Dispatching method based on distributed photovoltaic grid-connected operation and related device

Abstract

The invention discloses a scheduling method and related device based on distributed photovoltaic grid-connected operation, and the method comprises the steps: obtaining the data information of each distributed photovoltaic region in real time, the data information of each distributed photovoltaic region comprising minute-level generation power and uploading power of an uploading power grid; subtracting uploading power of an uploading power grid from the minute-level generated power to obtain minute-level average electric quantity consumption power of each distributed photovoltaic area, and calculating the average electric quantity consumption power of each distributed photovoltaic area according to the minute-level generated power and the clock-level average electric quantity consumption power of each distributed photovoltaic area. Calculating the optimal electric quantity demand power consumption and the optimal power generation power of each distributed photovoltaic group based on an ACO algorithm; according to the optimal electric quantity demand power consumption and the optimal generation power, the method and the related device can realize the optimal scheduling of a distributed power generation system, and ensure the stable and safe operation of photovoltaic power generation.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic power generation, and relates to a scheduling method and related device based on the grid-connected operation of distributed photovoltaic power generation. Background Art

[0002] Distributed photovoltaic power generation technology converts light energy into electrical energy, and reduces the use of fossil fuels through the sustainable development and utilization of solar energy. It has the advantages of being clean, environmentally friendly, and having high economic benefits. Under the average sunshine conditions in China, installing a 1-kilowatt photovoltaic power generation system generates an average of 1,200 degrees of electricity per year, which can reduce the consumption of coal (standard coal) by about 400 kilograms and reduce carbon dioxide emissions by about 1 ton. In the process of promoting energy conservation and emission reduction, distributed photovoltaic power generation systems significantly reduce CO2 emissions, meeting the development needs of low-carbon environmental protection. Therefore, widely applying distributed photovoltaic power generation systems is an important way to effectively solve energy problems and promote the sustainable development of society.

[0003] Although the current distributed photovoltaic power generation technology is becoming increasingly mature, there is still a problem of large fluctuations in the power output of the system, which not only affects the normal use of users, but also causes unstable voltage in the grid connection. Especially, weather factors in the external environment have a great impact on the power generation efficiency of distributed photovoltaic panels. How to ensure a stable power supply to users and the grid by the power generation system is the core element for the further development of this technology. Therefore, distributed photovoltaic power generation systems need to develop more effective optimal scheduling strategies to ensure the stable and safe operation of photovoltaic power generation. Summary of the Invention

[0004] The purpose of the present invention is to overcome the above-mentioned disadvantages of the prior art, and provide a scheduling method and related device based on the grid-connected operation of distributed photovoltaic power generation. The method and related device can achieve the optimal scheduling of distributed power generation systems and ensure the stable and safe operation of photovoltaic power generation.

[0005] To achieve the above purpose, the present invention discloses a scheduling method based on the grid-connected operation of distributed photovoltaic power generation, including:

[0006] Real-time acquisition of data information of each distributed photovoltaic area, where the data information of the distributed photovoltaic area includes minute-level power generation power and the power uploaded to the grid;

[0007] Subtract the power uploaded to the grid from the minute-level power generation power to obtain the minute-level average power consumption power of each distributed photovoltaic area. Based on the minute-level power generation power and the minute-level average power consumption power of each distributed photovoltaic area, calculate the optimal power consumption demand and the optimal power generation power of each distributed photovoltaic group using the ACO algorithm;

[0008] Optimize and control the power generation of the distributed photovoltaic group according to the optimal power consumption demand and the optimal power generation power.

[0009] A further improvement of the scheduling method based on distributed photovoltaic grid-connected operation according to the present invention lies in:

[0010] Furthermore, the process of optimizing and regulating the power generation of the distributed photovoltaic group according to the optimal power demand power consumption and the optimal power generation power is as follows:

[0011] When V < V 耗 , the power generation of the photovoltaic group and the power of the energy storage module are supplied to local users, where V 耗 is the optimal power demand power consumption;

[0012] When V 耗 < V < V 优 , the excess power generation of the photovoltaic group is uploaded to the power grid, where V 优 is the optimal power generation power;

[0013] When V > V 优 , a part of the excess power generation of the photovoltaic group is supplied to the power grid, and the other part is supplied to the energy storage device.

