Power grid multi-target operation domain generation method and system based on interval optimization

Abstract

The invention discloses a power grid multi-target operation domain generation method and system based on interval optimization, and the method comprises the steps: firstly constructing a power grid multi-target scheduling operation model for the requirements of the operation of a power grid, such as safety, economy and low carbon; considering the uncertainty of new energy output, and constructing an optimistic Pareto frontier and a pessimistic Pareto frontier of a multi-target scheduling operation problem; solving an optimistic Pareto leading edge, and depicting a lower boundary of a multi-target operation domain; solving a pessimistic Pareto leading edge, and completing depiction of an upper boundary of a multi-target operation domain; and an optimistic Pareto frontier and a pessimistic Pareto frontier are combined to finally obtain a power grid operation domain considering multiple targets of safety, economy and low carbon. According to the method, multi-target scheduling decision support can be provided for operation of a novel power system in an uncertain environment, and the safe-economic-low-carbon operation level of a power grid is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the field of power system dispatching, and particularly relates to a method and system for generating a multi-objective operation domain of a power grid based on interval optimization. Background Art

[0002] In recent years, with the new power system mainly composed of new energy sources such as wind and light has become an important means for the low-carbon transformation of the power industry. However, in the process of the transformation of the power system, the uncertainty problem of the power system has become increasingly prominent. On the energy side, a high proportion of new energy sources such as wind power and photovoltaic power are connected to the power grid. Since their power generation capacity is greatly affected by weather conditions, their output shows strong uncertainty and randomness, posing challenges to the stability of the power grid. On the load side, with the large-scale access of electric vehicles and the rapid development of distributed photovoltaic and energy storage devices on the user side, the power load characteristics have become more complex, showing strong randomness and uncertainty. In the process of the low-carbon transformation of the power system, how to cope with the uncertainties on both the source and load sides and ensure the safe, stable and economic operation of the power system has become a key problem to be solved urgently. Therefore, it is of great significance to carry out research on multi-objective optimal dispatching of the power grid considering uncertainty.

[0003] However, most traditional dispatching methods are deterministic dispatching methods and are difficult to effectively cope with uncertainties. For example, the patent document CN107546781A discloses a multi-objective operation optimization method for a microgrid based on an improved PSO algorithm, but this method is a deterministic dispatching method and lacks consideration of uncertainties, and cannot effectively guide the power grid to cope with the uncertainty problem of new energy output.

[0004] Therefore, there is an urgent need to propose a method and system for generating a multi-objective operation domain of a power grid based on interval optimization to guide power system dispatching and improve the safety, economy and low-carbon performance of power system operation. Summary of the Invention

[0005] In order to solve the deficiencies of the prior art and achieve the purpose of providing multi-objective dispatching decision support for the operation of a new power system in an uncertain environment and effectively improving the safe-economic-low-carbon operation level of the power grid, the present invention adopts the following technical solutions:

[0006] A method for generating a multi-objective operation domain of a power grid based on interval optimization includes the following steps:

[0007] According to the actual operation requirements of the power grid such as reducing carbon emissions, increasing the consumption of new energy, and reducing operating costs, a multi-objective source-load dispatching objective function of the power grid is constructed, and then based on the physical safety constraints of the power grid such as power balance constraints and power flow constraints, the constraint conditions of the model are established to construct a multi-objective source-load dispatching model of the power grid;

[0008] Based on interval programming, the uncertainty of new energy in the power grid is characterized in the form of an uncertain interval, and then combined with the multi-objective source-load scheduling model of the power grid, the optimistic problem and the pessimistic problem of multi-objective source-load collaborative scheduling are constructed;

[0009] For the optimistic problem of the multi-objective source-load collaborative scheduling, the weights of the objective function of the multi-objective source-load scheduling of the power grid are changed step by step, the original multi-objective problem is transformed into a series of single-objective optimization problems for solution, and then both the interval equality constraints and the interval inequality constraints are transformed into non-interval constraints, and the remaining non-interval constraints remain unchanged. After the above processing, a linear programming problem that is easy to solve in the optimistic case is finally formed; solve the transformed multiple single-objective optimistic problems, fit the obtained objective function values into a curve to generate the lower bound of the multi-objective operating region;

