Operation optimization method, device, equipment, medium and program product of power distribution microgrid

Abstract

The embodiment of the invention discloses an operation optimization method, device and equipment for a power distribution microgrid, a medium and a program product. Comprising the following steps: acquiring a micro-power distribution grid configuration parameter, a particle swarm algorithm parameter and a candidate power distribution scheme set, and initializing a particle swarm according to the particle swarm algorithm parameter and the candidate power distribution scheme set; wherein each particle in the particle population corresponds to one candidate power distribution scheme in the candidate power distribution scheme set; determining the fitness value of each candidate power distribution scheme according to the micro power distribution network configuration parameters and the power distribution network objective function; wherein the power distribution network objective function comprises an operation cost sub-objective function and a power abandoning cost sub-objective function; and substituting each fitness value into the particle swarm optimization model, carrying out iterative solution on the particle swarm, and determining a target power distribution scheme. The power distribution scheme determination requirement is met from the two aspects of economy and absorptivity, the operation cost of a distributed power distribution network-microgrid overall system is reduced, and new energy in the system can be fully absorbed.

Description

Technical Field

[0001] The present invention relates to the field of power network technology, and in particular to an operation optimization method, device, equipment, medium and program product for a distribution microgrid. Background Art

[0002] With the construction of new power systems based on new energy, the distribution network is gradually transforming from a power network that simply receives and distributes electric energy to users to a power network that integrates source, grid, load and storage and is flexibly coupled with microgrids. Its functions in promoting the local consumption of distributed power sources in microgrids and carrying new loads are becoming increasingly prominent.

[0003] Distributed distribution network-microgrid connects various power resources including traditional loads, adjustable loads, new energy generation and energy storage in a certain area, integrates and distributes power resources on a safe and fast basis, making energy utilization more efficient and reliable. Since the coordination between the operation of microgrid and distribution network will greatly affect the efficiency of distributed power sources, the existing operation coordination technology of new energy microgrid mostly focuses on the construction of multi-objective optimization model and the design of optimization algorithm. However, the optimization based on multi-objective is often complex, computationally intensive and difficult to implement, and is not suitable for the distribution plan determination needs in the process of implementing distributed intelligent distribution network. Summary of the invention

[0004] The present invention provides a method, device, equipment, medium and program product for optimizing the operation of a distribution microgrid, which determines the power distribution plan for a distributed distribution network-microgrid system from the perspectives of economy and absorptivity, thereby ensuring the reliable power supply of the distributed distribution network-microgrid while reducing the operating cost of the overall distributed distribution network-microgrid system, and enabling the new energy in the distributed distribution network-microgrid system to be fully absorbed.

[0005] In a first aspect, an embodiment of the present invention provides an operation optimization method of a distribution microgrid, comprising:

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

[0007] According to the configuration parameters of the distribution microgrid and the distribution network objective function, the fitness value of each candidate distribution scheme is determined; wherein the distribution network objective function includes an operation cost sub-objective function and a power abandonment cost sub-objective function;

[0008] Substitute each fitness value into the particle swarm optimization model, iteratively solve the particle population, and determine the target power distribution plan.

[0009] In a second aspect, an embodiment of the present invention further provides an operation optimization device for a distribution microgrid, comprising:

[0010] A data acquisition module is used to obtain the distribution microgrid configuration parameters, the particle swarm algorithm parameters and the candidate distribution scheme set, and initialize the 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;

[0011] The fitness determination module is used to determine the fitness value of each candidate distribution scheme according to the distribution microgrid configuration parameters and the distribution network objective function; wherein the distribution network objective function includes an operation cost sub-objective function and a power abandonment cost sub-objective function;

[0012] 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.

[0013] In a third aspect, an embodiment of the present invention further provides an operation optimization device for a distribution microgrid, the operation optimization device for the distribution microgrid comprising:

[0014] at least one processor; and a memory communicatively coupled to the at least one processor;

[0015] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can implement the operation optimization method of the distribution microgrid according to any embodiment of the present invention.

[0016] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute the operation optimization method of a distribution microgrid according to any embodiment of the present invention.

[0017] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, is used to execute the operation optimization method of a distribution microgrid according to any embodiment of the present invention.

