NSGA-III algorithm-based low-carbon economic unit commitment method and system

Abstract

The invention discloses a low-carbon economic unit commitment method and system based on an NSGA-III algorithm, and the method comprises the steps: building a unit commitment multi-objective optimization model considering the economy and environmental protection according to basic parameters; wherein the basic parameters comprise power system parameters, unit basic parameters, wind power generation parameters and solar power generation parameters; parameters of an NSGA-III algorithm are initialized, the unit commitment multi-objective optimization model is solved through the NSGA-III algorithm, and an optimal solution set is obtained; and based on the optimal solution set, performing simulation analysis on the unit commitment multi-objective optimization model by using an NSGA-III algorithm, performing comparative analysis with a traditional NSGA-II algorithm and an MOEA / D algorithm, and verifying the effectiveness of the NSGA-III algorithm. According to the method, the NSGA-III algorithm is used for solving the multi-target unit commitment model, and the method has the advantages of being high in speed, good in solution set convergence and the like.

Description

Technical Field

[0001] This invention relates to the field of energy planning technology, and in particular to a method and system for combining low-carbon economic units based on the NSGA-III algorithm. Background Technology

[0002] Electricity is a fundamental element of human life and social development. Rapid global economic development has led to a continuous increase in the demand for electricity, resulting in the massive consumption of fossil fuels and exacerbating global warming and environmental pollution. Wind and solar energy are mature and abundant clean energy sources, and utilizing renewable energy for power generation is an important way to address environmental problems. However, the integration of renewable energy brings significant uncertainties to the operation of power distribution networks, making the combination of generating units under renewable energy integration an important research topic.

[0003] Current research primarily focuses on optimizing unit configuration with economic cost as the objective, employing algorithms such as dynamic programming, mixed-integer linear programming, and Lagrange relaxation for solutions. However, with the increasing scale of networks, dynamic programming faces the "curse of dimensionality" problem. To further improve solution efficiency, some researchers have begun to use heuristic algorithms, achieving good results and solving some problems. However, none of the aforementioned unit configuration optimization methods consider renewable energy sources, and their robustness in uncertain environments needs further verification.

[0004] In the context of the construction of new power systems, environmental and carbon emissions are also key concerns. Some scholars have developed real-time energy management systems that utilize binary particle swarm optimization (BSO) to minimize energy consumption, CO2 emissions, and other pollutant emissions; however, the algorithm's efficiency is relatively low.

[0005] To address the aforementioned issues, there is an urgent need for a method that comprehensively considers the combination of wind and solar power generator sets, taking into account economic costs and CO2 emissions. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for low-carbon economic unit combination based on the NSGA-III algorithm. It utilizes the NSGA-III algorithm to solve multi-objective unit combination models and has the advantages of fast speed and good solution set convergence.

[0007] To achieve the above objectives, this invention provides a low-carbon economic unit combination method based on the NSGA-III algorithm, comprising:

[0008] Based on the basic parameters, a multi-objective optimization model for unit combination considering both economic and environmental factors is established; wherein, the basic parameters include power system parameters, unit basic parameters, wind power generation parameters, and solar power generation parameters;

[0009] Initialize the NSGA-III algorithm parameters, and use the NSGA-III algorithm to solve the multi-objective optimization model of the unit combination to obtain the optimal solution set;

[0010] Based on the optimal solution set, the NSGA-III algorithm is used to simulate and analyze the multi-objective optimization model of the unit combination, and the results are compared with the traditional NSGA-II algorithm and MOEA / D algorithm to verify the effectiveness of the NSGA-III algorithm.

[0011] Furthermore, based on the fundamental parameters, a multi-objective optimization model for unit combination considering both economic and environmental factors is established, including:

[0012] Based on the basic parameters, establish the objective function of a multi-objective optimization model for unit combination that considers the lowest economic cost and the lowest carbon emissions;

[0013] Establish the constraints for the objective function;

[0014] The power system parameters include: reserve ratio and total load;

[0015] The basic parameters of the unit include: minimum upper output limit, minimum lower output limit and unit fuel characteristic coefficient;

[0016] The wind power generation parameters include wind power generation capacity; the solar power generation parameters include photovoltaic power generation capacity.

