A method, system, device, and medium for processing energy optimization management models.

Abstract

This application proposes a method, system, device, and medium for processing energy optimization management models, relating to the field of power system automation technology. The method includes: obtaining an initial energy optimization management model of a microgrid cluster; determining dual variables based on decision variables, with the dual variables used for transmission between sub-microgrids; performing distributed processing on the initial energy optimization management model based on the dual variables to obtain a distributed energy optimization management model; obtaining consistency constraints during the iterative calculation process; obtaining data communication volume between sub-microgrids; applying communication constraints to the dual variables based on the data communication volume to obtain communication conditions for the dual variables; and iteratively calculating the distributed energy optimization management model based on the consistency constraints and the communication conditions for the dual variables to obtain a target optimization management strategy. This application can solve the energy optimization management model of a microgrid cluster and reduce the external communication of internal characteristic information of sub-microgrids.

Description

Technical Field

[0001] This application relates to the field of power system automation technology, and in particular to a processing method, system, device and medium for an energy optimization management model. Background Technology

[0002] Microgrid cluster technology is an emerging technology that improves the overall reliability and energy efficiency of a system by connecting multiple geographically adjacent microgrids. Achieving reliable and economical operation of a microgrid cluster depends on the rational and effective formulation of optimized operating strategies. To calculate these optimal strategies, power operators typically collect data on all power equipment and load users within each sub-microgrid and use centralized optimization to calculate the best operating strategy. However, this usually comes at the cost of excessively collecting local power supplier generation characteristic data and electricity user consumption habits from each sub-microgrid. Therefore, providing a method for processing energy optimization management models that can solve the energy optimization management model of a microgrid cluster and reduce the external communication of internal sub-microgrid characteristic information has become a pressing technical problem. Summary of the Invention

[0003] The main objective of this application is to propose a method, system, device, and medium for processing energy optimization management models, which can solve the energy optimization management model of microgrid clusters and reduce the external communication of internal characteristic information of sub-microgrids.

[0004] To achieve the above objectives, a first aspect of this application proposes a method for processing an energy optimization management model, applied to a microgrid cluster, wherein the microgrid cluster includes multiple sub-microgrids, and the method includes:

[0005] Obtain the initial energy optimization management model of the microgrid cluster, wherein the initial energy optimization management model includes the decision variables of the sub-microgrids;

[0006] The dual variables are determined based on the decision variables; the dual variables are used for transmission between the sub-microgrids;

[0007] The initial energy optimization management model is processed in a distributed manner based on the dual variables to obtain a distributed energy optimization management model.

[0008] Obtain the consistency constraints during the iterative calculation process;

[0009] Acquire the data communication volume between the sub-microgrids;

[0010] Based on the data communication volume, communication constraints are applied to the dual variables to obtain the communication conditions of the dual variables.

[0011] The distributed energy optimization management model is iteratively calculated based on the consistency constraints and the dual variable communication conditions to obtain the target optimization management strategy.

[0012] In some embodiments, the step of performing distributed processing on the initial energy optimization management model based on the dual variables to obtain a distributed energy optimization management model includes:

[0013] Auxiliary variables are obtained based on the dual variables;

[0014] The initial energy optimization management model is processed in a distributed manner based on the dual variables and the auxiliary variables to obtain a distributed energy optimization management model.

[0015] In some embodiments, obtaining the consistency constraints during the iterative computation process includes:

[0016] Selected sub-microgrids are selected from the plurality of said sub-microgrids;

[0017] Consistency constraints are constructed based on the decision variables, the dual variables, and the auxiliary variables. In each iteration of the consistency constraints, the dual variables of the selected sub-microgrid and the adjacent sub-microgrids are the same.

[0018] In some embodiments, the step of applying communication constraints to the dual variables based on the data communication volume between the sub-microgrids to obtain the communication conditions of the dual variables includes:

[0019] For the selected sub-microgrid, obtain the data communication volume between the selected sub-microgrid and the adjacent sub-microgrids;

[0020] The value of the dual variable is determined based on the data communication volume.

