Wind and light storage capacity distribution robust optimization configuration method, device, equipment and medium

Abstract

The invention relates to the technical field of power grid side large-scale wind and light and energy storage configuration, in particular to a wind and light storage capacity distribution robust optimization configuration method, device and equipment and a medium. Establishing a power grid side wind and light energy storage optimization configuration model considering inertia constraint; uncertain factors in the optimal configuration model are considered, and a probability distribution fuzzy set based on a mixed norm is constructed; establishing a two-stage distribution robust optimization configuration model based on data driving; converting the two-stage distribution robust optimization configuration model into a mixed integer linear programming model by using a linearization technology; and solving the mixed integer linear programming model by using a column and number generation algorithm to obtain an optimal configuration and operation scheme of each device. According to the method, the economical efficiency and load fluctuation can be balanced, and the problems of frequency instability and the like of a large-scale wind and light energy grid-connected power grid are avoided by considering inertia constraint.

Description

Technical Field

[0001] The present invention relates to the technical field of large-scale wind-solar and energy storage configuration on the power grid side, and in particular to a method, device, equipment and medium for optimizing the configuration of wind-solar and energy storage capacity distribution rods. Background Art

[0002] With the construction of new power systems and the advancement of the "dual carbon" goals, new energy sources such as wind power and photovoltaics are developing rapidly. Although large-scale access to new energy will significantly reduce dependence on fossil fuels and promote the green transformation of the energy structure, it is also accompanied by many challenges, including the stability of the power system, scheduling flexibility, and the need for energy storage. Energy storage technology can not only cooperate with thermal power units in the power grid to achieve peak shaving and valley filling of the power system and increase the absorption rate of new energy, but also provide virtual inertia to stabilize the grid frequency and improve the stability of the grid frequency. Therefore, the reasonable optimization of the configuration of wind and solar storage capacity on the grid side is of great significance for fully tapping the wide-area temporal and spatial complementarity of wind power and photovoltaic resources and ensuring the safe access of new energy to the grid.

[0003] In the existing technology, the research on the optimal configuration of wind, solar and storage on the grid side mainly focuses on increasing the absorption rate of new energy in the grid or reducing the comprehensive operating cost of the multi-energy system by configuring energy storage. However, there is little research on how to solve the frequency stability problem brought to the grid after large-scale wind and solar grid connection by configuring energy storage, and the optimal wind, solar and storage configuration capacity of the grid under the condition of meeting the minimum inertia constraint.

[0004] Therefore, we should pay attention to the grid frequency stability issues brought about by the large-scale grid connection of new energy, consider the minimum inertia constraint of the system, and configure the wind power, photovoltaic and energy storage capacity. This kind of practical planning problem is the problem that needs to be solved urgently.

[0005] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a method, device, equipment and medium for optimizing the configuration of wind-solar-storage capacity distribution blue rods, thereby effectively solving the problems in the background technology.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a method for optimizing the configuration of wind-solar-storage capacity distribution blue rods, comprising the following steps:

[0008] Taking the lowest comprehensive cost as the objective function, considering the investment decision constraints and the operation constraints including the minimum inertia constraint of the system, a grid-side wind, solar and energy storage optimization configuration model considering the inertia constraint is established;

[0009] Considering the uncertain factors in the optimization configuration model, a probability distribution fuzzy set based on a mixed norm is constructed;

[0010] Establish a data-driven two-stage distributed robust optimization configuration model;

[0011] The two-stage distributed robust optimization configuration model is converted into a mixed integer linear programming model using a linearization technique;

[0012] The mixed integer linear programming model is solved using a column and number generation algorithm to obtain the optimal configuration and operation plan for each device.

[0013] Furthermore, the objective function includes:

[0014] ;

[0015] ;

[0016] ;

[0017] ;

[0018] ;

[0019] Where: , , They represent the daily operating cost, daily wind and solar power abandonment cost and daily average investment cost of thermal power units respectively; represents the number of typical day scenes; Indicates the number of time periods in each typical day, with one hour as one time period, and the number of time periods in each typical day is 24; Indicates the total number of conventional thermal power units; Indicates On a typical day, Fuel costs under Indicates On a typical day, Down Output power during the time period; Indicates On a typical day, Start-stop costs under Indicates On a typical day, The cost of deep peak-shaving state under ; Indicates the running status of the unit, 1 means running, 0 means shutdown; Indicates the typical day The probability of It represents the unit cost of wind and solar power abandonment; , Represents a typical day The scenery below The predicted output at the moment; , Represents a typical day The scenery below Actual output at the moment; Indicates the daily coefficient of investment cost; Indicates the capacity of the energy storage configuration; Indicates the maximum power of the energy storage configuration; and Represent the maximum power of wind power and photovoltaic configuration respectively; , , and They represent the unit cost of energy storage configuration capacity, the unit cost of energy storage configuration power, and the unit cost of wind power and photovoltaic configuration power respectively; Indicates the life cycle of the equipment.

[0020] Furthermore, the investment decision constraints include:

[0021] ;

[0022] Where: , , and They represent the wind turbine, photovoltaic unit, energy storage battery capacity, and energy storage battery power installation upper limit respectively. and Represent the maximum power of wind power and photovoltaic configuration respectively, Indicates the capacity of the energy storage configuration; Indicates the maximum power of the energy storage configuration.

[0023] Furthermore, the operation constraints include:

[0024] System minimum inertia constraints:

[0025] In order to ensure the system frequency stability requirement, the system inertia in each period should be greater than the system minimum inertia requirement, the expression is as follows:

[0026] ;

[0027] ;

[0028] Where: and Represents a typical day Down The system's current inertia and minimum inertia requirement for the time period; Indicates the total number of pumped storage units; , Respectively represent Rated power and inertia time constant of each synchronous unit; , Respectively represent Rated power and inertia time constant of each pumped storage unit; , They represent the rated power and inertia time constant of the energy storage device respectively; , Respectively represent the operating status of conventional thermal power units and pumped storage units, 1 represents operation, and 0 represents shutdown;

[0029] Constraints on thermal power units:

[0030] The upper and lower limits of thermal power unit output:

[0031] ;

[0032] Thermal power unit climbing constraints:

[0033] ;

[0034] Minimum start and stop time constraints for thermal power units:

