Urban building micro-grid cluster distributed low-carbon energy scheduling method and system

Abstract

The invention discloses an urban building micro-grid cluster distributed low-carbon energy scheduling method and system, and belongs to the technical field of urban building micro-grid optimization scheduling. The method comprises the following steps: constructing an upper layer model with the goal of minimizing the cost of an urban building micro-grid, constructing a lower-layer model by taking minimization of the cost of the users in the urban building microgrid as a target, and obtaining a target model through the upper-layer model and the lower-layer model; and updating the low-carbon influence factors in the target model through an ADMM algorithm until an updating result meets a convergence condition to obtain an optimal energy scheduling scheme, and performing energy scheduling through the optimal energy scheduling scheme. According to the invention, through considering city-level building micro-grid scheduling with a high renewable energy source ratio, reduction of carbon emission and efficient consumption of renewable energy sources are effectively promoted, meanwhile, in the whole updating process, only low-carbon influence factors need to be exchanged, leakage of user sensitive information is avoided, and the safety of user information is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban building microgrid optimization scheduling, and in particular to a distributed low-carbon energy scheduling method and system for urban building microgrid clusters. Background Art

[0002] With the rapid development of renewable energy, achieving effective carbon emission reduction and efficient renewable energy consumption is crucial. Existing technologies primarily focus on low-carbon operating modes under carbon trading mechanisms. For example, Patent Publication No. CN118586538A describes a coordinated and optimized scheduling method for virtual power plants based on demand response and carbon trading mechanisms. This method utilizes real-time monitoring and prediction of energy demand for industrial intelligent users; introduces a carbon trading mechanism; applies multi-energy integration and an intelligent energy management system; utilizes computing power analysis to alleviate system power supply pressure and improve overall system stability; combines demand response with carbon trading technologies to enhance optimal scheduling capabilities; and achieves optimal scheduling by dynamically adjusting energy production and consumption. Furthermore, an improved particle swarm optimization algorithm based on extreme learning machines (ELMs) proposes an optimal scheduling model, a stepped carbon trading mechanism, and the introduction of carbon capture devices. However, these methods do not consider the scheduling of urban-scale building microgrids with high renewable energy consumption. Therefore, existing technologies are unable to effectively promote carbon emission reduction and efficient renewable energy consumption. Summary of the Invention

[0003] In response to the technical problem that the existing technology does not take into account the scheduling of building microgrids with a high proportion of renewable energy at the urban level, the present invention provides a distributed low-carbon energy scheduling method and system for urban building microgrid clusters. By constructing a target model including an upper-level model and a lower-level model, a dynamic balance of upper and lower-level targets is achieved, avoiding the separation problem of main decision-making and user passive response in traditional centralized scheduling. The distributed characteristics of the ADMM algorithm are used to update the intermediate calculation results, namely the low-carbon influencing factors, and then the optimal energy scheduling plan is obtained for scheduling. By considering the scheduling of building microgrids with a high proportion of renewable energy at the urban level, the reduction of carbon emissions and the efficient absorption of renewable energy are effectively promoted. At the same time, in the entire update process, only the low-carbon influencing factors need to be exchanged, which avoids the leakage of user sensitive information and improves the security of user information.

[0004] To solve the above technical problems, the present invention provides a distributed low-carbon energy scheduling method for urban building microgrid clusters, comprising the following steps: An upper-layer model is constructed with the goal of minimizing the cost of urban building microgrids, and a lower-layer model is constructed with the goal of minimizing the cost of users in urban building microgrids. The target model is obtained through the upper-layer model and the lower-layer model. The low-carbon influencing factors in the target model are updated through the ADMM algorithm until the updated results meet the convergence conditions to obtain the optimal energy scheduling plan, and energy scheduling is performed based on the optimal energy scheduling plan.