[0017] Furthermore, the objective function is:

[0018]

[0019] In the formula, F e c represents the economic cost, T represents the total number of time periods, t represents the specific time period, n represents the total number of generating units, and j represents the specific generating unit. Let P represent the fossil fuel cost function of the j-th generating unit. j,t Let x represent the power of the j-th unit in the t-th time period. j,t A symbolic variable representing whether the j-th unit is running in the t-th time period, where 1 indicates it is running and 0 indicates it is offline; CS j,t This represents the unit start-up and shutdown cost in the t-th time period. x represents j,t The NOT operation, CO 2e Indicates CO2 pollutant emissions, Let represent the CO2 emission function of the j-th unit.

[0020] Furthermore, the cost of the fossil fuels and the unit start-up and shutdown cost CS j,t They are respectively:

[0021]

[0022] In the formula, Let a represent the fossil fuel cost function of the j-th unit. j b j and c j Both represent the unit's fuel characteristic coefficient, CS H,j Let CS represent the hot start cost of the j-th unit. C,j Tlimit represents the cold start cost of the j-th unit. j This represents the shortest downtime for the j-th generating unit. T represents the offline duration of the j-th unit during time period t. cold,j This represents the number of cold start hours for the j-th unit;

[0023] Among them, the CO2 emission function of the unit The relationship between the active power output of the generator unit and the active power output is quadratic:

[0024]

[0025] In the formula, Let α represent the CO2 emission function of the j-th unit. c,j ,β c,j and γ c,j Both are CO2 pollutant emission coefficients, P j,t This represents the power of the j-th unit in the t-th time period.

[0026] Furthermore, the constraints of the objective function include: functional balance constraint, power reserve limit constraint, minimum time limit constraint for the upper or lower limit of the unit, and unit power limit constraint.

[0027] The functional balance constraint is as follows:

[0028]

[0029] In the formula, t represents the nth time period, n represents the total number of units, j represents the nth unit, and P j,t Let x represent the power of the j-th unit in the t-th time period. j,t P is a symbolic variable representing whether the j-th unit is running in the t-th time period. wind,t P represents the wind power generation capacity in the t-th time period. solar,t P represents the photovoltaic power generation capacity. D,t This represents the predicted power load for the t-th time period;

[0030] The power reserve limit constraint is:

[0031]

[0032] In the formula, P j,max SR represents the maximum power of j units. t P represents the reserve power in the t-th time period. j,min This represents the minimum power of j generator units;

[0033] The minimum time limit constraint for the upper or lower limit of the unit is:

[0034]

[0035] In the formula, I j,t This represents the minimum time constraint that limits the upper or lower limits of a generator set. This represents the continuous operating time of the j-th unit in the t-th time period. Tlimit represents the offline duration of the j-th unit in time period t. j TlimitU represents the shortest downtime for the j-th unit. j This represents the minimum operating time of the j-th unit;

[0036] The power limit constraint of the unit is:

[0037] P j,min *x j,t ≤P j,t ≤P j,max *x j,t .

[0038] Furthermore, the NSGA-III algorithm is used to solve the multi-objective optimization model of the unit combination to obtain the optimal solution set, including:

[0039] Individuals in the population are divided into several levels according to their dominance relationships, so that all individuals are standardized and associated with a reference point;

[0040] Genetic operations such as selection, crossover, and mutation are used to produce offspring populations;

[0041] The parent and child generations are merged, and non-dominant sorting, normalization, and individual selection operations are repeated.

[0042] The resulting offspring population serves as the parents of the next generation, and the process is repeated until convergence is achieved, at which point the optimal solution set is output.

[0043] Furthermore, based on the optimal solution set, the NSGA-III algorithm is used to simulate and analyze the multi-objective optimization model of the unit combination, and a comparative analysis is conducted with the traditional NSGA-II algorithm and the MOEA / D algorithm to verify the effectiveness of the NSGA-III algorithm, including:

[0044] Based on the optimal solution set, the NSGA-III algorithm is simulated and analyzed, and its target optimization results are compared with those of the traditional NSGA-II algorithm and the MOEA / D algorithm.

[0045] Among them, Case 1 and Case 2 are set up for comparative analysis. Case 1 is: optimization of low-carbon unit combination without wind power and photovoltaic power generation system, and Case 2 is: optimization of low-carbon unit combination with wind power and photovoltaic power generation system.

