Control and optimization method and system for carbon emission reduction quota of power grid carrying capacity constraint

Abstract

This invention discloses a method and system for optimizing carbon reduction quotas in controlled emission industries under grid carrying capacity constraints. It relates to the field of grid carrying capacity and carbon reduction quota optimization technology, and includes: acquiring candidate carbon reduction measures, process operation data, and the carrying capacity margin of the target grid for each time period; determining the production state to be restored based on process state deviations and generating a replenishment load boundary; mapping the replenishment load boundary to grid constraint elements in subsequent time periods, determining the executable amount of carbon reduction measures and the replenishment scheme based on available carrying capacity margins, and updating the available carrying capacity margins for subsequent time periods; and determining the target carbon reduction quota based on the emission reduction amount during the implementation phase and the emission amount during the replenishment process. This invention can reduce the redundant allocation of grid carrying capacity margins and the artificial increase of carbon reduction quotas.

Description

Technical Field

[0001] This invention relates to the field of power grid carrying capacity and carbon reduction quota optimization technology, and more specifically, to a method and system for optimizing carbon reduction quotas in emission-controlled industries under the constraint of power grid carrying capacity. Background Technology

[0002] With the increasing demand for energy conservation and carbon reduction in emission-controlled industries, existing technologies typically reduce energy consumption during the implementation phase by reducing the operating power of production equipment, adjusting the execution time of production tasks, or changing energy usage patterns. Enterprise carbon reduction quotas are then determined based on the corresponding load reduction and emission reduction amounts. However, some carbon reduction measures only transfer production tasks or energy demand to subsequent periods. The unfinished production tasks and equipment status deviations resulting from their implementation still require load replenishment to restore them. Existing technologies, when determining the feasible amount and carbon reduction quota of carbon reduction measures, typically do not include the occupancy of the replenished load on the line, transformer, and node capacity margins in subsequent periods in a unified verification process. Furthermore, they do not promptly deduct the corresponding capacity margin after the replenishment plan is determined. This leads to multiple candidate carbon reduction measures potentially using the same grid capacity in the same subsequent period, resulting in inaccurate determination of the feasible amount of carbon reduction measures and inflated carbon reduction quotas.

[0003] The above-disclosed technical solutions have at least the following technical problems: Existing technologies usually determine carbon reduction quotas based only on the load reduction and emission reduction during the implementation phase of candidate carbon reduction measures, without considering the continuous occupation of the grid carrying capacity margin by the production state to be restored caused by carbon reduction measures and the subsequent replenishment load. This results in the replenishment load of multiple candidate carbon reduction measures potentially occupying the grid carrying capacity margin in the same subsequent period, thereby causing inaccurate determination of the executable amount of candidate carbon reduction measures and the overstatement of carbon reduction quotas. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for optimizing carbon reduction quotas in emission-controlled industries under grid carrying capacity constraints. By associating the replenishment load formed by carbon reduction measures with the grid carrying capacity margin in subsequent periods, and determining the feasible amount and net emission reduction amount accordingly, the present invention addresses the problem of redundant allocation of carrying capacity margin and false increase of carbon reduction quotas caused by the replenishment load not being included in the grid carrying capacity verification.

[0005] To achieve the above objectives, the present invention provides the following technical solution: The method for optimizing carbon reduction quotas in controlled emission industries under grid carrying capacity constraints includes the following steps: obtaining candidate carbon reduction measures, process operation data, and the carrying capacity margin of the target grid for each time period for controlled emission enterprises; determining the production state to be restored based on the process state deviation caused by the candidate carbon reduction measures, and generating a replenishment load boundary that satisfies the process restoration constraint based on the production state to be restored; mapping the replenishment load boundary to the grid constraint elements in subsequent time periods, and determining the executable amount of the candidate carbon reduction measures and the corresponding replenishment scheme based on the available carrying capacity margin in subsequent time periods; and determining the target carbon reduction quota of controlled emission enterprises based on the emission reduction amount in the implementation stage and the emission amount in the replenishment process corresponding to the executable amount.

[0006] In a preferred embodiment, determining the production state to be restored includes: determining the baseline process state when the candidate carbon reduction measures are not implemented and the adjusted process state after implementation, based on the implementation period of the candidate carbon reduction measures; comparing the baseline process state and the adjusted process state to obtain the state deviation of production tasks, process materials and equipment operation; and determining the production tasks to be supplemented and the equipment state to be restored based on the connection relationship between the state deviation and subsequent processes, thus forming the production state to be restored.

[0007] In a preferred embodiment, generating the replenishment load boundary that satisfies the process recovery constraint includes: determining the deviation propagation path of the production state to be restored along the process material transfer relationship; determining the recovery verification process based on the deviation propagation path and the absorption capacity of subsequent processes for state deviation, and reversely determining the minimum cumulative deviation elimination amount at each recovery time; determining the maximum cumulative deviation elimination amount at each recovery time based on the recovery capacity of the production equipment; and determining the upper and lower limits of the replenishment load for each recovery period based on the change in the cumulative deviation elimination amount of adjacent recovery times when the minimum cumulative deviation elimination amount is not greater than the maximum cumulative deviation elimination amount, thereby forming the replenishment load boundary.

[0008] In a preferred embodiment, mapping the replenished load boundary to the grid constraint elements in subsequent time periods includes: generating candidate recovery schemes based on the replenished load boundary; superimposing the time-sharing replenished loads of the candidate recovery schemes onto the corresponding grid access nodes and performing time-series power flow calculations to obtain the carrying capacity margin occupancy of each grid constraint element; establishing the correspondence between the time-sharing replenished load, subsequent time periods, grid constraint objects, and carrying capacity margin occupancy to form a mapping result.

[0009] In a preferred embodiment, determining the executable amount and corresponding replenishment plan of candidate carbon reduction measures based on the available carrying capacity margin in subsequent periods includes: determining the production state to be restored and the replenishment load boundary based on the candidate execution amount; determining the maximum replenishment load for each candidate restoration period based on the mapping result and available carrying capacity margin, and converting the maximum replenishment load into the maximum state deviation elimination amount that can be eliminated in the corresponding period; determining the maximum remaining restoration amount and the state deviation elimination amount that must be completed in the current candidate restoration period based on the maximum state deviation elimination amount; comparing the state deviation elimination amount that must be completed with the corresponding maximum state deviation elimination amount, adjusting the candidate execution amount or forming a replenishment plan, and determining the maximum candidate execution amount as the executable amount.