[0005] After adopting the above technical solution, the present invention has the following advantages: By constructing a target model that includes upper and lower models, a dynamic balance between the upper and lower layer targets is achieved, avoiding the separation of subject decision-making and user passive response in traditional centralized scheduling. The distributed characteristics of the ADMM algorithm are used to update the intermediate calculation results, namely the low-carbon influencing factors, and then obtain the optimal energy scheduling plan for scheduling. By considering the scheduling of building microgrids with a high proportion of renewable energy at the urban level, carbon emissions are effectively reduced and renewable energy is efficiently absorbed. At the same time, since urban building microgrids are directly related to user privacy, the distributed characteristics of the ADMM algorithm also ensure that only low-carbon influencing factors are exchanged during the entire update process, avoiding the leakage of user sensitive information and improving the security of user information. The distributed nature of the ADMM algorithm allows urban building microgrids and users to exchange only intermediate calculation results, namely low-carbon impact factors, with adjacent nodes without having to upload original data. This significantly reduces communication bandwidth requirements and the computing pressure of central nodes, thereby reducing computational complexity and improving energy scheduling efficiency.

[0006] Preferably, the upper model is: Y represents the minimum cost of urban building microgrid, g represents the global cost function of urban building microgrid, N represents the number of users in the urban building microgrid, Indicates the amount of electricity purchased by user i.

[0007] Preferably, the lower layer model is: Where M represents the minimum cost of user i, f i represents the local cost function of user i, represents the gas-fired power generation power of user i, represents the photovoltaic power generation power of user i, represents the wind power generation power of user i, Represents the energy storage charging and discharging power of user i.

[0008] Preferably, the updating of the low-carbon influencing factors in the target model by the ADMM algorithm until the updated result satisfies the convergence condition to obtain the optimal energy scheduling solution includes: Sa: Integrate the constraints with the target model and introduce Lagrangian multipliers and penalty parameters to construct the augmented Lagrangian function; Sb: Decompose the augmented Lagrangian function to obtain sub-functions, and update the low-carbon influencing factors through the sub-functions. When the low-carbon influencing factors do not meet the convergence conditions, use the low-carbon influencing factors to update the augmented Lagrangian function and execute Sb. When the low-carbon influencing factors meet the convergence conditions, obtain the optimal energy scheduling plan based on the low-carbon influencing factors.

[0009] In this scheme, by integrating constraints with the target model and introducing Lagrange multipliers and penalty parameters to construct an augmented Lagrangian function, the resulting optimal energy scheduling solution meets both the target requirements and actual operating constraints, ensuring its rationality. This not only effectively promotes carbon emission reduction and the efficient consumption of renewable energy, but also improves scheduling security. Because urban building microgrids contain a large number of entities, centralizing the calculation of all node data would lead to communication congestion and computational delays. Therefore, the augmented Lagrangian function is decomposed to obtain sub-functions, allowing each entity to update its low-carbon influence factors based solely on local data and exchange them with neighboring nodes. This enables distributed parallel computing, significantly reducing communication and computational pressures, preventing the leakage of sensitive user information, and improving user information security.

[0010] Preferably, the decomposing the augmented Lagrangian function to obtain sub-functions, and updating the low-carbon influencing factors through the sub-functions, includes: According to the characteristics of low-carbon influencing factors, low-carbon influencing factors are divided into the first low-carbon influencing factors and the second low-carbon influencing factors; The function term related to the first low-carbon influencing factor in the augmented Lagrangian function is retained to obtain the first sub-function; The function term related to the second low-carbon influencing factor in the augmented Lagrangian function is retained to obtain the second sub-function; Obtaining a third sub-function based on the first sub-function and the second sub-function; The first low-carbon influencing factor and the second low-carbon influencing factor are updated respectively through the first sub-function and the second sub-function; Based on the first low-carbon influencing factor and the second low-carbon influencing factor, a third low-carbon influencing factor is obtained using a third sub-function, and the third low-carbon influencing factor is classified as a low-carbon influencing factor.

