Micro-grid group cooperative optimization method and device, electronic equipment, medium and product

Abstract

This invention relates to the field of power system analysis technology, and particularly to a method, device, electronic equipment, medium, and product for collaborative optimization of microgrid groups. The method includes: constructing a collaborative optimization model of a target microgrid group; identifying inter-group interaction coupling variables among microgrids in the collaborative optimization model, and predicting lost boundary coupling variables among microgrids; and, based on a preset asynchronous alternating direction multiplier method, asynchronously iteratively solving the collaborative optimization model of the microgrid group according to the inter-group interaction coupling variables and / or boundary coupling variables to obtain the collaborative optimization result of the target microgrid group. This solves the problem of poor collaborative optimization calculation accuracy and high computational resource consumption caused by the delay or loss of boundary variables during inter-microgrid communication due to computation and communication delays in related technologies, thereby improving the collaborative optimization accuracy and saving computational resources.

Description

Technical Field

[0001] This invention relates to the field of power system analysis technology, and in particular to a method, device, electronic equipment, medium and product for collaborative optimization of microgrid groups. Background Technology

[0002] As a micro-energy system integrating multiple types of resources such as power generation, load, and storage, microgrids can address the safety and stability issues arising from the integration of numerous distributed power sources through reasonable control methods. Furthermore, multiple adjacent microgrids connected by distribution lines form an interconnected and mutually supportive microgrid cluster. Through complementary and coordinated operation among the clusters, energy consumption differences between microgrids can be effectively compensated, the absorption rate of distributed renewable energy can be improved, and the economic characteristics of the microgrid cluster can be enhanced.

[0003] In related technologies, the coordination optimization model of microgrid groups is mainly solved in a distributed manner under ideal conditions.

[0004] However, in practical engineering, the delay or loss of boundary variables during communication between microgrid groups due to computation and communication delays often greatly affects the difficulty and accuracy of optimization solutions, which urgently needs to be addressed. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, medium, and product for collaborative optimization of microgrid groups, in order to solve the problem of poor collaborative optimization calculation accuracy and high computational resource consumption caused by the delay or loss of boundary variables during communication between microgrid groups due to computation and communication latency. This invention improves the accuracy of collaborative optimization and saves computational resources.

[0006] To achieve the above objectives, a first aspect of the present invention provides a microgrid group collaborative optimization method, comprising the following steps: constructing a microgrid group collaborative optimization model of a target microgrid group; identifying inter-group interaction coupling variables among the microgrids in the microgrid group collaborative optimization model, and predicting the lost boundary coupling variables among the microgrids; and asynchronously iteratively solving the microgrid group collaborative optimization model based on a preset asynchronous alternating direction multiplier method, according to the inter-group interaction coupling variables and / or the lost boundary coupling variables, to obtain the collaborative optimization result of the target microgrid group.

[0007] Furthermore, in some embodiments, constructing the microgrid group collaborative optimization model for the target microgrid group includes: obtaining the resource operation constraints and power constraints of the target microgrid group; constructing the objective function of the target microgrid group; and constructing the microgrid group collaborative optimization model based on the resource operation constraints and the power constraints, with the objective function as the goal.

[0008] Furthermore, in some embodiments, the objective function is: ; Where S represents the set of scenarios where there is uncertainty in the output of new energy sources. This represents the probability of occurrence in each scenario. The start-up and shutdown costs of micro gas turbines, For the operating cost of micro gas turbines, For the operating costs of energy storage systems, For the cost of power exchange between microgrids, The penalty cost for curtailing wind and solar energy.

[0009] Furthermore, in some embodiments, the prediction of the lost boundary coupling variables between the microgrids includes: determining whether each microgrid has not received the boundary coupling variables of the other microgrids in the current iteration period; if each microgrid has not received the lost boundary coupling variables of the other microgrids in the current iteration period, then obtaining the historical boundary coupling variables of the corresponding microgrid before the current iteration period, and predicting the lost boundary coupling variables between the microgrids in the current iteration period based on the historical boundary coupling variables.