[0010] In a preferred embodiment, converting the maximum replenishment load into the maximum state deviation elimination amount that can be eliminated in the corresponding time period includes: keeping the replenishment load of other candidate recovery time periods unchanged, increasing the replenishment load of the access node in the current candidate recovery time period, and determining the carrying capacity occupancy of each relevant grid constraint element; determining the maximum replenishment load when the carrying capacity occupancy does not exceed the corresponding available carrying capacity as the maximum replenishment load of the current candidate recovery time period; and determining the maximum state deviation elimination amount that can be eliminated in the corresponding time period based on the available replenishment power corresponding to the maximum replenishment load and the maximum recovery capacity of the production equipment.

[0011] In a preferred embodiment, determining the maximum remaining recovery amount and the state deviation elimination amount that must be completed in the current candidate recovery period includes: accumulating the maximum state deviation elimination amounts of each candidate recovery period after the current candidate recovery period to obtain the maximum remaining recovery amount; determining a first recovery amount based on the difference between the current minimum cumulative state deviation elimination amount and the amount already completed in the previous period; determining a second recovery amount based on the difference between the total state deviation to be eliminated minus the amount already completed and the maximum remaining recovery amount; and determining the maximum value among zero, the first recovery amount, and the second recovery amount as the state deviation elimination amount that must be completed in the current candidate recovery period.

[0012] In a preferred embodiment, the method further includes updating the available capacity margin: determining the capacity occupancy of relevant grid-constrained components for each subsequent time period based on the time-sharing replenishment load and access nodes of the replenishment scheme; deducting the capacity occupancy from the corresponding available capacity margin to obtain the updated available capacity margin; and using the updated available capacity margin to determine the feasible amount and replenishment scheme of the next candidate carbon reduction measure.

[0013] In a preferred embodiment, determining the target carbon reduction quota for controlled enterprises includes: determining the emission reduction amount during the implementation phase based on the feasible amount and the difference in energy consumption before and after implementation; determining the emission amount during the replenishment process based on the time-of-use replenishment load, equipment recovery energy consumption, and additional fuel consumption corresponding to the replenishment plan, combined with the corresponding carbon emission factors; determining the net emission reduction amount based on the difference between the emission reduction amount during the implementation phase and the emission amount during the replenishment process, and summing up the net emission reduction amounts of each candidate carbon reduction measure to obtain the target carbon reduction quota.

[0014] On the other hand, the carbon reduction quota optimization system for controlled emission industries under grid carrying capacity constraints includes: a basic data acquisition module, used to acquire candidate carbon reduction measures, process operation data, and the carrying capacity margin of the target grid for each time period for controlled emission enterprises; a replenishment boundary generation module, used to determine the production state to be restored based on the process state deviation caused by the candidate carbon reduction measures, and generate a replenishment load boundary that meets the process restoration constraints based on the production state to be restored; a replenishment decision module, used to map the replenishment load boundary to the grid constraint elements of subsequent time periods, and determine the executable amount of the candidate carbon reduction measures and the corresponding replenishment scheme based on the available carrying capacity margin of subsequent time periods; and a carbon reduction quota determination module, used to determine the target carbon reduction quota of controlled emission enterprises based on the emission reduction amount in the implementation stage corresponding to the executable amount and the emission amount in the replenishment process.

[0015] The technical effects and advantages of the method and system for optimizing carbon reduction quotas in controlled emission industries under power grid carrying capacity constraints of this invention are as follows: This invention identifies the production status to be restored and the subsequent replenishment load formed by candidate carbon reduction measures, and determines the executable amount and replenishment plan of carbon reduction measures by combining the available carrying capacity margin of the target power grid at each time period. At the same time, it deducts the carrying capacity margin already occupied by the replenishment plan, which can avoid multiple carbon reduction measures from repeatedly using the same power grid carrying capacity. Furthermore, it determines the carbon reduction quota based on the emission reduction amount during the implementation phase and the emission amount during the replenishment process, thereby improving the matching degree between the implementation of carbon reduction measures and the actual carrying capacity of the power grid and reducing the situation of failure to complete production replenishment and the false increase of carbon reduction quota. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the method for optimizing carbon reduction quotas in controlled emission industries under the constraints of power grid carrying capacity, as described in this invention. Figure 2 This is a schematic diagram of the structure of the emission reduction quota optimization system for controlled industries under the power grid carrying capacity constraint of the present invention; Figure 3 This is a schematic diagram showing the relationship between the process recovery boundary and the cumulative state deviation elimination amount; Figure 4 This diagram illustrates the risks of margin updates and redundant allocation. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, Figure 1 This invention presents a method for optimizing carbon reduction quotas in controlled emission industries under power grid carrying capacity constraints, comprising the following steps: S1, obtain the candidate carbon reduction measures, process operation data and the carrying capacity margin of the target power grid for each time period of the emission-controlled enterprise; In this embodiment, candidate carbon reduction measures and process operation data are obtained from the production management system, energy management system, and equipment control system of the emission-controlled enterprise. The candidate carbon reduction measures include the corresponding production process, the equipment involved, the execution period, the load adjustment amount, and the adjustment duration. The process operation data includes at least one of the following: production plan, equipment operating status, production task completion amount, work-in-process inventory, intermediate inventory, and energy consumption.

[0019] Candidate carbon reduction measures can include reducing the operating power of production equipment, shifting the execution time of production tasks, adjusting the operating mode of production equipment, or changing the operating combination between different energy equipment. Based on the power supply relationship of the production equipment corresponding to each candidate carbon reduction measure, the access node of the corresponding emission-controlled enterprise in the target power grid is determined.

[0020] The system obtains grid topology, line and transformer parameters, node loads, power output, and equipment operating limits from the target grid's dispatch system. Power flow calculations are then performed according to the defined dispatch periods to obtain line transmission power, transformer loads, and node voltages for each period. Based on the difference between the operating limits of each grid device and its corresponding operating state, the carrying capacity margins of lines, transformers, and nodes for each period are determined.

[0021] By aligning the candidate carbon reduction measures, process operation data, and capacity margins for each time period, the process operation status and grid connection nodes corresponding to each candidate carbon reduction measure are obtained.

