CN-122022326-A - Industrial energy consumption collaborative control method based on dynamic carbon cost mapping and random optimization

CN122022326ACN 122022326 ACN122022326 ACN 122022326ACN-122022326-A

Abstract

The invention discloses an industrial energy consumption collaborative management and control method based on dynamic carbon cost mapping and random optimization, and belongs to the technical field of industrial energy management. The method comprises the steps of collecting running power data of industrial equipment, carbon emission factors of a power grid and carbon transaction prices in real time, constructing an objective function with the minimum total cost as a target, carrying out optimization solution on the objective function by utilizing an improved alternate direction multiplier method, establishing a multi-dimensional stability index system and a stability scoring function, carrying out simulation test and stability judgment according to optimal decision variables, constructing a multi-target cooperative control scheme by combining the real-time collected data, generating a control instruction, remotely issuing the control instruction to each execution equipment end, and continuously monitoring the execution effect to realize dynamic cooperative control. The invention aims to solve the problems of non-uniform dimension of carbon emission cost and economic cost, difficult multi-target coordination and the like in the traditional energy consumption, and can effectively improve the control level of industrial energy consumption.

Inventors

HUANG PENG
WANG LE
WANG JIANGUO
WANG SHILIANG
Shu Yefan
Ni Annan
ZHANG QIANG
HUANG HUI
JI JIE
DING ZUJUN
ZHUANG XUZHOU

Assignees

淮阴工学院

Dates

Publication Date: 20260512
Application Date: 20260129

Claims (10)

