Receiving end power grid multi-source collaborative optimization method based on information gap decision theory

Abstract

The invention discloses a receiving-end power grid multi-source collaborative optimization method based on an information gap decision theory, and relates to the technical field of power system dispatching. The method comprises the following steps: establishing a multi-source collaborative optimization scheduling model of a receiving-end power grid by taking the minimum operation cost of the receiving-end power grid as a target function; establishing an implicit uncertainty model of an information gap decision theory by taking maximized adverse disturbance of uncertain parameters of wind power output and load demand as a target; an implicit uncertainty model is used as an upper objective function, a multi-source collaborative optimization scheduling model is used as a lower objective function, and an information gap decision theory model considering wind power output and load uncertainty is established; and solving the information gap decision theory model to obtain a receiving-end power grid multi-source cooperative scheduling scheme. According to the method, the receiving end network multi-source collaborative optimization model considering wind power output and load uncertainty is provided based on the information gap decision theory, it is guaranteed that decision takes economical efficiency and robustness into consideration, and the power receiving capacity and the consumption capacity of a receiving end power grid are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a multi-source collaborative optimization method for a receiving-end power grid based on information gap decision theory. Background Art

[0002] With the continuous advancement of the "dual carbon" goals, the installed capacity of clean energy has been continuously increasing across the country, bringing major challenges to the safe operation of the power grid and the consumption of new energy. At the same time, new energy and large hydropower bases are usually far away from the load center. The increasing proportion of DC transmission equipment and new energy systems, and the input of energy through DC lines, are likely to cause "hollowing out" problems in the receiving-end power grid, making the voltage stability problem of the receiving-end system increasingly serious.

[0003] To improve the power receiving capacity of the power receiving system, the key is to achieve multi-source coordinated optimization dispatching in the receiving power grid, and it is necessary to comprehensively consider the positive role of thermal power units, cascade hydropower units, wind power units, energy storage equipment and DC input lines in the receiving power grid in optimizing the dispatching operation of the receiving power grid. However, there are many uncertain factors in wind power output and load, which affect the normal and stable operation of the system.

[0004] Therefore, how to consider the uncertainty of renewable energy output and load demand within the receiving network system, achieve multi-energy coordination, improve the power receiving capacity of the power receiving system, and promote the consumption of renewable energy is an urgent problem that the receiving network needs to solve. Summary of the invention

[0005] The present invention provides a receiving-end power grid multi-source collaborative optimization method based on information gap decision theory to solve the problem of how to improve the power receiving capacity of the receiving-end network under the synergistic effect of multiple sources and in an environment of new energy and load uncertainty.

[0006] The present invention is achieved through the following technical solutions:

[0007] A first aspect of the present invention provides a multi-source collaborative optimization method for a receiving-end power grid based on information gap decision theory, comprising:

[0008] Taking the minimization of the operation cost of the receiving-end power grid as the objective function, a multi-source collaborative optimization scheduling model of the receiving-end power grid is established;

[0009] Taking the maximization of adverse disturbances of uncertain parameters of wind power output and load demand as the goal, an uncertainty model of information gap decision theory is established;

[0010] Taking the uncertainty model as the upper objective function and the multi-source collaborative optimization scheduling model as the lower objective function, an information gap decision theory model considering the uncertainty of wind power output and load is established;

[0011] The information gap decision theory model is solved to obtain a multi-source coordinated scheduling solution for the receiving-end power grid.

[0012] The present invention takes into account the extreme cases of uncertainty in wind power output and load demand, takes ensuring that the total operating cost of the receiving network is less than the expected value and avoiding the risks brought by uncertainty as the decision-making goal, establishes a two-layer optimization model based on information gap decision theory, and constrains the operating cost model with wind power output and load uncertainty, thereby ensuring that the decision takes into account both economy and robustness, improving the power receiving capacity of the receiving power grid, and playing a positive role in achieving multi-energy synergy and improving the capacity to absorb new energy.

[0013] Furthermore, the objective function of minimizing the operating cost of the receiving-end power grid is:

[0014]

[0015] Among them, min is the minimization function, t is the time period number, g represents the thermal power unit, w represents the wind power unit, e is the segment number of the coal consumption cost curve, c is the ge represents the incremental coal consumption of thermal power unit g in the eth section, P get represents the power generation of thermal power unit g in the eth period during period t, They represent the fuel consumption of starting and stopping the thermal power unit g during period t, C fuel represents the unit price of fuel, C w represents the unit wind curtailment penalty cost, It represents the wind abandonment amount of wind turbine w in period t.

[0016] Furthermore, the constraints of the multi-source collaborative optimization scheduling model include: branch flow constraints, node balance constraints, thermal power unit operation constraints, cascade hydropower unit operation constraints, wind turbine unit operation constraints, energy storage equipment constraints, DC transmission line constraints and SVC operation constraints.

[0017] Furthermore, before solving the information gap decision theory model, the operation constraints of the cascade hydropower units are linearized by dividing the power generation flow and the reservoir capacity into a plurality of continuous power generation flow intervals and reservoir capacity intervals respectively, dividing the plane projection composed of the power generation capacity and the reservoir capacity into (m-1)×(n-1)-dimensional grids, wherein m-1 is the number of power generation flow intervals, n-1 is the number of reservoir capacity intervals, m and n are integers greater than or equal to 2, and each grid is divided into an upper left triangle and a lower right triangle along the diagonal line. The operation constraints of the cascade hydropower units are represented by the current flow interval, the reservoir capacity interval and the triangular grid, and the linearized operation constraints of the cascade hydropower units are obtained, which are represented as follows:

[0018] P=ηL(H0+αE)

[0019]

[0020] φ ij ≤λ i,j+1 +λ i-1,j +λ ij +μ i+1,j +μ i,j-1 +μ ij

[0021]

[0022]

[0023] Where P represents the power generation capacity of the hydropower station, p ij Indicates l i With e j The value of the power generation capacity of the lower hydropower station;

[0024] η represents the hydropower conversion efficiency of the hydropower station, L represents the power generation flow, E represents the reservoir capacity, l i represents the lower limit of the ith flow interval, e j represents the lower limit of the j-th reservoir capacity interval, H0 represents the initial water head of the hydropower station, and α represents the water head coefficient of the hydropower station;

[0025] λ ij is a 0-1 variable, indicating whether the lower right triangle of the grid numbered (i, j+1) of the plane projection composed of power generation flow and storage capacity is obtained. Similarly, λ i-1,j Indicates whether the lower left triangle of the grid numbered (i-1, j) is obtained, λ i,j+1 Indicates whether the upper right triangle of the grid numbered (i, j+1) is obtained; μ ij is a 0-1 variable, indicating whether the upper left triangle of the grid numbered (i, j+1) is obtained. Similarly, μ i+1,j Indicates whether the upper right triangle of the grid numbered (i+1,j) is obtained, μ i,j-1 Indicates whether the lower left triangle of the grid numbered (i, j-1) is obtained; φ ij is the introduced continuous auxiliary variable.