[0046] Based on the same inventive concept, this invention also provides a low-carbon economic unit combination system based on the NSGA-III algorithm, comprising:

[0047] The model building unit is used to build a multi-objective optimization model for unit combination that considers economic efficiency and environmental protection based on basic parameters; wherein, the basic parameters include power system parameters, basic unit parameters, wind power generation parameters, and solar power generation parameters;

[0048] The solution unit is used to initialize the NSGA-III algorithm parameters and use the NSGA-III algorithm to solve the multi-objective optimization model of the unit combination to obtain the optimal solution set.

[0049] The analysis unit is used to perform simulation analysis on the multi-objective optimization model of the unit combination based on the optimal solution set using the NSGA-III algorithm, and to compare and analyze it with the traditional NSGA-II algorithm and MOEA / D algorithm to verify the effectiveness of the NSGA-III algorithm.

[0050] Based on the same inventive concept, embodiments of the present invention also provide an electronic device, including: a memory and a processor; the processor is used to read and execute a computer program stored in the memory to implement the aforementioned method for combining low-carbon economic units based on the NSGA-III algorithm.

[0051] Based on the same inventive concept, this embodiment of the invention also provides a computer storage medium storing computer-executable instructions, which, when executed, implement the aforementioned low-carbon economic unit combination method based on the NSGA-III algorithm.

[0052] The technical effects and advantages of this invention are as follows: The NSGA-III algorithm used in this invention has a fast solution speed and good solution set convergence, which can effectively solve multi-objective unit combination models; This invention incorporates CO2 emissions into the low-carbon multi-objective unit combination model, thus coordinating the optimal economic and environmental value of the system.

[0053] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart illustrating a low-carbon economic unit combination method based on the NSGA-III algorithm according to an embodiment of the present invention.

[0056] Figure 2 This is a schematic diagram of the target optimization results of the three algorithms when there are no wind power and photovoltaic power generation systems and the number of units is 10 in an embodiment of the present invention.

[0057] Figure 3 This is a schematic diagram of the two-dimensional spatial distribution of the two optimal solution sets corresponding to the three algorithms when there are no wind power and photovoltaic power generation systems in this embodiment of the invention;

[0058] Figure 4 This is a schematic diagram of the power data of each unit when there are 10 units and no wind power and photovoltaic power generation systems in an embodiment of the present invention.

[0059] Figure 5 This is a schematic diagram showing the calculation results of three algorithms when optimizing a low-carbon unit combination containing wind power and photovoltaic power generation systems in an embodiment of the present invention.

[0060] Figure 6 This is a schematic diagram of the two-dimensional spatial distribution of the two optimal solution sets corresponding to the three algorithms in an embodiment of the present invention when wind power and photovoltaic power generation systems are included;

[0061] Figure 7 This is a schematic diagram of the power data of each unit when there are 10 units, including wind power and photovoltaic power generation systems, in an embodiment of the present invention.

[0062] Figure 8 This is a schematic diagram of a low-carbon economic unit combination system based on the NSGA-III algorithm in an embodiment of the present invention;

[0063] Figure 9 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] To address the shortcomings of existing technologies, this invention discloses a low-carbon economic unit combination method based on the NSGA-III algorithm, comprising the following steps:

[0066] Step S1: Based on the basic parameters, establish a multi-objective optimization model for unit combination that considers both economic and environmental factors; specifically including:

[0067] Based on the basic parameters, a model of the equipment involved in the unit combination is established, and an objective function of the multi-objective optimization model of the unit combination considering the lowest economic cost and the lowest carbon emissions is established; where carbon emissions are calculated in terms of total carbon dioxide.

[0068] Among them, the basic parameters include power system parameters, unit basic parameters, wind power generation parameters, and solar power generation parameters;

[0069] Power system parameters include: reserve rate and total load; unit basic parameters include: minimum upload limit, minimum download limit and unit fuel characteristic coefficient; wind power generation parameters include wind power generation capacity; solar power generation parameters include photovoltaic power generation capacity; the various basic parameters are shown in Table 1:

[0070] Table 1

[0071]

[0072]

[0073] The objective function is:

[0074]

[0075] In the formula, F e c represents the economic cost, T represents the total number of time periods, t represents the specific time period (typically 15 minutes), n represents the total number of generator sets, and j represents the specific generator set. Let P represent the fossil fuel cost function of the j-th generating unit. j,t Let x represent the power of the j-th unit in the t-th time period. j,t A symbolic variable representing whether the j-th unit is running in the t-th time period, where 1 indicates it is running and 0 indicates it is offline; CS j,t This represents the unit start-up and shutdown cost in the t-th time period. x represents j,t The NOT operation, CO 2e Indicates CO2 pollutant emissions, Let represent the CO2 emission function of the j-th unit.

