Micro-grid operation optimization method, device, equipment, medium and program product

Abstract

Embodiments of the present application disclose a micro-grid operation optimization method, device, equipment, medium and program product. Including: obtaining micro-grid configuration parameters, particle swarm algorithm parameters and candidate power distribution scheme set, and initializing particle population according to particle swarm algorithm parameters and candidate power distribution scheme set; wherein each particle in the particle population corresponds to a candidate power distribution scheme in the candidate power distribution scheme set; determine the fitness value of each candidate power distribution scheme according to the micro-grid configuration parameters and the power distribution network objective function; wherein the power distribution network objective function includes an operation cost sub-objective function and an abandoned power cost sub-objective function; substitute each fitness value into the particle swarm optimization model, iteratively solve the particle population, and determine the target power distribution scheme. From the economic and consumption angles, the power distribution scheme determination demand is met, the operation cost of the distributed power distribution network-micro-grid overall system is reduced, and the new energy in the system can be fully consumed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power network, and particularly relates to a micro-grid operation optimization method, device, equipment, medium and program product. BACKGROUND

[0002] With the construction of new power system taking new energy as the main body, the distribution network is gradually changing from a power network that simply accepts and distributes electric energy to users into a power network that is flexibly coupled with a micro-grid and interacts with source, network, load and storage, and the functions of promoting the nearby consumption of distributed power sources of the micro-grid and bearing new loads are increasingly significant.

[0003] The distributed distribution network-micro-grid connects various power resources including traditional loads, adjustable loads, new energy power generation and energy storage in a certain area, integrates and distributes the power resources on the basis of safety and speed, and makes energy utilization more efficient and reliable. Since the coordination between the micro-grid and the distribution network will greatly affect the efficiency of the distributed power source, the existing operation coordination technology of the new energy micro-grid mostly focuses on the construction of a multi-objective optimization model and the design of an optimization algorithm. The optimization based on multiple objectives is often complex, has a large amount of calculation and is difficult to implement, and is not suitable for determining the distribution scheme in the implementation process of the distributed intelligent distribution network. SUMMARY

[0004] The present application provides a micro-grid operation optimization method, device, equipment, medium and program product, which determines the distribution scheme of the distributed distribution network-micro-grid system from the aspects of economy and consumption, ensures the reliable power supply of the distributed distribution network-micro-grid, reduces the operation cost of the overall system of the distributed distribution network-micro-grid, and enables the new energy in the distributed distribution network-micro-grid system to be fully consumed.

[0005] In a first aspect, an embodiment of the present application provides a micro-grid operation optimization method, comprising:

[0006] Obtaining micro-grid configuration parameters, particle swarm algorithm parameters and a candidate distribution scheme set, and initializing a particle population according to the particle swarm algorithm parameters and the candidate distribution scheme set; wherein each particle in the particle population corresponds to a candidate distribution scheme in the candidate distribution scheme set;

[0007] Determining the fitness values of the candidate distribution schemes according to the micro-grid configuration parameters and a distribution network objective function; wherein the distribution network objective function includes an operation cost sub-objective function and an abandoned power cost sub-objective function;

[0008] Substituting the fitness values into a particle swarm optimization model, iteratively solving the particle population, and determining a target distribution scheme.

[0009] In a second aspect, the embodiments of the present application further provide an operation optimization device for micro-grid distribution, comprising:

[0010] a data acquisition module, configured to acquire micro-grid distribution configuration parameters, particle swarm algorithm parameters and a candidate distribution scheme set, and initialize a particle population according to the particle swarm algorithm parameters and the candidate distribution scheme set; each particle in the particle population corresponds to a candidate distribution scheme in the candidate distribution scheme set;

[0011] a fitness determination module, configured to determine fitness values of the candidate distribution schemes according to the micro-grid distribution configuration parameters and a distribution network objective function; the distribution network objective function comprises an operation cost sub-objective function and an abandoned power cost sub-objective function;

[0012] a scheme determination module, configured to substitute the fitness values into a particle swarm optimization model, iteratively solve the particle population, and determine a target distribution scheme.

[0013] In a third aspect, the embodiments of the present application further provide an operation optimization device for micro-grid distribution, comprising:

[0014] at least one processor; and a memory connected with the at least one processor in communication;

[0015] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can implement the operation optimization method for micro-grid distribution of any embodiment of the present application.

[0016] In a fourth aspect, the embodiments of the present application further provide a storage medium containing computer executable instructions, which are used to execute the operation optimization method for micro-grid distribution of any embodiment of the present application when executed by a computer processor.

[0017] In a fifth aspect, the embodiments of the present application further provide a computer program product, comprising a computer program, which is used to execute the operation optimization method for micro-grid distribution of any embodiment of the present application when executed by a processor.