[0011] Preferably, the Sb further comprises: The main residual is obtained by the constraint term, the first low-carbon influencing factor and the second low-carbon influencing factor in the augmented Lagrangian function, and the secondary residual is obtained by the Lagrangian multiplier and the penalty parameter in the augmented Lagrangian function. When both the main residual and the secondary residual meet the preset conditions, it means that the low-carbon influencing factor meets the convergence condition. Otherwise, it means that the low-carbon influencing factor does not meet the convergence condition.

[0012] In this scheme, the primary residual reflects the degree to which low-carbon influencing factors satisfy the constraints during the iteration process, while the secondary residual reflects the degree of optimization of the energy scheduling scheme. The optimal energy scheduling scheme is obtained only when both the primary and secondary residuals meet the preset conditions. This avoids grid failures caused by constraint violations and resource waste caused by insufficient optimization. By setting these dual conditions, the feasible solution and the optimal solution are unified, further ensuring the rationality of the optimal energy scheduling scheme and effectively promoting the reduction of carbon emissions and the efficient consumption of renewable energy.

[0013] Preferably, the constraint condition includes at least a power balance constraint.

[0014] Beneficial effects of this program: By constructing a target model that includes upper and lower models, a dynamic balance between the upper and lower layer targets is achieved, avoiding the separation of subject decision-making and user passive response in traditional centralized scheduling. The distributed characteristics of the ADMM algorithm are used to update the intermediate calculation results, namely the low-carbon influencing factors, and then obtain the optimal energy scheduling plan for scheduling. By considering the scheduling of building microgrids with a high proportion of renewable energy at the urban level, carbon emissions are effectively reduced and renewable energy is efficiently absorbed. At the same time, since urban building microgrids are directly related to user privacy, the distributed characteristics of the ADMM algorithm also ensure that only low-carbon influencing factors are exchanged during the entire update process, avoiding the leakage of user sensitive information and improving the security of user information. The distributed nature of the ADMM algorithm allows urban building microgrids and users to exchange intermediate calculation results, namely low-carbon impact factors, with adjacent nodes without having to upload original data. This significantly reduces communication bandwidth requirements and the computing pressure on central nodes, thereby reducing computational complexity and improving energy scheduling efficiency. The optimal energy dispatch solution is obtained by combining the primary residual, which reflects the degree to which low-carbon influencing factors satisfy the constraints during the iteration process, with the secondary residual, which reflects the degree of optimization of the energy dispatch solution. This avoids grid failures caused by constraint violations and resource waste caused by insufficient optimization. By setting dual conditions, the feasible solution and the optimal solution are unified, ensuring the rationality of the optimal energy dispatch solution and further effectively promoting the reduction of carbon emissions and the efficient consumption of renewable energy.

[0015] The present invention also provides a distributed low-carbon energy dispatching system for urban building microgrid clusters, which is applicable to the above-mentioned distributed low-carbon energy dispatching method for urban building microgrid clusters, and includes a target model acquisition module and an optimal energy dispatching solution acquisition module; The target model acquisition module is used to construct an upper model with the goal of minimizing the cost of the urban building microgrid, and to construct a lower model with the goal of minimizing the cost of users in the urban building microgrid, and to obtain the target model through the upper model and the lower model; The optimal energy scheduling scheme acquisition module is used to iteratively update the low-carbon influencing factors in the target model through the ADMM algorithm until the optimal energy scheduling scheme is obtained when the iterative update result meets the convergence condition, and energy scheduling is performed according to the optimal energy scheduling scheme.

[0016] Preferably, the target model acquisition module includes an upper layer model acquisition module and a lower layer model acquisition module; The upper model acquisition module is used to construct an upper model with the goal of minimizing the cost of the urban building microgrid; The lower-layer model acquisition module is used to construct a lower-layer model with the goal of minimizing the cost of users in the urban building microgrid.

