An interval economic dispatch method for new energy power system based on security limit definition method

Abstract

The application provides a new energy power system interval economic dispatching method based on a security limit definition method, relates to the power grid dispatching technical field, and comprises the following steps: S1, constructing an interval economic dispatching model, including an objective function and constraint conditions; S2, expressing line power flow by using power distribution factors and node injection power; S3, solving interval power flow in the interval economic dispatching model to determine a power flow interval radius; and S4, converting the interval economic dispatching model into a deterministic model by using a security limit definition and solving a dispatching plan, so that the technical problem that an existing interval dispatching method cannot simultaneously satisfy system power balance and line power flow safety constraints when large-scale new energy is accessed is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and in particular to a new energy power system interval economic dispatching method based on a safety limit definition method. Background Art

[0002] In recent years, the large-scale integration of renewable energy has introduced significant uncertainty into power systems, further complicating the dynamic characteristics of power grids. Traditional economic dispatch is no longer applicable to these new power systems. Therefore, how to rationally allocate unit output under the uncertain participation of renewable energy units has become a key focus of current research in power system economic dispatch. Existing optimization methods for uncertainty management primarily include stochastic optimization, robust optimization, and interval optimization. Stochastic optimization solves problems by establishing probability distribution models. However, probabilistic models require extensive statistical data to determine the distribution of uncertain variables. This means that stochastic programming, when dealing with multiple uncertainties, requires a large number of scenarios. Robust optimization aims to find the optimal solution by satisfying variable constraints within an uncertainty interval, without requiring a probability distribution for the uncertain variables. However, robust optimization is prone to the curse of dimensionality when dealing with multiple scenarios. Both stochastic and robust optimization methods treat the time-varying uncertain variables as distributions that follow a certain rule, while interval optimization focuses solely on the boundary information of the uncertain variables, generating dispatch results based on interval descriptions. Existing interval scheduling methods cannot simultaneously meet system power balance and line flow security constraints when integrating large-scale renewable energy, posing the risk of unfeasible dispatch solutions. Summary of the Invention

[0003] The purpose of the present invention is to provide a new energy power system interval economic dispatch method based on the safety limit definition method to solve the technical problem that the interval dispatch method in the prior art cannot simultaneously meet the system power balance and line flow safety constraints when large-scale new energy is connected.

[0004] The present invention provides a new energy power system interval economic dispatch method based on the safety limit definition method, comprising the following steps: S1. constructing an interval economic dispatch model, including an objective function and constraints; S2. expressing the line flow using a power distribution factor and a node injection power; S3. solving the interval flow in the interval economic dispatch model and determining the flow interval radius; S4. converting the interval economic dispatch model into a deterministic model through the safety limit definition, and solving the dispatch plan.

[0005] Furthermore, the objective function in S1 is:

[0006] min(C(P G )+C(P FL )) (1)

[0007] Where, P G is the total output of the traditional unit during the dispatch cycle, C(PG ) is the power generation cost of traditional units, P FL is the total amount of flexible load in the dispatching period, C(P FL ) is the electricity cost of flexible load, specifically:

[0008]

[0009]

[0010] Where, T = 24h, that is, 24 periods in a day, S G is the set of generators, S FL is a set of flexible load nodes, P Gi,t is the planned output of the i-th generator in the t-th period, a, b, c are cost coefficients, p i,t is the electricity price of the load of the i-th node in the t-th period, Δt=1, P FLi,t is the total load of the i-th node in the t-th period, which can be expressed as:

[0011]

[0012] Where, For transferable loads, For a translatable load, To reduce the load.