[0021] In some embodiments, determining the value of the dual variable based on the data communication volume includes:

[0022] The data communication volume is compared with a preset data communication volume threshold;

[0023] If the data communication volume is greater than the data communication volume threshold, then the value of the dual variable of the selected sub-microgrid is retained as the current iteration calculation result of the selected sub-microgrid;

[0024] If the data communication volume is less than or equal to the data communication volume threshold, then the value of the dual variable of the selected sub-microgrid is retained as the result of the previous iteration calculation of the selected sub-microgrid.

[0025] In some embodiments, acquiring the data communication volume between the sub-microgrids includes:

[0026] Obtain the value of the first dual variable obtained in the current iteration and the value of the second dual variable obtained in the previous iteration;

[0027] The data communication volume is obtained by calculating the difference between the values ​​of the first dual variable and the second dual variable.

[0028] In some embodiments, the step of iteratively calculating the distributed energy optimization management model based on the consistency constraints and the dual variable communication conditions to obtain the target optimization management strategy includes:

[0029] Obtain the convergence criterion for iterative computation;

[0030] The distributed energy optimization management model is iteratively calculated using the consistency constraint and the dual variable communication condition to obtain the current calculation result.

[0031] The convergence of the current calculation result is judged according to the convergence criterion of the iterative calculation.

[0032] If convergence is achieved, then the target optimization management strategy is output.

[0033] If the calculation does not converge, proceed to the next iteration.

[0034] A second aspect of this application proposes a processing system for an energy optimization management model, applied to a microgrid cluster, the microgrid cluster comprising multiple sub-microgrids, including:

[0035] The model acquisition module is used to acquire the initial energy optimization management model of the microgrid cluster, wherein the initial energy optimization management model includes the decision variables of the sub-microgrid;

[0036] A dual variable determination module is used to determine dual variables based on the decision variables; the dual variables are used for transmission between the sub-microgrids.

[0037] A distributed processing module is used to perform distributed processing on the initial energy optimization management model according to the dual variables to obtain a distributed energy optimization management model.

[0038] The constraint acquisition module is used to acquire consistency constraints during the iterative calculation process;

[0039] The data communication volume acquisition module is used to acquire the data communication volume between the sub-microgrids;

[0040] A communication constraint module is used to impose communication constraints on the dual variables based on the data communication volume to obtain the communication conditions of the dual variables.

[0041] The target calculation module is used to iteratively calculate the distributed energy optimization management model based on the consistency constraints and the dual variable communication conditions to obtain the target optimization management strategy.

[0042] A third aspect of this application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor is used to perform a processing method for an energy optimization management model as described in any one of the embodiments of the first aspect of this application.

[0043] A fourth aspect of this application provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a computer, the computer is configured to perform a processing method for an energy optimization management model as described in any one of the embodiments of the first aspect of this application.

[0044] This application proposes a method, system, device, and medium for processing an energy optimization management model. It obtains an initial energy optimization management model of a microgrid cluster, determines dual variables based on decision variables (used for transmission between sub-microgrids), performs distributed processing on the initial energy optimization management model based on the dual variables, and obtains a distributed energy optimization management model. Iterative calculations are then performed on the distributed energy optimization management model based on consistency constraints and dual variable communication conditions to obtain the target optimization management strategy. This application can solve the energy optimization management model of a microgrid cluster and reduces the need for external communication of internal characteristic information between sub-microgrids. Attached Figure Description

[0045] Figure 1 This is a schematic diagram illustrating the principle of a processing method for an energy optimization management model provided in one embodiment of this application;

[0046] Figure 2 This is a flowchart illustrating the steps of a processing method for an energy optimization management model provided in one embodiment of this application;

[0047] Figure 3 yes Figure 2 Flowchart of the sub-steps in step S103;

[0048] Figure 4 yes Figure 2 Flowchart of the sub-steps in step S104;

[0049] Figure 5 This is a flowchart of the steps of a processing method for an energy optimization management model provided in another embodiment of this application;