[0014] Furthermore, in the process of calculating the optimal power demand power consumption and the optimal power generation power of each distributed photovoltaic group based on the ACO algorithm, the probability of an ant going to different cities is:

[0015]

[0016] where α is the pheromone factor, β is the heuristic function factor, A k is the taboo list of all cities that the k-th ant has visited at time t, and η i,j (t) is the heuristic value on the path from city i to j for each ant at time t.

[0017] Furthermore, the heuristic value η i,j (t) on the path from city i to j for each ant at time t is:

[0018]

[0019] where d i,j is the distance from city i to city j.

[0020] Furthermore, in the process of calculating the optimal power demand power consumption and the optimal power generation power of each distributed photovoltaic group based on the ACO algorithm, in the f-th iteration, the update formula of the pheromone concentration is:

[0021]

[0022] where ρ is the evaporation coefficient, Δτ i,j$(t)$ represents the process of pheromone increase on the path from city $i$ to city $j$ at time $t$ due to the passage of ants, $\Delta\tau$ i,j $(t)$ is:

[0023]

[0024] Wherein, represents the pheromone left by the $k$-th ant on the path between city $i$ and city $j$ at time $t$.

[0025] The present invention discloses a dispatching system based on distributed photovoltaic grid-connected operation, including:

[0026] An acquisition module, configured to acquire the data information of each distributed photovoltaic area in real time, and the data information of the distributed photovoltaic area includes the minute-level power generation power and the upload power to the power grid;

[0027] A calculation module, configured to subtract the upload power to the power grid from the minute-level power generation power to obtain the minute-level average power consumption power of each distributed photovoltaic area, and calculate the optimal power demand power consumption and the optimal power generation power of each distributed photovoltaic group based on the ACO algorithm according to the minute-level power generation power and the minute-level average power consumption power of each distributed photovoltaic area;

[0028] A regulation module, configured to optimize and regulate the power generation amount of the distributed photovoltaic group according to the optimal power demand power consumption and the optimal power generation power.

[0029] A further improvement of the dispatching system based on distributed photovoltaic grid-connected operation according to the present invention lies in:

[0030] Further, the process of optimizing and regulating the power generation amount of the distributed photovoltaic group according to the optimal power demand power consumption and the optimal power generation power is:

[0031] When $V < V$ 耗 , the power generation amount of the photovoltaic group and the power of the energy storage module are supplied to local users, wherein $V$ 耗 is the optimal power demand power consumption;

[0032] When $V$ 耗 $< V < V$ 优 , the excess power generation of the photovoltaic group is uploaded to the power grid, wherein $V$ 优 is the optimal power generation power;

[0033] When $V > V$ 优 , a part of the excess power generation of the photovoltaic group is supplied to the power grid, and the other part is supplied to the energy storage device.

[0034] Further, in the process of calculating the optimal power demand power consumption and the optimal power generation power of each distributed photovoltaic group based on the ACO algorithm, the probability of ants going to different cities is:

[0035]

[0036] where α is the pheromone factor, β is the heuristic function factor, and A k is the taboo list of all cities that the k-th ant has visited at time t, and η i,j (t) is the heuristic value on the path from city i to j for each ant at time t.

[0037] The present invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the scheduling method based on distributed photovoltaic grid-connected operation are implemented.

[0038] The present invention discloses a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the scheduling method based on distributed photovoltaic grid-connected operation are implemented.