[0026] Furthermore, before solving the information gap decision theory model, the branch power flow constraint is linearized by: according to the actual situation of the transmission network, the phase angle difference θ between the nodes is set ij,t The value satisfies sinθ ij,t ≈θ ij,t , each node voltage is close to the rated voltage, and define v ij,t =v it -v jt ,Will and Considered as independent variables, the linearized constraint forms of active power flow and reactive power flow are obtained, which are expressed as:

[0027]

[0028] Among them, P ij,t represents the active power flow on the transmission line (i, j) during period t, Q ij,t represents the reactive power flow on the transmission line (i, j) during period t;

[0029] v it represents the voltage amplitude of node i during period t, v jt represents the voltage amplitude of node j at time period t, v ij,t represents the voltage difference between node i and node j during period t;

[0030] g ij represents the real part of the corresponding line (i, j) in the admittance matrix, b ij represents the imaginary part of the admittance matrix corresponding to line (i, j), θ ij,t It is expressed as the phase angle difference between nodes i and j during period t;

[0031] According to the node voltage amplitude and voltage phase angle under the reference operating state, the first-order Taylor series expansion of the active power loss and reactive power loss is performed to obtain the linearized constraint form of the active power loss and reactive power loss, which is expressed as:

[0032]

[0033]

[0034] in, represents the active power loss on line (i, j) during period t, represents the reactive power loss on line (i, j) during period t;

[0035] g ij represents the real part of the corresponding line (i, j) in the admittance matrix, b ij represents the imaginary part of the admittance matrix corresponding to line (i, j), θ ij,t represents the phase difference between nodes i and j during period t, v it represents the voltage amplitude of node i during period t, v jt represents the voltage amplitude of node j at time period t;

[0036] represents the reactive power flow on the transmission line (i, j) during period t under the reference operating state, It represents the voltage amplitude of node i in period t under the reference operation state, It represents the voltage amplitude of node j in period t under the reference operating state.

[0037] Furthermore, the abstract form of the deterministic model is expressed as:

[0038] min a T x+b T y+c T z

[0039] stx∈{0,1}

[0040] Ax+By+Dz≤d

[0041] Among them, a, b, c, d, A, B, and D are abstract matrix forms and vectors, representing the coefficients of the objective function and constraints. x is a binary variable, referring to the start and stop of the unit, the power adjustment of the DC line, the linearization auxiliary variable of the hydropower conversion constraint, and the charging and discharging strategy of the energy storage equipment. y is a continuous variable, including the active and reactive output of the unit, the reservoir capacity of the hydropower station, the DC input power, the charging and discharging power of the energy storage, the energy storage capacity, the SVC reactive support power, the voltage, the phase angle, and the model linearization auxiliary variable. z represents the wind abandonment variable. The first row of constraints limits the value range of the variable x. The second row of constraints is the overall operation constraints of the system, including power flow constraints, node balance constraints, output constraints of each unit, energy storage equipment constraints, DC transmission line constraints, and SVC operation constraints.

[0042] The implicit uncertainty model is expressed as:

[0043] maxβ

[0044] stmax a T x+b T y+c T z≤(1+ω)C0

[0045] x∈{0,1}

[0046] Ax+By+Dz≤d

[0047]

[0048] Among them, β represents the uncertainty of wind power output and load demand, the formula after st is the constraint condition, a, b, c, d, A, B, D are abstract matrix forms and vectors, representing the coefficients of the objective function and constraint conditions, x is a binary variable, referring to the start and stop of the unit, DC line power adjustment, hydropower conversion constraint linearization auxiliary variable, energy storage equipment charging and discharging strategy, y is a continuous variable, including unit active and reactive output, hydropower station storage capacity, DC input power, energy storage charging and discharging power, energy storage capacity, SVC reactive support power, voltage, phase angle, model linearization auxiliary variable, z represents the wind abandonment variable, C0 is the system cost obtained under the deterministic model, ω is the deviation coefficient of the system cost, are the predicted values ​​of some variables; the first row of constraints limits the total cost of system operation under uncertainty and does not exceed the deviation coefficient limit; the second row of constraints limits the value range of variable x; the second row of constraints are the overall operation constraints of the system, including power flow constraints, node balance constraints, output constraints of each unit, energy storage equipment constraints, DC transmission line constraints and SVC operation constraints; the fourth row of constraints describes the implicit restrictions of variables under uncertainty.

[0049] Furthermore, before solving the information gap decision theory model, the upper objective function and the lower objective function are combined into a single-layer model. Since the uncertainty of load and wind power is considered, the implicit uncertainty variable constraint can be converted into an explicit expression, which is specifically expressed as follows:

[0050] maxβ

[0051] sta T x+b T y+c T z≤(1+ω)C0

[0052] x∈{0,1}

[0053] Ax+By+Dz≤d

[0054]

[0055] Among them, β represents the uncertainty of wind power output and load demand, the formula after st is the constraint condition, a, b, c, d, A, B, D are abstract matrix forms and vectors, representing the coefficients of the objective function and constraint conditions, x is a binary variable, referring to the start and stop of the unit, DC line power adjustment, hydropower conversion constraint linearization auxiliary variable, energy storage equipment charging and discharging strategy, y is a continuous variable, including unit active and reactive output, hydropower station storage capacity, DC input power, energy storage charging and discharging power, energy storage capacity, SVC reactive support power, voltage, phase angle, model linearization auxiliary variable, z represents the wind abandonment variable, C0 is the system cost obtained under the deterministic model, ω is the deviation coefficient of the system cost; E and F are operation matrices; Predict values ​​for some variables.

[0056] A second aspect of the present invention provides a receiving-end power grid multi-source collaborative optimization device based on information gap decision theory, comprising:

[0057] The first modeling module is used to establish a multi-source collaborative optimization scheduling model of the receiving-end power grid with the minimization of the operating cost of the receiving-end power grid as the objective function;

[0058] The second modeling module is used to establish an implicit uncertainty model of information gap decision theory with the goal of maximizing adverse disturbances of uncertain parameters of wind power output and load demand;

[0059] A third modeling module is used to establish an information gap decision theory model considering wind power output and load uncertainty by using the implicit uncertainty model as an upper objective function and the multi-source collaborative optimization scheduling model as a lower objective function;

[0060] The optimization solution module is used to solve the information gap decision theory model to obtain a multi-source coordinated scheduling solution for the receiving-end power grid.