[0050] Figure 6 yes Figure 2Flowchart of the sub-steps in step S107;

[0051] Figure 7 A block diagram of the module structure of a processing system for an energy optimization management model provided in one embodiment of this application;

[0052] Figure 8 This is a schematic diagram of the hardware structure of a computer device provided in one embodiment of this application. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0056] First, let's analyze some of the terms used in this application:

[0057] Artificial intelligence (AI) is a new branch of computer science that studies, develops, and applies theories, methods, technologies, and systems to simulate, extend, and expand human intelligence. It aims to understand the essence of intelligence and produce intelligent machines that can react in a way similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information processes of human consciousness and thought. Furthermore, AI utilizes digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceiving the environment, acquiring knowledge, and using that knowledge to achieve optimal results.

[0058] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0059] The energy optimization management model processing method provided in this application embodiment can be applied to artificial intelligence. Basic artificial intelligence technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operating / interactive systems, mechatronics, and other technologies. Artificial intelligence software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0060] Microgrid clustering technology is an emerging technology that improves the overall reliability and energy efficiency of a system by connecting multiple geographically adjacent microgrids. Achieving reliable and economical operation of microgrid clusters depends on the rational and effective formulation of optimized operation strategies. To calculate these optimal strategies, power operators typically collect data on all power equipment and load users within each sub-microgrid and use centralized optimization to calculate the best operating strategy. However, this usually comes at the cost of excessively collecting local power supplier generation characteristic data and electricity user consumption habit information from each sub-microgrid. Therefore, designing an optimization algorithm that maximizes the protection of individual information privacy on both the generation and consumption sides is an urgent need in the field of microgrid cluster economic operation optimization. In response, this application provides a processing method for an energy optimization management model, which can obtain the optimal microgrid cluster economic optimization operation strategy (target optimization management strategy) in a fully distributed computing manner while significantly reducing the external communication of internal characteristic information of sub-microgrids. Furthermore, this application also provides a processing system, computer equipment, and computer storage medium for executing the energy optimization management model processing method.

[0061] The energy optimization management model processing method provided in this application can be applied to a server-side application, or it can be software running on the server-side application. In some embodiments, the server-side application can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the above method, but is not limited to the above forms.

[0062] The embodiments of this application can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer computer devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0063] The present application provides a processing method for an energy optimization management model, which is specifically illustrated through the following embodiments.

[0064] Reference Figure 1 A microgrid cluster comprises multiple sub-microgrids, each containing decision variables. Dual variables are determined based on these decision variables and are used for communication between sub-microgrids. Decision variables represent the current calculation result of the sub-microgrid. If the dual variable communication condition is met, the dual variable represents the current calculation result of the sub-microgrid; otherwise, it represents the previous calculation result. In summary, in this embodiment, each sub-microgrid obtains its calculation result based on its internal characteristic information (device data, user data) and transmits this result to other sub-microgrids, eliminating the need to output its own device and user data externally, thus reducing the external communication of internal characteristic information within the sub-microgrid.

[0065] Reference Figure 2 According to an embodiment of this application, a processing method for an energy optimization management model includes, but is not limited to, steps S101 to S107.

[0066] Step S101: Obtain the initial energy optimization management model of the microgrid cluster, wherein the initial energy optimization management model includes the decision variables of the sub-microgrids;

[0067] Step S102: Determine the dual variables based on the decision variables; the dual variables are used for transfer between sub-microgrids.

[0068] Step S103: Perform distributed processing on the initial energy optimization management model based on the dual variables to obtain the distributed energy optimization management model;

[0069] Step S104: Obtain the consistency constraints during the iterative calculation process;

[0070] Step S105: Obtain the data communication volume between sub-microgrids;

[0071] Step S106: Apply communication constraints to the dual variables based on the data communication volume to obtain the communication conditions of the dual variables;

[0072] Step S107: Iteratively calculate the distributed energy optimization management model based on the consistency constraint and the dual variable communication condition to obtain the target optimization management strategy.

