Distributed energy access power distribution network stability optimization method and system

Abstract

The invention discloses a distributed energy access power distribution network stability optimization method and system, and relates to the field of power system control, and the method comprises the steps: building an objective function of a mathematical model through the parameter variables of photovoltaic power generation and wind power generation; determining constraint conditions of photovoltaic power generation, wind power generation and power balance; carrying out optimization solution by adopting an improved optimization algorithm; and finally, determining an optimal solution through optimization. According to the power distribution network system power generation multi-target optimization model considering the economic benefits, the model gives consideration to the photovoltaic power generation cost and the wind power generation cost, the output energy consumption of photovoltaic and wind power generation and other factors, and the stability of the system is optimized and adjusted. According to the method, the multi-objective optimization model is optimized and solved by adopting a bacterial population drug property algorithm, so that the optimization accuracy is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of power system control, and in particular to a method and system for optimizing the stability of a distribution network with access to distributed energy sources. Background Art

[0002] With the adjustment of industrial structure and the increase of average income of residents, the proportion of electricity consumption by residents has also shown a trend of gradual increase. In the power system, the distribution network is a key component to ensure the quality of power and the reliability of power supply. Due to the low level of technological development and the lack of rationality in planning, the traditional distribution network has disadvantages such as insufficient line power supply capacity, increased sensitive loads and increasing demand for DER access, which cannot meet the current electricity demand of residents. Therefore, in order to reduce resource consumption, distributed energy is connected to the distribution network. Distributed energy has the characteristics of reliability and efficiency, which can improve the stability of the distribution network. However, the current problem is that the demand for electricity increases after the large-scale distributed energy is connected, which leads to greater uncertainty in the total amount of the power system after the distributed energy is connected, and at the same time, the energy loss of the distribution network is relatively serious, which not only increases the cost of power transportation, but also may bring safety problems.

[0003] In order to improve the stability of the distribution network, researchers have proposed a variety of optimization methods, including methods based on mathematical programming, such as linear programming, mixed integer programming, etc.; and methods based on model predictive control, etc. These methods have improved the energy stability of the distribution network after the access of distributed energy to a certain extent, but these methods have high computational complexity, strong dependence on accuracy, and are difficult to adapt to the rapidly changing operating conditions of the distribution network. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that the existing technical solutions have high computational complexity, strong dependence on accuracy, and are difficult to adapt to the rapidly changing operating conditions of the distribution network.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a distribution network stability optimization method for distributed energy access, which includes constructing an objective function of a mathematical model with parameter variables of photovoltaic power generation and wind power generation; determining constraints of photovoltaic power generation, wind power generation and power balance; using an improved optimization algorithm for optimization and solution; and finally determining the optimal solution by searching for the best solution.

[0007] As a preferred solution of the distribution network stability optimization method for distributed energy access described in the present invention, the objective function of the mathematical model constructed with photovoltaic power generation and wind power generation as parameter variables includes maximizing the system benefit under the integration of renewable energy as the goal, taking 24 hours as an operation cycle and a step length of 1 hour to construct a mathematical model expressed as:

[0008]

[0009] Where F is the total profit of the distribution network system with renewable energy integrated, ρ g is the transaction price of the system participating in green power trading, P t is the total power of the system at time t, Δt is the time interval, F P is the total cost of electricity generated by the photovoltaic power station, F w The total cost of generating electricity for a wind farm.

[0010] As a preferred solution of the method for optimizing the stability of the distribution network for distributed energy access described in the present invention, the constraint condition of photovoltaic power generation is expressed as follows:

[0011]

[0012] in, is the power generation of the photovoltaic power station at time t, is the equivalent utilization rate of the output power allowed by the photovoltaic power station at time t, is the total power generation of the photovoltaic power station without abandoning light, P p is the total installed capacity of photovoltaic power stations, is the charging power connected to the system by the photovoltaic power station at time t, is the power fed into the grid by the photovoltaic power station at time t.

[0013] As a preferred solution of the method for optimizing the stability of the distribution network with distributed energy access described in the present invention, the constraint condition of wind power generation is expressed as follows:

[0014]

[0015] in, is the power generation of the wind farm at time t, is the equivalent utilization rate of the wind farm’s output power allowed at time t, is the total power generation of the wind farm without abandoning wind power, P w is the total installed capacity of the wind farm, is the charging power connected to the system by the wind farm at time t, is the power fed into the grid by the wind farm at time t; the power balance constraint is expressed as,

[0016]

[0017] in, is the total power generation of the photovoltaic power station, is the total power generation of the wind farm, is the power generation power of the distribution network energy storage system, PL It is the active power required by all loads in the distribution network.

