Distributed power supply optimization access method based on big data analysis

Abstract

The invention discloses a distributed power supply optimization access method based on big data analysis, and relates to the technical field of distributed power supply optimization access, and the method comprises the steps: collecting and analyzing the historical operation data of a distributed power supply, constructing a distributed power supply evaluation model, selecting a part of the distributed power supply as a dynamic scheduling object, and building an optimization access model. The optimal access capacity and the optimal access time of each distributed power supply are calculated, the access strategy of the distributed power supply is adjusted in real time, and coordinated optimization operation of the power grid and the distributed power supply is achieved. According to the method, historical data of the distributed power supply are collected and analyzed, an evaluation model is constructed, the supply and demand balance prediction capability of the system is enhanced, the operation risk is reduced, an optimal access model is established, the optimal access capacity and time are accurately calculated, the resource configuration is optimized, and the access strategy is adjusted in real time, so that the distributed power supply is optimized. Coordinated and optimized operation of the power grid and the distributed power supply is ensured, and system stability and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed power supply optimization access, and in particular to a distributed power supply optimization access method based on big data analysis. Background Art

[0002] In the field of distributed power optimization access technology, existing methods face many challenges, including the impact of distributed power access on the system load growth pattern, the randomness of output energy caused by meteorological factors, and the reverse power flow problem that may occur as the capacity of distributed power increases. These problems are particularly prominent in traditional power grids because they involve the analysis and processing of large amounts of data, and existing methods are difficult to effectively cope with these complex data integration and analysis requirements.

[0003] Distributed Generation (DG) technology is an ideal way to make full use of renewable energy. It has the advantages of low investment, environmental protection, flexible control and high safety, and can effectively supplement the deficiencies of centralized power supply systems. However, during the access of distributed power sources, the output fluctuates with the changes in meteorological conditions, which increases the uncertainty of load forecasting in the distribution system. With the popularization of distributed power sources, especially the continuous growth of their capacity, the system has seen phenomena such as reduced transmission power of upstream feeders, gradually reduced power flow, and increased feeder voltage, which may even lead to the generation of reverse power flow problems.

[0004] Therefore, in response to the above problems, a distributed power optimization access method based on big data analysis is proposed to deal with the load uncertainty, random influence and reverse flow problems that occur during the distributed power access process. This method can optimize the access of distributed power sources by analyzing and processing massive data, thereby improving the stability and operation efficiency of the power grid. Summary of the invention

[0005] In view of the problems of existing distributed power access methods in processing massive data, coping with load uncertainty and randomness of meteorological factors, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is how to optimize the access strategy of distributed power sources through a method based on big data analysis, so as to achieve coordinated and optimized operation of the power grid and distributed power sources and improve the stability and operation efficiency of the power grid.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, an embodiment of the present invention provides a distributed power supply optimization access method based on big data analysis, which includes collecting and analyzing historical operation data of distributed power supplies, and constructing a distributed power supply evaluation model; when the system monitors that the current power grid load is greater than a preset load threshold and the access capacity of the distributed power supply is less than the maximum carrying capacity of the system, some distributed power supplies are selected as dynamic scheduling objects; according to the distribution characteristics and power supply requirements of the dynamic scheduling objects, an optimization access model with the goal of maximizing system benefits is established, and the optimal access capacity and optimal access time of each distributed power supply are calculated; during the operation of the power grid, the access strategy of the distributed power supply is adjusted in real time to achieve coordinated and optimized operation of the power grid and the distributed power supply.

[0009] As a preferred solution of the distributed power supply optimization access method based on big data analysis described in the present invention, the distributed power supply evaluation model includes the following steps: using the Monte Carlo simulation method to simulate the fault scenario, calculate the system average power outage frequency index SAIFI and the system average power outage duration index SAIDI, and the specific calculation formula is as follows:

[0010]

[0011] Among them, λ is the failure rate, U is the annual average power outage time, N is the number of affected users, and NT is the total number of users.

[0012] The grid coverage area is divided into different power supply zones, a number of distributed power sources are randomly distributed in each zone, and data is collected and analyzed for each zone in turn, and the data collection cycle of all zones is the same; during the collection cycle of each zone, the power supply and demand balance of this zone is judged, and the impact of the access of distributed power sources on the system is statistically analyzed; the supply and demand balance index, the system average power outage frequency index SAIFI and the system average power outage duration index SAIDI within the coverage of the grid are accumulated and summed, and the overall balance and reliability evaluation index of the system are calculated; according to the overall balance and the reliability evaluation index, some distributed power sources are selected as dynamic scheduling objects.

