An electric-carbon synergistic optimization regulation method and system

Abstract

The application discloses a kind of electric carbon synergic optimization regulation and control method and system, belong to electric power system optimization scheduling technical field, the method is: based on load prediction data, new energy output data and thermal power generating unit operation data, constructs multi-objective electric carbon synergic scheduling model;Based on collaborative balance factor and dynamic tolerance feedback penalty factor, obtain improved optimization algorithm;Based on thermal power generating unit operation data, generate several initial scheduling schemes, and based on improved optimization algorithm and multi-objective electric carbon synergic scheduling model, initial scheduling scheme is evolved iteration, generates Pareto solution set;From Pareto solution set, the initial scheduling scheme that meets the requirement of evaluation verification is selected as target optimization scheduling scheme, to regulate and control electric power equipment.Therefore, by implementing the present application can realize introduction dynamic electric carbon coupling model and adaptive evolution mechanism, enhance global exploration, constraint convergence and low carbon sensitive area solution set coverage ability, improve electric carbon synergic optimization regulation and control strategy reliability.

Description

Technical Field

[0001] This invention relates to the field of power system optimization dispatching technology, and in particular to a method and system for coordinated optimization control of electricity and carbon emissions. Background Technology

[0002] The power system is a typical coupled system of "energy-environment-economy," and its operation is accompanied by significant carbon emissions. Currently, the power supply structure is still dominated by coal-fired power units, with carbon emissions primarily originating from the combustion of fossil fuels. However, the continuous increase in the installed capacity of renewable energy sources such as wind power and photovoltaics has led to strong time-varying and structurally dependent characteristics in the carbon emission levels of the power system. Therefore, how to achieve coordinated optimization with a low-carbon priority while ensuring the safe and stable operation of the power grid and maintaining acceptable economic efficiency has become a problem that needs further research.

[0003] Currently, existing technologies typically employ single-objective weighted optimization or simple phased optimization methods. These methods often transform multi-objective problems into single-objective problems through linear weighting, or simply handle economic scheduling and carbon emission control in stages. However, existing approaches struggle to accurately characterize the dynamic coupling relationship between electricity and carbon emissions, neglecting the impact of dynamic changes in power supply structure on carbon emissions, leading to deviations in optimization results under real-world operating scenarios. Therefore, existing single-objective weighted optimization or simple phased optimization methods fail to accurately characterize the dynamic coupling relationship between electricity and carbon emissions, resulting in poor reliability of the electricity-carbon coordinated optimization and control strategy. Summary of the Invention

[0004] This invention proposes a method and system for coordinated optimization and control of electricity and carbon, which solves the problem of poor reliability of coordinated optimization and control strategies caused by the inability of existing single-objective weighted or simple staged optimization methods to accurately characterize the dynamic coupling relationship between electricity and carbon. This invention introduces a dynamic electricity-carbon coupling model and an adaptive evolutionary mechanism to enhance global exploration, constraint convergence, and solution set coverage of low-carbon sensitive regions, thereby improving the reliability of the coordinated optimization and control strategy for electricity and carbon.

[0005] To achieve the above objectives, embodiments of the present invention provide a method for coordinated optimization and control of power generation and carbon emissions, comprising: constructing a multi-objective coordinated power generation and carbon emissions scheduling model based on pre-acquired load forecast data, new energy output data, and thermal power unit operation data; improving the algorithm structure of a preset optimization algorithm based on a pre-constructed coordinated balance factor and a pre-constructed dynamic tolerance feedback penalty factor to obtain an improved optimization algorithm; generating several initial scheduling schemes based on the thermal power unit operation data, and iterating the initial scheduling schemes through the coordinated balance factor and the dynamic tolerance feedback penalty factor based on the improved optimization algorithm and the multi-objective coordinated power generation and carbon emissions scheduling model to generate a Pareto solution set; and selecting initial scheduling schemes that meet preset evaluation and verification requirements from the Pareto solution set as target optimized scheduling schemes for the control of power equipment.

[0006] This invention proposes a method for coordinated optimization and control of electricity and carbon emissions. A multi-objective coordinated scheduling model for electricity and carbon emissions is constructed, and a coordinated balance factor and a dynamic tolerance feedback penalty factor are introduced to improve the preset optimization algorithm. An initial scheduling scheme is generated using thermal power unit operating data. Subsequently, the improved optimization algorithm and the multi-objective model are used for iterative evolution to generate a Pareto solution set. From the solution set, the target optimized scheduling scheme that meets the evaluation and verification requirements is selected to regulate the power equipment. Therefore, the construction of a multi-objective coordinated scheduling model for electricity and carbon emissions overcomes the shortcomings of traditional single-objective weighted methods that struggle to balance economic efficiency and low carbon emissions. The improved optimization algorithm is then used for adaptive evolutionary solving, enabling the control strategy to accurately characterize the dynamic coupling relationship between electricity and carbon emissions. This effectively solves the problem of poor strategy reliability caused by single-objective weighted or simple staged optimization in existing technologies. It enhances the ability of global exploration, constraint convergence, and solution set coverage of low-carbon sensitive areas, thereby improving the reliability of the coordinated optimization and control strategy for electricity and carbon emissions.

[0007] Furthermore, based on pre-acquired load forecast data, renewable energy output data, and thermal power unit operation data, a multi-objective power-carbon coordinated scheduling model is constructed, including: acquiring load forecast data, renewable energy output data, and thermal power unit operation data within a preset scheduling period; calculating actual renewable energy output data based on the load forecast data, renewable energy output data, and thermal power unit operation data; constructing a first objective function based on the thermal power unit operation data and a preset fuel cost coefficient; constructing a second objective function based on the thermal power unit operation data and a preset carbon emission coefficient; constructing a third objective function based on the renewable energy output data and the actual renewable energy output data; and constructing the multi-objective power-carbon coordinated scheduling model based on the load forecast data, renewable energy output data, thermal power unit operation data, and the set constraints, and according to the first objective function, the second objective function, and the third objective function.

[0008] In the above scheme, based on the load forecast data, new energy output data and thermal power unit operation data within the preset scheduling cycle, multiple objective functions are constructed to build a multi-objective electricity-carbon coordinated scheduling model. This can overcome the shortcomings of the traditional single-objective weighted method, which is difficult to balance economy and low carbon emissions, and helps to improve the reliability of the electricity-carbon coordinated optimization and control strategy.

[0009] Furthermore, based on the pre-constructed collaborative balance factor and the pre-constructed dynamic tolerance feedback penalty factor, the preset optimization algorithm is improved to obtain an improved optimization algorithm, including: constructing an evolutionary population based on the preset optimization algorithm and the thermal power unit operation data, and introducing the collaborative balance factor as a guiding indicator to guide the evolution of the evolutionary population during the individual selection stage, to obtain a first improved algorithm structure; calculating the supply-demand balance deviation based on the preset optimization algorithm and the load forecast data, and using the dynamic tolerance feedback penalty factor to perform segmented penalties on the supply-demand balance deviation during the constraint processing stage, to obtain a second improved algorithm structure; and obtaining an improved optimization algorithm based on the first improved algorithm structure and the second improved algorithm structure.

[0010] In the above scheme, the structure of the preset optimization algorithm is specifically improved. A collaborative balance factor is introduced as a guiding indicator in the individual selection stage to increase the retention probability of individuals with better low-carbon benefits and enhance the algorithm's responsiveness to low-carbon priority guidance. At the same time, a dynamic tolerance feedback penalty factor is used in the constraint processing stage to punish the supply and demand balance deviation in stages. This allows the improved optimization algorithm to maintain a certain search flexibility in the early stage of evolution to improve population diversity, while gradually strengthening constraint guidance in the later stage to improve the feasibility and convergence stability of the solution. This improved algorithm structure significantly enhances the ability of global exploration, constraint convergence, and solution set coverage in low-carbon sensitive areas, thereby improving the solution efficiency and the reliability of the electric carbon collaborative optimization control strategy.