[0022] S2, determine the production state to be restored based on the deviation of the process state caused by the candidate carbon reduction measures, and generate the replenishment load boundary that satisfies the process restoration constraint based on the production state to be restored; In this embodiment, determining the production state to be restored based on the deviation of the process state caused by the candidate carbon reduction measures includes: Based on the execution period corresponding to the candidate carbon reduction measures, extract the production task completion status and equipment operation status that the corresponding production process should achieve at the end of the execution period from the production plan when the candidate carbon reduction measures have not been implemented, and form the baseline process status. Based on the load adjustment amount, adjustment duration and actual operating data of production equipment corresponding to the candidate carbon reduction measures, determine the post-adjustment process status of the corresponding production process at the end of the execution period after implementing the candidate carbon reduction measures. By comparing the baseline process state with the adjusted process state, the deviations in production task completion, material state between processes, and equipment operating state caused by the candidate carbon reduction measures are determined. Based on the connection between each state deviation and subsequent production processes, the production tasks that need to be made up in the subsequent period and the equipment operating status that needs to be restored are determined, forming a production status to be restored.

[0023] Specifically, the baseline process status refers to the state that the corresponding production process should reach when it operates according to the original production plan until the end of the period for implementing the candidate carbon reduction measures, without the implementation of the candidate carbon reduction measures. This includes the planned production volume, the planned input or output of materials, the intermediate inventory, and the target operating status of the production equipment.

[0024] The adjusted process status refers to the actual state reached by the corresponding production process at the same time after the implementation of the candidate carbon reduction measures, or the state calculated based on the equipment operation data. This includes the actual production volume, the actual input or output of materials, the actual intermediate inventory, and the actual operating status of the production equipment.

[0025] The production task completion deviation is the difference between the planned production volume under the baseline process state and the actual production volume under the adjusted process state, and is used to characterize the unfinished production tasks caused by reduced load, shutdown or transfer of production equipment.

[0026] The material state deviation between processes includes the difference between the output of the upstream process and the demand of the downstream process, the difference between the intermediate inventory and the normal connection range, and the change of the position of work-in-process in the process, which is used to characterize the impact of candidate carbon reduction measures on the continuous operation of upstream and downstream processes.

[0027] The equipment operating status deviation includes the difference between the equipment temperature, pressure, current, speed, load rate, or other state parameters that affect the recovery of equipment operation and the baseline process state, and is used to characterize the compensation process required for the production equipment to recover to the normal production state.

[0028] After determining each state deviation, based on the material transfer relationship and execution sequence between production processes, it is determined whether the state deviation can be eliminated automatically by subsequent normal production processes. State deviations that can be eliminated automatically without increasing the subsequent production load are not included in the state of pending production recovery; for state deviations that require increasing the running time and power of subsequent production equipment or performing additional equipment recovery processes to be eliminated, their corresponding unfinished production tasks, material replenishment needs, and equipment recovery needs are included in the state of pending production recovery.

[0029] Therefore, the production state to be restored includes at least one of the production task to be made up and the equipment state restoration requirement. The production task to be made up is used to characterize the production tasks that have been delayed to a later period due to candidate carbon reduction measures; the equipment state restoration requirement is used to characterize the heating, pressurization, flow increase, speed increase or stable operation process required for the production equipment to recover from the adjusted operating state to the operating state that can perform the production tasks.

[0030] By determining the production status to be restored based on the baseline process status and the adjusted process status at the same time, it is possible to distinguish between the energy demand actually eliminated by the candidate carbon reduction measures and the energy demand that is only transferred to subsequent periods, and to provide a basis for subsequent production tasks to replenish load and equipment status to restore load.

[0031] Further, the step of generating a replenishment load boundary that satisfies the process recovery constraints based on the production state to be restored includes: Based on the material type, production tasks to be supplemented, and equipment operating status corresponding to the production state to be restored, the state deviation propagation path formed by the propagation of the production state to be restored to subsequent processes is determined along the material transfer relationship and execution sequence between each production process. According to the propagation path of state deviation, the demand time of each subsequent process for upstream materials, the adjustable range of intermediate inventory, the planned idle time and the adjustable capacity of equipment are determined in turn. It is also determined whether each subsequent process can absorb the state deviation caused by the production state to be restored. The subsequent processes that cannot continue to absorb the state deviation and whose normal execution conditions will be affected are determined as the recovery and verification processes. Based on the planned execution time, material input requirements, allowable intermediate inventory range, and equipment start-up conditions of each recovery and verification process, determine the maximum amount of unrecovered state that each recovery and verification process can have at different candidate recovery times; Along the state deviation propagation path, the maximum amount of unrecovered state allowed in each recovery verification process is reverse-calculated to the production process corresponding to the candidate carbon reduction measures, and the minimum cumulative state deviation that should be eliminated by the time of each candidate recovery is determined based on the reverse-calculation results. Starting from the adjusted process status at the end of the implementation of candidate carbon reduction measures, the maximum cumulative state deviation that can be eliminated up to each candidate recovery time is determined according to the state recovery sequence of production equipment, the rate of state change, the operating power range and the amount of materials available. The minimum cumulative state deviation corresponding to each candidate recovery time is compared with the maximum cumulative state deviation. If the minimum cumulative state deviation at any candidate recovery time is greater than the maximum cumulative state deviation, it is determined that the production state to be restored formed by the current carbon reduction measure execution amount cannot be restored under the condition of meeting the operation requirements of subsequent processes, and the execution amount of the candidate carbon reduction measure is reduced. When the minimum cumulative state deviation at each candidate recovery time is not greater than the maximum cumulative state deviation, the lower limit and upper limit of the replenishment load for each candidate recovery period are determined based on the changes in the minimum and maximum cumulative state deviation between adjacent candidate recovery times, combined with the production power consumption and equipment state recovery power consumption corresponding to the elimination of unit state deviation. This forms the replenishment load boundary.

[0032] In this embodiment, the process recovery constraint includes a state deviation elimination schedule constraint and a state deviation elimination capability constraint.

[0033] The state deviation elimination schedule constraint is determined based on the material requirements of subsequent production processes, the adjustable range of intermediate inventory, and the planned execution time, and is used to limit the minimum amount of state deviation that needs to be eliminated by each candidate recovery time. The state deviation elimination capability constraint is determined based on the state recovery sequence of production equipment, the rate of state change, the operating power range, and the available material quantity, and is used to limit the maximum amount of state deviation that can be eliminated by each candidate recovery time.

[0034] The recovery verification process refers to the subsequent process where the material input conditions, equipment start-up conditions, or planned production tasks cannot be met when the production state to be restored is not partially or completely eliminated before the corresponding time.

