Power system low-carbon optimization scheduling method based on two-stage robust optimization model

Abstract

The invention belongs to the field of electric power system dispatching optimization, and particularly relates to an electric power system low-carbon optimization dispatching method based on a two-stage robust optimization model, which comprises the following steps of: constructing an objective function of electric power system low-carbon optimization dispatching by combining the operation cost of a carbon capture module, a power-to-gas module, a demand response module and an energy storage module and the cost of energy supply; converting the objective function of low-carbon optimization scheduling of the power system into a two-stage robust optimization model; decomposing the two-stage robust optimization model into a main problem and a sub-problem; iteratively solving the main problem and the sub-problems to obtain an optimal solution of the two-stage robust optimization model; and performing optimization scheduling on the power system according to the optimal solution of the two-stage robust optimization model. Various carbon reduction technical means are comprehensively utilized, and the environmental protection performance of an electric power system is remarkably improved; aiming at the uncertainty of wind power and photovoltaic power generation output, the robustness of a renewable energy power generation scheduling scheme is enhanced by establishing a two-stage robust optimization model.

Description

Technical Field

[0001] The present invention belongs to the field of power system dispatch optimization, and in particular relates to a low-carbon optimization dispatch method for a power system based on a two-stage robust optimization model. Background Art

[0002] The power system mainly uses coal-fired units and gas-fired units to supply power to the grid. Generally speaking, coal-fired power generation has lower costs but is accompanied by higher carbon emissions. In contrast, gas-fired power generation has lower carbon emissions. Therefore, the combined power generation mode of coal-fired power generation and gas-fired power generation has good application prospects.

[0003] In recent years, the gradually developed and mature low-carbon technologies such as carbon capture, power-to-gas and renewable energy have helped to reduce carbon emissions from the power system. However, renewable energy generation such as wind power and photovoltaic power generation has great uncertainty, which affects the reliable and stable operation of the power system and power grid. Summary of the invention

[0004] The technical problem of the present invention is that the prior art lacks an optimization method that takes into account the uncertainty of renewable energy generation in the power system and the coordinated scheduling of multiple low-carbon measures.

[0005] The purpose of the present invention is to address the above-mentioned problems and provide a low-carbon optimization scheduling method for an electric power system based on a two-stage robust optimization model, equip the coal-fired power generation nodes of the power system with carbon capture modules, equip the renewable energy power generation nodes of the power system with power-to-gas modules, equip the coal-fired power generation nodes and the renewable energy power generation nodes with reserve energy modules, comprehensively utilize a variety of carbon reduction technical means to achieve energy conservation and emission reduction, and equip the power system with a demand response module to promote the balance of power supply and demand; construct an objective function for optimizing the scheduling of the power system with the goal of minimizing the operating cost of the power system, and establish a two-stage robust optimization model for the uncertainty of wind power and photovoltaic power generation output, so as to improve the robustness of the renewable energy power generation scheduling scheme, while taking into account the economy, reliability and stability of power generation in the power system.

[0006] In order to achieve the above purpose, the technical solution adopted by the present invention is: The low-carbon optimal dispatching method of the power system based on the two-stage robust optimization model includes the following steps: Step 1: Combine the operating costs of the carbon capture module, power-to-gas module, demand response module and energy reserve module as well as the cost of energy supply to construct the objective function of low-carbon optimal dispatch of the power system; Step 2: Convert the objective function of low-carbon optimal dispatch of the power system obtained in step 1 into a two-stage robust optimization model; Step 3: Decompose the two-stage robust optimization model into a main problem and sub-problems; Step 4: Iteratively solve the main problem and sub-problems to obtain the optimal solution of the two-stage robust optimization model; Step 4.1: Iteration initialization, setting the upper bound UB and lower bound LB of the iteration; Step 4.2: Solve the main problem, obtain the optimal solution of the main problem, and update the lower bound LB; Step 4.3: Solve the subproblem, obtain the optimal solution of the subproblem, and update the upper bound UB; Step 4.4: Determine whether the iteration termination condition is met. If so, output the optimal solution. Otherwise, execute step 4.2. Step 5: According to the optimal solution of the two-stage robust optimization model obtained in step 4, the power system is optimally dispatched.