[0011] Furthermore, the proposed method for coordinated optimization and control of electricity and carbon in this embodiment of the invention further includes: performing constraint repair operations on any individual in the evolutionary population based on the constraints, wherein the constraints include upper and lower limits of thermal power output constraints and ramping constraints; the constraint repair operations include: for any individual, obtaining the thermal power unit operation data for the current time period and the thermal power unit operation data for adjacent time periods; truncating the boundaries of individuals whose thermal power unit operation data for the current time period does not meet the upper and lower limits of thermal power output constraints to obtain the first constraint repair thermal power unit operation data for the current time period; adjusting the thermal power unit operation data for the current time period that does not meet the ramping constraints based on the first constraint repair thermal power unit operation data for the current time period to obtain the second constraint repair thermal power unit operation data for the current time period; calculating and optimizing the actual new energy output data based on the second constraint repair thermal power unit operation data for the current time period and the load forecast data, and calculating the instantaneous supply and demand deviation; and compensating the second constraint repair thermal power unit operation data for the current time period according to the preset downward adjustment space ratio or preset upward adjustment space ratio for each individual based on the instantaneous supply and demand deviation, and ending the constraint repair operation.

[0012] In the aforementioned scheme, a repair mechanism is designed to address the upper and lower limits of thermal output and the ramp-up constraint. Individual units that do not meet the upper and lower limits of thermal output are truncated at their boundaries, while those that do not meet the ramp-up constraint are adjusted. Then, based on optimized data, the instantaneous supply-demand deviation is calculated, and compensation is made according to the proportion of each unit's adjustable capacity. This repair operation not only ensures that the unit output meets the safety requirements of equipment operation and avoids equipment damage caused by excessive ramp-up, but also avoids excessive regulation pressure on a single unit through proportional correction, effectively reducing the constraint repair pressure in subsequent evolutionary solution stages and contributing to improving the reliability of the electricity-carbon co-optimization control strategy.

[0013] Furthermore, based on the thermal power unit operating data, several initial scheduling schemes are generated. Based on the improved optimization algorithm and the multi-objective electricity-carbon collaborative scheduling model, the initial scheduling schemes are iteratively evolved using the collaborative balance factor and the dynamic tolerance feedback penalty factor to generate a Pareto solution set. This includes: constructing an initial evolutionary population based on the thermal power unit operating data, and performing the constraint repair operation on each individual in the initial evolutionary population to obtain target thermal power unit operating data; encoding the target thermal power unit operating data to construct several candidate scheduling schemes; calculating the target original evaluation value and supply-demand balance deviation corresponding to each candidate scheduling scheme based on the candidate scheduling schemes and the multi-objective electricity-carbon collaborative scheduling model; and iteratively evolving the target original evaluation value and supply-demand balance deviation corresponding to each candidate scheduling scheme using the collaborative balance factor and the dynamic tolerance feedback penalty factor based on the improved optimization algorithm to generate a Pareto solution set.

[0014] In the above scheme, after constructing an initial evolutionary population using thermal power unit operating data, the aforementioned constraint repair operation is performed on each individual in the population to ensure the feasibility of the initial data. Subsequently, the repaired data is encoded to construct candidate scheduling schemes, and the target original evaluation value and supply-demand balance deviation corresponding to each scheme are calculated. Based on this, the candidate scheduling schemes are iteratively evolved using the collaborative balance factor and dynamic tolerance feedback penalty factor in the improved optimization algorithm to generate a Pareto solution set. Thus, by combining the theoretical model with actual data, scheduling schemes are continuously screened and optimized, ensuring that the generated Pareto solution set can truly reflect the performance boundaries of the power system under different operating conditions. This provides a reliable data foundation for subsequently selecting the optimal control strategy and helps improve the reliability of the power-carbon coordinated optimization control strategy.

[0015] Furthermore, based on the improved optimization algorithm, the target original evaluation value and supply-demand balance deviation corresponding to each candidate scheduling scheme are iteratively evaluated using the collaborative balance factor and the dynamic tolerance feedback penalty factor to generate a Pareto solution set. This includes: obtaining a tolerance threshold within a preset scheduling period based on the supply-demand balance deviation; constructing a dynamic penalty weight and a piecewise penalty function based on the tolerance threshold and the dynamic penalty weight; calculating the daily power deviation corresponding to each candidate scheduling scheme based on any candidate scheduling scheme in the current evolutionary generation; and, based on the piecewise penalty function, if the daily power deviation is less than or equal to the tolerance threshold, no penalty is applied to the target original evaluation value of the candidate scheduling scheme; if the daily power deviation is greater than the tolerance threshold, a penalty term is constructed and added to the... From the original target evaluation values ​​of the candidate scheduling schemes, penalized candidate scheduling schemes are obtained; based on any two penalized candidate scheduling schemes, non-dominated sorting and crowding distance calculations are performed to obtain non-dominated sorting results and crowding distance calculation results; a benchmark scheduling scheme is selected, and the target benchmark value corresponding to the benchmark scheduling scheme is obtained; based on the target benchmark value, the benchmark value improvement rate corresponding to any candidate scheduling scheme is calculated; based on the benchmark value improvement rate, the cooperative balance factor is constructed; individuals in the evolutionary population are screened based on the non-dominated sorting results and the crowding distance calculation results. When the non-dominated sorting results and crowding distance calculation results of two individuals are the same, individuals whose cooperative balance factor meets the preset screening requirements are selected to enter the next generation of the population until the evolutionary generation meets the preset evolutionary iteration requirements, thus obtaining the Pareto solution set.

[0016] In the above scheme, dynamic penalty weights and piecewise penalty functions are constructed to achieve fine-grained control over the evolutionary process. Then, a tolerance threshold is obtained based on the supply-demand balance deviation, and the penalty intensity is dynamically adjusted according to the daily electricity consumption deviation. This ensures that the algorithm maintains diversity in the early stages of the search, avoiding getting trapped in local optima. In the later stages of the search, the non-dominated sorting results and crowding distance calculation results are combined with a collaborative balance factor for selection. Thus, the relationship between exploration and exploitation is balanced, ensuring that when the evolutionary generations meet the iteration requirements, a Pareto solution set that satisfies both engineering constraints and possesses excellent electro-carbon synergistic performance can be obtained, which helps improve the reliability of the electro-carbon synergistic optimization and control strategy.

[0017] Furthermore, selecting initial scheduling schemes that meet preset evaluation and verification requirements from the Pareto solution set as target optimal scheduling schemes for regulating power equipment includes: based on the Pareto solution set, selecting individuals whose daily power deviation is less than or equal to the tolerance threshold to form an implementable candidate solution set; if the implementable candidate solution set meets a first output requirement, calculating the cooperative balance factor of each individual in the implementable candidate solution set, and selecting individuals whose cooperative balance factor meets the preset first evaluation and verification requirement as the target optimal scheduling scheme output for regulating power equipment; if the implementable candidate solution set meets a second output requirement, selecting individuals whose daily power deviation meets the preset second evaluation and verification requirement from the Pareto solution set as the target optimal scheduling scheme output for regulating power equipment.

[0018] In the above scheme, individuals with daily power deviations less than or equal to the tolerance threshold are selected from the Pareto solution set to form an implementable candidate solution set, ensuring the supply and demand balance of the scheme. Then, selection is carried out according to different output requirements, prioritizing low carbon emissions or ensuring the stability of system operation. Thus, the hierarchical selection strategy not only ensures the engineering feasibility of the scheduling scheme, but also prioritizes the optimization of carbon emission intensity within an acceptable economic cost range, which helps to improve the reliability of the power-carbon coordinated optimization and control strategy.

[0019] This invention also provides an electric power-carbon coordinated optimization and control system, including a model building module, an algorithm improvement module, a solution module, and a control module. The model building module is used to construct a multi-objective electric power-carbon coordinated scheduling model based on pre-acquired load forecast data, new energy output data, and thermal power unit operation data. The algorithm improvement module is used to improve the structure of a preset optimization algorithm based on a pre-built coordinated balance factor and a pre-built dynamic tolerance feedback penalty factor, resulting in an improved optimization algorithm. The solution module is used to generate several initial scheduling schemes based on the thermal power unit operation data, and based on the improved optimization algorithm and the multi-objective electric power-carbon coordinated scheduling model, iterates the initial scheduling schemes using the coordinated balance factor and the dynamic tolerance feedback penalty factor to generate a Pareto solution set. The control module is used to select initial scheduling schemes that meet preset evaluation and verification requirements from the Pareto solution set as target optimized scheduling schemes for controlling power equipment.