[0035] Specifically, for each subsequent process along the state deviation propagation path, the amount of state deviation that the subsequent process can absorb is determined based on the material demand and available intermediate inventory at the candidate recovery time. When the state deviation corresponding to the production state to be restored does not exceed the absorbable state deviation, the subsequent process can digest the state deviation through intermediate inventory or planned idle periods, without needing to complete the full recovery before the subsequent process is executed.

[0036] When the deviation in state exceeds the amount that subsequent processes can absorb, the excess will cause insufficient material supply, equipment waiting, or delay of planned tasks in subsequent processes. Therefore, the subsequent process is identified as a recovery and verification process, and the maximum amount of unrecovered state that the recovery and verification process can have is determined based on the material gap that is allowed to exist during its normal execution.

[0037] Let the initial state deviation formed at the end of the implementation of the candidate carbon reduction measures be . The maximum number of unrecovered states allowed at candidate recovery time t is Then, the minimum cumulative state deviation that should be eliminated by the candidate recovery time t for:

[0038] in, This represents the minimum cumulative state deviation that should be eliminated by the candidate recovery time t; This indicates the deviation from the initial state that occurred at the end of the implementation of the candidate carbon reduction measures; This represents the maximum amount of unrecovered state that the production process corresponding to the candidate carbon reduction measure is allowed to retain at the candidate recovery time t after reverse conversion of the recovery and verification process.

[0039] When there are multiple recovery verification processes in the state deviation propagation path, the maximum unrecovered state quantity allowed by each recovery verification process is reverse-calculated to the production process corresponding to the candidate carbon reduction measure, and the reverse-calculation result with the strictest constraint is taken as the maximum unrecovered state quantity corresponding to the candidate recovery time.

[0040] Furthermore, the order of equipment state recovery is determined based on the sequential changes in equipment state that must be completed for the production equipment to return from the adjusted operating state to the normal production state. For example, when the production equipment needs to complete heating, pressurization, flow increase, and stable operation in sequence before it can make up for the production task, the energy input to the production equipment is first used to eliminate the deviation in the equipment's operating state. After the equipment reaches the allowable production state, the remaining equipment capacity is then used to make up for the unfinished production task.

[0041] Based on the rate of change of state, maximum operating power, minimum stable operating power, and sustainable operating duration at each recovery stage, the amount of state deviation that the production equipment can eliminate in each candidate recovery period is determined, and then accumulated to obtain the maximum cumulative state deviation that can be eliminated up to the candidate recovery time t. .

[0042] Cumulative recovery amount that satisfies process recovery constraints Should meet:

[0043] in, This represents the cumulative state deviation actually eliminated up to the candidate recovery time t. The minimum cumulative state deviation elimination amount is determined by the normal operation requirements of the subsequent restoration and verification process; This is the maximum cumulative state deviation elimination amount determined by the state recovery capability of the production equipment.

[0044] When any candidate recovery moment satisfies:

[0045] This indicates that the minimum recovery progress required for subsequent processes has exceeded the maximum recovery progress that the production equipment can achieve at the corresponding time. The current implementation of candidate carbon reduction measures will result in a process status deviation that cannot be eliminated in time. Therefore, the load adjustment amount corresponding to the candidate carbon reduction measures should be reduced or their adjustment duration shortened, and the production status to be restored and the recovery time for each candidate measure should be redefined. and until all candidate recovery times meet the requirements. .

[0046] After all the feasible recovery conditions are met at each candidate recovery time, the range of state deviations that need to be eliminated within each candidate recovery period is determined based on the cumulative recovery amount boundary corresponding to adjacent candidate recovery times.

[0047] For adjacent candidate recovery times And t, the minimum state deviation that needs to be eliminated during this period. for:

[0048] The maximum state deviation elimination amount that can be eliminated during this period for:

[0049] in, This indicates that to ensure the candidate recovery time t meets the minimum recovery progress, during the time period... The minimum amount of state deviation that needs to be eliminated between time period t; This represents the maximum amount of state deviation that can be eliminated within this time period without disrupting the minimum recovery progress at the previous moment and without exceeding the current recovery capacity of the equipment. and They represent the time up to the candidate recovery time. The minimum cumulative state deviation that should be eliminated by t; and They represent the time up to the candidate recovery time. The maximum cumulative state deviation that t can eliminate.

[0050] Based on the production power consumption required to eliminate a unit state deviation of the production equipment, the minimum state deviation elimination amount and the maximum state deviation elimination amount are converted into production task replenishment power. At the same time, based on the equipment temperature, pressure, current, speed or other operating state deviations, the equipment state recovery power within the corresponding candidate recovery period is determined.

[0051] The replenishment power of the production task is superimposed with the power of equipment status restoration, and then divided by the duration of the corresponding candidate recovery period to obtain the lower limit and upper limit of the replenishment load for the candidate recovery period.

[0052] The resulting replenishment load boundary is not directly set according to historical curves or fixed ratios, but is determined by the minimum requirements of subsequent processes for recovery progress and the maximum recovery capacity that the current production equipment can achieve. This ensures that the replenishment plan within the replenishment load boundary will not cause subsequent processes to be unable to perform normally due to insufficient materials or tasks, nor will it exceed the actual recovery capacity of the production equipment in the corresponding time period.

[0053] like Figure 3 The diagram illustrates the feasible recovery range of the production state to be restored during subsequent recovery periods after the implementation of candidate carbon reduction measures, and the arrangement of recovery tasks according to this invention. The horizontal axis represents the candidate recovery period, and the vertical axis represents the normalized cumulative state deviation elimination amount. The minimum cumulative state deviation elimination amount is determined by the requirements of subsequent recovery verification processes regarding material supply, production task completion progress, and equipment start-up conditions, representing the minimum state deviation elimination amount that should be completed by the corresponding recovery period. The maximum cumulative state deviation elimination amount is determined by the recovery capabilities of the production equipment, such as the rate of state change, operating power range, and available materials, representing the maximum state deviation elimination amount achievable by the corresponding recovery period. The two represent the feasible recovery interval for the process. The cumulative recovery amount determined by this invention always lies within this feasible interval, indicating that the replenishment scheme formed according to this invention can both meet the minimum recovery progress requirements of subsequent processes and will not exceed the actual recovery capacity of the production equipment. In contrast, when replenishment tasks are continuously postponed based solely on the carrying capacity margin of subsequent periods, the early cumulative recovery amount is lower than the minimum recovery requirement, and a large number of tasks to be restored are concentrated in the later part of the recovery window, causing a rapid increase in the replenishment load in the later part. This shows that the present invention does not simply transfer the replenishment load to a period when the grid margin is large, but simultaneously considers the minimum recovery progress of the process and the remaining recovery capacity, thereby avoiding the formation of a replenishment scheme that cannot be completed within the specified recovery window.