[0076] Among them, fossil fuel costs and unit start-up and shutdown costs CS j,t They are respectively:

[0077]

[0078] In the formula, Let a represent the fossil fuel cost function of the j-th unit. j b j and c j Both represent the unit's fuel characteristic coefficient, CS H,j Let CS represent the hot start cost of the j-th unit. C,j Tlimit represents the cold start cost of the j-th unit. j This represents the shortest downtime for the j-th generating unit. T represents the offline duration of the j-th unit in the t-th time period. cold,j This represents the number of cold start hours for the j-th unit;

[0079] Among them, the CO2 emission function of the unit The relationship between the active power output of the generator unit and the active power output is quadratic:

[0080]

[0081] In the formula, Let α represent the CO2 emission function of the j-th unit. c,j ,β c,j and γ c,j Both are CO2 pollutant emission coefficients, P j,t This represents the power of the j-th unit in the t-th time period.

[0082] Establish the constraints of the objective function; the constraints of the objective function include: functional balance constraint, power reserve limit constraint, minimum time limit constraint of the upper or lower limit of the unit, and unit power limit constraint.

[0083] The functional balance constraint is as follows:

[0084]

[0085] In the formula, t represents the nth time period, n represents the total number of units, j represents the nth unit, and P j,t Let x represent the power of the j-th unit in the t-th time period. j,tA symbolic variable representing whether the j-th unit is running in the t-th time period, where 1 indicates it is running and 0 indicates it is offline; P wind,t P represents the wind power generation capacity in the t-th time period. solar,t P represents the photovoltaic power generation in the t-th time period. D,t This represents the predicted power load value for the t-th time period.

[0086] The power reserve limit constraint is:

[0087]

[0088] In the formula, t represents the nth time period, n represents the total number of units, j represents the nth unit, and P j,max Let x represent the maximum power of j units. j,t A symbolic variable representing whether the j-th unit is running in the t-th time period, where 1 indicates it is running and 0 indicates it is offline; P D,t SR represents the predicted power load for the t-th time period. t P represents the power system reserve capacity during the t-th time period. j,min This represents the minimum power of j units.

[0089] The minimum time limit constraint for the upper or lower limit of the unit is:

[0090]

[0091] In the formula, I j,t This represents the minimum time constraint that limits the upper or lower limits of a generator set. This represents the continuous operating time of the j-th unit in the t-th time period. Tlimit represents the offline duration of the j-th unit in the t-th time period. j TlimitU represents the shortest downtime for the j-th unit. j This represents the minimum operating time of the j-th unit.

[0092] The unit power limit constraint is:

[0093] P j,min *x j,t ≤P j,t ≤P j,max *x j,t ,

[0094] In the formula, P j,max P represents the maximum power of j generator units. j,min Let x represent the minimum power of j units. j,t A symbolic variable representing whether the j-th unit is running in the t-th time period, where 1 indicates it is running and 0 indicates it is offline; P j,tThis represents the power of the j-th unit in the t-th time period.

[0095] Step S2: Initialize the NSGA-III algorithm parameters, and use the NSGA-III algorithm to solve the multi-objective optimization model of the unit combination to obtain the optimal solution set; specifically including:

[0096] The NSGA-III algorithm parameters are initialized, and the relevant parameters are shown in Table 2:

[0097] Table 2 Other parameter settings

[0098] parameter numerical values Reserve rate 0.15 Population size 20 Total Algebra 900 The number of neighborhoods in three algorithms 10 coefficient of variation 0.02 .

[0099] In some specific embodiments, the NSGA-III algorithm is used to solve the multi-objective optimization model of the unit combination to obtain the optimal solution set, specifically including:

[0100] Individuals in the population are divided into several levels according to their dominance relationships, so that all individuals are standardized and associated with a reference point;

[0101] Genetic operations such as selection, crossover, and mutation are used to produce offspring populations;

[0102] The parent and child generations are merged, and non-dominant sorting, normalization, and individual selection operations are repeated.

[0103] The resulting offspring population serves as the parents of the next generation, entering the next iteration process until convergence is achieved, and the optimal solution set is output.