[0020] This invention proposes an electric-carbon coordinated optimization control system. A multi-objective electric-carbon coordinated scheduling model is constructed, and a coordinated balance factor and a dynamic tolerance feedback penalty factor are introduced to improve the preset optimization algorithm. An initial scheduling scheme is generated using thermal power unit operating data. Subsequently, the improved optimization algorithm and the multi-objective model are used for iterative evolution to generate a Pareto solution set. From the solution set, the target optimized scheduling scheme that meets the evaluation and verification requirements is selected to regulate the power equipment. Therefore, the construction of the multi-objective electric-carbon coordinated scheduling model overcomes the shortcomings of traditional single-objective weighted methods in balancing economic efficiency and low carbon emissions. The improved optimization algorithm is then used for adaptive evolutionary solving, enabling the control strategy to accurately characterize the dynamic coupling relationship between electric and carbon emissions. This effectively solves the problem of poor strategy reliability caused by single-objective weighted or simple staged optimization in existing technologies. It enhances the global exploration, constraint convergence, and solution set coverage capabilities of low-carbon sensitive areas, thereby improving the reliability of the electric-carbon coordinated optimization control strategy.

[0021] Furthermore, the model construction module includes a data acquisition unit, a data processing unit, a first objective function construction unit, a second objective function construction unit, a third objective function construction unit, and a multi-objective electricity-carbon coordinated scheduling model construction unit; wherein: the data acquisition unit is used to acquire load forecast data, renewable energy output data, and thermal power unit operation data within a preset scheduling period; the data processing unit is used to calculate actual renewable energy output data based on the load forecast data, renewable energy output data, and thermal power unit operation data; the first objective function construction unit is used to construct a first objective function based on the thermal power unit operation data and a preset fuel cost coefficient; the second objective function construction unit is used to construct a second objective function based on the thermal power unit operation data and a preset carbon emission coefficient; the third objective function construction unit is used to construct a third objective function based on the renewable energy output data and the actual renewable energy output data; the multi-objective electricity-carbon coordinated scheduling model construction unit is used to construct a multi-objective electricity-carbon coordinated scheduling model based on the load forecast data, the renewable energy output data, the thermal power unit operation data, and the set constraints, and according to the first objective function, the second objective function, and the third objective function.

[0022] Furthermore, the algorithm improvement module includes a first structural improvement unit, a second structural improvement unit, and an improvement combination unit; wherein: the first structural improvement unit is used to construct an evolutionary population based on the preset optimization algorithm and the thermal power unit operation data, and introduce the collaborative balance factor as a guiding indicator to guide the evolution of the evolutionary population during the individual selection stage, thereby obtaining a first improved algorithm structure; the second structural improvement unit is used to calculate the supply-demand balance deviation based on the preset optimization algorithm and the load forecast data, and use the dynamic tolerance feedback penalty factor to perform segmented penalty on the supply-demand balance deviation during the constraint processing stage, thereby obtaining a second improved algorithm structure; the improvement combination unit is used to obtain an improved optimization algorithm based on the first improved algorithm structure and the second improved algorithm structure. Attached Figure Description

[0023] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating the steps of a method for an electro-carbon synergistic optimization and control system according to a certain embodiment of the present invention. Figure 2 This is a schematic diagram of the module structure of an electro-carbon synergistic optimization and control system provided in a certain embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0027] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0029] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0030] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0031] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0032] To address the problem of poor reliability in synergistic optimization and control strategies for electricity and carbon due to the inability of existing single-objective weighted or simple staged optimization methods to accurately characterize the dynamic coupling relationship between electricity and carbon, see [reference needed]. Figure 1 , Figure 1 This is a flowchart illustrating the steps of a method for an electro-carbon synergistic optimization and control system according to a certain embodiment of the present invention. Figure 1 As shown in the figure, this embodiment of the invention proposes a method for synergistic optimization and control of electricity and carbon, including steps 101 to 104, the specific steps of which are as follows: Step 101: Based on the pre-acquired load forecast data, new energy output data and thermal power unit operation data, construct a multi-objective power-carbon coordinated scheduling model; Step 102: Based on the pre-constructed collaborative balance factor and the pre-constructed dynamic tolerance feedback penalty factor, the algorithm structure of the preset optimization algorithm is improved to obtain the improved optimization algorithm. Step 103: Based on the thermal power unit operation data, generate several initial scheduling schemes, and based on the improved optimization algorithm and the multi-objective electric carbon cooperative scheduling model, iterate the initial scheduling schemes through the cooperative balance factor and the dynamic tolerance feedback penalty factor to generate a Pareto solution set. Step 104: Select an initial scheduling scheme that meets the preset evaluation and verification requirements from the Pareto solution set as the target optimized scheduling scheme for regulating the power equipment.

[0033] One possible implementation involves first acquiring load forecasting data, renewable energy output data, and thermal power unit operation data for a 24-hour day-ahead dispatching scenario of a power system with renewable energy integration. Based on this, a multi-objective power-carbon collaborative dispatching model is constructed. This model comprehensively considers three objective functions: power generation economic cost, total carbon emissions, and renewable energy curtailment rate. After constructing the multi-objective power-carbon collaborative dispatching model, a multi-objective evolutionary algorithm is needed to solve for the Pareto non-dominated solution set under strong constraints and high-dimensional continuous decision variables. The classic NSGA-II algorithm has the advantages of non-dominated sorting and maintaining solution set diversity through congestion distance, but it still faces two typical engineering problems in the power grid day-ahead dispatching scenario: First, The constraints of power balance and ramping are rigid and the feasible region is narrow. If hard constraints or static penalty mechanisms are used, the search space is prone to premature shrinkage and the convergence process is unstable. Secondly, in practical engineering applications, power systems are more concerned with prioritizing the improvement of carbon emissions and renewable energy consumption within an economically acceptable range. However, the standard NSGA-II only selects based on non-dominated level and congestion distance, lacking a directional guidance mechanism for the dynamic coupling characteristics of electricity and carbon. Therefore, this invention proposes to structurally improve the constraint processing mechanism and selection operator within the NSGA-II framework, and construct an improved NSGA-II algorithm suitable for electricity-carbon coordinated scheduling scenarios to enhance the proportion of feasible solutions and improve low-carbon sensitivity. The solution set distribution density of the region is determined, and the convergence stability and engineering applicability of the algorithm are improved. Specifically, based on the pre-constructed collaborative balance factor and dynamic tolerance feedback penalty factor, the NSGA-II algorithm is improved to obtain an improved NSGA-II algorithm suitable for coordinated power and carbon scheduling. In this embodiment, the improved NSGA-II algorithm is characterized as an improved optimization algorithm. Then, after completing the construction of the multi-objective coordinated power and carbon scheduling model and the design of the improved NSGA-II algorithm, this embodiment uses the improved NSGA-II algorithm to perform evolutionary solving on the multi-objective coordinated power and carbon scheduling model. Under the premise of satisfying unit operation constraints, power balance, and economic tolerance requirements, the Pareto solution set is obtained, i.e., the root solution set. Several initial scheduling schemes are generated based on the operating data of thermal power units. These initial schemes are then iteratively evolved using an improved optimization algorithm and a multi-objective power-carbon coordinated scheduling model to generate a Pareto solution set. Finally, the initial scheduling scheme that meets the preset evaluation and verification requirements is selected from the Pareto solution set as the target optimized scheduling scheme for the regulation of power equipment. In practical applications, after users input typical daily load forecast data, new energy forecast output data, thermal power unit operating parameters, and carbon emission parameters, the power system can automatically generate multi-objective scheduling optimization results for the next 24 hours, outputting auxiliary decision-making information including unit output curves, carbon emission levels, curtailment rates, and representative target optimized scheduling schemes.