[0018] This invention provides a method, apparatus, device, medium, and program product for optimizing the operation of a distribution microgrid. It acquires microgrid configuration parameters, particle swarm optimization (PSO) algorithm parameters, and a set of candidate distribution schemes, and initializes a particle population based on these parameters. Each particle in the particle population corresponds to a candidate distribution scheme in the set. The fitness value of each candidate distribution scheme is determined based on the microgrid configuration parameters and the distribution network objective function. The distribution network objective function includes an operating cost sub-objective function and a curtailment cost sub-objective function. The fitness values ​​are substituted into the PSO optimization model, and the particle population is iteratively solved to determine the target distribution scheme. By employing this technical solution, after acquiring the PSO algorithm parameters and the set of candidate distribution schemes, the particle population required for subsequent PSO algorithm calculations is initialized, allowing each candidate distribution scheme to exist as a particle in the population. The microgrid configuration parameters are then substituted into the distribution network objective function, which includes both operating cost and curtailment cost sub-objective functions, to initialize the distribution network objective function. Based on this objective function, the fitness values ​​of each candidate distribution scheme in the particle swarm are calculated. Since the distribution network objective function includes both operating cost and curtailment cost sub-objective functions, the fitness value of a candidate distribution scheme can simultaneously indicate the operating cost of the distributed distribution network-microgrid and its capacity to absorb renewable energy when that scheme is adopted. This ensures that the target distribution scheme obtained through iterative solving of the particle swarm based on each fitness value meets the requirements for determining the distribution scheme from both economic and absorption perspectives. This guarantees reliable power supply from the distributed distribution network-microgrid while reducing the overall operating cost of the distributed distribution network-microgrid system and enabling sufficient absorption of renewable energy within the distributed distribution network-microgrid system.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating an operation optimization method for a distribution microgrid provided in Embodiment 1 of the present invention;

[0022] Figure 2This is a system structure example diagram of a distributed distribution network-microgrid provided in Embodiment 1 of the present invention;

[0023] Figure 3 This is a flowchart of an operation optimization method for a distribution microgrid provided in Embodiment 2 of the present invention;

[0024] Figure 4 This is a schematic diagram of the operation optimization device for a distribution microgrid provided in Embodiment 3 of the present invention;

[0025] Figure 5 This is a schematic diagram of the structure of a microgrid operation optimization device provided in Embodiment 4 of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Example 1

[0029] Figure 1This is a flowchart of a method for optimizing the operation of a distribution microgrid according to Embodiment 1 of the present invention. This embodiment is applicable to situations where a distribution scheme is determined for a distributed distribution network-microgrid to optimize its operation. This method can be executed by a distribution microgrid operation optimization device, which can be implemented by software and / or hardware and can be configured within a distribution microgrid operation optimization equipment. Optionally, the distribution microgrid operation optimization device can be an electronic device, such as a laptop, desktop computer, or smart tablet, etc. This embodiment of the present invention does not impose any limitations on this.

[0030] To clearly describe this solution, a distributed distribution network-microgrid system architecture is provided here, which can also be understood as the distribution microgrid system architecture proposed in the embodiments of this invention. Figure 2 This is an example diagram of a distributed distribution network-microgrid system structure provided in Embodiment 1 of the present invention. Solid lines represent energy flow, and dashed lines with arrows represent information flow. The system may include rigid loads located in the main grid (i.e., traditional loads in the distribution network); adjustable loads located in the main grid; and multiple microgrids. Each microgrid may include a renewable energy generation module, an energy storage module, and an adjustable load module. The microgrid operation optimization method provided in this embodiment of the invention is a method for determining the distribution scheme of renewable energy generation, energy storage, and curtailment parameters within the microgrids in the system, with the goal of optimizing renewable energy absorption capacity and total system operating cost.

[0031] like Figure 1 As shown in the figure, the operation optimization method for a distribution microgrid provided by this embodiment of the invention specifically includes the following steps:

[0032] S101. Obtain the microgrid configuration parameters, particle swarm algorithm parameters, and candidate power distribution scheme set, and initialize the particle swarm based on the particle swarm algorithm parameters and candidate power distribution scheme set.

[0033] Each particle in the particle swarm corresponds to a candidate power distribution scheme in the set of candidate power distribution schemes.

[0034] In this embodiment, the microgrid configuration parameters can be specifically understood as follows: Figure 2This diagram illustrates the inherent characteristic parameters of each component in a distributed distribution network-microgrid, as well as the set of configuration-related parameters generated during power network operation. Particle swarm optimization (PSO) parameters can be specifically understood as the parameters required for initializing and configuring the improved Particle Swarm Optimization (PSO) algorithm based on actual computational needs. For example, PSO parameters may include population size, maximum number of iterations, and acceleration factor, etc., but this embodiment of the invention does not impose limitations on these. Candidate distribution schemes can be specifically understood as a combination of parameters for new energy generation, energy storage, and curtailment in the microgrid within the system; that is, a scheme used as an alternative that can be applied to, for example... Figure 2 The system shown illustrates a scheme for configuring parameters for each component. In some examples, candidate power distribution schemes may include the power of the microgrid's adjustable load modules, the charging and discharging status of the energy storage modules, and the power exchange strategy between the microgrid and the main grid, etc., but this embodiment of the invention does not impose limitations on these aspects.

[0035] Specifically, when optimizing the operation of a distribution microgrid, the inherent characteristic parameters of each component within the microgrid requiring distribution scheme configuration, as well as configuration-related parameters generated during power network operation, are first obtained. This yields distribution microgrid configuration parameters that clearly define the normal operating requirements of each component. Simultaneously, particle swarm optimization (PSO) parameters for configuring the PSO algorithm can be directly obtained based on processing needs, along with a pre-defined set of candidate distribution schemes. Each candidate distribution scheme in this set can then be treated as a particle in the PSO algorithm. The velocity and position of each particle in the PSO algorithm population are initialized using the PSO algorithm parameters, resulting in an initialized particle population.

[0036] S102. Determine the fitness value of each candidate distribution scheme based on the microgrid configuration parameters and the distribution network objective function.

[0037] The objective function of the distribution network includes an operating cost sub-objective function and a curtailment cost sub-objective function.

