stochastic method.

2. The method according to claim 1, characterized in that, Based on the types of electrical appliances and living habits of farmers, a farmer electricity consumption prediction model is established to obtain the hourly electricity load curve of farmers, including: Based on the usage time and frequency of various electrical devices, and combined with the power of each type of electrical device, a stochastic process is introduced to calculate the hourly power consumption of farmers, thereby generating the hourly power load curve of the farmers.

3. The method according to claim 1, characterized in that, The method combines the diode model and local meteorological parameters to obtain hourly photovoltaic power generation curves for different seasons and weather conditions, including: The hourly photovoltaic power generation curve is generated by calculating the hourly total irradiance, hourly direct radiation, and hourly outdoor temperature using a diode model corrected for meteorological parameters and the incremental conductance method.

4. A design device for a rural photovoltaic flexible DC distribution network system, characterized in that, include: The first acquisition module is used to obtain the hourly electricity load curve of farmers based on the types of electrical appliances and living habits of farmers; The second acquisition module is used to combine the diode model and local meteorological parameters to obtain the hourly photovoltaic power generation curves under different seasons and weather conditions; The first design module is used to establish an optimization equation and solve it using the first optimization algorithm, with the first objective function being economy and smooth interaction with the transformer area, and the first constraint being nodal power balance and meeting the electricity demand of farmers at night. This results in the photovoltaic installed capacity, indoor energy storage capacity and first operation strategy for each farmer. as well as The second design module, with economic efficiency and stable grid interaction as the second objective function, and node power balance and meeting the electricity demand of the distribution area during consecutive rainy days as the second constraint, establishes optimization equations and solves them using a second optimization algorithm to obtain the centralized energy storage capacity of the distribution area and the second operating strategy; among which... The first design module includes: The first solution unit is used to maximize the system revenue as the first sub-objective function to obtain the indoor energy storage and photovoltaic capacity, and to minimize the hourly power standard deviation sent into the transformer area during the day as the second sub-objective function, so as to solve the first operation strategy using the first optimization algorithm. The second design module includes: The second solution unit is used to determine the lower limit of the centralized storage capacity based on the total electricity consumption of the distribution area under the longest consecutive cloudy days in historical meteorological data. It takes maximizing system revenue as the first sub-objective function and minimizing the hourly power standard deviation fed into the grid during the day as the second sub-objective function, using the second optimization algorithm to solve for the second operating strategy. The lower limit of the capacity of indoor power storage equipment is the electricity consumption of farmers at night; among which, Cluster analysis is used to simplify calculations based on different types of feature data; among them, The electricity consumption of the transformer substation is calculated based on the electricity consumption model of farmers and a Monte Carlo-based stochastic method. The total power generation of the transformer substation is calculated based on the photovoltaic power generation and a Monte Carlo stochastic method.

5. The apparatus according to claim 4, characterized in that, The first acquisition module includes: The first generation unit is used to calculate the hourly electricity consumption of farmers by introducing a random process based on the usage time and frequency of various electrical devices and the power of each type of electrical device, and to generate the hourly electricity load curve of the farmers.

6. The apparatus according to claim 5, characterized in that, The second acquisition module includes: The second generation unit is used to calculate the hourly power generation of photovoltaics based on the hourly total irradiance, hourly direct radiation, and hourly outdoor temperature, combined with a diode model corrected by meteorological parameters and the incremental conductance method, and to generate the hourly power generation curve of photovoltaics.

7. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the rural photovoltaic flexible DC distribution network system design method as described in any one of claims 1-3.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the design method for a rural photovoltaic flexible DC distribution network system as described in any one of claims 1-3.

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