[0104] Step S3: Based on the optimal solution set, the NSGA-III algorithm is used to perform simulation analysis on the multi-objective optimization model of the unit combination, and the results are compared with the traditional NSGA-II algorithm and the MOEA / D algorithm to verify the effectiveness of the NSGA-III algorithm; specifically including:

[0105] Based on the optimal solution set, the NSGA-III algorithm is used to perform simulation analysis on the multi-objective optimization model of the unit combination, and the optimization results of the respective objectives are compared with those of the traditional NSGA-II algorithm and the MOEA / D algorithm.

[0106] Among them, Case 1 and Case 2 are set up for comparative analysis. Case 1 is: optimization of low-carbon unit combination without wind power and photovoltaic power generation system, and Case 2 is: optimization of low-carbon unit combination with wind power and photovoltaic power generation system.

[0107] (1) Low-carbon unit combination optimization excluding wind power and photovoltaic power generation systems:

[0108] When the number of units is 10, the optimization results of each objective obtained by the three algorithms NSGA-III, NSGA-II, and MOEA / D are as follows: Figure 2 As shown. By Figure 2 It can be seen that when the number of units is 10, the economic cost obtained by NSGA-III is 568,827.88 yuan / day, while the optimal value obtained by NSGA-II is 572,768.11 yuan / day, and the optimal result of MOEA / D is 573,537.66 yuan / day. The target value obtained by NSGA-III is better than the other two algorithms. When the number of units increases to 80 and 100, the experimental data obtained by these algorithms show the same trend.

[0109] However, considering only economic costs or CO2 emission targets, and with 80 units, the values ​​obtained by NSGA-II are relatively better. The CO2 emissions obtained by NSGA-II are 652,953.59 kg / day, while those obtained by NSGA-III are 653,959.68 kg / day, but the difference is not significant.

[0110] To further verify the superiority of the method of this invention, the two-dimensional spatial distribution of the two optimal solutions corresponding to three algorithms is given, as follows: Figure 3 As shown. By Figure 3 It can be seen that when the number of units is 80, the Pareto front distribution obtained by NSGA-III is smaller than that of NSGA-II and MOEA / D, and the overall Pareto solution is relatively downward. The Pareto front of NSGA-III is at the lowest level, followed by NSGA-II, and MOEA / D is at the highest level, indicating that NSGA-III can obtain values ​​much smaller than other algorithms, which fully demonstrates the good performance of NSGA-III in optimizing unit combination optimization problems.

[0111] When the number of generating units is 10, the power data of each unit is obtained as follows: Figure 4 As shown in the figure, the data indicates that Unit 1 and Unit 2 are always online, and their power output varies with the system load.

[0112] (2) Optimize the combination of low-carbon units including wind power and photovoltaic power generation systems:

[0113] When the number of generating units is 10, the calculation results of the three algorithms are as follows: Figure 5 As shown. By Figure 5It can be seen that when the number of generating units is 10, the economic cost range obtained by NSGA-III is 549,649.33 yuan / day to 557,719.37 yuan / day. The optimal value obtained by NSGA-II is 550,624.12 yuan / day, while the optimal value of MOEA / D is 550,598.45 yuan / day. The target value obtained by NSGA-III is also better than the other two algorithms when wind and solar energy are integrated. When the number of generating units increases to 80 and 100, the above conclusions remain unchanged.

[0114] However, when the number of units is 100, NSGA-II yields relatively better economic costs and CO2 emission values. For example, NSGA-II yields a CO2 emission value of 778,713.29 kg / day, while NSGA-III yields a CO2 emission value of 779,727.42 kg / day, which is close to the optimal value of NSGA-II. Clearly, NSGA-III achieves most results better than other algorithms.

[0115] It can be observed that after wind and solar power are connected to the grid, regardless of the number of generating units, the optimal values ​​for both objectives are lower than the optimal values ​​when wind and solar power are not connected to the grid. This means that wind and solar power generation can reduce the total economic cost of the power system and is environmentally friendly, reducing pollutant emissions. The two-dimensional spatial distribution of the two optimal solution sets corresponding to the three algorithms is as follows: Figure 6 As shown in the figure, the comparison reveals that regardless of whether renewable energy sources are integrated, the Pareto front of NSGA-III remains at its lowest level, further validating NSGA-III's suitability for optimizing low-carbon power units.