[0013] As a preferred solution of the distributed power supply optimization access method based on big data analysis described in the present invention, wherein: the dynamic scheduling object serves as the power supply and load to achieve a dynamic balance between the power supply and the load, wherein the selection process of the dynamic scheduling object includes the following steps: sorting the N distributed power sources in the power grid system from large to small according to the access capacity; selecting K distributed power sources with small access capacity as dynamic scheduling objects based on the overall balance and reliability evaluation indicators; the number K of dynamic scheduling objects is dynamically determined based on the difference between the current load of the system and the preset load threshold; when the difference between the load and the preset load threshold exceeds a predetermined range, the K value is dynamically adjusted based on the size of the difference.

[0014] As a preferred solution of the distributed power optimization access method based on big data analysis described in the present invention, the optimized access model is constructed by using a mixed integer programming algorithm; the specific formula of the objective function of the optimized access model is as follows:

[0015] minF=Σ(α i *P i -β i *C i );

[0016] Among them, P i is the access capacity of each distributed power source, C i is the access cost, α i is the profit coefficient, β i is the cost coefficient, and i is the number of the distributed generation.

[0017] As a preferred solution of the distributed power optimization access method based on big data analysis described in the present invention, it also includes calculating the optimal access capacity of each distributed power source according to the genetic algorithm combined with the power flow calculation, and obtaining the optimal solution of the optimization objective function, which is expressed as:

[0018] P i * =arg F(P i ,C i ,α i ,β i );

[0019] Among them, P i * is the optimal access capacity of the i-th distributed generation, and F() is the optimization objective function.

[0020] As a preferred solution of the distributed power optimization access method based on big data analysis described in the present invention, the optimal access time of each distributed power source is calculated by using a time series analysis method, and the specific formula of its objective function is as follows:

[0021] minG=Σ(γ j *T j +δ j *D j );

[0022] Among them, T j is the power grid loss in the jth time period, D j is the corresponding load fluctuation, γ j and δ j are the weight coefficients of loss and fluctuation respectively.

[0023] The constraint condition of the objective function includes that the access capacity of the i-th distributed power source in the j-th time period is less than or equal to the maximum capacity P of the i-th distributed power source. i,j ≤P imax ; The voltage deviation in the jth time period is less than or equal to the maximum voltage deviation |ΔV j |≤ΔV max .

[0024] As a preferred solution of the distributed power optimization access method based on big data analysis described in the present invention, the access strategy includes the following steps: real-time monitoring of grid load changes and distributed power output fluctuations, dynamically adjusting the access strategy, with the goal of minimizing system fluctuations while ensuring the balance of power supply and demand, and the specific formula of the optimization objective function is as follows:

[0025]

[0026] Among them, ΔP i is the active power adjustment of the i-th distributed generation, ΔQ i is the reactive power adjustment of the i-th distributed generation, w i and are the weight coefficients for active and reactive power adjustment respectively.

[0027] According to the adjusted access strategy, the access or exit of distributed power sources is controlled in real time to stabilize the overall balance of the system and achieve coordinated and optimized operation of the power grid and distributed power sources.

[0028] In the second aspect, an embodiment of the present invention provides a distributed power supply optimization access system based on big data analysis, which includes: an acquisition module, used to collect and analyze historical operation data of distributed power supplies, and construct a distributed power supply evaluation model; a selection module, used to select part of the distributed power supplies as dynamic scheduling objects when the system monitors that the current power grid load is greater than a preset load threshold and the access capacity of the distributed power supplies is less than the maximum carrying capacity of the system; a calculation module, used to establish an optimized access model with the goal of maximizing system benefits according to the distribution characteristics and power supply requirements of the dynamic scheduling objects, and calculate the optimal access capacity and optimal access time of each distributed power supply; a coordination module, used to adjust the access strategy of the distributed power supply in real time during the operation of the power grid, so as to realize the coordinated and optimized operation of the power grid and the distributed power supply.

[0029] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the distributed power supply optimization access method based on big data analysis as described in the first aspect of the present invention are implemented.

[0030] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the distributed power supply optimization access method based on big data analysis as described in the first aspect of the present invention are implemented.