[0035] ;

[0036] Where: , Represents conventional thermal power units The upper and lower limits of output; , Respectively represent The continuous operation time and continuous shutdown time of conventional thermal power plants; and Respectively represent The upward and downward climbing rates of conventional thermal power generation; Indicates the unit scheduling duration; , Respectively represent The minimum continuous operation time and minimum continuous shutdown time allowed for conventional thermal power plants;

[0037] Energy storage constraints:

[0038] Energy storage output upper and lower limits:

[0039] ;

[0040] Energy Storage constraint:

[0041] ;

[0042] Where: , Respectively represent the maximum discharge and charge power of energy storage; , Represents a typical day Down Energy storage, discharge and charging power at all times; Indicates the discharge state of the energy storage device. It is 1 when the energy storage device is discharging and 0 when it is not discharging. Indicates the charging status of the energy storage device. It is 1 when the energy storage device is charging and 0 when it is not charging. and Respectively represent the maximum and minimum values ​​of energy storage capacity; Indicates a typical day Down The remaining energy storage capacity at all times; , Indicates the charging efficiency and discharging efficiency of the energy storage device;

[0043] System constraints:

[0044] Node power balance constraints:

[0045] ;

[0046] DC power flow constraints:

[0047] ;

[0048] Phase Angle Constraint:

[0049] ;

[0050] Where: , Represents a typical day Down Pumped storage unit The discharge power and charging power of Indicates a typical day Next Node With Node The active power between Indicates a typical day Down Time Node The load size; Indicates a typical day Down Time period connection node and Active power of the line; Representation Node and nodes The admittance between , Represents a typical day Down Time period node and nodes The phase angle difference between , Represents a typical day Down Time period connection node and The lower and upper limits of the active power of the line; , Respectively represent nodes The lower and upper limits of the phase angle; Indicates a typical day Down Time Node The phase angle.

[0051] Furthermore, the consideration of uncertain factors in the optimization configuration model and the construction of a probability distribution fuzzy set based on a mixed norm include the following steps:

[0052] Assume that Historical scenes are obtained by K-means clustering method. discrete typical days, assuming that the initial probability distribution of all uncertain typical days is , then the first The initial probability of a typical day is ;

[0053] Considering the randomness of wind power and photovoltaic output, based on Norm and The norm establishes confidence sets under different confidence values, as shown in the following formula:

[0054] ;

[0055] Where: Fuzzy set representing the mixed probability distribution of typical daily force; Indicates The actual probability of a typical day; and Respectively Norm and The probability deviation limit of typical scenarios under norm constraints;

[0056] The probability of occurrence of typical daily power generation scenarios of wind power and photovoltaic power generation needs to satisfy the following formula:

[0057] ;

[0058] Where: represents the probability that the inequality in brackets is true; , Respectively represent the confidence that the two probability distribution inequalities hold; by definition , get:

[0059] ;

[0060] From the above formula, we can see that when the historical data scale is When increasing, The values ​​decrease as Increase to hour, and The value of will approach 0 infinitely.

[0061] Furthermore, the establishment of a two-stage distributed robust optimization configuration model based on data-driven includes:

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] In the formula, represents the decision variables in the first stage, including the configuration capacity of energy storage batteries and wind power and photovoltaic power generation units; is the set of investment decision variables for energy storage and wind and photovoltaic power; represents the average daily investment cost; It represents the expected value of the operating cost corresponding to the worst probability distribution in the probability distribution uncertainty set under a given wind, solar and storage planning scheme; represents the first clustering of historical scene data of load, wind power, photovoltaic power and hybrid pumped storage water. A typical day; Indicates Operational decision variables for a typical day; It represents the set of system operation decision variables under different typical days when the investment decision variables in the first stage are given, including the power generation of thermal power units, the power generation of wind power and photovoltaic power generation units, the power generation of energy storage batteries, etc.; Indicates the number of typical days; , , , , , , , , , They represent the corresponding coefficient matrices respectively.

[0068] Furthermore, the use of linearization technology to transform the two-stage distributed robust optimization configuration model into a mixed integer linear programming model includes:

[0069] The nonlinear constraint is converted into a linear constraint based on the McCormick envelope:

[0070] ;

[0071] ;

[0072] Where: and is the transformed linear variable; , They are respectively the lower and upper limits of the rated power of energy storage allowed to be installed.

[0073] Furthermore, the use of linearization technology to transform the two-stage distributed robust optimization configuration model into a mixed integer linear programming model also includes:

[0074] Decomposing the optimization configuration problem of the mixed integer linear programming model into two parts: a main problem and a sub-problem;

[0075] The main problem is to obtain the optimal solution that meets the conditions under a known finite bad probability distribution, which provides a lower bound value for the two-stage distributed robust capacity optimization model as follows:

[0076] ;

[0077] Where: is the number of iterations;

[0078] The sub-problem is to take a given first-stage variable In the case of , the worst probability distribution is found, which is then provided to the main problem for further iterative calculation. The subproblem provides an upper bound value for the following formula;

[0079] When a first-stage variable is given , we can get the following sub-questions:

[0080] ;

[0081] The meaning of sub-problem is given in the main problem On this basis, find the worst scenario probability distribution within the confidence interval, provide iterative operations for the main problem, and obtain the lower bound of the model;

[0082] The constraints of the inner and outer min-max optimization problems are unrelated and can be decomposed into two steps, as shown in the following formula:

[0083] ;

[0084] ;

[0085] Introduce two auxiliary variables , Respectively represent the probability relatively The positive and negative offset marks of , thus converting the absolute value constraint into a linear constraint equivalently, and we get:

[0086] .

[0087] Further, the using a column and number generation algorithm to solve the mixed integer linear programming model includes:

[0088] Yalmip is used to call the Gurobi solver, and the column and number generation C&CG algorithm is used to solve the mixed integer linear programming model to obtain the optimal configuration plan for each device.