[0010] Further, in some embodiments, the step of asynchronously iteratively solving the microgrid group collaborative optimization model based on the preset asynchronous alternating direction multiplier method, according to the inter-group interactive coupling variables and / or the lost boundary coupling variables, to obtain the collaborative optimization result of the target microgrid group, includes: asynchronously and independently iterating each microgrid in the target microgrid group collaborative optimization model according to the preset asynchronous alternating direction multiplier method based on the inter-group interactive coupling variables and / or the boundary coupling variables, and determining whether the optimization result of each microgrid in the current iteration calculation cycle meets the preset convergence condition; if the optimization result of the microgrid in the current iteration calculation cycle does not meet the preset convergence condition, then iteratively calculating the microgrid based on the preset asynchronous alternating direction multiplier method; if the optimization result of the microgrid in the current iteration calculation cycle meets the preset convergence condition, then controlling the microgrid to exit the iteration, and simultaneously sending the converged microgrid coupling variable data to the remaining microgrids, and jumping to the remaining microgrids for iterative calculation, until the optimization results of all microgrids in the microgrid group meet the preset convergence condition, thereby obtaining the collaborative optimization result of the target microgrid group.

[0011] Furthermore, in some embodiments, the resource operation constraints of the target microgrid group include distributed renewable energy output constraints, energy storage operation constraints, and gas turbine operation constraints; the microgrid group power constraints of the target microgrid group include power exchange constraints between interconnected microgrid groups and power balance constraints within the microgrid.

[0012] According to the microgrid group collaborative optimization method provided in the embodiments of the present invention, a microgrid group collaborative optimization model is constructed, interactive coupling variables between microgrids are extracted, and the lost boundary coupling variables are estimated by extrapolation when communication is abnormal. The asynchronous alternating direction multiplier method is combined with the asynchronous iteration of effective coupling variables to solve the microgrid group collaborative optimization result. This solves the problem of poor collaborative optimization calculation accuracy and high computational resource consumption caused by the delay or loss of boundary variables during communication between microgrid groups due to computation and communication delays in related technologies. It improves the collaborative optimization accuracy and saves computational resources.

[0013] To achieve the above objectives, a second aspect of the present invention provides a microgrid group collaborative optimization device, comprising: a construction module for constructing a microgrid group collaborative optimization model of a target microgrid group; a prediction module for identifying inter-group interaction coupling variables among microgrids in the microgrid group collaborative optimization model and predicting lost boundary coupling variables among the microgrids; and a calculation module for asynchronously iteratively solving the microgrid group collaborative optimization model based on a preset asynchronous alternating direction multiplier method, according to the inter-group interaction coupling variables and / or the lost boundary coupling variables, to obtain the collaborative optimization result of the target microgrid group.

[0014] Furthermore, in some embodiments, the construction module is specifically used to: obtain the resource operation constraints and power constraints of the target microgrid group; construct the objective function of the target microgrid group; and construct the microgrid group collaborative optimization model based on the resource operation constraints and the power constraints of the microgrid group, with the objective function being minimized.

[0015] Furthermore, in some embodiments, the objective function is: ; Where S represents the set of scenarios where there is uncertainty in the output of new energy sources. This represents the probability of occurrence in each scenario. The start-up and shutdown costs of micro gas turbines, For the operating cost of micro gas turbines, For the operating costs of energy storage systems, For the cost of power exchange between microgrids, The penalty cost for curtailing wind and solar energy.

[0016] Furthermore, in some embodiments, the prediction module is specifically used to: determine whether each microgrid has not received the boundary coupling variables of the other microgrids in the current iteration period; if each microgrid has not received the boundary coupling variables lost by the other microgrids in the current iteration period, then obtain the historical boundary coupling variables of the corresponding microgrid before the current iteration period, and predict the boundary coupling variables lost between the microgrids in the current iteration period based on the historical boundary coupling variables.

[0017] Further, in some embodiments, the calculation module is specifically used to: perform asynchronous independent iteration on each microgrid in the target microgrid group collaborative optimization model based on the inter-group interaction coupling variables and / or the lost boundary coupling variables, according to a preset asynchronous alternating direction multiplier method, and determine whether the optimization result of each microgrid in the current iteration calculation cycle meets the preset convergence condition; if the microgrid optimization result in the current iteration calculation cycle does not meet the preset convergence condition, then perform iterative calculation on the microgrid based on the preset asynchronous alternating direction multiplier method; if the microgrid optimization result in the current iteration calculation cycle meets the preset convergence condition, then control the microgrid to exit the iteration, and simultaneously send the converged microgrid coupling variable data to the remaining microgrids, and jump to the remaining microgrids for iterative calculation, until the optimization results of all microgrids in the microgrid group meet the preset convergence condition, and obtain the collaborative optimization result of the target microgrid group.