[0073] Steps S101 to S107 of this application embodiment enable the global optimization solution of the initial energy optimization management model of the microgrid cluster through limited communication between adjacent sub-microgrids, even without a power dispatch center. Since this method requires only a small amount of information exchange between sub-microgrids during the model solution process (it does not require a power dispatch center to collect all power equipment data and load user information for unified calculation), it maximizes the protection of the privacy of internal characteristic information of each sub-microgrid. This application embodiment can solve the energy optimization management model of the microgrid and reduces the external communication of internal characteristic information of sub-microgrids.

[0074] In step S101 of some embodiments, the initial energy optimization management model of the microgrid cluster includes: a cost model for the economic operation of the microgrid cluster and a model of operating constraints of the microgrid cluster.

[0075] In one example, the economic operating cost model of a microgrid cluster can include the generator cost model of each sub-microgrid and the total generator cost model of the microgrid cluster. The generator cost model of each sub-microgrid represents the generator cost calculated by each sub-microgrid based on its own power generation, as shown in formula (1). The total generator cost model represents the total generator cost obtained within the microgrid cluster based on the generator costs of all sub-microgrids, as shown in formula (2). It is understood that each sub-microgrid does not need to transmit its own power generation information externally; it only needs to transmit the generator cost calculated from its power generation information to the total generator cost model of the microgrid cluster, thus reducing information leakage.

[0076]

[0077]

[0078] Where i is the sub-microgrid number, and there are N sub-microgrids in the microgrid cluster; a i b i c ip represents the generation cost coefficient for each sub-microgrid; i Let p be the decision variable. i If p ≥ 0, it means that the sub-microgrid i outputs power to the outside. i If f < 0, it indicates that the sub-microgrid i is absorbing power from external sources; i Let C be the generation cost function of sub-microgrid i; and let C be the total generation cost function of the microgrid group.

[0079] The economic operation constraint model of the microgrid group includes the upper and lower limit constraint model of power interaction between sub-microgrids and the power balance constraint model, as shown in formulas (3) and (4), respectively.

[0080]

[0081]

[0082] in, p i and p i The lower and upper limits, and when p i When indicating generator output, When p i When indicating electrical load, When p i When referring to an energy storage system, p i ≤0,

[0083] Formulas (1) to (4) constitute the initial energy optimization management model for the microgrid cluster referred to in the embodiments of this application. In related technologies, the characteristic information parameter a of each sub-microgrid is collected uniformly. i b i c i , p i and The optimal decision strategy p is obtained by using a centralized optimization approach. i This minimizes the overall operating cost C of the microgrid cluster. However, this centralized information collection and unified optimization approach typically comes at the cost of excessively collecting characteristic information from each sub-microgrid. Therefore, this application proposes a distributed solution for the initial energy optimization management model to reduce the external communication of characteristic information within sub-microgrids.

[0084] In step S102 of some embodiments, a dual variable is determined based on the decision variable; the dual variable is used for transmission between sub-microgrids. Specifically, the decision variable represents the current calculation result of the sub-microgrid. If the dual variable communication condition is met, the dual variable represents the current calculation result of the sub-microgrid; if the dual variable communication condition is not met, the dual variable represents the previous calculation result. Therefore, in a microgrid cluster, the dual variable is transmitted among the sub-microgrids, and the sub-microgrids do not need to transmit internal characteristic information (device data, user data, etc.) externally. Each sub-microgrid can only receive the current calculation result or the previous calculation result transmitted by other sub-microgrids, so that each sub-microgrid can perform iterative calculations to derive the target optimization management strategy, while reducing the external communication of internal characteristic information of the sub-microgrids.

[0085] In step S103 of some embodiments, the initial energy optimization management model (formulas (1)-(4)) is processed in a distributed manner according to the dual variables to obtain a distributed energy optimization management model, as shown in formulas (5) and (6).