[0089] The present invention also includes a wind-solar storage capacity distribution blue rod optimization configuration device, using the above method, the device includes:

[0090] The system modeling unit is used to establish a grid-side wind, solar and energy storage optimization configuration model taking inertia constraints into account, taking the lowest comprehensive cost as the objective function, considering investment decision constraints and operation constraints including system minimum inertia constraints;

[0091] A fuzzy set modeling unit, used to consider uncertain factors in the optimization configuration model and construct a probability distribution fuzzy set based on a mixed norm;

[0092] A distributed robust optimization model modeling unit, which is used to establish a two-stage distributed robust optimization configuration model based on data-driven;

[0093] A linearization unit, used for converting the two-stage distributed robust optimization configuration model into a mixed integer linear programming model using a linearization technique;

[0094] The solving unit is used to solve the mixed integer linear programming model using a column and number generation algorithm to obtain the optimal configuration and operation plan of each device.

[0095] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0096] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0097] The beneficial effects of the present invention are as follows: by establishing an objective function with the minimum comprehensive cost, establishing investment decision constraints and operating constraints including system minimum inertia constraints, establishing a grid-side wind, solar and energy storage optimization configuration model that takes inertia constraints into account; considering uncertain factors in the model, to ensure that the system has a certain robustness to uncertain factors, constructing a probability distribution fuzzy set based on a mixed norm; establishing a two-stage distributed robust optimization configuration model based on data-driven; using linearization technology to convert the system model into a mixed integer linear programming model; using the column and constraint generation (C&CG) algorithm to solve the mixed integer linear programming model, and obtaining the optimal configuration and operation plan of each device. The present invention can balance economy and load fluctuations, flexibly configure grid-side wind, solar and energy storage capacity, introduce energy storage equipment to absorb larger-scale wind power and photovoltaic resources, and the consideration of inertia constraints avoids problems such as frequency instability of the grid connected to the large-scale wind and solar energy grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0099] Figure 1 is a flow chart of the method in Example 1;

[0100] Figure 2 It is a structural schematic diagram of the device in Example 1;

[0101] Figure 3 is a schematic structural diagram of a power generation system in Example 2;

[0102] Figure 4 This is a flow chart for solving the two-stage distributed blue rod optimization configuration model in Example 2;

[0103] Figure 5It is a structural schematic diagram of the computer device of the present invention. DETAILED DESCRIPTION

[0104] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0105] Embodiment 1:

[0106] like Figure 1 As shown: A method for optimizing the configuration of wind-solar-solar storage capacity distribution rods includes the following steps:

[0107] Taking the lowest comprehensive cost as the objective function, considering the investment decision constraints and the operation constraints including the minimum inertia constraint of the system, a grid-side wind, solar and energy storage optimization configuration model considering the inertia constraint is established;

[0108] Considering the uncertain factors in the optimization configuration model, a probability distribution fuzzy set based on the mixed norm is constructed;

[0109] Establish a data-driven two-stage distributed robust optimization configuration model;

[0110] The two-stage distributed robust optimization model is transformed into a mixed integer linear programming model using linearization techniques.

[0111] The mixed integer linear programming model is solved using the column and number generation algorithm to obtain the optimal configuration and operation plan for each device.

[0112] By establishing an objective function with the minimum comprehensive cost, establishing investment decision constraints and operating constraints including the system minimum inertia constraints, a grid-side wind, solar and energy storage optimization configuration model considering inertia constraints is established; considering the uncertain factors in the model, to ensure that the system has a certain robustness to uncertain factors, a probability distribution fuzzy set based on a mixed norm is constructed; a two-stage distributed robust optimization configuration model based on data-driven is established; the system model is converted into a mixed integer linear programming model using linearization technology; the mixed integer linear programming model is solved using a column and constraint generation (C&CG) algorithm to obtain the optimal configuration and operation plan of each device. The present invention can balance economy and load fluctuations, flexibly configure grid-side wind, solar and energy storage capacity, introduce energy storage equipment to absorb larger-scale wind power and photovoltaic resources, and the consideration of inertia constraints avoids problems such as frequency instability of the grid when large-scale wind and solar energy is connected to the grid.

[0113] In this embodiment, the objective function includes:

[0114] ;

[0115] ;

[0116] ;

[0117] ;

[0118] ;

[0119] Where: , , They represent the daily operating cost, daily wind and solar power abandonment cost and daily average investment cost of thermal power units respectively; represents the number of typical day scenes; Indicates the number of time periods in each typical day, with one hour as one time period, and the number of time periods in each typical day is 24; Indicates the total number of conventional thermal power units; Indicates On a typical day, Fuel costs under Indicates On a typical day, Down Output power during the time period; Indicates On a typical day, Start-stop costs under Indicates On a typical day, The cost of deep peak-shaving state under ; Indicates the running status of the unit, 1 means running, 0 means shutdown; Indicates the typical day probability; It represents the unit cost of wind and solar power abandonment; , Represents a typical day The scenery below The predicted output at the moment; , Represents a typical day The scenery below Actual output at the moment; Indicates the daily coefficient of investment cost; Indicates the capacity of the energy storage configuration; Indicates the maximum power of the energy storage configuration; and Represent the maximum power of wind power and photovoltaic configuration respectively; , , and They represent the unit cost of energy storage configuration capacity, the unit cost of energy storage configuration power, and the unit cost of wind power and photovoltaic configuration power respectively; Indicates the life cycle of the equipment.

[0120] The comprehensive cost includes two parts: investment cost and operating cost. The goal is to minimize the sum of the average daily investment cost and the operating cost. The average daily investment cost includes the daily investment cost of wind power, photovoltaic power and energy storage, and the daily operating cost includes the fuel cost of the thermal power unit, the start-stop cost and the penalty cost caused by the abandonment of wind and solar power.

[0121] The constraints of the grid-side wind, photovoltaic and energy storage optimization configuration model considering inertia constraints mainly include the investment decision constraints of wind power, photovoltaic and energy storage, and the operation constraints including inertia constraints under given investment decisions.

[0122] Among them, investment decision constraints include:

[0123] ;

[0124] Where: , , and They represent the wind turbine, photovoltaic unit, energy storage battery capacity, and energy storage battery power installation upper limit respectively. and Represent the maximum power of wind power and photovoltaic configuration respectively, Indicates the capacity of the energy storage configuration; Indicates the maximum power of the energy storage configuration.