[0061] According to a third aspect of the present invention, an electronic device is provided, comprising 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 multi-source collaborative optimization method for a receiving-end power grid based on information gap decision theory as described in any one of the first aspects of the present invention is implemented.

[0062] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the receiving-end power grid multi-source collaborative optimization method based on information gap decision theory as described in any one of the first aspects of the present invention is implemented.

[0063] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0064] This multi-source collaborative optimization of the receiving power grid that takes into account the uncertainty of renewable energy output and load demand can better balance economy and robustness, effectively realize the multi-source collaborative optimization of the system, and promote the consumption of renewable energy.

[0065] Comprehensively consider the positive role of thermal power units, cascade hydropower units, wind turbines, energy storage equipment and DC input lines in the receiving power grid in optimizing the dispatching and operation of the receiving power grid, reduce wind abandonment in the receiving system, and improve the absorption capacity of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0067] Figure 1 It is a flow chart of a multi-source collaborative optimization method of a receiving-end power grid based on information gap decision theory in an embodiment of the present invention;

[0068] Figure 2 It is a schematic diagram of projection grid numbering of a power generation flow and a storage capacity plane according to an embodiment of the present invention;

[0069] Figure 3 is a system schematic diagram of a receiving-end power grid according to an embodiment of the present invention;

[0070] Figure 4 It is an output diagram of cascade hydropower and thermal power units of a receiving-end system according to an embodiment of the present invention;

[0071] Figure 5 Schematic diagram of wind abandonment of a receiving-end power grid system according to an embodiment of the present invention;

[0072] Figure 6 It is a schematic diagram of energy storage charging and discharging conditions of a receiving-end power grid system according to an embodiment of the present invention;

[0073] Figure 7 This is a sensitivity analysis diagram of the number of DC input line adjustments of a receiving-end power grid system according to an embodiment of the present invention;

[0074] Figure 8 It is a DC line transmission power diagram of a receiving-end power grid system in an embodiment of the present invention. DETAILED DESCRIPTION

[0075] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0076] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to or inherent to other steps or units of the device.

[0077] The terms used in the various embodiments of the application are only used to describe the purpose of specific embodiments and are not intended to limit the various embodiments of the application. As used herein, the singular form is intended to also include the plural form, unless the context clearly indicates otherwise. Unless otherwise limited, all terms used here (including technical terms and scientific terms) have the same meaning as the meanings commonly understood by ordinary technicians in the field of the various embodiments of the application. The terms (such as the terms defined in the dictionary generally used) will be interpreted as having the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning, unless clearly defined in the various embodiments of the application.

[0078] The embodiment of the present invention provides a multi-source collaborative optimization method for a receiving-end power grid based on information gap decision theory, which is suitable for optimal scheduling of the receiving-end power grid, is beneficial to improving the power receiving capacity of the receiving-end power grid, and promotes the consumption of new energy.

[0079] like Figure 1 The flowchart of a multi-source collaborative optimization method for a receiving-end power grid based on information gap decision theory according to an embodiment of the present invention includes the following steps.

[0080] S1, taking the minimization of the operating cost of the receiving power grid as the objective function, a multi-source collaborative optimization scheduling model of the receiving power grid is established.

[0081] S2, with the goal of maximizing the adverse disturbance of uncertain parameters of wind power output and load demand, an implicit uncertainty model of information gap decision theory is established.

[0082] S3, taking the implicit uncertainty model as the upper objective function and the multi-source collaborative optimization scheduling model as the lower objective function, establishes an information gap decision-making theoretical model considering the uncertainty of wind power output and load.

[0083] S4, solve the information gap decision theory model in S3 and obtain the multi-source coordinated dispatching scheme of the receiving power grid.

[0084] In step S1, the positive effects of thermal power units, cascade hydropower units, wind turbines, energy storage equipment and DC input lines in the receiving power grid on the optimal dispatching operation of the receiving power grid are comprehensively considered, and an objective function to minimize the total operating cost of the receiving power grid is established, which is expressed as follows:

[0085]

[0086] Among them, min is the minimization function, t is the time period number, g represents the thermal power unit, w represents the wind power unit, e is the segment number of the coal consumption cost curve, c is the ge represents the incremental coal consumption of thermal power unit g in the eth section, P get represents the power generation of thermal power unit g in the eth period during period t, They represent the fuel consumption of starting and stopping the thermal power unit g during period t, C fuel represents the unit price of fuel, C w represents the unit wind curtailment penalty cost, It represents the wind abandonment amount of wind turbine w in period t.

[0087] Considering branch power flow constraints, node balance constraints, thermal power unit operation constraints, cascade hydropower unit operation constraints, wind turbine unit operation constraints, energy storage equipment constraints, DC transmission line constraints and SVC operation constraints as the constraints of the objective function, a multi-source collaborative optimization scheduling model of the receiving power grid is established. Each constraint can be specifically expressed as follows.

[0088] (1) Branch flow constraints:

[0089]

[0090] θ ij,t =θ it -θ jt

[0091]

[0092]

[0093] Where i is the generator node number, i∈Ω G ,Ω G Represents the generator node set, j is the load node number, j∈Ω L ,Ω L represents a set of load nodes;

[0094] P ij,t represents the active power flow on the transmission line (i, j) during period t, Q ij,t represents the reactive power flow on the transmission line (i, j) during period t;

[0095] g ij represents the real part of the corresponding line (i, j) in the admittance matrix, b ij represents the imaginary part of the corresponding line (i, j) in the admittance matrix;

[0096] v it represents the voltage amplitude of node i at time period t, v jt represents the voltage amplitude of node j at time period t;

[0097] θ it represents the voltage phase angle of node i during period t, θ jt represents the voltage phase angle of node j during period t, θ ij,t is the phase angle difference between nodes i and j during period t, (·) max 、(·) max denote the maximum and minimum values ​​of (·), respectively.

