A method and system for optimizing the operation of multiple microgrids based on coordinated scheduling

Abstract

This invention relates to the field of microgrid dispatching technology, and discloses a method and system for optimizing the operation of multiple microgrids based on collaborative dispatching. The method includes: receiving a grid operation optimization command; determining a multi-microgrid group based on the command; sequentially extracting microgrids from the group; constructing a fitness function based on the microgrids; obtaining real-time power generation groups and real-time load power; setting a load power range; identifying the regional grid; formulating an optimal microgrid dispatching strategy; identifying a target microgrid based on the optimal strategy; calculating the total power generation based on the real-time power generation groups; and executing a first power supply strategy, a second power supply strategy, or a third power supply strategy based on the real-time load power, the load power range, and the total power generation. This invention can improve the intelligence level of microgrid operation and enhance the accuracy of power allocation during microgrid operation.

Description

Technical Field

[0001] This invention relates to the field of microgrid dispatching technology, and in particular to a method and system for optimizing the operation of multiple microgrids based on coordinated dispatching. Background Technology

[0002] With the transformation of energy structure and the widespread application of distributed energy, microgrids, as independent power systems that integrate distributed power sources and energy storage devices, are of great significance to the efficient use of energy, grid stability and economy through their operation and scheduling.

[0003] Traditional microgrid dispatching relies on fixed priorities to schedule distributed power sources and energy storage devices. While this approach can generally meet the operational needs of microgrids, it suffers from shortcomings such as insufficient dispatching precision and lack of real-time adaptability. Consequently, the intelligence level of microgrids is low, and the power allocation accuracy during operation is also low. Summary of the Invention

[0004] This invention provides a method and system for optimizing the operation of multiple microgrids based on coordinated scheduling. Its main purpose is to improve the intelligence level of microgrid operation and enhance the accuracy of power allocation during microgrid operation.

[0005] To achieve the above objectives, the present invention provides a multi-microgrid operation optimization method based on cooperative scheduling, comprising:

[0006] Receive grid operation optimization instructions, and determine multiple microgrid groups based on the grid operation optimization instructions, wherein the multiple microgrid groups include multiple microgrids;

[0007] Microgrids are extracted sequentially from multiple microgrid groups, wherein each microgrid includes a distributed power generation group, and each distributed power generation group includes an energy storage device.

[0008] Based on microgrids, microgrid operation loss functions and energy storage device loss functions are constructed. The microgrid operation loss function includes power generation variables and energy storage power variables, and the energy storage device loss function includes energy storage power variables.

[0009] A fitness function is constructed based on the microgrid operation loss function and the energy storage device loss function.

[0010] The microgrid is monitored in real time to obtain the real-time power generation groups and real-time load power, the load power range is set, and the regional power grid is identified. The load power range includes: peak power range, valley power range, and average power range.

[0011] The optimal microgrid scheduling strategy is formulated based on the real-time power generation group and fitness function, and the target microgrid is identified based on the optimal microgrid scheduling strategy.

[0012] Calculate the total power generation based on the real-time power generation group, and compare the real-time load power with the load power range;

[0013] If the real-time load power is within the peak power range of the load power range, then the preset first power supply strategy is executed on the target microgrid based on the regional power grid, total power generation and real-time load power.

[0014] If the real-time load power is within the valley power range of the load power range, then the preset second power supply strategy is executed on the target microgrid based on the regional power grid, total power generation and real-time load power.

[0015] If the real-time load power is within the average power range of the load power range, then a preset third power supply strategy is executed on the target microgrid based on the regional power grid, total power generation, and real-time load power.

[0016] Optionally, the construction of the microgrid operation loss function and energy storage device loss function based on the microgrid includes:

[0017] Construct time period groups;

[0018] Distributed power sources are sequentially extracted from the distributed power source groups in the microgrid, and the energy storage devices of the distributed power sources are determined.

[0019] Obtain the unit generation loss of distributed power sources and the unit energy storage loss of energy storage devices;

[0020] By summarizing the unit power generation loss and the unit energy storage loss respectively, we obtain the unit power generation loss group and the unit energy storage loss group;

[0021] Construct a microgrid operation loss function based on time period groups, unit generation loss groups, and unit energy storage loss groups;

[0022] Construct the loss function for the energy storage device.

[0023] Optionally, the microgrid operating loss function is expressed as:

[0024] ;

[0025] in, This represents the microgrid operating loss function. This indicates the number of distributed generation sources in a distributed generation group within a microgrid. Indicates the first in the unit power generation loss group Unit power generation loss, This indicates the number of time periods in the time period group. Indicates the first The power generation variable of a distributed power source Indicates the first The unit energy storage loss of an energy storage device for a distributed power source. Indicates the first The energy storage device of the distributed power source in the first Energy storage power variable over a time period.

[0026] Optionally, the loss function for constructing the energy storage device includes:

[0027] Obtain the historical energy storage power group of the energy storage device of each distributed power source in the distributed power group to obtain the historical energy storage power group set, wherein the historical energy storage power group includes multiple historical energy storage powers.

[0028] The average energy storage power group is obtained by calculating the mean of each historical energy storage power group in the historical energy storage power group set. In the average energy storage power group, one average energy storage power corresponds to one energy storage device.

[0029] The energy storage device loss function is constructed based on the average energy storage power group, where the energy storage device loss function is expressed as:

[0030] ;

[0031] in, This represents the loss function of the energy storage device. This represents an exponential function with the natural constant as its base. Represents the first in the distributed power supply group The energy storage device of the distributed power source is in the first time period group. Energy storage power variable over a time period Represents the first in the average energy storage power group Average energy storage capacity.

[0032] Optionally, the step of formulating the optimal microgrid dispatch strategy based on real-time power generation groups and fitness functions includes:

[0033] Construct a particle swarm, wherein the particle swarm consists of multiple particles, and the position of each particle is a matrix composed of the energy storage power of each distributed power source in the microgrid during each time period in the time period group.

[0034] The particle swarm is initialized based on the real-time power generation group and fitness function to obtain the initial particle swarm.

[0035] The global particle positions are obtained by iterating based on the initial particle swarm, and the optimal microgrid scheduling strategy is determined based on the global particle positions.

[0036] Optionally, the initialization of the particle swarm based on the real-time power generation group and fitness function to obtain an initial particle swarm includes:

[0037] Construct particle constraints, which include: energy storage constraints;

[0038] Particles are extracted sequentially from the particle swarm, and the initial position and initial velocity of the particles are generated based on the particle constraints. The initial position includes multiple energy storage powers, and each energy storage power corresponds to a time period and an energy storage device of a distributed power source.

