Method and system for generating multi-objective operation domain of power grid based on interval optimization

Abstract

This invention discloses a method and system for generating a multi-objective operation domain for a power grid based on interval optimization. First, a multi-objective dispatching and operation model for the power grid is constructed, targeting the safety, economy, and low-carbon requirements of power grid operation. Taking into account the uncertainty of renewable energy output, an optimistic Pareto frontier and a pessimistic Pareto frontier for the multi-objective dispatching and operation problem are constructed. The optimistic Pareto frontier is solved to characterize the lower boundary of the multi-objective operation domain. The pessimistic Pareto frontier is solved to characterize the upper boundary of the multi-objective operation domain. Finally, the optimistic and pessimistic Pareto frontiers are combined to obtain a power grid operation domain that takes into account multiple objectives: safety, economy, and low carbon. This invention can provide multi-objective dispatching decision support for the operation of new power systems in uncertain environments, effectively improving the safe, economical, and low-carbon operation level of the power grid.

Description

Technical Field

[0001] The present invention belongs to the field of power system dispatching, and in particular 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, as new power systems based on renewable energy sources such as wind and solar have become a key component of the power industry's low-carbon transition, uncertainty in the power system has become increasingly prominent during this transition. On the energy side, a high proportion of renewable energy sources such as wind and photovoltaic power are connected to the grid. Because their generation capacity is significantly affected by weather conditions, their output exhibits strong uncertainty and randomness, posing challenges to grid stability. On the load side, with the large-scale integration of electric vehicles and the rapid development of distributed photovoltaic and energy storage equipment on the user side, power load characteristics have become more complex, exhibiting strong randomness and uncertainty. In the process of low-carbon transformation of the power system, how to cope with uncertainty on both the source and load sides and ensure the safe, stable, and economical operation of the power system has become a key issue that needs to be addressed urgently. Therefore, research on multi-objective optimal scheduling of power grids that considers uncertainty is of great significance.

[0003] However, traditional scheduling methods are mostly deterministic scheduling, which makes it difficult to effectively deal with uncertainty. For example, patent document CN107546781A discloses a microgrid multi-objective operation optimization method based on an improved PSO algorithm. However, this method is a deterministic scheduling method that lacks consideration of uncertainty and cannot effectively guide the power grid to deal with the uncertainty of renewable energy output.

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

[0005] To address the shortcomings of existing technologies, provide multi-objective dispatch decision support for new power system operations under uncertain environments, and effectively improve the safe, economical, and low-carbon operation levels of power grids, the present invention adopts the following technical solutions:

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

[0007] Based on 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 scheduling objective function for the power grid is constructed. Then, based on the physical security constraints of the power grid, such as power balance constraints and power flow constraints, the model's constraints are established to construct a multi-objective source-load scheduling model for the power grid.

[0008] Based on interval planning, the uncertainty of renewable energy in the power grid is characterized in the form of uncertain intervals. Combined with the multi-objective source-load scheduling model of the power grid, the optimistic and pessimistic problems of multi-objective source-load coordinated scheduling are constructed.

[0009] In response to the optimistic problem of multi-objective source-load coordinated scheduling, the weights of the objective function of the multi-objective source-load scheduling of the power grid are changed in equal steps, and the original multi-objective problem is converted into a series of single-objective optimization problems for solution. Then, the interval equality constraints and interval inequality constraints therein are converted into non-interval constraints, and the other non-interval constraints remain unchanged. After the above processing, an easy-to-solve linear programming problem is finally formed under the optimistic situation; the multiple converted single-objective optimistic problems are solved, and the solved objective function values are fitted into a curve to generate the lower bound of the multi-objective operation domain;

[0010] In response to the pessimistic problem of the multi-objective source-load coordinated scheduling, the weights of the objective function of the multi-objective source-load scheduling of the power grid are changed in equal steps, the original multi-objective problem is converted into a series of single-objective optimization problems for solution, and the difficult-to-solve pessimistic problems are then converted into easy-to-solve linear programming problems; the converted multiple single-objective pessimistic problems are solved, and the solved objective function values are fitted into a curve to generate the upper bound of the multi-objective operation domain;

[0011] The multi-objective operation domain of the power grid, which is low-carbon, safe and economical according to the actual operation requirements of the power grid and the physical safety constraints of the power grid, is obtained through the area surrounded by the lower bound of the multi-objective operation domain and the upper bound of the multi-objective operation domain.