[0054] S3 maps the load boundary to the grid constraint elements in the subsequent time period, determines the feasible amount of candidate carbon reduction measures and the corresponding load replacement scheme based on the available carrying capacity margin in the subsequent time period, and updates the available carrying capacity margin in the subsequent time period based on the carrying capacity margin occupied by the load replacement scheme. In this embodiment, the step of mapping the replenishment load boundary to the grid constraint element in subsequent time periods includes: Based on the minimum and maximum cumulative state deviation elimination amounts in the replenishment load boundary, early recovery plans and delayed recovery plans are generated respectively. The early recovery plan prioritizes eliminating the production state to be restored according to the maximum recoverable capacity of the production equipment, while the delayed recovery plan postpones the execution of the replenishment task under the condition of meeting the minimum recovery progress of each recovery verification process. Based on the power supply relationship of the production equipment corresponding to the candidate carbon reduction measures, the access node of the replenishment load in the target power grid is determined. The time-sharing replenishment loads corresponding to the early recovery plan and the delayed recovery plan are superimposed to the access node respectively, and the time-series power flow calculation is performed in combination with the base load, power output and power grid operation mode of each subsequent period. Based on the time-series power flow calculation results, the changes in line and transformer load status and node voltage caused by each replenishment scheme are determined, and the amount of change in the corresponding operating status that approaches the operating limit is determined as the load margin occupancy. By comparing the grid constraint elements corresponding to the early recovery scheme and the delayed recovery scheme, grid constraint elements that are affected by the replenishment load under both recovery schemes are identified as common associated objects, and grid constraint elements that are affected only under one of the recovery schemes are identified as scheme associated objects. Based on the time-sharing replenishment load of each replenishment scheme and the carrying capacity occupancy of the common associated objects and the scheme associated objects, establish the correspondence between the time-sharing replenishment load, subsequent time periods and grid constraint elements to form a mapping result.

[0055] The early recovery plan does not simply take the upper limit of the replenishment load for each time period, but rather eliminates the production state to be restored as early as possible, under the condition that it does not exceed the maximum recovery capacity of the equipment; the delayed recovery plan does not concentrate all replenishment tasks in the last time period, but rather ensures that the cumulative recovery amount at each recovery time is not less than the minimum cumulative state deviation elimination amount.

[0056] For recovery period t, the cumulative recovery amount corresponding to the early recovery plan. The following relationship can be used to determine this:

[0057] in, This refers to the cumulative state deviation that has been eliminated up to the recovery period t in the early recovery plan; This represents the initial state deviation resulting from candidate carbon reduction measures; This represents the maximum amount of state deviation that the production equipment can eliminate within the recovery period t.

[0058] Cumulative recovery amount corresponding to the delayed recovery plan The minimum recovery progress that must be achieved at the current moment and the maximum recovery capacity for the remaining recovery period are determined to satisfy the following:

[0059] and:

[0060] in, The cumulative state deviation that has been eliminated up to the recovery period t is the amount of time required to postpone the recovery plan. The minimum cumulative state deviation that should be eliminated by the recovery period t; T is the latest recovery period; This represents the maximum amount of state deviation that the production equipment can eliminate after the current time period.

[0061] The difference between the cumulative recovery amounts in adjacent time periods is used to obtain the state deviation elimination amount for the corresponding time period. Then, based on the production power consumption and additional power consumption for equipment recovery required to eliminate the unit state deviation amount, the time-sharing replenishment load corresponding to the early recovery plan and the delayed recovery plan is determined.

[0062] For the q-th replenishment scheme, the replenishment active power in the subsequent time period t is... Superimposed on access node n, we get:

[0063] in, To implement the q-th recovery plan, the active load of access node n in the subsequent time period t; This refers to the basic active load connected to node n without additional replenishment load. Let q be the active power of the replenishment scheme in the subsequent time period t.

[0064] By calculating the time-series power flow, the operating status of each line, transformer, and node before and after the implementation of the replenishment scheme is obtained. For line or transformer m, its load margin occupancy is determined. for:

[0065] in, This refers to the load-bearing margin occupied by the line or transformer m. The apparent power of line or transformer m in time period t after superimposing the qth replenishment scheme; This represents the apparent power of line or transformer m during time period t without additional load.

[0066] For node voltage constraints, the amount of voltage upper or lower margin occupied by the replenishment scheme is determined based on the node voltage changes before and after the load is added.

[0067] When the same grid constraint element generates load margin occupancy under both the early recovery scheme and the delayed recovery scheme, it means that no matter how the replenishment task is adjusted within the recovery window, the grid constraint element needs to bear the corresponding replenishment load, and it is identified as a common constrained object.

[0068] The minimum required load capacity of the jointly restricted objects in time period t can be determined as follows:

[0069] in, The minimum required load capacity for the shared restricted object m during time period t; To restore the load capacity of the object in advance; The load occupancy of this object is determined by the delayed recovery plan.

[0070] When a certain grid constraint element generates significant load occupancy only under the early recovery scheme or the delayed recovery scheme, it indicates that the load occupancy can be transferred by adjusting the execution time of the replenishment task. The grid constraint element is identified as a scheme-related object, and its load occupancy continues to correspond with the corresponding replenishment scheme.

