Phase condenser parameter optimization method for AC / DC hybrid receiving-end power grid

Abstract

The present invention relates to a method for optimizing phase shifter parameters for an AC / DC hybrid receiving-end power grid, including: constructing a converter station dynamic model and a converter station steady-state model of the AC / DC hybrid receiving-end power grid; setting a planning period and an operating section of the converter station dynamic model and the converter station steady-state model, and establishing an optimal planning model for an AC / DC hybrid power grid with the goal of minimizing the amount of load loss within the planning period; obtaining the amount of system load loss under each operating section, and establishing a new scenario set representation including generator power and system load loss; screening out corresponding typical scenarios; and in the screened typical scenarios, establishing a corresponding phase shifter parameter optimal configuration model according to the installation position, capacity and operating parameters of the synchronous phase shifter to determine the optimal configuration parameters of the phase shifter within the planning period. The phase shifter parameter optimization method of the present invention can fully consider the operating parameters of the phase shifter and the constraints of the power grid, and thus can maximize the role of the phase shifter.

Description

Technical Field

[0001] The present invention relates to the technical field of AC / DC hybrid power grid operation control, and in particular to a phase regulator parameter optimization method for an AC / DC hybrid receiving-end power grid. Background Art

[0002] The commutation failure of the DC converter is one of the common faults in DC operation. Generally, a single converter station will have dozens of commutation failures in a year. Commutation failure may be caused by the DC control system, or it may be caused by the voltage drop of the AC nearby grid, which causes the thyristor turn-off time to be less than its minimum value, thereby inducing commutation failure. Since the DC on the inverter side needs to consume a large amount of reactive power when it is in steady-state operation, and the main source of the converter reactive power is the filter, it cannot provide effective and fast reactive voltage support when the voltage of the receiving grid drops, and because its reactive output is proportional to the square of the voltage, the reactive power provided by the filter will drop sharply as the voltage level drops. At the same time, during the period of DC commutation failure, the converter will consume a large amount of reactive power, further lowering the grid voltage level, forming a situation where the voltage operating level between the AC grid and the DC grid deteriorates, and ultimately leads to multi-circuit DC interlocking lockout, causing huge economic losses. Therefore, it is crucial to avoid the chain failure of the receiving grid at multiple DC points induced by commutation failure.

[0003] As a type of synchronous motor, phase regulators can effectively improve the system's short-circuit capacity, enhance the grid voltage strength, and improve the grid's voltage support capability after a fault disturbance, thereby reducing voltage fluctuations during the fault period and suppressing overvoltage. In response to the problem of optimizing the configuration of phase regulators at renewable energy stations, a Chinese patent with publication number CN113381445A discloses a method and system for optimizing the configuration of phase regulators for suppressing transient overvoltages of renewable energy sources. The method includes: calculating the short-circuit ratio of new energy multiple stations of each renewable energy source and the transient overvoltage of each renewable energy source when the target AC / DC hybrid power grid fails; when there is a transient overvoltage of a renewable energy source greater than the transient overvoltage threshold, determining the short-circuit ratio threshold of the multiple stations of the renewable energy source; configuring phase regulators on the low-voltage side of the grid connection point of the renewable energy source with the smallest short-circuit ratio of the multiple stations of the renewable energy source, and recalculating the short-circuit ratio of the multiple stations of the renewable energy source for each renewable energy source; when the short-circuit ratio of the multiple stations of the renewable energy source of each renewable energy source is greater than or equal to the short-circuit ratio threshold of the multiple stations of the renewable energy source, recalculating the transient overvoltage of each renewable energy source; when the transient overvoltage of each renewable energy source is less than or equal to the transient overvoltage threshold, determining that the current phase regulator configuration scheme is the optimal phase regulator configuration scheme.