[0038] In this embodiment, the distribution network objective function can be specifically understood as a function set according to the actual needs of the distributed distribution network-microgrid system, aiming to ensure that the operating state of each component in the system meets the target requirements. The operating cost sub-objective function can be specifically understood as a function that aims to minimize the operating cost of each component in the system. The curtailment cost sub-objective function can be specifically understood as a function that aims to minimize the curtailment cost of renewable energy generation when each component in the system is operating; it can be understood that the lower the curtailment cost, the higher the absorption capacity. The fitness value can be specifically understood as a value used to measure the merits of a particle in solving the problem. In this embodiment of the invention, the fitness value can be specifically understood as a value used to evaluate the system operating cost and curtailment cost when adopting candidate distribution schemes.

[0039] Specifically, the microgrid configuration parameters are substituted into the distribution network objective function to initialize the values ​​of all fixed configurations in the distribution network objective function. Then, each candidate distribution scheme is substituted into the configured distribution network objective function to solve for the minimum operating cost and the minimum curtailment cost. The fitness value of each candidate distribution scheme is determined based on the solution results.

[0040] S103. Substitute each fitness value into the particle swarm optimization model, iteratively solve the particle population, and determine the target power distribution scheme.

[0041] In this embodiment, the particle swarm optimization model can be specifically understood as a stochastic search optimization model based on swarm intelligence, constructed based on the particle swarm optimization algorithm.

[0042] Specifically, each fitness value is substituted into a pre-constructed particle swarm optimization model initialized with particle swarm algorithm parameters. The particle swarm is iteratively solved using this particle swarm optimization model until the iteration exit condition is met. At the end of the iteration, the candidate power distribution scheme corresponding to the best individual particle in the particle swarm is determined as the target power distribution scheme.

[0043] The technical solution of this embodiment obtains the microgrid configuration parameters, particle swarm optimization (PSO) algorithm parameters, and a set of candidate distribution schemes, and initializes a particle swarm based on the PSO algorithm parameters and the set of candidate distribution schemes. Each particle in the particle swarm corresponds to a candidate distribution scheme in the set of candidate distribution schemes. The fitness value of each candidate distribution scheme is determined based on the microgrid configuration parameters and the distribution network objective function. The distribution network objective function includes an operating cost sub-objective function and a curtailment cost sub-objective function. Each fitness value is substituted into the PSO optimization model, and the particle swarm is iteratively solved to determine the target distribution scheme. By adopting the above technical solution, after obtaining the PSO algorithm parameters and the set of candidate distribution schemes, the particle swarm required for subsequent PSO algorithm calculations is initialized, allowing each candidate distribution scheme to exist as a particle in the swarm. Then, the microgrid configuration parameters are substituted into the distribution network objective function, which simultaneously includes the operating cost sub-objective function and the curtailment cost sub-objective function, completing the initialization of the distribution network objective function. Finally, the fitness value of each candidate distribution scheme in the particle swarm is calculated based on the distribution network objective function. Since the objective function of the distribution network includes both the sub-objective function of operating cost and the sub-objective function of curtailment cost, the fitness value of the candidate distribution scheme can be used to indicate both the operating cost of the distributed distribution network-microgrid and its capacity to absorb new energy sources when the scheme is adopted. This allows the target distribution scheme obtained by iteratively solving the particle population based on each fitness value to meet the requirements for determining the distribution scheme from both economic and absorption perspectives. This ensures reliable power supply from the distributed distribution network-microgrid while reducing the overall operating cost of the distributed distribution network-microgrid system and enabling the new energy sources in the distributed distribution network-microgrid system to be fully absorbed.

[0044] Example 2

[0045] Figure 3This is a flowchart of a method for optimizing the operation of a distribution microgrid according to Embodiment 2 of the present invention. Based on the aforementioned optional technical solutions, this embodiment further optimizes the method by first acquiring real-time electricity price data, rigid load parameter information in the distribution microgrid, adjustable load parameter information in the main grid, and microgrid parameter information to predict the generation and load values ​​of the distribution network during the time period required for determining the distribution scheme. Then, the generation and load prediction values ​​calculated from all acquired information sets are used as configuration parameters for the distribution microgrid and substituted into the distribution network objective function to initialize the distribution network objective function and determine the constraints required for its calculation. When calculating the fitness value of each candidate distribution scheme, a distribution network objective function that simultaneously includes the lowest system operating cost and the optimal absorption capacity is adopted, improving the applicability of the calculated fitness value to the distributed distribution network-microgrid system. By substituting the fitness values ​​into the particle swarm optimization model and optimizing the velocity and position update formulas, the convergence speed and accuracy of the iterative solution are improved. This further enhances the particle swarm optimization algorithm's ability to control diversity and search accuracy, increases the flexibility of the particle swarm optimization model, and strengthens its adaptability to the problems and complex search spaces required by dynamically changing distributed distribution network-microgrid systems. From both economic and absorption perspectives, it meets the requirements for determining distribution schemes, ensuring reliable power supply to distributed distribution networks-microgrids while reducing the overall operating cost of the distributed distribution network-microgrid system. It also enables the full absorption of new energy sources in the distributed distribution network-microgrid system.

[0046] like Figure 3 As shown in the figure, the operation optimization method for a distribution microgrid provided by this embodiment of the invention specifically includes the following steps:

[0047] S201. Obtain real-time electricity price data, as well as rigid load parameter information, main grid adjustable load parameter information, and microgrid parameter information in the distribution microgrid.

[0048] In this embodiment, the distribution microgrid can be specifically understood as described above. Figure 2 The distributed distribution network-microgrid system shown; real-time electricity price data can be specifically understood as the electricity price of the power network at the current moment and in a relatively short period of time in the future, which can be used to assess the cost of renewable energy curtailment and the load cost of the distributed distribution network-microgrid.