[0018] Furthermore, in some embodiments, the resource operation constraints of the target microgrid group include distributed renewable energy output constraints, energy storage operation constraints, and gas turbine operation constraints; the microgrid group power constraints of the target microgrid group include power exchange constraints between interconnected microgrid groups and power balance constraints within the microgrid.

[0019] According to the microgrid group collaborative optimization device provided in the embodiments of the present invention, a microgrid group collaborative optimization model is constructed, interactive coupling variables between microgrids are extracted, and the lost boundary coupling variables are estimated by extrapolation when communication is abnormal. The asynchronous alternating direction multiplier method is combined with the asynchronous iteration of effective coupling variables to solve the microgrid group collaborative optimization result. This solves the problem of poor collaborative optimization calculation accuracy and high computational resource consumption caused by the delay or loss of boundary variables during communication between microgrid groups due to computation and communication delays in related technologies. It improves the collaborative optimization accuracy and saves computational resources.

[0020] To achieve the above objectives, a third aspect of the present invention provides an electronic device / vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the microgrid group collaborative optimization method as described in the above embodiments.

[0021] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the microgrid group collaborative optimization method as described in the above embodiments.

[0022] A fifth aspect of the present invention provides a computer program product, including a computer program that is executed to implement the microgrid group collaborative optimization method as described in the above embodiments.

[0023] Therefore, the present invention has the following beneficial effects: (1) The present invention uses an extrapolation strategy to predict the missing neighbor boundary information, which can effectively alleviate the impact of missing boundary data, avoid the microgrid from falling into invalid waiting, and improve the overall solution efficiency.

[0024] (2) The present invention is based on the asynchronous communication solution mechanism of the asynchronous alternating direction multiplier method, which breaks through the synchronization constraint in the iteration process of each microgrid, so that it can iterate independently according to its own solution progress, avoids the waste of computing resources caused by waiting for slow nodes, and significantly accelerates the system convergence speed. Attached Figure Description

[0025] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart of a microgrid group collaborative optimization method provided according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a microgrid group topology provided according to a specific embodiment of the present invention; Figure 3 This is a schematic diagram of a solution method provided according to a specific embodiment of the present invention; Figure 4 This is a schematic diagram of the collaborative optimization results provided according to a specific embodiment of the present invention; Figure 5 This is a schematic diagram of an asynchronous iteration method according to a specific embodiment of the present invention; Figure 6 This is a schematic diagram of a synchronous iteration method according to a specific embodiment of the present invention; Figure 7 This is a block diagram of a microgrid group collaborative optimization device provided according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0026] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0027] The following description, with reference to the accompanying drawings, describes the microgrid group collaborative optimization method, apparatus, electronic equipment, medium, and product provided according to embodiments of the present invention. First, the microgrid group collaborative optimization method provided according to embodiments of the present invention will be described with reference to the accompanying drawings.

[0028] Figure 1 This is a flowchart of a microgrid group collaborative optimization method provided according to an embodiment of the present invention.

[0029] like Figure 1 As shown, the microgrid group collaborative optimization method includes the following steps: In step S101, a microgrid group collaborative optimization model for the target microgrid group is constructed.

[0030] In some embodiments, constructing a microgrid group collaborative optimization model for the target microgrid group includes: obtaining the resource operation constraints and power constraints of the target microgrid group; constructing the objective function of the target microgrid group; and constructing a microgrid group collaborative optimization model based on the resource operation constraints and power constraints, with the objective function as the goal.

[0031] Specifically, the embodiments of the present invention first obtain the annual predicted and actual output data of distributed photovoltaic and wind power in typical regions, use the scenario method to handle the uncertainty of new energy output, generate a set of new energy output scenarios based on the historical output data of new energy, and reduce the set of scenarios to obtain typical new energy output scenarios.