[0004] The phase regulator optimization configuration method in the above-mentioned existing scheme can maximize the configuration effect of the synchronous phase regulator and minimize the configuration capacity of the synchronous phase regulator, thereby obtaining better economy and ensuring the safe grid connection of new energy. However, the applicant found that the above-mentioned existing scheme only focused on the installation location and capacity of the phase regulator, but did not consider the operating parameters of the phase regulator, as well as the flow constraints, voltage constraints and many physical constraints of the AC / DC hybrid receiving-end power grid, so that the role of the phase regulator cannot be maximized, and the operating stability of the system is difficult to ensure. Therefore, how to design a phase regulator parameter optimization method that can take into account the operating parameters of the phase regulator and the grid constraints to improve the operating stability of the system is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of the deficiencies of the above-mentioned prior art, the technical problem to be solved by the present invention is: how to design a phase modulator parameter optimization method for the AC / DC hybrid receiving-end power grid, so as to fully consider the operating parameters of the phase modulator and the flow constraints, voltage constraints and numerous physical constraints of the AC / DC hybrid receiving-end power grid, and thus maximize the role of the phase modulator, thereby achieving the operating stability of the system.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] The method for optimizing phase modulation parameters of a phase shifting device for an AC / DC hybrid receiving-end power grid comprises the following steps:

[0008] S1: Construct the dynamic model and steady-state model of the converter station of the AC / DC hybrid receiving-end power grid;

[0009] S2: Set the planning period and operation section of the converter station dynamic model and the converter station steady-state model, and establish the optimal planning model of the AC / DC hybrid power grid with the goal of minimizing the load loss within the planning period;

[0010] S3: Based on the optimal planning model of AC / DC hybrid power grid, the system load loss under each operating section is obtained, and a new scenario set representation including generator power and system load loss is established;

[0011] S4: Cluster the new scene set representation to screen out the corresponding typical scenes;

[0012] S5: Under the typical scenarios screened, the corresponding phase condenser parameter optimal configuration model is established according to the installation location, capacity and operating parameters of the synchronous phase condenser; then, in the phase condenser parameter optimal configuration model, the goal is to minimize the sum of the total operating and planning costs within the planning period, and based on the impact of dynamic reactive equipment capacity and parameter changes on the system load, the optimal configuration parameters of the phase condenser within the planning period are determined.

[0013] Preferably, in step S1, the converter station dynamic model is represented by the following formula:

[0014]

[0015] Where: γ represents the arc extinction angle; T I1 Represents the time constant of the first-order inertia link in constant current control; K I1 It represents the proportional coefficient of the first-order inertia link in constant current control; β1′ represents the trigger lead angle output after passing through the first-order inertia link in constant current control; Indicates the DC current command value; Indicates the actual value of the DC current on the inverter side; T I2 Indicates the time constant of the first-order inertia link in the fixed arc extinction angle control; K I2 represents the proportional coefficient of the first-order inertia link in the fixed arc extinction angle control; β2 represents the trigger lead angle after the output of the first-order inertia link in the fixed arc extinction angle control; γ ref Indicates the arc extinction angle control value;

[0016] The steady-state model of the converter station is expressed by the following formula:

[0017] stf acdc (p,q,v,θ,v dc ,i dc ,α,γ)=0;

[0018]

[0019]

[0020] Where: p represents the active power in the system; q represents the reactive power in the system; v represents the voltage amplitude; θ represents the voltage phase angle; v dc Indicates the DC voltage value; i dc represents the DC current value; α represents the trigger delay angle; γ represents the turn-off angle; f acdc represents the power flow constraint; Represents reactive power exchange between AC and DC systems; ψ represents a dummy variable, where 0 represents constant current control on the rectifier side and constant arc extinction angle control on the inverter side; 1 represents constant trigger angle control on the rectifier side and constant current control on the inverter side; α represents the minimum value of the trigger delay angle; Indicates the maximum value of the trigger delay angle; Indicates the DC current on the rectifier side; Indicates the upper limit of DC current on the rectifier side, Indicates the lower limit of DC current on the rectifier side; γ indicates the arc extinction angle; Indicates the maximum value of the arc extinction angle; γIt represents the minimum value of the arc extinction angle; the g function represents the general form of the algebraic equation group describing the system state.