[0098] (2) Node balance constraints:

[0099]

[0100] Where k is the node number of the power system, b is the SVC (static var compensation device) number, h is the hydropower unit number; c is the converter station number, Ω j is the device set of node j;

[0101] They represent the active and reactive power demands of node j in period t respectively; is the reactive power adjustment of node j to meet the voltage margin in period t, is the reactive power adjustment for node j to meet the voltage margin in period t;

[0102] are respectively the active power and reactive power transmitted from the AC side of converter station c to the power grid during period t;

[0103] P ij,t , Q ij,t They represent the active power flow and reactive power flow on the transmission line (i, j) in period t respectively;

[0104] P jk,t , Q jk,t They represent the active power flow and reactive power flow on the transmission line (i, k) during period t respectively;

[0105] P ht , Q ht They represent the active and reactive outputs of hydropower unit h during period t respectively;

[0106] P gt is the active power output of thermal power unit g during period t, Q gt is the reactive power output of thermal power unit g during period t;

[0107] P wt is the active power output of wind turbine w during period t, Q wt is the reactive power output of wind turbine w during period t;

[0108] are the charging and discharging power of energy storage s in period t respectively;

[0109] Q bt is the reactive power support of SVC in period t.

[0110] (3) Operation constraints of thermal power units:

[0111]

[0112] Among them, P gt is the output of thermal power unit g during period t, P g,t-1 The output of thermal power unit g during period t-1;

[0113] They are the upward and downward climbing limits of thermal power unit g respectively;

[0114] I gt is a 0-1 variable, indicating the start and stop status of the thermal power unit g in period t (1 means start, 0 means stop), I g,t-1 Indicates the start and stop status of the t-1 period;

[0115] They represent the upper and lower limits of the active output of thermal power unit g respectively;

[0116] are the unit start-up and shutdown costs of thermal power unit g, are the unit start-up and shutdown cost limits of thermal power unit g respectively;

[0117] They represent the time that the thermal power unit g is continuously running and remains in shutdown state before the period t-1, are the minimum startup and shutdown time of thermal power unit g respectively;

[0118] P get It represents the active output of thermal power unit g segment e in period t, It indicates the upper limit of active output of thermal power unit g section e.

[0119] (4) Operation constraints of cascade hydropower units:

[0120] P ht =η h L ht H ht

[0121]

[0122] E ht =E h,t-1 +q ht -L ht -S ht

[0123] q ht =L u,t-τu +S u,t-τu +n ht

[0124]

[0125] E h,0 =E h,T

[0126] Among them, P ht is the active power output of hydropower unit h during period t, η h represents the hydropower conversion efficiency of cascade hydropower station h, L ht is the power generation flow of hydropower station h during period t, H ht is the generating head of the hydropower station h during period t;

[0127] a hare the initial water head and water head coefficient of the hydropower station h, which are related to the physical characteristics of the hydropower station itself, E ht is the storage capacity of hydropower station h in period t;

[0128] I ht is the operating status of the hydropower station h during period t, are the maximum and minimum power generation flows allowed for the hydropower station h;

[0129] E h,t-1 is the generating head of the hydropower station h during period t-1, q ht is the inflow of hydropower station h in period t, S ht is the water discharge of hydropower station h in period t;

[0130] u is the upstream hydropower station of hydropower station h, t is the time period number, τ u is the time it takes for the water from hydropower station u to flow directly downstream, They represent the power generation flow and abandoned water flow of the hydropower station u directly upstream of the hydropower station h at time t, considering the water flow time lag, and n ht is the amount of running water of hydropower station h in period t;

[0131] is the minimum and maximum storage capacity of hydropower station h in period t;

[0132] E h,0 、E h,T They respectively represent the initial and final storage capacities of the hydropower station h.

[0133] (5) Wind turbine operation constraints:

[0134]

[0135] In the formula, represents the wind curtailment of wind turbine w in period t, represents the predicted output of wind turbine w in period t, Q wt represents the reactive power output of wind turbine w in period t, Indicates the upper limit of reactive power output of wind turbine w.

[0136] (6) Energy storage equipment constraints:

[0137]

[0138] In the formula, E st is the capacity of energy storage s in period t, are the upper and lower limits of the capacity of energy storage s;

[0139] E s,t-1 is the capacity of energy storage s in period t-1, are the charging and discharging efficiency of energy storage s, are the charging power and discharging power of energy storage s respectively;

[0140] are the upper and lower limits of the energy storage s charging power, are the upper and lower limits of the energy storage s discharge power, They are 0-1 variables, representing the charge and discharge status of the energy storage s in time period t (1 represents charging, 0 represents discharging).

[0141] (7) DC transmission line constraints:

[0142]

[0143] In the formula, They represent the AC active and reactive power transmitted by converter station c during period t, They represent the active power and reactive power flowing from converter station c during period t, They represent the active power loss and reactive power loss of converter station c during period t, They represent the lower and upper limits of the AC active power transmitted by converter station c, They represent the lower and upper limits of the AC reactive power transmitted by converter station c, They represent the upper and lower limits of reactive power flowing from converter station c during period t respectively;

[0144] v ct represents the voltage value of converter station c in period t, They represent the upper and lower limits of the voltage at converter station c respectively;

[0145] represents the active power flowing from converter station c during period t-1, are the maximum upward and downward adjustment limits of the DC receiving line transmission power connected to converter station c in adjacent time periods, They respectively represent the upward and downward adjustment states of the DC receiving-end line connected to the converter station c during the period t;

[0146] They respectively represent the upward and downward adjustment states of the DC receiving-end line connected to the converter station c in the period t-1;

[0147] N c Indicates the maximum number of adjustments to the DC receiving-end line connected to converter station c within the dispatching period;

[0148] The contracted transmission power of the DC receiving line connected to converter station c.

[0149] (8) SVC (static VAR compensation device) operation constraints:

[0150]

[0151] Among them, Q bt represents the reactive power of SVC in period t, Indicates the upper limit of SVC reactive power.

[0152] The objective function of minimizing the operating cost of the receiving power grid and the system operation constraints (1)-(8) together constitute the multi-source collaborative optimization scheduling model of the receiving power grid. Its abstract form is as follows:

[0153] min a T x+b T y+c T z

[0154] stx∈{0,1}

[0155] Ax+By+Dz≤d

[0156] Among them, a, b, c, d, A, B, and D are abstract matrix forms and vectors, representing the coefficients of the objective function and constraints. x is a binary variable, referring to the start and stop of the unit, the power adjustment of the DC line, the linearization auxiliary variable of the hydropower conversion constraint, and the charging and discharging strategy of the energy storage equipment. y is a continuous variable, including the active and reactive output of the unit, the reservoir capacity of the hydropower station, the DC input power, the charging and discharging power of the energy storage, the energy storage capacity, the SVC reactive support power, the voltage, the phase angle, and the model linearization auxiliary variable. z represents the wind abandonment variable. The first row of constraints limits the value range of the variable x, and the second row of constraints is the overall operation constraint of the system, including power flow constraints, node balance constraints, output constraints of each unit, energy storage equipment constraints, DC transmission line constraints, and SVC operation constraints.