[0032] Furthermore, in some embodiments, the objective function is: ; Where S represents the set of scenarios where there is uncertainty in the output of new energy sources. This represents the probability of occurrence in each scenario. The start-up and shutdown costs of micro gas turbines, For the operating cost of micro gas turbines, For the operating costs of energy storage systems, For the cost of power exchange between microgrids, The penalty cost for curtailing wind and solar energy.

[0033] Specifically, since each microgrid operates independently, its operational goal is to minimize its own operating costs. Because microgrids can enhance their inter-grid interaction capabilities and reduce dependence on the external power grid through inter-grid mutual assistance, the operating costs of a microgrid include the operating costs of various types of regulation resources and the power exchange costs between microgrids. These costs include the operating costs of energy storage systems, the costs of curtailment penalties for wind and solar power, the operating costs of micro gas turbines, and the power exchange costs between microgrids. The operating cost of the energy storage system is as follows:

[0034] in, The charging cost coefficient for energy storage. This is the discharge cost coefficient for energy storage. The charging power for energy storage, The discharge power of the stored energy. and These represent the charge / discharge states of energy storage, specifically the 0-1 variables. s This indicates an index of renewable energy power generation scenarios. The penalty cost for wind and solar curtailment is:

[0035] in, The cost coefficient for curtailment of wind and solar power. To contribute the most to new energy, Contributing to the dispatch of new energy sources. The start-up and shutdown costs and operating costs of micro gas turbines are:

[0036]

[0037]

[0038] in, For the start-up and shutdown decision variables of micro gas turbines, This refers to the output value of the micro gas turbine. , and This represents the output cost coefficient of the micro gas turbine. and These are the start-up cost and shutdown cost of a micro gas turbine, respectively. This is to switch the gas turbine from the start-up state to the shutdown state. To facilitate the transition of the gas turbine from shutdown to startup, the interconnected microgrid groups achieve stable operation through collaborative support. The power exchange cost between the groups is shared equally by the two adjacent microgrids. The power exchange cost between the microgrids is as follows:

[0039] in, For micro-network i and j The power exchange cost factor between them For micro-network i and j The exchange power between them.

[0040] Furthermore, in some embodiments, the resource operation constraints of the target microgrid cluster include distributed renewable energy output constraints, energy storage operation constraints, and gas turbine operation constraints; the microgrid cluster power constraints of the target microgrid cluster include power exchange constraints between interconnected microgrid clusters and power balance constraints within the microgrid.

[0041] Specifically, distributed renewable energy operates in maximum power point tracking mode, and the allowable amount of wind and solar curtailment must be controlled within a certain proportion. The operational constraints for distributed renewable energy are as follows:

[0042] in, Distributed new energy i In scenario s t Maximum output at any moment Distributed new energy i In scenario s t Real-time scheduling of output, The permitted curtailment rate of wind and solar power for distributed renewable energy sources.

[0043] Furthermore, the charging and discharging power of energy storage is limited by its own upper and lower limits. Simultaneously, during operation, the energy must be maintained within a certain range. Therefore, the operational constraints of energy storage are:

[0044] in, For the first i Energy storage scenario s Below t Charging power at any time For the first i Energy storage scenario s Below t Discharge power at any given moment; Indicates the first i Energy storage scenario s Below t The energy of a moment; For the first i The upper limit of the charging and discharging power of an energy storage device. For the first i The lower limit of the charging and discharging power of an energy storage device. For the first iIn terms of the charging and discharging energy of individual energy storage, For the first i The lower limit of the charging and discharging energy of an energy storage device.

[0045] Furthermore, the operating power constraint of the micro gas turbine is:

[0046] in, This represents the maximum output power of the micro gas turbine. This represents the minimum output power of the micro gas turbine.

[0047] Furthermore, since there is power exchange between each microgrid in an interconnected microgrid group, the power exchange constraint between interconnected microgrid groups is set as follows:

[0048] in, This represents the maximum interaction power between microgrids. This represents the minimum interaction power between microgrids.

[0049] Furthermore, each microgrid needs to maintain an internal source-load supply-demand balance; therefore, the internal power balance constraint of a microgrid is:

[0050] in, microgrid i exist t The load demand at any given moment.