[0017] Beneficial effects of this program: By constructing a target model that includes upper and lower models, a dynamic balance between the upper and lower layer targets is achieved, avoiding the separation of subject decision-making and user passive response in traditional centralized scheduling. The distributed characteristics of the ADMM algorithm are used to update the intermediate calculation results, namely the low-carbon influencing factors, and then obtain the optimal energy scheduling plan for scheduling. By considering the scheduling of building microgrids with a high proportion of renewable energy at the urban level, carbon emissions are effectively reduced and renewable energy is efficiently absorbed. At the same time, since urban building microgrids are directly related to user privacy, the distributed characteristics of the ADMM algorithm also ensure that only low-carbon influencing factors are exchanged during the entire update process, avoiding the leakage of user sensitive information and improving the security of user information. The distributed nature of the ADMM algorithm allows urban building microgrids and users to exchange only intermediate calculation results, namely low-carbon impact factors, with adjacent nodes without having to upload original data. This significantly reduces communication bandwidth requirements and the computing pressure of central nodes, thereby reducing computational complexity and improving energy scheduling efficiency.

[0018] The present invention also provides a computer device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor and the memory communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the distributed low-carbon energy scheduling method for urban building microgrid clusters. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects, and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are provided for the purpose of illustrating preferred embodiments only and are not to be construed as limiting the present invention. Like reference characters are used throughout the drawings to designate like parts.

[0020] Figure 1 Schematic diagram of the process of the distributed low-carbon energy dispatching method for urban building microgrid clusters of the present invention; Figure 2 It is a structural schematic diagram of the urban building microgrid cluster in the distributed low-carbon energy dispatching system of the urban building microgrid cluster of the present invention. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be performed in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but can also have additional steps not included in the figures; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0023] Example 1: like Figure 1 As shown in FIG, the distributed low-carbon energy dispatching method for urban building microgrid clusters includes the following steps: An upper-layer model is constructed with the goal of minimizing the cost of urban building microgrids, and a lower-layer model is constructed with the goal of minimizing the cost of users in urban building microgrids. The target model is obtained through the upper-layer model and the lower-layer model.

[0024] Specifically, the upper model is: Y represents the minimum cost of urban building microgrid, g represents the global cost function of urban building microgrid, N represents the number of users in the urban building microgrid, Indicates the amount of electricity purchased by user i.

[0025] Specifically, the lower layer model is: Where M represents the minimum cost of user i, f i represents the local cost function of user i, represents the gas-fired power generation power of user i, represents the photovoltaic power generation power of user i, represents the wind power generation power of user i, Represents the energy storage charging and discharging power of user i.

[0026] In this embodiment, the target model, i.e., the target function, is: Where C total represents the sum of the costs of each user, C cost represents the total power generation cost of urban building microgrid components, represents the total carbon emission cost of urban building microgrid, C i,t represents the basic cost of user i in time period t, represents the unit price of electricity purchased by user i, represents the electricity purchased by user i in period t, represents the carbon emission cost coefficient of user i, Indicates the carbon emissions per unit of electricity purchased, a 1,i represents the cost coefficient of gas power generation equipment of user i, a 2,i represents the cost coefficient of photovoltaic power generation equipment of user i, a 3,i represents the cost coefficient of wind power generation equipment of user i, a 4,i represents the cost coefficient corresponding to the electricity purchasing behavior of user i, a 5,i represents the cost coefficient for user i's energy storage charging and discharging equipment, and T represents the total number of scheduling time periods. In this embodiment, by constructing a target model that includes both upper- and lower-level models, a dynamic balance between upper- and lower-level objectives is achieved, avoiding the disconnect between decision-making and passive user response in traditional centralized scheduling.

[0027] The low-carbon influencing factors in the target model are updated through the ADMM algorithm until the updated results meet the convergence conditions to obtain the optimal energy scheduling plan, and energy scheduling is performed based on the optimal energy scheduling plan.

[0028] The ADMM algorithm (Alternating Direction Method of Multipliers) is the core algorithm for dealing with separable block convex optimization problems. It decomposes large-scale problems into sub-problems through a decomposition strategy and solves them alternately.

