A dynamic optimization scheduling method for low-carbon building loads under multi-energy flow network constraints

Abstract

The present invention provides a method for dynamic optimization scheduling of low-carbon building loads under multi-energy flow network constraints, which relates to the field of data processing technology, and comprises: obtaining a multi-energy flow network of a monitored low-carbon building and corresponding total energy flow data, wherein the energy flow data comprises water carbon energy flow information, electricity carbon energy flow information, gas carbon energy flow information and heat carbon energy flow information; performing structural decomposition on the monitored low-carbon building according to a preset decomposition strategy, and determining first sub-energy flow data corresponding to each decomposition structure in the total energy flow data in the multi-energy flow network; constructing a corresponding energy consumption target function based on the energy consumption scenario of each decomposition structure, and establishing constraint conditions corresponding to each energy flow information; decomposing and calculating the first sub-energy flow data based on the energy consumption target function and the constraint conditions, and obtaining distributed optimization scheduling information of the building load of the low-carbon building.

Description

Technical Field

[0001] The invention relates to data processing technology, and in particular to a dynamic optimization scheduling method for low-carbon building loads under multi-energy flow network constraints. Background Art

[0002] The energy supply system of low-carbon buildings is usually diverse, including multiple energy flow information such as electricity, gas, heat and water. The coordination and optimization management of these different energy flows are crucial. In order to achieve energy efficiency improvement and carbon emission control, the energy flow network of low-carbon buildings must be monitored and optimized in real time.

[0003] The existing low-carbon building energy flow management system mainly relies on the monitoring and control of a single energy flow, and cannot fully consider the mutual conversion and influence between multiple energy flows. This approach usually only focuses on the optimization of local energy flows, ignoring the energy efficiency and carbon emissions of the overall system, resulting in unbalanced energy flow distribution and failure to fully utilize the information of multiple energy flows.

[0004] Therefore, how to achieve comprehensive processing of multiple energy flows and load optimization scheduling to meet the needs of modern low-carbon buildings has become an urgent problem to be solved. Summary of the invention

[0005] The embodiment of the present invention provides a method for dynamically optimizing and dispatching loads of low-carbon buildings under the constraints of a multi-energy flow network, which can realize comprehensive processing of multiple energy flows and load optimization dispatching to meet the needs of modern low-carbon buildings.

[0006] A first aspect of an embodiment of the present invention provides a method for dynamically optimizing and dispatching low-carbon building loads under multi-energy flow network constraints, comprising:

[0007] Acquire a multi-energy flow network of a monitored low-carbon building and corresponding total energy flow data, wherein the energy flow data includes water carbon energy flow information, electric carbon energy flow information, gas carbon energy flow information, and thermal carbon energy flow information;

[0008] Decomposing the monitored low-carbon building structure according to a preset decomposition strategy, and determining the first sub-energy flow data corresponding to each decomposition structure in the total energy flow data in the multi-energy flow network;

[0009] Construct the corresponding energy consumption objective function based on the energy consumption scenario of each decomposition structure, and establish the constraint conditions corresponding to each energy flow information;

[0010] Based on the energy consumption objective function and constraint conditions, the first sub-energy flow data is decomposed and calculated to obtain the distributed optimization scheduling information of the building load of the low-carbon building.

[0011] Optionally, in a possible implementation manner of the first aspect, obtaining a multi-energy flow network of a monitored low-carbon building and corresponding total energy flow data includes:

[0012] The functional flow pipelines of the monitored low-carbon building are obtained, and the relationship between the pipeline parts and pipeline endpoints of all functional flow pipelines and the monitored low-carbon building is obtained to obtain a multi-energy flow network;

[0013] Based on the metering equipment, the energy flow information corresponding to the pipeline parts and pipeline endpoints is collected to obtain the corresponding total energy flow data.

[0014] Optionally, in a possible implementation manner of the first aspect, performing structural decomposition on the monitored low-carbon building according to a preset decomposition strategy to determine first sub-energy flow data corresponding to each decomposition structure in the total energy flow data in the multi-energy flow network includes:

[0015] The spatial structure of the monitored low-carbon building is obtained to obtain multiple subspaces, and the subspaces are processed based on a preset decomposition strategy to obtain the corresponding decomposition structure;

[0016] The pipeline part and the pipeline end point corresponding to each decomposition structure are determined, and the energy flow information of each decomposition structure is collected based on the corresponding metering equipment to obtain the first sub-energy flow data decomposed by the total energy flow data.

[0017] Optionally, in a possible implementation manner of the first aspect, acquiring the spatial structure of the monitored low-carbon building to obtain a plurality of subspaces, and processing the subspaces based on a preset decomposition strategy to obtain a corresponding decomposition structure includes:

[0018] If it is determined that the user selects the preset decomposition strategy based on the decomposition of the metering device, a monitoring mapping relationship between the metering device and the subspace corresponding to each energy flow information is constructed, and each subspace has a mapping relationship with at least one metering device;

[0019] Based on the monitoring mapping relationship between the metering equipment and the subspace corresponding to each energy flow information, the corresponding relationship between the metering equipment between the energy flows is determined to obtain a decomposition structure, wherein the decomposition structure includes at least one subspace, and each subspace corresponds to at least one metering equipment under each type of energy flow information.

[0020] Optionally, in a possible implementation manner of the first aspect, determining the correspondence relationship between the metering devices between the energy flows to obtain the decomposition structure based on the monitoring mapping relationship between the metering devices and the subspace corresponding to each energy flow information includes:

[0021] The metering equipment corresponding to each subspace under each energy flow information is sequentially traversed as the metering equipment to be compared, and the subspace corresponding to each metering equipment to be compared is counted to obtain the corresponding space set;

[0022] If it is determined that the space sets of the to-be-compared measuring devices have a complete inclusion relationship, then the space set with the largest number of subspaces is determined to be the first space set;

[0023] All subspaces in the first space set are counted to form a decomposition structure, and metering devices mapped under all energy flow information of the corresponding decomposition structure are counted.