[0051] In step S102, the inter-group interaction coupling variables among the microgrids in the microgrid group collaborative optimization model are identified, and the lost boundary coupling variables among the microgrids are predicted.

[0052] Among them, the interactive coupling variable refers to the interconnection and interaction power between microgrids, and the boundary coupling variable refers to the coupling variable that cannot be received normally due to communication delay and data packet loss.

[0053] Specifically, embodiments of the present invention introduce coupling variables that satisfy... ,in microgrid i and j Coupled variables between them microgrid j and i In the distributed optimization model, the coupling variables between microgrids only need to interact with each other to achieve distributed optimization scheduling of the entire network.

[0054] Furthermore, in some embodiments, predicting the lost boundary coupling variables between microgrids includes: determining whether each microgrid has not received the boundary coupling variables of the other microgrids in the current iteration period; if each microgrid has not received the lost boundary coupling variables of the other microgrids in the current iteration period, then obtaining the historical boundary coupling variables of the corresponding microgrid before the current iteration period, and predicting the lost boundary coupling variables between microgrids in the current iteration period based on the historical boundary coupling variables.

[0055] As one possible way to achieve this, if microgrids i In the If the coupling variable information of other microgrids is received normally in the next iteration, then the microgrid i Normally, if other microgrids experience communication delays or packet loss, causing the microgrid to... i If the coupling variable information from other microgrids is not received in a timely manner, then the microgrid... i By extrapolating and combining the coupling variable information from the previous two tests of other microgrids, the prediction of this microgrid's performance in [the following context is missing]. The values ​​of the coupling variables in the next iteration, i.e.: , ,in, This indicates a term that considers the slope trend between two consecutive points; and These are the weighting coefficients. This is an extrapolation term used to predict boundary coupling information that cannot be obtained from each microgrid using the extrapolation method.

[0056] In step S103, based on the preset asynchronous alternating direction multiplier method, the microgrid group collaborative optimization model is solved asynchronously and iteratively according to the inter-group interactive coupling variables and / or lost boundary coupling variables to obtain the collaborative optimization result of the target microgrid group.

[0057] Further, in some embodiments, based on a preset asynchronous alternating direction multiplier method, the microgrid group collaborative optimization model is asynchronously iteratively solved according to inter-group interactive coupling variables and / or lost boundary coupling variables to obtain the collaborative optimization result of the target microgrid group. This includes: based on inter-group interactive coupling variables and / or boundary coupling variables, each microgrid in the target microgrid group collaborative optimization model is asynchronously and independently iterated according to the preset asynchronous alternating direction multiplier method, and it is determined whether the optimization result of each microgrid in the current iteration calculation cycle meets the preset convergence condition; if the microgrid optimization result in the current iteration calculation cycle does not meet the preset convergence condition, the microgrid is iteratively calculated according to the preset asynchronous alternating direction multiplier method; if the microgrid optimization result in the current iteration calculation cycle meets the preset convergence condition, the microgrid is controlled to exit the iteration, and the converged microgrid coupling variable data is sent to the remaining microgrids, and the iteration calculation is switched to the remaining microgrids until the optimization results of all microgrids in the microgrid group meet the preset convergence condition, thus obtaining the collaborative optimization result of the target microgrid group.

[0058] Specifically, for a single microgrid, after each iteration of its calculation, the coupling variables and Lagrange multipliers between it and other microgrids are updated. During the iteration process, it is first determined whether the optimization result of the microgrid meets the convergence condition. If the optimization result of the microgrid does not meet the convergence condition, it continues to enter the next iteration. If the optimization result of the microgrid has met the convergence condition, the microgrid exits the calculation, but continues to send the converged coupling variables to other microgrids until the optimization results of all microgrids meet the convergence condition.

[0059] For example, under asynchronous communication conditions, determining the update rules for coupling variables and Lagrange multipliers between microgrids is crucial for microgrids. i microgrid i and j The Lagrange multiplier update rule between them is expressed as:

[0060]

[0061] in, and They represent microgrids i and j The actual values ​​(i.e., without communication delay) and predicted values ​​(i.e. with communication delay) of the coupling variables, convergence conditions include the original residuals and the dual residuals:

[0062]

[0063] in, and The convergence coefficient is used to determine the convergence condition. If the optimization result of the microgrid does not meet the convergence condition, the next iteration continues. If the optimization result of the microgrid meets the convergence condition, the microgrid exits the calculation, but continues to send the converged coupling variables to other microgrids until the optimization results of all microgrids meet the convergence condition, thus obtaining the collaborative optimization result of the target microgrid group.