[0125] As a preference of the above embodiment, the operation constraint conditions include:

[0126] System minimum inertia constraints:

[0127] In order to ensure the system frequency stability requirement, the system inertia in each period should be greater than the system minimum inertia requirement, the expression is as follows:

[0128] ;

[0129] ;

[0130] Where: and Represents a typical day Down The system's current inertia and minimum inertia requirement for the time period; Indicates the total number of pumped storage units; , Respectively represent Rated power and inertia time constant of each synchronous unit; , Respectively represent Rated power and inertia time constant of each pumped storage unit; , They represent the rated power and inertia time constant of the energy storage device respectively; , Respectively represent the operating status of conventional thermal power units and pumped storage units, 1 represents operation, and 0 represents shutdown;

[0131] Constraints on thermal power units:

[0132] The upper and lower limits of thermal power unit output:

[0133] ;

[0134] Thermal power unit climbing constraints:

[0135] ;

[0136] Minimum start and stop time constraints for thermal power units:

[0137] ;

[0138] Where: , Represents conventional thermal power units The upper and lower limits of output; , Respectively represent The continuous operation time and continuous shutdown time of conventional thermal power plants; and Respectively represent The upward and downward climbing rates of conventional thermal power generation; Indicates the unit scheduling duration; , Respectively represent The minimum continuous operation time and minimum continuous shutdown time allowed for conventional thermal power plants;

[0139] Energy storage constraints:

[0140] Energy storage output upper and lower limits:

[0141] ;

[0142] Energy Storage constraint:

[0143] ;

[0144] Where: , Respectively represent the maximum discharge and charge power of energy storage; , Represents a typical day Down Energy storage, discharge and charging power at all times; Indicates the discharge state of the energy storage device. It is 1 when the energy storage device is discharging and 0 when it is not discharging. Indicates the charging status of the energy storage device. It is 1 when the energy storage device is charging and 0 when it is not charging. and Respectively represent the maximum and minimum values ​​of energy storage capacity; Indicates a typical day Down The remaining energy storage capacity at all times; , Indicates the charging efficiency and discharging efficiency of the energy storage device;

[0145] System constraints:

[0146] Node power balance constraints:

[0147] ;

[0148] DC power flow constraints:

[0149] ;

[0150] Phase Angle Constraint:

[0151] ;

[0152] Where: , Represents a typical day Down Pumped storage unit The discharge power and charging power of Indicates a typical day Next Node With Node The active power between Indicates a typical day Down Time Node The load size; Indicates a typical day Down Time period connection node and Active power of the line; Representation Node and nodes The admittance between , Represents a typical day Down Time period node and nodes The phase angle difference between , Represents a typical day Down Time period connection node and The lower and upper limits of the active power of the line; , Respectively represent nodes The lower and upper limits of the phase angle; Indicates a typical day Down Time Node The phase angle.

[0153] In this embodiment, considering the uncertain factors in the optimization configuration model, a probability distribution fuzzy set based on a mixed norm is constructed, including the following steps:

[0154] The wind power output and photovoltaic output in the system model are uncertain. Therefore, in order to ensure that the system has a certain robustness to uncertain factors, a mixed norm fuzzy set is used to describe the probability distribution uncertainty of wind power and photovoltaic output. Assume that Historical scenes are obtained by K-means clustering method. discrete typical days, assuming that the initial probability distribution of all uncertain typical days is , then the first The initial probability of a typical day is At the same time, due to the randomness of wind power and photovoltaic output, the actual probability distribution is different from the initial probability obtained based on historical data clustering.

[0155] Considering the randomness of wind power and photovoltaic output, based on Norm and The norm establishes confidence sets under different confidence values, as shown in the following formula:

[0156] ;

[0157] Where: Fuzzy set representing the mixed probability distribution of typical daily force; Indicates The actual probability of a typical day; and Respectively Norm and The probability deviation limit of a typical scenario under norm constraint, where the probability distribution approaches the actual probability distribution of wind power and photovoltaic output;

[0158] The probability of occurrence of typical daily power generation scenarios of wind power and photovoltaic power generation needs to satisfy the following formula:

[0159] ;

[0160] Where: represents the probability that the inequality in brackets is true; , Respectively represent the confidence that the two probability distribution inequalities hold; by definition , get:

[0161] ;

[0162] From the above formula, we can see that when the historical data scale is When increasing, The values ​​decrease as Increase to hour, and The value of will approach 0 infinitely.

[0163] Among them, a two-stage distributed robust optimization configuration model based on data-driven is established, including:

[0164] The first stage is an investment decision problem, which optimizes the configuration capacity of energy storage batteries, wind power, and photovoltaic generators; the second stage is an operation optimization problem, with the goal of minimizing the coal consumption cost, start-up and shutdown cost, and wind and solar power abandonment cost of thermal power in the power grid. Therefore, the matrix form of the data-driven two-stage distributed robust capacity optimization configuration model is as follows:

[0165] ;

[0166] ;

[0167] ;

[0168] ;

[0169] ;

[0170] In the formula, represents the decision variables in the first stage, including the configuration capacity of energy storage batteries and wind power and photovoltaic power generation units; is the set of investment decision variables for energy storage and wind and photovoltaic power; represents the average daily investment cost; It represents the expected value of the operating cost corresponding to the worst probability distribution in the probability distribution uncertainty set under a given wind, solar and storage planning scheme; represents the first clustering of historical scene data of load, wind power, photovoltaic power and hybrid pumped storage water. A typical day; Indicates Operational decision variables for a typical day; It represents the set of system operation decision variables under different typical days when the investment decision variables in the first stage are given, including the power generation of thermal power units, the power generation of wind power and photovoltaic power generation units, the power generation of energy storage batteries, etc.; Indicates the number of typical days; , , , , , , , , , They represent the corresponding coefficient matrices respectively.

[0171] The two-stage distributed robust optimization model is transformed into a mixed integer linear programming model using linearization techniques, including:

[0172] In the grid-side wind-solar energy storage optimization configuration model considering inertia constraints, the maximum charge and discharge power of the energy storage battery needs to be configured, and the maximum charge and discharge power is related to , These two continuous variables are related and are related to the running state 0-1 variable , After multiplication, a nonlinear constraint is formed.

[0173] Therefore, the nonlinear constraint is transformed into a linear constraint based on the McCormick envelope:

[0174] ;

[0175] ;

[0176] Where: and is the transformed linear variable; , They are respectively the lower and upper limits of the rated power of energy storage allowed to be installed.