[0018] The embodiment of the present invention provides an operation optimization method, device, equipment, medium and program product for a distribution microgrid, which obtains the configuration parameters of the distribution microgrid, the parameters of a particle swarm algorithm and a set of candidate distribution schemes, and initializes the particle population according to the parameters of the particle swarm algorithm and the set of candidate distribution schemes; wherein each particle in the particle population corresponds to a candidate distribution scheme in the set of candidate distribution schemes; the fitness value of each candidate distribution scheme is determined according to the configuration parameters of the distribution microgrid and the objective function of the distribution network; wherein the objective function of the distribution network includes an operating cost sub-objective function and a power abandonment cost sub-objective function; each fitness value is substituted into the particle swarm optimization model, and the particle population is iteratively solved to determine the target distribution scheme. By adopting the above technical solution, after obtaining the parameters of the particle swarm algorithm and the set of candidate distribution schemes, the particle population required for the subsequent particle swarm algorithm calculation is initialized so that each candidate distribution scheme can exist as a particle in the population. Then, the distribution microgrid configuration parameters are substituted into the distribution network objective function that includes both the operating cost sub-objective function and the power abandonment cost sub-objective function, and the initialization of the distribution network objective function is completed. Then, the fitness value of each candidate distribution scheme in the particle population is calculated based on the distribution network objective function. Since the distribution network objective function includes both the operating cost sub-objective function and the power abandonment cost sub-objective function, that is, the fitness value of the candidate distribution scheme can be used to indicate the operating cost and the absorption capacity of the distributed distribution network-microgrid when the scheme is adopted, so that the target distribution scheme obtained by iteratively solving the particle population based on each fitness value can meet the distribution scheme determination requirements from both the economic and absorption perspectives, ensuring the reliable power supply of the distributed distribution network-microgrid while reducing the operating cost of the overall distributed distribution network-microgrid system, and enabling the new energy in the distributed distribution network-microgrid system to be fully absorbed.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. 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.

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

[0022] Figure 2A diagram showing an example of a system structure of a distributed distribution network-microgrid provided in the first embodiment of the present invention;

[0023] Figure 3 A flow chart of an operation optimization method of a distribution microgrid provided in Embodiment 2 of the present invention;

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

[0025] Figure 5 A schematic diagram of the structure of an operation optimization device for a distribution microgrid provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work 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 and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1The present invention provides a flowchart of a method for optimizing the operation of a distribution microgrid in accordance with the first embodiment of the present invention. The present invention can be applied to the case where a distribution scheme is determined for a distributed distribution network-microgrid to optimize the operation of the distribution microgrid. The method can be executed by an operation optimization device for the distribution microgrid. The operation optimization device for the distribution microgrid can be implemented by software and / or hardware. The operation optimization device for the distribution microgrid can be configured in an operation optimization device for the distribution microgrid. Optionally, the operation optimization device for the distribution microgrid can be an electronic device, which can be a notebook, a desktop computer, a smart tablet, etc. The present invention does not limit this.

[0030] In order to clearly describe this solution, a distributed distribution network-microgrid system structure is provided here, which can also be understood as the system structure of the distribution microgrid proposed in the embodiment of the present invention. Figure 2 A system structure example diagram of a distributed distribution network-microgrid provided in the first embodiment of the present invention, in which the solid line represents the energy flow, and the dashed line with an arrow represents the information flow. The system may include a rigid load located in the main grid, which can be understood as a traditional load in the distribution network; an adjustable load located in the main grid; and multiple microgrids, wherein each microgrid may include a new energy power generation module, an energy storage module, and a microgrid adjustable load module. The operation optimization method of the distribution microgrid provided in the embodiment of the present invention is a method for determining the distribution scheme of the parameters of the new energy power generation, energy storage, and power abandonment contained in the microgrid in the system under the condition of optimizing the new energy absorption capacity and the total operating cost of the system.

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

[0032] S101, obtaining distribution microgrid configuration parameters, particle swarm algorithm parameters and a set of candidate distribution schemes, and initializing a particle population according to the particle swarm algorithm parameters and the set of candidate distribution schemes.

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

[0034] In this embodiment, the distribution microgrid configuration parameters can be specifically understood as follows: Figure 2The distributed distribution network-microgrid shown is a collection of inherent characteristic parameters of each component and configuration-related parameters generated during the operation of the power network. The particle swarm algorithm parameters can be specifically understood as the parameters required for initializing the configuration of the improved particle swarm optimization algorithm (ParticleSwarm Optimization, PSO) according to actual computing needs. Exemplarily, the particle swarm algorithm parameters may include population size, maximum number of iterations, acceleration factor, etc., which are not limited in the embodiments of the present invention. The candidate distribution scheme can be specifically understood as a combination of new energy power generation, energy storage and power abandonment parameters in a microgrid within the system, that is, it can be understood as an alternative, which can be applied to Figure 2 In some examples, the candidate power distribution scheme may include the power of the microgrid adjustable load module, the charge and discharge status of the energy storage module, and the power exchange strategy between the microgrid and the main grid, etc., which is not limited in the embodiments of the present invention.