[0064] To enable those skilled in the art to better understand the microgrid group collaborative optimization method of the present invention, the following explanation will be provided in conjunction with specific embodiments.

[0065] Figure 2 This is a schematic diagram of a microgrid group topology according to a specific embodiment of the present invention. Figure 3 This is a schematic diagram of a solution method provided according to a specific embodiment of the present invention, combined with... Figure 2 and Figure 3 The microgrid group is composed of multiple microgrids equipped with wind power, photovoltaics, energy storage, generators, and electrical loads connected end to end. In the solution process, the generation, consumption, energy storage costs and power limitations of all equipment are first calculated to construct an overall optimal power dispatch model. The interactive power between adjacent microgrids is extracted as interactive coupling variables. In the iterative calculation of the asynchronous iterative alternating direction multiplier method, when a single microgrid is iterated, it first tries to receive the coupling data of the neighboring network. If the communication is normal and the data is obtained, it directly performs its own optimization solution. When the communication is delayed or packet loss prevents the reception of neighbor information, the boundary coupling variables are predicted and supplemented by extrapolation based on the data of the two previous iterations. By updating the variables and multipliers and transmitting its own coupling information outward, the calculation is terminated after the convergence criterion is met, and the collaborative optimization result of the target microgrid group is obtained.

[0066] To verify the effectiveness of the microgrid group collaborative optimization method of the present invention, the following detailed explanation is based on experimental results.

[0067] Figure 4 This is a schematic diagram of the collaborative optimization results provided according to a specific embodiment of the present invention, wherein, Figure 4 (a)- Figure 4 (e) are schematic diagrams of the collaborative optimization results of microgrids 1 to 5, as shown below. Figure 4 As shown, in the asynchronous iterative collaborative optimization scheduling over a 24-hour period, the collaborative operation of the microgrid group can fully utilize the operating characteristics of each microgrid to achieve balanced control of the target microgrid group. Furthermore, Figure 5 This is a schematic diagram of an asynchronous iteration method according to a specific embodiment of the present invention. Figure 6 This is a schematic diagram of a synchronous iteration method according to a specific embodiment of the present invention, wherein, Figure 5 (a)- Figure 5(e) are the asynchronous iterative method curves for microgrid 1 to microgrid 5, respectively. Table 1 is a comparison table of calculation time provided according to a specific embodiment of the present invention.

[0068] Table 1

[0069] Combination Figure 5 , Figure 6 As shown in Table 1, when using the synchronous iteration method, if the neighbor node information is lost, the system waits in place. Although the number of iterations required to reach convergence is reduced, the waiting time slows down the overall calculation process and greatly increases the calculation time. However, the asynchronous iteration method can significantly reduce the number of iterations and the calculation time, which proves the effectiveness of the microgrid group collaborative optimization method in this embodiment of the invention.

[0070] According to the microgrid group collaborative optimization method provided in the embodiments of the present invention, a microgrid group collaborative optimization model is constructed, interactive coupling variables between microgrids are extracted, and the lost boundary coupling variables are estimated by extrapolation when communication is abnormal. The asynchronous alternating direction multiplier method is combined with the asynchronous iteration of effective coupling variables to solve the microgrid group collaborative optimization result. This solves the problem of poor collaborative optimization calculation accuracy and high computational resource consumption caused by the delay or loss of boundary variables during communication between microgrid groups due to computation and communication delays in related technologies. It improves the collaborative optimization accuracy and saves computational resources.

[0071] Next, the microgrid group collaborative optimization device provided according to an embodiment of the present invention is described with reference to the accompanying drawings.

[0072] Figure 7 This is a block diagram of a microgrid group collaborative optimization device provided according to an embodiment of the present invention.