[0013] Furthermore, the constraints in S1 include:

[0014] -Unit output constraints,

[0015] The planned output of the i-th unit in the t-th period satisfies:

[0016]

[0017] Where, and are the minimum and maximum planned output of the i-th unit respectively;

[0018] - Spare capacity constraints in extreme scenarios,

[0019] The spare capacity of the i-th unit satisfies:

[0020]

[0021]

[0022] Where, Δ P Gi 、 are the lower and upper reserve capacities of the i-th unit respectively;

[0023] - Considering the ramp constraints of the unit in extreme scenario conversion,

[0024] For the i-th traditional unit, considering the spare capacity, the unit output ramp rate from period t to period t+1 satisfies:

[0025]

[0026]

[0027] Where, are the downward climbing rate and upward climbing rate of the i-th unit respectively;

[0028] -Flexible load total amount constraint,

[0029] The transferable load of the i-th node in the t-th period satisfies:

[0030]

[0031] Where, is the maximum transferable load that can be borne by the i-th node in the t-th period;

[0032] The total amount of transferable load within the scheduling period of the i-th node satisfies:

[0033]

[0034] Where, P FLi,total is the total amount of transferable load that the i-th node can bear within the scheduling period;

[0035] The total amount of transferable load set by the system in the tth period satisfies:

[0036]

[0037] Where, P FLt,total The total amount of transferable load set for the system in the tth period;

[0038] - predicting power balance constraints for wind power scenarios,

[0039] In the prediction scenario, the line transmission power in the tth period satisfies:

[0040]

[0041] Where, P Wi，t are the upper and lower limits of the output of the i-th wind turbine in the t-th period respectively;

[0042] -Power balance constraints in extreme wind power scenarios,

[0043] In the extreme scenario, considering the reserve capacity of traditional units, the line transmission power in the tth period satisfies:

[0044]

[0045]

[0046] - Line interval flow constraints,

[0047] In the tth period, the power flow of the line section connecting the i-th node and the j-th node satisfies:

[0048]

[0049] Where, is the maximum transmission power from node i to node j.

[0050] Furthermore, the variables, objective functions and constraints of the interval economic dispatch model in S1 are expressed as vectors and functions:

[0051]

[0052] Where f(X,u) is the sum of power generation cost and flexible load electricity cost, [f min , f max ] is the sum of power generation cost and flexible load electricity cost, f min and f max are the lower and upper limits of f(X,u), respectively. X is the state variable representing the transmission power or phase angle, u is the control variable representing the planned output of the traditional unit, and u min and u max They represent the lower and upper limits of u respectively, A is the coefficient matrix of the traditional unit output, B is the coefficient matrix of the line flow, [C min , C max ] is the fluctuation range of wind power and load, C min and C max are the lower limit and upper limit of the interval respectively.

[0053] Furthermore, the calculation method for expressing the line power flow using the power distribution factor and the node injection power in S2 includes:

[0054] Line flow P ij Get the minimum value Maximum The conditions are:

[0055]

[0056]

[0057] Where n is the number of lines, and are the lower and upper limits of the injected power, respectively, D = [B'] -1 , B' represents the node admittance matrix of the reference node row;

[0058] The DC line power flow between node i and node j can be expressed as:

[0059] P ij =B ij θ ij (20)

[0060] Where B ij is the element in row i and column j of the admittance matrix, θ ij is the phase difference between the i-th and j-th nodes, and the maximum value of the line flow Minimum It can be expressed as:

[0061]

[0062]

[0063] Where, P s is the injected power,

[0064] Define the power distribution factor GGDF ki =B ij (D i-1,k-1 -D j-1,k-1 ),

[0065] The above line power flow can be specifically expressed as:

[0066]

[0067]

[0068] Where S N A collection of system nodes.

[0069] Furthermore, the calculation method of the interval tidal radius in S3 includes:

[0070] For the linear constraint AX+Bu=[C min ,C max ], suppose that when the control variable u takes the value u0, u0∈[u min ,u max ], the state variable x takes the value x0, and available:

[0071] A(X-x0)+B(u-u0)=[-ΔC,ΔC] (25)

[0072] Where, ΔC=(C max -C min ) / 2, since the A matrix is ​​a reversible matrix, through matrix operations we can get:

[0073] X-x0=[-|A -1 |ΔC,|A -1 |ΔC]-A -1 B(u-u0) (26)

[0074] The value range of the state variable X is [X min ,X max ], so X-x0=[-(X max -X min ) / 2,(X max -X min ) / 2], we can see that under any value of the control variable, the radius of the state variable remains unchanged, that is:

[0075] rad(X)=|A -1 |ΔC (27)

[0076] The midpoint of the interval of the line flow is taken to obtain its interval radius. The radius is subtracted from the set upper limit of the flow and the radius is added to the lower limit to meet the flow constraint. The interval radius of the flow of each line can be expressed as:

[0077]

[0078] Furthermore, the specific method of converting the interval scheduling model into a deterministic scheduling model by defining the safety limit in S4 includes:

[0079] According to the safety limit definition method, the line power flow constraint can be expressed as:

[0080]

[0081] Taking into account the positive and negative power distribution factors, the line power flow constraints can be expressed as a lower safety limit and an upper safety limit as follows:

[0082]

[0083] Where, For the set upper and lower limits of the line flow, the line flow interval constraints in the interval scheduling model are transformed into the above deterministic constraints through the definition of safety limits, so that the deterministic economic scheduling model after transformation is obtained as follows:

[0084]

[0085] The interval economic dispatch method for a new energy power system based on a security limit definition method provided by the application regards uncertain wind power output as an interval value, considers power balance and line flow constraints, and takes into account the response characteristics of demand-side transferable load, establishes an interval economic dispatch model, and solves the model by converting it into a deterministic model through the security limit definition method, thereby solving the technical problem in the prior art that the interval dispatch method cannot simultaneously satisfy system power balance and line flow safety constraints when large-scale new energy is connected, reducing dispatch cost, playing a role in peak load shifting of transferable load, and achieving good results in handling power balance. BRIEF DESCRIPTION OF DRAWINGS

[0086] In order to more clearly illustrate the technical solutions in the specific embodiments or the prior art, the drawings needed in the specific embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative effort.

[0087] Figure 1 is a flowchart of the interval economic dispatch method for a new energy power system based on a security limit definition provided by the embodiment;

[0088] Figure 2 is a total load curve before and after transferable load dispatching in each period provided by the embodiment;

[0089] Figure 3 is a comparison chart of total load curves in each period after interval optimization dispatching, robust optimization and interval optimization dispatching provided by the embodiment without considering limit scenarios;

[0090] Figure 4 is a line flow curve before and after interval optimization dispatching in the first period provided by the embodiment;

[0091] Figure 5 is a system power balance bar chart under the minimum limit scenario of total wind power output provided by the embodiment;

[0092] Figure 6 is a system power balance bar chart under the maximum limit scenario of total wind power output provided by the embodiment;

[0093] Figure 7 is a unit plan output and reserve capacity curve in the lowest load period provided by the embodiment;

[0094] Figure 8 is a unit plan output and reserve capacity curve in the highest load period provided by the embodiment. DETAILED DESCRIPTION

[0095] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort should fall into the protection scope of the present application.

[0096] The embodiment provides a new energy power system interval economic dispatching method based on a security limit definition method, and includes the following steps.

[0097] S1. Construct an interval economic dispatching model. Based on interval theory, the wind power output is regarded as an interval value, while considering power balance and line flow constraints, and considering the response characteristics of the demand side transferable load, an interval economic dispatching model is constructed, which is an uncertain optimization model. The objective function of the interval economic dispatching model is to minimize the traditional unit generation cost and the transferable load power cost.

[0098] The objective function can be expressed as:

[0099] min(C(P G )+C(P FL ))(1)

[0100] In the formula, P G is the total output in the traditional unit dispatching period, C(P G ) is the traditional unit generation cost, P FL is the total amount of transferable load in the dispatching period, C(P FL ) is the transferable load power cost, and specifically:

[0101]

[0102]

[0103] In the formula, T=24h, that is, 24 time periods in a day, S G is a set of generators, S FL is a set of transferable load nodes, P Gi,t is the planned output of the i-th generator at the t-th time period, a, b, and c are cost coefficients, p i,t is the electricity price of the i-th node at the t-th time period, Δt=1, and P FLi,t is the total load of the i-th node at the t-th time period, which can be expressed as:

[0104]

[0105] In the formula, P is the transferable load, which satisfies that the total load amount in a day remains unchanged. The load can be shifted to ensure that the total load remains unchanged throughout the day, but the load needs to be shifted according to time periods. In order to reduce the load, it can be reduced according to the actual situation within the constraints.