[0018] As a preferred solution of the distribution network stability optimization method for distributed energy access described in the present invention, the optimization solution using the improved optimization algorithm includes using the bacterial population chemotaxis algorithm for optimization solution, initializing and configuring the parameters related to photovoltaic power generation and wind power generation, and the parameter initialization formula is expressed as:

[0019] T0=ε 0.30 10 -1.73

[0020]

[0021] Among them, T0 is the minimum average movement time of bacteria, ε is the system calculation accuracy, b is the system parameter related to the bacterial movement time, τ c is the quantity related to the change of direction of bacteria; the moving speed v of the bacterial population

[0022] =const, usually 1; the movement time τ of the bacterial population in the new direction is determined by the probability distribution function:

[0023]

[0024] The bacterial population initialization includes generating an initial population consisting of N bacterial individuals, calculating the fitness values ​​of the individuals in the initial population, and recording the individuals with higher fitness.

[0025] As a preferred solution of the distributed energy access distribution network stability optimization method of the present invention, the optimization solution using the improved optimization algorithm also includes calculating the bacterial movement time and the new movement direction and angle, solving the new position of a single bacterium after movement and Discretization improvement is performed, and the formula is expressed as:

[0026]

[0027] Among them, f pr is the fitness difference between the current position of the bacteria and the position before moving, l pr is the vector modulus of the current position of the bacteria and the position before movement; the method for determining the new movement direction and angle of the bacterial population includes: if the position of the bacteria in space is x, the angle vector of the movement direction is The angle between the new direction of bacterial movement and the previous direction is The Gaussian distribution is expressed as:

[0028]

[0029] Among them, α∈[0,180], μ is the mathematical expectation, σ is the variance; the new position of the bacteria in space is expressed as,

[0030]

[0031] in, is the new location of the bacteria after they move. is the position of the bacteria before they moved. is the direction vector of the regularized new collection, l is the length of the new bacterial movement trajectory; the bacterial population optimization process includes that the bacterial population tends to move to the bacterial center point with a better position through information perception, and the bacterium a moves to the bacterial colony center point with a better position when the number of moving steps is k. At this time, the position of the bacteria is And the new position is discretized and expressed as:

[0032]

[0033] in, is the distance between bacteria a and bacteria b, SenseLimit is the sensing range; generate a reference population The reference population is and Individuals with better positions between them are generated.

[0034] As a preferred solution of the distribution network stability optimization method for distributed energy access described in the present invention, wherein: the final determination of the optimal solution through optimization includes improving the optimization process of the bacterial population, when the spatial density of the bacterial population is greater than a preset threshold, inducing the bacteria to perform directional mutation operations in areas with smaller spatial density, thereby avoiding the occurrence of local optimality; the termination condition judgment includes, if the judgment basis is not met, updating the bacterial movement speed, perception range and algorithm accuracy, and recalculating the bacterial movement time and the new movement direction and angle; if the judgment basis is met, stopping the algorithm and outputting the final population information.

[0035] Another object of the present invention is to provide a distribution network stability optimization system for distributed energy access, which can find the best deployment plan based on the parameter variables of photovoltaic power generation and wind power generation.

[0036] To solve the above technical problems, the present invention provides the following technical solutions: a system for a method for optimizing the stability of a distribution network with distributed energy access, comprising: a mathematical model construction module, a constraint determination module and an optimization module; the mathematical model construction module constructs the objective function of the mathematical model with parameter variables of photovoltaic power generation and wind power generation; the constraint determination module determines the constraint conditions of photovoltaic power generation, wind power generation and power balance; the optimization module uses an improved optimization algorithm to perform optimization and solution, and finally determines the optimal solution through optimization.

[0037] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the distribution network stability optimization method for distributed energy access as described above are implemented.

[0038] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the distribution network stability optimization method for distributed energy access as described above.

[0039] The beneficial effects of the present invention are as follows: the multi-objective optimization model for power generation of the distribution network system considering economic benefits proposed by the present invention takes into account the cost of photovoltaic power generation and wind power generation, as well as the output energy consumption of photovoltaic and wind power generation, and optimizes and adjusts the stability of the system. The present invention also uses a bacterial population drug property algorithm to optimize and solve the multi-objective optimization model, effectively improving the accuracy of the optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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:

[0041] Figure 1 This is a flow chart of the method for optimizing the stability of a distribution network with distributed energy access in Example 1.

[0042] Figure 2 This is a flow chart of the optimization of the distribution network stability optimization method for distributed energy access in Example 1.

[0043] Figure 3 This is a module structure diagram of the distribution network stability optimization system for distributed energy access in Example 3. DETAILED DESCRIPTION

[0044] 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.