[0073] like Figure 7 As shown, the microgrid group collaborative optimization device 10 includes: The construction module 100 is used to construct a microgrid group collaborative optimization model for the target microgrid group; the prediction module 200 is used to identify the inter-group interaction coupling variables among the microgrids in the microgrid group collaborative optimization model and predict the lost boundary coupling variables among the microgrids; the calculation module 300 is used to perform asynchronous iterative solution of the microgrid group collaborative optimization model based on a preset asynchronous alternating direction multiplier method, according to the inter-group interaction coupling variables and / or the lost boundary coupling variables, to obtain the collaborative optimization result of the target microgrid group.

[0074] Furthermore, in some embodiments, the construction module 100 is specifically used to: obtain the resource operation constraints and power constraints of the target microgrid group; construct the objective function of the target microgrid group; and construct a collaborative optimization model of the microgrid group based on the resource operation constraints and power constraints of the microgrid group, with the goal of minimizing the objective function.

[0075] Furthermore, in some embodiments, the objective function is: ; Where S represents the set of scenarios where there is uncertainty in the output of new energy sources. This represents the probability of occurrence in each scenario. The start-up and shutdown costs of micro gas turbines, For the operating cost of micro gas turbines, For the operating costs of energy storage systems, For the cost of power exchange between microgrids, The penalty cost for curtailing wind and solar energy.

[0076] Furthermore, in some embodiments, the prediction module 200 is specifically used to: determine whether each microgrid has not received the boundary coupling variables of the other microgrids in the current iteration period; if each microgrid has not received the boundary coupling variables lost by the other microgrids in the current iteration period, then obtain the historical boundary coupling variables of the corresponding microgrid before the current iteration period, and predict the boundary coupling variables lost between the microgrids in the current iteration period based on the historical boundary coupling variables.

[0077] Further, in some embodiments, the calculation module 300 is specifically used to: perform asynchronous independent iteration on each microgrid in the target microgrid group collaborative optimization model based on inter-group interactive coupling variables and / or lost boundary coupling variables, according to a preset asynchronous alternating direction multiplier method, and determine whether the optimization result of each microgrid in the current iteration calculation cycle meets the preset convergence condition; if the microgrid optimization result in the current iteration calculation cycle does not meet the preset convergence condition, then perform iterative calculation on the microgrid based on the preset asynchronous alternating direction multiplier method; if the microgrid optimization result in the current iteration calculation cycle meets the preset convergence condition, then control the microgrid to exit the iteration, and simultaneously send the converged microgrid coupling variable data to the remaining microgrids, and jump to the remaining microgrids for iterative calculation, until the optimization results of all microgrids in the microgrid group meet the preset convergence condition, and obtain the collaborative optimization result of the target microgrid group.

[0078] Furthermore, in some embodiments, the resource operation constraints of the target microgrid cluster include distributed renewable energy output constraints, energy storage operation constraints, and gas turbine operation constraints; the microgrid cluster power constraints of the target microgrid cluster include power exchange constraints between interconnected microgrid clusters and power balance constraints within the microgrid.

[0079] It should be noted that the foregoing explanation of the microgrid group collaborative optimization method embodiment also applies to the microgrid group collaborative optimization device of this embodiment, and will not be repeated here.

[0080] According to the microgrid group collaborative optimization device provided in the embodiments of the present invention, a microgrid group collaborative optimization model is constructed, interactive coupling variables between microgrids are extracted, and the lost boundary coupling variables are estimated by extrapolation when communication is abnormal. The asynchronous alternating direction multiplier method is combined with the asynchronous iteration of effective coupling variables to solve the microgrid group collaborative optimization result. This solves the problem of poor collaborative optimization calculation accuracy and high computational resource consumption caused by the delay or loss of boundary variables during communication between microgrid groups due to computation and communication delays in related technologies. It improves the collaborative optimization accuracy and saves computational resources.

[0081] Figure 8 This is a schematic diagram of an electronic device provided according to an embodiment of the present invention. The electronic device may include: The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0082] When the processor 802 executes the program, it implements the microgrid group collaborative optimization method provided in the above embodiments.

[0083] Furthermore, electronic devices also include: Communication interface 803 is used for communication between memory 801 and processor 802.

[0084] The memory 801 is used to store computer programs that can run on the processor 802.

[0085] The memory 801 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0086] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0087] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0088] The processor 802 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention.

