Energy efficiency-emission coordinated optimization control method and system under carbon quota constraints

Abstract

This invention discloses a method and system for collaborative optimization control of energy efficiency and emissions under carbon quota constraints. This method relates to the technical field of collaborative optimization control of energy efficiency and emissions, and includes the following steps: constructing a system topology structure containing relay nodes, using the relay nodes to complete missing data on emission paths to generate a complete boundary dataset; calculating the exergy loss of each energy conversion unit based on the boundary dataset; constructing a carbon emission mapping model based on exergy loss and carbon emission intensity, and generating a unit-level carbon deviation index based on carbon quota information; identifying control paths based on the carbon deviation index and topological connectivity, and constructing an operating parameter optimization model to generate a control strategy. This method addresses the problem that traditional exergy methods are difficult to adapt to complex coupled systems.

Description

Technical Field

[0001] The present invention relates to the technical field of energy efficiency-emission collaborative optimization control, and more specifically, to an energy efficiency-emission collaborative optimization control method and system under carbon quota constraints. Background Art

[0002] While meeting diverse load demands and ensuring energy supply security, power grid systems urgently need to achieve strict carbon emissions constraints and coordinated optimization of energy efficiency. With the implementation of carbon emissions trading mechanisms in multiple regions, carbon quotas have gradually become a key constraint affecting energy system operation strategies. Within the carbon quota framework, the system must coordinate energy efficiency improvements and carbon emissions control to balance operating costs and environmental performance.

[0003] However, in practical engineering, power grid systems exhibit complex characteristics such as multi-source coupling, multi-scale structures, and open boundaries. Significant differences in energy efficiency losses and carbon emission pathways exist between different energy devices, leading to increased spatial heterogeneity in the distribution of system carbon emissions. In this context, relying solely on economic costs or traditional energy efficiency indicators for operational optimization often makes it difficult to accurately identify high-carbon source units and potential regulation pathways, making it difficult to respond to the needs of coordinated system regulation under carbon quota constraints. This can easily lead to localized regulation failures or excessive carbon emissions, increasing the risk of carbon trading penalties.

[0004] In the field of energy efficiency assessment and optimal control, some research has introduced exergy analysis methods to reveal irreversible losses in energy utilization and improve the physical consistency of energy efficiency control. Exergy analysis, by measuring the "availability" of different energy forms, has demonstrated valuable theoretical guidance in thermal systems and closed-loop structures. It has been widely used in applications with clear thermodynamic boundaries, such as power plants and aviation propulsion.

[0005] However, the promotion of Exergy optimization in power grid systems by existing technologies still faces key technical bottlenecks:

[0006] On the one hand, power grid systems typically have characteristics such as open structure, high path uncertainty, and incomplete observation data, making it difficult to meet the traditional Exergy analysis requirements for stable system boundaries and clear energy flow paths. This makes it difficult to accurately estimate path-level losses, distorted Exergy results, and difficult to implement optimization.

[0007] On the other hand, the existing methods have not established a synergistic relationship between Exergy loss and system operating units, carbon emission intensity and carbon quota constraints. There is a lack of a mechanism to bind the path-level Exergy distribution with the unit-level carbon emission quota for analysis and regulation optimization, making it difficult to identify and control operating deviations under carbon quotas.

[0008] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0009] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an energy efficiency-emission collaborative optimization control method and system under carbon quota constraints. By introducing relay nodes to construct an improved topology structure and calculate the Exergy loss amount, the Exergy assessment accuracy and operation control coordination of the system under carbon quota constraints are improved, effectively supporting carbon excess identification and carbon surplus utilization, and realizing the collaborative optimization of energy efficiency improvement and emission control.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] A method for collaborative optimization control of energy efficiency and emissions under carbon quota constraints comprises the following steps: constructing a system topology structure including relay nodes, and generating a complete boundary data set by supplementing missing data of emission paths through relay nodes; calculating the Exergy loss of each energy conversion unit based on the boundary data set; constructing a carbon emission mapping model based on Exergy loss and carbon emission intensity, and generating a unit-level carbon deviation index based on carbon quota information; identifying the control path based on the carbon deviation index and the topological connection relationship, and constructing an operation parameter optimization model to generate a control strategy.