[0035] Specifically, when it is necessary to optimize the operation of the distribution microgrid, the inherent characteristic parameters of each component in the distribution microgrid that needs to be configured for the distribution scheme and the configuration-related parameters generated during the operation of the power network are first obtained to obtain the distribution microgrid configuration parameters that can clarify the normal working requirements of each component in the distribution microgrid. At the same time, the particle swarm algorithm parameters used to configure the particle swarm algorithm can be directly obtained according to the processing requirements, as well as the candidate distribution scheme set consisting of multiple candidate distribution schemes given in advance according to the actual situation. Then, each candidate distribution scheme in the candidate distribution scheme set can be used as a particle in the particle swarm algorithm, and the speed and position of the particles corresponding to each candidate distribution scheme in the particle population are initialized through the particle swarm algorithm parameters to obtain an initialized particle population.

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

[0037] Among them, the distribution network objective function includes the operation cost sub-objective function and the power abandonment cost sub-objective function.

[0038] In this embodiment, the distribution network objective function can be specifically understood as a function set according to the needs of the distributed distribution network-microgrid system in actual work, and it is expected that the working state of each component in the system when working meets the target demand. The operating cost sub-objective function can be specifically understood as a function with the goal of minimizing the operating cost of each component in the system when working. The abandoned power cost sub-objective function can be specifically understood as a function with the goal of minimizing the abandoned power cost for renewable energy power generation when each component in the system is working; it can be understood that the lower the abandoned power cost, the higher the absorption capacity can be considered. The fitness value can be specifically understood as a value used to measure the pros and cons of a particle in solving the problem to be solved. In an embodiment of the present invention, the fitness value can be specifically understood as a value used to evaluate the system operating cost and the abandoned power cost when adopting a candidate distribution scheme.

[0039] Specifically, the distribution 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, and then each candidate distribution scheme is substituted into the configured distribution network objective function to solve the problem with the lowest operating cost and the lowest power abandonment cost as the goal, and the fitness value of each candidate distribution scheme is comprehensively determined based on the solution results.

[0040] S103, substituting each fitness value into the particle swarm optimization model, iteratively solving the particle population, and determining the target power distribution plan.

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

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

[0043] The technical solution of this embodiment is to obtain the configuration parameters of the distribution microgrid, the particle swarm algorithm parameters and the set of candidate distribution schemes, and initialize the particle population according to the particle swarm algorithm parameters and the set of candidate distribution schemes; wherein each particle in the particle population corresponds to a candidate distribution scheme in the set of candidate distribution schemes; determine the fitness value of each candidate distribution scheme according to the configuration parameters of the distribution microgrid and the distribution network objective function; wherein the distribution network objective function includes the operating cost sub-objective function and the power abandonment cost sub-objective function; substitute each fitness value into the particle swarm optimization model, iteratively solve the particle population, and determine the target distribution scheme. By adopting the above technical solution, after obtaining the particle swarm algorithm parameters and the set of candidate distribution schemes, the particle population required for the subsequent particle swarm algorithm calculation is initialized, so that each candidate distribution scheme can exist as a particle in the population. Then, the configuration parameters of the distribution microgrid are substituted into the distribution network objective function that includes the operating cost sub-objective function and the power abandonment cost sub-objective function, and the initialization of the distribution network objective function is completed, and then the fitness value of each candidate distribution scheme in the particle population is calculated based on the distribution network objective function. Since the distribution network objective function contains both the operating cost sub-objective function and the power abandonment cost sub-objective function, that is, the fitness value of the candidate distribution scheme can be used to indicate the operating cost of the distributed distribution network-microgrid and the absorption capacity of new energy when the scheme is adopted, so that the target distribution scheme obtained by iteratively solving the particle population based on each fitness value can meet the distribution scheme determination requirements from both the economic and absorption perspectives, ensuring the reliable power supply of the distributed distribution network-microgrid while reducing the operating cost of the overall distributed distribution network-microgrid system, and enabling the new energy in the distributed distribution network-microgrid system to be fully absorbed.