[0007] Preferably, the power system includes a carbon trading module, which is used for trading carbon emission quotas of power generation nodes of the power system. When the carbon emissions of a power generation node exceed the issued carbon emission quotas, the remaining carbon emission quotas of other power generation nodes are purchased through the carbon trading module.

[0008] Further, in step 1, the objective function of the low-carbon optimal dispatch of the power system is:

[0009] In the formula is the operating cost of the carbon capture module, is the operating cost of the carbon trading module, is the operating cost of the demand response module, is the operating cost of the power-to-gas module, To save the operating cost of the energy module, The cost of energy supply.

[0010] Preferably, in step 2, the two-stage robust optimization model is:

[0011] Where x is the decision variable in the first stage, and y is the decision variable in the second stage; It represents the coefficient of the previous objective function converted into a vector form; u is the uncertainty variable of photovoltaic and wind power output; U represents the set of uncertainty variables; N(u, x) represents the feasible domain interval of variable y; Indicates the operating status of the reserve energy module, that is, whether the reserve energy module is started or shut down; is the coal-fired power generation power in period t, is the gas-fired power generation power in period t, is the power of the carbon capture equipment at the coal-fired carbon power generation node c during period t, is the reserve energy charging power during period t, is the reserve energy discharge power in period t, is the power dispatching power of the demand response module during period t, and These are auxiliary variables in the demand response module. is the wind power generation power in period t, is the photovoltaic power generation power in period t, is the power of the power-to-gas device during period t.

[0012] Preferably, in step 3, the main question is:

[0013] In the formula They represent the photovoltaic and wind power outputs during period t respectively; , Respectively represent the average output of photovoltaic and wind power based on historical data; , Respectively represent the maximum deviation of photovoltaic and wind power output within the specified range, , is the uncertainty adjustment parameter of the introduced photovoltaic and wind power output, which takes the value of an integer between 0 and 24; B is the introduced binary variable, which takes the value of 0 and 1.

[0014] Preferably, in step 3, the sub-problem is:

[0015] In the given Down, This is a linear problem. According to the strong duality theory, it can be transformed into a maximum value. form, and on the outer Combined, we get the following dual form of the sub-problem:

[0016] In the formula , , and are the dual variables introduced for the constraints of the main problem.

[0017] Preferably, in step 4, a column constraint generation method CCG is used to solve the main problem and the sub-problems to obtain the optimal solution of the two-stage robust optimization model.

[0018] Compared with the prior art, the beneficial effects of the present invention include: 1) The present invention effectively reduces carbon emissions by equipping coal-fired power generation nodes with carbon capture modules; at the same time, the power-to-gas modules equipped with renewable energy power generation nodes are used to convert excess electricity into natural gas storage, which not only improves energy utilization but also further reduces carbon emissions. The present invention makes comprehensive use of a variety of carbon reduction technical means to significantly improve the environmental performance of the power system. In view of the uncertainty of wind power and photovoltaic power generation output, the robustness of the renewable energy power generation scheduling scheme is enhanced by establishing a two-stage robust optimization model. This means that even when the output of renewable energy fluctuates greatly, the stable operation of the power system can be guaranteed, thereby improving the actual utilization rate of renewable energy.

[0019] 2) The present invention constructs the objective function of optimal scheduling with the goal of minimizing the operating cost of the power system, and comprehensively considers the operating costs of modules such as carbon capture, power-to-gas, demand response and energy reserve in the model. This design not only ensures the economy of the power system, but also improves the reliability and stability of the power system through the introduction of energy reserve modules and demand response modules.

[0020] 3) The present invention uses an iterative solution method to decompose the two-stage robust optimization model into a main problem and sub-problems, and uses the column constraint generation method CCG to solve the problem through continuous iterations, gradually approaching the optimal solution. This solution method not only ensures the accuracy of the optimization results, but also improves the solution efficiency, making the method more feasible in practical applications.

[0021] 4) By introducing a demand response module, the present invention can flexibly adjust the power supply according to the change of power demand, thereby achieving a dynamic balance between power supply and demand, helping to reduce power waste and improve the overall efficiency of the power system.