[0157] The traditional optimization scheduling method is to solve and construct the network scheduling model through optimization algorithms such as particle swarm and linear regression, but there are problems such as complex calculations and large differences between the solution results and the actual situation. In order to improve the power receiving capacity of the power receiving system, the present invention considers the comprehensive effects of thermal power units, cascade hydropower units, wind turbines, energy storage equipment and DC input lines in the receiving power grid, establishes a receiving network operation cost model that considers the uncertainty of new energy output and load demand, and uses information gap decision theory to establish a two-layer optimization model. The upper objective function is an uncertainty model with the maximum adverse disturbance of wind power output and load demand as the optimization target, and the lower objective function is a multi-source collaborative optimization scheduling model with the total system operating cost as the optimization target, so that the scheduling decision ensures that the total system cost is less than the expected value and avoids the risks brought by uncertainty, thereby improving the system's power receiving capacity and the ability to absorb new energy.

[0158] In step S2, the adverse disturbance of the uncertain parameters of wind power output and load demand is maximized, and the corresponding mathematical model of the implicit uncertainty model is expressed as follows:

[0159]

[0160]

[0161] Where β represents the uncertainty of wind power output and load demand, formula (3) is the constraint condition, a, b, c, d, A, B, D are abstract matrix forms and vectors, representing the coefficients of the objective function and constraint conditions, x is a binary variable, referring to the start and stop of the unit, DC line power adjustment, hydropower conversion constraint linearization auxiliary variable, energy storage equipment charging and discharging strategy, y is a continuous variable, including unit active and reactive output, hydropower station storage capacity, DC input power, energy storage charging and discharging power, energy storage capacity, SVC reactive support power, voltage, phase angle, model linearization auxiliary variable, z represents the wind curtailment variable, C0 is the system cost obtained under the deterministic model, ω is the deviation coefficient of the system cost, The first line of constraints restricts the total cost of system operation under uncertainty to not exceed the deviation coefficient limit, the second line of constraints restricts the value range of variable x, the second line of constraints is the overall operation constraint of the system, including power flow constraints, node balance constraints, output constraints of each unit, energy storage equipment constraints, DC transmission line constraints and SVC operation constraints, and the fourth line of constraints describes the implicit restrictions of variables under uncertainty. Among them, setting the deviation coefficient ensures that the optimized cost will not exceed the limit of the deviation coefficient when considering uncertainty.

[0162] The information gap decision theory model adopted in the present invention adopts an implicit uncertainty model as the upper objective function, and the lower objective function is to maximize the total cost of the system, and the lower objective function is restricted by the calculation results of the deterministic scenario.

[0163] Since the uncertainty set is implicitly expressed, it is difficult to solve directly. The maximum value of the objective function of the lower layer is limited by an upper limit. At this time, no matter how the objective function is taken, it will not exceed the upper limit, so the max function can be omitted. At the same time, the explicit expression of the uncertain parameter set can be replaced by an equation. For example, for load, a larger load leads to a greater cost for the system, so it can be used Indicates, where E is the operation matrix, y is a continuous variable, Ey represents the load value under the information gap decision theory (IGDT) method, Indicates the load forecast value. For wind power, a smaller wind power output will increase the system cost, so Where F is the operation matrix, Fy represents the upper limit of wind power output under the information gap decision method, Represents the wind power forecast value. Therefore, the two-layer model can be converted into a single-layer information gap decision theory model considering the uncertainty of wind power output and system load. The single-layer model is expressed as follows:

[0164] maxβ (4)

[0165]

[0166] Among them, β represents the uncertainty of wind power output and load demand.

[0167] Furthermore, before solving the information gap decision theory model, in order to simplify the calculation, the operation constraints of the cascade hydropower units and the AC power flow constraints are linearized.

[0168] To linearize the operating constraints of cascade hydropower units, first simplify the formula subscript numbers to make the formula expression more concise and convenient for subsequent processing. Then, the power generation flow L (i.e. L in all periods) ht The collection of storage capacity E (i.e., E ht The set of) is divided into multiple continuous intervals [l i ,l i+1 ] and [e j ,e j+1 ], where i = 1, 2, ..., m-1, i.e. there are m-1 continuous power generation flow intervals, j = 1, 2, ..., n-1, i.e. there are n-1 continuous storage capacity intervals, m and n are integers greater than or equal to 2, and the specific values ​​can be set. Figure 2 As shown in the figure, the projection of the original equation on the plane composed of power generation flow L and reservoir capacity E is divided into (m-1)×(n-1) dimensional quadrilateral grids, and each grid is divided into two triangles along the diagonal line, respectively with μ ij and λ ij represents the upper left triangle and the lower right triangle, where μ ij represents the upper left triangle of the grid numbered (i, j), λ ij represents the lower right triangle of the grid numbered (i, j), i and j represent the horizontal and vertical coordinates of the quadrilateral grid respectively, and introduces a continuous auxiliary variable φ ij The linearized operation constraints of the cascade hydropower units are expressed as follows:

[0169] P=ηL(H0+αE)

[0170] p ij =ηl i (H0+αe j )

[0171]

[0172] φij ≤λ i,j+1 +λ i-1,j +λ ij +μ i+1,j +μ i,j-1 +μ ij

[0173]

[0174] Where P represents the power generation capacity of the hydropower station, p ij Indicates l i With e j The value of the power generation capacity of the lower hydropower station;

[0175] η represents the hydropower conversion efficiency of the hydropower station, L represents the power generation flow, E represents the reservoir capacity, l i represents the lower limit of the ith flow interval, e j represents the lower limit of the j-th reservoir capacity interval, H0 represents the initial water head of the hydropower station, and α represents the water head coefficient of the hydropower station;

[0176] λ ij is a 0-1 variable, indicating whether the lower right triangle of the grid numbered (i, j+1) of the plane projection composed of power generation flow and storage capacity is obtained. Similarly, λ i-1,j Indicates whether the lower left triangle of the grid numbered (i-1, j) is obtained, λ ,j+1 Indicates whether the upper right triangle of the grid numbered (i, j+1) is obtained; μ ij is a 0-1 variable, indicating whether the upper left triangle of the grid numbered (i, j+1) is obtained. Similarly, μ i+1,j Indicates whether the upper right triangle of the grid numbered (i+1,j) is obtained, μ i,j-1 Indicates whether the lower left triangle of the grid numbered (i, j-1) is obtained; φ ij is the introduced continuous auxiliary variable.