[0044] Embodiment 2

[0045] Figure 3The flowchart of a method for optimizing the operation of a distribution microgrid provided in the second embodiment of the present invention is further optimized on the basis of the above-mentioned optional technical solutions. The real-time electricity price data, as well as the rigid load parameter information, the main grid adjustable load parameter information and the microgrid parameter information in the distribution microgrid are first obtained to complete the prediction of the power generation value and the load value of the distribution network in the time period required to determine the distribution plan. Then, the power generation prediction value and the load prediction value calculated by all the acquired information sets are substituted into the distribution network objective function as the configuration parameters of the distribution microgrid to complete the initialization of the distribution network objective function and the determination of the constraints required for the operation. When calculating and determining the fitness value of each candidate distribution plan, the distribution network objective function that includes both the lowest system operating cost and the best absorption capacity is adopted, which improves the applicability of the calculated fitness value to the distributed distribution network-microgrid system. Each fitness value is substituted into the particle swarm optimization model. By optimizing the speed and position update formula in the particle swarm optimization model, the convergence speed and accuracy of the iterative solution are improved, the particle swarm optimization algorithm's control ability over diversity and search accuracy is further enhanced, the flexibility of the particle swarm optimization model is improved, and the adaptability to the problem solving and complex search space required for the dynamically changing distributed distribution network-microgrid system is enhanced. The distribution scheme determination requirements are met from both the economic and absorptive perspectives, and the reliable power supply of the distributed distribution network-microgrid is ensured while reducing the operating cost of the overall distributed distribution network-microgrid system, and the new energy in the distributed distribution network-microgrid system can be fully absorbed.

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

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

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

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

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

[0051] In this embodiment, the microgrid parameter information can be specifically understood as the parameter information used to determine the cost of each microgrid in the distributed distribution network-microgrid system during the construction and production process, and the power consumption of the microgrid adjustable load module contained in the microgrid during the time period when the power distribution plan needs to be determined. Exemplarily, the microgrid parameter information may include the equipment age, fixed construction cost, unit capacity construction cost rate and depreciation rate of the new energy power generation module and energy storage module in the microgrid, and the penalty cost of power abandonment of the microgrid, etc., and the embodiment of the present invention does not limit this.

[0052] S202: Perform new energy power generation forecast and power consumption forecast based on rigid load parameter information, main grid adjustable load parameter information and microgrid parameter information to determine 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 the 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 plan needs to be determined, as well as the power consumption of each load during the time period when the power distribution plan needs to be determined, that 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, power generation forecast value and load forecast value as distribution microgrid configuration parameters, and execute S205.

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

[0056] S204, obtaining particle swarm algorithm parameters and a set of candidate power distribution solutions, and initializing a particle population according to the particle swarm algorithm parameters and the set of candidate power distribution solutions, and executing S205.

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

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

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

[0060] Among them, the distribution network objective function includes the operation cost sub-objective function and the power abandonment cost sub-objective function.

[0061] Among them, the distribution network objective function is

[0062]

[0063] in, is the operating cost sub-objective function; is the curtailment cost sub-objective function; C SLoad is the rigid load cost; C VLoad The main network can adjust the load cost; C MGrid is the microgrid cost; M is the number of microgrids in the distribution microgrid; N is the number of new energy generation modules in the microgrid; p PVW (n,t) is the amount of power abandoned by the nth renewable energy generation module at the tth moment; S t is the real-time electricity price; Z The penalty cost for curtailing electricity.

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

[0065] In this embodiment, the main grid adjustable load cost can be specifically understood as the system operating cost generated by the load power consumption of the main grid adjustable load in the distributed distribution network-microgrid system during the time period when the distribution plan needs to be determined, which is 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 the distributed distribution network-microgrid system during the construction and production process determined based on the microgrid parameter information, as well as the cost incurred by the load power consumption of the microgrid's adjustable load module during the time period when the distribution plan needs to be determined.

[0067] In this embodiment, the power abandonment penalty cost can be specifically understood as a cost that is preset according to actual conditions and is caused by a penalty when the power generated by the new energy power generation module is abandoned.

[0068] In some examples, the rigid load cost C SLoadThe method of determining is:

[0069]

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

[0071] In some examples, the main network can adjust the load cost C VLoad The method of determining is:

[0072]

[0073] Among them, D 2,t is the power consumption of the adjustable load of the main grid in the distributed distribution network-microgrid at time t, which can be included in the load forecast value; ΔD 2,t The change in power consumption of the adjustable load of the main grid; ΔS t is the change in electricity price.