[0010] For the pessimistic problem of the multi-objective source-load collaborative scheduling, the weights of the objective function of the multi-objective source-load scheduling of the power grid are changed step by step, the original multi-objective problem is transformed into a series of single-objective optimization problems for solution, and then the difficult-to-solve pessimistic problem is transformed into a linear programming problem that is easy to handle; solve the transformed multiple single-objective pessimistic problems, fit the obtained objective function values into a curve to generate the upper bound of the multi-objective operating region;

[0011] Through the region enclosed by the lower bound of the multi-objective operating region and the upper bound of the multi-objective operating region, the multi-objective operating region of the power grid with low carbon, safety and economy of the actual operating requirements of the power grid and the physical safety constraints of the power grid is obtained.

[0012] Furthermore, constructing the objective function of the multi-objective source-load scheduling of the power grid according to the actual operating requirements of the power grid includes: taking minimizing the operating cost and minimizing the carbon emission as the objective functions:

[0013]

[0014]

[0015] where cost represents the operating cost, and CO2 represents the carbon dioxide emission, represents the output power of the generator set at time t, c represents the generator cost coefficient, and e represents the generator carbon dioxide emission coefficient.

[0016] Furthermore, the constraint conditions for establishing the model based on the physical safety constraints of the power grid include: taking line power flow constraints, generator output constraints, generator ramp constraints and transferable load constraints as constraint conditions:

[0017] Line power flow constraint:

[0018]

[0019]

[0020] In the formula, represents the output power of the generating unit at time t, represents the power upper limit vector of line l, represents the power flow transfer coefficient matrix of line l, represents the node load vector at time t, represents the interval variable of the wind turbine output at time t, represents the node transferable load vector at time t.

[0021] Generating unit output constraint:

[0022]

[0023] In the formula, represents the upper limit vector of the generator output.

[0024] Generating unit ramp constraint:

[0025]

[0026] In the formula, represents the downward ramp constraint, represents the upward ramp constraint, , r represents the ramp rate coefficient, .

[0027] Transferable load constraint:

[0028]

[0029] In the formula, represents the total demand of the node transferable load.

[0030] Furthermore, the construction of the optimistic problem of multi-objective source-load collaborative scheduling is the optimistic Pareto front of the multi-objective source-load collaborative scheduling problem, which is to convert the multi-objective source-load scheduling model of the power grid into the form of the optimistic Pareto front based on interval linear programming:

[0031]

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038] In the formula, represents the output power of the generator set at time t, c represents the generator cost coefficient, and e represents the generator carbon dioxide emission coefficient. represents the power upper limit vector of line l. represents the power flow transfer coefficient matrix of line l. represents the interval variable of the fan output at time t. represents the node load vector at time t. represents the node transferable load vector at time t. represents the generator output upper limit vector. represents the downward ramp constraint. represents the upward ramp constraint. , r represents the ramp rate coefficient. , represents the total demand of the node transferable load. 、 represent the maximum value and the minimum value of the fan processing interval respectively.

[0039] Furthermore, the conversion of the interval equality constraint into a non-interval constraint is to convert the interval equality constraint into a pair of non-interval inequality constraints, that is, the interval equality constraint in the optimistic problem is converted into the following constraint:

[0040]

[0041]

[0042] The conversion of the interval inequality constraint into a non-interval constraint is to relax the interval inequality constraint to the most optimistic situation, that is, the interval inequality constraint in the optimistic problem is relaxed into the following constraint:

[0043]

[0044]

[0045] Finally, the interval constraint is removed to obtain the following representation form of the single-objective optimistic problem:

[0046]

[0047] In the formula, represents the objective function. represents a series of inequality constraint conditions in the processed single-objective optimistic problem. Represents a series of equality constraint conditions in the processed single-objective optimistic problem.