[0045] 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.

[0046] Example 1, reference Figure 1 and Figure 2, which is the first embodiment of the present invention, and the embodiment provides a method for optimizing the stability of a distribution network with distributed energy access, including: Figure 1 As shown:

[0047] S1. Construct the objective function of the mathematical model with the parameter variables of photovoltaic power generation and wind power generation.

[0048] The present invention describes and optimizes the stability of the distribution network from the perspective of economic benefits: the objective function of the mathematical model is constructed with the parameter variables of photovoltaic power generation and wind power generation. The participation of renewable energy in green electricity trading can solve the problem of reduced system stability under large-scale energy access. Therefore, in order to coordinate the optimal operation of the distribution network system, the goal is to maximize the system benefits under the integration of renewable energy, with 24h as an operation cycle and a step length of 1h to construct a mathematical model expressed as:

[0049]

[0050] Where F is the total profit of the distribution network system with renewable energy integrated, ρ g is the transaction price of the system participating in green power trading, P t is the total power of the system at time t, Δt is the time interval, F P is the total cost of electricity generated by the photovoltaic power station, F w The total cost of generating electricity for a wind farm.

[0051] In addition, the power generation cost of wind and solar units can be divided into fixed costs and variable costs. Fixed costs refer to investment costs, depreciation costs, and operation and maintenance costs, while variable costs can be raw material costs or equipment costs.

[0052] S2. Determine the constraints of photovoltaic power generation, wind power generation and power balance.

[0053] The constraints of photovoltaic power generation are expressed as,

[0054]

[0055] in, is the power generation of the photovoltaic power station at time t, is the equivalent utilization rate of the output power allowed by the photovoltaic power station at time t, is the total power generation of the photovoltaic power station without abandoning light, P p is the total installed capacity of photovoltaic power stations, is the charging power connected to the system by the photovoltaic power station at time t, is the power fed into the grid by the photovoltaic power station at time t.

[0056] The constraints for wind power generation are expressed as,

[0057]

[0058] in, is the power generation of the wind farm at time t, is the equivalent utilization rate of the wind farm’s output power allowed at time t, is the total power generation of the wind farm without abandoning wind power, P w is the total installed capacity of the wind farm, is the charging power connected to the system by the wind farm at time t, is the power fed into the grid by the wind farm at time t;

[0059] The power balance constraint of the distribution network system refers to the total power generation of the system connected by renewable energy equal to the power that meets the demand, which is expressed as:

[0060]

[0061] in, is the total power generation of the photovoltaic power station, is the total power generation of the wind farm, is the power generation power of the distribution network energy storage system, P L It is the active power required by all loads in the distribution network.

[0062] S3. Use improved optimization algorithm to optimize and solve.

[0063] Multi-objective optimization is a mixed integer nonlinear programming problem with multiple constraints. To solve the above multi-objective optimization model, the present invention uses a bacterial population chemotaxis algorithm for optimization. The algorithm uses a global search method and has strong convergence and high solution accuracy, making the stability optimization result obtained based on this method more accurate. The specific steps are as follows: Figure 2 shown.

[0064] Initialize and configure the parameters related to photovoltaic power generation and wind power generation. The parameter initialization formula is as follows:

[0065] T0=ε 0.30 10 -1.73

[0066]

[0067] Among them, T0 is the minimum average movement time of bacteria, ε is the system calculation accuracy, b is the system parameter related to the bacterial movement time, τ c The amount of change in direction for bacteria;

[0068] The movement speed of the bacterial population v = const, usually 1;

[0069] The time τ for the bacterial population to move in a new direction is determined by the probability distribution function:

[0070]

[0071] The bacterial population initialization includes generating an initial population consisting of N bacterial individuals, calculating the fitness values ​​of the individuals in the initial population, and recording the individuals with higher fitness.

[0072] Bacteria individual optimization process: calculate the bacteria movement time and new movement direction and angle, solve the new position of a single bacterium after movement and adjust the new position of the bacteria Discretization improvement is performed, and the formula is expressed as:

[0073]

[0074] Among them, f pr is the fitness difference between the current position of the bacteria and the position before moving, l pr is the vector modulus of the bacteria’s current position and its position before movement.

[0075] The new method for determining the movement direction and angle of the bacterial population includes: if the position of the bacteria in space is x, the angle vector of the movement direction is The angle between the new direction of bacterial movement and the previous direction is The Gaussian distribution is expressed as:

[0076]

[0077] Among them, α∈[0,180], μ is the mathematical expectation, and σ is the variance.