[0039] Identify the real-time generation loss corresponding to each real-time generation power in the real-time generation power group to obtain the real-time generation loss group;

[0040] The initial energy storage power group is determined based on the initial location, and the initial energy storage loss corresponding to each initial energy storage power in the initial energy storage power group is identified, thus obtaining the initial energy storage loss group;

[0041] Substitute the real-time power generation set, the initial energy storage power set, the real-time power generation loss set, and the initial energy storage loss set into the fitness function to obtain the initial fitness.

[0042] The initial particles are initialized based on their initial position, initial velocity, and initial fitness to obtain initial particles. The initial particles are then aggregated to obtain an initial particle swarm.

[0043] Optionally, the execution of a preset first power supply strategy on the target microgrid based on the regional power grid, total power generation, and real-time load power includes:

[0044] Determine whether the total power generation is greater than the real-time load power;

[0045] If the total power generation is greater than the real-time load power, the transferable power is calculated based on the total power generation and the real-time load power, and the multiple energy storage devices in the target microgrid are charged based on the transferable power to complete the first power supply strategy.

[0046] If the total power generation is not greater than the real-time load power, then the target power demand is calculated based on the total power generation and the real-time load power.

[0047] The current energy storage power group in the target microgrid is obtained based on the optimal microgrid scheduling strategy, and the total energy storage power is calculated based on the current energy storage power group;

[0048] Determine whether the total energy storage capacity exceeds the target demand power.

[0049] If the total energy storage capacity is greater than the target demand power, then the energy storage devices in the target microgrid will be used to supply power, thus completing the first power supply strategy.

[0050] If the total energy storage capacity is not greater than the target demand power, the real-time power deficit between the total energy storage capacity and the target demand power is calculated, and power is dispatched to the regional power grid based on the real-time power deficit to complete the first power supply strategy.

[0051] Optionally, the implementation of a preset second power supply strategy for the target microgrid based on the regional power grid, total power generation, and real-time load power includes:

[0052] Determine the maximum energy storage capacity of the target microgrid and record the real-time energy storage capacity of multiple energy storage devices in the target microgrid.

[0053] The total charging power is calculated based on the total power generation and the real-time load power, where the total charging power is the difference between the total power generation and the real-time load power.

[0054] The total charging power is used to charge the energy storage device until the real-time energy storage capacity reaches the maximum energy storage capacity.

[0055] Once the real-time energy storage capacity reaches its maximum, the system stores energy in the regional power grid based on the total charging power, thus completing the second power supply strategy.

[0056] Optionally, the implementation of a preset third power supply strategy for the target microgrid based on the regional power grid, total power generation, and real-time load power includes:

[0057] Determine whether the total power generation is greater than the real-time load power;

[0058] If the total power generation is greater than the real-time load power, then the real-time energy storage power is calculated based on the total power generation and the real-time load power.

[0059] The multiple energy storage devices in the target microgrid are charged based on real-time energy storage power until the real-time energy storage capacity of the multiple energy storage devices in the target microgrid reaches the maximum energy storage capacity.

[0060] When the real-time energy storage capacity of multiple energy storage devices in the target microgrid reaches the maximum energy storage capacity, the energy is stored in the regional power grid based on the real-time energy storage power to complete the third power supply strategy.

[0061] If the total power generation is not greater than the real-time load power, then multiple energy storage devices in the target microgrid will be used to generate electricity, thus completing the third power supply strategy.

[0062] To achieve the above objectives, the present invention also provides a multi-microgrid operation optimization system based on cooperative scheduling, comprising:

[0063] The microgrid determination module is used to receive grid operation optimization instructions and determine multiple microgrid groups based on the grid operation optimization instructions. The multiple microgrid groups include multiple microgrids. The microgrids are extracted sequentially from the multiple microgrid groups. Each microgrid includes a distributed power generation group, and each distributed power generation in the distributed power generation group includes an energy storage device.

[0064] The fitness function construction module is used to construct the microgrid operation loss function and the energy storage device loss function based on the microgrid. The microgrid operation loss function includes: power generation power variable and energy storage power variable, and the energy storage device loss function includes: energy storage power variable. The fitness function is constructed based on the microgrid operation loss function and the energy storage device loss function.

[0065] The target grid acquisition module is used to monitor the microgrid in real time, obtain the real-time power generation group and real-time load power, set the load power range, and identify the regional power grid. The load power range includes: peak power range, valley power range and average power range. Based on the real-time power generation group and fitness function, the optimal microgrid scheduling strategy is formulated for the microgrid, and the target microgrid is identified based on the optimal microgrid scheduling strategy.

[0066] The power supply strategy execution module is used to calculate the total power generation based on the real-time power generation group, compare the real-time load power with the load power range, and if the real-time load power is within the peak power range of the load power range, then a preset first power supply strategy is executed on the target microgrid based on the regional power grid, the total power generation, and the real-time load power. If the real-time load power is within the valley power range of the load power range, then a preset second power supply strategy is executed on the target microgrid based on the regional power grid, the total power generation, and the real-time load power. If the real-time load power is within the average power range of the load power range, then a preset third power supply strategy is executed on the target microgrid based on the regional power grid, the total power generation, and the real-time load power.

[0067] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0068] Memory, storing at least one instruction; and

[0069] The processor executes the instructions stored in the memory to implement the above-described multi-microgrid operation optimization method based on cooperative scheduling.

[0070] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the aforementioned multi-microgrid operation optimization method based on cooperative scheduling.

[0071] To address the problems described in the background art, this invention first constructs a microgrid operation loss function and an energy storage device loss function based on the microgrid. These functions comprehensively quantify the power loss during microgrid operation, including not only generation losses but also losses during the charging and discharging of energy storage devices, thus contributing to efficient microgrid operation. Next, a fitness function is constructed based on these functions. This fitness function integrates various power losses in microgrid operation, enabling a comprehensive evaluation of the merits of a scheduling strategy. This provides a crucial evaluation criterion for finding the optimal scheduling strategy using particle swarm optimization (PSO) algorithms, allowing the algorithm to effectively search towards reducing power losses. Furthermore, an optimal microgrid scheduling strategy is formulated based on the real-time power generation group and fitness function. The target microgrid is then identified based on this optimal scheduling strategy. This strategy ensures that the microgrid's scheduling scheme is optimal under the current operating conditions, achieving a rational arrangement of power generation and energy storage, thereby improving the safety of microgrid operation. Then, the real-time load power is compared with the load power range to ensure that the microgrid can adopt the optimal operating mode within different load power ranges. Finally, based on the different relationships between the real-time load power and the load power range, a first, second, and third power supply strategy are set. These different power supply strategies improve the operating efficiency of the microgrid at different times. Therefore, this invention can improve the intelligence level of microgrid operation and enhance the accuracy of power allocation during microgrid operation. Attached Figure Description

[0072] Figure 1 A flowchart illustrating a multi-microgrid operation optimization method based on coordinated scheduling provided in an embodiment of the present invention;

[0073] Figure 2 This is a functional block diagram of a multi-microgrid operation optimization system based on cooperative scheduling provided in an embodiment of the present invention;

[0074] Figure 3 This is a schematic diagram of the structure of an electronic device that implements the multi-microgrid operation optimization method based on cooperative scheduling, according to an embodiment of the present invention.