[0024] Optionally, in a possible implementation of the first aspect, if it is determined that the administrator selects a preset decomposition strategy for manual matching; or,

[0025] If it is determined that there is no complete inclusion relationship between the spatial sets of the to-be-compared measuring devices, interaction is performed with the administrator to obtain a decomposition structure by correlating the subspaces accordingly.

[0026] Optionally, in a possible implementation manner of the first aspect, constructing a corresponding energy usage objective function based on the energy usage scenario of each decomposition structure, and establishing a constraint condition corresponding to the energy flow information in each, includes:

[0027] Constructing an energy consumption objective function for each decomposition structure under an energy consumption scenario, wherein the energy consumption objective function includes a sub-function for each type of energy flow information and a total function for comprehensive energy flow information;

[0028] The sub-functions include:

[0029] ,

[0030] The overall function includes:

[0031] ,

[0032] in, For the The decomposition structure is The function value of the energy consumption objective function in the energy flow information dimension, For the Energy consumption value of each energy flow information dimension, For the The carbon conversion coefficient of the energy flow information dimension, For the The weight coefficient of each energy flow information dimension, is the function value of the total function, is the upper limit of the number of energy flow information dimensions, is the upper limit of the number of decomposition structures, For the One-degree parameter of the energy flow information dimension;

[0033] Interact with the administrator to determine the constraint conditions of each energy flow information dimension, wherein the constraint conditions include boundary values ​​and conversion relationships of corresponding energy flow information.

[0034] Optionally, in a possible implementation manner of the first aspect, the decomposing and calculating the first sub-energy flow data based on the energy consumption objective function and the constraint condition to obtain the distributed optimization scheduling information of the building load of the low-carbon building includes:

[0035] Extract the first sub-energy flow data of each decomposition structure and input it into the sub-function to obtain the corresponding first function value, count all the first function values ​​and input them into the total function to obtain the second function value;

[0036] If it is determined that the energy flow value of any energy flow information of any decomposition structure meets the constraint condition, energy flow conversion is performed based on the conversion relationship of the energy flow information, and distributed optimization scheduling information of the building load of the low-carbon building is obtained based on the energy flow conversion.

[0037] Optionally, in a possible implementation manner of the first aspect, if it is determined that the energy flow value of any energy flow information of any decomposition structure meets the constraint condition, energy flow conversion is performed based on the conversion relationship of the energy flow information, and the distributed optimization scheduling information of the building load of the low-carbon building is obtained based on the energy flow conversion, including:

[0038] If it is determined that the energy flow value of any energy flow information of any decomposition structure is greater than or equal to the constraint value, it is determined that the constraint condition is met, and the energy flow that meets the constraint condition is obtained as the first conversion energy flow;

[0039] Acquire a second conversion energy flow and a convertible module corresponding to the first conversion energy flow in a conversion relationship, each convertible module corresponds to at least one second conversion energy flow, and the device included in the convertible module can use multiple energy flows;

[0040] Based on the convertible module and the second conversion energy flow, corresponding load distribution optimization scheduling information is generated so that energy can be provided based on the second conversion energy flow when the convertible module is required to work at the next moment.

[0041] Optionally, in a possible implementation manner of the first aspect, the generating corresponding load distributed optimization scheduling information based on the convertible module and the second conversion energy flow includes:

[0042] Obtaining a first conversion energy flow and a second conversion energy flow converted by the convertible module within a monitoring period, and calculating a carbon emission difference corresponding to the first conversion energy flow and the second conversion energy flow per unit time;

[0043] Obtaining the conversion time of the convertible module during the monitoring period, and obtaining the corresponding excess carbon emission value based on the conversion time and the carbon emission difference;

[0044] If the excess carbon emission value is negative, no constraint adjustment is made;

[0045] If the excess carbon emission value is a positive value, the constraint condition is adjusted based on the excess carbon emission value according to a preset strategy.

[0046] Optionally, in a possible implementation manner of the first aspect, if the excess carbon emission value is a positive value, adjusting the constraint condition based on the excess carbon emission value according to a preset strategy includes:

[0047] Calculate the difference between the excess carbon emission value and the preset value to obtain a first difference; if the first difference is less than or equal to 0, retrieve the benchmark adjustment coefficient; if the first difference is greater than 0, calculate the dynamic adjustment coefficient based on the first difference and the benchmark adjustment value; each energy flow information has a preset benchmark adjustment coefficient;

[0048] Calculate the first energy flow value of the first conversion energy flow based on the reference adjustment coefficient or the dynamic adjustment coefficient to obtain an adjusted energy flow value;

[0049] If it is determined that the same energy flow information dimension in other decomposition structures does not meet the constraint condition, then based on the proportion of the values ​​of the constraint condition that are not met in other decomposition structures, the constraint values ​​of the constraint conditions of other decomposition structures are adjusted to be smaller according to the energy flow value adjustment;

[0050] If it is determined that the same energy flow information dimensions in other decomposition structures meet the constraint conditions, the constraint conditions in other decomposition structures will not be adjusted.

[0051] Optionally, in a possible implementation manner of the first aspect, if it is determined that the same energy flow information dimension in other decomposition structures does not meet the constraint condition, then based on the proportion of other decomposition structures that do not meet the constraint condition, the constraint value of the constraint condition of other decomposition structures is adjusted to a smaller value according to the energy flow value, including:

[0052] The energy flow value is calculated by the following formula:

[0053] ,

[0054] in, is the excess carbon emission value, is the default value, The energy flow value increased by the adjusted constraint. is the energy flow value of the constraint before adjustment, is the benchmark adjustment factor, is the normalized value of energy flow, For other The decomposition structure is adjusted to reduce the constraint value. For other The number of unmet constraints for the decomposition structure. For other The number of unmet constraints for the decomposition structure. is the upper limit of the decomposition structure.

[0055] Technical effect:

[0056] 1. Realize refined energy flow management

[0057] The present invention obtains the multi-energy flow network and corresponding energy flow data of low-carbon buildings to ensure that subsequent optimization scheduling is based on accurate and comprehensive data. Specifically, by decomposing the building space structure, the energy flow distribution of each subspace is clarified, and then the monitoring mapping relationship is constructed using metering equipment. In this way, the water carbon energy flow information, electric carbon energy flow information, gas carbon energy flow information and heat carbon energy flow information in each decomposed structure can be monitored and managed in detail in real time, realizing the refined collection and processing of energy flow data, and providing strong data support for subsequent optimization scheduling.