[0074] In some examples, the microgrid cost C MGrid The method of determining is:

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

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

[0077] The above-mentioned new energy costs, energy storage costs and microgrid load costs are determined as follows:

[0078]

[0079] Among them, Y is the equipment life of the new energy power generation module and energy storage module, r is the depreciation rate, a PV is the fixed construction cost of the new energy power generation module, a Store is the fixed construction cost of the energy storage module, b PV is the unit capacity construction cost rate of the new energy power generation module, b Store is the unit capacity construction cost rate of the energy storage module, E PV is the configuration capacity of the new energy power generation module, E Store is the configuration capacity of the energy storage module, σ PV is the new energy operation and maintenance cost coefficient, σ Store N is the energy storage operation and maintenance cost coefficient PVis the number of new energy generation modules, N Store is the number of energy storage modules, D 3,t is the power consumption of the microgrid adjustable load in the microgrid at time t, which can be included in the load forecast value; ΔD 3,t The change in electricity consumption of the load that can be adjusted for the main grid.

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

[0081] S206. For each candidate power distribution scheme, substitute the candidate power distribution scheme into the distribution network objective function to solve the constraint conditions determined according to the distribution microgrid configuration parameters.

[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 may be expressed as:

[0084]

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

[0086] In some examples, the renewable energy output constraint can be expressed as:

[0087]

[0088] Among them, P E It is the rated power output by the new energy power generation module.

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

[0090]

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

[0092] S207: Determine the fitness value of the candidate power distribution scheme according to the solved system operation cost and renewable energy power abandonment cost.

[0093] Specifically, the solved system operating cost and system energy curtailment cost are integrated according to the method preset in the particle swarm algorithm to obtain the fitness value of the candidate distribution scheme that considers both the system operating cost and the new energy curtailment cost, that is, the system operating cost and the new energy absorption capacity.

[0094] S208, substituting each fitness value into the particle swarm optimization model, and iteratively solving the particle population through the speed position update formula of the particle swarm optimization model.

[0095] The speed position update formula is:

[0096]

[0097] Among them, v i is the velocity of the i-th particle in the particle population; n is the number of iterations; ω(n) is the inertia weight; x i is the position of the i-th particle in the particle population; x pbest is a local extreme value; x gbest is the global optimal solution; c 1 and c 2 is the learning factor; r 1 and r 2 are independent random numbers.

[0098] in,

[0099] Among them, ω max is the maximum inertia weight; n max is the maximum number of iterations; c 1,max is the maximum c 1 Learning factor; c 2,max is the maximum c 2 Learning factor; c 2,min is the minimum c 2 Learning factor.

[0100] In the embodiment of the present invention, according to the maximum inertia weight, the maximum number of iterations, the maximum c 1 Learning factor, maximum c 2 Learning Factor and Minimum c 2The learning factor redefines the inertia weight and learning factor in the speed and position update formula, realizes the optimization of the speed and position update formula, improves the convergence speed and accuracy of the iterative solution, further enhances the particle swarm optimization algorithm's control over diversity and search accuracy, improves the flexibility of the particle swarm optimization model, and enhances its adaptability to the problem solving and complex search space required for the dynamically changing distributed distribution network-microgrid system.

[0101] In some examples, each fitness value is substituted into the particle swarm optimization model, and the particle population is iteratively solved by the speed position update formula of the particle swarm optimization model, which can be specifically implemented by the following steps:

[0102] 1) Each fitness value is input into the initialized particle swarm optimization model as the individual fitness of each particle in the particle swarm, and the next generation of population is obtained through the speed and position update formula of the particle swarm optimization model.

[0103] 2) Calculate the local extreme value x pbest , according to the individual historical optimal solution x pbest and the group's historical optimal solution x gbest , to determine whether the solution of the current particle is better than its historical optimal solution. If the current solution is better than the historical optimal solution, the current solution is used as the new historical optimal solution; otherwise, the historical optimal solution remains unchanged.

[0104] 3) Store non-dominated solutions. Sort all individual particles in the population by non-domination, and then calculate the crowding distance of each individual particle. Among them, the crowding distance can reflect the density of distribution around the individual particle. The larger the value, the sparser the distribution around the individual particle. After comprehensively considering the crowding distance and the non-dominated level, the non-dominated Pareto solution set is stored in the external archive set. At the same time, when the size of the external archive set reaches the 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 selected and added to the external archive set.

[0105] 4) Select the global optimal solution x pbest . Select the global optimal solution x from the external archive collection pbest The comprehensive fitness value of each particle is calculated according to the non-dominated level and crowding distance of each particle, and the particles are sorted according to the fitness value. Then, one particle is randomly selected from the first few particles as the global optimal solution x pbest .