[0177] In this embodiment, the two-stage distributed robust optimization configuration model is transformed into a mixed integer linear programming model using a linearization technique, which also includes:

[0178] Decompose the optimization configuration problem of the mixed integer linear programming model into two parts: the main problem and the sub-problem;

[0179] Since the two-stage distributed robust capacity optimization configuration problem is a multi-layer optimization problem and cannot be solved directly, it is decomposed into two parts: the main problem and the sub-problem, as shown in the following two equations, and then the C&CG algorithm is used to iteratively solve these two parts. The main problem is to obtain the optimal solution that meets the conditions under a known finite bad probability distribution, which provides a lower bound value for the two-stage distributed robust capacity optimization configuration model, as follows:

[0180] ;

[0181] Where: is the number of iterations;

[0182] The sub-problem is to take a given first-stage variable In the case of , the worst probability distribution is found, which is then provided to the main problem for further iterative calculation. The subproblem provides an upper bound value for the following formula;

[0183] When a first-stage variable is given , we can get the following sub-questions:

[0184] ;

[0185] The meaning of sub-problem is given in the main problem On this basis, find the worst scenario probability distribution within the confidence interval, provide iterative operations for the main problem, and obtain the lower bound of the model;

[0186] The constraints of the inner and outer min-max optimization problems are unrelated and can be decomposed into two steps, as shown in the following formula:

[0187] ;

[0188] ;

[0189] Introduce two auxiliary variables , Respectively represent the probability relatively The positive and negative offset marks of , thus converting the absolute value constraint into a linear constraint equivalently, and we get:

[0190] .

[0191] At this time, the sub-problem model can be transformed into a hybrid linear programming problem for rapid solution, thereby passing the optimal result to the upper-level main problem for iterative calculation.

[0192] In summary, the nonlinear constraints in the grid-side wind-solar-energy storage optimization configuration model considering inertia constraints are transformed into linear constraints, and the two-stage distributed robust optimization configuration model is also transformed into a linear model. Therefore, the optimization model is transformed into a mixed integer linear programming model.

[0193] Among them, the mixed integer linear programming model is solved using the column and number generation algorithm, including:

[0194] Yalmip is used to call the Gurobi solver, and the column and number generation C&CG algorithm is used to solve the mixed integer linear programming model to obtain the optimal configuration plan for each device.

[0195] like Figure 2 As shown, this embodiment also includes a wind-solar-storage capacity distribution blue rod optimization configuration device, using the above method, the device includes:

[0196] The system modeling unit is used to establish a grid-side wind, solar and energy storage optimization configuration model taking inertia constraints into account, taking the lowest comprehensive cost as the objective function, considering investment decision constraints and operation constraints including system minimum inertia constraints;

[0197] The fuzzy set modeling unit is used to consider the uncertain factors in the optimization configuration model and construct the probability distribution fuzzy set based on the mixed norm;

[0198] A distributed robust optimization model modeling unit, which is used to establish a two-stage distributed robust optimization configuration model based on data-driven;

[0199] A linearization unit, used to transform the two-stage distributed robust optimization configuration model into a mixed integer linear programming model using a linearization technique;

[0200] The solving unit is used to solve the mixed integer linear programming model using a column and number generation algorithm to obtain the optimal configuration and operation plan of each device.

[0201] Embodiment 2:

[0202] like Figure 3 As shown, the embodiment of the present invention includes a wind farm to be planned, a photovoltaic power station to be planned, and the regulating energy includes a thermal power plant, a hybrid pumped storage power station and energy storage equipment to be planned.

[0203] First, establish the objective function of minimizing the comprehensive cost, establish the investment decision constraints and the operation constraints including the system minimum inertia constraints, and establish the grid-side wind, solar and energy storage optimization configuration model considering the inertia constraints, including:

[0204] 1) Considering the inertia support capacity that energy storage equipment can provide, on the basis of meeting the operating requirements of the power system inertia constraints, the grid-side wind, photovoltaic and storage joint planning is carried out with the goal of minimizing the sum of the average daily investment cost and the operating cost. The average daily investment cost includes the daily investment cost of wind power, photovoltaic and energy storage, and the operating cost includes the fuel cost of thermal power units, start-up and shutdown costs, and penalty costs caused by wind and solar abandonment.

[0205] ;

[0206] ;

[0207] ;

[0208] ;

[0209] ;

[0210] Where: , , They represent the daily operating cost, daily wind and solar power abandonment cost and daily average investment cost of thermal power units respectively; represents the number of typical day scenes; Indicates the number of time periods in each typical day, with one hour as one time period, and the number of time periods in each typical day is 24; Indicates the total number of conventional thermal power units; Indicates On a typical day, Fuel costs under Indicates On a typical day, Down Output power during the time period; Indicates On a typical day, Start-stop costs under Indicates On a typical day, The cost of deep peak-shaving state under ; Indicates the unit operating status (1 means running, 0 means shutdown); Indicates the typical day probability; It represents the unit cost of wind and solar power abandonment; , Represents a typical day The scenery below The predicted output at the moment; , Represents a typical day The scenery below Actual output at the moment; Indicates the daily coefficient of investment cost; Indicates the capacity of the energy storage configuration; Indicates the maximum power of the energy storage configuration; and Represent the maximum power of wind power and photovoltaic configuration respectively; , , and They represent the unit cost of energy storage configuration capacity, the unit cost of energy storage configuration power, and the unit cost of wind power and photovoltaic configuration power respectively; Indicates the life cycle of the equipment.

[0211] 2) The constraints of the grid-side wind, photovoltaic and energy storage optimization configuration model considering inertia constraints mainly include the investment decision constraints of wind power, photovoltaic and energy storage, and the operation constraints including inertia constraints under given investment decisions.

[0212] Investment decision constraints include wind, solar and energy storage investment decision constraints.

[0213] Wind, solar and energy storage investment decision constraints:

[0214] ;

[0215] Where: , , and They respectively represent the installation upper limit of wind turbines, photovoltaic units, energy storage battery capacity, and energy storage battery power.

[0216] The operating constraints include system minimum inertia constraints, thermal power unit constraints, energy storage constraints and system constraints.