[0058] 2. Dynamic optimization scheduling

[0059] The present invention constructs an energy consumption objective function and corresponding constraints, and performs real-time energy flow data decomposition and optimization calculation based on these two core models. By dynamically monitoring the energy flow information of each decomposition structure, it is determined whether the constraints are met, and then the energy flow conversion is performed based on the energy flow conversion relationship. In particular, by adjusting and optimizing the energy flow value, it is ensured that the energy distribution is more balanced, so as to meet the carbon emission constraints. In this way, it is possible to make accurate energy flow value adjustments according to the actual carbon emission situation and the proportion of non-compliance, dynamically adapt to different carbon emission situations, and flexibly adjust the constraint values ​​of the decomposition structure.

[0060] 3. Improve energy efficiency and carbon emission control

[0061] In the scheduling of multi-energy flow networks, the present invention not only takes into account the information of a single energy flow, but also makes full use of the complementarity between multiple energy flows through energy flow conversion relationships. By calculating and adjusting the excess carbon emission value, the energy flow is dynamically adjusted and optimized, so that the overall energy efficiency of the building is improved and carbon emissions are effectively controlled. For example, when a certain energy flow information exceeds the constraint conditions, it is replaced by another energy flow through energy flow conversion, thereby reducing carbon emissions. In addition, by presetting and dynamically adjusting the constraint conditions, the stability and consistency of the system are ensured, and the negative impact of excessive adjustment is avoided. Through the optimization scheduling method under the constraints of the multi-energy flow network of the present invention, low-carbon buildings can not only realize the refined management of multi-energy flow information, but also can dynamically optimize scheduling, improve energy efficiency and carbon emission control. In particular, through energy flow conversion and dynamic adjustment of constraints, the flexibility and stability of the system are ensured, providing strong technical support for improving the energy efficiency of modern low-carbon buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a flow chart of a method for dynamic optimization scheduling of low-carbon building loads under multi-energy flow network constraints provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] See also Figure 1 , is a flow chart of a method for dynamic optimization scheduling of low-carbon building loads under multi-energy flow network constraints provided by an embodiment of the present invention, including:

[0065] S1, obtaining a multi-energy flow network of a monitored low-carbon building and corresponding total energy flow data, wherein the energy flow data includes water carbon energy flow information, electricity carbon energy flow information, gas carbon energy flow information and heat carbon energy flow information.

[0066] This step mainly obtains the multi-energy flow network of the monitored low-carbon building and its corresponding total energy flow data to ensure that the subsequent optimization scheduling is based on accurate and comprehensive data. Among them, the water carbon energy flow information is the building water consumption and the corresponding carbon emissions. The electric carbon energy flow information is the building electricity consumption and the corresponding carbon emissions. The gas carbon energy flow information is the building gas consumption and the corresponding carbon emissions. The heat carbon energy flow information is the building heat supply and the corresponding carbon emissions.

[0067] In some embodiments, the step of obtaining the multi-energy flow network and the corresponding total energy flow data of the monitored low-carbon building includes:

[0068] S11, acquiring the functional flow pipelines of the monitored low-carbon building, obtaining the relationship between the pipeline parts and pipeline endpoints of all functional flow pipelines and the monitored low-carbon building, and obtaining a multi-energy flow network.

[0069] Among them, functional flow pipelines are, for example, heating pipelines, gas supply pipelines, cooling pipelines, etc. It is understandable that the functional flow pipelines are distributed inside the monitored low-carbon building. This solution will record the location of the endpoints of each pipeline and its connection with each energy-consuming area or equipment in the building, and integrate the information of all pipeline parts and endpoints to form a multi-energy flow network. For example, if a part of the pipeline is located on a certain floor or a certain room of the monitored low-carbon building, it can be known through the multi-energy flow network.

[0070] S12, based on the metering equipment, the energy flow information corresponding to the pipeline part and the pipeline end point is collected to obtain the corresponding total energy flow data.

[0071] This solution will install appropriate metering equipment, such as flow meters, power meters, heat meters, etc., at key nodes of the multi-energy flow network (such as pipeline endpoints). The metering equipment will monitor and record energy flow information in real time. The collected energy flow information will be integrated and processed to form complete total energy flow data.

[0072] S2, structurally decomposing the monitored low-carbon building according to a preset decomposition strategy, and determining the first sub-energy flow data corresponding to each decomposition structure in the total energy flow data in the multi-energy flow network.

[0073] This step aims to achieve detailed load distribution and optimized scheduling by structurally decomposing low-carbon buildings and determining the energy flow data corresponding to each decomposed structure.

[0074] In some embodiments, the step of performing structural decomposition on the monitored low-carbon building according to a preset decomposition strategy and determining first sub-energy flow data corresponding to each decomposition structure in the total energy flow data in the multi-energy flow network includes:

[0075] S21, obtaining the spatial structure of the monitored low-carbon building to obtain multiple subspaces, and processing the subspaces based on a preset decomposition strategy to obtain corresponding decomposition structures.

[0076] This solution can obtain the complete spatial structure information of the monitored low-carbon building through architectural design drawings, BIM (Building Information Modeling) and other means. The building is divided into multiple subspaces according to different dimensions such as functional divisions, floors, rooms, etc. For example, it is divided according to office areas, public areas, etc. Select an appropriate decomposition strategy, process the divided subspaces, and form a decomposition structure. Each decomposition structure can be a room, a floor, or a functional area.

[0077] The step of obtaining the spatial structure of the monitored low-carbon building to obtain a plurality of subspaces and processing the subspaces based on a preset decomposition strategy to obtain a corresponding decomposition structure includes:

[0078] S211, if it is determined that the user selects the preset decomposition strategy based on metering device decomposition, a monitoring mapping relationship between the metering device and the subspace corresponding to each energy flow information is constructed, and each subspace has a mapping relationship with at least one metering device.