[0071] Furthermore, the step of determining the feasible amount of candidate carbon reduction measures and corresponding remediation plans based on the available carrying capacity margin in subsequent periods includes: The initial candidate execution quantities are formed by the maximum allowable load adjustment amount and adjustment duration of the candidate carbon reduction measures, and the production state to be restored and its corresponding replenishment load boundary are re-determined based on the initial candidate execution quantities; the candidate execution quantities are used to compare the load adjustment scale of the candidate carbon reduction measures, and each candidate execution quantity is still associated with the load adjustment power and adjustment duration that formed the candidate execution quantity; Based on the mapping results from the replenishment load boundary to the grid constraint element, the maximum replenishment load that the target grid can support is determined time-by-time under the condition that it does not exceed the available carrying capacity of each grid constraint element, and the maximum replenishment load is converted into the maximum state deviation elimination amount that can be eliminated in the corresponding time period. Based on the maximum state deviation elimination amount that can be eliminated in each candidate recovery period, the maximum remaining recovery amount that can be completed after the current candidate recovery period is determined in reverse. Based on the cumulative state deviation elimination amount completed up to the previous candidate recovery period, the minimum cumulative state deviation elimination amount that should be achieved up to the current candidate recovery period, and the maximum remaining recovery amount that can be completed after the current candidate recovery period, determine the state deviation elimination amount that must be completed in the current candidate recovery period. When the amount of state deviation elimination that must be completed in any candidate recovery period exceeds the maximum amount of state deviation elimination that can be eliminated in that period, the execution amount of the candidate carbon reduction measures is reduced according to the production task replenishment requirement or equipment state recovery requirement corresponding to the excess part, the production state to be restored and the replenishment load boundary are redefined, the redefined replenishment load boundary is mapped to the grid constraint element in the subsequent period, and the maximum amount of state deviation elimination that can be eliminated in each candidate recovery period is redefined. When the amount of state deviation elimination that must be completed in each candidate recovery period does not exceed the maximum amount of state deviation elimination that can be eliminated in the corresponding period, the amount of state deviation elimination for each candidate recovery period is allocated between the corresponding upper and lower limits, and the amount of state deviation elimination is converted into time-sharing replenishment load to form a candidate replenishment scheme. The maximum candidate execution quantity corresponding to the candidate replenishment scheme that can form a candidate replenishment scheme that satisfies the process recovery constraint and the power grid carrying capacity constraint is determined as the executable quantity of the candidate carbon reduction measure, and the corresponding candidate replenishment scheme is determined as the replenishment scheme.

[0072] For candidate carbon reduction measures where load regulation power varies over time, the candidate implementation amount x is:

[0073] in, This refers to the set of implementation periods for candidate carbon reduction measures. The load regulation power for execution period k; The duration of execution period k; These are candidate execution quantities.

[0074] The maximum state deviation elimination amount is specifically as follows:

[0075] in, The maximum state deviation elimination amount that can be eliminated during the candidate recovery period t; This is the maximum state deviation elimination amount determined based on the maximum operating power of the production equipment, the rate of state change, and the material supply conditions. The maximum replenishment load that the target power grid can support during the candidate recovery period t; The duration of the candidate recovery period; The power to restore the equipment status used for heating, voltage boosting, current increase, speed-up, or stable operation during the candidate recovery period; The electricity required to eliminate deviations in the unit production task status; .

[0076] The determination of the state deviation elimination amount that must be completed in the current candidate recovery period is specifically as follows:

[0077] in, This represents the minimum amount of state deviation elimination that must be completed during the candidate recovery period t. This represents the minimum cumulative state deviation elimination amount that should be achieved by the candidate recovery period t. This represents the cumulative amount of state deviation elimination completed up to the previous candidate recovery period; The deviation from the total amount represents the state to be eliminated resulting from candidate carbon reduction measures; The maximum state deviation elimination amount that can be eliminated during the candidate recovery period r; This is the latest recovery period.

[0078] In this embodiment, the prior art typically arranges replenishment loads periodically based on the current available capacity margin for each time period, or prioritizes allocating replenishment loads to time periods with larger capacity margins. This approach only determines whether the current time period can accommodate replenishment loads, without determining whether, after reducing the replenishment tasks in the current time period, the remaining recovery time period still has sufficient process recovery capacity and grid capacity to complete the remaining production recovery.

[0079] For example, in the early part of the recovery window, a certain line has a small but still usable capacity margin, while in the later part of the recovery window, it has a larger predicted capacity margin. If only local optimization is performed based on the current margin in each time period, it may be possible to postpone all the replenishment tasks in the early part. However, the maximum recovery power of the equipment in subsequent time periods, the completion time limit of production tasks, or other allocated loads may prevent the remaining replenishment tasks from being completed. Therefore, this embodiment not only determines how much replenishment load the current recovery period can carry, but also reverses this to determine how much recovery task can be completed at most after the current recovery period, thereby determining the minimum replenishment amount that the current period cannot be postponed any further.

[0080] Furthermore, the conversion of the maximum replenishment load into the maximum state deviation elimination amount that can be eliminated in the corresponding time period includes: Based on the mapping results from the replenishment load boundary to the grid constraint element, while keeping the replenishment load unchanged in other time periods, the replenishment load of the access node in the current candidate recovery period is increased, and the carrying capacity occupancy of each relevant grid constraint element is determined. When the capacity occupancy of any relevant grid constraint element does not exceed its available capacity, the maximum replenishment load that can be achieved during the current candidate recovery period is determined as the maximum replenishment load that the target grid can support. Based on the available replenishment power corresponding to the maximum replenishment load and the maximum recovery capacity that the production equipment can achieve in the current candidate recovery period, determine the maximum state deviation elimination amount that can be eliminated in the current candidate recovery period.

[0081] The determination of the state deviation elimination amount that must be completed in the current candidate recovery period includes: The maximum state deviation elimination amount that can be eliminated by each candidate recovery period after the current candidate recovery period is accumulated to obtain the maximum remaining recovery amount that can be completed after the current candidate recovery period. The first recovery amount required to meet the current minimum recovery progress is determined based on the difference between the minimum cumulative state deviation elimination amount that should be achieved up to the current candidate recovery period and the cumulative state deviation elimination amount that has been completed up to the previous candidate recovery period. Based on the total amount of state deviation to be eliminated, the second recovery amount is determined by deducting the cumulative state deviation elimination amount completed up to the previous candidate recovery period and the maximum remaining recovery amount that can be completed after the current candidate recovery period, to ensure that the remaining recovery tasks can be completed. The maximum value among zero, the first recovery amount, and the second recovery amount is determined as the state deviation elimination amount that must be completed in the current candidate recovery period.

[0082] In this embodiment, updating the available capacity margin for subsequent time periods based on the capacity margin occupied by the replenishment scheme includes: Based on the time-sharing replenishment load and grid access nodes corresponding to the replenishment scheme, determine the load occupancy of relevant grid constraint components in each subsequent time period; Establish the correspondence between candidate carbon reduction measures, replenishment schemes, subsequent time periods and grid constraint components, and mark the load occupancy as the allocated load of the corresponding candidate carbon reduction measures; The updated available capacity margin is obtained by subtracting the allocated capacity from the available capacity margin of the corresponding grid constraint element in each subsequent time period. When determining the feasible amount and replenishment plan for multiple candidate carbon reduction measures in sequence, the updated available carrying capacity margin is used to determine the feasible amount and replenishment plan for the next candidate carbon reduction measure.