[0086]

[0087]

[0088] Where k is the number of iterations; ρ is the step size factor for the iterative calculation; N i Let d be the set of all neighboring sub-microgrids of sub-microgrid i; i For set N i The metric; j is the number of the neighboring microgrid of sub-microgrid i; Decision variables The dual variable; P i (·) is a mapping operator, indicating that the result calculated from formula (5) will be mapped. The values ​​are mapped to the feasible regions corresponding to constraints (3) and (4); ||·|| 2 This is a 2-norm operator.

[0089] Observing formula (5), it can be seen that in order to calculate the decision strategy p of sub-microgrid i, i The distributed iterative computation method only needs to utilize the characteristic information of the sub-microgrid i itself and its neighboring sub-microgrid j, thereby greatly reducing the amount of data communication.

[0090] In some embodiments, see Figure 3 Step S103 specifically includes, but is not limited to, steps S201 to S202:

[0091] Step S201: Obtain auxiliary variables based on dual variables;

[0092] Step S202: The initial energy optimization management model is processed in a distributed manner based on the dual variables and auxiliary variables to obtain the distributed energy optimization management model.

[0093] Specifically, the initial energy optimization management model (formulas (1)-(4)) is processed in a distributed manner according to the dual variables and auxiliary variables to obtain the distributed energy optimization management model, as shown in formulas (6) and (7).

[0094]

[0095] in, dual variables Auxiliary variables.

[0096] Specifically, since the embodiments of this application solve the model based on the propagation of dual variables, although this reduces the external communication of internal microgrid characteristic information, it also introduces instability in the solution. Therefore, the embodiments of this application improve the stability of the model during the iterative calculation process by introducing auxiliary variables of dual variables.

[0097] In step S104 of some embodiments, the existence of the consistency constraint ensures that the dual variables of sub-microgrid i and its neighboring sub-microgrid j are kept consistent during each iteration calculation, which can ensure that the final optimization strategy of the calculated microgrid group achieves global convergence.

[0098] In some embodiments, see Figure 4 Step S104 specifically includes, but is not limited to, steps S301 to S302:

[0099] Step S301: Select a sub-microgrid from multiple sub-microgrids;

[0100] Step S302: Construct consistency constraints based on decision variables, dual variables, and auxiliary variables. In each iteration of the calculation, the consistency constraints select the dual variables of the sub-microgrid and the adjacent sub-microgrid to be the same.

[0101] Specifically, the consistency constraints are shown in equations (8) and (9).

[0102]

[0103]

[0104] Specifically, the existence of consistency constraints (8) and (9) forces the execution of the dual variables of sub-microgrid i during each iteration calculation. The dual variables of its neighboring sub-microgrid j Maintaining consistency ensures that the calculated final optimization strategy for the microgrid cluster achieves global convergence.

[0105] In step S105 of some embodiments, the data communication volume is used to characterize the amount of information exchanged between adjacent sub-microgrids in consecutive iterations. A larger data communication volume indicates a greater difference in information exchange between adjacent sub-microgrids in consecutive iterations. Conversely, a smaller data communication volume indicates a smaller difference in information exchange between adjacent sub-microgrids in consecutive iterations.

[0106] In one embodiment, step S105 specifically includes:

[0107] Obtain the value of the first dual variable obtained in the current iteration and the value of the second dual variable obtained in the previous iteration;

[0108] The data communication volume is obtained by calculating the difference between the values ​​of the first and second dual variables.

[0109] Specifically,

[0110] In step S106 of some embodiments, communication constraints are applied to the dual variables based on the data communication volume to obtain the communication conditions of the dual variables. Specifically, based on the magnitude of the data communication volume, it is determined whether the dual variables between adjacent sub-microgrids are transmitted, that is, the values ​​of the dual variables are determined. For a selected sub-microgrid, the data communication volume between the selected sub-microgrid and its adjacent sub-microgrids is obtained; the values ​​of the dual variables are determined based on the data communication volume.