[0031] The beneficial effects of the present invention are as follows: the present invention collects and analyzes historical data of distributed power sources, constructs an evaluation model, enhances the system's supply and demand balance prediction capability, and reduces operational risks; when the grid load exceeds a threshold and the access capacity is insufficient, the system dynamically selects part of the power supply for scheduling, flexibly responds to load fluctuations, and improves operational efficiency; by establishing an optimized access model with the goal of maximizing system benefits, accurately calculates the optimal access capacity and time, optimizes resource allocation, and improves economic benefits; by adjusting the access strategy in real time, ensures coordinated and optimized operation of the grid and distributed power sources, and improves system stability and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0033] Figure 1 This is a flow chart of a distributed power supply optimization access method based on big data analysis in Example 1. DETAILED DESCRIPTION

[0034] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0035] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0036] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0037] Example 1

[0038] Reference Figure 1, which is the first embodiment of the present invention, and provides a distributed power supply optimization access method based on big data analysis, including:

[0039] S1: Collect and analyze the historical operation data of distributed power sources and build a distributed power source evaluation model.

[0040] Specifically, the distributed power generation evaluation model includes the following steps: using the Monte Carlo simulation method, by simulating the fault scenario, the system average power outage frequency index SAIFI and the system average power outage duration index SAIDI are calculated. The specific calculation formula is as follows:

[0041]

[0042] Among them, λ is the failure rate, U is the annual average power outage time, N is the number of affected users, and NT is the total number of users.

[0043] It should be noted that when the grid load exceeds the preset load threshold, the scheduling priority of distributed power sources will be increased; if a fault scenario occurs in the system, the SAIFI and SAIDI indicators will be recalculated according to the Monte Carlo simulation results; if there is a deviation in the power supply and demand balance in a certain power supply zone, the amount of distributed power sources connected in the zone will be adjusted; when the impact of distributed power access on the system exceeds the set threshold, the overall balance and reliability evaluation indicators will be re-evaluated and adjusted.

[0044] Furthermore, the grid coverage area is divided into different power supply zones, a number of distributed power sources are randomly distributed in each zone, and data is collected and analyzed for each zone in turn, and the data collection cycle of all zones is the same; during the collection cycle of each zone, the power supply and demand balance of this zone is judged, and the impact of the access of distributed power sources on the system is statistically analyzed; the supply and demand balance index, the system average power outage frequency index SAIFI and the system average power outage duration index SAIDI within the coverage of the grid are accumulated and summed to calculate the overall balance and reliability evaluation index of the system; according to the overall balance and reliability evaluation index, some distributed power sources are selected as dynamic scheduling objects.

[0045] Furthermore, the dynamic scheduling object acts as the power supply and load to achieve a dynamic balance between the power supply and the load, wherein the selection process of the dynamic scheduling object includes the following steps: sorting the N distributed power sources in the power grid system from large to small according to the access capacity; selecting K distributed power sources with small access capacity as dynamic scheduling objects based on the overall balance and reliability evaluation indicators; the number K of dynamic scheduling objects is dynamically determined based on the difference between the current load of the system and the preset load threshold; when the difference between the load and the preset load threshold exceeds a predetermined range, the K value is dynamically adjusted based on the size of the difference.

[0046] S2: When the system detects that the current grid load is greater than the preset load threshold and the access capacity of the distributed generation is less than the maximum carrying capacity of the system, some distributed generation is selected as the dynamic scheduling object.

[0047] It should be noted that the preset load threshold refers to the upper limit of the grid load set in advance by the system, which is usually determined based on factors such as historical load data, load forecasting models, and system design standards. This threshold is used to determine whether the grid load exceeds the normal operating range, thereby triggering dynamic scheduling of distributed power sources.

[0048] S3: Based on the distribution characteristics and power supply requirements of the dynamic scheduling objects, an optimization access model is established with the goal of maximizing system benefits, and the optimal access capacity and optimal access time of each distributed power source are calculated.

[0049] Specifically, a mixed integer programming algorithm is used to construct an optimized access model; the specific formula of the objective function of the optimized access model is as follows:

[0050] minF=Σ(α i *P i -β i *C i );

[0051] Among them, P i is the access capacity of each distributed power source, C i is the access cost, α i is the profit coefficient, β i is the cost coefficient, and i is the number of the distributed generation.

[0052] Furthermore, according to the genetic algorithm combined with the power flow calculation, the optimal access capacity of each distributed generation is calculated, and the optimal solution of the optimization objective function is obtained, which is expressed as:

[0053] P i * =arg F(P i ,C i ,α i ,β i );

[0054] Among them, P i * is the optimal access capacity of the i-th distributed generation, and F() is the optimization objective function.