[0012] In a preferred embodiment, the specific steps of constructing a system topology structure including relay nodes are: identifying the positional relationship and energy transmission path of each functional device in the target system to construct an energy flow topology network diagram, mapping the functional devices into nodes, and mapping the energy transmission paths between devices into edges to construct a first topology structure; based on the boundary data of known nodes and the topological energy conservation relationship, evaluating the boundary uncertainty state of each path in the first topology structure, and generating a set of path uncertainty indicators; according to the set of path uncertainty indicators, inserting relay nodes on paths with unclear boundaries to form a second topology structure including relay nodes.

[0013] In a preferred embodiment, the evaluation of the boundary uncertainty state of each path in the first topological structure and the generation of a path uncertainty indicator set are specifically as follows: identifying the missing parameters of each path in the first topological structure and counting the number of missing parameters of each path; performing closed-loop energy analysis on all energy closed loops in the first topological structure and calculating the closed-loop energy residual; allocating the closed-loop energy residual to the path level according to the proportion of the energy flux per unit time of each path in the corresponding closed loop; weightedly calculating the path boundary uncertainty score according to the number of missing parameters and the closed-loop energy residual of the path level, and generating a path uncertainty indicator set containing all path scores; the path uncertainty indicator set includes the path number and the boundary uncertainty score of the path.

[0014] In a preferred embodiment, according to the path uncertainty indicator set, relay nodes are inserted on the path with unclear boundaries to form a second topological structure containing relay nodes. The specific steps are as follows: the path uncertainty indicator set is arranged in descending order by score to generate a relay node insertion queue; the mean and standard deviation of all path uncertainty scores are calculated to obtain an insertion threshold; all insertion queues are traversed and when the score is greater than or equal to the insertion threshold, the path and its subsequent paths are marked as candidate relay node paths; and relay nodes are deployed at the geometric midpoint of the candidate path to form a second topological structure containing relay nodes.

[0015] In a preferred embodiment, the method of completing the missing data of the emission path through relay nodes to generate a complete boundary data set is specifically as follows: performing topological identification on each path segment of the second topological structure, and the topological identification includes: if the two end nodes of the path segment contain at least one relay node, it is marked as an estimated path segment; otherwise it is marked as a directly calculated path segment; for the estimated path segment, the known thermodynamic boundary data of its two end nodes are obtained, the energy flux conservation equation group of the relay node is constructed, and the thermodynamic state parameters of the relay node, including temperature, pressure, etc., are estimated using a numerical inversion method; the estimated relay node thermodynamic state parameters are merged with the original boundary observation data to complete the boundary thermodynamic parameters of each path segment in the second topological structure to form a complete boundary data set.

[0016] In a preferred embodiment, the calculation of the Exergy loss of each energy conversion unit in combination with the boundary data set is specifically as follows: based on the boundary data set, the energy flux of each path segment in the second topological structure is calculated; according to the physical category to which the path belongs, the Exergy flux calculation model corresponding to the energy flux is matched; according to the corresponding Exergy flux calculation model, the Exergy input and output of each path segment and relay node are calculated respectively, and the difference is calculated to calculate the respective Exergy losses; the Exergy loss data of the path segments and relay nodes are summarized to generate the system Exergy loss distribution matrix.

[0017] In a preferred embodiment, the carbon emission mapping model is constructed based on Exergy loss and carbon emission intensity, and a unit-level carbon deviation index is generated in combination with carbon quota information, specifically including the following steps: consulting a standard carbon factor database or according to an equipment emission factor table to determine the carbon emission intensity coefficient of each unit to form a carbon emission intensity vector; calculating the estimated carbon emission value of each unit by element-by-element multiplication of the carbon emission intensity vector and the unit Exergy loss matrix; obtaining the upper limit of the carbon quota allocated to each unit in the system; and calculating the unit-level carbon deviation index by taking the difference between the estimated carbon emission value of each unit and the upper limit of the carbon quota.

[0018] In a preferred embodiment, the method of identifying the control path based on the carbon deviation index and the topological connection relationship, and constructing an operating parameter optimization model to generate a control strategy is specifically as follows: based on the carbon deviation index and the topological connection relationship, the carbon control path between units is identified to obtain a set of feasible control candidate paths; based on the set of feasible control candidate paths, an operating parameter optimization model is constructed to generate an optimized control strategy.

[0019] In a preferred embodiment, the operating parameter optimization model includes an optimization objective function and joint constraints.