[0049] In this embodiment, rigid load parameter information can be specifically understood as parameter information used to determine the electricity consumption of rigid loads in a distributed distribution network-microgrid system during the time period in which the power distribution scheme needs to be determined. For example, rigid load parameter information may include the electricity consumption of the rigid load at each moment during the time period in which the power distribution scheme needs to be determined.

[0050] In this embodiment, the adjustable load parameters of the main grid can be specifically understood as parameter information used to determine the electricity consumption of the adjustable load of the main grid in the distributed distribution network-microgrid system during the time period in which the power distribution scheme needs to be determined. For example, the adjustable load parameters of the main grid may include the electricity consumption and changes in electricity consumption of the adjustable load at each time point during the time period in which the power distribution scheme needs to be determined.

[0051] In this embodiment, microgrid parameter information can be specifically understood as parameters used to determine the costs of each microgrid in the construction and operation process within the distributed distribution network-microgrid system, as well as the electricity consumption parameters of the adjustable load modules included in the microgrid during the time period when the power distribution scheme needs to be determined. For example, microgrid parameter information may include the equipment lifespan, fixed construction costs, unit capacity construction cost rate, and depreciation rate of the new energy generation modules and energy storage modules in the microgrid, as well as the curtailment penalty cost of the microgrid, etc., and this embodiment of the invention does not impose limitations on these aspects.

[0052] S202. Based on rigid load parameter information, main grid adjustable load parameter information and microgrid parameter information, perform new energy power generation forecast and power consumption forecast, and determine the power generation forecast value and load forecast value.

[0053] Specifically, the rigid load parameter information, the main grid adjustable load parameter information, and the microgrid parameter information are substituted into the pre-trained power generation prediction model and load prediction model to obtain the power generation prediction value of each new energy power generation module in the microgrid during the time period when the power distribution scheme needs to be determined, as well as the power consumption of each load during the time period when the power distribution scheme needs to be determined, which is the load prediction value.

[0054] S203. Determine the real-time electricity price data, rigid load parameter information, main grid adjustable load parameter information, microgrid parameter information, generation forecast value and load forecast value as the distribution microgrid configuration parameters, and execute S205.

[0055] Specifically, real-time electricity price data, rigid load parameter information, main grid adjustable load parameter information, microgrid parameter information, generation forecast value and load forecast value are used together as distribution microgrid configuration parameters, and S205 is further implemented.

[0056] S204. Obtain the particle swarm optimization (PSO) algorithm parameters and the candidate power distribution scheme set, initialize the particle swarm based on the PSO algorithm parameters and the candidate power distribution scheme set, and execute S205.

[0057] Each particle in the particle swarm corresponds to a candidate power distribution scheme in the set of candidate power distribution schemes.

[0058] It should be noted that S201-S203 and S204 can be executed simultaneously or in any order. In this embodiment of the invention, the flowchart is shown as an example of the simultaneous execution of the two.

[0059] S205. Initialize the distribution network objective function by configuring the microgrid configuration parameters.

[0060] The objective function of the distribution network includes an operating cost sub-objective function and a curtailment cost sub-objective function.

[0061] Wherein, the objective function of the distribution network is

[0062]

[0063] in, The operating cost is the sub-objective function; The sub-objective function for the cost of curtailed electricity; C SLoad Cost of rigid load; C VLoad Main grid adjustable load cost; C MGrid For microgrid cost; M is the number of microgrids in the distribution microgrid; N is the number of renewable energy generation modules in the microgrid; p PVW (n,t) represents the amount of electricity wasted by the nth renewable energy generation module at time t; S t For real-time electricity price; for S Z Costs of power curtailment penalties.

[0064] In this embodiment, rigid load cost can be specifically understood as the system operating cost incurred by the rigid load in the distributed distribution network-microgrid system due to load electricity consumption during the time period when the power distribution scheme needs to be determined, based on rigid load parameter information.

[0065] In this embodiment, the main grid adjustable load cost can be specifically understood as the system operating cost incurred by the main grid adjustable load in the distributed distribution network-microgrid system due to load electricity consumption during the time period when the power distribution scheme needs to be determined, based on the main grid adjustable load parameter information.

[0066] In this embodiment, the microgrid cost can be specifically understood as the cost of a microgrid in a distributed distribution network-microgrid system during construction and production, determined based on microgrid parameter information, as well as the cost incurred by the microgrid's adjustable load modules due to load power consumption during the time period when a power distribution scheme needs to be determined.

[0067] In this embodiment, the cost of power curtailment penalty can be understood as the cost incurred due to penalty when power generated by the new energy power generation module is curtailed, which is pre-set according to the actual situation.

[0068] In some examples, the rigid load cost C SLoadThe method for determining it is as follows:

[0069]

[0070] Where T is the length of the time period for which the power distribution plan needs to be determined; D 1,t The electricity consumption of rigid loads in a distributed distribution network-microgrid at time t can be included in the load forecast.

[0071] In some examples, the main network adjustable load cost C VLoad The method for determining it is as follows:

[0072]

[0073] Among them, D 2,t The electricity consumption of the adjustable load in the main grid of a distributed distribution network-microgrid at time t can be included in the load forecast; ΔD 2,t The change in electricity consumption of the main grid's adjustable load; ΔS t This represents the change in electricity prices.

[0074] In some examples, the microgrid cost C MGrid The method for determining it is as follows:

[0075] C MGrid =C PV +C Store +C MGLoad

[0076] Among them, C PV For the cost of new energy, C Store For energy storage costs, C MGLoad For microgrid load costs.