[0012] Furthermore, the multi-objective source-load scheduling objective function of the power grid is constructed based on the actual operation requirements of the power grid, including: minimizing the operation cost and minimizing the carbon emissions as the objective functions:

[0013]

[0014]

[0015] Among them, cost represents the operating cost, 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.

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

[0017] Line flow constraints:

[0018]

[0019]

[0020] Where, represents the output power of the generator set 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 wind turbine output interval variable at time t, represents the node transferable load vector at time t.

[0021] Unit output constraints:

[0022]

[0023] Where, Represents the generator output upper limit vector.

[0024] Unit climbing constraints:

[0025]

[0026] Where, represents the downward climbing constraint, represents the upward climbing constraint, , r represents the climbing rate coefficient, .

[0027] Transferable load constraints:

[0028]

[0029] Where, Indicates the total amount of load demand that can be transferred to the node.

[0030] Furthermore, the optimistic problem of constructing multi-objective source-load coordinated scheduling is an optimistic Pareto frontier of the multi-objective source-load coordinated scheduling problem, which is obtained by converting the multi-objective source-load scheduling model of the power grid into an optimistic Pareto frontier form based on interval linear programming:

[0031]

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038] Where, 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 power upper limit vector of line l, represents the power flow transfer coefficient matrix of line l, represents the wind turbine output interval variable at time t, represents the node load vector at time t, represents the node transferable load vector at time t, represents the upper limit vector of the generator output, represents the downward climbing constraint, represents the upward climbing constraint, , r represents the climbing rate coefficient, , represents the total amount of load demand that can be transferred from the node, 、 Respectively represent the maximum and minimum values of the fan processing range.

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

[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 case, that is, to convert the interval inequality constraint in the optimistic problem into a non-interval constraint. Relax the constraints as follows:

[0043]

[0044]

[0045] Finally, remove the interval constraints , we get the following representation of the single-objective optimistic problem:

[0046]

[0047] Where, represents the objective function, represents a series of inequality constraints in the processed single-objective optimistic problem; Represents a set of equality constraints in the processed single-objective optimistic problem.

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

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] Where, 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 wind turbine output interval variable at time t, 、 Respectively represent the maximum and minimum values of the fan processing range, 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 upper limit vector of the generator output, represents the total amount of load demand that can be transferred from the node, represents the downward climbing constraint, represents the upward climbing constraint, , r represents the climbing rate coefficient, .

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

[0059] After converting the multi-objective pessimistic problem into multiple single-objective pessimistic problems, the pessimistic solution of the interval optimization problem can be written as a max-min two-level optimization problem of the following form:

[0060]

[0061] Where, represents the objective function, represents a series of inequality constraints of the scheduling model, A series of equality constraints representing the scheduling model, Represents the uncertainty, that is, the output of new energy in the scheduling model, 、 Represent the minimum and maximum values of the uncertainty respectively;

[0062] List the KKT conditions for the inner layer problem:

[0063]

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

[0065] The inner layer problem is converted into a set of constraints through KKT conditions, thereby converting the two-layer optimization problem into a single-layer optimization problem:

[0066]

[0067] Using the Big M method, the complementary relaxation condition Transformed into the following linear constraints with integer variables:

[0068]

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

[0070] The original difficult-to-solve pessimistic problem is transformed into an easy-to-solve standard mixed integer linear programming problem. Multiple single-objective pessimistic problems after transformation are solved. After fitting the solved objective function values into a curve, the upper bound of the multi-objective operating domain is obtained.

[0071] A 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 power grid multi-objective source-load scheduling module, the problem construction module, and the multi-objective operation domain generation module are sequentially used to construct a power grid multi-objective source-load scheduling model, construct optimistic problems and pessimistic problems for multi-objective source-load coordinated scheduling, and obtain upper and lower bounds by solving the optimistic problem and the pessimistic problem to generate the power grid multi-objective operation domain.