[0020] In a preferred embodiment, the optimization objective function takes minimizing the system carbon deviation as the optimization goal; the joint constraints include control parameter range constraints, energy conservation constraints, equipment adjustment capability limitation constraints, topological coupling consistency constraints and multi-unit shared structural unit coordination constraints.

[0021] An energy efficiency-emission collaborative optimization control system under carbon quota constraints includes a topology modeling module, an Exergy estimation module, a unit attribution module, a carbon deviation calculation module, a path identification module and a parameter optimization module; the topology modeling module is used to construct an energy flow network topology diagram, identify path boundary uncertainties and insert relay nodes to form a second topology structure; the Exergy estimation module is used to calculate the Exergy loss of each path and relay node based on the second topology structure and node thermodynamic parameters; the unit attribution module is used to establish a unit number set according to the node function identifier to form a unit attribution mapping relationship between the path and the relay node; the carbon deviation calculation module is used to aggregate the unit structure Exergy loss, combine the carbon emission intensity and carbon quota information, and calculate the unit-level carbon emission deviation; the path identification module is used to identify the connection path between high-carbon deviation units based on the topological connection relationship to form a feasible control path set; the parameter optimization module is used to construct an operation parameter optimization model with the minimum carbon deviation as the goal, and output the system optimization control strategy.

[0022] The technical effects and advantages of the energy efficiency-emission coordinated optimization control method under carbon quota constraints of the present invention are as follows:

[0023] 1. This paper introduces relay nodes to construct energy flow topology, complete boundary data, and calculate path-level Exergy loss, thereby improving the accuracy and applicability of Exergy estimation in open and complex systems.

[0024] 2. The present invention models the association between Exergy loss and unit carbon emission intensity and carbon quota constraints, constructs a parameter optimization model to achieve coordinated control, and enhances the responsiveness of operation and control to carbon deviations and the regulatory effect of quota execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1Schematic diagram of the flow of the energy efficiency-emission coordinated optimization control method under the carbon quota constraint of the present invention;

[0026] Figure 2 A flowchart of a system topology including relay nodes is constructed for the present invention;

[0027] Figure 3 Schematic diagram of the structure of the first topology of the present invention;

[0028] Figure 4 is a schematic structural diagram of the second topology of the present invention;

[0029] Figure 5 This is a schematic diagram of the structure of the energy efficiency-emission collaborative optimization control system under the carbon quota constraint of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0031] Example 1, Figure 1 The present invention provides an energy efficiency-emission coordinated optimization control method under carbon quota constraints, comprising the following steps:

[0032] S1, constructs a system topology structure including relay nodes, and completes the missing data of the emission path through relay nodes to generate a complete boundary dataset.

[0033] In this embodiment, if Figure 2 As shown, the specific steps of constructing the system topology structure including relay nodes are:

[0034] Identify the positional relationship and energy transmission path of each functional device in the target system to construct an energy flow topology network diagram, map the functional devices into nodes, and map the energy transmission paths between devices into edges to construct a first topological structure;

[0035] Based on the boundary data of known nodes and the topological energy conservation relationship, the boundary uncertainty state of each path in the first topological structure is evaluated to generate a set of path uncertainty indicators;

[0036] According to a set of path uncertainty indicators, relay nodes are inserted on paths with unclear boundaries to form a second topology structure containing relay nodes;

[0037] The topological energy conservation relationship refers to the energy conservation principle followed between energy transmission paths in the system topology structure;

[0038] Furthermore, the step of evaluating the boundary uncertainty state of each path in the first topological structure and generating the path uncertainty index set is specifically:

[0039] Identifying missing parameters of each path in the first topology structure, and counting the number of missing parameters of each path;

[0040] Performing closed-loop energy analysis on all energy closed loops in the first topological structure and calculating closed-loop energy residuals;

[0041] The closed-loop energy residual is allocated to the path level according to the proportion of the energy flux per unit time of each path in the corresponding closed loop;

[0042] The path boundary uncertainty score is calculated based on the number of missing parameters and the path level closed-loop energy residual, and a path uncertainty index set containing all path scores is generated;

[0043] The path uncertainty indicator set includes a path number and a boundary uncertainty score of the path.