[0077] The methods for determining the costs of new energy sources, energy storage, and microgrid loads are as follows:

[0078]

[0079] Where Y represents the equipment life of the new energy power generation module and the energy storage module, r represents the depreciation rate, and a represents the depreciation rate. PV For the fixed construction cost of the new energy power generation module, a Store For the fixed construction cost of energy storage modules, b PV b represents the unit capacity construction cost rate of the new energy power generation module. Store E represents the unit capacity construction cost rate of energy storage modules. PV For the configuration capacity of the new energy power generation module, E Store For the configured capacity of the energy storage module, σ PV σ represents the operation and maintenance cost coefficient for new energy sources. Store N is the energy storage operation and maintenance cost coefficient. PVN represents the number of new energy power generation modules. Store D represents the number of energy storage modules. 3,t The electricity consumption of the adjustable load in the microgrid at time t can be included in the load forecast; ΔD 3,t The change in electricity consumption of the main grid's adjustable load.

[0080] It is understandable that p in the above formula PVW (n,t), E PV and E S tore is an unknown quantity, which is the quantity that needs to be included in the candidate power distribution scheme.

[0081] S206. For each candidate power distribution scheme, substitute the candidate power distribution scheme into the objective function of the power distribution network and solve for the constraint conditions determined based on the configuration parameters of the distribution microgrid.

[0082] Among them, the constraints include at least: power balance constraints, new energy output constraints, and energy storage unit constraints.

[0083] In some examples, the power balance constraint can be expressed as:

[0084]

[0085] Where N is the number of new energy power generation modules, p PV For the actual output of the new energy power generation module, p Out p represents the discharge power of the energy storage module. in For the charging power of the energy storage module, p Sell p represents the electricity sold by the microgrid to the distribution network. buy p represents the power purchased by the microgrid from the distribution network. PVW This refers to the amount of electricity wasted from renewable energy sources.

[0086] In some examples, the power output constraints of new energy sources can be expressed as:

[0087]

[0088] Among them, P E The rated power output of the new energy power generation module.

[0089] In some examples, the constraints of energy storage units can be expressed as:

[0090]

[0091] Among them, P S This refers to the rated power of the energy storage module.

[0092] S207. Based on the system operating cost and renewable energy curtailment cost obtained from the solution, determine the fitness value of the candidate power distribution scheme.

[0093] Specifically, the system operating cost and the cost of curtailed renewable energy obtained from the solution are combined according to the pre-set method in the particle swarm optimization algorithm to obtain the fitness value of the candidate power distribution scheme that simultaneously considers the system operating cost and the cost of curtailed renewable energy, that is, simultaneously considers the system operating cost and the renewable energy absorption capacity.

[0094] S208. Substitute each fitness value into the particle swarm optimization model, and iteratively solve the particle population using the velocity and position update formula of the particle swarm optimization model.

[0095] The velocity and position update formula is as follows:

[0096]

[0097] Among them, v i The velocity of the i-th particle in the particle swarm; n is the iteration number; ω(n) is the inertia weight; x i x represents the position of the i-th particle in the particle swarm; pbest It is a local extremum; x gbest The global optimal solution is represented by c1 and c2, which are learning factors; and r1 and r2 are independent random numbers.

[0098] in,

[0099] Where, ω max The maximum inertia weight; n max c is the maximum number of iterations. 1,max c is the maximum c1 learning factor; 2,max c is the maximum c2 learning factor; 2,min It is the minimum c2 learning factor.

[0100] In this embodiment of the invention, the inertia weight and learning factor in the velocity-position update formula are redefined based on the maximum inertia weight, the maximum number of iterations, the maximum c1 learning factor, the maximum c2 learning factor, and the minimum c2 learning factor. This achieves optimization of the velocity-position update formula, improves the convergence speed and accuracy of the iterative solution, further enhances the particle swarm optimization algorithm's ability to control diversity and search accuracy, improves the flexibility of the particle swarm optimization model, and enhances its adaptability to the problems and complex search spaces required by dynamically changing distributed distribution network-microgrid systems.

[0101] In some examples, the method of substituting each fitness value into the particle swarm optimization model and iteratively solving the particle population using the velocity and position update formula of the particle swarm optimization model can be implemented through the following steps:

[0102] 1) Input each fitness value as the individual fitness of each particle in the particle swarm into the initialized particle swarm optimization model, and obtain the next generation of the swarm through the velocity and position update formula of the particle swarm optimization model.

[0103] 2) Calculate the local extremum x pbest Based on the individual's historical optimal solution x pbest and the group's historical optimal solution x gbest The algorithm determines whether the current solution is better than the historical best solution. If the current solution is better than the historical best solution, then the current solution is taken as the new historical best solution; otherwise, the historical best solution remains unchanged.

[0104] 3) Storing Non-Dominated Solutions. All particles in the population are sorted according to their non-dominance level, and then the crowding distance of each particle is calculated. Crowding distance reflects the density of the distribution around a particle; a larger value indicates a sparser distribution. After considering both crowding distance and non-dominance level, the non-dominated Pareto solution set is stored in an external archive set. Simultaneously, when the size of the external archive set reaches its capacity limit, all solutions are sorted according to the crowding distance between particles, and the top 10% of particles with better fitness values ​​and larger crowding distances are added to the external archive set.

[0105] 4) Select the globally optimal solution x pbest Select the globally optimal solution x from the external archive set. pbest The overall fitness value of each particle is calculated based on its non-dominated level and crowding distance. Particles are then sorted according to their fitness values, and a single particle is randomly selected from the top few as the global optimum x. pbest .

[0106] 5) Return to step 1) and, based on the comparison between the current iteration count and the maximum iteration count, decide whether to continue iterating. If the iteration count reaches the maximum iteration count, output the final Pareto non-dominated solution; otherwise, update the velocity and position of each particle using the velocity-position update formula to find a new individual solution and proceed to the next iteration.