[0039] The present invention has the following beneficial effects:

[0040] When the scheduling method and related device based on distributed photovoltaic grid-connected operation of the present invention are specifically operated, according to the real-time data of the operation of the distributed photovoltaic power generation system, the ant optimization algorithm is used to process the data, and the obtained optimal value can improve the timeliness and applicability of the scheduling method, and the energy storage technology is used to solve the problem of grid connection impact caused by the instability of distributed photovoltaic power generation, and improve the stability and reliability of the photovoltaic power generation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0042] Figure 1 is the flowchart of the method of the present invention;

[0043] Figure 2 is the optimization flowchart in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] In the description of the present invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0046] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0047] It should be further understood that the term " / and" used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. For example, A and / or B may represent: the case where A exists alone, the case where A and B exist simultaneously, and the case where B exists alone. In addition, the character " / " in the present invention generally represents an "or" relationship between the contextually related objects.

[0048] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present invention to describe preset ranges, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0049] Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determining" or "when detected (stated condition or event)" or "in response to detecting (stated condition or event)".

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the present invention described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0051] Various schematic structural diagrams according to the disclosed embodiments of the present invention are shown in the accompanying drawings. These figures are not drawn to scale, where for the purpose of clear expression, some details are enlarged and some details may be omitted. The shapes of various regions and layers shown in the figures and their relative sizes and positional relationships are merely exemplary. In practice, there may be deviations due to manufacturing tolerances or technical limitations, and those skilled in the art can design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0052] As is well known, the ant algorithm, also known as the ant colony optimization (ACO) algorithm, is a probabilistic algorithm used to find an optimal path in a graph. It was introduced by Marco Dorigo in his doctoral thesis in 1992, and its inspiration comes from the behavior of ants finding paths during the process of searching for food. Ants release a substance called "pheromone" during movement. They can sense this substance and use it to guide their movement direction. The collective behavior of a large number of ants will exhibit a phenomenon of positive feedback of information: the more ants that have walked on a certain path, the greater the probability that later ants will choose that path. It is a bionic optimization algorithm based on the foraging behavior of ants, with characteristics such as high efficiency and global optimization, and has been widely applied in many fields.

[0053] Embodiment 1

[0054] Refer to Figure 1 , the scheduling method based on distributed photovoltaic grid-connected operation of the present invention includes the following steps:

[0055] 1) Obtain the data information of each distributed photovoltaic area in real time. The data information of the distributed photovoltaic area includes the minute-level power generation power and the upload power to the power grid.

[0056] 2) Subtract the uploading power to the power grid from the minute-level power generation to obtain the minute-level average power consumption of each distributed photovoltaic area. Based on the minute-level power generation and the minute-level average power consumption of each distributed photovoltaic area, calculate the optimal power consumption demand and the optimal power generation of each distributed photovoltaic group using the ACO algorithm. As Figure 2 shown, the specific process is as follows:

[0057] 21) Initialize the pheromone matrix, process the minute-level power generation data and the average power consumption to convert them into matrix information between cities, and initialize parameters such as the population size, pheromone factor, heuristic function factor, evaporation coefficient, pheromone constant, and maximum number of iterations. Among them, the initialized population size is 1.5 times the number of matrices, the value range of the pheromone factor α is 1 to 4, the value range of the heuristic function factor β is 3 to 5, the value range of the evaporation coefficient ρ is 0.2 to 0.5, the pheromone constant Q is 1, and the taboo list A k is the set of cities visited by the ants, and the value range of the maximum number of iterations is 100 to 500;

[0058] 22) Randomly place the ants at different starting points, with at most one ant distributed in each city. Calculate the next city to be visited by each ant. Each ant in the new population selects an unvisited city based on the pheromone matrix and the city distance as the next city to go to. The pheromone is:

[0059] τ i,j (0) = τ 初 , i, j = 1, 2, …, n

[0060] where τ 初 is the initial pheromone.

[0061] 23) Calculate the objective function value of each solution in the current population, that is, calculate the path length passed by each ant, and record the optimal solution in the current iteration. The probability calculation formula for the ants to go to different cities is:

[0062]

[0063] where α is the pheromone factor, β is the heuristic function factor, A k is the taboo list of all cities that the kth ant has visited at time t, and η i,j (t) is the heuristic value on the path from city i to j for each ant at time t, and η i,j (t) is:

[0064]

[0065] where d i,j is the distance from city i to city j.