[0029] In this embodiment, the low-carbon influencing factors in the target model include local power generation / energy storage decisions and global grid interaction power. The optimal energy scheduling plan includes the power consumption strategy of each user in the urban building microgrid and the scheduling strategy of the urban building microgrid.

[0030] As a preferred embodiment, the low-carbon influencing factors in the target model are updated by the ADMM algorithm until the updated result satisfies the convergence condition to obtain the optimal energy scheduling solution, including: Sa: Integrate the constraints with the target model and introduce Lagrangian multipliers and penalty parameters to construct the augmented Lagrangian function; Sb: Decompose the augmented Lagrangian function to obtain sub-functions, and update the low-carbon influencing factors through the sub-functions. When the low-carbon influencing factors do not meet the convergence conditions, use the low-carbon influencing factors to update the augmented Lagrangian function and execute Sb. When the low-carbon influencing factors meet the convergence conditions, obtain the optimal energy scheduling plan based on the low-carbon influencing factors.

[0031] In this embodiment, the penalty parameter can be set to 2 or greater. The specific value can be fine-tuned based on the complexity of the actual system and the required convergence speed. The Lagrange multiplier can be initially set to 0. In this embodiment, through the distributed nature of the ADMM algorithm, each urban building microgrid and user only needs to exchange intermediate calculation results, namely low-carbon impact factors, with adjacent nodes, without having to upload original data. This significantly reduces communication bandwidth requirements and the computational pressure on the central node, thereby reducing computational complexity and improving energy scheduling efficiency.

[0032] As a preferred embodiment, the decomposing the augmented Lagrangian function to obtain sub-functions, and updating the low-carbon influencing factors through the sub-functions, includes: According to the characteristics of low-carbon influencing factors, low-carbon influencing factors are divided into the first low-carbon influencing factors and the second low-carbon influencing factors; The function term related to the first low-carbon influencing factor in the augmented Lagrangian function is retained to obtain the first sub-function; The function term related to the second low-carbon influencing factor in the augmented Lagrangian function is retained to obtain the second sub-function; Obtaining a third sub-function based on the first sub-function and the second sub-function; The first low-carbon influencing factor and the second low-carbon influencing factor are updated respectively through the first sub-function and the second sub-function; Based on the first low-carbon influencing factor and the second low-carbon influencing factor, a third low-carbon influencing factor is obtained using a third sub-function, and the third low-carbon influencing factor is classified as a low-carbon influencing factor.

[0033] In this embodiment, the first low-carbon influencing factor is the local power generation / storage decision, the second low-carbon influencing factor is the global grid interaction power, and the third low-carbon influencing factor is the Lagrange multiplier. The global grid interaction power can be obtained based on the amount of electricity purchased by the user. The first sub-function is specifically: in, represents the local power generation / storage decision made by user i after the k+1th iteration, ρ represents the penalty parameter, represents the amount of electricity purchased by user i after the kth iteration, represents the Lagrange multiplier obtained by user i after the kth iteration, The second sub-function is specifically: in, represents the amount of electricity purchased by user i after the k+1th iteration; The third sub-function is specifically: in, represents the Lagrange multiplier obtained by user i after the k+1th iteration.

[0034] As a preferred embodiment, the Sb further includes: The main residual is obtained by the constraint term, the first low-carbon influencing factor and the second low-carbon influencing factor in the augmented Lagrangian function, and the secondary residual is obtained by the Lagrangian multiplier and the penalty parameter in the augmented Lagrangian function. When both the main residual and the secondary residual meet the preset conditions, it means that the low-carbon influencing factor meets the convergence condition. Otherwise, it means that the low-carbon influencing factor does not meet the convergence condition.