[0106] The constraints of the uncertainty optimization model include unit output constraints, reserve capacity constraints under extreme scenarios, unit ramping constraints considering extreme scenario conversions, total transferable load constraints, power balance constraints for predicted wind power scenarios, power balance constraints for extreme wind power scenarios, and line section power flow constraints. Specifically, they include:

[0107] 1) Unit output constraints

[0108] The planned output of the i-th unit in the t-th period satisfies:

[0109]

[0110] Where, and are the minimum and maximum planned output of the i-th unit respectively.

[0111] 2) Reserve capacity constraints in extreme scenarios

[0112] Wind power output volatility cannot be coordinated solely through load scheduling. Traditional units also need to provide spare capacity to cope with its uncertainty. The spare capacity of the i-th unit satisfies:

[0113]

[0114]

[0115] Where ΔP Gi 、 are the lower spare capacity and upper spare capacity of the i-th unit respectively.

[0116] 3) Considering the unit ramp constraints for extreme scenario conversion

[0117] For the i-th traditional unit, considering the spare capacity, the unit output ramp rate from period t to period t+1 satisfies:

[0118]

[0119]

[0120] Where, are the downward climbing rate and upward climbing rate of the i-th unit respectively.

[0121] 4) Constraints on the total amount of transferable load

[0122] The transferable load of the i-th node in the t-th period satisfies:

[0123]

[0124] Where, is the maximum transferable load that can be borne by the i-th node in the t-th period.

[0125] The total amount of transferable load within the scheduling period of the i-th node satisfies:

[0126]

[0127] Where, P FLi,total is the total amount of transferable load that the i-th node can bear within the scheduling period.

[0128] The total amount of transferable load set by the system in the tth period satisfies:

[0129]

[0130] Where, P FLt,total The total amount of transferable load set for the system in the tth period.

[0131] 5) Predicting power balance constraints in wind power scenarios

[0132] In the prediction scenario, the line transmission power in the tth period satisfies

[0133]

[0134] Where, P Wi，t are the upper and lower limits of the output of the i-th wind turbine in the t-th period respectively;

[0135] 6) Power balance constraints in extreme wind power scenarios

[0136] In the extreme scenario, considering the reserve capacity of traditional units, the line transmission power in the tth period should meet the following requirements:

[0137]

[0138]

[0139] 7) Line section power flow constraints

[0140] The power flow of the line section connecting the i-th node and the j-th node in the t-th period satisfy:

[0141]

[0142] Where, is the maximum transmission power from node i to node j. Since the wind power output is an interval value, is an interval value.

[0143] The variables, objective functions and constraints of the interval economic dispatch model are expressed as vectors and functions:

[0144]

[0145] Where f(X,u) is the sum of power generation cost and flexible load electricity cost, [f min , f max ] is the sum of power generation cost and flexible load electricity cost, f min and f max are the lower and upper limits of f(X,u), respectively. X is the state variable representing the transmission power or phase angle, u is the control variable representing the planned output of the traditional unit, and u min and u max They represent the lower and upper limits of u respectively, A is the coefficient matrix of the traditional unit output, B is the coefficient matrix of the line flow, [C min , C max ] is the fluctuation range of wind power and load, C min and C max are the lower limit and upper limit of the interval respectively.

[0146] S2. Calculate the power distribution factor GGDF based on the system data, and express the line power flow using the power distribution factor and the node injection power.

[0147] Line flow P ij Get the minimum value Maximum The conditions are:

[0148]

[0149]

[0150] Where n is the number of lines, and are the lower and upper limits of the injected power, respectively, D = [B'] -1 , assuming that node 1 is the reference node, B' represents the node admittance matrix of the reference node row.