[0075] Explanation of reference numerals in the attached figures:

[0076] 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0077] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0078] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0079] This application provides a method for optimizing the operation of multiple microgrids based on coordinated scheduling. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for optimizing the operation of multiple microgrids based on coordinated scheduling can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0080] Reference Figure 1 The diagram shown is a flowchart illustrating a multi-microgrid operation optimization method based on cooperative scheduling according to an embodiment of the present invention. In this embodiment, the multi-microgrid operation optimization method based on cooperative scheduling includes:

[0081] S1. Receive grid operation optimization instructions, and determine multiple microgrid groups based on the grid operation optimization instructions, wherein the multiple microgrid groups include multiple microgrids.

[0082] Understandably, the grid operation optimization instruction refers to a human-initiated instruction to schedule a specific microgrid, and the multi-microgrid group refers to multiple specific microgrids indicated by the grid operation optimization instruction. The multi-microgrid group includes multiple microgrids (i.e., corresponding to multiple specific microgrids). Here, a microgrid refers to an independent power system composed of multiple distributed power sources and corresponding energy storage devices.

[0083] S2. Extract microgrids sequentially from the multi-microgrid group, wherein each microgrid includes a distributed power source group, and each distributed power source in the distributed power source group includes an energy storage device.

[0084] It is understood that the distributed power group includes multiple distributed power sources, wherein distributed power sources refer to renewable energy power generation equipment, such as wind turbine generators, photovoltaic generators, etc., and the energy storage device refers to the equipment used to store electrical energy. Each distributed power source corresponds to one energy storage power source.

[0085] For example, a distributed power source is a photovoltaic generator set, and the distributed power source is equipped with a lithium-ion battery energy storage device.

[0086] S3. Construct a microgrid operation loss function and an energy storage device loss function based on the microgrid. The microgrid operation loss function includes: power generation variable and energy storage power variable, and the energy storage device loss function includes: energy storage power variable.

[0087] It should be explained that the microgrid operation loss function refers to the function that quantifies the power loss of distributed power sources in the microgrid during power generation, and the energy storage device loss function refers to the function that quantifies the power loss of the energy storage device during discharge. Here, power loss refers to the phenomenon of reduced or ineffective utilization of electrical energy due to factors such as resistance and equipment efficiency during the power transmission or conversion process (the power generation process of distributed power sources or the charging and discharging process of energy storage devices).

[0088] Furthermore, the power generation variable refers to the power generated by the distributed power generation group of the microgrid, in kW, and the energy storage power variable refers to the charging and discharging power of multiple energy storage devices corresponding to the distributed power generation group of the microgrid, in kW.

[0089] Specifically, the construction of the microgrid operation loss function and energy storage device loss function based on the microgrid includes:

[0090] Construct time period groups;

[0091] Distributed power sources are sequentially extracted from the distributed power source groups in the microgrid, and the energy storage devices of the distributed power sources are determined.

[0092] Obtain the unit generation loss of distributed power sources and the unit energy storage loss of energy storage devices;

[0093] By summarizing the unit power generation loss and the unit energy storage loss respectively, we obtain the unit power generation loss group and the unit energy storage loss group;

[0094] Construct a microgrid operation loss function based on time period groups, unit generation loss groups, and unit energy storage loss groups;

[0095] Construct the loss function for the energy storage device.

[0096] It is clear that the aforementioned time period group refers to a combination of various time periods within a day, artificially set. A time period refers to a specific time period within a 24-hour period, for example, the time period from 0:00 to 2:00 is considered a single time period. Energy storage devices within the same time period have the same energy storage power, where energy storage power refers to the charging and discharging power of the energy storage device. The unit power generation loss refers to the proportion of electricity lost per hour of distributed power generation, expressed as a percentage (%). The unit energy storage loss refers to the proportion of electricity lost per hour of charging and discharging by the energy storage device, also expressed as a percentage (%). Both the unit power generation loss and unit energy storage loss can be obtained from historical data or from relevant equipment specifications.

[0097] In detail, the microgrid operating loss function is expressed as follows:

[0098] ;

[0099] in, This represents the microgrid operating loss function. This indicates the number of distributed generation sources in a distributed generation group within a microgrid. Indicates the first in the unit power generation loss group Unit power generation loss, This indicates the number of time periods in the time period group. Indicates the first The power generation variable of a distributed power source Indicates the first The unit energy storage loss of an energy storage device for a distributed power source. Indicates the first The energy storage device of the distributed power source in the first Energy storage power variable over a time period.

[0100] It needs to be explained that during the operation of a microgrid, each distributed power source within the microgrid incurs power losses during generation; that is, power losses are introduced into the microgrid operating loss function. Meanwhile, each distributed power source's energy storage device incurs power loss during charging and discharging, which is introduced into the microgrid's operating loss function. .

[0101] Specifically, the loss function for constructing the energy storage device includes:

[0102] Obtain the historical energy storage power group of the energy storage device of each distributed power source in the distributed power group to obtain the historical energy storage power group set, wherein the historical energy storage power group includes multiple historical energy storage powers.

[0103] The average energy storage power group is obtained by calculating the mean of each historical energy storage power group in the historical energy storage power group set. In the average energy storage power group, one average energy storage power corresponds to one energy storage device.

[0104] The energy storage device loss function is constructed based on the average energy storage power group, where the energy storage device loss function is expressed as:

[0105] ;

[0106] in, This represents the loss function of the energy storage device. This represents an exponential function with the natural constant as its base. Represents the first in the distributed power supply group The energy storage device of the distributed power source is in the first time period group. Energy storage power variable over a time period Represents the first in the average energy storage power group Average energy storage capacity.

[0107] It is clear that the historical energy storage power group refers to the combination of multiple energy storage powers of the energy storage device under multiple previous periods, and the average energy storage power group includes multiple average energy storage powers, and the average energy storage power refers to the average value of all historical energy storage powers in the historical energy storage power group.