[0044] The steps of identifying the missing parameters of each path in the first topology structure and counting the number of the missing parameters of each path are as follows:

[0045] For any edge in the first topological structure, check whether its starting node and ending node have observation data of the following thermodynamic parameters: temperature, heat flow / power, mass flow rate and pressure;

[0046] If there are some missing items in the above parameters, the path will be marked as incomplete and the number of missing parameters will be recorded;

[0047] The closed-loop energy analysis is performed on all energy closed loops in the first topology structure to calculate the closed-loop energy residuals as follows:

[0048] Based on the first topological structure, a depth-first search algorithm is used to identify all closed loops;

[0049] Each path in the closed loop is classified according to the following rules:

[0050] If the path starts outside the loop and ends inside the loop, it is considered an external input path;

[0051] If the path's starting point is inside the loop and its end point is outside the loop, it is considered an external output path.

[0052] The starting and ending points are both within the loop and are not included in the input / output energy;

[0053] The energy flow of each external input / output path is calculated using the corresponding energy flow analysis method according to the physical category to which the path belongs, and the total input / output energy is obtained by summing them. The specific formula is as follows:

[0054]

[0055] in, and are the total amount of input / output energy per unit time of each closed loop; and are external input / output path sets respectively; is the energy flux of path k, where is the thermodynamic state parameter of the relay node, is the parameter to be estimated; is a set of known physical parameters of path k, including mass flow rate Specific heat capacity , upstream and downstream node temperatures, thermal resistance R; is the flux function model determined by the physical type of the path. If the path is a fluid path, then , if the path is a heat conduction path, then ,in, 、 、 、 and are the mass flow rate, specific heat capacity, starting node temperature, ending node temperature and path thermal resistance of path k respectively; Indicates the path k The corresponding time window;

[0056] By comparing the difference between the total input energy and the total output energy, the closed-loop energy residual is calculated. The specific formula is as follows:

[0057]

[0058] in, is the closed-loop energy residual;

[0059] The closed-loop energy residual is allocated to the path level according to the proportion of the energy flux per unit time of each path in the corresponding closed loop, specifically:

[0060] The energy flux per unit time of each path in the closed loop is calculated using an energy flow analysis method that matches the physical category to which the path belongs.

[0061] The energy flux values ​​of all paths in the closed loop are summed to obtain the total energy flux of the closed loop;

[0062] The weight coefficient of each path is calculated based on the proportion of the energy flux per unit time of each path in the total closed-loop energy flux. The specific formula is as follows:

[0063]

[0064] in, , is the set of paths within the closed loop; For path k Energy flux ratio weight

[0065] The above closed-loop energy residual is distributed to each path according to the weight coefficient to obtain the energy residual of each path. The specific calculation formula is as follows:

[0066]

[0067] in, is the energy residual of path k;

[0068] The path boundary uncertainty score is calculated based on the number of missing parameters and the path level closed-loop energy residual weighted. The specific calculation formula is as follows:

[0069]

[0070] in, For path k The number of missing parameters; and are the maximum values ​​of the corresponding indicators of all paths; and are the weight coefficients of the missing parameters and energy residuals, respectively, satisfying In practical applications, it can be set as follows: When the number of missing parameters has a more significant impact on the system path uncertainty, it is preferred to set , Otherwise, you can set , The present invention also supports parameter fitting of weights using statistical learning or empirical regression methods based on error sensitivity experiments in actual engineering scenarios or historical data regression methods.

[0071] Furthermore, the steps of inserting relay nodes on the path with unclear boundaries to form a second topology structure including relay nodes are as follows:

[0072] Arrange the path uncertainty indicator set in descending order of score, generate relay nodes and insert them into the queue;

[0073] Calculate the mean and standard deviation of all path uncertainty scores to obtain the insertion threshold;

[0074] Traverse all insertion queues. When the score is greater than or equal to the insertion threshold, mark the path and its subsequent paths as candidate relay node paths;

[0075] Relay nodes are deployed at geometric midpoints of the candidate paths to form a second topology structure including the relay nodes.

[0076] The specific calculation formula of the insertion threshold is as follows:

[0077]

[0078]

[0079]

[0080] in, is the insertion threshold, and are the mean and standard deviation of all path uncertainty scores, N is the total number of paths in the current topology, The 75th percentile of all path uncertainty scores;

[0081] like Figure 4 As shown, based on Figure 3 The missing parameters and closed-loop energy residual in the first topology shown The path uncertainty scores (U = 0.81, U = 0.92) are calculated, and relay nodes (R1, R2) are inserted at the midpoints of the paths where the scores exceed the threshold to form a second topology, which provides a structural basis for calculating the Exergy loss in subsequent steps.