[0034] This invention proposes a method for coordinated optimization and control of electricity and carbon emissions. A multi-objective coordinated scheduling model for electricity and carbon emissions is constructed, and a coordinated balance factor and a dynamic tolerance feedback penalty factor are introduced to improve the preset optimization algorithm. An initial scheduling scheme is generated using thermal power unit operating data. Subsequently, the improved optimization algorithm and the multi-objective model are used for iterative evolution to generate a Pareto solution set. From the solution set, the target optimized scheduling scheme that meets the evaluation and verification requirements is selected to regulate the power equipment. Therefore, the construction of a multi-objective coordinated scheduling model for electricity and carbon emissions overcomes the shortcomings of traditional single-objective weighted methods that struggle to balance economic efficiency and low carbon emissions. The improved optimization algorithm is then used for adaptive evolutionary solving, enabling the control strategy to accurately characterize the dynamic coupling relationship between electricity and carbon emissions. This effectively solves the problem of poor strategy reliability caused by single-objective weighted or simple staged optimization in existing technologies. It enhances the ability of global exploration, constraint convergence, and solution set coverage of low-carbon sensitive areas, thereby improving the reliability of the coordinated optimization and control strategy for electricity and carbon emissions.

[0035] A preferred embodiment involves constructing a multi-objective power-carbon coordinated scheduling model based on pre-acquired load forecast data, renewable energy output data, and thermal power unit operation data. The model includes: acquiring load forecast data, renewable energy output data, and thermal power unit operation data within a preset scheduling period; calculating actual renewable energy output data based on the load forecast data, renewable energy output data, and thermal power unit operation data; constructing a first objective function based on the thermal power unit operation data and a preset fuel cost coefficient; constructing a second objective function based on the thermal power unit operation data and a preset carbon emission coefficient; constructing a third objective function based on the renewable energy output data and the actual renewable energy output data; and constructing the multi-objective power-carbon coordinated scheduling model based on the load forecast data, renewable energy output data, thermal power unit operation data, and the set constraints, and according to the first objective function, the second objective function, and the third objective function.

[0036] For example, the scheduling period is discretized into 24 time periods with a time step of 1 hour, and defined as the scheduling time domain. The time period number is The number of conventional thermal power units is Unit number is The number of wind farms is The station number is Then obtain the power system's first... Total load demand for a given period, i.e., load forecast data, is characterized as... The unit is MW. Load forecast data can be obtained from the load forecast module of the dispatch automation system, historical SCADA data curves, EMS load forecast results, or manually corrected day-ahead forecasts; obtain the first The first wind farm station The available wind power during a time period, i.e., the new energy output data, is characterized as , with the unit of MW. This data can be sourced from a wind power prediction system, a meteorological numerical prediction model, or a new energy power station prediction platform; Obtain the output power of the th thermal power unit during the th time period, i.e., the thermal power unit operation data, which is characterized as , with the unit of MW; Since in the embodiments of the present invention, the actual utilization power of wind power is not used as an independent decision variable, but follows the principle of preferential consumption of new energy and is automatically determined according to the remaining load space between the thermal power output and the system load. Specifically, define the total available wind power of the system during the th time period as: ; Define the total actual utilized wind power of the system during the th time period as: ; The actual new energy output data can be obtained by allocating the actual utilized wind power of the th wind power station according to its available output ratio, which is expressed as: ; Then, the first objective function is characterized as the total system operation cost objective function . Specifically, the total system operation cost objective function is defined as the total fuel cost of the thermal power units during the entire scheduling period, that is: ; In the formula, , , are respectively the quadratic fuel cost coefficients of the th thermal power unit, which are used to reflect the characteristics of the heat consumption rate change of the unit at different output levels. The total system operation cost objective function is the core index of the economic dispatch of the power grid enterprise.

[0037] The second objective function is characterized as the total system carbon emission objective function . Specifically, the total system carbon emission objective function is represented by a linear carbon emission accounting model, that is: ; In the formula, is the unit output carbon emission coefficient of the th thermal power unit. The total system carbon emission objective function is used to quantify the carbon emission level of the scheduling plan during the entire day-ahead period and can be docked with carbon accounting, carbon assessment, or carbon cost settlement modules.

[0038] The third objective function is represented by the system's wind curtailment rate objective function. The objective function of the system's wind curtailment rate Defined as the proportion of daily curtailed wind power to daily available wind power, i.e.: ; In the formula, Indicates the first Time period The wind curtailment power of each wind farm station; when When the denominator is zero, it can be agreed that... System wind curtailment rate objective function Reflecting the system's capacity to absorb new energy sources, when When the value approaches 0, it indicates that new energy sources have basically achieved full consumption.

[0039] After constructing the first, second, and third objective functions, the power system operation constraints are established. In this embodiment, these are represented as upper and lower limits of thermal power unit output and ramping constraints. Specifically, the upper and lower limits of thermal power unit output are expressed as follows: The ramp constraint is represented as: ;in, For minimum output, To maximize output For climbing ability and For downhill climbing ability.

[0040] It is worth mentioning that the minimum output , maximum output Climbing ability Downhill climbing ability Secondary fuel cost coefficient Secondary fuel cost coefficient Secondary fuel cost coefficient Carbon emission coefficient per unit output The parameters can be derived from the unit design specifications, regression fitting results of historical operating data of power generation enterprises, dispatching procedure parameter tables, or parameters given by the IEEE 30-node standard test system.

[0041] Finally, based on the first objective function, the second objective function, and the third objective function, a multi-objective electric carbon coordinated scheduling model is constructed, expressed as: .

[0042] In the above scheme, based on the load forecast data, new energy output data and thermal power unit operation data within the preset scheduling cycle, multiple objective functions are constructed to build a multi-objective electricity-carbon coordinated scheduling model. This can overcome the shortcomings of the traditional single-objective weighted method, which is difficult to balance economy and low carbon emissions, and helps to improve the reliability of the electricity-carbon coordinated optimization and control strategy.

[0043] A preferred embodiment involves improving the structure of a preset optimization algorithm based on a pre-constructed collaborative balance factor and a pre-constructed dynamic tolerance feedback penalty factor to obtain an improved optimization algorithm. This includes: constructing an evolutionary population based on the preset optimization algorithm and the thermal power unit operating data; introducing the collaborative balance factor as a guiding indicator to guide the evolution of the population during the individual selection phase to obtain a first improved algorithm structure; calculating the supply-demand balance deviation based on the preset optimization algorithm and the load forecast data; and applying the dynamic tolerance feedback penalty factor to segmentally penalize the supply-demand balance deviation during the constraint processing phase to obtain a second improved algorithm structure; and obtaining the improved optimization algorithm based on the first and second improved algorithm structures.

[0044] For example, the operating data of thermal power units is first encoded to characterize the hourly output scheme of the thermal power units. Each hourly output scheme of a thermal power unit is treated as an individual, and an evolutionary population is constructed. One interpretation is that the population size is . The maximum number of generations is The current evolutionary generation is The coding format for the hourly output scheme of each thermal power unit is as follows: ;in, Indicates the first Taiwan thermal power unit in the The output power for a given time period, i.e., the operating data of thermal power units; then, within the NSGA-II algorithm framework, structural improvements are made to the constraint handling mechanism and selection operator. Specifically, in the individual selection phase, the difference in improvement rate between the baseline scheduling scheme and any candidate scheduling scheme is calculated, and the aforementioned collaborative balance factor is introduced. As a guiding indicator, the evolutionary population is guided to shift towards a region with better electro-carbon synergistic benefits, thus obtaining the first improved algorithm structure.

[0045] Then, the supply and demand balance deviation within the entire scheduling cycle is calculated based on the load forecast data, and a segmented penalty function is constructed based on the supply and demand balance deviation to impose a penalty on the multi-objective electric carbon collaborative scheduling model. That is, the dynamic tolerance feedback penalty factor is used to segmentally penalize the supply and demand balance deviation during the constraint processing stage, resulting in the second improved algorithm structure.

[0046] The individual selection and constraint processing phases of the NSGA-II algorithm are structurally improved to obtain an improved optimization algorithm, which is more suitable for the electric carbon cooperative scheduling scenario.