[0021] Preferably, in step S1, the converter station dynamic model is converted into a corresponding continuous state equation by polynomial approximation; specifically, the following steps are included:

[0022] S101: Construct the following basis functions and linear combinations;

[0023] Basis function: Ξ λ (e λkx )=span(1,e λx ,e 2λx ,...,e λMx );

[0024] Linear combinations:

[0025] Where: e λkx represents the basis of the λ order; z represents the input independent variable parameter; y represents the output dependent variable parameter; y min ,y max Respectively represent the minimum and maximum allowed values ​​of the output parameters; and They represent the best coefficients of approximation respectively; e λiz+jy represents the linear combination of the basis of the approximate limiting link; M represents the number of combined basis; l represents the linear combination function of the approximate limiting link; l max It represents the linear combination function of the minimum and maximum comparison input and output links; A and B represent the slope and intercept of the linear function of the non-limiting part in the limiting link;

[0026] S102: Calculate the approximation-related parameters based on the basis function and the linear combination in combination with the following formula;

[0027]

[0028]

[0029] Where: h(x) represents the linearized objective function, expressed as h(x)=Ax+B; a k The coefficients of the basis combination that approximates the limiting link; represents the coefficients of the basis combination of the approximate minimum and maximum comparison input and output links; arg represents the function of finding the parameters of the function;

[0030] S103: When a fault or disturbance occurs in the power system, the dynamic characteristics of the system are expressed by the following formula;

[0031]

[0032] Where: x, y, and u represent the state variable, algebraic variable, and control variable of the system respectively; i represents the i-th trigger event; t + and t - They represent the time before and after the trigger event respectively; the f function here represents the general form of the differential equations describing the system dynamics; the g function represents the general form of the algebraic equations describing the system state, where represents the set of algebraic equations describing the system dynamics at the moment before the i-th trigger event occurs; Represents the set of algebraic equations describing the dynamic characteristics of the system at one moment after the i-th trigger event occurs; and Respectively represent t - State variables and algebraic variables of the system at each moment; and Indicates t + State variables and algebraic variables of the system at each moment;

[0033] S104: Differentiate the time interval h of the system dynamic characteristics through a continuous differential algebraic equation to obtain the following partial differential expressions of the state variables and algebraic variables with respect to the control variables;

[0034]

[0035] Where: h represents the step size of the calculation; I represents the unit matrix; represents the partial derivative of the g function with respect to the state variable at time t; represents the partial derivative of the g function with respect to the algebraic variable at time t; The partial derivative of the g function with respect to the control variable at time t; represents the partial derivative of function f with respect to the state variable at time t; represents the partial derivative of the function f with respect to the algebraic variable at time t; The partial derivative of the f function with respect to the control variable at time t; represents the partial derivative of the state variable with respect to the control variable at time t; represents the partial derivative of the algebraic variable with respect to the control variable at time t;

[0036] S105: For the fault occurrence and fault clearing moments, the trajectory sensitivity under the state change condition is calculated by differentiating the state variable and combining the following trajectory sensitivity solution formula;

[0037]

[0038]

[0039] Where: represents the partial derivative of the g function with respect to the algebraic variable after the i-th trigger event occurs; represents the partial derivative of the derivative variable with respect to the control variable after the i-th trigger event occurs; represents the partial derivative of the g function with respect to the control variable after the i-th trigger event occurs; represents the partial derivative of the state variable with respect to the control variable after the i-th trigger event occurs; represents the partial derivative of the state variable with respect to the control variable before the i-th trigger event occurs; represents the partial derivative of the g function with respect to the control variable after the i-th trigger event occurs;

[0040] S106: Solve the partial differential equations of the system dynamic characteristics, state variables and algebraic variables to the control variables, and the trajectory sensitivity solution formula to obtain the full-process trajectory sensitivity of the state variables and algebraic variables to the control variables; then use the state variables and algebraic variables as the continuous state equations of the dynamic model in the form of Taylor series expansion as follows;

[0041]

[0042] In the formula: x represents the state variable; y represents the algebraic variable; x0 represents the initial point of the state variable; y0 represents the initial point of the algebraic variable; x u represents the partial derivative of the state variable with respect to the control variable; y u represents the partial derivative of the algebraic variable with respect to the control variable; Δu represents the increment of the control variable; o(Δu) 2 represents higher-order terms with respect to the control variables.