[0079] It is understandable that if the user chooses the preset decomposition strategy, then it is necessary to obtain a list of all metering devices in the building, including water meters, electricity meters, gas meters, heat meters and other devices. Identify the various subspaces of the building (such as rooms, floors, functional areas, etc.). Then construct a monitoring mapping relationship between each energy flow information and the subspace. Specifically, determine the energy flow information (such as water, electricity, gas, heat) monitored by each metering device and the subspace covered by the device. Ensure that each subspace has a mapping relationship with at least one metering device. Store the constructed mapping relationship in the system for use in subsequent steps.

[0080] S212, based on the monitoring mapping relationship between the metering equipment and the subspace corresponding to each energy flow information, determine the corresponding relationship between the metering equipment between the energy flows to obtain a decomposition structure, wherein the decomposition structure includes at least one subspace, and each subspace corresponds to at least one metering equipment under each type of energy flow information.

[0081] First, the monitoring mapping relationship constructed in step S211 is read from the storage system. According to different types of energy flow information (water, electricity, gas, heat, etc.), the monitoring mapping relationship is classified and sorted. After that, it is necessary to determine the corresponding relationship of the metering equipment, and sort out the metering equipment corresponding to each subspace for each energy flow information (such as water, electricity, gas, and heat). The corresponding relationship of the metering equipment of different energy flow information is integrated to ensure that each decomposition structure has at least one metering device under each energy flow information. Each subspace and its corresponding metering equipment (including metering equipment under multiple energy flow information) are defined as a decomposition structure. Verify whether the metering equipment configuration in each decomposition structure meets the requirements to ensure that at least one metering equipment is covered under each energy flow information. The determined decomposition structure is stored in the system for use in subsequent steps. This method realizes the refined management and monitoring of building energy flow by constructing a monitoring mapping relationship between metering equipment and subspaces, and determining the decomposition structure based on this relationship, which provides strong data support and structural foundation for subsequent energy flow optimization and scheduling.

[0082] The step of determining the corresponding relationship between the metering devices between the energy flows to obtain the decomposition structure based on the monitoring mapping relationship between the metering devices and the subspace corresponding to each energy flow information includes:

[0083] Traverse each subspace in turn and take the metering equipment corresponding to each energy flow information as the metering equipment to be compared, and count the subspaces corresponding to each metering equipment to be compared to obtain the corresponding space set. This solution will traverse the subspaces and energy flow information to obtain a list of all subspaces of the building to obtain various types of energy flow information (such as water, electricity, gas, heat, etc.) and their corresponding metering equipment lists. For each subspace, find its corresponding metering equipment under each energy flow information. Store the subspace set covered by each metering equipment. For each metering equipment, record its corresponding subspace to form a space set. Record the space set of each metering equipment. It can be understood that a space set of a subspace may correspond to multiple metering equipment to be compared.

[0084] If it is determined that the spatial sets of the metrological equipment to be compared have a complete inclusion relationship, the spatial set with the largest number of subspaces is determined to be the first spatial set. This solution will traverse all spatial sets to check whether there is a complete inclusion relationship. If spatial set A completely contains spatial set B, that is, B is a subset of A, then this inclusion relationship is marked. For each spatial set, the number of subspaces it contains is counted. From all inclusion relationships, the spatial set with the largest number of subspaces is selected as the first spatial set.

[0085] Count all subspaces in the first space set to form a decomposition structure, and count the metering devices mapped by the corresponding decomposition structure under all energy flow information. This solution will record all subspaces in the first space set to form a subspace list of the decomposition structure. Create an empty list to store the metering devices mapped by the decomposition structure under each energy flow information. For each energy flow information, find the metering devices corresponding to all subspaces in the first space set. Add all the metering devices found to the metering device list to ensure that the metering device list covers all energy flow information. Define all subspaces in the first space set and their corresponding metering devices as a decomposition structure. Store the generated decomposition structure and its corresponding metering device list in the system for subsequent use.

[0086] Based on the above embodiment, it also includes:

[0087] If it is determined that the administrator selects the preset decomposition strategy of manual matching, or if it is determined that there is no complete inclusion relationship between the spatial sets of the metrological equipment to be compared, the administrator is interacted with to obtain a decomposition structure by correspondingly associating the subspaces.

[0088] Based on the above embodiments, this solution adds the function of manual intervention by the administrator to deal with some special situations. For example, when automatic matching cannot be completed, the system will request the administrator to perform manual matching. The following is a detailed description of the steps: Determine whether the administrator chooses to manually match the preset decomposition strategy, and check whether the administrator has selected the preset decomposition strategy for manual matching. If the administrator selects manual matching in the settings, this information is recorded. If manual matching is enabled, the system will show the administrator the correspondence between the current metering device and the subspace. The administrator manually selects or adjusts the correspondence between the metering device and the subspace to form a new decomposition structure.

[0089] This solution requires traversing all the spatial sets of the metrological equipment to be compared to check whether there is a complete inclusion relationship between them. If there is no complete inclusion relationship between all spatial sets, this situation is recorded. The system displays an interactive interface where the administrator can view and adjust the correspondence between the metrological equipment and the subspace.

[0090] Through interaction with the administrator, manually determine and adjust the association between the subspace and the metering equipment to obtain the final decomposition structure. The system displays the correspondence between the current metering equipment and the subspace on the interface for the administrator to view. The administrator can adjust the correspondence between the metering equipment and the subspace by dragging and dropping. The administrator can also specify the correspondence between the metering equipment and the subspace by direct input. The administrator confirms the adjusted correspondence between the metering equipment and the subspace. The system saves the decomposition structure confirmed by the administrator to the database for subsequent use. The system records the administrator's adjustments and refers to these adjustments in the subsequent optimization algorithm to improve the accuracy of automatic matching.

[0091] Through the above method, based on the monitoring mapping relationship between the metering equipment and the subspace corresponding to each energy flow information, this solution sequentially traverses and counts the space sets corresponding to the metering equipment, determines the inclusion relationship, and generates and stores multiple decomposition structures. Each decomposition structure ensures that there is a corresponding metering equipment under each energy flow information, realizing efficient and refined management and monitoring of building energy flow.