[0048] Furthermore, the construction of the pessimistic problem of multi-objective source-load coordinated scheduling is the pessimistic Pareto front of the multi-objective source-load coordinated scheduling problem, which transforms the multi-objective source-load scheduling model of the power grid into the form of the pessimistic Pareto front based on interval linear programming:

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] In the formula, represents the output power of the generator set at time t, c represents the generator cost coefficient, e represents the generator carbon dioxide emission coefficient, represents the interval variable of the wind turbine output at time t, and represent the maximum and minimum values of the wind turbine processing interval respectively, represents the power upper limit vector of line l, represents the power flow transfer coefficient matrix of line l, represents the node load vector at time t, represents the node transferable load vector at time t, represents the generator output upper limit vector, represents the total demand of the node transferable load, represents the downward ramp constraint, represents the upward ramp constraint, , r represents the ramp rate coefficient, .

[0058] Furthermore, the generation process of the upper bound of the multi-objective operating region includes the following steps:

[0059] After transforming the multi-objective pessimistic problem into multiple single-objective pessimistic problems, the pessimistic solution of the interval optimization problem is written in the form of a max-min two-layer optimization problem:

[0060]

[0061] In the formula, represents the objective function, represents a series of inequality constraint conditions of the scheduling model, represents a series of equality constraint conditions of the scheduling model, represents the uncertain quantity, that is, the new energy output in the scheduling model, and represent the minimum value and the maximum value of the uncertain quantity respectively;

[0062] Write out the KKT conditions of the inner layer problem:

[0063]

[0064] In the formula, represents the gradient of the Lagrangian function, x represents the decision variable (i.e., the output power of the generator set), λ represents the Lagrange multiplier of the equality constraint condition, μ represents the Lagrange multiplier of the inequality constraint condition, represents the slack condition;

[0065] Transform the inner layer problem into a set of constraints through the KKT conditions, so as to transform the bilevel optimization problem into a single-level optimization problem:

[0066]

[0067] Use the big M method to transform the complementary slackness condition into the following linear constraint containing integer variables:

[0068]

[0069] In the formula, M represents a sufficiently large constant, and u represents a variable between 0 and 1.

[0070] Transform the original difficult-to-solve pessimistic problem into an easy-to-solve standard mixed-integer linear programming problem, solve the multiple single-objective pessimistic problems after transformation, and fit the obtained objective function values into a curve to obtain the upper bound of the multi-objective operating region.

[0071] The power grid multi-objective operating region generation system based on interval optimization includes a power grid multi-objective source-load scheduling module, a problem construction module, and a multi-objective operating region generation module. According to the power grid multi-objective operating region generation method based on interval optimization, the power grid multi-objective source-load scheduling module, the problem construction module, and the multi-objective operating region generation module are sequentially passed to construct the power grid multi-objective source-load scheduling model, construct the optimistic problem and the pessimistic problem of the multi-objective source-load collaborative scheduling, and generate the power grid multi-objective operating region by solving the optimistic problem and the pessimistic problem to obtain the upper and lower bounds.

[0072] An electronic device, comprising a memory and a processor, where the memory is used to store computer-executable instructions or computer programs, and when the processor executes the computer-executable instructions or computer programs stored in the memory, the method for generating a multi-objective operation domain of a power grid based on interval optimization is implemented.

[0073] A computer storage medium stores computer-executable instructions or computer programs, and when the computer-executable instructions or computer programs are executed by a processor, the method for generating a multi-objective operation domain of a power grid based on interval optimization is implemented.

[0074] The advantages and beneficial effects of the present invention are as follows:

[0075] The method and system for generating a multi-objective operation domain of a power grid based on interval optimization according to the present invention generate a multi-objective operation domain of the power grid on the basis of fully considering the uncertainty of new energy output, more intuitively display the relationship between multi-objectives of the power grid, and can be used to guide power system dispatching, thereby improving the safety, economy, and low-carbon performance of power system operation. Description of the Drawings

[0076] Figure 1 is the flowchart of the method of the embodiment of the present invention.