[0072] An electronic device includes a memory and a processor, the memory being used to store computer-executable instructions or computer programs, and the processor being used to implement the interval-optimization-based method for generating a multi-objective operating domain for a power grid when executing the computer-executable instructions or computer programs stored in the memory.

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

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

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

[0076] Figure 1 It is a flow chart of a method according to an embodiment of the present invention.

[0077] Figure 2 Schematic diagram of the system structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The following describes the specific embodiments of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

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

[0080] Step S1: Based on 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 scheduling objective function of the power grid is constructed; then, based on the physical safety constraints of the power grid, such as power balance constraints and flow constraints, the constraint conditions of the model are established, and finally a multi-objective source-load scheduling model of the power grid is constructed.

[0081] In one embodiment, minimizing operating costs and carbon emissions is used as the objective function, and line flow constraints, unit output constraints, unit ramp constraints, and transferable load constraints are used as constraints to construct the following source-load scheduling model:

[0082] Objective function:

[0083]

[0084]

[0085] Among them, cost represents the operating cost, 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] Constraints:

[0087] 1. Line flow constraints:

[0088]

[0089]

[0090] Where, 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, It represents the wind turbine output interval variable at time t, recorded as , represents the node transferable load vector at time t.

[0091] 2. Unit output constraints:

[0092]

[0093] Where, Represents the generator output upper limit vector.

[0094] 3. Unit climbing constraints:

[0095]

[0096] Where, represents the downward climbing constraint, represents the upward climbing constraint, , r represents the climbing rate coefficient, .

[0097] 4. Transferable load constraints:

[0098]

[0099] Where, Indicates the total amount of load demand that can be transferred from the node. Except for , the rest are variables and parameters in classical mathematical optimization.

[0100] Step S2: Based on interval planning, the uncertainty of renewable energy is characterized in the form of uncertain intervals. Then, combined with the established multi-objective source-load scheduling model of the power grid, the optimistic Pareto frontier (optimistic problem) and the pessimistic Pareto frontier (pessimistic problem) of the multi-objective source-load coordinated scheduling problem are constructed.

[0101] In one embodiment, the above source-load scheduling model is further written as follows based on the optimistic Pareto front and the pessimistic Pareto front of interval linear programming:

[0102] The optimistic Pareto front can be expressed as follows:

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110] The meanings of variables and parameters have been described above, so I will not repeat them here.

[0111] Pessimistic Pareto front:

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120] Step S3: For the optimistic problem of interval optimization, the weight of the objective function is changed in equal steps, and the original multi-objective problem is converted into a series of single-objective optimization problems for solution. Then, the interval equality constraints and interval inequality constraints are converted into non-interval constraints, and the other non-interval constraints remain unchanged. After the above processing, a linear programming problem that is easy to solve under optimistic conditions is finally formed.

[0121] In one embodiment, for interval equality constraints, they are converted into non-interval inequality constraint pairs, that is, the interval equality constraints in the optimistic problem are converted into non-interval inequality constraints. Transformed into the following constraints:

[0122]

[0123]

[0124] For the interval inequality constraints, relax them to the most optimistic case, that is, the interval inequality constraints in the optimistic problem Relax the constraints as follows:

[0125]

[0126]

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

[0128]

[0129] Where, represents the objective function, represents a series of inequality constraints in the processed single-objective optimistic problem; represents a series of equality constraints in the processed single-objective optimistic problem;

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

[0131] Step S4: For the pessimistic problem of interval optimization, the weight of the objective function is changed in equal steps, transforming the original multi-objective problem into a series of single-objective optimization problems. The pessimistic problem that is difficult to solve is then transformed into a more tractable linear programming problem.

[0132] In one embodiment, after converting the multi-objective pessimistic problem into multiple single-objective pessimistic problems, the single-objective pessimistic problem is formulated as a two-level max-min optimization problem. The inner-level problem is converted into a set of constraints using the Karush-Kuhn-Tucker (KKT) condition, which is a set of necessary conditions in optimization theory used to solve nonlinear programming problems with equality and inequality constraints. This converts the two-level optimization problem into a single-level optimization problem, and ultimately into a standard MILP problem, which can be solved within a reasonable time. The specific steps include the following:

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

[0134]

[0135] Where, represents the objective function, Represents a series of inequality constraints for the scheduling model in step 4.1; Represents a series of equality constraints for the scheduling model in step 4.1; Represents the uncertainty, that is, the output of new energy in the scheduling model, 、 They represent the minimum and maximum values of the uncertainty respectively.