[0177] According to the actual situation of the transmission network, the phase angle difference θ between nodes ij,t The value of is very small, and the voltage of each node in the transmission network is close to the rated voltage. Therefore, the branch power flow constraint condition is linearized to meet the following formula:

[0178] sinθ ij,t ≈θ ij,t

[0179]

[0180] v it ≈1

[0181]

[0182] Among them, θij,t represents the phase difference between nodes i and j during period t, v it represents the voltage amplitude of node i during period t, v jt represents the voltage amplitude of node j at time period t, P ij,t represents the active power flow on the transmission line (i, j) during period t, Q ij,t represents the reactive power flow on the transmission line (i, j) during period t, g ij represents the real part of the corresponding line (i, j) in the admittance matrix, b ij Represents the imaginary part of the admittance matrix corresponding to line (i, j).

[0183] For ease of expression, define v ij,t =v it -v jt , if and If it is regarded as an independent variable, then equations (6)-(7) are linear constraints, while the active and reactive power losses of the line are still nonlinear, expressed as:

[0184]

[0185] in, They represent the active power loss and reactive power loss on line (i, j) during period t respectively.

[0186] The first-order Taylor series expansion method is used to perform Taylor series expansion under the reference operating state, that is, the rated voltage (the voltage amplitude and voltage phase angle are taken as the values ​​under the reference operating state) to linearize the expressions of active and reactive power losses of the line. For the convenience of analysis, the active power loss is first decomposed into two parts. The Taylor series expansion is performed on the active power loss and reactive power loss under the reference operating state, and the linearized expressions are as follows:

[0187]

[0188] Then there is,

[0189]

[0190] in, represents the part of active power loss related to the phase angle difference, Indicates the part of active power loss related to voltage difference; represents the reactive power flow on the transmission line (i, j) during period t under the reference operating state, It represents the voltage amplitude of node i in period t under the reference operation state, It represents the voltage amplitude of node j in period t under the reference operating state.

[0191] By linearizing the constraints, the complexity of the solution can be reduced and the optimized scheduling results can be obtained more quickly.

[0192] In step S4, when solving the information gap decision theory model, a solver can be used for solving, and the specific theoretical algorithm can adopt branch and bound, simplex method, etc. The parameters to be solved include unit combination, DC input power and its adjustment, energy storage device charging and discharging strategy, and the known parameters include system data and generator set, energy storage device information, DC input power contract quantity, etc.

[0193] The present invention is further described below in conjunction with specific optimization examples.

[0194] like Figure 3 The figure shows a system diagram of a receiving-end power grid, which includes two thermal power units (G1, G2), two cascade hydropower units (H1, H2), one wind farm (W1), one energy storage power station (E1), and two DC transmission lines (C1, C2). In the figure, B1 to B6 represent bus numbers, and arrows represent loads. The present invention uses four scenario scenarios to solve the multi-source collaborative optimization model of the receiving-end power grid. The factors considered in each scenario are shown in Table 1, and the total cost, operating cost, and wind abandonment cost results of each scenario are shown in Table 2.

[0195] Table 1 Elements included in each plan

[0196]

[0197]

[0198] Comparative Example 1: A multi-source collaborative optimization scheduling model for the receiving power grid is established based on Scheme 1 without considering the energy storage equipment, DC power conditions, and wind power and load uncertainties.

[0199] like Figure 4 The figure shows the output diagram of cascade hydropower and thermal power units in the receiving system based on Scheme 1. Since hydropower has no power generation cost, it can be used more limitedly than thermal power units under the premise of meeting the constraints. The output of the H2 hydropower station located downstream in the cascade hydropower is more stable than that of the H1 hydropower station located upstream, and it remains at the maximum output during most periods. The output of the upstream hydropower station H1 is adjusted more frequently, and it can adjust the output to a certain extent in line with the fluctuation of wind power, alleviating the peak load pressure of thermal power units. At 3:00-5:00, when the wind power output is at its peak, in order to minimize wind abandonment, the output of thermal power and hydropower is 0.

[0200] Comparative Example 2: Based on Scheme 2, the regulating effect of energy storage is considered on the basis of Scheme 1.

[0201] Compared with Scheme 1, the system cost of Scheme 2 decreased by RMB 38,618.3, the penalty for wind curtailment was reduced by 56.20%, and the operating cost was also reduced. Figure 5 As shown in Figure 2, although there is still wind curtailment in Scheme 2 from 3:00 to 5:00, the value is lower than that in Scheme 1. The charging and discharging power and storage capacity of energy storage in Scheme 2 change over the entire period of time as shown in Figure 2. Figure 6 As shown, it can be observed that the energy storage maintains the maximum charging power from 3:00 to 5:00, alleviating the pressure of wind power consumption.

[0202] Comparative Example 3: Based on Scheme 3, the power regulation function of the DC input line is taken into account on the basis of Scheme 2, and a single line is allowed to be adjusted 4 times during the entire dispatching period.

[0203] Compared with Scheme 2, the total system cost of Scheme 3 is reduced by 40,473.0 yuan, and the system operation cost and wind curtailment cost are reduced to a certain extent. The results are as follows: Figure 7 Compared with scheme 2, which cannot adjust the DC input power, scheme 3 allows the DC input power to be adjusted, and its DC input power is shown in 8. Figure 5 It can be seen that compared with Scheme 2, Scheme 3 only has wind curtailment at 4:00. Since the wind power peak-shaving pressure is the greatest from 3:00 to 5:00, the power input to the system in Scheme 3 is lower, which promotes the consumption of wind power to a certain extent. Therefore, the wind curtailment cost of Scheme 3 is greatly reduced.

[0204] The method of the present invention is based on Scheme 4 and considers adding the uncertainty of wind power output and load demand on the basis of Scheme 3.

[0205] The basic cost is 83514.1 yuan from the solution of Scheme 3, and the deviation coefficient ω is set to 0.2. On this basis, the uncertainty of the model for solving the information gap decision is 0.0163, that is, under the premise that the system loss cost shall not exceed the information gap decision theory, the maximum uncertainty of wind power output is 0.0163. The decision of Scheme 4 can take into account the influence of wind power output and load demand uncertainty on the basis of multi-source coordinated optimization of the receiving power grid, and ensure that the economy and robustness of the system are taken into account through the information gap decision theory.