[0077] Figure 2 is the schematic structural diagram of the system of the embodiment of the present invention. Detailed Embodiments

[0078] The following further describes the detailed embodiments of the present invention with reference to the drawings. It should be understood that the detailed embodiments described herein are only for explaining and illustrating the present invention, and are not used to limit the present invention.

[0079] As Figure 1 shown, the method for generating a multi-objective operation domain of a power grid based on interval optimization includes the following steps:

[0080] Step S1: According to the actual operation requirements of the power grid such as reducing carbon emissions, increasing new energy consumption, and reducing operation costs, construct a multi-objective source-load scheduling objective function of the power grid; then, based on the physical security constraints of the power grid such as power balance constraints and power flow constraints, establish the constraint conditions of the model, and finally construct a multi-objective source-load scheduling model of the power grid.

[0081] In one embodiment, minimizing the operation cost and minimizing the carbon emissions are used as the objective function, and the line power flow constraint, unit output constraint, unit ramp constraint, and shiftable load constraint are used as the constraint conditions to construct the following source-load scheduling model:

[0082] Objective function:

[0083]

[0084]

[0085] Among them, cost represents the operating cost, and CO2 represents the carbon dioxide emissions. represents the output power of the generator set at time t, c represents the generator cost coefficient, and e represents the generator carbon dioxide emission coefficient.

[0086] Constraint conditions:

[0087] 1. Line power flow constraint:

[0088]

[0089]

[0090] In the formula, represents the upper limit vector of the power of line l. represents the power flow transfer coefficient matrix of line l. represents the node load vector at time t. represents the interval variable of the wind turbine output at time t, denoted as , represents the node transferable load vector at time t.

[0091] 2. Generator output constraint:

[0092]

[0093] In the formula, represents the upper limit vector of the generator output.

[0094] 3. Generator ramping constraint:

[0095]

[0096] In the formula, represents the downward ramping constraint. represents the upward ramping constraint. , r represents the ramping rate coefficient. .

[0097] 4. Transferable load constraint:

[0098]

[0099] In the formula, represents the total demand of the node transferable load. In the above formulas, except , the rest are variables and parameters in classical mathematical optimization.

[0100] Step S2: Based on interval programming, characterize the uncertainty of new energy in the form of an uncertain interval, and then combine it with the constructed multi-objective source-load scheduling model of the power grid to construct the optimistic Pareto front (optimistic problem) and pessimistic Pareto front (pessimistic problem) of the multi-objective source-load collaborative scheduling problem.

[0101] In one implementation, further, write the above source-load scheduling model as the following optimistic Pareto front and pessimistic Pareto front based on interval linear programming:

[0102] The optimistic Pareto front can be expressed in the following form:

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110] The meanings of the variables and parameters have been described above, so they will not be elaborated here.

[0111] Pessimistic Pareto front:

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120] Step S3: For the optimistic problem of interval optimization, vary the weights of the objective function at equal step lengths, transform the original multi-objective problem into a series of single-objective optimization problems for solution, and then transform both the interval equality constraints and interval inequality constraints into non-interval constraints, while keeping the remaining non-interval constraints unchanged. After the above processing, a linear programming problem that is easy to solve in the optimistic case is finally formed.

[0121] In one embodiment, for the interval equality constraint, transform it into a pair of non-interval inequality constraints, that is, the interval equality constraint in the optimistic problem is transformed into the following constraints:

[0122]

[0123]

[0124] For the interval inequality constraint, relax it to the most optimistic case, that is, the interval inequality constraint in the optimistic problem is relaxed into the following constraints:

[0125]

[0126]

[0127] Remove the interval constraints , after the above processing, a single-objective optimistic problem can be expressed in the following form:

[0128]

[0129] In the formula, represents the objective function, represents a series of inequality constraint conditions in the processed single-objective optimistic problem; represents a series of equality constraint conditions in the processed single-objective optimistic problem;

[0130] Solve the transformed multiple single-objective optimistic problems, and after fitting the obtained objective function values into a curve, the lower bound of the multi-objective operating region can be obtained.