[0136] Step 4.2: Write down the KKT conditions for the inner problem:

[0137]

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

[0139]

[0140] Step 4.4: Use the Big M method to relax the complementary conditions Transformed into the following linear constraints with integer variables:

[0141]

[0142] Step 4.5: Finally, the original pessimistic problem that is difficult to solve is transformed into a standard mixed integer linear programming problem that is easy to solve. After solving the transformed multiple single-objective pessimistic problems, the obtained objective function values are fitted into a curve to obtain the upper bound of the multi-objective operating domain.

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

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

[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, and the processor is used to execute the computer-executable instructions or computer programs stored in the memory to implement the steps performed in the above-mentioned embodiment of the method for generating a multi-objective operating domain of a power grid based on interval optimization.

[0146] An embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions or a computer program. When the computer-executable instructions or the computer program are executed by a processor, the steps performed in the embodiment of the method for generating a multi-objective operating domain of a power grid 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 the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. However, 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 multi-objective operation domains of power grids based on interval optimization, characterized by The steps include: According to the actual operation requirements of the power grid, the objective function of the multi-objective source-load scheduling of the power grid is constructed. Then, based on the physical security constraints of the power grid, the constraint conditions of the model are established to construct the multi-objective source-load scheduling model of the power grid. Based on interval planning, the uncertainty of renewable energy in the power grid is characterized in the form of uncertain intervals. Combined with the multi-objective source-load scheduling model of the power grid, the optimistic and pessimistic problems of multi-objective source-load coordinated scheduling are constructed. In response to the optimistic problem of multi-objective source-load coordinated scheduling, the weights of the objective function of the multi-objective source-load scheduling of the power grid are changed in equal steps, the original multi-objective problem is converted into a series of single-objective optimization problems for solution, and then the interval equality constraints and interval inequality constraints therein are converted into non-interval constraints, ultimately forming a linear programming problem under the optimistic condition; the converted multiple single-objective optimistic problems are solved, and the solved objective function values are fitted into a curve to generate the lower bound of the multi-objective operation domain; In response to the pessimistic problem of the multi-objective source-load coordinated scheduling, the weights of the objective function of the multi-objective source-load scheduling of the power grid are changed in equal steps, the original multi-objective problem is converted into a series of single-objective optimization problems for solution, and the pessimistic problem is then converted into a linear programming problem; the converted multiple single-objective pessimistic problems are solved, and the solved objective function values are fitted into a curve to generate the upper bound of the multi-objective operation domain; The multi-objective operation domain of the power grid of the actual operation requirements of the power grid and the physical safety constraints of the power grid is obtained through the area surrounded by the lower bound of the multi-objective operation domain and the upper bound of the multi-objective operation domain.

2. 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 objective function of constructing a multi-objective source-load dispatching of a power grid based on the actual operation requirements of the power grid includes: minimizing the operation cost and minimizing the carbon emissions as the objective function: Among them, cost represents the operating cost, 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 constraint conditions for establishing the model based on the physical security constraints of the power grid include: using line flow constraints, unit output constraints, unit ramp constraints and transferable load constraints as constraint conditions: Line flow constraints: Where, 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 node load vector at time t, represents the wind turbine output interval variable at time t, represents the node transferable load vector at time t; Unit output constraints: Where, Represents the generator output upper limit vector; Unit climbing constraints: Where, represents the downward climbing constraint, represents the upward climbing constraint, , r represents the climbing rate coefficient, ; Transferable load constraints: Where, Indicates the total amount of load demand that can be transferred to 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 optimistic problem of constructing multi-objective source-load coordinated scheduling is the optimistic Pareto frontier of the multi-objective source-load coordinated scheduling problem, which is to convert the multi-objective source-load scheduling model of the power grid into an optimistic Pareto frontier form based on interval linear programming: Where, represents the generator output power at time t, c represents the generator cost coefficient, 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 wind turbine output interval variable at time t, represents the node load vector at time t, represents the node transferable load vector at time t, represents the upper limit vector of the generator output, represents the downward climbing constraint, represents the upward climbing constraint, , r represents the climbing rate coefficient, , represents the total amount of load demand that can be transferred from the node, 、 Respectively represent the maximum and minimum values of the fan processing range.