[0111] In some embodiments, see Figure 5 The determination of the value of the dual variable based on the data communication volume specifically includes, but is not limited to, steps S401 to S403:

[0112] Step S401: Compare the data communication volume with the preset data communication volume threshold;

[0113] Step S402: If the data communication volume is greater than the data communication volume threshold, then the value of the dual variable of the selected sub-microgrid is retained as the current iteration calculation result of the selected sub-microgrid.

[0114] Step S403: If the data communication volume is less than or equal to the data communication volume threshold, then the value of the dual variable of the selected sub-microgrid is retained as the result of the previous iteration calculation of the selected sub-microgrid.

[0115] Specifically, as shown in formula (10).

[0116]

[0117] Where, δ kThis is the data communication threshold required in the k-th iteration calculation.

[0118] Based on formula (10), information exchange between adjacent sub-microgrids occurs only when there are significant differences between successive iterations. Otherwise, the dual variables... The previous iteration calculation results for each sub-microgrid i will be retained. This information exchange, known as "conditional communication," avoids unnecessary communication, thereby further protecting the characteristic information of each sub-microgrid.

[0119] In some embodiments, see Figure 6 Step S107 specifically includes, but is not limited to, steps S501 to S505:

[0120] Step S501: Obtain the convergence criterion for iterative calculation;

[0121] Step S502: Iteratively calculate the distributed energy optimization management model using consistency constraints and dual variable communication conditions to obtain the current calculation result;

[0122] Step S503: Judge the convergence of the current calculation result according to the convergence criterion of iterative calculation;

[0123] Step S504: If convergence is achieved, output the target optimization management strategy;

[0124] Step S505: If the convergence is not achieved, proceed to the next iteration.

[0125] Specifically, the convergence of the calculation results is judged by the convergence criterion calculated iteratively, as shown in formula (11).

[0126]

[0127] Where ε is the convergence threshold for iterative calculation.

[0128] After k iterations of calculation, the dual variables of each sub-microgrid If the criteria in formula (11) are met, the iterative calculation is terminated, and the corresponding optimal decision variables for the microgrid group are output. Otherwise, the calculation proceeds to the next iteration.

[0129] In steps S501 to S505, the distributed energy optimization management model (i.e., formulas (6) to (10)) containing consistency constraints and dual variable communication conditions is iteratively calculated and solved. Figure 1 As shown, it can be observed that in the k-th iteration calculation, the sub-microgrid i only considers the decision variables. dual variables (Constrained by communication conditions) The information is transmitted to its neighboring sub-microgrid j, thereby protecting the privacy of the internal characteristic parameters and decision variables of sub-microgrid i. Therefore, the embodiments of this application can have a good feature information privacy protection function and solve the model in a fully distributed iterative computation manner.

[0130] Please see Figure 7 This application also provides a processing system for an energy optimization management model, applied to a microgrid cluster comprising multiple sub-microgrids, capable of implementing the aforementioned energy optimization management model processing method. Figure 7 The block diagram of the processing system for the energy optimization management model provided in this application embodiment is shown. The system includes: a model acquisition module 601, a dual variable determination module 602, a distributed processing module 603, a constraint condition acquisition module 604, a data communication volume acquisition module 605, a communication constraint module 606, and a target calculation module 607. The system comprises the following modules: Model Acquisition Module 601, which acquires the initial energy optimization management model of the microgrid cluster, including decision variables of the sub-microgrids; Dual Variable Determination Module 602, which determines dual variables based on the decision variables; Dual variables are used for transmission between sub-microgrids; Distributed Processing Module 603, which performs distributed processing on the initial energy optimization management model based on the dual variables to obtain a distributed energy optimization management model; Constraint Acquisition Module 604, which acquires the consistency constraints during the iterative calculation process; Data Communication Volume Acquisition Module 605, which acquires the data communication volume between sub-microgrids; Communication Constraint Module 606, which applies communication constraints to the dual variables based on the data communication volume to obtain the communication conditions of the dual variables; and Objective Calculation Module 607, which iteratively calculates the distributed energy optimization management model based on the consistency constraints and the communication conditions of the dual variables to obtain the objective optimization management strategy.