1. The industrial energy consumption collaborative management and control method based on dynamic carbon cost mapping and random optimization is characterized by comprising the following steps of: Step 1, collecting operation power data of industrial equipment, a power grid carbon emission factor and a carbon transaction price in real time, and preprocessing the data; Step 2, constructing a dynamic mapping relation for the preprocessed data, constructing an objective function by taking the minimum total cost as a target, and carrying out optimization solution on the objective function by utilizing an improved alternate direction multiplier method to obtain an optimal decision variable; Step 3, establishing a multi-dimensional stability index system and a stability scoring function, and performing simulation test and stability judgment according to an optimal decision variable; step 4, constructing a multi-target cooperative control scheme according to the judging result and combining real-time acquisition data to generate a control instruction; and 5, remotely issuing a control instruction to each execution equipment end, continuously monitoring the execution effect, triggering the re-execution of the optimization flow by establishing a real-time feedback closed loop, and guiding the system to dynamically adjust parameters so as to realize dynamic collaborative management and control.
2. The industrial energy consumption collaborative management and control method based on dynamic carbon cost mapping and random optimization according to claim 1, wherein step 2 comprises: establishing a direct quantification relation of carbon cost, and calculating the instantaneous carbon emission, wherein the formula is as follows: , Wherein, the Representation of The instant carbon emission amount is calculated at the moment, Representation of The power data is run at the moment in time, Representation of The carbon emission factor of the power grid at moment; Combined carbon trade price The carbon cost is calculated as: , the constructed objective function is expressed as: , Wherein, the A set of decision variables that are carbon cost dependent; for a set of decision variables that are related to economic costs, The representation takes the minimum value of the value, Representing the weight of the carbon cost to be added, The weight of the economic cost is represented by, ; Representing an economic cost function, the expression is: , Wherein, the The dynamic weight coefficient is an adjusting factor reflecting the economic cost; the price of the energy source is; Operating power for the device.
3. The method for collaborative management of industrial energy consumption based on dynamic carbon cost mapping and stochastic optimization according to claim 2, wherein the step of optimizing the solution of the objective function using the modified alternate direction multiplier method comprises: Setting constraint conditions to ensure that the optimization result meets the equipment operation limit and the power grid dispatching requirement, wherein the constraint conditions are expressed as follows: , Wherein, the And Representing the carbon cost variable as a coefficient matrix And economic cost variable Is a matrix Constraint coefficient, matrix of a coke cost-related decision variable Constraint coefficient of focusing economic cost related decision variable, constraint constant vector Physical limitations covering the operational limits of the device; introducing Lagrangian multipliers And penalty term Construction of an augmented Lagrangian function Converting the constraint problem into unconstrained optimization, which is expressed as: , Solving the extended Lagrangian function through an alternate updating mechanism until the convergence condition is met, thereby obtaining the objective function And taking the minimized optimal solution as an optimal decision variable.
4. The industrial energy consumption collaborative management method based on dynamic carbon cost mapping and random optimization according to claim 1, wherein the alternate update mechanism comprises: Firstly, updating the carbon cost variable, wherein the updating formula is as follows: , Wherein, the Representing the number of alternating iterations; and then updating the economic cost variable, wherein the updating formula is as follows: , and updating the multiplier, wherein the updating formula is as follows: , Calculating residual norms and quantifying the current solution , ) The degree of deviation from the constraint is expressed as: , wherein the convergence condition is: 。
5. The industrial energy consumption collaborative management and control method based on dynamic carbon cost mapping and random optimization according to claim 1, wherein the expression of the multi-dimensional stability index system in step 3 is: , Wherein, the At the moment for the system Is used for the combination stability index of (a), For the number of stability dimensions, Is the first The dimensions are at the moment Is satisfied by the dynamic weight of ; Is the first Normalized stability score for each dimension, value range [0,1], score for each dimension Is defined as follows: The carbon cost stability score is expressed as: , The economic cost stability score is expressed as: , the algorithm convergence stability score is expressed as: , the equipment operational stability score is expressed as: , Wherein, the Indicating time of day The carbon cost optimizes the target value of the carbon, Is extremely small constant, and prevents denominator from being zero; Indicating time of day The economic cost is optimized to a target value, For the initial residual norm, Indicating the device power rating.
6. The method for collaborative management of industrial energy consumption based on dynamic carbon cost mapping and stochastic optimization according to claim 4, wherein the stability scoring function in step 3 is expressed as: , Wherein, the Is the first Item sub-policies at time of day Reflecting the importance of the dynamic weight in the overall strategy; is the first The real-time failure probability of the item sub-strategy is calculated by combining historical data with real-time monitoring; for the strategy gain fluctuation rate, the uncertainty of the synergy of economy and carbon emission reduction double targets is represented; evaluating the adaptability of the strategy under the disturbance condition for the robustness score; is a global environmental factor.
7. The method for collaborative management of industrial energy consumption based on dynamic carbon cost mapping and stochastic optimization according to claim 6, wherein step 3 comprises: an adaptive stability threshold is set and a three-level response mechanism is triggered when the stability score is below the threshold.
8. The method for collaborative management of industrial energy consumption based on dynamic carbon cost mapping and stochastic optimization according to claim 7, wherein the rules triggering the tertiary response mechanism include: When scoring When the stability of the system is lower than the threshold value, the first-stage response is triggered preferentially, and if the stability score is not recovered to be higher than the threshold value after the first-stage response, the second-stage response is triggered; wherein the first-stage response is to adjust the weight distribution of the carbon cost and the economic cost based on the real-time data stream, namely to adjust the weight coefficient And ; The second level of response is to dynamically adjust by monitoring the original residual error in the iterative process A value; And the third stage of response is to re-conduct the data collection and preprocessing flow.
9. The industrial energy consumption co-management method based on dynamic carbon cost mapping and random optimization according to any one of claims 1 to 8, wherein the multi-objective co-management scheme in step 4 comprises carbon cost optimization strategy, economic cost optimization strategy, dynamic weight allocation, stability check result and continuous real-time data flow.
10. An industrial energy consumption collaborative management and control system based on dynamic carbon cost mapping and random optimization, which is characterized by comprising: the multi-source data sensing module is used for sensing and collecting data of the power sensor, the carbon-emission factor detection unit and the external data interface; The data preprocessing module is used for cleaning and normalizing the acquired data; The carbon cost-economic cost dynamic mapping model is used for accurately calculating the cost; The stability verification model is used for judging the stability of the related conditions and issuing corresponding strategies; the cooperative control module is used for generating a cooperative control scheme by combining the sensing data and the stability verification result; the intelligent decision center is used for realizing the remote monitoring function and generating a decision scheme.