[0151] The DC line power flow between node i and node j can be expressed as:

[0152] P ij =B ij θ ij (20)

[0153] Where B ijis the element in row i and column j of the admittance matrix, θ ij is the phase difference between the i-th and j-th nodes, then the maximum value of the line flow is Minimum It can be expressed as:

[0154]

[0155]

[0156] Where, P s is the injected power,

[0157] Define the power distribution factor GGDF ki =B ij (D i-1,k-1 -D j-1,k-1 ), the above line power flow can be specifically expressed as:

[0158]

[0159]

[0160] Where S N A collection of system nodes.

[0161] S3. Using the optimization scenario method, the interval uncertainty is regarded as a variable within the boundary, the pre-optimization interval flow of the interval flow constraint in the model is solved, and the flow interval radius is determined.

[0162] For the linear constraint AX+Bu=[C min ,C max ], suppose that when the control variable u takes the value u0, u0∈[u min ,u max ], the state variable x takes the value x0, and available:

[0163] A(X-x0)+B(u-u0)=[-ΔC,ΔC] (25)

[0164] Where, ΔC=(C max -C min ) / 2, since the A matrix is ​​a reversible matrix, through matrix operations we can get:

[0165] X-x0=[-|A -1 |ΔC,|A -1 |ΔC]-A -1 B(u-u0) (26)

[0166] The value range of the state variable X is [X min ,X max], so X-x0=[-(X max -X min ) / 2,(X max -X min ) / 2], we can see that under any value of the control variable, the radius of the state variable remains unchanged, that is:

[0167] rad(X)=|A -1 |ΔC (27)

[0168] Take the midpoint of the interval of the line flow to get its interval radius, subtract the radius from the set upper limit of the flow, and add the radius to the lower limit to meet the flow constraint. The interval radius of each line flow can be expressed as:

[0169]

[0170] S4. Convert the interval scheduling model into a deterministic model by defining safety limits. Form a coefficient matrix based on the objective function and constraints, and use the quadprog quadratic programming function to solve the scheduling plan.

[0171] According to the safety limit definition method, the line power flow constraint can be expressed as:

[0172]

[0173] Taking into account the positive and negative power distribution factors, the line power flow constraints can be expressed as a lower safety limit and an upper safety limit as follows:

[0174]

[0175] Where, For the set upper and lower limits of the line flow, the line flow interval constraints in the interval scheduling model are transformed into the above deterministic constraints through the definition of safety limits, so that the transformed deterministic economic scheduling model is obtained as follows:

[0176]

[0177] This example analyzes an IEEE 118-node system consisting of 169 transmission lines, 54 generators, 64 loads, and 9 transformers, with a system baseline of 100 MVA. Ten generators at nodes 10, 18, 26, 32, 61, 65, 74, 90, 104, and 111 are randomly selected as wind turbines. It is assumed that only the transferable load within the flexible load of residential users is considered for scheduling. The residential users within the transferable load are distributed across nodes with only loads. Assuming that the transferable load accounts for 10%, the load data for each time period is shown in Table 1. All data below are standard values.

[0178] Table 1 Total load and transferable load of IEEE118 node system in each period

[0179]

[0180] according to Figure 1 The process shown solves this example, using the membership function to divide the daily electricity consumption period and form an electricity price matrix. According to Table 1, the daily minimum load a = 29.1200 pu and the daily maximum load b = 44.8000 pu. Setting the confidence level to 0.7, the boundary loads for the off-peak and peak periods can be calculated as follows:

[0181] x l =b-(ba)×0.7=34.1376pu

[0182] x u =a+(ba)×0.7=40.2304pu

[0183] From the boundary load, we know that 1-7 is the off-peak period, 8 and 22-24 are the normal period, and 9-21 is the peak period. Assume that the electricity price of the residential user's transferable load during the normal period is 0.56 yuan / kWh. Assuming that the electricity price fluctuation ratio of the peak and off-peak period is +50% and -45%, the electricity price of the residential user during the off-peak and peak periods is:

[0184] p v =p0×(1-f v ) = 0.308 yuan / kWh

[0185] p p =p0×(1+f p ) = 0.840 yuan / kWh

[0186] The power distribution factor (GGDF) is calculated, and the optimized scenario method is used to solve the interval DC power flow before optimized scheduling. Assuming the upper and lower limits of the line power flow are ±6.0 pu, the radius of the line power flow can be obtained, thereby determining its safety margin. Based on the definition of the safety margin, the interval scheduling model is transformed into a deterministic quadratic programming model for solution. This method is compared with other scheduling methods shown in Table 2.