[0066] 24) Update the pheromone concentration on the connection paths of each city. The pheromone includes the pheromone remaining in the iteration and the pheromone generated currently. In the f - th iteration, the update formula for the pheromone concentration is as follows:

[0067]

[0068] Among them, ρ is the evaporation coefficient, and Δτ i,j (t) represents the process of the increase in pheromone on the path from city i to city j at time t due to the passing of ants, and Δτ i,j (t) is:

[0069]

[0070] Among them, represents the pheromone left by the k - th ant on the path between city i and city j at time t.

[0071] 25) Judge according to the termination condition. If the termination condition is met, output the optimal value; if the termination condition is not met, go to step 22).

[0072] 3) The judgment criterion is a dual judgment criterion composed of the optimal power consumption and the optimal power generation. Compare the minute - level power generation power of the distributed photovoltaic group monitored in real - time with the judgment criterion. When V < V 耗 , issue instruction a; when V 耗 < V < V 优 , issue instruction b; when V > V 优 , issue instruction c. The three - level standard reasonably distributes the redundant power generation of the photovoltaic system and improves the regulation efficiency of the scheduling method.

[0073] 4) Optimally regulate the power generation of the distributed photovoltaic group according to different instructions. Instruction a is: supply the power generation of the photovoltaic group and the power of the energy storage module to local users; Instruction b is: upload the redundant power generation of the photovoltaic group to the power grid; Instruction c is: supply a part of the redundant power generation of the photovoltaic group to the power grid and the other part to the energy storage device. Such distribution not only ensures the stable power consumption of users but also uses the energy storage module to reduce the voltage peak of the photovoltaic power generation system connected to the power grid.

[0074] The present invention has the following characteristics:

[0075] The present invention calculates the optimal operating parameters of the photovoltaic power generation system based on the operating data of the photovoltaic power generation system, and performs optimal regulation according to the real - time monitoring data information, timely improving the power supply efficiency of the photovoltaic power generation system to obtain highly stable electric energy.

[0076] The present invention adopts the ant optimization algorithm, and through the continuous iterative crawling calculation of each group of ants according to the target requirements, the global optimal value is found, effectively improving the accuracy and efficiency of the regulation method. At the same time, the evaluation criteria are divided into three instruction results, and the excess power generation is reasonably allocated to the energy storage device and the power grid according to the real-time power generation situation of the photovoltaic power generation system. The use of the energy storage module not only ensures the stable power consumption of users, but also reduces the voltage peak of the photovoltaic power generation system connected to the power grid.

[0077] Embodiment 2

[0078] The scheduling system based on distributed photovoltaic grid-connected operation described in the present invention includes:

[0079] An acquisition module, configured to acquire the data information of each distributed photovoltaic area in real time, where the data information of the distributed photovoltaic area includes the minute-level power generation power and the upload power to the power grid;

[0080] A calculation module, configured to subtract the upload power to the power grid from the minute-level power generation power to obtain the minute-level average power consumption power of each distributed photovoltaic area, and based on the minute-level power generation power and the minute-level average power consumption power of each distributed photovoltaic area, calculate the optimal power demand power consumption and the optimal power generation power of each distributed photovoltaic group based on the ACO algorithm;

[0081] A regulation module, configured to optimize and regulate the power generation amount of the distributed photovoltaic group according to the optimal power demand power consumption and the optimal power generation power.

[0082] In this embodiment, the process of optimizing and regulating the power generation amount of the distributed photovoltaic group according to the optimal power demand power consumption and the optimal power generation power is as follows:

[0083] When V < V 耗 , then the power generation amount of the photovoltaic group and the power of the energy storage module are supplied to local users, where V 耗 is the optimal power demand power consumption;

[0084] When V 耗 < V < V 优 , then the excess power generation of the photovoltaic group is uploaded to the power grid, where V 优 is the optimal power generation power;

[0085] When V > V 优 , then a part of the excess power generation of the photovoltaic group is supplied to the power grid, and the other part is supplied to the energy storage device.