[0047] In the above scheme, the structure of the preset optimization algorithm is specifically improved. A collaborative balance factor is introduced as a guiding indicator in the individual selection stage to increase the retention probability of individuals with better low-carbon benefits and enhance the algorithm's responsiveness to low-carbon priority guidance. At the same time, a dynamic tolerance feedback penalty factor is used in the constraint processing stage to punish the supply and demand balance deviation in stages. This allows the improved optimization algorithm to maintain a certain search flexibility in the early stage of evolution to improve population diversity, while gradually strengthening constraint guidance in the later stage to improve the feasibility and convergence stability of the solution. This improved algorithm structure significantly enhances the ability of global exploration, constraint convergence, and solution set coverage in low-carbon sensitive areas, thereby improving the solution efficiency and the reliability of the electric carbon collaborative optimization control strategy.

[0048] In a preferred embodiment of the present invention, a method for coordinated optimization and control of electricity and carbon energy further includes: performing a constraint repair operation on any individual in the evolutionary population based on the constraints, wherein the constraints include upper and lower limits of thermal power output constraints and ramping constraints; the constraint repair operation includes: for any individual, obtaining the thermal power unit operation data for the current time period and the thermal power unit operation data for adjacent time periods; truncating the boundaries of individuals whose thermal power unit operation data for the current time period does not meet the upper and lower limits of thermal power output constraints to obtain the first constraint repair thermal power unit operation data for the current time period; adjusting the thermal power unit operation data for the current time period that does not meet the ramping constraints based on the first constraint repair thermal power unit operation data for the current time period to obtain the second constraint repair thermal power unit operation data for the current time period; calculating and optimizing the actual new energy output data based on the second constraint repair thermal power unit operation data for the current time period and the load forecast data, and calculating the instantaneous supply and demand deviation; and compensating the second constraint repair thermal power unit operation data for the current time period according to the preset downward adjustment space ratio or preset upward adjustment space ratio for each individual based on the instantaneous supply and demand deviation, and ending the constraint repair operation.

[0049] For example, during the initialization phase of the evolutionary population, initial individuals that satisfy the upper and lower limits of thermal power output constraints are randomly generated. Then, the ramp-up constraints of each initial individual are checked hourly. When an initial individual fails to meet the ramp-up constraints, the operating data of the thermal power unit for the corresponding time period is corrected to bring it back to the allowable constraint range, thereby ensuring that the initial population has a high feasibility ratio in terms of unit operation constraints. One specific explanation is that the constraint correction operation steps are performed sequentially for each individual: For any initial individual thermal power unit i and time period t, the following definition is given: ; This ensures the initial individual fire output upper and lower limits constraints for the current period, and the first constraint repairs the thermal power unit operation data; A ramp constraint check is performed on the operating data of thermal power units in adjacent time periods, as shown below: ; ; If the ramp constraint is violated, the output for the corresponding period will be adjusted back to the allowable range, usually back to the boundary value, to obtain the second constraint repair thermal power unit operation data for the current period.

[0050] Then, define the first The total output of thermal power during the period is: ; Then, according to the new energy priority principle proposed in step 101, the optimized actual new energy output data is determined and expressed as follows: ; Then calculate the instantaneous supply-demand deviation: ;like Then, reductions will be made according to the proportion of space that can be reduced for each unit. (Definition of the first...) Taiwanese unit in The time period can be adjusted downwards as follows: ; The operating data of the second constraint-repaired thermal power unit for the current time period is then compensated as follows to obtain the operating data of the target thermal power unit: ; like Then compensation will be made according to the proportion of space that each unit can adjust upwards. Define the first... Taiwanese unit in The adjustable range for the time period is as follows: ; The operating data of the second constraint-repaired thermal power unit for the current time period is then compensated as follows to obtain the operating data of the target thermal power unit: ; The above-mentioned correction method can allocate the adjustment amount according to the current operating status of each unit, so that the load deviation can be balanced under the condition of meeting the upper and lower limits of thermal power output constraints, thereby avoiding excessive adjustment pressure on a single unit and improving the feasibility and stability of the dispatching scheme. Since the above-mentioned power redistribution may disrupt the consistency of adjacent time periods, it is necessary to perform ramp-up repair again. That is, after the thermal power repair is completed, the renewable energy consumption power and supply-demand deviation are recalculated, and the remaining deficit is compensated proportionally again.

[0051] In the aforementioned scheme, a repair mechanism is designed to address the upper and lower limits of thermal output and the ramp-up constraint. Individual units that do not meet the upper and lower limits of thermal output are truncated at their boundaries, while those that do not meet the ramp-up constraint are adjusted. Then, based on optimized data, the instantaneous supply-demand deviation is calculated, and compensation is made according to the proportion of each unit's adjustable capacity. This repair operation not only ensures that the unit output meets the safety requirements of equipment operation and avoids equipment damage caused by excessive ramp-up, but also avoids excessive regulation pressure on a single unit through proportional correction, effectively reducing the constraint repair pressure in subsequent evolutionary solution stages and contributing to improving the reliability of the electricity-carbon co-optimization control strategy.

[0052] A preferred embodiment involves generating several initial scheduling schemes based on the thermal power unit operating data, and then iteratively evolving the initial scheduling schemes using the collaborative balance factor and the dynamic tolerance feedback penalty factor based on the improved optimization algorithm and the multi-objective power-carbon collaborative scheduling model to generate a Pareto solution set. This includes: constructing an initial evolutionary population based on the thermal power unit operating data, and performing the constraint repair operation on each individual in the initial evolutionary population to obtain target thermal power unit operating data; encoding the target thermal power unit operating data to construct several candidate scheduling schemes; calculating the target original evaluation value and supply-demand balance deviation corresponding to each candidate scheduling scheme based on the candidate scheduling schemes and the multi-objective power-carbon collaborative scheduling model; and iteratively evolving the target original evaluation value and supply-demand balance deviation corresponding to each candidate scheduling scheme using the collaborative balance factor and the dynamic tolerance feedback penalty factor based on the improved optimization algorithm to generate a Pareto solution set.

[0053] For example, after performing the constraint repair operations proposed in the above steps on each individual in the initial evolutionary population, the operating data of the target thermal power unit is obtained. Let the initial population be... Population size is The maximum number of generations is , No. Generation population is recorded as It contains One candidate scheduling individual, namely There are 10 candidate scheduling schemes, where, for any individual First, the operating data of each thermal power unit is obtained by encoding and decoding its thermal power output. , Then, combining the multi-objective electricity-carbon coordinated scheduling model, the original objective evaluation value and supply-demand balance deviation corresponding to each candidate scheduling scheme are calculated. The original objective evaluation value is characterized as follows: The supply-demand imbalance is characterized as The supply-demand imbalance can be characterized as follows: ; Supply-demand imbalance is used to measure the degree to which a scheduling scheme deviates from the system's power balance; Finally, based on the improved optimization algorithm, the original target evaluation value and supply-demand balance deviation corresponding to each candidate scheduling scheme are iteratively evaluated through the collaborative balance factor and dynamic tolerance feedback penalty factor to generate the Pareto solution set.

[0054] In the above scheme, after constructing an initial evolutionary population using thermal power unit operating data, the aforementioned constraint repair operation is performed on each individual in the population to ensure the feasibility of the initial data. Subsequently, the repaired data is encoded to construct candidate scheduling schemes, and the target original evaluation value and supply-demand balance deviation corresponding to each scheme are calculated. Based on this, the candidate scheduling schemes are iteratively evolved using the collaborative balance factor and dynamic tolerance feedback penalty factor in the improved optimization algorithm to generate a Pareto solution set. Thus, by combining the theoretical model with actual data, scheduling schemes are continuously screened and optimized, ensuring that the generated Pareto solution set can truly reflect the performance boundaries of the power system under different operating conditions. This provides a reliable data foundation for subsequently selecting the optimal control strategy and helps improve the reliability of the power-carbon coordinated optimization control strategy.