[0043] Preferably, in step S1, the steady-state model is solved by a step search algorithm to obtain the constraints of the converter station; specifically, the following steps are included:

[0044] S111: Solve the following formula by a step search algorithm to obtain the maximum and minimum values ​​of reactive power exchange and AC bus voltage;

[0045]

[0046] Where: Indicates the reactive exchange value of AC / DC system; Indicates the minimum value of reactive exchange value of AC / DC system; Indicates the maximum value of the reactive power exchange value of the AC / DC system; v ac Indicates the AC side bus voltage value; v ac Indicates the minimum value of the AC side bus voltage; Indicates the maximum value of the AC side bus voltage;

[0047] S112: performing piecewise linearization operation on the maximum and minimum values ​​of reactive power exchange and AC bus voltage, and obtaining the following linearized boundary set as constraints of the converter station;

[0048]

[0049] Where: v ex Indicates the external grid voltage value; v ac Indicates the AC side bus voltage value; It represents the reactive exchange value of the AC / DC system; A, B, C, and D represent the coefficients obtained by linear fitting between the external grid voltage and the reactive exchange value.

[0050] Preferably, in step S2, the planning period is set to one year, and four operating sections are selected every day within the planning period, totaling 1460 operating sections; then, combined with the constraints of the AC system, an optimal planning model for the AC / DC hybrid power grid is established with the goal of minimizing the load loss in each operating section.

[0051] Preferably, in step S2, the optimal planning model of the AC / DC hybrid power grid is expressed by the following formula:

[0052]

[0053] stf acdc (p,q,v,θ,v dc ,i dc ,α,γ)=0;

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] v dc | t＝0.4s ≥0.85;

[0061] Where: Indicates the amount of system load shedding; vo indicates the AC bus voltage; v , They represent the minimum and maximum values ​​of the AC bus voltage respectively; r represents the reactive power output of the synchronous condenser in the AC system; r and Indicates the minimum and maximum reactive power output of the synchronous condenser in the flow system; p l It represents the active power transmitted by the lines in the AC system; p l and They represent the lower and upper limits of the active power transmitted by the lines in the AC system respectively; T represents the transformer tap ratio in the AC system, T and They represent the lower and upper limits of the transformer tap ratio adjustment in the AC system respectively; N represents the number of capacitor groups allowed to be switched in the AC system, N and Respectively represent the minimum and maximum number of capacitor groups allowed to be switched in the AC system; Indicates the area where the bus voltage drops after a fault; t ocr Indicates the time when the fault occurs; t end Indicates fault clearing time; v base Indicates the AC bus voltage reference value; v i Indicates the AC bus voltage value during the fault period; Indicates the maximum allowable AC bus voltage drop area; v dc | t＝0.4s represents the DC bus voltage value 0.4s after the fault is removed; p represents the active power in the system; θ represents the voltage phase angle; v dc Indicates the DC voltage value; i dc represents the DC current value; α represents the trigger delay angle; γ represents the turn-off angle; f acdc Represents power flow constraints.

[0062] Preferably, step S3 specifically includes the following steps:

[0063] S301: Converting the optimal planning model of the AC / DC hybrid power grid into the following general form;

[0064]

[0065] Where: h(u) is the general form of the objective function of the optimal planning model of the initial AC / DC hybrid power grid, indicating that the system load shedding is minimized; x and u represent the state variable and control variable of the system, respectively; f(x,u)=0 represents the equality constraint in the optimal planning model of the initial AC / DC hybrid power grid; g(x,u)≥0 represents the inequality constraint in the optimal planning model of the initial AC / DC hybrid power grid;

[0066] S302: Add variable b to the general form to obtain the following equality constraint form;

[0067]

[0068] Where: b is a newly added variable to facilitate the transformation of the inequality constraint in the original general form into an equality constraint;

[0069] S303: by a=[aB a N ], Decompose the coefficients a and u of the equality constraint form and obtain the following decomposition formula;

[0070]

[0071] Where: a represents the coefficient of the relevant control parameter before the equation constraint, a B represents the basic variable matrix after the coefficient splitting before the relevant control parameter equality constraint; a N represents the non-basic variable matrix after the coefficient splitting before the relevant control parameter equality constraint; u B represents the basic variable matrix about the control variable; u N represents the non-basic variable matrix about the control variables;

[0072] S304: When the variable b increases to b+Δb, the objective function h increases to h+Δh, and then Δh is calculated based on the decomposition formula combined with the following update formula;

[0073]

[0074] Where: Δh represents the increment of the objective function; represents the feasible solution of the basic variable of the control variable split; Δb represents the increment of the newly added variable; Represents the Hessian matrix obtained for the objective function; Δu represents the change in the control variable.