[0206] Compared with the prior art, the multi-source collaborative optimization method of the receiving-end power grid based on the information gap decision theory proposed in the present invention can comprehensively consider the positive effects of thermal power units, cascade hydropower units, wind turbines, energy storage equipment and DC input lines in the receiving-end power grid on the optimized dispatching and operation of the receiving-end power grid, reduce the wind abandonment of the receiving-end system, and improve the absorption capacity of new energy. The multi-source collaborative optimization of the receiving-end power grid considering the uncertainty of new energy output and load demand can better balance the economy and robustness, effectively realize the multi-source collaborative optimization of the system, and promote the absorption of new energy.

[0207] An embodiment of the present invention further provides a receiving-end power grid multi-source collaborative optimization device based on information gap decision theory, which is used to execute the receiving-end power grid multi-source collaborative optimization method based on information gap decision theory of the above embodiment of the present invention, and the device includes:

[0208] The first modeling module is used to establish a multi-source collaborative optimization scheduling model of the receiving-end power grid with the minimization of the operating cost of the receiving-end power grid as the objective function;

[0209] The second modeling module is used to establish an implicit uncertainty model of information gap decision theory with the goal of maximizing adverse disturbances of uncertain parameters of wind power output and load demand;

[0210] The third modeling module is used to establish an information gap decision-making theory model considering the uncertainty of wind power output and load, using the implicit uncertainty model as the upper objective function and the multi-source collaborative optimization scheduling model as the lower objective function;

[0211] The optimization solution module is used to solve the information gap decision theory model and obtain the multi-source coordinated scheduling plan of the receiving power grid.

[0212] An embodiment of the present invention further provides an electronic device, which includes a processor and a memory, and the number of processors may be one or more. The memory, as a computer-readable storage medium, can be used to store software programs, computer executable programs, and modules. The processor executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory, so as to implement the multi-source collaborative optimization method of the receiving-end power grid based on the information gap decision theory of any of the above embodiments of the present invention.

[0213] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0214] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the receiving-end power grid multi-source collaborative optimization method based on information gap decision theory according to any embodiment of the present invention.

[0215] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0216] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0217] An embodiment of the present invention further provides a computer program product, which, when executed on a computer, enables the computer to execute the receiving-end power grid multi-source collaborative optimization method based on information gap decision theory of any of the above embodiments of the present invention. The above-described specific implementation methods further elaborate on the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-source collaborative optimization method for receiving-end power grid based on information gap decision theory, characterized in that: include: Taking the minimization of the operation cost of the receiving-end power grid as the objective function, a multi-source collaborative optimization scheduling model of the receiving-end power grid is established; Taking the maximization of adverse disturbances of uncertain parameters of wind power output and load demand as the goal, an implicit uncertainty model of information gap decision theory is established; Taking the implicit uncertainty model as the upper objective function and the multi-source collaborative optimization scheduling model as the lower objective function, an information gap decision theory model considering the uncertainty of wind power output and load is established; The information gap decision theory model is solved to obtain a multi-source coordinated scheduling solution for the receiving-end power grid.

2. The receiving-end power grid multi-source collaborative optimization method based on information gap decision theory according to claim 1 is characterized in that: The objective function of minimizing the operating cost of the receiving power grid is: Among them, min is the minimization function, t is the time period number, g represents the thermal power unit, w represents the wind power unit, e is the segment number of the coal consumption cost curve, c is the ge represents the incremental coal consumption of thermal power unit g in the eth section, P get represents the power generation of thermal power unit g in the eth period during period t, They represent the fuel consumption of starting and stopping the thermal power unit g during period t, C fuel represents the unit price of fuel, C w represents the unit wind curtailment penalty cost, It represents the wind abandonment amount of wind turbine w in period t.

3. The receiving-end power grid multi-source collaborative optimization method based on information gap decision theory according to claim 2 is characterized in that: The constraints of the multi-source collaborative optimization scheduling model include: branch flow constraints, node balance constraints, thermal power unit operation constraints, cascade hydropower unit operation constraints, wind turbine unit operation constraints, energy storage equipment constraints, DC transmission line constraints and SVC operation constraints.

4. The receiving-end power grid multi-source collaborative optimization method based on information gap decision theory according to claim 3 is characterized in that: Before solving the information gap decision theory model, the operation constraints of the cascade hydropower units are linearized by dividing the power generation flow and the reservoir capacity into a plurality of continuous power generation flow intervals and reservoir capacity intervals respectively, and dividing the plane projection composed of the power generation capacity and the reservoir capacity into (m-1)×(n-1)-dimensional grids, where m-1 is the number of power generation flow intervals, n-1 is the number of reservoir capacity intervals, and m and n are integers greater than or equal to 2. Each grid is divided into an upper left triangle and a lower right triangle along the diagonal line, and the operation constraints of the cascade hydropower units are represented by the current flow interval, the reservoir capacity interval and the triangular grid to obtain the linearized operation constraints of the cascade hydropower units, which are represented as follows: P=ηL(H0+αE) p ij =ηl i (H0+αe j ) f ij ≤λ i,j+1 +λ i-1,j +λ ij +m i+1,j +m i,j-1 +m ij Where P represents the power generation capacity of the hydropower station, p ij Indicates l i With e j The value of the power generation capacity of the lower hydropower station; η represents the hydropower conversion efficiency of the hydropower station, L represents the power generation flow, E represents the reservoir capacity, l i represents the lower limit of the ith flow interval, e j represents the lower limit of the j-th reservoir capacity interval, H0 represents the initial water head of the hydropower station, and α represents the water head coefficient of the hydropower station; λ ij is a 0-1 variable, indicating whether the lower right triangle of the grid numbered (i, j+1) of the plane projection composed of power generation flow and storage capacity is obtained. Similarly, λ i-1,j Indicates whether the lower left triangle of the grid numbered (i-1, j) is obtained, λ i,j+1 Indicates whether the upper right triangle of the grid numbered (i, j+1) is obtained; μ ij is a 0-1 variable, indicating whether the upper left triangle of the grid numbered (i, j+1) is obtained. Similarly, μ i+1,j Indicates whether the upper right triangle of the grid numbered (i+1,j) is obtained, μ i,j-1 Indicates whether the lower left triangle of the grid numbered (i, j-1) is obtained; φ ij is the introduced continuous auxiliary variable.