[0055] A preferred embodiment, based on the improved optimization algorithm, iterates the target original evaluation value and the supply-demand balance deviation corresponding to each candidate scheduling scheme through the collaborative balance factor and the dynamic tolerance feedback penalty factor to generate a Pareto solution set, including: obtaining a tolerance threshold within a preset scheduling period based on the supply-demand balance deviation; constructing a dynamic penalty weight and constructing a piecewise penalty function based on the tolerance threshold and the dynamic penalty weight; calculating the daily power deviation corresponding to each candidate scheduling scheme based on any candidate scheduling scheme in the current evolutionary generation; based on the piecewise penalty function, if the daily power deviation is less than or equal to the tolerance threshold, no penalty is applied to the target original evaluation value of the candidate scheduling scheme; if the daily power deviation is greater than the tolerance threshold, a penalty term is constructed and the penalty term is superimposed. From the target original evaluation values ​​of the candidate scheduling schemes, penalized candidate scheduling schemes are obtained; based on any two penalized candidate scheduling schemes, non-dominated sorting and crowding distance calculations are performed to obtain non-dominated sorting results and crowding distance calculation results; a benchmark scheduling scheme is selected, and the target benchmark value corresponding to the benchmark scheduling scheme is obtained; based on the target benchmark value, the benchmark value improvement rate corresponding to any candidate scheduling scheme is calculated; based on the benchmark value improvement rate, the cooperative balance factor is constructed; individuals in the evolutionary population are screened based on the non-dominated sorting results and the crowding distance calculation results. When the non-dominated sorting results and crowding distance calculation results of two individuals are the same, individuals whose cooperative balance factor meets the preset screening requirements are selected to enter the next generation of the population until the evolutionary generation meets the preset evolutionary iteration requirements, thus obtaining the Pareto solution set.

[0056] For example, the dynamic penalty weights are updated by generation during each iteration. Furthermore, a supply-demand deviation penalty term is introduced into the original target evaluation value. One interpretation is to define the absolute daily electricity tolerance threshold as: ; In the formula, This is the tolerance ratio parameter; Define the dynamic penalty weight for generation g. , represented as: ; In the formula, The initial penalty coefficient, This represents the maximum number of generations.

[0057] Then, based on the absolute daily electricity tolerance threshold and dynamic penalty weights Construct a piecewise penalty function, expressed as: ; Based on this, construct the objective function value after penalty. , and It serves as a unified evaluation criterion for non-dominated ranking and selection. Specifically, the penalized candidate scheduling scheme is represented as follows: ; ; ; In the formula, , and The representation is the corresponding original evaluation value of the target, for the individual. Then its original target evaluation value is characterized as .

[0058] Then, the penalized candidate scheduling schemes are used for non-dominated ranking and crowding calculation, allowing the supply-demand balance deviation to participate in multi-objective evolutionary evaluation as a soft constraint. Specifically, a penalized objective vector is constructed, denoted as... Perform a fast non-dominated sort on the population, if for any two individuals and ,satisfy: If at least one objective satisfies the strict inequality, then the individual is considered to be... Dominant Individual This assigns a non-dominant hierarchy to each individual. .

[0059] After completing the non-dominated ordination, the population is divided into several Pareto rank, and the crowding distance is calculated within the same rank. The crowding distance in each target dimension is defined as: ; The total crowding distance for an individual is defined as: ; In this embodiment, the greater the crowding distance, the more sparse the individual is in the solution space, and the higher its value in maintaining diversity.

[0060] To quantitatively measure the relative improvement effect of candidate scheduling schemes on the goal of coordinated electricity and carbon emissions, a baseline scheduling scheme was selected. As an evaluation reference, the corresponding target benchmark values ​​are denoted as follows: , and Baseline scheduling scheme It can be obtained from the lowest-cost feasible solution in the initial population or the result of traditional economic scheduling; then, for any candidate individual The improvement rate relative to the baseline scheduling scheme is defined as: ; ; ; In the formula, To reduce carbon emissions; This represents the percentage change in operating costs. The percentage of improvement in the wind curtailment rate of renewable energy sources; among which, This indicates the achievement of carbon emission reduction. This indicates an increase in costs. This indicates a decrease in the wind curtailment rate. The above three indicators quantify the optimization effect from three dimensions: low-carbon benefits, economic changes, and improvement in new energy consumption.

[0061] Based on this, a collaborative balance factor is constructed, expressed as: ; In the formula, It is a very small positive number, used to avoid the denominator being zero. The higher the value, the more significant the improvement in carbon emissions and wind curtailment rate, and the better the benefits of electricity-carbon synergy, with relatively small changes in cost.

[0062] Finally, in the parent selection phase, a multi-level priority selection rule based on the tournament concept is adopted, prioritizing the selection of non-dominant generations. Lower-ranking individuals, when in a non-dominant hierarchy When the distances are the same, prioritize the crowded distance. Larger individuals, when in a non-dominant hierarchy distance from crowds When all are the same, the cooperative balance factor should be selected first. For larger individuals, this rule, while maintaining Pareto frontier diversity, guides the evolutionary direction toward regions where the synergistic benefits of electricity and carbon are better.

[0063] After the selection is completed, taking the g-th generation as an example, crossover and mutation operations are performed on the parent individuals to generate the offspring population. The crossover probability employs an adaptive strategy that varies with algebra: ; In the formula, This represents the initial crossover probability. Then, the generated offspring... Each individual in the population is checked against its firepower output limits and climbing constraints. Individuals violating these constraints undergo repair operations to ensure physical feasibility. In each generation iteration, the parent and offspring populations are merged to form a joint population, represented as: ; An elite retention strategy is implemented based on non-dominated ranking and crowding distance, selecting the top... Individuals constitute the next generation population. The algorithm terminates when any of the following conditions are met: ; Or the Pareto front changes by less than a given threshold over several consecutive generations. The algorithm terminates. After termination, the Pareto solution set is output, denoted as... .

[0064] In the above scheme, dynamic penalty weights and piecewise penalty functions are constructed to achieve fine-grained control over the evolutionary process. Then, a tolerance threshold is obtained based on the supply-demand balance deviation, and the penalty intensity is dynamically adjusted according to the daily electricity consumption deviation. This ensures that the algorithm maintains diversity in the early stages of the search, avoiding getting trapped in local optima. In the later stages of the search, the non-dominated sorting results and crowding distance calculation results are combined with a collaborative balance factor for selection. Thus, the relationship between exploration and exploitation is balanced, ensuring that when the evolutionary generations meet the iteration requirements, a Pareto solution set that satisfies both engineering constraints and possesses excellent electro-carbon synergistic performance can be obtained, which helps improve the reliability of the electro-carbon synergistic optimization and control strategy.

[0065] A preferred embodiment involves selecting an initial scheduling scheme from the Pareto solution set that meets preset evaluation and verification requirements as a target optimized scheduling scheme for regulating power equipment. This includes: based on the Pareto solution set, selecting individuals whose daily power deviation is less than or equal to the tolerance threshold to form an implementable candidate solution set; if the implementable candidate solution set meets a first output requirement, calculating the collaborative balance factor of each individual in the implementable candidate solution set, and selecting individuals whose collaborative balance factor meets the preset first evaluation and verification requirements as the target optimized scheduling scheme output for regulating power equipment; if the implementable candidate solution set meets a second output requirement, selecting individuals from the Pareto solution set whose daily power deviation meets the preset second evaluation and verification requirements as the target optimized scheduling scheme output for regulating power equipment.

[0066] For example, since only one specific scheduling scheme can ultimately be executed in actual power grid operation, this embodiment of the invention proposes to start from the output Pareto solution set. Representative scheduling schemes are selected as the target optimized scheduling scheme output. Specifically, the allowable daily power deviation threshold is first defined based on the load scale. Based on this, a set of feasible candidates is constructed, represented as: ; like If the feasible candidate set is not empty, representing the first output requirement, then the individual with the largest collaborative balance factor from the feasible candidate set is selected as the recommended scheduling scheme, expressed as: ; Among them, the first pre-set evaluation and verification requirement is characterized by the maximum synergistic balance factor; like If the candidate set is empty, representing the second output requirement, then it degenerates into selecting the individual with the smallest daily electricity deviation as the output scheme to ensure the output result has the greatest feasibility: ; Among them, the preset second evaluation and verification requirement is characterized by the minimum daily electricity consumption deviation.