[0108] Understandably, the larger the energy storage range of an energy storage device, the faster it ages. The energy storage range is defined in the energy storage device's loss function. The larger the value of this term, the greater the range of energy storage variation, and consequently the smaller the output value of the energy storage device's loss function.

[0109] S4. Construct a fitness function based on the microgrid operation loss function and the energy storage device loss function.

[0110] It is clear that the fitness function is the function used to calculate the fitness of particles in the subsequent particle swarm optimization algorithm, and the fitness function is expressed as:

[0111] ;

[0112] in, Represents the fitness function. This represents the preset running loss weight. This represents the loss function of the energy storage device. This indicates the preset energy storage loss weight. The loss function of the energy storage device is represented by the operation loss weight, which is a constant that quantifies the importance of power loss during the operation of the microgrid, and the energy storage loss weight is a constant that quantifies the importance of power loss during the charging and discharging of the energy storage device. Optionally, the operation loss weight and the energy storage loss weight are set to 0.6 and 0.4, respectively.

[0113] S5. Perform real-time monitoring of the microgrid to obtain real-time power generation groups and real-time load power, set the load power range, and identify the regional power grid. The load power range includes: peak power range, valley power range, and average power range.

[0114] Understandably, the real-time power generation group includes multiple real-time power generation units, and each real-time power generation unit corresponds one-to-one with a distributed power source. The real-time load power refers to the power consumption required by the user end of the microgrid. The regional power grid refers to the main power grid or large power network, which is larger in scale and has a wider coverage area than the microgrid.

[0115] Furthermore, the load power range refers to the power range of electricity consumption at the user end of the target microgrid, which is set by the user. This range includes: peak power range, valley power range, and average power range. The peak power range refers to the time period when the power consumption at the user end is the highest, such as the peak electricity consumption period. The valley power range refers to the time period when the power consumption at the user end is the lowest, such as the off-peak electricity consumption period. The average power range refers to the time period when the power consumption at the user end is moderate (between the highest and lowest power), such as the normal electricity consumption period.

[0116] S6. Formulate the optimal microgrid scheduling strategy for the microgrid based on the real-time power generation group and fitness function, and identify the target microgrid based on the optimal microgrid scheduling strategy.

[0117] It is clear that the optimal microgrid scheduling strategy refers to the combination of energy storage power of various energy storage devices in a microgrid at different time periods. For example, if a certain time period is set as (time period A, time period B), and a microgrid contains the following energy storage devices: energy storage device A and energy storage device B, where the energy storage power of energy storage device A in time period A and time period B are power A1 and power A2 respectively, and the energy storage power of energy storage device B in time period A and time period B are power B1 and power B2 respectively, then the optimal microgrid scheduling strategy corresponding to this microgrid is (power A1, power A2, power B1, power B2). The target microgrid refers to the microgrid executed according to the optimal microgrid scheduling strategy.

[0118] In detail, the step of formulating the optimal microgrid scheduling strategy based on real-time power generation groups and fitness functions includes:

[0119] Construct a particle swarm, wherein the particle swarm consists of multiple particles, and the position of each particle is a matrix composed of the energy storage power of multiple energy storage devices of each distributed power source in the microgrid during each time period in the time period group.

[0120] The particle swarm is initialized based on the real-time power generation group and fitness function to obtain the initial particle swarm.

[0121] The global particle positions are obtained by iterating based on the initial particle swarm, and the optimal microgrid scheduling strategy is determined based on the global particle positions.

[0122] Clearly, the particle swarm refers to a set of candidate solutions (particles), where each particle represents a possible microgrid scheduling strategy matrix. Initialization refers to initializing the velocity, position, and fitness of the particles in the particle swarm; the initial particle swarm refers to the particle swarm after initialization. The global particle position refers to the optimal particle position found during the iteration process, where the optimal particle position is the position corresponding to the particle with the lowest fitness, and the solution corresponding to this global particle position is the optimal microgrid scheduling strategy.

[0123] In detail, the initialization of the particle swarm based on the real-time power generation group and fitness function to obtain the initial particle swarm includes:

[0124] Construct particle constraints, which include: energy storage constraints;

[0125] Particles are extracted sequentially from the particle swarm, and the initial position and initial velocity of the particles are generated based on the particle constraints. The initial position includes multiple energy storage powers, and each energy storage power corresponds to a time period and an energy storage device of a distributed power source.

[0126] Identify the real-time generation loss corresponding to each real-time generation power in the real-time generation power group to obtain the real-time generation loss group;

[0127] The initial energy storage power group is determined based on the initial location, and the initial energy storage loss corresponding to each initial energy storage power in the initial energy storage power group is identified, thus obtaining the initial energy storage loss group;

[0128] Substitute the real-time power generation set, the initial energy storage power set, the real-time power generation loss set, and the initial energy storage loss set into the fitness function to obtain the initial fitness.

[0129] The initial particles are initialized based on their initial position, initial velocity, and initial fitness to obtain initial particles. The initial particles are then aggregated to obtain an initial particle swarm.

[0130] It should be explained that the energy storage constraints refer to the range of energy storage power of different distributed power sources constructed artificially, for example: for the first distributed power source in a distributed power source group... An energy storage device for a distributed power source, the energy storage capacity of which ranges as follows: ( ),in, Indicates the first The minimum energy storage capacity of a distributed power source's energy storage device. Indicates the first The maximum energy storage capacity of the energy storage device of a distributed power source is then determined for each time period. All of them have the following constraints: The initial position is represented as:

[0131] ;

[0132] in, Indicates the initial position. This represents the energy storage power of the first distributed power source's energy storage device during the first time period. This indicates that the energy storage device of the first distributed power source is in the [missing information]. Energy storage capacity within a time period Indicates the first The energy storage power of a distributed power source's energy storage device during the first time period. Indicates the first The energy storage device of the distributed power source in the first Energy storage capacity within a specific time period.

[0133] Understandably, the initial position and initial velocity are generated using a random function while satisfying particle constraints. The real-time power generation loss refers to the unit power generation loss of the distributed power source corresponding to the real-time power generation. The initial energy storage power group refers to the combination of energy storage powers included in the initial position. The initial energy storage loss refers to the unit energy storage loss of the energy storage device corresponding to the initial energy storage loss.