[0083] S4. Determine the target carbon reduction quota for controlled enterprises based on the emission reductions during the implementation phase and the emissions during the replenishment process corresponding to the executable amount.

[0084] In this embodiment, determining the target carbon reduction quota for controlled emission enterprises includes: Based on the feasibility of the candidate carbon reduction measures, determine the baseline energy consumption of the corresponding production process when the candidate carbon reduction measures are not implemented, and the actual energy consumption after the candidate carbon reduction measures are implemented. The emission reduction amount in the implementation stage is determined based on the difference between the two. Based on the time-sharing replenishment load, equipment status restoration energy consumption, and additional fuel consumption corresponding to the replenishment plan, determine the incremental energy consumption caused by the production status to be restored; Based on the carbon emission factors of electricity and the corresponding energy sources in each replenishment period, the incremental energy consumption is converted into emissions during the replenishment process. The net emission reduction corresponding to the candidate carbon reduction measures is obtained by subtracting the emission reduction during the remediation process from the emission reduction during the implementation phase. The net emission reductions corresponding to each candidate carbon reduction measure that meets the process recovery constraint and grid carrying capacity constraint are summarized to determine the target carbon reduction quotas for controlled emission enterprises.

[0085] In this embodiment, the emission reduction during the implementation phase includes the reduction in electricity consumption and fuel consumption emissions during the implementation of candidate carbon reduction measures. The emissions during the replenishment process only include the increased emissions due to the pending production recovery state caused by the candidate carbon reduction measures, and do not include the baseline emissions generated by controlled emission enterprises operating normally according to the original production plan during the corresponding replenishment period.

[0086] The net emission reduction corresponding to candidate carbon reduction measure i is:

[0087] in, For candidate carbon reduction measure i, the net emission reduction is; The emission reductions generated during the implementation phase of candidate carbon reduction measure i; The amount of emissions from the replenishment process corresponding to candidate carbon reduction measure i.

[0088] The emissions during the refill process for:

[0089] in, For the set of replenishment periods corresponding to candidate carbon reduction measure i; This refers to the increase in electricity consumption relative to the baseline energy consumption during the replenishment period t. The electricity carbon emission factor for the time period t is used to replenish the energy. This refers to the set of fuel types consumed during the replenishment process. This represents the increase in consumption of fuel type j relative to the baseline fuel consumption. denoted as the carbon emission factor for fuel type j.

[0090] The target carbon reduction quotas for the controlled emission enterprises are:

[0091] in, This is a set of candidate carbon reduction measures for which the feasible amount, replenishment plan, and subsequent carrying capacity margin occupancy have been determined; To reduce carbon emission quotas for enterprises subject to emission control.

[0092] like Figure 4The diagram illustrates the role of dynamically updating the available carrying capacity margin in subsequent time periods when multiple candidate carbon reduction measures are sequentially determined for replenishment schemes. The horizontal axis represents subsequent time periods, and the vertical axis represents the normalized carrying capacity margin or replenishment load. The original available carrying capacity margin represents the carrying capacity that grid-constrained components can provide in each subsequent time period before replenishment loads have been allocated to candidate carbon reduction measures. After measure A's replenishment scheme is determined, its time-sharing replenishment load occupies the corresponding grid-constrained components. Therefore, the allocated carrying capacity of measure A is subtracted from the original available carrying capacity margin to obtain the updated available carrying capacity margin. The candidate replenishment load demand of measure B should then be compared with this updated carrying capacity margin. In subsequent time periods 3 and 4, the candidate replenishment load of measure B is lower than the original available carrying capacity margin, but higher than the remaining available carrying capacity margin after measure A's occupation. If the original carrying capacity margin is still used to determine whether measure B is feasible, the carrying capacity already occupied by measure A will be redistributed to measure B, forming the double allocation risk shown in the diagram. This invention avoids the reuse of the same grid carrying capacity by promptly deducting the allocated carrying capacity of the corresponding time period and grid constraint elements after the replenishment scheme of each candidate carbon reduction measure is determined, and using the updated carrying capacity margin for the next candidate carbon reduction measure. This ensures that the executable amount and target carbon reduction quota of the subsequently determined candidate carbon reduction measures match the actual carrying capacity that the grid can provide.

[0093] Example 2, Figure 2 This invention presents an optimization system for carbon reduction quotas in controlled emission industries under power grid carrying capacity constraints, comprising: The basic data acquisition module is used to acquire candidate carbon reduction measures, process operation data, and the carrying capacity margin of the target power grid for each time period for emission-controlled enterprises. The replenishment boundary generation module is used to determine the production state to be restored based on the deviation of the process state caused by the candidate carbon reduction measures, and to generate a replenishment load boundary that satisfies the process restoration constraints based on the production state to be restored. The replenishment decision module is used to map the replenishment load boundary to the grid constraint elements in subsequent time periods, and determine the feasibility of candidate carbon reduction measures and corresponding replenishment schemes based on the available carrying capacity margin in subsequent time periods. The carbon reduction quota determination module is used to determine the target carbon reduction quota for controlled enterprises based on the emission reduction amount in the implementation phase and the emission amount in the replenishment process corresponding to the executable amount.

[0094] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0095] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0096] Those skilled in the art will recognize that the modules and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art 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.

[0097] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0098] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0099] In conclusion, 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 within the protection scope of the present invention.

Claims

1. A method for optimizing carbon reduction quotas in emission-controlled industries under power grid carrying capacity constraints, characterized in that... Includes the following steps: Obtain candidate carbon reduction measures, process operation data, and the carrying capacity margin of the target power grid for each time period for enterprises subject to emission control. The production status to be restored is determined based on the deviation of the process status caused by the candidate carbon reduction measures, and a replenishment load boundary that satisfies the process restoration constraint is generated based on the production status to be restored. The replenishment load boundary is mapped to the grid constraint elements in subsequent time periods, and the feasibility of candidate carbon reduction measures and corresponding replenishment schemes are determined based on the available carrying capacity margin in subsequent time periods. The target carbon reduction quotas for controlled enterprises are determined based on the emission reductions during the implementation phase and the emissions during the replenishment process corresponding to the feasible amount.