[0086] In this embodiment, in the process of calculating the optimal power demand power consumption and the optimal power generation power of each distributed photovoltaic group based on the ACO algorithm, the probability that an ant goes to different cities is:

[0087]

[0088] Among them, α is the pheromone factor, β is the heuristic function factor, and A k is the taboo list of all cities that the k-th ant has visited at time t, and η i,j (t) is the heuristic value on the path from city i to j for each ant at time t.

[0089] The division of modules in the embodiments of the present application is illustrative, only a logical function division. In actual implementation, there may be other division methods. In addition, in each embodiment of the present application, each functional module can be integrated in a processor, can also exist physically alone, or two or more modules can be integrated in one module. The above integrated modules can be implemented in the form of hardware or in the form of software function modules.

[0090] Embodiment Three

[0091] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the scheduling method for distributed photovoltaic grid-connected operation. For example, it includes: obtaining data information of each distributed photovoltaic area in real time, where the data information of the distributed photovoltaic area includes minute-level power generation power and the power uploaded to the power grid; subtracting the power uploaded to the power grid from the minute-level power generation power to obtain the minute-level average power consumption power of each distributed photovoltaic area, and based on the ACO algorithm, calculating the optimal power demand power consumption and the optimal power generation power of each distributed photovoltaic group according to the minute-level power generation power and the minute-level average power consumption power of each distributed photovoltaic area; optimizing and controlling the power generation of the distributed photovoltaic group according to the optimal power demand power consumption and the optimal power generation power. Among them, the memory may include internal memory, such as high-speed random access memory, and may also include non-volatile memory, such as at least one disk memory, etc.; the processor, network interface, and memory are interconnected through an internal bus, and this internal bus can be an Industry Standard Architecture bus, a Peripheral Component Interconnect standard bus, an Extended Industry Standard Architecture bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include program code, and the program code includes computer operation instructions. The memory can include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0092] Embodiment Four

[0093] A computer-readable storage medium stores a computer program which, when executed by a processor, implements the steps of the scheduling method for distributed photovoltaic grid-connected operation. For example, it includes: obtaining data information of each distributed photovoltaic area in real time, where the data information of the distributed photovoltaic area includes minute-level power generation and the power uploaded to the grid; subtracting the power uploaded to the grid from the minute-level power generation to obtain the minute-level average power consumption of each distributed photovoltaic area, and based on the minute-level power generation and the minute-level average power consumption of each distributed photovoltaic area, calculating the optimal power demand and the optimal power generation of each distributed photovoltaic group based on the ACO algorithm; optimizing and controlling the power generation of the distributed photovoltaic group according to the optimal power demand and the optimal power generation. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disc, magnetic disk, etc.

[0094] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one or more flows or multiple flows and / or blocks Figure 1 one or more blocks or multiple blocks.

[0096] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the specified functions in the Figure 1one process or multiple processes and / or boxes Figure 1 the functions specified in one box or multiple boxes.

[0097] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or boxes Figure 1 the one box or multiple boxes.

[0098] After considering the specification and the disclosure of the invention, those skilled in the art will readily conceive of other embodiments of the present invention. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include known common general knowledge or conventional technical means in the technical field not disclosed in the present invention. The specification and examples are only regarded as exemplary, and the true scope and spirit of the present invention are pointed out by the following claims.

[0099] It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

[0100] As described above, the above are only the preferred embodiments of the present invention and do not impose any limitations on the present invention. Any simple modifications, changes, and equivalent structural changes made to the above embodiments according to the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A dispatching method based on distributed photovoltaic grid-connected operation, characterized in that: include: Acquire data information of each distributed photovoltaic area in real time, wherein the data information of the distributed photovoltaic area includes minute-level power generation and power uploaded to the power grid; The minute-level power generation is subtracted from the power uploaded to the power grid to obtain the minute-level average power consumption of each distributed photovoltaic area. According to the minute-level power generation and the hour-level average power consumption of each distributed photovoltaic area, the optimal power demand power consumption and optimal power generation of each distributed photovoltaic group are calculated based on the ACO algorithm; The power generation of the distributed photovoltaic group is optimized and regulated according to the optimal power demand power consumption and the optimal power generation power.