Description

Industrial energy consumption collaborative control method based on dynamic carbon cost mapping and random optimization Technical Field The invention relates to the technical field of industrial energy management, in particular to an industrial energy consumption collaborative management and control method based on dynamic carbon cost mapping and random optimization. Background In the technical field of industrial energy management, energy consumption management and control need to be conducted on carbon emission reduction and economic cost control, but traditional industrial energy consumption management and control modes are focused on single energy cost optimization, and dynamic carbon cost cannot be effectively integrated. In the existing method, the dimension difference between the carbon cost and the economic cost is difficult to map cooperatively, and the acquisition quality of multi-source data (such as power and carbon emission factors) is poor, so that the cost calculation deviation is caused. In addition, the traditional static model cannot adapt to dynamic changes of a carbon market, the multi-objective collaborative optimization capability is weak, a stability verification mechanism is lacked, and policy execution fluctuation is easy to cause. Therefore, there is a need for an energy consumption management and control system that can achieve dynamic carbon cost mapping, multi-objective synergy, and stability assurance. Disclosure of Invention Aiming at the problems, the invention aims to provide an industrial energy consumption collaborative management and control method based on dynamic carbon cost mapping and random optimization. The technical scheme is that the industrial energy consumption collaborative management and control method based on dynamic carbon cost mapping and random optimization of the first aspect comprises the following steps: Step 1, collecting operation power data of industrial equipment, a power grid carbon emission factor and a carbon transaction price in real time, and preprocessing the data; Step 2, constructing a dynamic mapping relation for the preprocessed data, constructing an objective function by taking the minimum total cost as a target, and carrying out optimization solution on the objective function by utilizing an improved alternate direction multiplier method to obtain an optimal decision variable; Step 3, establishing a multi-dimensional stability index system and a stability scoring function, and performing simulation test and stability judgment according to an optimal decision variable; step 4, constructing a multi-target cooperative control scheme according to the judging result and combining real-time acquisition data to generate a control instruction; and 5, remotely issuing a control instruction to each execution equipment end, continuously monitoring the execution effect, triggering the re-execution of the optimization flow by establishing a real-time feedback closed loop, and guiding the system to dynamically adjust parameters so as to realize dynamic collaborative management and control. Further, step 2 includes: establishing a direct quantification relation of carbon cost, and calculating the instantaneous carbon emission, wherein the formula is as follows: , Wherein, the Representation ofThe instant carbon emission amount is calculated at the moment,Representation ofThe power data is run at the moment in time,Representation ofThe carbon emission factor of the power grid at moment; Combined carbon trade price The carbon cost is calculated as: , the constructed objective function is expressed as: , Wherein, the A set of decision variables that are carbon cost dependent; for a set of decision variables that are related to economic costs, The representation takes the minimum value of the value,Representing the weight of the carbon cost to be added,The weight of the economic cost is represented by,;Representing an economic cost function, the expression is: , Wherein, the The dynamic weight coefficient is an adjusting factor reflecting the economic cost; the price of the energy source is; Operating power for the device. Further, the step of optimally solving the objective function using the modified alternate direction multiplier method includes: Setting constraint conditions to ensure that the optimization result meets the equipment operation limit and the power grid dispatching requirement, wherein the constraint conditions are expressed as follows: , Wherein, the AndRepresenting the carbon cost variable as a coefficient matrixAnd economic cost variableIs a matrixConstraint coefficient, matrix of a coke cost-related decision variableConstraint coefficient of focusing economic cost related decision variable, constraint constant vectorPhysical limitations covering the operational limits of the device; introducing Lagrangian multipliers And penalty termConstruction of an augmented Lagrangian functionConverting the constraint problem into unconstrai