[0055] Furthermore, the time series analysis method is used to calculate the optimal access time of each distributed power source, and the specific formula of its objective function is as follows:

[0056] minG=Σ(γ j *T j +δj *D j );

[0057] Among them, T j is the power grid loss in the jth time period, D j is the corresponding load fluctuation, γ j and δ j are the weight coefficients of loss and fluctuation respectively.

[0058] It should be noted that the constraints of the objective function include that the access capacity of the i-th distributed generation in the j-th time period is less than or equal to the maximum capacity P of the i-th distributed generation. i,j ≤P imax ; The voltage deviation in the jth time period is less than or equal to the maximum voltage deviation |ΔV j |≤ΔV max .

[0059] S4: Adjust the access strategy of distributed power sources in real time during the operation of the power grid to achieve coordinated and optimized operation of the power grid and distributed power sources.

[0060] Specifically, the access strategy includes the following steps: real-time monitoring of grid load changes and distributed power output fluctuations, and dynamic adjustment of the access strategy to minimize system fluctuations while ensuring the balance of power supply and demand. The specific formula of the optimization objective function is as follows:

[0061]

[0062] Among them, is the active power adjustment of the ith distributed power source, is the reactive power adjustment of the ith distributed power source, and are the weight coefficients of active and reactive power adjustment respectively.

[0063] It should be noted that the load changes of the power grid and the output fluctuations of distributed power sources are monitored in real time, and the access strategy is adjusted dynamically. The goal is to minimize the power fluctuations of the system while maintaining the balance of power supply and demand. The optimization objective function clarifies the adjustment of the system's active power and reactive power, and sets weight coefficients for these two adjustments to achieve a proper balance during the optimization process. With the fluctuation of load and the uncertainty of distributed power output, the access strategy needs to constantly adapt to the current operating conditions of the system.

[0064] Furthermore, according to the adjusted access strategy, the access or exit of distributed power sources is controlled in real time to stabilize the overall balance of the system and achieve coordinated and optimized operation of the power grid and distributed power sources.

[0065] Furthermore, this embodiment also provides a distributed power supply optimization access system based on big data analysis, including: an acquisition module, used to collect and analyze historical operation data of distributed power supplies, and construct a distributed power supply evaluation model; a selection module, used to select part of the distributed power supplies as dynamic scheduling objects when the system monitors that the current grid load is greater than the preset load threshold and the access capacity of the distributed power supplies is less than the maximum carrying capacity of the system; a calculation module, used to establish an optimized access model with the goal of maximizing system benefits according to the distribution characteristics and power supply requirements of the dynamic scheduling objects, and calculate the optimal access capacity and optimal access time of each distributed power supply; a coordination module, used to adjust the access strategy of the distributed power supply in real time during the operation of the power grid, so as to realize the coordinated and optimized operation of the power grid and the distributed power supply.

[0066] This embodiment also provides a computer device, which is suitable for the case of a distributed power supply optimization access method based on big data analysis, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the distributed power supply optimization access method based on big data analysis proposed in the above embodiment.

[0067] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0068] The present embodiment also provides a storage medium on which a computer program is stored, and when the program is executed by a processor, the following steps are implemented: collecting and analyzing historical operating data of distributed power sources, and constructing a distributed power source evaluation model; when the system monitors that the current grid load is greater than a preset load threshold and the access capacity of the distributed power sources is less than the maximum carrying capacity of the system, some distributed power sources are selected as dynamic scheduling objects; according to the distribution characteristics and power supply requirements of the dynamic scheduling objects, an optimized access model with the goal of maximizing system benefits is established, and the optimal access capacity and optimal access time of each distributed power source are calculated; during the operation of the power grid, the access strategy of the distributed power source is adjusted in real time to achieve coordinated and optimized operation of the power grid and the distributed power source.

[0069] In summary, the present invention collects and analyzes historical data of distributed power sources, constructs an evaluation model, enhances the system's supply and demand balance prediction capability, and reduces operational risks; when the grid load exceeds the threshold and the access capacity is insufficient, the system dynamically selects part of the power supply for scheduling, flexibly responds to load fluctuations, and improves operational efficiency; by establishing an optimized access model with the goal of maximizing system benefits, accurately calculates the optimal access capacity and time, optimizes resource allocation, and improves economic benefits; by adjusting the access strategy in real time, ensures the coordinated and optimized operation of the power grid and distributed power sources, and improves system stability and efficiency.