[0131] This application provides a processing system for an energy optimization management model. This system can achieve global optimization of the initial energy optimization management model of a microgrid cluster without a power dispatch center, relying solely on limited communication between adjacent sub-microgrids. Because this method requires only minimal information exchange between sub-microgrids during the model solution process (eliminating the need for a power dispatch center to collect all power equipment data and load user information for unified calculation), it maximizes the protection of the privacy of internal characteristic information within each sub-microgrid. This application embodiment can solve the energy optimization management model of a microgrid cluster and reduces the external communication of internal characteristic information of sub-microgrids.

[0132] The processing system of the energy optimization management model in this application embodiment is used to execute the processing method of the energy optimization management model in the above embodiment. The specific processing process is the same as the processing method of the energy optimization management model in the above embodiment, and will not be described in detail here.

[0133] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, which, when executed by the processor, is used by the processor to perform a processing method of an energy optimization management model as described in any of the embodiments of this application.

[0134] The following is combined Figure 8 The hardware structure of the computer device is described in detail. The computer device includes: a processor 701, a memory 702, an input / output interface 703, a communication interface 704, and a bus 705.

[0135] The processor 701 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0136] The memory 702 can be implemented in the form of ROM (Read Only Memory), static storage device, dynamic storage device, or RAM (Random Access Memory). The memory 702 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 702 and is called and executed by the processor 701 to execute a processing method for an energy optimization management model according to an embodiment of this application.

[0137] The input / output interface 703 is used to implement information input and output;

[0138] The communication interface 704 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.); and the bus 705 is used to transmit information between the various components of the device (such as processor 701, memory 702, input / output interface 703 and communication interface 704).

[0139] The processor 701, memory 702, input / output interface 703, and communication interface 704 are connected to each other within the device via bus 705.

[0140] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a computer, enables the computer to perform a processing method for an energy optimization management model as described in any of the embodiments of this application.

[0141] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0142] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0143] It will be understood by those skilled in the art that Figures 2 to 6 The technical solutions shown do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0144] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; 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.

[0145] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0146] It should be understood that the data used in this way can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than those illustrated or described herein. Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus.

[0147] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0149] The units described as separate components may or may not be physically separate. The components shown as units 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] Furthermore, the functional units in the various embodiments of this application 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 as a software functional unit.

[0151] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0152] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A processing method for an energy optimization management model, characterized in that, Applied to a microgrid cluster, the microgrid cluster comprising multiple sub-microgrids, the method includes: Obtain the initial energy optimization management model of the microgrid cluster, wherein the initial energy optimization management model includes the decision variables of the sub-microgrids, and the initial energy optimization management model includes: , , , ,in, i This is the numbering system for sub-microgrids; there are a total of [number] sub-microgrids within the microgrid cluster. N Individual microgrids; , , This represents the generation cost coefficient for each sub-microgrid; If (the variable is used for decision purposes) This indicates a sub-microgrid. i Outputting electricity to the outside, if This indicates a sub-microgrid. i Absorbing electricity from the outside; For sub-microgrids i The power generation cost function; Let be the total generation cost function of the microgrid group. and They are respectively The lower and upper limits, and when When indicating generator output, ;when When indicating electrical load, ;when When referring to an energy storage system, ; The dual variables are determined based on the decision variables; the dual variables are used for transmission between the sub-microgrids; The initial energy optimization management model is processed in a distributed manner based on the dual variables to obtain a distributed energy optimization management model. Obtain the consistency constraints during the iterative calculation process; Acquire the data communication volume between the sub-microgrids; Based on the data communication volume, communication constraints are applied to the dual variables to obtain the communication conditions of the dual variables. The distributed energy optimization management model is iteratively calculated based on the consistency constraints and the dual variable communication conditions to obtain the target optimization management strategy.