5. The method for generating a multi-objective operation domain of a power grid based on interval optimization according to claim 4, characterized in that: The conversion of the interval equality constraint into a non-interval constraint is to convert the interval equality constraint into a non-interval inequality constraint pair, that is, to convert the interval equality constraint in the optimistic problem into a non-interval inequality constraint pair. Transformed into the following constraints: The conversion of the interval inequality constraint into a non-interval constraint is to relax the interval inequality constraint to the most optimistic case, that is, to convert the interval inequality constraint in the optimistic problem into a non-interval constraint. Relax the constraints as follows: Remove interval constraints , we get the following representation of the single-objective optimistic problem: Where, represents the objective function, represents a series of inequality constraints in the processed single-objective optimistic problem; Represents a set of equality constraints in the processed single-objective optimistic problem.

6. 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 pessimistic problem of constructing multi-objective source-load coordinated scheduling is the pessimistic Pareto front of the multi-objective source-load coordinated scheduling problem, which is obtained by converting the multi-objective source-load scheduling model of the power grid into a pessimistic Pareto front form based on interval linear programming: Where, represents the generator output power at time t, c represents the generator cost coefficient, e represents the generator carbon dioxide emission coefficient, represents the wind turbine output interval variable at time t, 、 Respectively represent the maximum and minimum values of the fan processing range, 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 upper limit vector of the generator output, represents the total amount of load demand that can be transferred from the node, represents the downward climbing constraint, represents the upward climbing constraint, , r represents the climbing rate coefficient, .

7. The method for generating a multi-objective operation domain of a power grid based on interval optimization according to claim 6, characterized in that: The process of generating the upper bound of the multi-objective operating domain includes the following steps: After converting the multi-objective pessimistic problem into multiple single-objective pessimistic problems, the pessimistic solution of the interval optimization problem can be written as a max-min two-level optimization problem of the following form: Where, represents the objective function, represents a series of inequality constraints of the scheduling model, A series of equality constraints representing the scheduling model, Represents the uncertainty, that is, the output of new energy in the scheduling model, 、 Represent the minimum and maximum values of the uncertainty respectively; List the KKT conditions for the inner layer problem: Where, represents the gradient of the Lagrangian function, x represents the output power of the generator set, λ represents the Lagrangian multiplier of the equality constraint, μ represents the Lagrangian multiplier of the inequality constraint, represents the relaxation condition; The inner layer problem is converted into a set of constraints through KKT conditions, thereby converting the two-layer optimization problem into a single-layer optimization problem: Using the Big M method, the complementary relaxation condition Transformed 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; The pessimistic problem is transformed into a standard mixed integer linear programming problem, and multiple single-objective pessimistic problems after transformation are solved. After fitting the solved objective function values into a curve, the upper bound of the multi-objective operating domain is obtained.

8. A power grid multi-objective operation domain generation system based on interval optimization, including a power grid multi-objective source and load scheduling module, a problem construction module, and a multi-objective operation domain generation module, characterized by: According to the method for generating a multi-objective operation domain of a power grid based on interval optimization according to any one of claims 1 to 7, a multi-objective source-load scheduling model of the power grid, an optimistic problem and a pessimistic problem of multi-objective source-load coordinated scheduling are constructed in sequence through a multi-objective source-load scheduling module, a problem construction module, and a multi-objective operation domain generation module, and upper and lower bounds are obtained by solving the optimistic problem and the pessimistic problem to generate a multi-objective operation domain of the power grid.

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, and the processor is used to execute the computer-executable instructions or computer programs stored in the memory to implement the method for generating a multi-objective operating domain of a power grid based on interval optimization as described in any one of claims 1 to 7.

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

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