[0134] Importantly, substituting the real-time power generation group, initial energy storage power group, real-time power generation loss group, and initial energy storage loss group into the fitness function includes: substituting the real-time power generation from the real-time power generation group into the power generation variable in the fitness function; substituting the initial energy storage power from the initial energy storage power group into the energy storage power variable in the fitness function; substituting the real-time power generation loss from the real-time power generation loss group into the unit power generation loss in the fitness function; and substituting the initial energy storage loss from the initial energy storage loss group into the unit energy storage loss in the fitness function, thereby obtaining the output value of the fitness function. This output value is the initial fitness. The above substitution refers to substituting the same distributed power source or the same energy storage device, for example: Indicates the first The energy storage device of the distributed power source in the first The energy storage power variable over a time period is determined by identifying the energy storage power of the energy storage device of the a-th distributed power source in the b-th time period from the initial energy storage power group, and then substituting this energy storage power into the... .

[0135] S7. Calculate the total power generation based on the real-time power generation group, and compare the real-time load power with the load power range.

[0136] Understandably, the total power generation refers to the sum of all real-time power generation in the real-time power generation group. For real-time loads located within different load power ranges, the microgrid also employs different power supply strategies, namely the subsequent first, second, and third power supply strategies.

[0137] S8. If the real-time load power is within the peak power range of the load power range, then the preset first power supply strategy is executed on the target microgrid based on the regional power grid, total power generation and real-time load power.

[0138] Understandably, the first power supply strategy refers to the power supply strategy of the target microgrid that is manually set when the real-time load power is within the peak power range of the load power range.

[0139] Specifically, the execution of a preset first power supply strategy on the target microgrid based on the regional power grid, total power generation, and real-time load power includes:

[0140] Determine whether the total power generation is greater than the real-time load power;

[0141] If the total power generation is greater than the real-time load power, the transferable power is calculated based on the total power generation and the real-time load power, and the multiple energy storage devices in the target microgrid are charged based on the transferable power to complete the first power supply strategy.

[0142] If the total power generation is not greater than the real-time load power, then the target power demand is calculated based on the total power generation and the real-time load power.

[0143] The current energy storage power group in the target microgrid is obtained based on the optimal microgrid scheduling strategy, and the total energy storage power is calculated based on the current energy storage power group;

[0144] Determine whether the total energy storage capacity exceeds the target demand power.

[0145] If the total energy storage capacity is greater than the target demand power, then the energy storage devices in the target microgrid will be used to supply power, thus completing the first power supply strategy.

[0146] If the total energy storage capacity is not greater than the target demand power, the real-time power deficit between the total energy storage capacity and the target demand power is calculated, and power is dispatched to the regional power grid based on the real-time power deficit to complete the first power supply strategy.

[0147] It is clear that the transferable power refers to the difference between the total power generation and the real-time load power, that is, the total power generation minus the real-time load power. When the total power generation is greater than the real-time load power, it means that the power generation of the distributed power generation group in the target microgrid can meet the power demand of the user. At this time, the remaining power can be allocated to the regional grid so that the regional grid can allocate this part of the power to other microgrids in the multi-microgrid group (microgrids other than the target microgrid), thereby alleviating the power shortage of other microgrids during peak power consumption periods.

[0148] Furthermore, the target demand power refers to the difference between the total generated power and the real-time load power, i.e., the real-time load power minus the total generated power. Since the total generated power at this time is not greater than the real-time load power, the generated power of the distributed power generation group in the target microgrid alone cannot meet the electricity demand of the user end, and multiple energy storage devices in the target microgrid need to provide electricity. The current energy storage power group refers to the combination of the energy storage power of each energy storage device in the target microgrid at the current moment. The current energy storage power group is obtained by: determining the current time period, where the current time period is a certain time period in a time period group, and determining the current energy storage power group in the optimal microgrid scheduling strategy based on the current time period. The current energy storage power in the current energy storage power group corresponds one-to-one with the distributed power generation in the distributed power generation group of the target microgrid. The total energy storage power is the sum of the current energy storage power of each current energy storage power in the current energy storage power group.

[0149] It should be explained that when the total energy storage capacity is greater than the target demand power, it means that the energy storage devices in the target microgrid can meet the electricity demand of the users. The real-time power deficit refers to the difference between the total energy storage capacity and the target demand power. When the total energy storage capacity is not greater than the target demand power, it means that the energy storage devices cannot meet the real-time power deficit at this time. In this case, it is necessary to draw electricity from the regional power grid with a power equal to the real-time power deficit in order to meet the electricity demand of the users.

[0150] S9. If the real-time load power is within the valley power range of the load power range, then the preset second power supply strategy is executed on the target microgrid based on the regional power grid, total power generation and real-time load power.

[0151] Understandably, the second power supply strategy refers to a power supply strategy that is manually set when the real-time load power is within the valley power range of the load power range.

[0152] Specifically, the implementation of a preset second power supply strategy for the target microgrid based on the regional power grid, total power generation, and real-time load power includes:

[0153] Determine the maximum energy storage capacity of the target microgrid and record the real-time energy storage capacity of multiple energy storage devices in the target microgrid.

[0154] The total charging power is calculated based on the total power generation and the real-time load power, where the total charging power is the difference between the total power generation and the real-time load power.

[0155] The total charging power is used to charge the energy storage device until the real-time energy storage capacity reaches the maximum energy storage capacity.

[0156] Once the real-time energy storage capacity reaches its maximum, the system stores energy in the regional power grid based on the total charging power, thus completing the second power supply strategy.

[0157] It is clear that the maximum energy storage capacity refers to the sum of the maximum energy storage capacity of multiple energy storage devices in the target microgrid. The real-time energy storage capacity refers to the sum of the energy currently stored by each energy storage device in the target microgrid. The total charging power is the power of the microgrid charging the energy storage devices. This total charging power is the total power generation minus the real-time load power. Since the real-time load power is within the valley power range, the power generation of the microgrid's own distributed power generation group can meet the real-time load power. Therefore, the remaining power (i.e., the total charging power) after the microgrid meets the real-time load power can be stored in the microgrid's own multiple energy storage devices for use during peak electricity consumption periods (i.e., when the real-time load power is within the peak power range of the load power range).

[0158] Furthermore, when the real-time energy storage capacity reaches its maximum, it indicates that the multiple energy storage devices in the target microgrid have completed charging, and the remaining power can then be stored in the regional power grid. It should be noted that in both the charging of the energy storage devices and the storage of power in the regional power grid, the target microgrid needs to meet the power consumption of users according to the real-time load power.

[0159] S10. If the real-time load power is within the average power range of the load power range, then the preset third power supply strategy is executed on the target microgrid based on the regional power grid, total power generation and real-time load power.

[0160] Understandably, the third power supply strategy refers to the power supply strategy of the target microgrid that is artificially set when the real-time load power is within the average power range of the load power range.