[0187] Table 2 Comparison of four scheduling methods

[0188]

[0189] The interval optimization method used in this embodiment reduces the operating cost of the system after considering the transferable load, and the power generation cost is reduced by 48.595 million yuan. The operating cost when using the interval optimization method of this embodiment is higher than the interval optimization and robust optimization that do not consider the power balance of extreme scenarios. The method provided by this embodiment can ensure the feasibility of the scheduling plan and improve the safety of system operation. The total system load in each period after scheduling by the four methods is as follows Figure 2 、 Figure 3 As shown in the figure, the shiftable loads involved in scheduling have the effect of shaving peak loads and filling valleys. A total of 18.77 pu of loads were shifted to the valley period, reducing the user's electricity bill by 93,300 yuan, which can improve their electricity satisfaction. In addition, the interval optimization method in this embodiment achieves the most reasonable distribution of total load during the scheduling cycle. Figure 2 The two curves show a consistent trend, which means that when only the transferable load is considered, the transferable load in the 24 time periods of a day is transferred to the period with low electricity price, that is, the valley period.

[0190] Taking the first period as an example, the upper and lower limits of the system line flow before and after interval optimization scheduling in this embodiment are as follows: Figure 4 Before optimization, the power flows on lines 93 and 177 in the first period exceeded the limits by 0.97 pu and 2.79 pu, respectively. Using the method proposed in this embodiment, the optimized line power flows meet the set constraints, and the economic dispatch of the power system is carried out while ensuring system security and stability.

[0191] In this embodiment, based on the interval optimization of the safety limit, there are two scenarios: the minimum total wind power output (extreme scenario 1) and the maximum total wind power output (extreme scenario 2). The power balance conditions satisfied by the unit reserve capacity scheduling results under the two extreme scenarios are as follows: Figure 5 、 Figure 6 As shown in the figure, the actual output and upper and lower limits of spare capacity of 44 traditional units after dispatching during the load valley period and load peak period are as follows: Figure 7 、 Figure 8 As shown in the figure. The optimized reserve capacity is set at the median of wind power fluctuations, or the planned output value. The optimization result satisfies the system power balance constraint. In the wind power limit scenario, the total upward reserve capacity of each unit during the lowest load period is 8.3600 pu, and the total downward reserve capacity is 11.7548 pu. During the highest load period, the total upward reserve capacity of each unit is 9.4037 pu, and the total downward reserve capacity is 9.4038 pu. Traditional units respond to wind power fluctuations by calling on reserve capacity. Traditional units can provide reserve capacity or reduce output when wind power fluctuates (deviations from the predicted value).