2. The dispatching method based on distributed photovoltaic grid-connected operation according to claim 1 is characterized in that: The process of optimizing and regulating the power generation of the distributed photovoltaic group according to the optimal power demand power consumption and the optimal power generation power is as follows: When V<V 耗 When V 耗 Power consumption for optimal power demand; When V 耗 <V<V 优 When V 优 is the optimal power generation; When V>V 优 When the power is too high, part of the excess power generated by the photovoltaic group is supplied to the grid, and the other part is supplied to the energy storage device.

3. The dispatching method based on distributed photovoltaic grid-connected operation according to claim 1 is characterized in that: The probability of ants going to different cities in the process of calculating the optimal power demand and optimal power generation of each distributed photovoltaic group based on the ACO algorithm for: Among them, α is the pheromone factor, β is the heuristic function factor, A k is the taboo list of all cities visited by the kth ant at time t, η i,j (t) is the inspiration value of each ant on the path from city i to j at time t.

4. The dispatching method based on distributed photovoltaic grid-connected operation according to claim 3 is characterized in that: The heuristic value η of each ant on the path from city i to j at time t i,j (t) is: Among them, d i,j is the distance from city i to city j.

5. The dispatching method based on distributed photovoltaic grid-connected operation according to claim 1 is characterized in that: In the process of calculating the optimal power demand and optimal power generation of each distributed photovoltaic group based on the ACO algorithm, the update formula of the pheromone concentration in the fth iteration is: Where ρ is the evaporation coefficient, Δτ i,j (t) represents the process of the increase of pheromone on the path from city i to city j at time t due to the passage of ants, Δτ i,j (t) is: in, represents the pheromone left by the kth ant on the path between city i and city j at time t.

6. A dispatching system based on distributed photovoltaic grid-connected operation, characterized in that: include: An acquisition module is used to acquire data information of each distributed photovoltaic area in real time, wherein the data information of the distributed photovoltaic area includes minute-level generated power and uploaded power to the power grid; A calculation module is used to subtract the uploaded power of the uploaded power grid from the minute-level generated power to obtain the minute-level average power consumption of each distributed photovoltaic area, and calculate the optimal power demand power consumption and optimal power generation power of each distributed photovoltaic group based on the ACO algorithm according to the minute-level generated power and the hour-level average power consumption of each distributed photovoltaic area; The control module is used to optimize and control the power generation of the distributed photovoltaic group according to the optimal power demand power consumption and the optimal power generation power.

7. The dispatching system based on distributed photovoltaic grid-connected operation according to claim 6 is characterized in that: The process of optimizing and regulating the power generation of the distributed photovoltaic group according to the optimal power demand power consumption and the optimal power generation power is as follows: When V<V 耗 When V 耗 Power consumption for optimal power demand; When V 耗 <V<V 优 When V 优 is the optimal power generation; When V>V 优 When the power is too high, part of the excess power generated by the photovoltaic group is supplied to the grid, and the other part is supplied to the energy storage device.

8. The dispatching system based on distributed photovoltaic grid-connected operation according to claim 6 is characterized in that: The probability of ants going to different cities in the process of calculating the optimal power demand and optimal power generation of each distributed photovoltaic group based on the ACO algorithm for: Among them, α is the pheromone factor, β is the heuristic function factor, A k is the taboo list of all cities visited by the kth ant at time t, η i,j (t) is the inspiration value of each ant on the path from city i to j at time t.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the scheduling method based on distributed photovoltaic grid-connected operation as described in any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the scheduling method based on distributed photovoltaic grid-connected operation as described in any one of claims 1 to 5 are implemented.

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