[0022] 5) The low-carbon optimization scheduling method for the power system of the present invention not only helps to reduce carbon emissions and improve the utilization rate of renewable energy, but also takes into account the economy, reliability and stability of the power system, providing strong support for the low-carbon transformation and sustainable development of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0024] Figure 1 FIG. 4 is a schematic diagram of a power system according to an embodiment of the present invention.

[0025] Figure 2 A low-carbon optimization scheduling method for a power system based on a two-stage robust optimization model according to an embodiment of the present invention.

[0026] Figure 3 This is a photovoltaic unit power generation output curve diagram obtained by solving the third situation in the embodiment of the present invention.

[0027] Figure 4 This is a wind turbine generator output curve diagram obtained by solving the third situation in the embodiment of the present invention.

[0028] Figure 5 This is a time period distribution curve diagram of the uncertainty adjustment parameter obtained by solving the third situation in the embodiment of the present invention. DETAILED DESCRIPTION

[0029] like Figure 1 As shown, the power system of the embodiment includes a gas power generation node G1, a coal-fired power generation node G2, a coal-fired power generation node G3, a renewable energy power generation node, a natural gas node, a reserve energy module SE, a power-to-gas module PtG, a load L1 and a load L2. The natural gas node includes a gas well N1 and a gas well N2; the renewable energy power generation node includes a photovoltaic generator set PV and a wind turbine set WT; in the embodiment, the coal-fired power generation node G2 and the coal-fired power generation node G3 are respectively equipped with a carbon capture module; the photovoltaic generator set PV, the wind turbine set WT and the coal-fired power generation node G3 supply power to the reserve energy module SE and the power-to-gas module PtG, and the power-to-gas module PtG produces methane and transports it to the gas well N2.

[0030] like Figure 2 As shown in FIG, the low-carbon optimization dispatch method for the power system based on the two-stage robust optimization model includes the following steps: Step 1: Combine the operating costs of the carbon capture module, carbon trading module, power-to-gas module, demand response module and energy reserve module as well as the cost of energy supply to construct the objective function of low-carbon optimal dispatch of the power system; Step 2: Convert the objective function of low-carbon optimal dispatch of the power system obtained in step 1 into a two-stage robust optimization model; Step 3: Decompose the two-stage robust optimization model into a main problem and sub-problems; Step 4: Iteratively solve the main problem and sub-problems to obtain the optimal solution of the two-stage robust optimization model; Step 4.1: Iteration initialization, setting the upper bound UB and lower bound LB of the iteration; Step 4.2: Solve the main problem, obtain the optimal solution of the main problem, and update the lower bound LB; Step 4.3: Solve the subproblem, obtain the optimal solution of the subproblem, and update the upper bound UB; Step 4.4: Determine whether the iteration termination condition is met. If so, output the optimal solution. Otherwise, execute step 4.2. Step 5: According to the optimal solution of the two-stage robust optimization model obtained in step 4, the power system is optimally dispatched.

[0031] In the embodiment, the operating cost of the capture module in step 1 is for: ; (1) In the formula is the carbon emission intensity of coal-fired power generation node c, represents the power consumption of the carbon capture module of the coal-fired power generation node c during period t, represents the carbon transmission and storage cost of coal-fired power generation node c, represents the amount of carbon captured by the coal-fired power generation node c in period t; t represents the tth period; c represents the cth coal-fired power generation node; Operating costs of demand response modules for: ; (2) In the formula represents the unit dispatch cost of demand response load, represents the actual power of the demand response load during period t, represents the expected power consumption of the demand response load during period t; Power-to-Gas Module Operating Costs for: ; (3) In the formula Indicates the time period t The dispatching power of each power-to-gas device, represents the operating cost of the power-to-gas equipment during period t; Indicates the serial number of the power-to-gas device; Operating costs of energy storage modules for: ; (4) In the formula represents the operating cost of the reserve energy module, represents the charging power during period t, Indicates the charging efficiency, represents the discharge power during period t, Indicates discharge efficiency; Cost of energy supply for: ; (5) In the formula represents the unit electricity generation cost; represents the net power output of coal-fired power generation node c during period t, represents the unit cost of gas-fired power generation, Indicates the amount of gas required for gas-fired power generation during period t.