Claims

1. A new energy power system interval economic dispatch method based on safety limit definition, characterized by: The steps include: S1. Construct an interval economic dispatch model, including the objective function and constraints; S2. Express the line power flow using the power distribution factor and the node injection power; S3. Solve the interval flow in the interval economic dispatch model and determine the flow interval radius; S4. Convert the interval economic dispatch model into a deterministic model by defining a safety limit, and solve the dispatch plan; The objective function in S1 is: (1) Where, is the total output during the traditional unit dispatch cycle, is the power generation cost of traditional units, is the total amount of flexible load in the dispatching period, is the electricity cost of flexible load, specifically: (2) (3) Where, , that is, 24 periods of a day, is a collection of generators, is a set of flexible load nodes, For the The generator is in The planned output for each period, is the cost coefficient, For the The load of the node is The electricity price for each period, , For the The load of the node is The total load in each period can be expressed as: (4) Where, For transferable loads, For a translatable load, To reduce the load; The calculation method of the interval tidal radius in S3 includes: For linear constraints , assuming that the control variable The value is , When the state variable The value of ,and ,available: (25) Where, ,because The matrix is ​​a reversible matrix, and can be obtained through matrix operations: (26) State variables The value range is ,so , we can see that under any value of the control variable, the radius of the state variable remains unchanged, that is: (27) The midpoint of the interval of the line flow is taken to obtain its interval radius. The radius is subtracted from the set upper limit of the flow and the radius is added to the lower limit to meet the flow constraint. The interval radius of the flow of each line can be expressed as: (28)。 2. The interval economic dispatch method according to claim 1, characterized in that: The constraints in S1 include: -Unit output constraints, No. Period Planned output of units P Gi,t satisfy: (5) Where, and Respectively The minimum and maximum planned output of each unit; - Spare capacity constraints in extreme scenarios, No. Spare capacity of units P Gi satisfy: (6) (7) Where, 、 Respectively The lower and upper reserve capacity of each unit; - Considering the ramp constraints of the unit in extreme scenario conversion, For the Traditional units, considering spare capacity, Time period is up The unit output ramp rate during the period meets the following requirements: (8) (9) Where, 、 Respectively The downward and upward climbing rates of the units; -Flexible load total amount constraint, No. Period The transferable load of each node satisfies: (10) Where, For the Period The maximum transferable load that can be borne by a node; No. The total amount of transferable load within a node scheduling cycle satisfies: (11) Where, For the The total amount of transferable load that a node can bear within the scheduling cycle; No. The total amount of transferable load set by the system in each time period meets the following requirements: (12) Where, For the The total amount of transferable load set by the system for each time period; - predicting power balance constraints for wind power scenarios, In the prediction scenario, The line transmission power in each period satisfies: (13) Where, 、 Respectively Wind turbines The upper and lower limits of output for each time period; -Power balance constraints in extreme wind power scenarios, In extreme scenarios, considering the reserve capacity of traditional units, The line transmission power in each period satisfies: (14) (15) - Line interval flow constraints, No. Time period, connect nodes and Line interval power flow of nodes satisfy: (16) Where, For nodes To Node The maximum transmission power.

3. The interval economic dispatch method according to claim 1, characterized in that: The variables, objective functions and constraints of the interval economic dispatch model in S1 are expressed as vectors and functions: (17) Where, is the sum of power generation cost and flexible load electricity cost, [ ] is the sum of power generation cost and flexible load electricity cost, f min and f max They are The lower and upper limits of is the state variable representing the transmission power or phase angle, is the control variable representing the planned output of the traditional unit, u min and u max Respectively u The lower and upper limits of is the coefficient matrix of traditional unit output, is the coefficient matrix of the line power flow, ] is the fluctuation range of wind power and load, C min and C max are the lower limit and upper limit of the interval respectively.

4. The interval economic dispatch method according to claim 1, characterized in that: The calculation method for expressing the line power flow using the power distribution factor and the node injection power in S2 includes: Line flow Get the minimum value , maximum value The conditions are: (18) (19) Where, is the number of lines, and are the lower and upper limits of the injected power, , represents the nodal admittance matrix of the reference node row; node With node DC line power flow between P ij It can be expressed as: (20) Where, is the first Rank Elements of the column, For the The first and Phase difference of each node, maximum value of line flow , minimum value It can be expressed as: (21) (22) Where, P s is the injected power, Defining Power Distribution Factors , The above line power flow can be specifically expressed as: (23) (24) Where, A collection of system nodes.

5. The interval economic dispatch method according to claim 1, characterized in that: The specific method of converting the interval scheduling model into a deterministic scheduling model by defining the safety limit in S4 includes: According to the safety limit definition method, the line power flow constraint can be expressed as: (29) Taking into account the positive and negative power distribution factors, the line power flow constraints can be expressed as a lower safety limit and an upper safety limit as follows: (30) Where, 、 For the set upper and lower limits of the line flow, the line flow interval constraints in the interval scheduling model are transformed into the above deterministic constraints through the definition of safety limits, so that the transformed deterministic economic scheduling model is obtained as follows: (31)。

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