[0106] 5) Return to step 1) and decide whether to continue iterating based on the comparison between the current number of iterations and the maximum number of iterations. If the number of iterations reaches the maximum number of iterations, the final Pareto non-inferior solution is output; otherwise, the speed and position update formula is used to update the speed and position of each particle to find a new individual solution and enter the next round of iteration.

[0107] 6) Apply the solved target 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 meet the distribution network objective function.

[0108] S209: Determine the optimal individual solution in the particle population at the end of the iteration as the target power distribution solution.

[0109] Optionally, after determining the target power distribution plan, it also includes:

[0110] The microgrid in the distribution microgrid is configured according to the target distribution plan.

[0111] Specifically, after determining the target power distribution plan, it is possible to clearly define the parameters that need to be configured within the execution time of the power distribution plan, including the power of the microgrid's 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 in the distributed distribution network-microgrid. The microgrid's adjustable load module, energy storage module, and new energy power generation module are configured with corresponding parameters to achieve control optimization for the microgrid.

[0112] The technical solution of this embodiment first obtains real-time electricity price data, as well as rigid load parameter information in the distribution microgrid, adjustable load parameter information of the main grid, and microgrid parameter information to complete the power generation value prediction and load value prediction for the distribution network in the time period required to determine the distribution plan, and then substitutes the power generation prediction value and load prediction value calculated by all acquired information sets as the distribution microgrid configuration parameters into the distribution network objective function to complete the initialization of the distribution network objective function and the determination of the constraints required for the operation. When calculating and determining the fitness value of each candidate distribution plan, the distribution network objective function that includes both the lowest system operating cost and the best absorption capacity is adopted, which improves the applicability of the calculated fitness value to the distributed distribution network-microgrid system. Each fitness value is substituted into the particle swarm optimization model. By optimizing the speed and position update formula in the particle swarm optimization model, the convergence speed and accuracy of the iterative solution are improved, the particle swarm optimization algorithm's control ability over diversity and search accuracy is further enhanced, the flexibility of the particle swarm optimization model is improved, and the adaptability to the problem solving and complex search space required for the dynamically changing distributed distribution network-microgrid system is enhanced. The distribution scheme determination requirements are met from both the economic and absorptive perspectives, and the reliable power supply of the distributed distribution network-microgrid is ensured while reducing the operating cost of the overall distributed distribution network-microgrid system, and the new energy in the distributed distribution network-microgrid system can be fully absorbed.

[0113] Embodiment 3

[0114] Figure 4 A schematic diagram of the structure of a distribution microgrid operation optimization device provided in the third embodiment of the present invention is shown in FIG. Figure 4 As shown, the operation optimization device of the distribution microgrid includes a data acquisition module 31, a fitness determination module 32 and a scheme determination module 33.

[0115] Among them, the data acquisition module 31 is used to obtain the distribution microgrid configuration parameters, the particle swarm algorithm parameters and the candidate distribution scheme set, and initialize the 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; the fitness determination module 32 is used to determine the fitness value of each candidate distribution scheme according to the distribution microgrid configuration parameters and the distribution network objective function; wherein the distribution network objective function includes an operating cost sub-objective function and a power abandonment 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 the embodiment of the present invention, after obtaining the particle swarm algorithm parameters and the set of candidate distribution schemes, initializes the particle population required for the subsequent particle swarm algorithm calculation, so that each candidate distribution scheme can exist as a particle in the population. Then, the distribution microgrid configuration parameters are substituted into the distribution network objective function that includes both the operating cost sub-objective function and the power abandonment cost sub-objective function, and the initialization of the distribution network objective function is completed, and then the fitness value of each candidate distribution scheme in the particle population is calculated based on the distribution network objective function. Since the distribution network objective function contains both the operating cost sub-objective function and the power abandonment cost sub-objective function, that is, the fitness value of the candidate distribution scheme can be used to indicate the operating cost of the distributed distribution network-microgrid and the absorption capacity of new energy when the scheme is adopted, so that the target distribution scheme obtained by iteratively solving the particle population based on each fitness value can meet the distribution scheme determination requirements from both the economic and absorption perspectives, ensuring the reliable power supply of the distributed distribution network-microgrid while reducing the operating cost of the overall distributed distribution network-microgrid system, and enabling the new energy 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, main grid adjustable load parameter information and microgrid parameter information;