2. The method for optimizing carbon reduction quotas in controlled emission industries under power grid carrying capacity constraints according to claim 1, characterized in that, The determination of the production status to be restored includes: Based on the implementation period of the candidate carbon reduction measures, determine the baseline process status when the candidate carbon reduction measures are not implemented and the adjusted process status after implementation; By comparing the baseline process status with the adjusted process status, the deviations in production tasks, process materials, and equipment operation status are obtained. Based on the relationship between the status deviation and subsequent processes, the production tasks to be supplemented and the equipment status to be restored are determined, thus forming a production status to be restored.

3. The method for optimizing carbon reduction quotas in controlled emission industries under power grid carrying capacity constraints according to claim 2, characterized in that, The generation of the replenishment load boundary that satisfies the process recovery constraint includes: Determine the deviation propagation path of the production state to be restored along the material transfer relationship of the process; Based on the deviation propagation path, the recovery and verification process is determined according to the absorption capacity of subsequent processes for state deviation, and the minimum cumulative deviation elimination amount at each recovery time is determined in reverse. The maximum cumulative deviation elimination amount at each recovery time is determined based on the recovery capability of the production equipment. When the minimum cumulative deviation elimination amount is not greater than the maximum cumulative deviation elimination amount, the upper and lower limits of the replenishment load for each recovery period are determined based on the changes in the cumulative deviation elimination amount at adjacent recovery times, thus forming the replenishment load boundary.

4. The method for optimizing carbon reduction quotas in controlled emission industries under power grid carrying capacity constraints according to claim 3, characterized in that, The grid constraint element that maps the replenishment load boundary to subsequent time periods includes: Candidate recovery schemes are generated based on the replenishment load boundary; The time-sharing replenishment loads of the candidate recovery schemes are superimposed onto the corresponding grid access nodes and time-series power flow calculations are performed to obtain the carrying margin occupancy of each grid constraint element. Establish the correspondence between time-sharing replenishment load, subsequent time periods, grid constraints, and carrying capacity occupancy to form a mapping result.

5. The method for optimizing carbon reduction quotas in controlled emission industries under power grid carrying capacity constraints according to claim 4, characterized in that, The process of determining the feasible amount of candidate carbon reduction measures and corresponding replenishment plans based on the available carrying capacity margin in subsequent periods includes: Determine the production status to be restored and the load recovery boundary based on the candidate execution volume; Based on the mapping results and available capacity margin, the maximum replenishment load for each candidate recovery period is determined, and the maximum replenishment load is converted into the maximum state deviation elimination amount that can be eliminated in the corresponding period. The maximum remaining recovery amount and the state deviation elimination amount that must be completed in the current candidate recovery period are determined based on the maximum state deviation elimination amount. Compare the required state deviation elimination amount with the corresponding maximum state deviation elimination amount, adjust the candidate execution amount or form a backup plan, and determine the maximum candidate execution amount as the executable amount.

6. The method for optimizing carbon reduction quotas in controlled emission industries under power grid carrying capacity constraints according to claim 5, characterized in that, The process of converting the maximum replenishment load into the maximum state deviation elimination amount that can be eliminated in the corresponding time period includes: Keep the replenishment load of other candidate recovery periods unchanged, increase the replenishment load of the access node in the current candidate recovery period, and determine the carrying capacity margin of each relevant power grid constraint element; The maximum replenishment load when the occupancy of the carrying capacity margin does not exceed the corresponding available carrying capacity margin is determined as the maximum replenishment load for the current candidate recovery period; Based on the available replenishment power corresponding to the maximum replenishment load and the maximum recovery capacity of the production equipment, determine the maximum state deviation that can be eliminated in the corresponding time period.

7. The method for optimizing carbon reduction quotas in controlled emission industries under power grid carrying capacity constraints according to claim 6, characterized in that, The determination of the maximum remaining recovery amount and the state deviation elimination amount that must be completed in the current candidate recovery period includes: The maximum remaining recovery amount is obtained by summing the maximum state deviation elimination amount of each candidate recovery period after the current candidate recovery period; The first recovery amount is determined based on the difference between the current minimum cumulative state deviation elimination amount and the amount completed in the previous period. The second recovery amount is determined based on the difference between the total deviation of the state to be eliminated and the completed amount and the maximum remaining recovery amount. The maximum value among zero, the first recovery amount, and the second recovery amount is determined as the state deviation elimination amount that must be completed in the current candidate recovery period.

8. The method for optimizing carbon reduction quotas in controlled emission industries under power grid carrying capacity constraints according to claim 5, characterized in that, This also includes updating available capacity margin: Based on the time-sharing replenishment load and access nodes of the replenishment scheme, determine the load occupancy of relevant power grid constraint components in each subsequent time period; The occupancy rate is deducted from the corresponding available capacity margin to obtain the updated available capacity margin; The updated available carrying capacity margin will be used to determine the feasibility and replenishment schemes for the next candidate carbon reduction measures.

9. The method for optimizing carbon reduction quotas in controlled emission industries under power grid carrying capacity constraints according to claim 1, characterized in that, The determination of target carbon reduction quotas for controlled emission enterprises includes: The emission reduction amount for the implementation phase is determined based on the feasible amount and the difference in energy consumption before and after implementation; The emissions during the replenishment process are determined based on the time-sharing replenishment load, equipment recovery energy consumption, and additional fuel consumption corresponding to the replenishment plan, combined with the relevant carbon emission factors. The net emission reduction is determined by the difference between the emission reduction during the implementation phase and the emission reduction during the replenishment process. The net emission reduction of each candidate carbon reduction measure is then aggregated to obtain the target carbon reduction quota.

10. A system for optimizing carbon reduction quotas in emission-controlled industries under grid carrying capacity constraints, used to execute the method for optimizing carbon reduction quotas in emission-controlled industries under grid carrying capacity constraints as described in any one of claims 1-9, characterized in that, include: The basic data acquisition module is used to acquire candidate carbon reduction measures, process operation data, and the carrying capacity margin of the target power grid for each time period for emission-controlled enterprises. The replenishment boundary generation module is used to determine the production state to be restored based on the deviation of the process state caused by the candidate carbon reduction measures, and to generate a replenishment load boundary that satisfies the process restoration constraints based on the production state to be restored. The replenishment decision module is used to map the replenishment load boundary to the grid constraint elements in subsequent time periods, and determine the feasibility of candidate carbon reduction measures and corresponding replenishment schemes based on the available carrying capacity margin in subsequent time periods. The carbon reduction quota determination module is used to determine the target carbon reduction quota for controlled enterprises based on the emission reduction amount in the implementation phase and the emission amount in the replenishment process corresponding to the executable amount.

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