5. The receiving-end power grid multi-source collaborative optimization method based on information gap decision theory according to claim 4 is characterized in that: Before solving the information gap decision theory model, the branch power flow constraint is linearized by: according to the actual situation of the transmission network, the phase angle difference θ between the nodes is set ij,t The value satisfies sinθ ij,t ≈θ ij,t , each node voltage is close to the rated voltage, and define v ij,t =v it -v jt ,Will and Considered as independent variables, the linearized constraint forms of active power flow and reactive power flow are obtained, which are expressed as: Among them, P ij,t represents the active power flow on the transmission line (i, j) during period t, Q ij,t represents the reactive power flow on the transmission line (i, j) during period t; v it represents the voltage amplitude of node i during period t, v jt represents the voltage amplitude of node j at time period t, v ij,t represents the voltage difference between node i and node j during period t; g ij represents the real part of the corresponding line (i, j) in the admittance matrix, b ij represents the imaginary part of the admittance matrix corresponding to line (i, j), θ ij,t It is expressed as the phase angle difference between nodes i and j during period t; According to the node voltage amplitude and voltage phase angle under the reference operating state, the first-order Taylor series expansion of the active power loss and reactive power loss is performed to obtain the linearized constraint form of the active power loss and reactive power loss, which is expressed as: in, represents the active power loss on line (i, j) during period t, represents the reactive power loss on line (i, j) during period t; g ij represents the real part of the corresponding line (i, j) in the admittance matrix, b ij represents the imaginary part of the admittance matrix corresponding to line (i, j), θ ij,t represents the phase difference between nodes i and j during period t, v it represents the voltage amplitude of node i during period t, v jt represents the voltage amplitude of node j at time period t; represents the reactive power flow on the transmission line (i, j) during period t under the reference operating state, It represents the voltage amplitude of node i in period t under the reference operation state, It represents the voltage amplitude of node j in period t under the reference operating state.

6. The receiving-end power grid multi-source collaborative optimization method based on information gap decision theory according to claim 1 is characterized in that: The abstract form of the deterministic model is expressed as: min a T x+b T y+c T z stx∈{0,1} Ax+By+Dx≤d Among them, a, b, c, d, A, B, and D are abstract matrix forms and vectors, representing the coefficients of the objective function and constraints. x is a binary variable, referring to the start and stop of the unit, the power adjustment of the DC line, the linearization auxiliary variable of the hydropower conversion constraint, and the charging and discharging strategy of the energy storage equipment. y is a continuous variable, including the active and reactive output of the unit, the reservoir capacity of the hydropower station, the DC input power, the charging and discharging power of the energy storage, the energy storage capacity, the SVC reactive support power, the voltage, the phase angle, and the model linearization auxiliary variable. z represents the wind abandonment variable. The first row of constraints limits the value range of the variable x. The second row of constraints is the overall operation constraints of the system, including power flow constraints, node balance constraints, output constraints of each unit, energy storage equipment constraints, DC transmission line constraints, and SVC operation constraints. The implicit uncertainty model is expressed as: maxβs.t.max a T x+b T y+c T z≤(1+ω)C0 x∈{0,1} Ax+By+Dz≤d Among them, β represents the uncertainty of wind power output and load demand, the formula after st is the constraint condition, a, b, c, d, A, B, D are abstract matrix forms and vectors, representing the coefficients of the objective function and constraint conditions, x is a binary variable, referring to the start and stop of the unit, DC line power adjustment, hydropower conversion constraint linearization auxiliary variable, energy storage equipment charging and discharging strategy, y is a continuous variable, including unit active and reactive output, hydropower station storage capacity, DC input power, energy storage charging and discharging power, energy storage capacity, SVC reactive support power, voltage, phase angle, model linearization auxiliary variable, z represents the wind abandonment variable, C0 is the system cost obtained under the deterministic model, ω is the deviation coefficient of the system cost, are the predicted values ​​of some variables; the first row of constraints limits the total cost of system operation under uncertainty and does not exceed the deviation coefficient limit; the second row of constraints limits the value range of variable x; the second row of constraints are the overall operation constraints of the system, including power flow constraints, node balance constraints, output constraints of each unit, energy storage equipment constraints, DC transmission line constraints and SVC operation constraints; the fourth row of constraints describes the implicit restrictions of variables under uncertainty.

7. The receiving-end power grid multi-source collaborative optimization method based on information gap decision theory according to claim 6 is characterized in that: Before solving the information gap decision theory model, the upper objective function and the lower objective function are combined into a single-layer model. Since the uncertainty of load and wind power is considered, the implicit uncertainty variable constraint can be converted into an explicit expression, which is specifically expressed as follows: maxβ s.t.a T x+b T y+c T z≤(1+ω)C0 x∈{0,1} Ax+By+Dz≤d Among them, β represents the uncertainty of wind power output and load demand, the formula after st is the constraint condition, a, b, c, d, A, B, D are abstract matrix forms and vectors, representing the coefficients of the objective function and constraint conditions, x is a binary variable, referring to the start and stop of the unit, DC line power adjustment, hydropower conversion constraint linearization auxiliary variable, energy storage equipment charging and discharging strategy, y is a continuous variable, including unit active and reactive output, hydropower station storage capacity, DC input power, energy storage charging and discharging power, energy storage capacity, SVC reactive support power, voltage, phase angle, model linearization auxiliary variable, z represents the wind abandonment variable, C0 is the system cost obtained under the deterministic model, ω is the deviation coefficient of the system cost; E and F are operation matrices; Predict values ​​for some variables.

8. A multi-source collaborative optimization device for a receiving-end power grid based on information gap decision theory, characterized in that: include: The first modeling module is used to establish a multi-source collaborative optimization scheduling model of the receiving-end power grid with the minimization of the operating cost of the receiving-end power grid as the objective function; The second modeling module is used to establish an implicit uncertainty model of information gap decision theory with the goal of maximizing adverse disturbances of uncertain parameters of wind power output and load demand; A third modeling module is used to establish an information gap decision theory model considering wind power output and load uncertainty by using the implicit uncertainty model as an upper objective function and the multi-source collaborative optimization scheduling model as a lower objective function; The optimization solution module is used to solve the information gap decision theory model to obtain a multi-source coordinated scheduling solution for the receiving power grid.

9. An electronic device comprising 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 receiving-end power grid multi-source collaborative optimization method based on information gap decision theory described in any one of claims 1-7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the receiving-end power grid multi-source collaborative optimization method based on information gap decision theory described in any one of claims 1 to 7 is implemented.

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