[0107] 6) Apply the solved target power distribution scheme in the distributed distribution network-microgrid system to ensure that each optimal individual solution meets the constraints, and finally output the optimization result that can satisfy the objective function of the distribution network.

[0108] S209. The optimal individual solution in the particle population at the end of the iteration is determined as the target power distribution scheme.

[0109] Optionally, after determining the target power distribution scheme, the following may also be included:

[0110] Configure the microgrids in the distribution microgrid according to the target power distribution scheme.

[0111] Specifically, after determining the target power distribution scheme, the parameters that need to be configured during the execution time of the power distribution scheme, including the power of the microgrid adjustable load module, the charging and discharging status of the energy storage module, and the power exchange strategy between the microgrid and the main grid, can be identified. The corresponding parameters are then used to configure the microgrid adjustable load module, energy storage module, and new energy generation module to achieve control optimization for the microgrid.

[0112] The technical solution of this embodiment first acquires real-time electricity price data, as well as rigid load parameter information, main grid adjustable load parameter information, and microgrid parameter information to predict the generation and load values ​​of the distribution network during the time period required for determining the distribution scheme. Then, the generation and load prediction values ​​calculated from all acquired information sets are used as configuration parameters for the distribution microgrid and substituted into the distribution network objective function to initialize the objective function and determine the constraints required for its calculation. When calculating the fitness value of each candidate distribution scheme, a distribution network objective function that simultaneously includes the lowest system operating cost and the optimal absorption capacity is adopted, improving the applicability of the calculated fitness value to the distributed distribution network-microgrid system. By substituting the fitness values ​​into the particle swarm optimization model and optimizing the velocity and position update formulas, the convergence speed and accuracy of the iterative solution are improved. This further enhances the particle swarm optimization algorithm's ability to control diversity and search accuracy, increases the flexibility of the particle swarm optimization model, and strengthens its adaptability to the problems and complex search spaces required by dynamically changing distributed distribution network-microgrid systems. From both economic and absorption perspectives, it meets the requirements for determining distribution schemes, ensuring reliable power supply to distributed distribution networks-microgrids while reducing the overall operating cost of the distributed distribution network-microgrid system. It also enables the full absorption of new energy sources in the distributed distribution network-microgrid system.

[0113] Example 3

[0114] Figure 4 This is a schematic diagram of the operation optimization device for a distribution microgrid provided in Embodiment 3 of the present invention, as shown below. Figure 4 As shown, the operation optimization device for the distribution microgrid includes a data acquisition module 31, an fitness determination module 32, and a scheme determination module 33.

[0115] The data acquisition module 31 is used to acquire the microgrid configuration parameters, particle swarm optimization (PSO) parameters, and candidate distribution scheme set, and initialize the particle population based on the PSO parameters and candidate distribution scheme set; wherein each particle in the particle population corresponds to a candidate distribution scheme in the candidate distribution scheme set; the fitness determination module 32 is used to determine the fitness value of each candidate distribution scheme based on the microgrid configuration parameters and the distribution network objective function; wherein the distribution network objective function includes an operating cost sub-objective function and a curtailment cost sub-objective function; the scheme determination module 33 is used to substitute each fitness value into the particle swarm optimization model, iteratively solve the particle population, and determine the target distribution scheme.

[0116] The technical solution of this invention, after obtaining the particle swarm optimization (PSO) algorithm parameters and the set of candidate power distribution schemes, initializes the particle population required for subsequent PSO calculations, so that each candidate power distribution scheme can exist as a particle in the population. Then, the microgrid configuration parameters are substituted into the power distribution network objective function, which simultaneously includes the operating cost sub-objective function and the curtailment cost sub-objective function, completing the initialization of the power distribution network objective function. Finally, the fitness value of each candidate power distribution scheme in the particle population is calculated based on the power distribution network objective function. Since the objective function of the distribution network includes both the sub-objective function of operating cost and the sub-objective function of curtailment cost, the fitness value of the candidate distribution scheme can be used to indicate both the operating cost of the distributed distribution network-microgrid and its capacity to absorb new energy sources when the scheme is adopted. This allows the target distribution scheme obtained by iteratively solving the particle population based on each fitness value to meet the requirements for determining the distribution scheme from both economic and absorption perspectives. This ensures reliable power supply from the distributed distribution network-microgrid while reducing the overall operating cost of the distributed distribution network-microgrid system and enabling the new energy sources in the distributed distribution network-microgrid system to be fully absorbed.

[0117] Optionally, the data acquisition module 31 is specifically used for:

[0118] Obtain real-time electricity price data, as well as rigid load parameter information in the distribution microgrid, adjustable load parameter information in the main grid, and microgrid parameter information;

[0119] Based on rigid load parameter information, main grid adjustable load parameter information and microgrid parameter information, new energy power generation and electricity consumption forecasts are made to determine the power generation forecast value and load forecast value.

[0120] Real-time electricity price data, rigid load parameter information, main grid adjustable load parameter information, microgrid parameter information, generation forecast value, and load forecast value are determined as the distribution microgrid configuration parameters.

[0121] Optional, fitness determination module 32, specifically used for:

[0122] Initialize the distribution network objective function by configuring the microgrid configuration parameters;

[0123] For each candidate power distribution scheme, the candidate power distribution scheme is substituted into the objective function of the power distribution network to solve for the constraints determined based on the configuration parameters of the distribution microgrid.