[0078] The new position of the bacteria in space is represented by,

[0079]

[0080] in, is the new location of the bacteria after they move. is the position of the bacteria before they moved. is the regularized newly collected direction vector, and l is the length of the new bacterial movement trajectory.

[0081] Bacterial colony optimization process: At this time, the bacterial optimization process is also called the perception process. The bacterial colony tends to move to the bacterial center point with a better position through information perception. The movement mode of bacteria a to the bacterial colony center point with a better position when the number of moving steps is k is as follows. At this time, the position of the bacteria is And the new position is discretized and expressed as:

[0082]

[0083] in, is the distance between bacteria a and bacteria b, and SenseLimit is the sensing range.

[0084] Generate reference population The reference population is and Individuals with better positions between them are generated.

[0085] S4. Finally, the optimal solution is determined through optimization.

[0086] Improve the optimization process of bacterial population: Since the distribution of the initial population may form crowding in space, the individual bacteria have great limitations in perceiving population information, and are more likely to cause congestion when moving in a certain direction, which leads to a decrease in individual differences in the population and makes it easy to fall into the local optimum. Therefore, when the spatial density of the bacterial population is high, the bacteria should be induced to perform directional mutation operations in areas with low spatial density to avoid the local optimum.

[0087] Termination condition judgment: If the judgment criteria are not met, the bacterial movement speed, perception range and algorithm accuracy are updated, and the bacterial movement time and new movement direction and angle are recalculated; if the judgment criteria are met, the algorithm is stopped and the final population information is output.

[0088] Embodiment 2 is the second embodiment of the present invention, which is different from the first embodiment in that the method for optimizing the stability of a distribution network with distributed energy access also includes: in order to verify and illustrate the technical effects adopted in this method, this embodiment adopts a traditional technical solution and the method of the present invention for comparative testing, and compares the test results by means of scientific argumentation to verify the actual effect of this method.

[0089] Experimental objectives:

[0090] The performance of the method of the present invention is compared with the prior art in optimizing the power distribution system, and the focus includes cost saving, system stability and optimization speed.

[0091] Test case:

[0092] Set up a specific distribution network model, including photovoltaic power generation, wind power generation and load demand. Simulate the power generation and consumption every hour within 24 hours.

[0093] Performance indicators:

[0094] System stability (number and duration of power outages); algorithm runtime.

[0095] Comparison algorithms:

[0096] Prior art solutions (linear programming, basic bacterial population algorithm), improved bacterial population chemotaxis algorithm of the present invention.

[0097] The experimental data obtained are shown in Table 1:

[0098] Table 1: Experimental data comparison table

[0099]

[0100] Power supply interruption: Compared with the prior art, the algorithm of the present invention performs better in terms of power supply stability and reduces the number and duration of power supply interruptions.

[0101] Algorithm running time: The improved algorithm also shows higher efficiency in running time, and it takes less time to process the same optimization problem.

[0102] Example 3, reference Figure 3 , which is the third embodiment of the present invention, and is different from the previous two embodiments in that: a system for a method for optimizing the stability of a distribution network with distributed energy access comprises a mathematical model building module 100, a constraint determination module 200 and an optimization module 300; the mathematical model building module 100 builds the objective function of the mathematical model with parameter variables of photovoltaic power generation and wind power generation; the constraint determination module 200 determines the constraint conditions of photovoltaic power generation, wind power generation and power balance; the optimization module 300 uses an improved optimization algorithm to perform optimization and solution, and finally determines the optimal solution through optimization.

[0103] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0104] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0105] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0106] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0107] 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 method for optimizing the stability of a distribution network with distributed energy access, characterized in that: include, The objective function of the mathematical model is constructed with the parameter variables of photovoltaic power generation and wind power generation; Determine constraints for photovoltaic power generation, wind power generation, and power balance; Use improved optimization algorithm to optimize and solve; Finally, the optimal solution is determined through optimization.

2. The method for optimizing the stability of a distribution network with distributed energy access according to claim 1, characterized in that: The objective function of constructing a mathematical model with photovoltaic power generation and wind power generation as parameter variables includes maximizing the system benefit under the integration of renewable energy as the goal, taking 24 hours as an operation cycle and a step length of 1 hour to construct a mathematical model expressed as: Where F is the total profit of the distribution network system with renewable energy integrated, ρ g is the transaction price of the system participating in green power trading, P t is the total power of the system at time t, Δt is the time interval, F P is the total cost of electricity generated by the photovoltaic power station, F w The total cost of generating electricity for a wind farm.