[0082] Furthermore, the missing data of the emission path is supplemented by relay nodes to generate a complete boundary data set, specifically:

[0083] Performing topology identification on each path segment of the second topology structure, wherein the topology identification includes:

[0084] If the nodes at both ends of a path segment contain at least one relay node, it is marked as an estimated path segment;

[0085] Otherwise, mark it as a path segment to be directly calculated;

[0086] For the estimated path segment, the known thermodynamic boundary data of the nodes at both ends are obtained, the energy flux conservation equations of the relay nodes are constructed, and the thermodynamic state parameters of the relay nodes, including temperature and pressure, are estimated using numerical inversion methods.

[0087] The estimated thermodynamic state parameters of the relay nodes are merged with the original boundary observation data to complete the boundary thermodynamic parameters of each path segment in the second topological structure and form a complete boundary data set.

[0088] The energy flux conservation equations are:

[0089]

[0090] in, is the set of paths pointing to the relay node; is the set of paths flowing out from the relay node.

[0091] The specific formula for estimating the thermodynamic state parameters of the relay node using the numerical inversion method is as follows:

[0092]

[0093] in, is the thermodynamic state parameter of the relay node estimated value; = represents the numerical inversion operation of the equation, and K is the total number of estimated path segments. The above equations are nonlinear energy conservation relations and are difficult to solve analytically. Therefore, the present invention adopts the minimum residual numerical inversion method to estimate the thermodynamic state parameters of the relay node.

[0094] S2, calculates the Exergy loss of each energy conversion unit based on the boundary data set.

[0095] In this embodiment, the calculation of the Exergy loss of each energy conversion unit by combining the boundary data set is specifically as follows:

[0096] calculating an energy flux of each path segment in the second topological structure based on the boundary data set;

[0097] According to the physical category to which the path belongs, the Exergy flux calculation model corresponding to the energy flux is matched;

[0098] According to the corresponding Exergy flux calculation model, the Exergy input and output of each path segment and relay node are calculated respectively, and the difference is used to calculate the respective Exergy loss;

[0099] Aggregate the Exergy loss data of path segments and relay nodes to generate the system Exergy loss distribution matrix.

[0100] The energy flux of each path segment in the second topological structure is calculated based on the boundary data set, specifically:

[0101] identifying physical categories of all path segments in the second topology;

[0102] For each path segment, the energy flux calculation model corresponding to the physical category to which it belongs is selected according to the boundary thermodynamic state parameters of its starting and ending nodes;

[0103] The thermodynamic state parameters in the boundary data set are substituted into the energy flux model to calculate the energy flux value of the path segment, providing an input basis for subsequent Exergy calculations.

[0104] The specific formula of the energy flux calculation model is as follows:

[0105]

[0106] in, For path k energy flux.

[0107] Furthermore, the Exergy flux calculation model corresponding to the energy flux is matched according to the physical category to which the path belongs. The specific formula is as follows:

[0108]

[0109] Among them, the function e() is the Exergy flux calculation function determined by the path physical type. If the path is a fluid path, the ideal gas model is used for calculation. The specific formula is as follows:

[0110]

[0111] If the path is a heat conduction path, the heat conduction Exergy calculation model is used. The specific formula is as follows:

[0112]

[0113] Among them, c p is the specific heat capacity at constant pressure; R is the gas constant; T 0, p 0 are the ambient reference temperature and pressure respectively; T is the current temperature of the system; p is the current pressure of the system; c is the specific heat capacity of the substance.

[0114] The Exergy input and output of each path segment and relay node are calculated separately, and the difference is used to calculate the respective Exergy losses. The specific formula is:

[0115]

[0116]

[0117]

[0118]

[0119] in, and Divided into paths kThermodynamic state parameters corresponding to the starting and ending nodes, , Path k Exergy input and output; For path k Exergy loss; Relay Node m A set of adjacent paths; is the path direction factor, Indicates that path k flows into relay node m, Indicates that path k flows out of relay node m; is the Exergy flux calculated using the thermodynamic state parameters of the relay node; For relay nodes m Exergy loss;

[0120] The Exergy loss data of the path segments and relay nodes are aggregated to generate the system Exergy loss distribution matrix. The specific steps are as follows:

[0121] According to the system operation function division, extract the function identification parameters of all nodes in the second topology structure and construct the unit number set ,in, represents the i-th energy functional unit;

[0122] For each path segment k and relay node m, the following attribution rules are used to determine the attribution unit number based on the functional identification information of its adjacent nodes: :

[0123] If the nodes at both ends of the path segment belong to the same unit , then the path segment is included in the unit;

[0124] If a path segment connects two different units, it is classified into the starting unit according to the direction of its main energy flow;

[0125] If all path segments connected by the relay node belong to the same unit , it is classified into this unit, otherwise it is classified into the unit to which the path segment with the maximum energy flux belongs.