[0035] In this embodiment, the constraint term is given by the formula Obtain, by subtracting the constraint term from the sum of the first low-carbon influencing factor and the second low-carbon influencing factor, the main residual can be obtained, and the penalty parameter is multiplied by the difference of the Lagrange multipliers of two adjacent iterations to obtain the secondary residual. When the main residual and the secondary residual are both greater than 0, it means that the preset conditions are met. The main residual reflects the degree to which the low-carbon influencing factor meets the constraint conditions during the iteration process, and the secondary residual reflects the degree of optimization of the energy scheduling plan. Only when the main residual and the secondary residual meet the preset conditions can the optimal energy scheduling plan be obtained, avoiding power grid failures caused by constraint violations and resource waste caused by insufficient optimization. By setting dual conditions, the unification of feasible solutions and optimal solutions is achieved, further ensuring the rationality of the optimal energy scheduling plan, and further effectively promoting the reduction of carbon emissions and the efficient absorption of renewable energy.

[0036] Specifically, the constraint condition includes at least a power balance constraint.

[0037] It's understandable that gas turbines generating electricity through gas must meet upper and lower output power constraints, as well as power ramping constraints, while photovoltaic and wind power systems must meet upper output power constraints. Energy storage systems must meet charging power constraints, discharging power constraints, charge and discharge state constraints, and state of charge constraints during the charging and discharging process.

[0038] Example 2: This embodiment also provides a distributed low-carbon energy dispatching system for urban building microgrid clusters, which is applicable to the above-mentioned distributed low-carbon energy dispatching method for urban building microgrid clusters, including a target model acquisition module and an optimal energy dispatching solution acquisition module; The target model acquisition module is used to construct an upper model with the goal of minimizing the cost of the urban building microgrid, and to construct a lower model with the goal of minimizing the cost of users in the urban building microgrid, and to obtain the target model through the upper model and the lower model; The optimal energy scheduling scheme acquisition module is used to iteratively update the low-carbon influencing factors in the target model through the ADMM algorithm until the optimal energy scheduling scheme is obtained when the iterative update result meets the convergence condition, and energy scheduling is performed according to the optimal energy scheduling scheme.

[0039] In this embodiment, if Figure 2 As shown in the figure, this system consists of several urban building microgrids, each of which includes a photovoltaic system, an energy storage system, a gas turbine and a load.

[0040] In this embodiment, by constructing a target model comprising upper and lower-level models, a dynamic balance between these targets is achieved, avoiding the disconnect between decision-making and passive user response in traditional centralized scheduling. Low-carbon factors in the target model include local generation / storage decisions and global grid interaction power. The optimal energy scheduling solution includes the power usage strategy of each user in the urban building microgrid and the scheduling strategy of the urban building microgrid.

[0041] Specifically, the low-carbon influencing factors in the target model are updated by the ADMM algorithm until the updated result meets the convergence condition to obtain the optimal energy scheduling solution, including: Sa: Integrate the constraints with the target model and introduce Lagrangian multipliers and penalty parameters to construct the augmented Lagrangian function; Sb: Decompose the augmented Lagrangian function to obtain sub-functions, and update the low-carbon influencing factors through the sub-functions. When the low-carbon influencing factors do not meet the convergence conditions, use the low-carbon influencing factors to update the augmented Lagrangian function and execute Sb. When the low-carbon influencing factors meet the convergence conditions, obtain the optimal energy scheduling plan based on the low-carbon influencing factors.

[0042] The penalty parameter can be specifically set to 2 or a larger value. The specific value can be fine-tuned according to the complexity of the actual system and the convergence speed requirements. The Lagrange multiplier can be initially set to 0.

[0043] Decomposing the augmented Lagrangian function to obtain sub-functions, and updating the low-carbon influencing factors through the sub-functions, include: According to the characteristics of low-carbon influencing factors, low-carbon influencing factors are divided into the first low-carbon influencing factors and the second low-carbon influencing factors; The function term related to the first low-carbon influencing factor in the augmented Lagrangian function is retained to obtain the first sub-function; The function term related to the second low-carbon influencing factor in the augmented Lagrangian function is retained to obtain the second sub-function; Obtaining a third sub-function based on the first sub-function and the second sub-function; The first low-carbon influencing factor and the second low-carbon influencing factor are updated respectively through the first sub-function and the second sub-function; Based on the first low-carbon influencing factor and the second low-carbon influencing factor, a third low-carbon influencing factor is obtained using a third sub-function, and the third low-carbon influencing factor is classified as a low-carbon influencing factor.