[0161] Specifically, the implementation of a preset third power supply strategy for the target microgrid based on the regional power grid, total power generation, and real-time load power includes:

[0162] Determine whether the total power generation is greater than the real-time load power;

[0163] If the total power generation is greater than the real-time load power, then the real-time energy storage power is calculated based on the total power generation and the real-time load power.

[0164] The multiple energy storage devices in the target microgrid are charged based on real-time energy storage power until the real-time energy storage capacity of the multiple energy storage devices in the target microgrid reaches the maximum energy storage capacity.

[0165] When the real-time energy storage capacity of multiple energy storage devices in the target microgrid reaches the maximum energy storage capacity, the energy is stored in the regional power grid based on the real-time energy storage power to complete the third power supply strategy.

[0166] If the total power generation is not greater than the real-time load power, then multiple energy storage devices in the target microgrid will be used to generate electricity, thus completing the third power supply strategy.

[0167] It is clear that the real-time energy storage power refers to the difference between the total power generation and the real-time load power. When the total power generation is greater than the real-time load power, it means that the distributed power sources in the target microgrid can meet the electricity demand of the users. Therefore, the remaining electricity after the target microgrid meets the users' electricity demand (i.e., the electricity stored according to the real-time storage power) can be stored in the various energy storage devices in the target microgrid. When the electricity in the various energy storage devices in the target microgrid reaches the maximum storage capacity, since it is impossible to continue storing electricity, the real-time storage power can be stored in the regional grid so that the regional grid can distribute the real-time storage power to other microgrids. When the total power generation is not greater than the real-time load power, it means that the distributed power sources in the target microgrid cannot meet the electricity demand of the users. At this time, it is necessary to call on the electricity of the energy storage devices in the target microgrid to supply power to the users, that is, to use multiple energy storage devices in the target microgrid to generate electricity to meet the users' electricity demand. It should be noted that since the real-time load power is in the average power range of the load power range at this time, the energy storage devices can meet the difference between the total power generation and the real-time load power.

[0168] To address the problems described in the background art, this invention first constructs a microgrid operation loss function and an energy storage device loss function based on the microgrid. These functions comprehensively quantify the power loss during microgrid operation, including not only generation losses but also losses during the charging and discharging of energy storage devices, thus contributing to efficient microgrid operation. Next, a fitness function is constructed based on these functions. This fitness function integrates various power losses in microgrid operation, enabling a comprehensive evaluation of the merits of a scheduling strategy. This provides a crucial evaluation criterion for finding the optimal scheduling strategy using particle swarm optimization (PSO) algorithms, allowing the algorithm to effectively search towards reducing power losses. Furthermore, an optimal microgrid scheduling strategy is formulated based on the real-time power generation group and fitness function. The target microgrid is then identified based on this optimal scheduling strategy. This strategy ensures that the microgrid's scheduling scheme is optimal under the current operating conditions, achieving a rational arrangement of power generation and energy storage, thereby improving the safety of microgrid operation. Then, the real-time load power is compared with the load power range to ensure that the microgrid can adopt the optimal operating mode within different load power ranges. Finally, based on the different relationships between the real-time load power and the load power range, a first, second, and third power supply strategy are set. These different power supply strategies improve the operating efficiency of the microgrid at different times. Therefore, this invention can improve the intelligence level of microgrid operation and enhance the accuracy of power allocation during microgrid operation.

[0169] like Figure 2 The diagram shown is a functional block diagram of a multi-microgrid operation optimization system based on collaborative scheduling provided in an embodiment of the present invention.

[0170] The multi-microgrid operation optimization system 100 based on cooperative scheduling described in this invention can be installed in an electronic device. Depending on the functions implemented, the multi-microgrid operation optimization system 100 based on cooperative scheduling may include a microgrid determination module 101, a fitness function construction module 102, a target grid acquisition module 103, and a power supply strategy execution module 104. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0171] The microgrid determination module 101 is used to receive grid operation optimization instructions and determine multiple microgrid groups based on the grid operation optimization instructions. The multiple microgrid groups include multiple microgrids. The microgrids are extracted sequentially from the multiple microgrid groups. The microgrids include distributed power generation groups, and each distributed power generation group includes an energy storage device.

[0172] The fitness function construction module 102 is used to construct a microgrid operation loss function and an energy storage device loss function based on the microgrid. The microgrid operation loss function includes a power generation variable and an energy storage power variable, and the energy storage device loss function includes an energy storage power variable. The fitness function is constructed based on the microgrid operation loss function and the energy storage device loss function.

[0173] The target power grid acquisition module 103 is used to monitor the microgrid in real time, obtain the real-time power generation group and the real-time load power, set the load power range, and identify the regional power grid. The load power range includes: peak power range, valley power range and average power range. Based on the real-time power generation group and fitness function, the optimal microgrid scheduling strategy of the microgrid is formulated, and the target microgrid is identified based on the optimal microgrid scheduling strategy.

[0174] The power supply strategy execution module 104 is used to calculate the total power generation based on the real-time power generation group, compare the real-time load power with the load power range, and if the real-time load power is within the peak power range of the load power range, then a preset first power supply strategy is executed on the target microgrid based on the regional power grid, the total power generation, and the real-time load power. If the real-time load power is within the valley power range of the load power range, then a preset second power supply strategy is executed on the target microgrid based on the regional power grid, the total power generation, and the real-time load power. If the real-time load power is within the average power range of the load power range, then a preset third power supply strategy is executed on the target microgrid based on the regional power grid, the total power generation, and the real-time load power.

[0175] In detail, the modules in the multi-microgrid operation optimization system 100 based on cooperative scheduling described in this embodiment of the invention adopt the same approach as described above. Figure 1 The method used here is the same as the collaborative scheduling-based multi-microgrid operation optimization method described above, and can produce the same technical effect, so it will not be repeated here.

[0176] like Figure 3 The diagram shown is a schematic representation of an electronic device for implementing a multi-microgrid operation optimization method based on collaborative scheduling, according to an embodiment of the present invention.

[0177] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a multi-microgrid operation optimization method program based on coordinated scheduling.

[0178] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as code for a multi-microgrid operation optimization method program based on coordinated scheduling, but also to temporarily store data that has been output or will be output.