[0217] System minimum inertia constraints:

[0218] In order to ensure the system frequency stability requirement, the system inertia in each period should be greater than the system minimum inertia requirement, the expression is as follows:

[0219] ;

[0220] ;

[0221] Where: and Represents a typical day Down The system's current inertia and minimum inertia requirement for the time period; Indicates the total number of pumped storage units; , Respectively represent Rated power and inertia time constant of each synchronous unit; , Respectively represent Rated power and inertia time constant of each pumped storage unit; , They represent the rated power and inertia time constant of the energy storage device respectively; , They respectively represent the operating status of conventional thermal power units and pumped storage units (1 means operation, 0 means shutdown).

[0222] Constraints on thermal power units:

[0223] Output upper and lower limits of thermal power units:

[0224] ;

[0225] Thermal power unit climbing constraints:

[0226] ;

[0227] Minimum start and stop time constraints for thermal power units:

[0228] ;

[0229] Where: , Represents conventional thermal power units The upper and lower limits of output; , Respectively represent The continuous operation time and continuous shutdown time of conventional thermal power plants; and Respectively represent The upward and downward climbing rates of conventional thermal power generation; Indicates the unit scheduling duration; , Respectively represent The minimum continuous operation time and minimum continuous shutdown time allowed for conventional thermal power plants.

[0230] Energy storage constraints:

[0231] Energy storage output upper and lower limits:

[0232] ;

[0233] Energy Storage constraint:

[0234] ;

[0235] Where: , Respectively represent the maximum discharge and charge power of energy storage; , Represents a typical day Down Energy storage, discharge and charging power at all times; Indicates the discharge status of the energy storage device (1 when the energy storage device is discharging, and 0 when it is not discharging); Indicates the charging status of the energy storage device (1 when the energy storage device is charging, and 0 when it is not charging); and Respectively represent the maximum and minimum values ​​of energy storage capacity; Indicates a typical day Down The remaining energy storage capacity at all times; , Indicates the charging efficiency and discharging efficiency of the energy storage device.

[0236] System constraints:

[0237] Node power balance constraints:

[0238] ;

[0239] DC power flow constraints:

[0240] ;

[0241] Phase Angle Constraint:

[0242] ;

[0243] Where: , Represents a typical day Down Pumped storage unit The discharge power and charging power of Indicates a typical day Next Node With Node The active power between Indicates a typical day Down Time Node The load size; Indicates a typical day Down Time period connection node and Active power of the line; Representation Node and nodes The admittance between , Represents a typical day Down Time period node and nodes The phase angle difference between , Represents a typical day Down Time period connection node and The lower and upper limits of the active power of the line; , Respectively represent nodes The lower and upper limits of the phase angle; Indicates a typical day Down Time Node The phase angle.

[0244] Next, considering the uncertain factors in the model, a probability distribution fuzzy set based on the mixed norm is constructed, including:

[0245] The wind power output and photovoltaic output in the system model are uncertain. Therefore, in order to ensure that the system has a certain robustness to uncertain factors, a mixed norm fuzzy set is used to describe the probability distribution uncertainty of wind power and photovoltaic output. Assume that Historical scenes are obtained by K-means clustering method. discrete typical days, assuming that the initial probability distribution of all uncertain typical days is , then the first The initial probability of a typical day is At the same time, due to the randomness of wind power and photovoltaic output, the actual probability distribution is different from the initial probability obtained based on historical data clustering. Norm and The norm establishes confidence sets under different confidence values, as shown below:

[0246] ;

[0247] Where: Fuzzy set representing the mixed probability distribution of typical daily force; Indicates The actual probability of a typical day; and Respectively Norm and Probability deviation limit values ​​for typical scenarios under norm constraints.

[0248] The probability of occurrence of typical daily power generation scenarios of wind power and photovoltaic power generation needs to satisfy the following formula:

[0249] ;

[0250] Where: represents the probability that the inequality in brackets is true; , They represent the confidence that the two probability distribution inequalities hold. By definition , You can get:

[0251] ;

[0252] From the above formula, we can see that when the historical data scale is When increasing, The values ​​decrease, which shows that Increase to hour, and The value of will approach 0 infinitely, and the probability distribution at this time approaches the actual probability distribution of wind power and photovoltaic output.

[0253] Next, based on the grid-side wind-solar-energy storage optimization configuration model considering inertia constraints and the mixed probability distribution fuzzy set established above, a data-driven two-stage distributed robust optimization configuration model is established, including:

[0254] The first stage is an investment decision problem, which optimizes the configuration capacity of energy storage batteries, wind power, and photovoltaic generators; the second stage is an operation optimization problem, with the goal of minimizing the coal consumption cost, start-up and shutdown cost, and wind and solar power abandonment cost of thermal power in the power grid. Therefore, the matrix form of the data-driven two-stage distributed robust capacity optimization configuration model is as follows:

[0255] ;

[0256] ;

[0257] ;

[0258] ;

[0259] ;

[0260] Where: represents the decision variables in the first stage, including the configuration capacity of energy storage batteries and wind power and photovoltaic power generation units; is the set of investment decision variables for energy storage and wind and photovoltaic power; represents the average daily investment cost; It represents the expected value of the operating cost corresponding to the worst probability distribution in the probability distribution uncertainty set under a given wind, solar and storage planning scheme; represents the first clustering of historical scene data of load, wind power, photovoltaic power and hybrid pumped storage water. A typical day; Indicates Operational decision variables for a typical day; It represents the set of system operation decision variables under different typical days when the investment decision variables in the first stage are given, including the power generation of thermal power units, the power generation of wind power and photovoltaic power generation units, the power generation of energy storage batteries, etc.; Indicates the number of typical days; , , , , , , , , , They represent the corresponding coefficient matrices respectively.

[0261] Next, the model is transformed into a mixed integer linear programming model using linearization techniques, including:

[0262] 1) In the grid-side wind-solar energy storage optimization configuration model considering inertia constraints, the maximum charge and discharge power of the energy storage battery needs to be configured, and the maximum charge and discharge power is related to , These two continuous variables are related and are related to the running state 0-1 variable , After multiplication, a nonlinear constraint is formed. Therefore, the present invention converts the nonlinear constraint formula into a linear constraint formula based on the McCormick envelope.

[0263] ;

[0264] ;

[0265] Where: and is the transformed linear variable; , They are respectively the lower and upper limits of the rated power of energy storage allowed to be installed.