[0126] A collection of mappings from building blocks to structural units ;

[0127] Each unit The Exergy loss values ​​of the included path segments and relay nodes are aggregated and summed to obtain the unit-level loss vector. The specific formula is as follows:

[0128]

[0129] Summarize the Exergy loss values ​​of all units by number and construct the Exergy loss distribution matrix of each energy conversion unit , used for subsequent carbon emission intensity mapping and regulation modeling.

[0130] S3, builds a carbon emission mapping model based on Exergy loss and carbon emission intensity, and generates unit-level carbon deviation indicators based on carbon quota information.

[0131] In this embodiment, the carbon emission mapping model is constructed based on Exergy loss and carbon emission intensity, and the unit-level carbon deviation index is generated in combination with carbon quota information. Specifically, the steps include:

[0132] Consult the standard carbon factor database or the equipment emission factor table to determine the carbon emission intensity coefficient for each unit and form a carbon emission intensity vector ;

[0133] Using the carbon intensity vector Multiply element-by-element with the unit Exergy loss matrix E to calculate the estimated carbon emissions for each unit ,in ;

[0134] Get the upper limit of carbon quota allocated to each unit in the system ;

[0135] Calculate the carbon deviation index of each unit based on the estimated carbon emissions of each unit and the allocated carbon quota cap;

[0136] The specific calculation formula for each unit carbon deviation index is as follows:

[0137]

[0138] in, Represents the carbon emission deviation index of the i-th unit, when >0 means excess emissions. <0 indicates that redundant quota exists.

[0139] This step establishes a mapping relationship between Exergy and carbon emissions and integrates carbon quota boundary information to achieve a quantitative assessment of the carbon quota execution deviation of each functional unit in the system, providing a basic criterion for subsequent regulatory path identification and optimized control.

[0140] S4, based on the carbon deviation index and topological connection relationship, the control path is identified and the operation parameter optimization model is constructed to generate the control strategy.

[0141] In this embodiment, the control path is identified based on the carbon deviation index and the topological connection relationship, and the operation parameter optimization model is constructed to generate the control strategy, specifically:

[0142] Based on the carbon deviation index and topological connection relationship, the carbon regulation path between units is identified to obtain a set of feasible candidate regulation paths;

[0143] Based on the set of feasible control candidate paths, an operation parameter optimization model is constructed to generate an optimization control strategy.

[0144] The specific steps for obtaining the set of feasible control candidate paths are as follows:

[0145] Based on the second topology Construct the unit connection matrix A ∈ {0,1} n×n ,in:

[0146]

[0147] Filter to meet >0 and <0 and = 1 unit pair , indicating the unit and There is an actual energy flow channel between Excess emissions, There are redundant quotas;

[0148] For all units that meet the conditions , extract the set of all path segments connecting them , forming a set of feasible control paths.

[0149] Furthermore, the operation parameter optimization model is constructed to generate an optimization control strategy, specifically:

[0150] Based on the set of feasible control paths, record the set of structural units contained in each path segment , and the complete set of regulatory structural units is obtained:

[0151]

[0152] in, Represents the set of structural units that need to participate in parameter optimization, which serves as the basis for the variable set of the subsequent parameter optimization model;

[0153] Based on the complete set of control structure units, the operation parameter optimization model is constructed and solved to obtain the optimal adjustment value of the control parameters and form an optimized control strategy;

[0154] The operating parameter optimization model includes an optimization objective function and joint constraints;

[0155] The optimization objective function is to minimize the system carbon deviation as the optimization goal, and the specific formula is as follows:

[0156]

[0157] in, x Control parameter vector for the structural unit to be optimized; Unit Carbon emission intensity coefficient; Unit In the control parameters x Exergy loss under Unit carbon quota limit.