[0119] According to the rigid load parameter information, the main grid adjustable load parameter information and the microgrid parameter information, new energy power generation forecast and power consumption forecast are carried out to determine the power generation forecast value and load forecast value;

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

[0121] Optionally, the fitness determination module 32 is specifically used for:

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

[0123] For each candidate power distribution scheme, substitute the candidate power distribution scheme into the distribution network objective function to solve the constraint conditions determined according to the distribution microgrid configuration parameters;

[0124] Determine the fitness value of the candidate power distribution scheme based on the system operation cost and renewable energy power abandonment cost obtained by solving;

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

[0126] Optionally, the distribution network objective function is

[0127]

[0128] in, is the operating cost sub-objective function; is the curtailment cost sub-objective function; C SLoad is the rigid load cost; C VLoad The main network can adjust the load cost; C MGrid is the microgrid cost; M is the number of microgrids in the distribution microgrid; N is the number of new energy generation modules in the microgrid; p PVW (n,t) is the amount of power abandoned by the nth renewable energy generation module at the tth moment; S t is the real-time electricity price; Z The penalty cost for curtailing electricity.

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

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

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

[0132] Optionally, the velocity position update formula is

[0133]

[0134] Among them, v i is the velocity of the i-th particle in the particle population; n is the number of iterations; ω(n) is the inertia weight; x i is the position of the i-th particle in the particle population; x pbest is a local extreme value; x gbest is the global optimal solution; c 1 and c 2 is the learning factor; r 1 and r 2 are independent random numbers.

[0135] in,

[0136] Among them, ω max is the maximum inertia weight; n max is the maximum number of iterations; c 1,max is the maximum c 1 Learning factor; c 2,max is the maximum c 2 Learning factor; c 2,min is the minimum c 2Learning factor.

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

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

[0139] Embodiment 4

[0140] Figure 5 A schematic diagram of the structure of an operation optimization device for a distribution microgrid provided in Embodiment 4 of the present invention. The operation optimization device 40 for the distribution microgrid may be intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The operation optimization device 40 for the distribution microgrid may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, 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 examples and are not intended to limit the implementation of the present invention described and / or required herein.

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

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

[0143] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 41 executes the various methods and processes described above, such as the operation optimization method of the distribution microgrid.

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

[0145] Optionally, an embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the operation optimization method of the distribution microgrid provided in any embodiment of the present invention.

[0146] Various implementations 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0147] Computer programs for implementing 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, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

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

[0149] To provide interaction with a user, the systems and techniques described herein may 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 a user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which a user may provide input to the power distribution scheme determination device. Other types of devices may also be used to provide interaction with a user; for example, feedback provided to a user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from a user may be received in any form (including acoustic input, voice input, or tactile input).

[0150] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0151] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0152] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0153] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing the operation of a distribution microgrid, characterized in that: include: Obtaining distribution microgrid configuration parameters, particle swarm algorithm parameters and a set of candidate distribution schemes, and initializing a particle population according to the particle swarm algorithm parameters and the set of candidate distribution schemes; wherein each particle in the particle population corresponds to a candidate distribution scheme in the set of candidate distribution schemes; Determine the fitness value of each candidate power distribution scheme according to the distribution microgrid configuration parameters and the distribution network objective function; wherein the distribution network objective function includes an operation cost sub-objective function and a power abandonment cost sub-objective function; Substitute each fitness value into a particle swarm optimization model, iteratively solve the particle population, and determine a target power distribution plan.

2. The operation optimization method of the distribution microgrid according to claim 1 is characterized in that: The obtaining of the distribution microgrid configuration parameters includes: Obtain real-time electricity price data, as well as rigid load parameter information in the distribution microgrid, main grid adjustable load parameter information and microgrid parameter information; Perform new energy power generation forecast and power consumption forecast according to the rigid load parameter information, the main grid adjustable load parameter information and the microgrid parameter information, and determine the power generation forecast value and the 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 distribution microgrid configuration parameters.

3. The operation optimization method of the distribution microgrid according to claim 2 is characterized in that: Determining the fitness value of each of the candidate power distribution schemes according to the distribution microgrid configuration parameters and the distribution network objective function includes: Initialize the distribution network objective function by configuring the distribution microgrid parameters; For each of the candidate power distribution schemes, substitute the candidate power distribution scheme into the distribution network objective function to solve the constraint conditions determined according to the distribution microgrid configuration parameters; Determine the fitness value of the candidate power distribution scheme according to the solved system operation cost and renewable energy power abandonment cost; Among them, the constraints include at least: power balance constraints, new energy output constraints and energy storage unit constraints.