[0266] 2) Since the two-stage distributed robust capacity optimization configuration problem is a multi-layer optimization problem and cannot be solved directly, it is decomposed into two parts: the main problem and the sub-problem, as shown in the following two equations, and then the C&CG algorithm is used to iteratively solve these two parts. The main problem is to obtain the optimal solution that meets the conditions under a known finite bad probability distribution, which provides a lower bound value for the two-stage distributed robust capacity optimization configuration model, as follows:

[0267] ;

[0268] Where: is the number of iterations. The subproblem is to In the case of , the worst probability distribution is found, which is then provided to the main problem for further iterative calculation. The subproblem actually provides an upper bound for the following formula. , we can get the following sub-questions:

[0269] ;

[0270] The meaning of sub-problem is given in the main problem On this basis, we find the worst scenario probability distribution within the confidence interval, provide iterative operations for the main problem, and obtain the lower bound of the model. From the above formula, we can see that the constraints of the inner and outer min-max optimization problems are unrelated, so they can be decomposed into two steps, that is, the solution order is first inner and then outer, as shown in the following formula:

[0271] ;

[0272] ;

[0273] The objective functions in the above two equations have been transformed into linear forms. However, the mixed probability distribution fuzzy set contains absolute value constraints and needs further equivalent transformation. Therefore, two auxiliary variables are introduced: , Respectively represent the probability relatively The positive and negative offset marks of , thus converting the absolute value constraint into a linear constraint equivalently, and we get:

[0274] ;

[0275] At this time, the sub-problem model can be transformed into a hybrid linear programming problem for rapid solution, thereby passing the optimal result to the upper-level main problem for iterative calculation.

[0276] In summary, the nonlinear constraints in the grid-side wind-solar-energy storage optimization configuration model considering inertia constraints are transformed into linear constraints, and the two-stage distributed robust optimization configuration model is also transformed into a linear model. Therefore, the optimization model is transformed into a mixed integer linear programming model.

[0277] Finally, the C&CG algorithm is used to solve the problem and obtain the optimal configuration scheme for each device, including:

[0278] Yalmip is used to call the Gurobi solver, and the C&CG algorithm is used to solve the mixed integer linear programming model to obtain the optimal configuration solution for each device.

[0279] See also Figure 5 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0280] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0281] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0282] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0283] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0284] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0285] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0286] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0287] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0288] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0289] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for optimizing the configuration of wind, solar and storage capacity distribution blue bars, characterized in that: The steps include: Taking the lowest comprehensive cost as the objective function, considering the investment decision constraints and the operation constraints including the minimum inertia constraint of the system, a grid-side wind, solar and energy storage optimization configuration model considering the inertia constraint is established; Considering the uncertain factors in the optimization configuration model, a probability distribution fuzzy set based on a mixed norm is constructed; Establish a data-driven two-stage distributed robust optimization configuration model; The two-stage distributed robust optimization configuration model is converted into a mixed integer linear programming model using a linearization technique; The mixed integer linear programming model is solved using a column and number generation algorithm to obtain the optimal configuration and operation plan for each device.

2. The method for optimizing the configuration of wind, solar and storage capacity distribution blue bars according to claim 1 is characterized in that: The objective function includes: ； ； ； ； ； Where: , , They represent the daily operating cost, daily wind and solar power abandonment cost and daily average investment cost of thermal power units respectively; represents the number of typical day scenes; Indicates the number of time periods in each typical day, with one hour as one time period, and the number of time periods in each typical day is 24; Indicates the total number of conventional thermal power units; Indicates On a typical day, Fuel costs under Indicates On a typical day, Down Output power during the time period; Indicates On a typical day, Start-stop costs under Indicates On a typical day, The cost of deep peak-shaving state under ; Indicates the running status of the unit, 1 means running, 0 means shutdown; Indicates the typical day probability; It represents the unit cost of wind and solar power abandonment; , Represents a typical day The scenery below The predicted output at the moment; , Represents a typical day The scenery below Actual output at the moment; Indicates the daily coefficient of investment cost; Indicates the capacity of the energy storage configuration; Indicates the maximum power of the energy storage configuration; and Represent the maximum power of wind power and photovoltaic configuration respectively; , , and They represent the unit cost of energy storage configuration capacity, the unit cost of energy storage configuration power, and the unit cost of wind power and photovoltaic configuration power respectively; Indicates the life cycle of the equipment.

3. The method for optimizing the configuration of wind, solar and storage capacity distribution blue bars according to claim 1 is characterized in that: The investment decision constraints include: ； Where: , , and They represent the wind turbine, photovoltaic unit, energy storage battery capacity, and energy storage battery power installation upper limit respectively. and Represent the maximum power of wind power and photovoltaic configuration respectively, Indicates the capacity of the energy storage configuration; Indicates the maximum power of the energy storage configuration.

4. The method for optimizing the configuration of wind, solar and storage capacity distribution blue bars according to claim 1 is characterized in that: The operating constraints include: System minimum inertia constraints: In order to ensure the system frequency stability requirement, the system inertia in each period should be greater than the system minimum inertia requirement, the expression is as follows: ； ； Where: and Represents a typical day Down The system's current inertia and minimum inertia requirement for the time period; Indicates the total number of pumped storage units; , Respectively represent Rated power and inertia time constant of each synchronous unit; , Respectively represent Rated power and inertia time constant of each pumped storage unit; , They represent the rated power and inertia time constant of the energy storage device respectively; , Respectively represent the operating status of conventional thermal power units and pumped storage units, 1 represents operation, and 0 represents shutdown; Constraints on thermal power units: Output upper and lower limits of thermal power units: ； Thermal power unit climbing constraints: ； Minimum start and stop time constraints for thermal power units: ； Where: , Represents conventional thermal power units The upper and lower limits of output; , Respectively represent The continuous operation time and continuous shutdown time of conventional thermal power plants; and Respectively represent The upward and downward climbing rates of conventional thermal power generation; Indicates the unit scheduling duration; , Respectively represent The minimum continuous operation time and minimum continuous shutdown time allowed for conventional thermal power plants; Energy storage constraints: Energy storage output upper and lower limits: ； Energy Storage constraint: ； Where: , Respectively represent the maximum discharge and charge power of energy storage; , Represents a typical day Down Energy storage, discharge and charging power at all times; Indicates the discharge state of the energy storage device. It is 1 when the energy storage device is discharging and 0 when it is not discharging. Indicates the charging status of the energy storage device. It is 1 when the energy storage device is charging and 0 when it is not charging. and Respectively represent the maximum and minimum values ​​of energy storage capacity; Indicates a typical day Down The remaining energy storage capacity at all times; , Indicates the charging efficiency and discharging efficiency of the energy storage device; System constraints: Node power balance constraints: ； DC power flow constraints: ； Phase Angle Constraint: ； Where: , Represents a typical day Down Pumped storage unit The discharge power and charging power of Indicates a typical day Next Node With Node The active power between Indicates a typical day Down Time Node The load size; Indicates a typical day Down Time period connection node and Active power of the line; Representation Node and nodes The admittance between , Represents a typical day Down Time period node and nodes The phase angle difference between , Represents a typical day Down Time period connection node and The lower and upper limits of the active power of the line; , Respectively represent nodes The lower and upper limits of the phase angle; Indicates a typical day Down Time Node The phase angle.