[0070] Example 2

[0071] Referring to Table 1, which is the second embodiment of the present invention, this embodiment provides a distributed power supply optimization access method based on big data analysis. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0072] Specifically, actual application tests were carried out in the distribution network system of a provincial power grid company. The distribution network system covers an area of ​​about 3,000 square kilometers, including 5 power supply divisions and a total of 100,000 users. Various types of distributed power sources have been connected to the system, including photovoltaic power generation, wind power generation and small gas generators. The operating data of this distribution network system in the past three years, including load curves, distributed power output and system fault records, were collected and analyzed. Using these historical data, a distributed power evaluation model was constructed using the Monte Carlo simulation method. During the simulation, 1,000 different fault scenarios were randomly generated, and the system average outage frequency index (SAIFI) and the system average outage duration index (SAIDI) were calculated.

[0073] Furthermore, the grid coverage area is divided into five power supply zones, and a number of distributed power sources are randomly distributed in each zone. A 15-minute data collection cycle is set to collect and analyze data from each zone to determine the power supply and demand balance in each zone, and to count the impact of distributed power source access on the system.

[0074] Furthermore, as shown in Table 1, it can be clearly seen that the method of the present invention has achieved significant improvement effects in many aspects compared with the traditional method. In improving system reliability, the method of the present invention reduces the system average power outage frequency index (SAIFI) from 1.5 times / year / household to 0.8 times / year / household, with an improvement of 46.67%. At the same time, the system average power outage duration index (SAIDI) is also reduced from 3.2 hours / year / household to 1.7 hours / year / household, with an improvement of 46.88%. This shows that the method of the present invention significantly improves the reliability of the distribution network system and greatly reduces the frequency and duration of power outages for users. In improving the overall balance of the system, after adopting the method of the present invention, the overall balance of the system is increased from 85% to 95%, with an improvement of 11.76%. This means that the power grid system can better maintain the balance of supply and demand when facing load fluctuations and changes in the output of distributed power sources, thereby improving the stability of the system.

[0075] Table 1 Comparison between the present invention and the traditional method

[0076] parameter Traditional methods Method of the present invention Improvement System average power outage frequency index (SAIFI) (times / year / household) 1.5 0.8 46.67％ System average outage duration index (SAIDI) (hours / year / household) 3.2 1.7 46.88％ Overall balance of the system (%) 85 95 11.76％ Average utilization rate of distributed power sources (%) 62 78 25.81％ Annual operating cost of the system (10,000 yuan) 1200 980 18.33％

[0077] Specifically, in improving the utilization rate of distributed power sources, the method of the present invention increases the average utilization rate of distributed power sources from 62% to 78%, with an improvement of up to 25.81%. The improvement shows that the method can more effectively utilize distributed power resources, reduce the phenomenon of wind and solar power abandonment, and improve energy utilization efficiency. In terms of economic benefit improvement, the annual operating cost of the system was reduced from 12 million yuan to 9.8 million yuan, saving 2.2 million yuan, with an improvement of 18.33%. This considerable cost saving fully reflects the advantages of the method of the present invention in terms of economic benefits.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distributed power supply optimization access method based on big data analysis, characterized in that: include, Collect and analyze historical operating data of distributed power sources and build a distributed power source evaluation model; When the system detects that the current grid load is greater than the preset load threshold and the access capacity of the distributed generation is less than the maximum carrying capacity of the system, some distributed generation is selected as the dynamic scheduling object; According to the distribution characteristics and power supply requirements of the dynamic scheduling object, an optimization access model with the goal of maximizing system benefits is established to calculate the optimal access capacity and optimal access time of each distributed power source; During the operation of the power grid, the access strategy of distributed power sources is adjusted in real time to achieve coordinated and optimized operation of the power grid and distributed power sources.

2. The distributed power supply optimization access method based on big data analysis according to claim 1, characterized in that: The distributed power supply evaluation model comprises the following steps: The Monte Carlo simulation method is used to simulate fault scenarios to calculate the system average power outage frequency index SAIFI and the system average power outage duration index SAIDI. The specific calculation formula is as follows: Among them, λ is the failure rate, U is the average annual power outage time, N is the number of affected users, and NT is the total number of users; The grid coverage area is divided into different power supply zones, several distributed power sources are randomly distributed in each zone, and data is collected and analyzed for each zone in turn, and the data collection cycle for all zones is the same; During the collection period of each partition, determine the power supply and demand balance of this partition, and count the impact of the access of distributed power sources on the system; The supply and demand balance index within the coverage area of ​​the power grid, the system average power outage frequency index SAIFI and the system average power outage duration index SAIDI are accumulated and summed to calculate the overall balance and reliability evaluation index of the system; According to the overall balance degree and the reliability evaluation index, some distributed power sources are selected as dynamic scheduling objects.