[0032] Operating costs of the carbon trading module for: ; (6) In the formula represents the transaction price of carbon quotas, represents the actual total carbon emissions during the scheduling period, is the rated total carbon emissions.

[0033] In the embodiment, the objective function of low-carbon optimization scheduling of the power system is: ; (7) In the formula is the operating cost of the carbon capture module, is the operating cost of the carbon trading module, is the operating cost of the demand response module, is the operating cost of the power-to-gas module, To save the operating cost of the energy module, The cost of energy supply.

[0034] In the embodiment, the two-stage robust optimization model is: ; (8) Where x is the decision variable in the first stage, and y is the decision variable in the second stage; It represents the coefficient of the previous objective function converted into a vector form; u is the uncertainty variable of photovoltaic and wind power output; U represents the set of uncertainty variables; N(u, x) represents the feasible domain interval of variable y; Indicates the operating status of the reserve energy module, i.e. whether the reserve energy module is started or shut down; is the coal-fired power generation power in period t, is the gas-fired power generation power in period t, is the power consumption of the carbon capture module of the coal-fired power generation node c during period t, is the charging power of the energy reserve module during period t, is the discharge power of the energy reserve module during period t, is the power dispatching power of the demand response module during period t, and These are auxiliary variables in the demand response module. is the wind power generation power in period t, is the photovoltaic power generation power in period t, is the power of the power-to-gas equipment during period t; The set of uncertain variables U for: ; (9) In the formula They represent the photovoltaic and wind power outputs during period t respectively; , Respectively represent the average output of photovoltaic and wind power based on historical data; , Respectively represent the maximum deviation of photovoltaic and wind power output within the specified range, , is the uncertainty adjustment parameter of the introduced photovoltaic and wind power output, which takes the value of an integer between 0 and 24; B is the introduced binary variable, which takes the value of 0 and 1.

[0035] In the embodiment, in step 3, the main question is: ; (10) In the formula is the number of iterations, Indicates that the sub-problem is in the scenario l The optimal solution under The index of the scene identified for the quilt problem; O represents the scene l A collection of ; is the auxiliary variable introduced, Representation scene l Down u The worst case value of ; F represents the coefficient matrix of the x constraint in the first stage, D, K, G, and I are the coefficient matrices of the y constraint in the second stage, and d and h are constant terms. The sub-problems are: ; (11) In the given Down, This is a linear problem. According to the strong duality theory, it can be transformed into a maximum value. The form of the outer Combined, we get the following dual form of the sub-problem: ; (12) In the formula , , and are the dual variables introduced for the constraints of the main problem.

[0036] In step 4, the specific process of solving the two-stage robust optimization model includes: Step 4.1: Initially, set the lower bound , upper bound , number of iterations k = 0 and l Collection ; Step 4.2: Solve the above main problem to get the optimal solution ,in The solution of the decision variable x obtained by solving the main problem for the k+1th time, is the auxiliary variable obtained for the k+1th time The solution, They represent the values ​​of the second-stage decision variable y obtained by the 1st, 2nd…kth iterations respectively; Update the Nether: ; (13) Step 4.3: Solve the subproblem and get the solution to the subproblem , and update the upper bound ,in ; Step 4.4: Determine whether the iteration termination condition is met. If , then return And terminate the iteration, Indicates the set error, otherwise judge size; (1) If , create the second-stage decision variables to be solved in the k+1th iteration , add the following constraints to the main problem, ; (14) Update the number of iterations after the solution is completed , execute step 4.2; (ii) If , create the second-stage decision variables to be solved in the k+1th iteration , add the following constraints to the main problem, ; (15) Update iterations , proceed to step 4.2.