[0067] In the above scheme, individuals with daily power deviations less than or equal to the tolerance threshold are selected from the Pareto solution set to form an implementable candidate solution set, ensuring the supply and demand balance of the scheme. Then, selection is carried out according to different output requirements, prioritizing low carbon emissions or ensuring the stability of system operation. Thus, the hierarchical selection strategy not only ensures the engineering feasibility of the scheduling scheme, but also prioritizes the optimization of carbon emission intensity within an acceptable economic cost range, which helps to improve the reliability of the power-carbon coordinated optimization and control strategy.

[0068] Based on the above method embodiments, corresponding apparatus embodiments are provided; see [link to apparatus embodiments]. Figure 2 , Figure 2 This is a schematic diagram of the module structure of an electro-carbon synergistic optimization and control system provided in one embodiment of the present invention. Figure 2 As shown in the figure, this embodiment of the invention also provides an electric-carbon coordinated optimization and control system, including a model construction module 201, an algorithm improvement module 202, a solution module 203, and a control module 204; wherein: the model construction module 201 is used to construct a multi-objective electric-carbon coordinated scheduling model based on pre-acquired load forecast data, new energy output data, and thermal power unit operation data; the algorithm improvement module 202 is used to improve the algorithm structure of a preset optimization algorithm based on a pre-constructed coordinated balance factor and a pre-constructed dynamic tolerance feedback penalty factor to obtain an improved optimization algorithm; the solution module 203 is used to generate several initial scheduling schemes based on the thermal power unit operation data, and based on the improved optimization algorithm and the multi-objective electric-carbon coordinated scheduling model, iterates the initial scheduling schemes through the coordinated balance factor and the dynamic tolerance feedback penalty factor to generate a Pareto solution set; the control module 204 is used to select initial scheduling schemes that meet preset evaluation and verification requirements from the Pareto solution set as target optimized scheduling schemes for controlling power equipment.

[0069] This invention proposes an electric-carbon coordinated optimization control system. A multi-objective electric-carbon coordinated scheduling model is constructed, and a coordinated balance factor and a dynamic tolerance feedback penalty factor are introduced to improve the preset optimization algorithm. An initial scheduling scheme is generated using thermal power unit operating data. Subsequently, the improved optimization algorithm and the multi-objective model are used for iterative evolution to generate a Pareto solution set. From the solution set, the target optimized scheduling scheme that meets the evaluation and verification requirements is selected to regulate the power equipment. Therefore, the construction of the multi-objective electric-carbon coordinated scheduling model overcomes the shortcomings of traditional single-objective weighted methods in balancing economic efficiency and low carbon emissions. The improved optimization algorithm is then used for adaptive evolutionary solving, enabling the control strategy to accurately characterize the dynamic coupling relationship between electric and carbon emissions. This effectively solves the problem of poor strategy reliability caused by single-objective weighted or simple staged optimization in existing technologies. It enhances the global exploration, constraint convergence, and solution set coverage capabilities of low-carbon sensitive areas, thereby improving the reliability of the electric-carbon coordinated optimization control strategy.

[0070] Furthermore, the model construction module 201 includes a data acquisition unit 301, a data processing unit 302, a first objective function construction unit 303, a second objective function construction unit 304, a third objective function construction unit 305, and a multi-objective electricity-carbon coordinated scheduling model construction unit 306; wherein: the data acquisition unit 301 is used to acquire load forecast data, new energy output data, and thermal power unit operation data within a preset scheduling period; the data processing unit 302 is used to calculate actual new energy output data based on the load forecast data, new energy output data, and thermal power unit operation data; the first objective function construction unit 303 is used to calculate actual new energy output data based on the thermal power unit operation data. The system uses the thermal power unit's operating data and a preset fuel cost coefficient to construct a first objective function; a second objective function construction unit 304 is used to construct a second objective function based on the thermal power unit's operating data and a preset carbon emission coefficient; a third objective function construction unit 305 is used to construct a third objective function based on the new energy output data and the actual new energy output data; and a multi-objective power-carbon coordinated scheduling model construction unit 306 is used to construct a multi-objective power-carbon coordinated scheduling model based on the load forecast data, the new energy output data, the thermal power unit's operating data, and the set constraints, and according to the first objective function, the second objective function, and the third objective function.

[0071] Furthermore, the algorithm improvement module 202 includes a first structural improvement unit 401, a second structural improvement unit 402, and an improvement combination unit 403; wherein: the first structural improvement unit 401 is used to construct an evolutionary population based on the preset optimization algorithm and the thermal power unit operation data, and introduce the collaborative balance factor as a guiding indicator to guide the evolution of the evolutionary population during the individual selection stage, thereby obtaining a first improved algorithm structure; the second structural improvement unit 402 is used to calculate the supply-demand balance deviation based on the preset optimization algorithm and the load forecast data, and use the dynamic tolerance feedback penalty factor to perform segmented penalty on the supply-demand balance deviation during the constraint processing stage, thereby obtaining a second improved algorithm structure; the improvement combination unit 403 is used to obtain an improved optimization algorithm based on the first improved algorithm structure and the second improved algorithm structure.

[0072] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can realize the electro-carbon synergistic optimization control method provided by any of the above-described method embodiments of the present invention.

[0073] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0074] Based on the above-described embodiment of the electro-carbon synergistic optimization control method, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements an electro-carbon synergistic optimization control method according to any embodiment of the present invention.

[0075] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0076] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0077] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0078] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the electro-carbon synergistic optimization and control method described in any of the above-described method embodiments of the present invention.

[0079] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0080] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for synergistic optimization and control of electricity and carbon, characterized in that, include: Based on pre-acquired load forecast data, new energy output data, and thermal power unit operation data, a multi-objective power-carbon coordinated scheduling model is constructed. Based on the pre-constructed collaborative balance factor and the pre-constructed dynamic tolerance feedback penalty factor, the algorithm structure of the preset optimization algorithm is improved to obtain the improved optimization algorithm. Based on the thermal power unit operating data, several initial scheduling schemes are generated. Based on the improved optimization algorithm and the multi-objective electric carbon cooperative scheduling model, the initial scheduling schemes are iterated through the cooperative balance factor and the dynamic tolerance feedback penalty factor to generate a Pareto solution set. The initial scheduling scheme that meets the preset evaluation and verification requirements is selected from the Pareto solution set as the target optimized scheduling scheme for the regulation of power equipment.

2. The method for synergistic optimization and control of electricity and carbon as described in claim 1, characterized in that, Based on pre-acquired load forecast data, renewable energy output data, and thermal power unit operation data, a multi-objective power-carbon coordinated scheduling model is constructed, including: Acquire load forecast data, new energy output data, and thermal power unit operation data within a preset scheduling cycle; Based on the load forecast data, new energy output data and thermal power unit operation data, calculate the actual new energy output data; Based on the operating data of the thermal power unit and the preset fuel cost coefficient, a first objective function is constructed; Based on the operating data of the thermal power units and the preset carbon emission coefficient, a second objective function is constructed. Based on the new energy output data and the actual new energy output data, a third objective function is constructed; A multi-objective electricity-carbon coordinated scheduling model is constructed based on the load forecast data, the new energy output data, the thermal power unit operation data, and the set constraints, and according to the first objective function, the second objective function, and the third objective function.

3. The method for synergistic optimization and control of electricity and carbon as described in claim 2, characterized in that, Based on the pre-constructed collaborative balance factor and the pre-constructed dynamic tolerance feedback penalty factor, the algorithm structure of the preset optimization algorithm is improved to obtain the improved optimization algorithm, including: Based on the preset optimization algorithm and the thermal power unit operation data, an evolutionary population is constructed, and the cooperative balance factor is introduced as a guiding indicator to guide the evolutionary population during the individual selection stage, thus obtaining the first improved algorithm structure. Based on the preset optimization algorithm and the load forecast data, the supply-demand balance deviation is calculated, and the dynamic tolerance feedback penalty factor is used to penalize the supply-demand balance deviation in segments during the constraint processing stage, thus obtaining the second improved algorithm structure. Based on the first improved algorithm structure and the second improved algorithm structure, an improved optimization algorithm is obtained.