2. The method according to claim 1, characterized in that, The step of performing distributed processing on the initial energy optimization management model based on the dual variables to obtain a distributed energy optimization management model includes: Auxiliary variables are obtained based on the dual variables; The initial energy optimization management model is processed in a distributed manner based on the dual variables and the auxiliary variables to obtain a distributed energy optimization management model.

3. The method according to claim 2, characterized in that, The process of obtaining consistency constraints during iterative computation includes: Selected sub-microgrids are selected from the plurality of said sub-microgrids; Consistency constraints are constructed based on the decision variables, the dual variables, and the auxiliary variables. In each iteration of the consistency constraints, the dual variables of the selected sub-microgrid and the adjacent sub-microgrids are the same.

4. The method according to claim 3, characterized in that, The step of applying communication constraints to the dual variables based on the data communication volume to obtain the communication conditions of the dual variables includes: For the selected sub-microgrid, obtain the data communication volume between the selected sub-microgrid and the adjacent sub-microgrids; The value of the dual variable is determined based on the data communication volume.

5. The method according to claim 4, characterized in that, Determining the value of the dual variable based on the data communication volume includes: The data communication volume is compared with a preset data communication volume threshold; If the data communication volume is greater than the data communication volume threshold, then the value of the dual variable of the selected sub-microgrid is retained as the current iteration calculation result of the selected sub-microgrid; If the data communication volume is less than or equal to the data communication volume threshold, then the value of the dual variable of the selected sub-microgrid is retained as the result of the previous iteration calculation of the selected sub-microgrid.

6. The method according to claim 1, characterized in that, The acquisition of data communication volume between the sub-microgrids includes: Obtain the value of the first dual variable obtained in the current iteration and the value of the second dual variable obtained in the previous iteration; The data communication volume is obtained by calculating the difference between the values ​​of the first dual variable and the second dual variable.

7. The method according to claim 1, characterized in that, The step of iteratively calculating the distributed energy optimization management model based on the consistency constraints and the dual variable communication conditions to obtain the target optimization management strategy includes: Obtain the convergence criterion for iterative computation; The distributed energy optimization management model is iteratively calculated using the consistency constraint and the dual variable communication condition to obtain the current calculation result. The convergence of the current calculation result is judged according to the convergence criterion of the iterative calculation. If convergence is achieved, then the target optimization management strategy is output. If the calculation does not converge, proceed to the next iteration.

8. A processing system for an energy optimization management model, characterized in that, Applied to microgrid clusters, the microgrid cluster comprising multiple sub-microgrids, the system includes: The model acquisition module is used to acquire the initial energy optimization management model of the microgrid cluster, wherein the initial energy optimization management model includes the decision variables of the sub-microgrids, and the initial energy optimization management model includes: , , , ,in, i This is the numbering system for sub-microgrids; there are a total of [number] sub-microgrids within the microgrid cluster. N Individual microgrids; , , This represents the generation cost coefficient for each sub-microgrid; If (the variable is used for decision purposes) This indicates a sub-microgrid. i Outputting electricity to the outside, if This indicates a sub-microgrid. i Absorbing electricity from the outside; For sub-microgrids i The power generation cost function; Let be the total generation cost function of the microgrid group. and They are respectively The lower and upper limits, and when When indicating generator output, ;when When indicating electrical load, ;when When referring to an energy storage system, ; A dual variable determination module is used to determine dual variables based on the decision variables; the dual variables are used for transmission between the sub-microgrids. A distributed processing module is used to perform distributed processing on the initial energy optimization management model according to the dual variables to obtain a distributed energy optimization management model. The constraint acquisition module is used to acquire consistency constraints during the iterative calculation process; The data communication volume acquisition module is used to acquire the data communication volume between the sub-microgrids; A communication constraint module is used to impose communication constraints on the dual variables based on the data communication volume to obtain the communication conditions of the dual variables. The target calculation module is used to iteratively calculate the distributed energy optimization management model based on the consistency constraints and the dual variable communication conditions to obtain the target optimization management strategy.

9. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following: The method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a computer, enables the computer to perform: The method as described in any one of claims 1 to 7.

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