[0089] In addition, embodiments of the present invention also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described microgrid group collaborative optimization method.

[0090] In addition, embodiments of the present invention also provide a computer program product, including a computer program, which is executed to implement the microgrid group collaborative optimization method described above.

[0091] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0092] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0093] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for collaborative optimization of microgrid groups, characterized in that, Includes the following steps: Construct a microgrid group collaborative optimization model for the target microgrid group; Identify the inter-group interaction coupling variables among the microgrids in the microgrid group collaborative optimization model, and predict the lost boundary coupling variables among the microgrids; Based on the preset asynchronous alternating direction multiplier method, the microgrid group collaborative optimization model is solved asynchronously and iteratively according to the inter-group interactive coupling variables and / or the lost boundary coupling variables to obtain the collaborative optimization result of the target microgrid group.

2. The method according to claim 1, characterized in that, The microgrid group collaborative optimization model for constructing the target microgrid group includes: Obtain the resource operation constraints and power constraints of the target microgrid group; An objective function for the target microgrid group is constructed, and based on the resource operation constraints and the power constraints of the microgrid group, a collaborative optimization model for the microgrid group is constructed with the objective function as the goal.

3. The method according to claim 2, characterized in that, The objective function is: ； Where S represents the set of scenarios with uncertainties in new energy output. This represents the probability of occurrence in each scenario. The start-up and shutdown costs of micro gas turbines, For the operating cost of micro gas turbines, For the operating costs of energy storage systems, For the cost of power exchange between microgrids, The penalty cost for curtailing wind and solar energy.

4. The method according to claim 1, characterized in that, The predicted boundary coupling variables lost between the microgrids include: Determine whether each microgrid has not received boundary coupling variables from other microgrids during the current iteration period; If each microgrid does not receive the boundary coupling variables lost by the other microgrids in the current iteration period, then the historical boundary coupling variables of the corresponding microgrid before the current iteration period are obtained, and the boundary coupling variables lost between the microgrids in the current iteration period are predicted based on the historical boundary coupling variables.

5. The method according to claim 1, characterized in that, The method based on a preset asynchronous alternating direction multiplier performs asynchronous iterative solution on the microgrid group collaborative optimization model according to the inter-group interactive coupling variables and / or the lost boundary coupling variables, to obtain the collaborative optimization result of the target microgrid group, including: Based on the inter-group interaction coupling variables and / or the boundary coupling variables, each microgrid in the target microgrid group collaborative optimization model is asynchronously and independently iterated according to the preset asynchronous alternating direction multiplier method, and it is determined whether the optimization result of each microgrid in the current iteration calculation cycle meets the preset convergence condition. If the microgrid optimization result of the current iteration calculation cycle does not meet the preset convergence condition, the microgrid is iteratively calculated based on the preset asynchronous alternating direction multiplier method. If the microgrid optimization result of the current iteration calculation cycle meets the preset convergence condition, then the microgrid is controlled to exit the iteration, and the converged microgrid coupling variable data is sent to the other microgrids. Then, the iteration calculation is performed on the other microgrids until the optimization results of all microgrids in the microgrid group meet the preset convergence condition, and the collaborative optimization result of the target microgrid group is obtained.

6. The method according to claim 2, characterized in that, The resource operation constraints of the target microgrid group include distributed renewable energy output constraints, energy storage operation constraints, and gas turbine operation constraints. The power constraints of the target microgrid group include power exchange constraints between interconnected microgrid groups and power balance constraints within the microgrid.

7. A microgrid group collaborative optimization device, characterized in that, include: The module is used to build a microgrid group collaborative optimization model for the target microgrid group; The prediction module is used to identify the inter-group interaction coupling variables among the microgrids in the microgrid group collaborative optimization model, and to predict the lost boundary coupling variables among the microgrids. The calculation module is used to perform asynchronous iterative solution of the microgrid group collaborative optimization model based on a preset asynchronous alternating direction multiplier method, according to the inter-group interactive coupling variables and / or the lost boundary coupling variables, to obtain the collaborative optimization result of the target microgrid group.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, the processor executing the program to implement the microgrid group collaborative optimization method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the microgrid group collaborative optimization method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the microgrid group collaborative optimization method as described in any one of claims 1-6.

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