[0044] The first low-carbon influencing factor is the local power generation / storage decision, the second low-carbon influencing factor is the global grid interaction power, and the third low-carbon influencing factor is the Lagrange multiplier. The global grid interaction power can be obtained based on the amount of electricity purchased by the user. The first sub-function is specifically: in, represents the local power generation / storage decision made by user i after the k+1th iteration, ρ represents the penalty parameter, represents the amount of electricity purchased by user i after the kth iteration, represents the Lagrange product obtained by user i after the kth iteration The second sub-function is specifically: in, represents the amount of electricity purchased by user i after the k+1th iteration; The third sub-function is specifically: in, represents the Lagrange multiplier obtained by user i after the k+1th iteration.

[0045] Said Sb further comprises: The main residual is obtained by the constraint term, the first low-carbon influencing factor and the second low-carbon influencing factor in the augmented Lagrangian function, and the secondary residual is obtained by the Lagrangian multiplier and the penalty parameter in the augmented Lagrangian function. When both the main residual and the secondary residual meet the preset conditions, it means that the low-carbon influencing factor meets the convergence condition. Otherwise, it means that the low-carbon influencing factor does not meet the convergence condition.

[0046] Specifically, the constraint term is given by the formula Obtain, by subtracting the constraint term from the sum of the first low-carbon influencing factor and the second low-carbon influencing factor, the main residual can be obtained, and the penalty parameter is multiplied by the difference of the Lagrange multipliers of two adjacent iterations to obtain the secondary residual. When the main residual and the secondary residual are both greater than 0, it means that the preset conditions are met. The main residual reflects the degree to which the low-carbon influencing factor meets the constraint conditions during the iteration process, and the secondary residual reflects the degree of optimization of the energy scheduling plan. Only when the main residual and the secondary residual meet the preset conditions can the optimal energy scheduling plan be obtained, avoiding power grid failures caused by constraint violations and resource waste caused by insufficient optimization. By setting dual conditions, the unification of feasible solutions and optimal solutions is achieved, further ensuring the rationality of the optimal energy scheduling plan, and further effectively promoting the reduction of carbon emissions and the efficient absorption of renewable energy.

[0047] Example 3: This embodiment also provides a computer device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor and the memory communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the distributed low-carbon energy scheduling method for urban building microgrid clusters.

[0048] The specific implementation method described above is a preferred implementation method of the distributed low-carbon energy scheduling method and system for urban building microgrid clusters of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation method. Any equivalent changes made in accordance with the shape and structure of the present invention are within the scope of protection of the present invention.

Claims

1. A distributed low-carbon energy dispatching method for urban building microgrid clusters, characterized by: The following steps are involved: An upper-layer model is constructed with the goal of minimizing the cost of urban building microgrids, and a lower-layer model is constructed with the goal of minimizing the cost of users in urban building microgrids. The target model is obtained through the upper-layer model and the lower-layer model. The low-carbon influencing factors in the target model are updated through the ADMM algorithm until the updated results meet the convergence conditions to obtain the optimal energy scheduling plan, and energy scheduling is performed based on the optimal energy scheduling plan.

2. The distributed low-carbon energy dispatching method for urban building microgrid clusters according to claim 1 is characterized in that: The upper model is: Y represents the minimum cost of the urban building microgrid, g represents the global cost function of the urban building microgrid, N represents the number of users in the urban building microgrid, Indicates the amount of electricity purchased by user i.

3. The distributed low-carbon energy dispatching method for urban building microgrid clusters according to claim 2 is characterized in that: The lower layer model is: Where M represents the minimum cost of user i, f i represents the local cost function of user i, represents the gas-fired power generation power of user i, represents the photovoltaic power generation power of user i, represents the wind power generation power of user i, Represents the energy storage charging and discharging power of user i.