4. The operation optimization method of the distribution microgrid according to claim 3 is characterized in that: The distribution network objective function is Among them, the is the operating cost sub-objective function; is the power abandonment cost sub-objective function; SLoad is the rigid load cost; VLoad The main network can adjust the load cost; MGrid is the microgrid cost; 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) is the amount of power abandoned by the nth new energy generation module at the tth moment; t is the real-time electricity price; Z The penalty cost for curtailing electricity.

5. The operation optimization method of the distribution microgrid according to claim 1, characterized in that: Substituting each fitness value into a particle swarm optimization model, iteratively solving the particle population, and determining a target power distribution scheme includes: Substituting each fitness value into a particle swarm optimization model, and iteratively solving the particle population through a speed position update formula of the particle swarm optimization model; The optimal individual solution in the particle population at the end of the iteration is determined as the target power distribution plan.

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

7. The operation optimization method of the distribution microgrid according to claim 1, characterized in that: After determining the target power distribution plan, the method further includes: The microgrid in the distribution microgrid is configured according to the target power distribution scheme.

8. An operation optimization device for a distribution microgrid, characterized in that: include: A data acquisition module, used to acquire the distribution microgrid configuration parameters, the particle swarm algorithm parameters and the candidate distribution scheme set, and initialize the 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; A fitness determination module is used to determine the fitness value of each of the candidate distribution schemes according to the distribution microgrid configuration parameters and the distribution network objective function; wherein the distribution network objective function includes an operation cost sub-objective function and a power abandonment cost sub-objective function; 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.

9. The operation optimization device for a distribution microgrid according to claim 8, characterized in that: 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, main grid adjustable load parameter information and microgrid parameter information; Perform new energy power generation forecast and power consumption forecast according to the rigid load parameter information, the main grid adjustable load parameter information and the microgrid parameter information, and determine the power generation forecast value and the 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 distribution microgrid configuration parameters.

10. The operation optimization device for a distribution microgrid according to claim 9, characterized in that: The fitness determination module is specifically used for: Initialize the distribution network objective function by configuring the distribution microgrid parameters; For each of the candidate power distribution schemes, substitute the candidate power distribution scheme into the distribution network objective function to solve the constraint conditions determined according to the distribution microgrid configuration parameters; Determine the fitness value of the candidate power distribution scheme according to the solved system operation cost and renewable energy power abandonment cost; Among them, the constraints include at least: power balance constraints, new energy output constraints and energy storage unit constraints.

11. The operation optimization device for a distribution microgrid according to claim 10, characterized in that: The distribution network objective function is Among them, the is the operating cost sub-objective function; is the power abandonment cost sub-objective function; SLoad is the rigid load cost; VLoad The main network can adjust the load cost; MGrid is the microgrid cost; 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) is the amount of power abandoned by the nth new energy generation module at the tth moment; t is the real-time electricity price; Z The penalty cost for curtailing electricity.

12. The operation optimization device for a distribution microgrid according to claim 8, characterized in that: The solution determination module is specifically used to: Substituting each fitness value into a particle swarm optimization model, and iteratively solving the particle population through a speed position update formula of the particle swarm optimization model; The optimal individual solution in the particle population at the end of the iteration is determined as the target power distribution plan.

13. The operation optimization device for a distribution microgrid according to claim 12, characterized in that: The speed position update formula is: Among them, v i is the velocity of the i-th particle in the particle population; n is the number of iterations; ω(n) is the inertia weight; x i is the position of the i-th particle in the particle population; x pbest is a local extreme value; x gbest is the global optimal solution; c1 and c2 are learning factors; r1 and r2 are independent random numbers; in, Among them, ω max is the maximum inertia weight; n max is the maximum number of iterations; c 1,max is the maximum c1 learning factor; c 2,max is the maximum c2 learning factor; c 2,min is the minimum c2 learning factor.

14. The operation optimization device for a distribution microgrid according to claim 8, characterized in that: Also includes: Distribution network configuration module, used for: The microgrid in the distribution microgrid is configured according to the target power distribution scheme.

15. An operation optimization device for a distribution microgrid, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; Wherein, 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 execute the operation optimization method of the distribution microgrid as described in any one of claims 1-7.

16. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to execute the operation optimization method of the distribution microgrid as described in any one of claims 1 to 7 when executed by a computer processor.

17. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the operation optimization method of the distribution microgrid according to any one of claims 1 to 7.

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