3. The distributed power supply optimization access method based on big data analysis according to claim 2 is characterized in that: The dynamic scheduling object acts as a power source and a load to achieve a dynamic balance between the power source and the load, wherein the selection process of the dynamic scheduling object includes the following steps: Sort the N distributed power sources in the power grid system from large to small according to their access capacity; According to the overall balance and reliability evaluation indicators, K distributed generation sources with small access capacity are selected as dynamic scheduling objects; The number K of dynamically scheduled objects is dynamically determined based on the difference between the current system load and the preset load threshold; When the difference between the load and the preset load threshold exceeds a predetermined range, the K value is dynamically adjusted according to the size of the difference.

4. The distributed power optimization access method based on big data analysis according to claim 3 is characterized in that: The optimized access model is constructed by using a mixed integer programming algorithm; the specific formula of the objective function of the optimized access model is as follows: minF=∑(α i *P i -b i *C i ); Among them, P i is the access capacity of each distributed power source, C i is the access cost, α i is the profit coefficient, β i is the cost coefficient, and i is the number of the distributed generation.

5. The distributed power supply optimization access method based on big data analysis according to claim 4 is characterized in that: Also includes, According to the genetic algorithm combined with power flow calculation, the optimal access capacity of each distributed generation is calculated, and the optimal solution of the optimization objective function is obtained, which is expressed as: P i * =arg F(P i ,C i ,a i ,b i ); Among them, P i * is the optimal access capacity of the i-th distributed generation, and F() is the optimization objective function.

6. The distributed power supply optimization access method based on big data analysis according to claim 5 is characterized in that: The time series analysis method is used to calculate the optimal access time of each distributed power source. The specific formula of its objective function is as follows: minG=∑(γ j *T j +δ j *D j ); Among them, T j is the power grid loss in the jth time period, D j is the corresponding load fluctuation, γ j and δ j are the weight coefficients of loss and fluctuation respectively; The constraint condition of the objective function includes that the access capacity of the i-th distributed power source in the j-th time period is less than or equal to the maximum capacity P of the i-th distributed power source. i,j ≤P imax ; The voltage deviation in the jth time period is less than or equal to the maximum voltage deviation |ΔV j |≤ΔV max .

7. The distributed power supply optimization access method based on big data analysis according to claim 1, characterized in that: The access strategy includes the following steps: Real-time monitoring of grid load changes and distributed power output fluctuations, dynamic adjustment of access strategies, with the goal of minimizing system fluctuations while ensuring the balance of power supply and demand. The specific formula of the optimization objective function is as follows: Among them, ΔP i is the active power adjustment of the i-th distributed generation, ΔQ i is the reactive power adjustment of the i-th distributed generation, w i and are the weight coefficients for active and reactive power adjustment respectively; According to the adjusted access strategy, the access or exit of distributed power sources is controlled in real time to stabilize the overall balance of the system and achieve coordinated and optimized operation of the power grid and distributed power sources.

8. A distributed power supply optimization access system based on big data analysis, based on the distributed power supply optimization access method based on big data analysis according to any one of claims 1 to 7, characterized in that: Also includes, The acquisition module is used to collect and analyze the historical operation data of distributed power sources and build a distributed power source evaluation model; A selection module is used to select some distributed power sources as dynamic scheduling objects when the system monitors that the current grid load is greater than a preset load threshold and the access capacity of the distributed power sources is less than the maximum carrying capacity of the system; A calculation module, used to establish an optimization access model with the goal of maximizing system benefits according to the distribution characteristics and power supply requirements of the dynamic scheduling object, and calculate the optimal access capacity and optimal access time of each distributed power source; The coordination module is used to adjust the access strategy of distributed power sources in real time during the operation of the power grid, so as to realize the coordinated and optimized operation of the power grid and distributed power sources.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the distributed power supply optimization access method based on big data analysis described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distributed power supply optimization access method based on big data analysis described in any one of claims 1 to 7 are implemented.

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