[0092] S22, determining the pipeline part and pipeline end point corresponding to each decomposition structure, and collecting energy flow information of each decomposition structure based on corresponding metering equipment to obtain first sub-energy flow data decomposed from the total energy flow data.

[0093] This solution can determine the pipeline sections and endpoint locations within each decomposition structure based on a multi-energy flow network. Ensure all energy flow paths (including water, electricity, gas, heat, etc.) within each decomposition structure. Install appropriate metering equipment, such as water meters, electricity meters, gas meters, heat meters, etc., at key nodes (such as pipeline endpoints) of each decomposition structure. Through the configured metering equipment, the energy flow information within each decomposition structure is monitored and recorded in real time. According to the specific situation of each decomposition structure, the corresponding water-carbon energy flow information, electricity-carbon energy flow information, gas-carbon energy flow information, and heat-carbon energy flow information are collected. Finally, the energy flow information collected from each decomposition structure is summarized to form the corresponding first sub-energy flow data. These sub-energy flow data together constitute the total energy flow data, which is convenient for subsequent optimization and scheduling.

[0094] S3, constructing a corresponding energy consumption objective function based on the energy consumption scenario of each decomposition structure, and establishing constraints corresponding to each energy flow information.

[0095] Among them, the constraint condition may be an upper limit of energy consumption. For example, corresponding upper limits of energy consumption may be set for heat energy, gas energy, electric energy, etc.

[0096] In some embodiments, the energy usage scenario based on each decomposition structure constructs a corresponding energy usage objective function, and establishes constraint conditions corresponding to the energy flow information in each, including:

[0097] S31, constructing an energy usage target function for each decomposition structure in an energy usage scenario, wherein the energy usage target function includes a sub-function for each type of energy flow information and a total function for comprehensive energy flow information;

[0098] The sub-functions include:

[0099] ,

[0100] The overall function includes:

[0101] ,

[0102] in, For the The decomposition structure is The function value of the energy consumption objective function in the energy flow information dimension, For the Energy consumption value of each energy flow information dimension, For the The carbon conversion coefficient of the energy flow information dimension, For the The weight coefficient of each energy flow information dimension, is the function value of the total function, is the upper limit of the number of energy flow information dimensions, is the upper limit of the number of decomposition structures, For the A first-degree parameter of the energy flow information dimension.

[0103] In the above formula, the energy flow information dimension under each decomposition structure is first determined. Each sub-function is calculated according to the energy value, carbon conversion coefficient and weight coefficient of the energy flow information dimension. It is worth mentioning that this scheme uses the carbon conversion coefficient for conversion so that the data is unified to the carbon data dimension. Among them, the carbon conversion coefficient and weight coefficient can be preset by the user. Finally, the total function is calculated based on each sub-function and the first-degree parameter.

[0104] S32, interacting with the administrator to determine the constraint conditions of each energy flow information dimension, wherein the constraint conditions include boundary values ​​and conversion relationships of corresponding energy flow information.

[0105] This solution shows the administrator the current value and possible boundary value range of each energy flow information dimension. The administrator can manually set the boundary value of each energy flow information dimension through a graphical interface or an input box. The conversion relationship between each energy flow information dimension and other energy flow information dimensions is displayed. The administrator confirms or adjusts these conversion relationships to ensure their accuracy and feasibility. After the administrator confirms the boundary value and conversion relationship, the system saves these constraints. The determined constraints are applied to the calculation process of the energy consumption objective function to ensure that the optimization results meet the actual constraints. By constructing the energy consumption objective function in detail and interacting with the administrator to determine the constraints, the present invention provides a systematic and refined energy flow information management and optimization method. The objective function includes sub-functions and total functions to ensure that the energy flow information under each decomposition structure is fully considered; the interaction with the administrator ensures the accuracy and practicality of the constraints. This improvement enhances the flexibility and adaptability of the system, and helps to achieve the optimal management of energy flow information and the effective control of carbon emissions.

[0106] S4, based on the energy consumption objective function and constraint conditions, the first sub-energy flow data is decomposed and calculated to obtain distributed optimization scheduling information of the building load of the low-carbon building.

[0107] This embodiment realizes the decomposition and calculation of the first sub-energy flow data based on the energy consumption objective function and the constraint conditions, and finally obtains the distributed optimization scheduling information of the building load of the low-carbon building. Step S41 describes in detail how to extract and calculate the sub-energy flow data of the decomposition structure, and step S42 describes how to monitor and convert the energy flow information and generate the final optimization scheduling information, thereby ensuring the optimization of building energy efficiency and carbon emissions.

[0108] In some embodiments, the decomposition and calculation of the first sub-energy flow data based on the energy consumption objective function and the constraint condition to obtain the distributed optimization scheduling information of the building load of the low-carbon building includes:

[0109] S41, extracting the first sub-energy flow data of each decomposition structure and inputting it into the sub-function to obtain the corresponding first function value, and counting all the first function values ​​and inputting them into the total function to obtain the second function value.

[0110] In this step, it is necessary to extract the first sub-energy flow data of each decomposition structure, then calculate the value of each sub-function, and finally summarize it into the total function. Extract the first sub-energy flow data of each decomposition structure from the system database or real-time monitoring system. Input the first sub-energy flow data of each decomposition structure into the corresponding sub-function, and calculate the corresponding first function value. Store the calculated first function value in the system for subsequent use. Summarize all the first function values ​​and input them into the total function to calculate the second function value. Store the calculated second function value in the system for subsequent analysis and decision-making.

[0111] S42, if it is determined that the energy flow value of any energy flow information of any decomposition structure meets the constraint condition, energy flow conversion is performed based on the conversion relationship of the energy flow information, and distributed optimization scheduling information of the building load of the low-carbon building is obtained based on the energy flow conversion.