[0116] The NSGA-III optimization results for 10 generating units are presented here. The power data for each unit are as follows: Figure 7 As shown. By Figure 7 As can be seen, Units 1 and 2 are always on, and their power output varies with the system load. Unit 10 is always off, which means that the power system does not need all units to operate to meet load demand, thereby reducing economic costs and pollutant emissions.

[0117] Based on the same inventive concept, this invention also provides a low-carbon economic unit combination system based on the NSGA-III algorithm, such as... Figure 8 As shown, it includes:

[0118] The model building unit is used to build a multi-objective optimization model for unit combination that considers economic efficiency and environmental protection based on basic parameters; wherein, the basic parameters include power system parameters, basic unit parameters, wind power generation parameters, and solar power generation parameters;

[0119] The solution unit is used to initialize the NSGA-III algorithm parameters and use the NSGA-III algorithm to solve the multi-objective optimization model of the unit combination to obtain the optimal solution set.

[0120] The analysis unit is used to perform simulation analysis on the multi-objective optimization model of the unit combination based on the optimal solution set using the NSGA-III algorithm, and to compare and analyze it with the traditional NSGA-II algorithm and MOEA / D algorithm to verify the effectiveness of the NSGA-III algorithm.

[0121] Regarding the system in the above embodiments, the specific manner in which each unit module performs operations has been described in detail in the embodiments related to the method, and will not be elaborated here.

[0122] Based on the same inventive concept, embodiments of the present invention also provide an electronic device, the structure of which is as follows: Figure 9 As shown, it includes: a memory and a processor, wherein the processor is used to read and execute the computer program stored in the memory to implement the aforementioned low-carbon economic unit combination method based on the NSGA-III algorithm.

[0123] Based on the same inventive concept, this embodiment of the invention also provides a computer storage medium storing computer-executable instructions, which, when executed, implement the aforementioned low-carbon economic unit combination method based on the NSGA-III algorithm.

[0124] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for combining low-carbon economical generating units based on the NSGA-III algorithm, characterized in that, include: Based on the basic parameters, a multi-objective optimization model for unit combination considering both economic and environmental factors is established; wherein, the basic parameters include power system parameters, unit basic parameters, wind power generation parameters, and solar power generation parameters; Initialize the NSGA-III algorithm parameters, and use the NSGA-III algorithm to solve the multi-objective optimization model of the unit combination to obtain the optimal solution set; Based on the optimal solution set, the NSGA-III algorithm is used to simulate and analyze the multi-objective optimization model of the unit combination, and the results are compared with the traditional NSGA-II algorithm and MOEA / D algorithm to verify the effectiveness of the NSGA-III algorithm.

2. The low-carbon economic unit combination method based on the NSGA-III algorithm according to claim 1, characterized in that, Based on the basic parameters, a multi-objective optimization model for unit combination considering both economic and environmental factors is established, including: Based on the basic parameters, establish the objective function of a multi-objective optimization model for unit combination that considers the lowest economic cost and the lowest carbon emissions; Establish the constraints for the objective function; The power system parameters include: reserve ratio and total load; The basic parameters of the unit include: minimum upper output limit, minimum lower output limit and unit fuel characteristic coefficient; The wind power generation parameters include wind power generation capacity; the solar power generation parameters include photovoltaic power generation capacity.

3. The low-carbon economic unit combination method based on the NSGA-III algorithm according to claim 2, characterized in that, The objective function is: In the formula, F e c represents the economic cost, T represents the total number of time periods, t represents the specific time period, n represents the total number of generating units, and j represents the specific generating unit. Let P represent the fossil fuel cost function of the j-th generating unit. j,t Let x represent the power of the j-th unit in the t-th time period. j,t A symbolic variable representing whether the j-th unit is running in the t-th time period, where 1 indicates it is running and 0 indicates it is offline; CS j,t This represents the unit start-up and shutdown cost in the t-th time period. x represents j,t The NOT operation, CO 2e Indicates CO2 pollutant emissions, Let represent the CO2 emission function of the j-th unit.