[0158] The joint constraints include control parameter range constraints, energy conservation constraints, equipment adjustment capability limit constraints, topology coupling consistency constraints, and multi-unit shared structural unit coordination constraints. The specific formulas are as follows:

[0159]

[0160] in, and are the upper and lower limits of the j-th control variable, respectively, which are determined by the equipment specifications; is the energy flux of path segment k under the control parameter x; and They represent the incoming path set and outgoing path set of node v respectively, and V is the set of all nodes in the second topological structure; represents the regulation index function of structural unit j; To adjust the upper limit of its capacity; A collection of structural units with regulatory capabilities; A set of structural unit pairs shared by multiple units; is the control coupling function between structures with coupling relationships; is the coupling deviation tolerance threshold.

[0161] This optimization model introduces a carbon quota deviation control target to generate parameter adjustment suggestions that meet carbon emission reduction constraints while ensuring energy conservation, physical feasibility of regulation, and system operation stability. It has good adaptability and engineering feasibility.

[0162] Figure 5 This is the structure diagram of the energy efficiency-emission coordinated optimization control system under carbon quota constraints, including topology modeling module, Exergy estimation module, unit attribution module, carbon deviation calculation module, path identification module and parameter optimization module;

[0163] A topology modeling module is used to construct the energy flow network topology diagram, identify path boundary uncertainties and insert relay nodes to form a second topology structure;

[0164] An Exergy estimation module, configured to calculate the Exergy loss of each path and relay node based on the second topology structure and node thermodynamic parameters;

[0165] The unit attribution module is used to establish a unit number set according to the node function identifier and form a unit attribution mapping relationship between the path and the relay node;

[0166] The carbon deviation calculation module is used to aggregate the unit structure Exergy loss, combine the carbon emission intensity and carbon quota information, and calculate the unit-level carbon emission deviation;

[0167] A path identification module is used to identify the connection paths between high-carbon deviation units based on topological connection relationships and form a set of feasible control paths;

[0168] The parameter optimization module is used to build an operating parameter optimization model with the goal of minimizing carbon deviation and output the system optimization control strategy.

[0169] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0170] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0171] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0172] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0173] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0174] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for energy efficiency-emission coordinated optimization control under carbon quota constraints, characterized in that: The steps include: Construct a system topology structure including relay nodes, and use relay nodes to complete the missing data of the emission path to generate a complete boundary dataset; Calculate the Exergy loss of each energy conversion unit by combining the boundary data set; A carbon emission mapping model is built based on Exergy loss and carbon emission intensity, and a unit-level carbon deviation indicator is generated by combining carbon quota information; Based on the carbon deviation index and topological connection relationship, the control path is identified and the operation parameter optimization model is constructed to generate the control strategy; The specific steps of constructing the system topology structure including relay nodes are: Identify the positional relationship and energy transmission path of each functional device in the target system to construct an energy flow topology network diagram, map the functional devices into nodes, and map the energy transmission paths between devices into edges to construct a first topological structure; Based on the boundary data of known nodes and the topological energy conservation relationship, the boundary uncertainty state of each path in the first topological structure is evaluated to generate a set of path uncertainty indicators; According to a set of path uncertainty indicators, relay nodes are inserted on paths with unclear boundaries to form a second topology structure containing relay nodes.

2. The energy efficiency-emission coordinated optimization control method under carbon quota constraints according to claim 1 is characterized in that: The boundary uncertainty state of each path in the first topological structure is evaluated to generate a path uncertainty index set, specifically: Identifying missing parameters of each path in the first topology structure, and counting the number of missing parameters of each path; Performing closed-loop energy analysis on all energy closed loops in the first topological structure and calculating closed-loop energy residuals; The closed-loop energy residual is allocated to the path level according to the proportion of the energy flux per unit time of each path in the corresponding closed loop; The path boundary uncertainty score is calculated based on the number of missing parameters and the path level closed-loop energy residual, and a path uncertainty index set containing all path scores is generated; The path uncertainty indicator set includes a path number and a boundary uncertainty score of the path.

3. The energy efficiency-emission coordinated optimization control method under carbon quota constraints according to claim 2 is characterized in that: The steps of inserting relay nodes on the path with unclear boundaries to form a second topology structure including relay nodes are as follows: Arrange the path uncertainty indicator set in descending order of score, generate relay nodes and insert them into the queue; Calculate the mean and standard deviation of all path uncertainty scores to obtain the insertion threshold; Traverse all insertion queues. When the score is greater than or equal to the insertion threshold, mark the path and its subsequent paths as candidate relay node paths; Relay nodes are deployed at geometric midpoints of the candidate paths to form a second topology structure including the relay nodes.