[0179] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a multi-microgrid operation optimization method program based on coordinated scheduling) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0180] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0181] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0182] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management system, thereby enabling functions such as charging management, discharging management, and power consumption management through the power management system. The power supply may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0183] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0184] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0185] The multi-microgrid operation optimization method program based on cooperative scheduling, stored in the memory 11 of the electronic device 1, is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0186] Receive grid operation optimization instructions, and determine multiple microgrid groups based on the grid operation optimization instructions, wherein the multiple microgrid groups include multiple microgrids;

[0187] Microgrids are extracted sequentially from multiple microgrid groups, wherein each microgrid includes a distributed power generation group, and each distributed power generation group includes an energy storage device.

[0188] Based on microgrids, microgrid operation loss functions and energy storage device loss functions are constructed. The microgrid operation loss function includes power generation variables and energy storage power variables, and the energy storage device loss function includes energy storage power variables.

[0189] A fitness function is constructed based on the microgrid operation loss function and the energy storage device loss function.

[0190] The microgrid is monitored in real time to obtain the real-time power generation groups and real-time load power, the load power range is set, and the regional power grid is identified. The load power range includes: peak power range, valley power range, and average power range.

[0191] The optimal microgrid scheduling strategy is formulated based on the real-time power generation group and fitness function, and the target microgrid is identified based on the optimal microgrid scheduling strategy.

[0192] Calculate the total power generation based on the real-time power generation group, and compare the real-time load power with the load power range;

[0193] If the real-time load power is within the peak power range of the load power range, then the preset first power supply strategy is executed on the target microgrid based on the regional power grid, total power generation and real-time load power.

[0194] If the real-time load power is within the valley power range of the load power range, then the preset second power supply strategy is executed on the target microgrid based on the regional power grid, total power generation and real-time load power.

[0195] If the real-time load power is within the average power range of the load power range, then a preset third power supply strategy is executed on the target microgrid based on the regional power grid, total power generation, and real-time load power.

[0196] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0197] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0198] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0199] Receive grid operation optimization instructions, and determine multiple microgrid groups based on the grid operation optimization instructions, wherein the multiple microgrid groups include multiple microgrids;

[0200] Microgrids are extracted sequentially from multiple microgrid groups, wherein each microgrid includes a distributed power generation group, and each distributed power generation group includes an energy storage device.

[0201] Based on microgrids, microgrid operation loss functions and energy storage device loss functions are constructed. The microgrid operation loss function includes power generation variables and energy storage power variables, and the energy storage device loss function includes energy storage power variables.

[0202] A fitness function is constructed based on the microgrid operation loss function and the energy storage device loss function.

[0203] The microgrid is monitored in real time to obtain the real-time power generation groups and real-time load power, the load power range is set, and the regional power grid is identified. The load power range includes: peak power range, valley power range, and average power range.

[0204] The optimal microgrid scheduling strategy is formulated based on the real-time power generation group and fitness function, and the target microgrid is identified based on the optimal microgrid scheduling strategy.

[0205] Calculate the total power generation based on the real-time power generation group, and compare the real-time load power with the load power range;

[0206] If the real-time load power is within the peak power range of the load power range, then the preset first power supply strategy is executed on the target microgrid based on the regional power grid, total power generation and real-time load power.

[0207] If the real-time load power is within the valley power range of the load power range, then the preset second power supply strategy is executed on the target microgrid based on the regional power grid, total power generation and real-time load power.

[0208] If the real-time load power is within the average power range of the load power range, then a preset third power supply strategy is executed on the target microgrid based on the regional power grid, total power generation, and real-time load power.

[0209] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0210] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0211] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0212] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0213] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing the operation of multiple microgrids based on coordinated scheduling, characterized in that, The method includes: Receive grid operation optimization instructions, and determine multiple microgrid groups based on the grid operation optimization instructions, wherein the multiple microgrid groups include multiple microgrids; Microgrids are extracted sequentially from multiple microgrid groups, wherein each microgrid includes a distributed power generation group, and each distributed power generation group includes an energy storage device. Based on microgrids, microgrid operation loss functions and energy storage device loss functions are constructed. The microgrid operation loss function includes power generation variables and energy storage power variables, and the energy storage device loss function includes energy storage power variables. A fitness function is constructed based on the microgrid operation loss function and the energy storage device loss function. The microgrid is monitored in real time to obtain the real-time power generation groups and real-time load power, the load power range is set, and the regional power grid is identified. The load power range includes: peak power range, valley power range, and average power range. The optimal microgrid scheduling strategy is formulated based on the real-time power generation group and fitness function, and the target microgrid is identified based on the optimal microgrid scheduling strategy. Calculate the total power generation based on the real-time power generation group, and compare the real-time load power with the load power range; If the real-time load power is within the peak power range of the load power range, then the preset first power supply strategy is executed on the target microgrid based on the regional power grid, total power generation and real-time load power. If the real-time load power is within the valley power range of the load power range, then the preset second power supply strategy is executed on the target microgrid based on the regional power grid, total power generation and real-time load power. If the real-time load power is within the average power range of the load power range, then a preset third power supply strategy is executed on the target microgrid based on the regional power grid, total power generation, and real-time load power.

2. The multi-microgrid operation optimization method based on cooperative scheduling as described in claim 1, characterized in that, The construction of the microgrid operation loss function and energy storage device loss function based on the microgrid includes: Construct time period groups; Distributed power sources are sequentially extracted from the distributed power source groups in the microgrid, and the energy storage devices of the distributed power sources are determined. Obtain the unit generation loss of distributed power sources and the unit energy storage loss of energy storage devices; By summarizing the unit power generation loss and the unit energy storage loss respectively, we obtain the unit power generation loss group and the unit energy storage loss group; Construct a microgrid operation loss function based on time period groups, unit generation loss groups, and unit energy storage loss groups; Construct the loss function for the energy storage device.

3. The multi-microgrid operation optimization method based on cooperative scheduling as described in claim 2, characterized in that, The microgrid operation loss function is expressed as follows: ； in, This represents the microgrid operating loss function. This indicates the number of distributed generation sources in a distributed generation group within a microgrid. Indicates the first in the unit power generation loss group Unit power generation loss, This indicates the number of time periods in the time period group. Indicates the first The power generation variable of a distributed power source Indicates the first The unit energy storage loss of an energy storage device for a distributed power source. Indicates the first The energy storage device of the distributed power source in the first Energy storage power variable over a time period.

4. The multi-microgrid operation optimization method based on cooperative scheduling as described in claim 3, characterized in that, The loss function for constructing the energy storage device includes: Obtain the historical energy storage power group of the energy storage device of each distributed power source in the distributed power group to obtain the historical energy storage power group set, wherein the historical energy storage power group includes multiple historical energy storage powers. The average energy storage power group is obtained by calculating the mean of each historical energy storage power group in the historical energy storage power group set. In the average energy storage power group, one average energy storage power corresponds to one energy storage device. The energy storage device loss function is constructed based on the average energy storage power group, where the energy storage device loss function is expressed as: ； in, This represents the loss function of the energy storage device. This represents an exponential function with the natural constant as its base. Represents the first in the distributed power supply group The energy storage device of the distributed power source is in the first time period group. Energy storage power variable over a time period Represents the first in the average energy storage power group Average energy storage capacity.