[0112] In this step, it is necessary to monitor the value of each energy flow information to determine whether the constraint conditions are met. If so, perform energy flow conversion and finally generate optimized scheduling information. Monitor the value of the energy flow information of each decomposition structure in real time. Compare the monitored energy flow value with the pre-set constraint conditions (boundary values) to determine whether the constraint conditions are met. Perform energy flow conversion based on the pre-defined energy flow information conversion relationship. Update the converted energy flow value to the system. Based on the latest energy flow value and the defined objective function and constraint conditions, the system performs optimization calculations to obtain a better building load distribution plan. Generate building load distributed optimization scheduling information with the results of the optimization calculation, including the energy flow distribution and scheduling strategies of each decomposition structure.

[0113] Wherein, if it is determined that the energy flow value of any energy flow information of any decomposition structure meets the constraint condition, energy flow conversion is performed based on the conversion relationship of the energy flow information, and distributed optimization scheduling information of building load of low-carbon building is obtained based on the energy flow conversion, including:

[0114] S421, if it is determined that the energy flow value of any energy flow information of any decomposed structure is greater than or equal to the constraint value, it is determined that the constraint condition is met, and the energy flow that meets the constraint condition is obtained as the first conversion energy flow.

[0115] This solution monitors the energy flow information of each decomposition structure in real time. If the energy flow information value of any decomposition structure is greater than or equal to the preset constraint value, it is considered that the constraint condition is met. The energy flow information that meets the constraint condition is marked as the first conversion energy flow.

[0116] S422, obtaining a second conversion energy flow and a convertible module corresponding to the first conversion energy flow in the conversion relationship, each convertible module corresponds to at least one second conversion energy flow, and the device included in the convertible module can use multiple energy flows.

[0117] This solution presupposes the conversion relationship of energy flow information. It is understandable that some energy flows can be converted. For example, for heating, it can use electric energy, thermal energy or gas energy. Therefore, electric energy, thermal energy or gas energy can be directly converted. For example, when electric energy exceeds the constraint condition, it can be converted into thermal energy or gas energy. Then thermal energy or gas energy is the second conversion energy flow mentioned above. The above is only for illustration and is not limited.

[0118] S423, generating corresponding load distribution optimization scheduling information based on the convertible module and the second conversion energy flow, so that energy can be provided based on the second conversion energy flow when the convertible module is required to work at the next moment.

[0119] This solution generates load distribution optimization scheduling information based on the convertible module and the corresponding second conversion energy flow, ensuring that the convertible module is provided with energy based on the second conversion energy flow when work is required at the next moment.

[0120] In the above embodiment, the generating corresponding load distributed optimization scheduling information based on the convertible module and the second conversion energy flow includes:

[0121] Obtain the first conversion energy flow and the second conversion energy flow converted by the convertible module within a monitoring period, and calculate the carbon emission difference corresponding to the first conversion energy flow and the second conversion energy flow per unit time. This solution will record the specific data of the energy flow conversion performed by the convertible module within a monitoring period (for example, one hour, a whole day, etc.). Obtain the actual values ​​of the first conversion energy flow and the second conversion energy flow, and then calculate them in combination with the preset carbon emission coefficient to obtain the carbon emission data, and finally calculate the carbon emission difference per unit time by difference. This difference represents the change in carbon emissions per unit time after the second conversion energy flow replaces the first conversion energy flow.

[0122] Obtain the conversion time of the convertible module during the monitoring period, and obtain the corresponding excess carbon emission value based on the conversion time and the carbon emission difference. Record the actual working time of the convertible module during the monitoring period, that is, the specific time periods in which these modules perform energy flow conversion. Based on the conversion time and the carbon emission difference, calculate the excess carbon emission value generated during the entire monitoring period. This value represents the increase or decrease in carbon emissions caused by energy flow conversion during the monitoring period.

[0123] If the excess carbon emission value is a negative value, the constraint condition is not adjusted. If it is a negative value, it means that the energy flow conversion reduces carbon emissions and there is no need to passively adjust the constraint condition.

[0124] If the excess carbon emission value is a positive value, the constraint condition is adjusted based on the excess carbon emission value according to the preset strategy. If it is a positive value, it means that the energy flow conversion increases carbon emissions, and the constraint condition needs to be adjusted to optimize carbon emissions.

[0125] In some embodiments, if the excess carbon emission value is a positive value, adjusting the constraint condition based on the excess carbon emission value according to a preset strategy includes:

[0126] Calculate the difference between the excess carbon emission value and the preset value to obtain a first difference. If the first difference is less than or equal to 0, call the benchmark adjustment coefficient. If the first difference is greater than 0, calculate the dynamic adjustment coefficient based on the first difference and the benchmark adjustment value. Each energy flow information has a preset benchmark adjustment coefficient. By comparing the actual value with the target value, this solution can intuitively understand whether the current carbon emissions exceed the standard and the degree of excess. Determine which adjustment coefficient to use based on the positive or negative value of the difference. If the difference is negative or zero, use the benchmark adjustment coefficient (preset fixed value); if the difference is positive, calculate and use the dynamic adjustment coefficient (depending on the actual difference change). The benchmark adjustment coefficient represents the default adjustment ratio of the system under normal circumstances, which is applicable to situations where there is no excess or slight excess. The dynamic adjustment coefficient can be flexibly adjusted according to the actual degree of excess, providing a more adaptable adjustment method.

[0127] The first energy flow value of the first conversion energy flow is calculated based on the reference adjustment coefficient or the dynamic adjustment coefficient to obtain an adjusted energy flow value. This solution adjusts the current energy flow value according to the selected adjustment coefficient to obtain a new energy flow value.

[0128] If it is determined that the same energy flow information dimension in other decomposition structures does not meet the constraint conditions, then based on the proportion of the values ​​that do not meet the constraint conditions in other decomposition structures, the constraint values ​​of the constraints of other decomposition structures are adjusted to a smaller value according to the adjusted energy flow values. This solution will check whether the energy flow information dimension in each decomposition structure meets the standard according to the preset constraints. By comparing the actual value with the preset constraints, it can be determined whether the current energy flow information needs to be adjusted. For the energy flow information dimension that does not meet the constraint conditions, calculate the proportion of its value in the entire decomposition structure. Through the proportion calculation, the intensity of the adjustment can be clearly determined, making the adjustment more targeted. According to the calculated proportion, the energy flow values ​​in the decomposition structure that does not meet the constraint conditions are adjusted to make them closer to the preset constraints. The proportional adjustment method ensures the scientificity and accuracy of the adjustment, and can effectively reduce the excessive energy flow information. Optimize the energy flow information so that it gradually meets the preset constraints and reduces the excessive carbon emissions in the system.