[0124] Based on the system operating cost and renewable energy curtailment cost obtained from the solution, the fitness value of the candidate power distribution scheme is determined;

[0125] Among them, the constraints include at least: power balance constraints, new energy output constraints, and energy storage unit constraints.

[0126] Optionally, the objective function of the distribution network is:

[0127]

[0128] in, The operating cost is the sub-objective function; The sub-objective function for the cost of curtailed electricity; C SLoad For rigid load costs; C VLoad Main grid adjustable load cost; C MGrid For microgrid cost; M is the number of microgrids in the distribution microgrid; N is the number of renewable energy generation modules in the microgrid; p PVW (n,t) represents the amount of electricity wasted by the nth renewable energy generation module at time t; S t For real-time electricity price; for S Z Costs of penalties for abandoning electricity.

[0129] Optionally, the solution determination module 33 is specifically used for:

[0130] Substitute each fitness value into the particle swarm optimization model, and iteratively solve the particle population using the velocity and position update formula of the particle swarm optimization model;

[0131] The optimal individual solution in the particle swarm at the end of the iteration is determined as the target power distribution scheme.

[0132] Optionally, the velocity-position update formula is as follows:

[0133]

[0134] Among them, v i The velocity of the i-th particle in the particle swarm; n is the iteration number; ω(n) is the inertia weight; x i x represents the position of the i-th particle in the particle swarm; pbest It is a local extremum; x gbest The global optimal solution is represented by c1 and c2, which are learning factors; and r1 and r2 are independent random numbers.

[0135] in,

[0136] Where, ω max The maximum inertia weight; n max c is the maximum number of iterations. 1,max c is the maximum c1 learning factor; 2,max c is the maximum c2 learning factor; 2,min It is the minimum c2 learning factor.

[0137] Optionally, the power distribution scheme determination device further includes: a distribution microgrid configuration module, used to: configure the microgrids in the distribution microgrid according to the target power distribution scheme.

[0138] The microgrid operation optimization device provided in this embodiment of the invention can execute the microgrid operation optimization method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0139] Example 4

[0140] Figure 5 This is a schematic diagram of a microgrid operation optimization device according to Embodiment 4 of the present invention. The microgrid operation optimization device 40 can represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The microgrid operation optimization device 40 can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0141] like Figure 5 As shown, the microgrid operation optimization device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 and a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 may also store various programs and data required for the operation of the power distribution scheme determination device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0142] Multiple components in the microgrid operation optimization device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, optical disk, etc.; and a communication unit 49, such as a network card, modem, wireless transceiver, etc. The communication unit 49 allows the microgrid operation optimization device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0143] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as operation optimization methods for distribution microgrids.

[0144] In some embodiments, the microgrid operation optimization method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on the microgrid operation optimization device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the microgrid operation optimization method described above can be performed. Alternatively, in other embodiments, processor 41 can be configured to execute the microgrid operation optimization method by any other suitable means (e.g., by means of firmware).

[0145] Optionally, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the microgrid operation optimization method provided in any embodiment of the present invention.

[0146] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0147] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0148] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0149] To provide user interaction, the systems and techniques described herein can be implemented on a power distribution scheme determination device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the power distribution scheme determination device. Other types of devices can also be used to provide user interaction; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0150] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0151] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0152] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0153] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for optimizing the operation of a distribution microgrid, characterized in that, include: The microgrid configuration parameters, particle swarm optimization (PSO) algorithm parameters, and candidate distribution scheme set are obtained, and a particle swarm is initialized based on the PSO algorithm parameters and the candidate distribution scheme set; wherein, each particle in the particle swarm corresponds to a candidate distribution scheme in the candidate distribution scheme set; Based on the microgrid configuration parameters and the distribution network objective function, the fitness value of each candidate distribution scheme is determined; wherein, the distribution network objective function includes an operating cost sub-objective function and a curtailment cost sub-objective function, and the curtailment cost sub-objective function is a function that aims to minimize the curtailment cost of renewable energy generation when each component of the system is working; Substitute the fitness values ​​into the particle swarm optimization model, iteratively solve the particle population, and determine the target power distribution scheme. The acquisition of microgrid configuration parameters includes: Obtain real-time electricity price data, as well as rigid load parameter information in the distribution microgrid, adjustable load parameter information in the main grid, and microgrid parameter information; Based on the rigid load parameter information, the main grid adjustable load parameter information, and the microgrid parameter information, new energy power generation and electricity consumption forecasts are performed to determine the power generation forecast value and load forecast value; The real-time electricity price data, the rigid load parameter information, the main grid adjustable load parameter information, the microgrid parameter information, the power generation forecast value, and the load forecast value are determined as the distribution microgrid configuration parameters.

2. The operation optimization method for distribution microgrids according to claim 1, characterized in that, The step of determining the fitness value of each candidate distribution scheme based on the distribution microgrid configuration parameters and the distribution network objective function includes: The distribution network objective function is initialized using the microgrid configuration parameters. For each candidate power distribution scheme, the candidate power distribution scheme is substituted into the objective function of the power distribution network to solve for the constraints determined based on the configuration parameters of the distribution microgrid. The fitness value of the candidate power distribution scheme is determined based on the system operating cost and the cost of curtailment of renewable energy obtained from the solution. The constraints include at least: power balance constraints, new energy output constraints, and energy storage unit constraints.

3. The method for optimizing the operation of a distribution microgrid according to claim 2, characterized in that, The objective function of the distribution network is: Among them, the The operating cost sub-objective function; The objective function for the cost of abandoned electricity is C; SLoad For rigid load costs; the C VLoad Main grid adjustable load cost; the C MGrid The cost of the microgrid; M is the number of microgrids in the distribution microgrid; N is the number of new energy power generation modules in the microgrid; p PVW (n,t) represents the amount of electricity wasted by the nth new energy power generation module at time t; the S t The real-time electricity price; S is the price mentioned above. Z Costs of penalties for abandoning electricity.