4. The low-carbon economic unit combination method based on the NSGA-III algorithm according to claim 3, characterized in that, The cost of fossil fuels and the unit start-up and shutdown cost CS j,t They are respectively: In the formula, Let a represent the fossil fuel cost function of the j-th unit. j b j and c j Both represent the unit's fuel characteristic coefficient, CS H,j Let CS represent the hot start cost of the j-th unit. C,j Tlimit represents the cold start cost of the j-th unit. j This represents the shortest downtime for the j-th generating unit. T represents the offline duration of the j-th unit during time period t. cold,j This represents the number of cold start hours for the j-th unit; Among them, the CO2 emission function of the unit The relationship between the active power output of the generator unit and the active power output is quadratic: In the formula, Let α represent the CO2 emission function of the j-th unit. c,j ,β c,j and γ c,j Both are CO2 pollutant emission coefficients, P j,t This represents the power of the j-th unit in the t-th time period.

5. A method for low-carbon economic unit combination based on the NSGA-III algorithm according to claim 2, characterized in that, The constraints of the objective function include: functional balance constraint, power reserve limit constraint, minimum time limit constraint of the upper or lower limit of the unit, and unit power limit constraint. The functional balance constraint is as follows: In the formula, t represents the nth time period, n represents the total number of units, j represents the nth unit, and P j,t Let x represent the power of the j-th unit in the t-th time period. j,t P is a symbolic variable representing whether the j-th unit is running in the t-th time period. wind,t P represents the wind power generation capacity in the t-th time period. solar,t P represents the photovoltaic power generation capacity. D,t This represents the predicted power load for the t-th time period; The power reserve limit constraint is: In the formula, P j,max SR represents the maximum power of j units. t P represents the reserve power in the t-th time period. j,min This represents the minimum power of j units; The minimum time limit constraint for the upper or lower limit of the unit is: In the formula, I j,t This represents the minimum time constraint that limits the upper or lower limits of a generator set. This represents the continuous operating time of the j-th unit in the t-th time period. Tlimit represents the offline duration of the j-th unit in time period t. j TlimitU represents the shortest downtime for the j-th unit. j This represents the minimum operating time of the j-th unit; The power limit constraint of the unit is: P j,min *x j,t ≤P j,t ≤P j,max *x j,t 。 6. The low-carbon economic unit combination method based on the NSGA-III algorithm according to claim 1, characterized in that, The NSGA-III algorithm is used to solve the multi-objective optimization model of the unit combination to obtain the optimal solution set, including: Individuals in the population are divided into several levels according to their dominance relationships, so that all individuals are standardized and associated with a reference point; Genetic operations such as selection, crossover, and mutation are used to produce offspring populations; The parent and child generations are merged, and non-dominant sorting, normalization, and individual selection operations are repeated. The resulting offspring population serves as the parents of the next generation, and the process is repeated until convergence is achieved, at which point the optimal solution set is output.

7. The method for low-carbon economic unit combination based on the NSGA-III algorithm according to claim 1, characterized in that, Based on the optimal solution set, the NSGA-III algorithm is used to simulate and analyze the multi-objective optimization model of the unit combination, and the results are compared with the traditional NSGA-II algorithm and the MOEA / D algorithm to verify the effectiveness of the NSGA-III algorithm, including: Based on the optimal solution set, the NSGA-III algorithm is simulated and analyzed, and its target optimization results are compared with those of the traditional NSGA-II algorithm and the MOEA / D algorithm. Among them, Case 1 and Case 2 are set up for comparative analysis. Case 1 is: optimization of low-carbon unit combination without wind power and photovoltaic power generation system, and Case 2 is: optimization of low-carbon unit combination with wind power and photovoltaic power generation system.

8. A low-carbon economic unit combination system based on the NSGA-III algorithm, characterized in that, include: The model building unit is used to build a multi-objective optimization model for unit combination that considers economic efficiency and environmental protection based on basic parameters; wherein, the basic parameters include power system parameters, basic unit parameters, wind power generation parameters, and solar power generation parameters; The solution unit is used to initialize the NSGA-III algorithm parameters and use the NSGA-III algorithm to solve the multi-objective optimization model of the unit combination to obtain the optimal solution set. The analysis unit is used to perform simulation analysis on the multi-objective optimization model of the unit combination based on the optimal solution set using the NSGA-III algorithm, and to compare and analyze it with the traditional NSGA-II algorithm and MOEA / D algorithm to verify the effectiveness of the NSGA-III algorithm.

9. An electronic device, characterized in that, include: Memory, processor; The processor is used to read and execute the computer program stored in the memory to implement the low-carbon economic unit combination method based on the NSGA-III algorithm as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, implement the low-carbon economic unit combination method based on the NSGA-III algorithm as described in any one of claims 1-7.

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