4. The energy efficiency-emission coordinated optimization control method under carbon quota constraints according to claim 3 is characterized in that: The method of generating a complete boundary dataset by supplementing the missing data of the emission path through relay nodes is as follows: Performing topology identification on each path segment of the second topology structure, wherein the topology identification includes: marking the path segment as an estimated path segment if at least one relay node is included in the nodes at both ends of the path segment, and marking the path segment as a directly calculated path segment otherwise; For the estimated path segment, the known thermodynamic boundary data of the nodes at both ends are obtained, the energy flux conservation equations of the relay nodes are constructed, and the thermodynamic state parameters of the relay nodes are estimated using the numerical inversion method; The estimated thermodynamic state parameters of the relay nodes are merged with the original boundary observation data to complete the boundary thermodynamic parameters of each path segment in the second topological structure and form a complete boundary data set.

5. The energy efficiency-emission coordinated optimization control method under carbon quota constraints according to claim 4 is characterized in that: The Exergy loss of each energy conversion unit is calculated by combining the boundary data set, specifically: calculating an energy flux of each path segment in the second topological structure based on the boundary data set; According to the physical category to which the path belongs, the Exergy flux calculation model corresponding to the energy flux is matched; According to the Exergy flux calculation model, the Exergy input and output of each path segment and relay node are calculated respectively, and the respective Exergy losses are calculated; Aggregate the Exergy loss data of path segments and relay nodes to generate the system Exergy loss distribution matrix.

6. The energy efficiency-emission coordinated optimization control method under carbon quota constraints according to claim 5 is characterized in that: The carbon emission mapping model is constructed based on Exergy loss and carbon emission intensity, and the unit-level carbon deviation index is generated in combination with carbon quota information, specifically: Based on the standard carbon factor database or equipment emission factor table, determine the carbon emission intensity coefficient of each unit to form a carbon emission intensity vector; Calculate the estimated value of each unit carbon emission by multiplying the carbon emission intensity vector by the unit Exergy loss matrix element by element; Obtain the upper limit of the carbon quota allocated to each unit in the system and calculate the carbon deviation index at the unit level.

7. The energy efficiency-emission coordinated optimization control method under carbon quota constraints according to claim 6 is characterized in that: The control path is identified based on the carbon deviation index and the topological connection relationship, and the operation parameter optimization model is constructed to generate the control strategy, specifically: Based on the carbon deviation index and topological connection relationship, the carbon regulation path between units is identified to obtain a set of feasible candidate regulation paths; Based on the set of feasible control candidate paths, an operation parameter optimization model is constructed to generate an optimization control strategy.

8. The energy efficiency-emission coordinated optimization control method under carbon quota constraints according to claim 7 is characterized in that: The operating parameter optimization model includes: Taking minimizing the system carbon deviation as the optimization goal, the optimization objective function is constructed: in, Control parameter vector for the structural unit to be optimized; Unit Carbon emission intensity coefficient; Unit In the control parameters Exergy loss under Unit Carbon quota limits; Establish constraints, including control parameter range constraints, energy conservation constraints, equipment adjustment capability limit constraints, topology coupling consistency constraints, and multi-unit shared structural unit coordination constraints.

9. A system using the energy efficiency-emission coordinated optimization control method under carbon quota constraints according to any one of claims 1 to 8, characterized in that: It includes topology modeling module, Exergy estimation module, unit attribution module, carbon deviation calculation module, path identification module and parameter optimization module; A topology modeling module is used to construct the energy flow network topology diagram, identify path boundary uncertainties and insert relay nodes to form a second topology structure; An Exergy estimation module, configured to calculate the Exergy loss of each path and relay node based on the second topology structure and node thermodynamic parameters; The unit attribution module is used to establish a unit number set according to the node function identifier and form a unit attribution mapping relationship between the path and the relay node; The carbon deviation calculation module is used to aggregate the unit structure Exergy loss, combine the carbon emission intensity and carbon quota information, and calculate the unit-level carbon emission deviation; A path identification module is used to identify the connection paths between high-carbon deviation units based on topological connection relationships and form a set of feasible control paths; The parameter optimization module is used to build an operating parameter optimization model with the goal of minimizing carbon deviation and output unit-level and system-level optimization control strategies.

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