3. The method for optimizing the stability of a distribution network with distributed energy access as claimed in claim 2, characterized in that: The constraints of photovoltaic power generation are expressed as: in, is the power generation of the photovoltaic power station at time t, is the equivalent utilization rate of the output power allowed by the photovoltaic power station at time t, is the total power generation of the photovoltaic power station without abandoning light, P p is the total installed capacity of photovoltaic power stations, is the charging power connected to the system by the photovoltaic power station at time t, is the power fed into the grid by the photovoltaic power station at time t.

4. The method for optimizing the stability of a distribution network with distributed energy access as claimed in claim 3, characterized in that: The constraints of wind power generation are expressed as: in, is the power generation of the wind farm at time t, is the equivalent utilization rate of the wind farm’s output power allowed at time t, is the total power generation of the wind farm without abandoning wind power, P w is the total installed capacity of the wind farm, is the charging power connected to the system by the wind farm at time t, is the power fed into the grid by the wind farm at time t; The power balance constraint is expressed as, in, is the total power generation of the photovoltaic power station, is the total power generation of the wind farm, is the power generation power of the distribution network energy storage system, P L It is the active power required by all loads in the distribution network.

5. The method for optimizing the stability of a distribution network with distributed energy access as claimed in claim 4, characterized in that: The improved optimization algorithm is used for optimization and solution, including using a bacterial population chemotaxis algorithm for optimization and solution, initializing and configuring the parameters related to photovoltaic power generation and wind power generation, and the parameter initialization formula is expressed as: T0=ε 0.30 ·10 -1.73 Among them, T0 is the minimum average movement time of bacteria, ε is the system calculation accuracy, b is the system parameter related to the bacterial movement time, τ c The amount of change in direction for bacteria; The movement speed of the bacterial population v = const, usually 1; The time τ for the bacterial population to move in a new direction is determined by the probability distribution function: The bacterial population initialization includes generating an initial population consisting of N bacterial individuals, calculating the fitness values ​​of the individuals in the initial population, and recording the individuals with higher fitness.

6. The method for optimizing the stability of a distribution network with distributed energy access as claimed in claim 5, characterized in that: The optimization solution using the improved optimization algorithm also includes calculating the bacterial movement time and the new movement direction and angle, solving the new position of a single bacterium after movement and calculating the new position of the bacterium. Discretization improvement is performed, and the formula is expressed as: Among them, f pr is the fitness difference between the current position of the bacteria and the position before moving, l pr is the vector modulus of the bacteria’s current position and its position before moving; The new method for determining the movement direction and angle of the bacterial population includes: if the position of the bacteria in space is x, the angle vector of the movement direction is The angle between the new direction of bacterial movement and the previous direction is The Gaussian distribution is expressed as: Among them, α∈[0,180], μ is the mathematical expectation, σ is the variance; The new position of the bacteria in space is represented by, in, is the new location of the bacteria after they move. is the position of the bacteria before they moved. is the direction vector of the regularized new collection, l is the length of the new bacterial movement trajectory; The process of bacterial colony optimization includes that the bacterial colony tends to move to the bacterial center point with a better position through information perception. Bacteria a moves to the bacterial colony center point with a better position when the number of moving steps is k. At this time, the position of the bacteria is And the new position is discretized and expressed as: in, is the distance between bacteria a and bacteria b, and SenseLimit is the sensing range; Generate reference population The reference population is and Individuals with better positions between them are generated.

7. The method for optimizing the stability of a distribution network with distributed energy access as claimed in claim 6, characterized in that: The final determination of the optimal solution by optimization includes improving the optimization process of the bacterial population, when the spatial density of the bacterial population is greater than a preset threshold, inducing the bacteria to perform a directional mutation operation to an area with a smaller spatial density, thereby avoiding a local optimum; The termination condition judgment includes: if the judgment basis is not met, the bacterial movement speed, perception range and algorithm accuracy are updated, and the bacterial movement time and new movement direction and angle are recalculated; if the judgment basis is met, the algorithm is stopped and the final population information is output.

8. A system using the method for optimizing the stability of a distribution network with distributed energy access as claimed in any one of claims 1 to 7, characterized in that: It includes a mathematical model building module (100), a constraint condition determination module (200) and an optimization module (300); The mathematical model construction module (100) constructs the objective function of the mathematical model using the parameter variables of photovoltaic power generation and wind power generation; The constraint condition determination module (200) determines the constraint conditions of photovoltaic power generation, wind power generation and power balance; The optimization module (300) uses an improved optimization algorithm to perform optimization and finally determines the optimal solution through optimization.

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 method for optimizing the stability of a distribution network with distributed energy access as 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 method for optimizing the stability of a distribution network with distributed energy access as described in any one of claims 1 to 7 are implemented.

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