[0129] If it is determined that the same energy flow information dimensions in other decomposition structures have met the constraint conditions, the constraint conditions in other decomposition structures will not be adjusted. For energy flow information dimensions that have already met the preset constraint conditions, no adjustment will be made. Maintain the state that has already met the standard, avoid unnecessary adjustments, and ensure system stability. Maintain the stability and consistency of the system and avoid the negative impact of excessive adjustments.

[0130] In the above embodiment, if it is determined that the same energy flow information dimension in other decomposition structures does not meet the constraint condition, then based on the proportion of other decomposition structures that do not meet the constraint condition, the constraint value of the constraint condition of other decomposition structures is adjusted to a smaller value according to the energy flow value adjustment, including:

[0131] The energy flow value is calculated by the following formula:

[0132] ,

[0133] in, is the excess carbon emission value, is the default value, The energy flow value increased by the adjusted constraint. is the energy flow value of the constraint before adjustment, is the benchmark adjustment factor, is the normalized value of energy flow, For other The decomposition structure is adjusted to reduce the constraint value. For other The number of unmet constraints for the decomposition structure. For other The number of unmet constraints for the decomposition structure. is the upper limit of the decomposition structure.

[0134] The purpose of the above formula is to adjust the energy flow value so that the energy distribution is more balanced, so as to meet the carbon emission constraint. When the excess carbon emission value is less than or equal to the preset value, it means that the actual carbon emission is already within the acceptable range. At this time, the adjusted energy flow value is proportionally adjusted to the energy flow value before adjustment according to the benchmark adjustment coefficient. When the excess carbon emission value is greater than the preset value, it means that the actual carbon emission exceeds the acceptable range. In this case, a more forceful adjustment is required. The adjusted energy flow value is based on the benchmark adjustment coefficient, plus the proportion of the excess part, multiplied by the energy flow value before adjustment. In the above formula, the energy flow normalization value can be preset by the user. The function of this formula is to calculate the constraint value that should be reduced for each decomposition structure based on the adjusted energy flow value and the value ratio of each decomposition structure that does not meet the constraint condition. In this way, it can ensure that the adjusted constraint value is more reasonable and meets the overall carbon emission control target. In the above method, the energy flow value can be accurately adjusted according to the actual carbon emission situation and the proportion of non-compliance. It can dynamically adapt to different carbon emission situations and flexibly adjust the constraint value of the decomposition structure. Through proportional allocation, we ensure that the adjusted energy flow value is reasonable and meets the overall carbon emission control target.

[0135] The present invention also provides a storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided by the various embodiments described above.

[0136] Among them, the storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, the storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the storage medium can also exist in a communication device as discrete components. The storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0137] The present invention also provides a program product, which includes an execution instruction, which is stored in a storage medium. At least one processor of a device can read the execution instruction from the storage medium, and at least one processor executes the execution instruction so that the device implements the methods provided in the above various embodiments.