[0037] It should be noted that It represents the scene that has been identified by the sub-problem. Meaning value in front In each iteration, the resulting cutting plane constraints are added to the main problem; It is equivalent to an auxiliary variable, which actually represents the target value of the second stage; About the calculation of upper and lower limits:

[0038] Regarding the lower limit, because the first stage of the two-stage robust optimization model only considers part of the constraints, relaxes the other part of the constraints, and the goal is to find the minimum value min, so the result must be smaller, providing a lower bound. Adding the relaxed part back will gradually increase the lower bound LB; Regarding the upper limit, its principle is to divide the two-stage problem into two independent problems, and then add their respective results. For the feasible solution of independent optimization, it must be a feasible solution of joint optimization, but it cannot be guaranteed that the solution is the optimal solution. However, the solution of joint optimization must be better than or equal to the solution of independent optimization, so It provides an upper bound for the two-stage problem. Then, in the iterative process, the results of the sub-problems will gradually improve through the continuous changes of the decision variables in the first stage, so that the upper bound will gradually decrease. Finally, it is determined by iteration. The value of satisfies the condition. If so, the iteration is stopped to obtain the optimal solution and output the result. If not, the iteration is continued until the condition is met.

[0039] Table 1 Planning schemes under five different uncertainty situations

[0040] In the embodiment, the solution result under situation three is as follows Figure 3-5 shown. Figure 3 , Figure 4 The following are the photovoltaic output and wind power output curves under scenario 3. Taking scenario 3 as an example, the uncertainty adjustment parameter is and , It means that the longest time for the photovoltaic output to reach the worst output condition is no more than 10 hours. It means that the longest time for wind power output to reach the worst case scenario is no more than 12 hours.

[0041] Figure 5 The solution for case 3 is shown below. The time distribution curve of Figure 5 It can be seen that in the time intervals from 6 to 11 o'clock and from 16 to 19 o'clock, the photovoltaic output takes the worst case. The worst case is that the uncertainty variable set U Get the minimum boundary value, which corresponds to Figure 3 In the time intervals of 6 to 11 o'clock and 16 to 19 o'clock, the photovoltaic output is lower than the average photovoltaic output of the current period. Figure 4 It can be seen that in the time period between 8:00 and 17:00 and between 19:00 and 23:00, the wind power output is lower than the average wind power output in the current time period, that is, the wind power output in this time period is the worst.

[0042] It is worth noting that the uncertainty variable u of the present invention is obtained based on the average output of renewable energy minus the maximum deviation. Because the present invention implements robust optimization and analyzes a relatively conservative solution, when the output of wind power and photovoltaic power is lower than the average output, that is, the output of wind power and photovoltaic power does not achieve the expected effect.

[0043] During the modeling process, in order to prevent the photovoltaic output from being too low, the output lower limit is set. , when it reaches the minimum boundary value, it is the worst case mentioned above. Regarding the introduced uncertainty adjustment parameters, in case three, It means that the total time period under the worst case scenario does not exceed 10 hours. In actual scheduling, the worst case time period may be less than 10. The value range of is [0, 24], but The value range of is [0, 14], because the photovoltaic system has no power output at night, so The value range is [0, 24] and The range of values ​​for is [0, 14] and makes no difference.

[0044] As can be seen from Table 1, starting from situation 2, as the uncertainty of renewable energy output continues to increase, the cost of the power system also increases accordingly, verifying the effectiveness of the present invention; in contrast, the cost of situation 1 is much lower than the other four situations, but it does not mean that the scheduling scheme in situation 1, i.e., the deterministic situation, is better than the robust optimization scheme. The uncertainty of situation 1 is 0, i.e., the model becomes a deterministic model, and the deterministic model can be converted into a mixed integer linear programming to solve, so the economic benefit is better than the robust optimization scheme. The present invention focuses on the benefits of robust optimization. In other words, the scheduling scheme obtained by robust optimization has stronger robustness and the ability to resist risks.

[0045] The system of low-carbon optimization dispatch method of power system based on two-stage robust optimization model includes: Carbon capture cost calculation module: used to calculate the carbon capture cost of the power system based on the power consumption of the carbon capture module of the coal-fired power generation node and the carbon transmission and storage cost; Demand response cost calculation module: used to calculate the demand response cost of the power system according to the unit dispatch cost of the demand response load and the actual power of the demand response load in each period; Power-to-gas cost calculation module: used to calculate the power-to-gas cost of the power system according to the dispatch power of the power-to-gas equipment in each period; Reserve energy operation cost calculation module: used to calculate the reserve energy operation cost of the power system according to the charge and discharge power of the reserve energy module in each period; Energy supply cost calculation module: used to calculate the energy supply cost of the power system based on the output of coal-fired units and gas-fired units; Carbon trading cost calculation module: used to calculate the carbon trading cost of the power system based on the actual total carbon emissions and rated carbon emissions during the dispatch period; Objective function modeling module: used to construct the objective function of low-carbon optimal dispatch of the power system and convert the objective function into a two-stage robust optimization model; Two-stage robust optimization solution module: used to solve the two-stage robust optimization model and obtain the optimal scheduling solution; Dispatch instruction sending module: According to the optimal dispatch plan output by the two-stage robust optimization solution module, dispatch instructions are issued to the operators of coal-fired power generation nodes, renewable energy power generation nodes, reserve energy modules, and demand response modules respectively.