4. The distributed low-carbon energy dispatching method for urban building microgrid clusters according to claim 1 is characterized in that: The low-carbon influencing factors in the target model are updated by the ADMM algorithm until the updated result meets the convergence condition to obtain the optimal energy scheduling solution, including: Sa: Integrate the constraints with the target model and introduce Lagrangian multipliers and penalty parameters to construct the augmented Lagrangian function; Sb: Decompose the augmented Lagrangian function to obtain sub-functions, and update the low-carbon influencing factors through the sub-functions. When the low-carbon influencing factors do not meet the convergence conditions, use the low-carbon influencing factors to update the augmented Lagrangian function and execute Sb. When the low-carbon influencing factors meet the convergence conditions, obtain the optimal energy scheduling plan based on the low-carbon influencing factors.

5. The distributed low-carbon energy dispatching method for urban building microgrid clusters according to claim 4 is characterized in that: Decomposing the augmented Lagrangian function to obtain sub-functions, and updating the low-carbon influencing factors through the sub-functions, include: According to the characteristics of low-carbon influencing factors, low-carbon influencing factors are divided into the first low-carbon influencing factors and the second low-carbon influencing factors; The function term related to the first low-carbon influencing factor in the augmented Lagrangian function is retained to obtain a first sub-function; the function term related to the second low-carbon influencing factor in the augmented Lagrangian function is retained to obtain a second sub-function; Obtaining a third sub-function based on the first sub-function and the second sub-function; The first low-carbon influencing factor and the second low-carbon influencing factor are updated respectively through the first sub-function and the second sub-function; Based on the first low-carbon influencing factor and the second low-carbon influencing factor, a third low-carbon influencing factor is obtained using a third sub-function, and the third low-carbon influencing factor is classified as a low-carbon influencing factor.

6. The distributed low-carbon energy dispatching method for urban building microgrid clusters according to claim 5 is characterized in that: Said Sb further comprises: The main residual is obtained by the constraint term, the first low-carbon influencing factor and the second low-carbon influencing factor in the augmented Lagrangian function, and the secondary residual is obtained by the Lagrangian multiplier and the penalty parameter in the augmented Lagrangian function. When both the main residual and the secondary residual meet the preset conditions, it means that the low-carbon influencing factor meets the convergence condition. Otherwise, it means that the low-carbon influencing factor does not meet the convergence condition.

7. The distributed low-carbon energy dispatching method for urban building microgrid clusters according to claim 4 is characterized in that: The constraint conditions include at least a power balance constraint.

8. A distributed low-carbon energy dispatching system for urban building microgrid clusters, applicable to the distributed low-carbon energy dispatching method for urban building microgrid clusters according to any one of claims 1 to 7, characterized in that: It includes target model acquisition module and optimal energy scheduling solution acquisition module; The target model acquisition module is used to construct an upper model with the goal of minimizing the cost of the urban building microgrid, and to construct a lower model with the goal of minimizing the cost of users in the urban building microgrid, and to obtain the target model through the upper model and the lower model; The optimal energy scheduling scheme acquisition module is used to iteratively update the low-carbon influencing factors in the target model through the ADMM algorithm until the optimal energy scheduling scheme is obtained when the iterative update result meets the convergence condition, and energy scheduling is performed according to the optimal energy scheduling scheme.

9. The urban building microgrid cluster distributed low-carbon energy dispatching system according to claim 8 is characterized in that: The target model acquisition module includes an upper layer model acquisition module and a lower layer model acquisition module; The upper model acquisition module is used to construct an upper model with the goal of minimizing the cost of the urban building microgrid; The lower-layer model acquisition module is used to construct a lower-layer model with the goal of minimizing the cost of users in the urban building microgrid.

10. A computer device comprising: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor and the memory communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the distributed low-carbon energy scheduling method for urban building microgrid clusters as described in any one of claims 1 to 7 above.

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