[0131] Step S4: For the pessimistic problem of interval optimization, vary the weights of the objective function at equal step lengths, transform the original multi-objective problem into a series of single-objective optimization problems for solution. Then transform the difficult-to-solve pessimistic problem to make it a linear programming problem that is easy to handle.

[0132] In one embodiment, after transforming the multi-objective pessimistic problem into multiple single-objective pessimistic problems, the single-objective pessimistic problem is formulated as a two-layer max-min optimization problem. By using the KKT (Karush-Kuhn-Tucker, a set of necessary conditions in optimization theory for solving nonlinear programming problems with equality and inequality constraints) conditions, the inner-layer problem is transformed into a set of constraints, thereby transforming the two-layer optimization problem into a single-layer optimization problem, and finally into a standard MILP problem, so that it can be solved within a reasonable time. The specific steps are as follows:

[0133] Step 4.1: Write the pessimistic solution of the interval optimization problem as a max-min two-layer optimization problem in the following form:

[0134]

[0135] In the formula, represents the objective function, represents a series of inequality constraint conditions of the scheduling model in Step 4.1; represents a series of equality constraint conditions of the scheduling model in Step 4.1; represents the uncertain quantity, that is, the new energy output in the scheduling model, and represent the minimum value and the maximum value of the uncertain quantity respectively.

[0136] Step 4.2: Write the KKT conditions of the inner-layer problem:

[0137]

[0138] Step 4.3: Use the KKT conditions to transform the problem into a single-layer optimization problem:

[0139]

[0140] Step 4.4: Use the big M method to transform the complementary slackness condition into the following linear constraint containing integer variables:

[0141]

[0142] Step 4.5: Finally, transform the original difficult-to-solve pessimistic problem into an easy-to-solve standard mixed-integer linear programming problem, solve the transformed multiple single-objective pessimistic problems, and after fitting the obtained objective function values into a curve, the upper bound of the multi-objective operation region can be obtained.

[0143] Step S5: Obtain the lower bound of the multi-objective operation domain by solving the optimistic Pareto front, and obtain the upper bound of the multi-objective operation domain by solving the pessimistic Pareto front. The area enclosed by these two front surfaces is the power grid multi-objective operation domain that takes into account low carbon, safety, and economy. Each point between these two front surfaces is the Pareto optimal solution corresponding to a certain new energy output.

[0144] As Figure 2 shown, the power grid multi-objective operation domain generation system based on interval optimization includes a power grid multi-objective source-load scheduling module, a problem construction module, and a multi-objective operation domain generation module. According to the power grid multi-objective operation domain generation method based on interval optimization, the construction of the power grid multi-objective source-load scheduling model, the construction of the optimistic problem and the pessimistic problem of multi-objective source-load collaborative scheduling, and the generation of the power grid multi-objective operation domain by solving the optimistic problem and the pessimistic problem to obtain the upper and lower bounds are sequentially carried out through the power grid multi-objective source-load scheduling module, the problem construction module, and the multi-objective operation domain generation module.

[0145] An embodiment of the present invention provides an electronic device, which includes a memory and a processor. The memory is used to store computer-executable instructions or computer programs. When the processor executes the computer-executable instructions or computer programs stored in the memory, the steps executed in the embodiment of the above-mentioned power grid multi-objective operation domain generation method based on interval optimization are implemented.

[0146] An embodiment of the present invention provides a computer-readable storage medium, which stores computer-executable instructions or computer programs. When the computer-executable instructions or computer programs are executed by a processor, the steps executed in the embodiment of the above-mentioned power grid multi-objective operation domain generation method based on interval optimization are implemented.