5. The multi-microgrid operation optimization method based on cooperative scheduling as described in claim 4, characterized in that, The process of formulating the optimal microgrid dispatch strategy based on real-time power generation groups and fitness functions includes: Construct a particle swarm, wherein the particle swarm consists of multiple particles, and the position of each particle is a matrix composed of the energy storage power of each distributed power source in the microgrid during each time period in the time period group. The particle swarm is initialized based on the real-time power generation group and fitness function to obtain the initial particle swarm. The global particle positions are obtained by iterating based on the initial particle swarm, and the optimal microgrid scheduling strategy is determined based on the global particle positions.

6. The multi-microgrid operation optimization method based on cooperative scheduling as described in claim 5, characterized in that, The initial particle swarm is obtained by initializing the particle swarm based on the real-time power generation group and fitness function, including: Construct particle constraints, which include: energy storage constraints; Particles are extracted sequentially from the particle swarm, and the initial position and initial velocity of the particles are generated based on the particle constraints. The initial position includes multiple energy storage powers, and each energy storage power corresponds to a time period and an energy storage device of a distributed power source. Identify the real-time generation loss corresponding to each real-time generation power in the real-time generation power group to obtain the real-time generation loss group; The initial energy storage power group is determined based on the initial location, and the initial energy storage loss corresponding to each initial energy storage power in the initial energy storage power group is identified, thus obtaining the initial energy storage loss group; Substitute the real-time power generation set, the initial energy storage power set, the real-time power generation loss set, and the initial energy storage loss set into the fitness function to obtain the initial fitness. The initial particles are initialized based on their initial position, initial velocity, and initial fitness to obtain initial particles. The initial particles are then aggregated to obtain an initial particle swarm.

7. The multi-microgrid operation optimization method based on cooperative scheduling as described in claim 6, characterized in that, The first power supply strategy, which is preset and applied to the target microgrid based on the regional power grid, total power generation, and real-time load power, includes: Determine whether the total power generation is greater than the real-time load power; If the total power generation is greater than the real-time load power, the transferable power is calculated based on the total power generation and the real-time load power, and the multiple energy storage devices in the target microgrid are charged based on the transferable power to complete the first power supply strategy. If the total power generation is not greater than the real-time load power, then the target power demand is calculated based on the total power generation and the real-time load power. The current energy storage power group in the target microgrid is obtained based on the optimal microgrid scheduling strategy, and the total energy storage power is calculated based on the current energy storage power group; Determine whether the total energy storage capacity exceeds the target demand power. If the total energy storage capacity is greater than the target demand power, then the energy storage devices in the target microgrid will be used to supply power, thus completing the first power supply strategy. If the total energy storage capacity is not greater than the target demand power, the real-time power deficit between the total energy storage capacity and the target demand power is calculated, and power is dispatched to the regional power grid based on the real-time power deficit to complete the first power supply strategy.

8. The multi-microgrid operation optimization method based on cooperative scheduling as described in claim 7, characterized in that, The second power supply strategy, which is preset based on the regional power grid, total power generation, and real-time load power, is applied to the target microgrid, including: Determine the maximum energy storage capacity of the target microgrid and record the real-time energy storage capacity of multiple energy storage devices in the target microgrid. The total charging power is calculated based on the total power generation and the real-time load power, where the total charging power is the difference between the total power generation and the real-time load power. The total charging power is used to charge the energy storage device until the real-time energy storage capacity reaches the maximum energy storage capacity. Once the real-time energy storage capacity reaches its maximum, the system stores energy in the regional power grid based on the total charging power, thus completing the second power supply strategy.

9. The multi-microgrid operation optimization method based on cooperative scheduling as described in claim 8, characterized in that, The third power supply strategy, which is based on the regional power grid, total power generation, and real-time load power, is implemented on the target microgrid and includes: Determine whether the total power generation is greater than the real-time load power; If the total power generation is greater than the real-time load power, then the real-time energy storage power is calculated based on the total power generation and the real-time load power. The multiple energy storage devices in the target microgrid are charged based on real-time energy storage power until the real-time energy storage capacity of the multiple energy storage devices in the target microgrid reaches the maximum energy storage capacity. When the real-time energy storage capacity of multiple energy storage devices in the target microgrid reaches the maximum energy storage capacity, the energy is stored in the regional power grid based on the real-time energy storage power to complete the third power supply strategy. If the total power generation is not greater than the real-time load power, then multiple energy storage devices in the target microgrid will be used to generate electricity, thus completing the third power supply strategy.

10. A multi-microgrid operation optimization system based on cooperative scheduling, characterized in that, The system includes: The microgrid determination module is used to receive grid operation optimization instructions and determine multiple microgrid groups based on the grid operation optimization instructions. The multiple microgrid groups include multiple microgrids. The microgrids are extracted sequentially from the multiple microgrid groups. Each microgrid includes a distributed power generation group, and each distributed power generation in the distributed power generation group includes an energy storage device. The fitness function construction module is used to construct the microgrid operation loss function and the energy storage device loss function based on the microgrid. The microgrid operation loss function includes: power generation power variable and energy storage power variable, and the energy storage device loss function includes: energy storage power variable. The fitness function is constructed based on the microgrid operation loss function and the energy storage device loss function. The target grid acquisition module is used to monitor the microgrid in real time, obtain the real-time power generation group and real-time load power, set the load power range, and identify the regional power grid. The load power range includes: peak power range, valley power range and average power range. Based on the real-time power generation group and fitness function, the optimal microgrid scheduling strategy is formulated for the microgrid, and the target microgrid is identified based on the optimal microgrid scheduling strategy. The power supply strategy execution module is used to calculate the total power generation based on the real-time power generation group, compare the real-time load power with the load power range, and if the real-time load power is within the peak power range of the load power range, then a preset first power supply strategy is executed on the target microgrid based on the regional power grid, the total power generation, and the real-time load power. If the real-time load power is within the valley power range of the load power range, then a preset second power supply strategy is executed on the target microgrid based on the regional power grid, the total power generation, and the real-time load power. If the real-time load power is within the average power range of the load power range, then a preset third power supply strategy is executed on the target microgrid based on the regional power grid, the total power generation, and the real-time load power.

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