Claims

1. A low-carbon optimization dispatching method for power system based on a two-stage robust optimization model, characterized in that: The power system includes coal-fired power generation nodes, renewable energy power generation nodes, reserve energy modules and demand response modules; A carbon capture module is provided on the coal-fired power generation node, wherein the carbon capture module is used to capture and store carbon dioxide generated during the power generation process of the coal-fired power generation unit; A power-to-gas module is provided at the renewable energy power generation node, wherein the power-to-gas module utilizes electrical energy to produce combustible gas through chemical reaction and transmits the combustible gas to the gas well; A reserve energy module is provided on the gas power generation node and the renewable energy power generation node, and the reserve energy module is responsible for charging and discharging the energy storage battery; The demand response module is used to encourage users to reduce electricity consumption during peak electricity demand periods and increase electricity consumption during off-peak periods, thereby promoting a balance between electricity supply and demand; The low-carbon optimization scheduling method comprises the following steps: Step 1: Combine the operating costs of the carbon capture module, power-to-gas module, demand response module and energy reserve module as well as the cost of energy supply to construct the objective function of low-carbon optimal dispatch of the power system; Step 2: Convert the objective function of low-carbon optimal dispatch of the power system obtained in step 1 into a two-stage robust optimization model; Step 3: Decompose the two-stage robust optimization model into a main problem and sub-problems; Step 4: Iteratively solve the main problem and sub-problems to obtain the optimal solution of the two-stage robust optimization model; Step 4.1: Iteration initialization, setting the upper bound UB and lower bound LB of the iteration; Step 4.2: Solve the main problem, obtain the optimal solution of the main problem, and update the lower bound LB; Step 4.3: Solve the subproblem, obtain the optimal solution of the subproblem, and update the upper bound UB; Step 4.4: Determine whether the iteration termination condition is met. If so, output the optimal solution. Otherwise, execute step 4.

2. Step 5: According to the optimal solution of the two-stage robust optimization model obtained in step 4, the power system is optimally dispatched.

2. The low-carbon optimization dispatching method for power system according to claim 1 is characterized in that The power system also includes a carbon trading module, which is used to trade the carbon emission quotas of the power generation nodes of the power system. When the carbon emissions of the power generation nodes exceed the issued carbon emission quotas, the remaining carbon emission quotas of other power generation nodes are purchased through the carbon trading module.

3. The low-carbon optimization dispatching method for power system according to claim 1 or 2, characterized in that In step 1, the operating cost of the carbon capture module for: ；（1） In the formula is the carbon emission intensity of coal-fired power generation node c, represents the power consumption of the carbon capture module of the coal-fired power generation node c during period t, represents the carbon transmission and storage cost of coal-fired power generation node c, represents the amount of carbon captured by the coal-fired power generation node c in period t; t represents the tth period; c represents the cth coal-fired power generation node; Operating costs of demand response modules for: ；（2） In the formula represents the unit dispatch cost of demand response load, represents the actual power of the demand response load during period t, represents the expected power consumption of the demand response load during period t; Power-to-Gas Module Operating Costs for: ；（3） In the formula Indicates the time period t The dispatching power of each power-to-gas device, represents the operating cost of the power-to-gas equipment during period t; Indicates the serial number of the power-to-gas device; Operating costs of energy storage modules for: ；（4） In the formula represents the operating cost of the reserve energy module, represents the charging power during period t, Indicates the charging efficiency, represents the discharge power during period t, Indicates discharge efficiency; Cost of energy supply for: ；（5） In the formula represents the unit electricity generation cost; represents the net power output of coal-fired power generation node c during period t, represents the unit cost of gas-fired power generation, Indicates the amount of gas required for gas-fired power generation during period t.