[0147] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating a multi-objective operation domain of a power grid based on interval optimization, characterized in that It includes the following steps: According to the actual operation requirements of the power grid, construct the multi-objective source-load dispatching objective function of the power grid, and then based on the physical security constraints of the power grid, establish the constraint conditions of the model to construct the multi-objective source-load dispatching model of the power grid; Based on interval programming, characterize the uncertainty of new energy in the power grid in the form of an uncertain interval, and then combine with the multi-objective source-load dispatching model of the power grid to construct the optimistic problem and the pessimistic problem of multi-objective source-load collaborative dispatching; For the optimistic problem of the multi-objective source-load collaborative dispatching, change the weight of the multi-objective source-load dispatching objective function of the power grid step by step, transform the original multi-objective problem into a series of single-objective optimization problems for solution, and then transform both the interval equality constraint and the interval inequality constraint into non-interval constraints, and finally form a linear programming problem in the optimistic case; Solve the transformed multiple single-objective optimistic problems, fit the obtained objective function values into a curve to generate the lower bound of the multi-objective operation domain; For the pessimistic problem of the multi-objective source-load collaborative dispatching, change the weight of the multi-objective source-load dispatching objective function of the power grid step by step, transform the original multi-objective problem into a series of single-objective optimization problems for solution, and then transform the pessimistic problem to make it a linear programming problem; Solve the transformed multiple single-objective pessimistic problems, fit the obtained objective function values into a curve to generate the upper bound of the multi-objective operation domain; Through the area enclosed by the lower bound of the multi-objective operation domain and the upper bound of the multi-objective operation domain, obtain the multi-objective operation domain of the actual operation requirements of the power grid and the physical security constraints of the power grid.

2. The method for generating a multi-objective operation region of a power grid based on interval optimization according to claim 1, wherein: The construction of the multi-objective source-load dispatching objective function of the power grid according to the actual operation requirements of the power grid includes: taking minimizing the operation cost and minimizing the carbon emission as the objective function: Among them, cost represents the operating cost, and CO2 represents the carbon dioxide emissions. represents the generator output power, c represents the generator cost coefficient, and e represents the generator carbon dioxide emission coefficient.

3. The method for generating a multi-objective operation domain of a power grid based on interval optimization according to claim 1, characterized in that: The establishment of the constraint conditions of the model based on the physical security constraints of the power grid includes: taking the line power flow constraint, the unit output constraint, the unit ramp constraint and the shiftable load constraint as the constraint conditions: Line power flow constraint: wherein, represents the generator output power at time t, represents the power upper limit vector of line l, represents the power flow transfer coefficient matrix of line l, represents the nodal load vector at time t, represents the interval variable of the wind turbine output at time t, represents the nodal transferable load vector at time t; Unit output constraint: In the formula, represents the upper limit vector of generator output; Unit ramp constraint: In the formula, represents the downward ramp constraint, represents the upward ramp constraint, , r represents the ramp rate coefficient, ; Shiftable load constraint: In the formula, represents the total transferable load demand of the node.

4. The method for generating a multi-objective operation domain of a power grid based on interval optimization according to claim 1, characterized in that: The construction of the optimistic problem of the multi-objective source-load collaborative dispatching is the optimistic Pareto front of the multi-objective source-load collaborative dispatching problem, which is to transform the multi-objective source-load dispatching model of the power grid into the form of an optimistic Pareto front based on interval linear programming: Wherein, represents the output power of the generator at time t, c represents the generator cost coefficient, and e represents the generator carbon dioxide emission coefficient, represents the power upper limit vector of line l, represents the line l power flow transfer coefficient matrix, represents the interval variable of the wind turbine output at time t, represents the node load vector at time t, represents the node transferable load vector at time t, represents the generator output upper limit vector, represents the downward ramp constraint, represents the upward ramp constraint, , r represents the ramp rate coefficient, , represents the total node transferable load demand, 、 represent the maximum value and the minimum value of the wind turbine processing interval respectively.