4. The operation optimization method for distribution microgrids according to claim 1, characterized in that, The step of substituting each fitness value into the particle swarm optimization model, iteratively solving the particle population, and determining the target power distribution scheme includes: Substitute each fitness value into the particle swarm optimization model, and iteratively solve the particle population using the velocity and position update formula of the particle swarm optimization model. The optimal individual solution in the particle swarm at the end of the iteration is determined as the target power distribution scheme.

5. The operation optimization method for a distribution microgrid according to claim 4, characterized in that, The velocity-position update formula is: Among them, v i The velocity of the i-th particle in the particle swarm; n is the iteration number; ω(n) is the inertia weight; x i x represents the position of the i-th particle in the particle swarm; pbest It is a local extremum; x gbest The global optimal solution is represented by c1 and c2, which are learning factors; and r1 and r2 are independent random numbers. in, Where, ω max The maximum inertia weight; n max c is the maximum number of iterations. 1,max c is the maximum c1 learning factor; 2,max c is the maximum c2 learning factor; 2,min It is the minimum c2 learning factor.

6. The method for optimizing the operation of a distribution microgrid according to claim 1, characterized in that, After determining the target power distribution scheme, the following is also included: Configure the microgrids in the distribution microgrid according to the target power distribution scheme.

7. A device for optimizing the operation of a distribution microgrid, characterized in that, include: The data acquisition module is used to acquire microgrid configuration parameters, particle swarm optimization (PSO) algorithm parameters, and a set of candidate power distribution schemes, and initialize a particle swarm based on the PSO algorithm parameters and the set of candidate power distribution schemes; wherein, each particle in the particle swarm corresponds to a candidate power distribution scheme in the set of candidate power distribution schemes; The fitness determination module is used to determine the fitness value of each candidate power distribution scheme based on the microgrid configuration parameters and the power distribution network objective function; wherein, the power distribution network objective function includes an operating cost sub-objective function and a curtailment cost sub-objective function, and the curtailment cost sub-objective function is a function that aims to minimize the curtailment cost of renewable energy generation when each component in the system is working; The scheme determination module is used to substitute each fitness value into the particle swarm optimization model, iteratively solve the particle population, and determine the target power distribution scheme. The data acquisition module is specifically used for: Obtain real-time electricity price data, as well as rigid load parameter information in the distribution microgrid, adjustable load parameter information in the main grid, and microgrid parameter information; Based on the rigid load parameter information, the main grid adjustable load parameter information, and the microgrid parameter information, new energy power generation and electricity consumption forecasts are performed to determine the power generation forecast value and load forecast value; The real-time electricity price data, the rigid load parameter information, the main grid adjustable load parameter information, the microgrid parameter information, the power generation forecast value, and the load forecast value are determined as the distribution microgrid configuration parameters.

8. The microgrid operation optimization device according to claim 7, characterized in that, The fitness determination module is specifically used for: The distribution network objective function is initialized using the microgrid configuration parameters. For each candidate power distribution scheme, the candidate power distribution scheme is substituted into the objective function of the power distribution network to solve for the constraints determined based on the configuration parameters of the distribution microgrid. The fitness value of the candidate power distribution scheme is determined based on the system operating cost and the cost of curtailment of renewable energy obtained from the solution. The constraints include at least: power balance constraints, new energy output constraints, and energy storage unit constraints.

9. The microgrid operation optimization device according to claim 8, characterized in that, The objective function of the distribution network is: Among them, the The operating cost sub-objective function; The objective function for the cost of abandoned electricity is C; SLoad For rigid load costs; the C VLoad Main grid adjustable load cost; the C MGrid The cost of the microgrid; M is the number of microgrids in the distribution microgrid; N is the number of new energy power generation modules in the microgrid; p PVW (n,t) represents the amount of electricity wasted by the nth new energy power generation module at time t; the S t The real-time electricity price; S is the price mentioned above. Z Costs of penalties for abandoning electricity.

10. The microgrid operation optimization device according to claim 7, characterized in that, The solution determination module is specifically used for: Substitute each fitness value into the particle swarm optimization model, and iteratively solve the particle population using the velocity and position update formula of the particle swarm optimization model. The optimal individual solution in the particle swarm at the end of the iteration is determined as the target power distribution scheme.

11. The microgrid operation optimization device according to claim 10, characterized in that, The velocity-position update formula is: Among them, v i The velocity of the i-th particle in the particle swarm; n is the iteration number; ω(n) is the inertia weight; x i x represents the position of the i-th particle in the particle swarm; pbest It is a local extremum; x gbest The global optimal solution is represented by c1 and c2, which are learning factors; and r1 and r2 are independent random numbers. in, Where, ω max The maximum inertia weight; n max c is the maximum number of iterations. 1,max c is the maximum c1 learning factor; 2,max c is the maximum c2 learning factor; 2,min It is the minimum c2 learning factor.

12. The microgrid operation optimization device according to claim 7, characterized in that, Also includes: Distribution network configuration module, used for: Configure the microgrids in the distribution microgrid according to the target power distribution scheme.

13. An operation optimization device for a distribution microgrid, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the operation optimization method for the distribution microgrid according to any one of claims 1-6.

14. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the operation optimization method for a distribution microgrid as described in any one of claims 1-6.

15. A computer program product comprising a computer program that, when executed by a processor, implements the operation optimization method for a distribution microgrid as described in any one of claims 1-6.

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