5. The method for optimizing the configuration of wind, solar and storage capacity distribution blue bars according to claim 1, characterized in that: The method of considering the uncertain factors in the optimization configuration model and constructing a probability distribution fuzzy set based on a mixed norm comprises the following steps: Assume that Historical scenes are obtained by K-means clustering method. discrete typical days, assuming that the initial probability distribution of all uncertain typical days is , then the first The initial probability of a typical day is ; Considering the randomness of wind power and photovoltaic output, based on Norm and The norm establishes confidence sets under different confidence values, as shown in the following formula: ； Where: Fuzzy set representing the mixed probability distribution of typical daily force; Indicates The actual probability of a typical day; and Respectively Norm and The probability deviation limit of typical scenarios under norm constraints; The probability of occurrence of typical daily power generation scenarios of wind power and photovoltaic power generation needs to satisfy the following formula: ； Where: represents the probability that the inequality in brackets is true; , Respectively represent the confidence that the two probability distribution inequalities hold; by definition , get: ； From the above formula, we can see that when the historical data scale is When increasing, The values ​​decrease as Increase to hour, and The value of will approach 0 infinitely.

6. The method for optimizing the configuration of wind, solar and storage capacity distribution blue bars according to claim 1, characterized in that: The establishment of a two-stage distributed robust optimization configuration model based on data-driven includes: ； ； ； ； ； In the formula, represents the decision variables in the first stage, including the configuration capacity of energy storage batteries and wind power and photovoltaic power generation units; is the set of investment decision variables for energy storage and wind and photovoltaic power; represents the average daily investment cost; It represents the expected value of the operating cost corresponding to the worst probability distribution in the probability distribution uncertainty set under a given wind, solar and storage planning scheme; represents the first clustering of historical scene data of load, wind power, photovoltaic power and hybrid pumped storage water. A typical day; Indicates Operational decision variables for a typical day; It represents the set of system operation decision variables under different typical days when the investment decision variables in the first stage are given, including the power generation of thermal power units, the power generation of wind power and photovoltaic power generation units, the power generation of energy storage batteries, etc.; Indicates the number of typical days; , , , , , , , , , They represent the corresponding coefficient matrices respectively.

7. The method for optimizing the configuration of wind, solar and storage capacity distribution blue bars according to claim 6 is characterized in that: The method of using a linearization technique to transform the two-stage distributed robust optimization configuration model into a mixed integer linear programming model includes: The nonlinear constraint is converted into a linear constraint based on the McCormick envelope: ； ； Where: and is the transformed linear variable; , They are respectively the lower and upper limits of the rated power of energy storage allowed to be installed.

8. The method for optimizing the configuration of wind, solar and storage capacity distribution blue bars according to claim 7 is characterized in that: The method of using a linearization technique to transform the two-stage distributed robust optimization configuration model into a mixed integer linear programming model also includes: Decomposing the optimization configuration problem of the mixed integer linear programming model into two parts: a main problem and a sub-problem; The main problem is to obtain the optimal solution that meets the conditions under a known finite bad probability distribution, which provides a lower bound value for the two-stage distributed robust capacity optimization model as follows: ； Where: is the number of iterations; The sub-problem is to take a given first-stage variable In the case of , the worst probability distribution is found, which is then provided to the main problem for further iterative calculation. The subproblem provides an upper bound value for the following formula; When a first-stage variable is given , we can get the following sub-questions: ； The meaning of sub-problem is given in the main problem On this basis, find the worst scenario probability distribution within the confidence interval, provide iterative operations for the main problem, and obtain the lower bound of the model; The constraints of the inner and outer min-max optimization problems are unrelated and can be decomposed into two steps, as shown in the following formula: ； ； Introduce two auxiliary variables , Respectively represent the probability relatively The positive and negative offset marks of , thus converting the absolute value constraint into a linear constraint equivalently, and we get: 。 9. The method for optimizing the configuration of wind, solar and storage capacity distribution blue bars according to claim 1, characterized in that: The using a column and number generation algorithm to solve the mixed integer linear programming model includes: Yalmip is used to call the Gurobi solver, and the column and number generation C&CG algorithm is used to solve the mixed integer linear programming model to obtain the optimal configuration plan for each device.

10. A wind-solar-storage capacity distribution blue rod optimization configuration device, characterized in that: Using the method according to any one of claims 1 to 9, the device comprises: The system modeling unit is used to establish a grid-side wind, solar and energy storage optimization configuration model taking inertia constraints into account, taking the lowest comprehensive cost as the objective function, considering investment decision constraints and operation constraints including system minimum inertia constraints; A fuzzy set modeling unit, used to consider uncertain factors in the optimization configuration model and construct a probability distribution fuzzy set based on a mixed norm; A distributed robust optimization model modeling unit, which is used to establish a two-stage distributed robust optimization configuration model based on data-driven; A linearization unit, used for converting the two-stage distributed robust optimization configuration model into a mixed integer linear programming model using a linearization technique; The solving unit is used to solve the mixed integer linear programming model using a column and number generation algorithm to obtain the optimal configuration and operation plan of each device.

11. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

12. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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