4. The method for synergistic optimization and control of electricity and carbon as described in claim 3, characterized in that, Also includes: Based on the constraints, a constraint repair operation is performed on any individual in the evolutionary population, wherein the constraints include upper and lower limits of firepower output and climbing constraints. The constraint repair operation includes: For any of the aforementioned individuals, obtain the thermal power unit operation data for the current time period and the thermal power unit operation data for adjacent time periods; For individual thermal power unit operating data that do not meet the upper and lower limits of thermal power output constraints in the current time period, boundary truncation is performed to obtain the first constraint-repaired thermal power unit operating data for the current time period; Based on the first constraint repair thermal power unit operation data of the current time period, the individual thermal power unit operation data of the current time period that does not meet the ramping constraint are adjusted to obtain the second constraint repair thermal power unit operation data of the current time period. Based on the second constraint repair thermal power unit operation data and the load forecast data for the current period, the actual new energy output data is calculated and optimized, and the instantaneous supply and demand deviation is calculated. Based on the instantaneous supply and demand deviation, the operating data of the second constraint repair thermal power unit in the current period are compensated according to the preset adjustable space ratio or preset adjustable space ratio of each individual, and the constraint repair operation ends.

5. The method for synergistic optimization and control of electricity and carbon as described in claim 4, characterized in that, Based on the operating data of the thermal power units, several initial scheduling schemes are generated. Then, based on the improved optimization algorithm and the multi-objective power-carbon coordinated scheduling model, the initial scheduling schemes are iteratively evolved using the coordinated balance factor and the dynamic tolerance feedback penalty factor to generate a Pareto solution set, including: An initial evolutionary population is constructed based on the thermal power unit operation data, and the constraint repair operation is performed on each individual in the initial evolutionary population to obtain the target thermal power unit operation data. The operating data of the target thermal power unit is encoded to construct several candidate scheduling schemes; Based on the candidate scheduling schemes and the multi-objective electricity-carbon coordinated scheduling model, calculate the original target evaluation value and supply-demand balance deviation for each candidate scheduling scheme. Based on the improved optimization algorithm, the original target evaluation value and the supply-demand balance deviation corresponding to each candidate scheduling scheme are iteratively evaluated through the collaborative balance factor and the dynamic tolerance feedback penalty factor to generate a Pareto solution set.

6. The method for synergistic optimization and control of electricity and carbon as described in claim 5, characterized in that, Based on the improved optimization algorithm, the original target evaluation value and the supply-demand balance deviation corresponding to each candidate scheduling scheme are iteratively evaluated using the collaborative balance factor and the dynamic tolerance feedback penalty factor to generate a Pareto solution set, including: Based on the supply and demand imbalance, obtain the tolerance threshold within the preset scheduling period; Construct dynamic penalty weights, and build a piecewise penalty function based on the tolerance threshold and the dynamic penalty weights; Based on any candidate scheduling scheme in the current generation, calculate the daily power deviation corresponding to each candidate scheduling scheme; Based on the piecewise penalty function, if the daily electricity consumption deviation is less than or equal to the tolerance threshold, no penalty is imposed on the target original evaluation value of the candidate scheduling scheme; If the daily power consumption deviation is greater than the tolerance threshold, a penalty term is constructed and added to the original target evaluation value of the candidate scheduling scheme to obtain the penalized candidate scheduling scheme. Based on any two candidate scheduling schemes after penalties, perform non-dominated sorting and congestion distance calculation to obtain the non-dominated sorting result and the congestion distance calculation result. Select a baseline scheduling scheme and obtain the target baseline value corresponding to the baseline scheduling scheme; Based on the target baseline value, calculate the baseline improvement rate corresponding to any candidate scheduling scheme; Based on the improvement rate of the benchmark value, the collaborative balance factor is constructed; Based on the non-dominated sorting results and the crowding distance calculation results, individuals in the evolutionary population are screened. When the non-dominated sorting results and crowding distance calculation results of two individuals are the same, individuals whose cooperative balance factor meets the preset screening requirements are selected to enter the next generation of the population until the number of evolutionary generations meets the preset evolutionary iteration requirements, thus obtaining the Pareto solution set.

7. The method for synergistic optimization and control of electricity and carbon as described in claim 6, characterized in that, Selecting initial scheduling schemes that meet preset evaluation and verification requirements from the Pareto solution set as target optimized scheduling schemes for regulating power equipment includes: Based on the Pareto solution set, individuals with daily electricity deviation less than or equal to the tolerance threshold are selected to form an implementable candidate solution set; If the feasible candidate solution set meets the first output requirement, then the cooperative balance factor of each individual in the feasible candidate solution set is calculated, and the individual whose cooperative balance factor meets the preset first evaluation and verification requirement is selected as the target optimized scheduling scheme output, so as to regulate the power equipment; If the feasible candidate solution set is the second output requirement, then individuals whose daily power deviation meets the preset second evaluation and verification requirements are selected from the Pareto solution set as the target optimized scheduling scheme output, so as to regulate the power equipment.

8. An electro-carbon synergistic optimization control system, characterized in that, It includes a model building module, an algorithm improvement module, a solution module, and a control module; among which: The model building module is used to construct a multi-objective power-carbon coordinated scheduling model based on pre-acquired load forecast data, new energy output data, and thermal power unit operation data. The algorithm improvement module is used to improve the algorithm structure of the preset optimization algorithm based on the pre-constructed collaborative balance factor and the pre-constructed dynamic tolerance feedback penalty factor, so as to obtain the improved optimization algorithm. The solution module is used to generate several initial scheduling schemes based on the thermal power unit operating data, and to iterate the initial scheduling schemes through the cooperative balance factor and the dynamic tolerance feedback penalty factor based on the improved optimization algorithm and the multi-objective electric carbon cooperative scheduling model to generate a Pareto solution set. The control module is used to select an initial scheduling scheme that meets the preset evaluation and verification requirements from the Pareto solution set as the target optimized scheduling scheme for controlling the power equipment.

9. The electro-carbon synergistic optimization and control system as described in claim 8, characterized in that, The model construction module includes a data acquisition unit, a data processing unit, a first objective function construction unit, a second objective function construction unit, a third objective function construction unit, and a multi-objective electric carbon collaborative scheduling model construction unit; wherein: The data acquisition unit is used to acquire load forecast data, new energy output data and thermal power unit operation data within a preset scheduling cycle. The data processing unit is used to calculate the actual new energy output data based on the load forecast data, new energy output data and thermal power unit operation data. The first objective function construction unit is used to construct a first objective function based on the thermal power unit's operating data and a preset fuel cost coefficient; The second objective function construction unit is used to construct a second objective function based on the thermal power unit's operating data and a preset carbon emission coefficient; The third objective function construction unit is used to construct a third objective function based on the new energy output data and the actual new energy output data; The multi-objective power-carbon coordinated scheduling model construction unit is used to construct a multi-objective power-carbon coordinated scheduling model based on the load forecast data, the new energy output data, the thermal power unit operation data, and the set constraints, and according to the first objective function, the second objective function, and the third objective function.

10. The electro-carbon synergistic optimization and control system as described in claim 9, characterized in that, The algorithm improvement module includes a first structural improvement unit, a second structural improvement unit, and an improvement combination unit; wherein: The first structural improvement unit is used to construct an evolutionary population based on the preset optimization algorithm and the thermal power unit operation data, and to introduce the cooperative balance factor as a guiding indicator to guide the evolutionary population during the individual selection stage, so as to obtain the first improved algorithm structure. The second structural improvement unit is used to calculate the supply-demand balance deviation based on the preset optimization algorithm and the load forecast data, and to use the dynamic tolerance feedback penalty factor to perform segmented penalty on the supply-demand balance deviation during the constraint processing stage, so as to obtain the second improved algorithm structure. The improved combination unit is used to obtain an improved optimization algorithm based on the first improved algorithm structure and the second improved algorithm structure.

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