4. The low-carbon optimization dispatching method for power system according to claim 3 is characterized in that ,In step 1, the operating cost of the carbon trading module for: ；（6） In the formula represents the transaction price of carbon quotas, represents the actual total carbon emissions during the scheduling period, is the rated total carbon emissions.

5. The low-carbon optimization dispatching method for power system according to claim 4 is characterized in that ,In step 1, the objective function of the low-carbon optimal dispatch of the power system is: ；（7） In the formula is the operating cost of the carbon capture module, is the operating cost of the carbon trading module, is the operating cost of the demand response module, is the operating cost of the power-to-gas module, To save the operating cost of the energy module, The cost of energy supply.

6. The low-carbon optimization dispatching method for power system according to claim 5 is characterized in that ,In step 2, the two-stage robust optimization model is: ；（8） Where x is the decision variable in the first stage, and y is the decision variable in the second stage; It represents the coefficient of the previous objective function converted into a vector form; u is the uncertainty variable of photovoltaic and wind power output; U represents the set of uncertainty variables; N(u, x) represents the feasible domain interval of variable y; Indicates the operating status of the reserve energy module, i.e. whether the reserve energy module is started or shut down; is the coal-fired power generation power in period t, is the gas-fired power generation power in period t, is the power consumption of the carbon capture module of the coal-fired power generation node c during period t, is the charging power of the energy reserve module during period t, is the discharge power of the energy reserve module during period t, is the power dispatching power of the demand response module during period t, and These are auxiliary variables in the demand response module. is the wind power generation power in period t, is the photovoltaic power generation power in period t, is the power of the power-to-gas equipment during period t; The set of uncertain variables U for: ； （9） In the formula They represent the photovoltaic and wind power outputs during period t respectively; , Respectively represent the average output of photovoltaic and wind power based on historical data; , Respectively represent the maximum deviation of photovoltaic and wind power output within the specified range, , is the uncertainty adjustment parameter of photovoltaic and wind power output introduced, and B is the introduced binary variable.

7. The low-carbon optimization dispatching method for power system according to claim 6 is characterized in that ,In step 3, the main problem is: ；（10） In the formula is the number of iterations, Indicates that the sub-problem is in the scenario l The optimal solution under The index of the scene identified for the quilt problem; O represents the scene l A collection of ; is the auxiliary variable introduced, Representation scene l Down u The worst case value of ; F represents the coefficient matrix of the x constraint in the first stage, D, K, G, and I are the coefficient matrices of the y constraint in the second stage, and d and h are constant terms.

8. The low-carbon optimization dispatching method for power system according to claim 7 is characterized in that ,In step 3, the sub-problem is: ；（11） In the formula is the objective function of the sub-problem; In the given Down, This is a linear problem. According to the strong duality theory, it can be transformed into a maximum value. The form of the outer Combined, we get the following dual subproblem: ； （12） In the formula , , and are the dual variables introduced for the constraints of the main problem.

9. The low-carbon optimization dispatching method for power system according to claim 8 is characterized in that ,The step 4 uses the column constraint generation method CCG to solve the two-stage robust optimization model, specifically including: Step 4.1: Initially, set the lower bound , upper bound , number of iterations k = 0 and l Collection ; Step 4.2: Solve the above main problem to get the optimal solution ,in The solution of the decision variable x obtained by solving the main problem for the k+1th time, is the auxiliary variable obtained for the k+1th time The solution, They represent the values ​​of the second-stage decision variable y obtained by the 1st, 2nd…kth iterations respectively; Update the Nether: ；（13） Step 4.3: Solve the subproblem and get the solution to the subproblem , and update the upper bound ,in ; Step 4.4: Determine whether the iteration termination condition is met. If , then return And terminate the iteration, Indicates the set error, otherwise judge size; (1) If , create the second-stage decision variables to be solved in the k+1th iteration , add the following constraints to the main problem, ；（14） Update the number of iterations after the solution is completed , execute step 4.2; (ii) If , create the second-stage decision variables to be solved in the k+1th iteration , add the following constraints to the main problem, ；（15） Update iterations , proceed to step 4.2.

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