[0138] In the above-mentioned terminal or server embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamic optimization scheduling of low-carbon building loads under multi-energy flow network constraints, characterized in that: include: Acquire a multi-energy flow network of a monitored low-carbon building and corresponding total energy flow data, wherein the energy flow data includes water carbon energy flow information, electric carbon energy flow information, gas carbon energy flow information, and thermal carbon energy flow information; Decomposing the monitored low-carbon building structure according to a preset decomposition strategy, and determining the first sub-energy flow data corresponding to each decomposition structure in the total energy flow data in the multi-energy flow network; Construct the corresponding energy consumption objective function based on the energy consumption scenario of each decomposition structure, and establish the constraint conditions corresponding to each energy flow information; Based on the energy consumption objective function and constraint conditions, the first sub-energy flow data is decomposed and calculated to obtain distributed optimization scheduling information of building loads of low-carbon buildings; The step of performing structural decomposition on the monitored low-carbon building according to a preset decomposition strategy and determining first sub-energy flow data corresponding to each decomposition structure in the total energy flow data in the multi-energy flow network includes: The spatial structure of the monitored low-carbon building is obtained to obtain multiple subspaces, and the subspaces are processed based on a preset decomposition strategy to obtain the corresponding decomposition structure; Determine the pipeline part and pipeline end point corresponding to each decomposition structure, and collect energy flow information of each decomposition structure based on corresponding metering equipment to obtain first sub-energy flow data decomposed from the total energy flow data; The step of obtaining the spatial structure of the monitored low-carbon building to obtain a plurality of subspaces and processing the subspaces based on a preset decomposition strategy to obtain a corresponding decomposition structure includes: If it is determined that the user selects the preset decomposition strategy based on the decomposition of the metering device, a monitoring mapping relationship between the metering device and the subspace corresponding to each energy flow information is constructed, and each subspace has a mapping relationship with at least one metering device; Based on the monitoring mapping relationship between the metering equipment and the subspace corresponding to each energy flow information, the corresponding relationship between the metering equipment between the energy flows is determined to obtain a decomposition structure, wherein the decomposition structure includes at least one subspace, and each subspace corresponds to at least one metering equipment under each type of energy flow information; The method of determining the corresponding relationship between the metering devices between the energy flows to obtain the decomposition structure based on the monitoring mapping relationship between the metering devices and the subspace corresponding to each energy flow information includes: The metering equipment corresponding to each subspace under each energy flow information is sequentially traversed as the metering equipment to be compared, and the subspace corresponding to each metering equipment to be compared is counted to obtain the corresponding space set; If it is determined that the space sets of the to-be-compared measuring devices have a complete inclusion relationship, then the space set with the largest number of subspaces is determined to be the first space set; Counting all subspaces in the first space set to form a decomposition structure, and counting the metering devices mapped by the corresponding decomposition structure under all energy flow information; The first sub-energy flow data is decomposed and calculated based on the energy consumption objective function and the constraint conditions to obtain the distributed optimization scheduling information of the building load of the low-carbon building, including: Extract the first sub-energy flow data of each decomposition structure and input it into the sub-function to obtain the corresponding first function value, count all the first function values ​​and input them into the total function to obtain the second function value; If it is determined that the energy flow value of any energy flow information of any decomposition structure meets the constraint condition, energy flow conversion is performed based on the conversion relationship of the energy flow information, and distributed optimization scheduling information of the building load of the low-carbon building is obtained based on the energy flow conversion; If it is determined that the energy flow value of any energy flow information of any decomposition structure meets the constraint condition, energy flow conversion is performed based on the conversion relationship of the energy flow information, and distributed optimization scheduling information of building load of low-carbon building is obtained based on the energy flow conversion, including: If it is determined that the energy flow value of any energy flow information of any decomposition structure is greater than or equal to the constraint value, it is determined that the constraint condition is met, and the energy flow that meets the constraint condition is obtained as the first conversion energy flow; Acquire a second conversion energy flow and a convertible module corresponding to the first conversion energy flow in a conversion relationship, each convertible module corresponds to at least one second conversion energy flow, and the device included in the convertible module can use multiple energy flows; Generate corresponding load distribution optimization scheduling information based on the convertible module and the second conversion energy flow, so that when the convertible module is required to work at the next moment, energy is provided based on the second conversion energy flow; The generating corresponding load distribution optimization scheduling information based on the convertible module and the second conversion energy flow includes: Obtaining a first conversion energy flow and a second conversion energy flow converted by the convertible module within a monitoring period, and calculating a carbon emission difference corresponding to the first conversion energy flow and the second conversion energy flow per unit time; Obtaining the conversion time of the convertible module during the monitoring period, and obtaining the corresponding excess carbon emission value based on the conversion time and the carbon emission difference; If the excess carbon emission value is negative, no constraint adjustment is made; If the excess carbon emission value is a positive value, the constraint condition is adjusted based on the excess carbon emission value according to a preset strategy; If the excess carbon emission value is a positive value, adjusting the constraint condition based on the excess carbon emission value according to a preset strategy includes: Calculate the difference between the excess carbon emission value and the preset value to obtain a first difference; if the first difference is less than or equal to 0, retrieve the benchmark adjustment coefficient; if the first difference is greater than 0, calculate the dynamic adjustment coefficient based on the first difference and the benchmark adjustment value; each energy flow information has a preset benchmark adjustment coefficient; Calculate the first energy flow value of the first conversion energy flow based on the reference adjustment coefficient or the dynamic adjustment coefficient to obtain an adjusted energy flow value; If it is determined that the same energy flow information dimension in other decomposition structures does not meet the constraint condition, then based on the proportion of the values ​​of the constraint condition that are not met in other decomposition structures, the constraint values ​​of the constraint conditions of other decomposition structures are adjusted to be smaller according to the energy flow value adjustment; If it is determined that the same energy flow information dimension in other decomposition structures meets the constraint conditions, the constraint conditions in other decomposition structures will not be adjusted; If it is determined that the same energy flow information dimension in other decomposition structures does not meet the constraint condition, then based on the proportion of other decomposition structures that do not meet the constraint condition, the constraint value of the constraint condition of other decomposition structures is adjusted to a smaller value according to the energy flow value adjustment, including: The energy flow value is calculated by the following formula: ， in, is the excess carbon emission value, is the default value, The energy flow value increased by the adjusted constraint. is the energy flow value of the constraint before adjustment, is the benchmark adjustment factor, is the normalized value of energy flow, For other The decomposition structure is adjusted to reduce the constraint value. For other The number of unmet constraints for the decomposition structure. For other The number of unmet constraints for the decomposition structure. is the upper limit of the decomposition structure.

2. According to the method of dynamic optimization scheduling of low-carbon building load under multi-energy flow network constraints in claim 1, it is characterized in that: The method of obtaining the multi-energy flow network of the monitored low-carbon building and the corresponding total energy flow data includes: The functional flow pipelines of the monitored low-carbon building are obtained, and the relationship between the pipeline parts and pipeline endpoints of all functional flow pipelines and the monitored low-carbon building is obtained to obtain a multi-energy flow network; Based on the metering equipment, the energy flow information corresponding to the pipeline parts and pipeline endpoints is collected to obtain the corresponding total energy flow data.

3. A method for dynamic optimization scheduling of low-carbon building loads under multi-energy flow network constraints according to any one of claim 1, characterized in that: If it is determined that the administrator selects the preset decomposition strategy of manual matching; or, If it is determined that there is no complete inclusion relationship between the spatial sets of the to-be-compared measuring devices, interaction is performed with the administrator to obtain a decomposition structure by correlating the subspaces accordingly.

4. According to the method of dynamic optimization scheduling of low-carbon building load under multi-energy flow network constraints in claim 1, it is characterized in that: The energy usage scenario based on each decomposition structure constructs a corresponding energy usage objective function, and establishes constraint conditions corresponding to the energy flow information in each, including: Constructing an energy consumption objective function for each decomposition structure under an energy consumption scenario, wherein the energy consumption objective function includes a sub-function for each type of energy flow information and a total function for comprehensive energy flow information; The sub-functions include: ， The overall function includes: ， in, For the The decomposition structure is The function value of the energy consumption objective function in the energy flow information dimension, For the Energy consumption value of each energy flow information dimension, For the The carbon conversion coefficient of the energy flow information dimension, For the The weight coefficient of each energy flow information dimension, is the function value of the total function, is the upper limit of the number of energy flow information dimensions, is the upper limit of the number of decomposition structures, For the One-degree parameter of the energy flow information dimension; Interact with the administrator to determine the constraint conditions of each energy flow information dimension, wherein the constraint conditions include boundary values ​​and conversion relationships of corresponding energy flow information.

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