5. The method for generating a multi-objective operation region of a power grid based on interval optimization according to claim 4, wherein: The conversion of the interval equality constraint into a non-interval constraint is to convert the interval equality constraint into a pair of non-interval inequality constraints, that is, the interval equality constraint in the optimistic problem is converted into the following constraints: The conversion of the interval inequality constraint into a non-interval constraint relaxes the interval inequality constraint to the most optimistic case, that is, the interval inequality constraint in the optimistic problem is relaxed to the following constraint: Remove interval constraints , obtaining the following representation of the single-objective optimistic problem: In the formula, represents the objective function, represents a series of inequality constraint conditions in the processed single-objective optimistic problem; represents a series of equality constraint conditions in the processed single-objective optimistic problem.

6. The method for generating a multi-objective operation region of a power grid based on interval optimization according to claim 1, wherein: The construction of the pessimistic problem of the multi-objective source-load collaborative dispatching is the pessimistic Pareto front of the multi-objective source-load collaborative dispatching problem, which is to transform the multi-objective source-load dispatching model of the power grid into the form of a pessimistic Pareto front based on interval linear programming: In the formula, represents the generator output power at time t, c represents the generator cost coefficient, and e represents the generator carbon dioxide emission coefficient. represents the wind turbine output interval variable at time t. and represent the maximum and minimum values of the wind turbine output interval, respectively. represents the power upper limit vector of line l. represents the power flow transfer coefficient matrix of line l. represents the node load vector at time t. represents the node transferable load vector at time t. represents the generator output upper limit vector. represents the total node transferable load demand. represents the downward ramp constraint. represents the upward ramp constraint. , r represents the ramp rate coefficient. .

7. The method for generating a multi-objective operation domain of a power grid based on interval optimization according to claim 6, wherein: The generation process of the upper bound of the multi-objective operation domain includes the following steps: After transforming the multi-objective pessimistic problem into multiple single-objective pessimistic problems, write the pessimistic solution of the interval optimization problem as a max-min two-layer optimization problem in the following form: In the formula, represents the objective function, represents a series of inequality constraint conditions of the scheduling model, represents a series of equality constraint conditions of the scheduling model, represents the uncertain quantity, i.e., the new energy output in the scheduling model, and represent the minimum value and the maximum value of the uncertain quantity respectively; Write the KKT conditions of the inner layer problem: In the formula, represents the gradient of the Lagrangian function, x represents the output power of the generator set, λ represents the Lagrange multiplier of the equality constraint, μ represents the Lagrange multiplier of the inequality constraint, represents the relaxation condition; Transform the inner layer problem into a set of constraints through the KKT conditions, so as to transform the two-layer optimization problem into a single-layer optimization problem: Using the Big M method to transform the complementary slackness conditions into the following linear constraints with integer variables: In the formula, M represents a large constant, and u represents a variable between 0 and 1; Convert the pessimistic problem into a standard mixed-integer linear programming problem, solve multiple single-objective pessimistic problems after conversion, and fit the obtained objective function values into a curve to obtain the upper bound of the multi-objective operating region.

8. A power grid multi-objective operation domain generation system based on interval optimization, including a power grid multi-objective source-load scheduling module, a problem construction module, and a multi-objective operation domain generation module, characterized in that: According to the method for generating a multi-objective operating region of a power grid based on interval optimization described in any one of claims 1 to 7, successively pass through the multi-objective source-load scheduling module, problem construction module, and multi-objective operating region generation module of the power grid to construct a multi-objective source-load scheduling model of the power grid, construct the optimistic problem and pessimistic problem of multi-objective source-load collaborative scheduling, and generate the multi-objective operating region of the power grid by solving the optimistic problem and pessimistic problem to obtain the upper and lower bounds.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory is used to store computer-executable instructions or computer programs. When the processor is used to execute the computer-executable instructions or computer programs stored in the memory, the method for generating a multi-objective operating region of a power grid based on interval optimization described in any one of claims 1 to 7 is implemented.

10. A computer storage medium stores computer-executable instructions or a computer program, characterized in that, When the computer-executable instructions or computer programs are executed by the processor, the method for generating a multi-objective operating region of a power grid based on interval optimization described in any one of claims 1 to 7 is implemented.

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