[0075] Preferably, in step S3, the new scenario set including the generator power and the system load loss is expressed by the following formula:

[0076]

[0077]

[0078] Where: Ω p Represents all scenarios of the power system within the planning period; N Total Indicates the total number of scenes; n g and n l Respectively represent vector p g and p l The dimension of p g and p l Indicates the generator output and load power in each scenario; T Represents the transpose of a matrix.

[0079] Preferably, step S4 specifically includes the following steps:

[0080] S401: constructing the following mapping relationship based on the new scene set representation;

[0081]

[0082] Where: Indicates the amount of load loss in the system under different generator output scenarios; Ω p Represents all scenarios of the power system within the planning period; p g represents the generator output in each scenario; p represents the active power in the system;

[0083] S402: Setting the initial scene set and Set the probability of each scene μi i →1 / N total , set the following scene filtering conditions; represents the set of scenes selected from the initial scene p; represents the empty set;

[0084] Scene filter conditions:

[0085] Where: ρ represents the scene screening condition; Δh N represents the deviation of the non-basis matrix of the objective function term under different conditions; Δb represents the change of the generator output power and the load power in the system under different scenarios; a B Represents the basis variable matrix after coefficient splitting before the equality constraint of related control parameters;

[0086] For p in the initial scene set i and p j , if the condition ρ(p i ,p j )>0.01, the system load loss is calculated by the optimal planning model of the AC / DC hybrid power grid; otherwise, the system load loss is calculated by updating the formula, and then the typical scenario set is screened and updated.

[0087] S403: When the updated number of scenes is still greater than the required value N require When , the Kantorovich distance between any two scenes in the selected scene set is calculated according to the following formula And let the probability of each scenario be

[0088] Where: P i and P j represents the i-th and j-th scenarios containing generator output information and system load loss information;

[0089] S404: Select the minimum scene probability value from the calculated scene probabilitiesm j =min{μ j |j=1,2,...,N Ω}, update the scene probability m j =min{μ j |j=1,2,...,N Ω} and remove scene j from the typical dataset

[0090] S405: Repeat steps S401 to S404 until the number of remaining typical scenes is equal to N. require When the user selects a typical scene, complete the screening of typical scenes.

[0091] Preferably, in step S5, the optimal configuration model of phase-shifting parameters is expressed by the following formula:

[0092]

[0093]

[0094]

[0095]

[0096]

[0097] Where: β represents the load shedding penalty cost; η Q It represents the configuration cost of the unit capacity of the condenser; Indicates the cost coefficient for adjusting the phase regulator parameters; It represents the system load loss under the ith triggering event in the nth scenario; Indicates the change in the amount of system load loss; The linear relationship coefficient that represents the change in system load loss and dynamic reactive capacity; The linear relationship coefficient that represents the change in system load loss and the change in dynamic reactive equipment parameters; ΔQ C Indicates the dynamic reactive capacity change; ΔQ I Indicates the change in dynamic reactive equipment parameters; Indicates the upper limit of the dynamic reactive capacity change; Q allocate Indicates the capacity of dynamic reactive equipment installed in the system; Indicates the maximum value of the dynamic reactive power equipment capacity allowed to be installed in the system; ΔQ I Indicates the minimum value of the dynamic reactive equipment parameter change; Indicates the maximum value of the dynamic reactive equipment parameter change; N select Indicates the number of selected scenes.

[0098] Compared with the prior art, the phase condenser parameter optimization method of the present invention has the following beneficial effects:

[0099] The present invention first constructs a converter station dynamic model and a converter station steady-state model, then sets the planning cycle and operation section of the model and establishes an optimal planning model of an AC / DC hybrid power grid with the goal of minimizing the amount of load loss within the planning cycle, and then obtains the amount of system load loss under each operation section and establishes a new scenario set representation including generator power and system load loss, and finally selects typical scenarios and establishes a corresponding optimal configuration model of phase-shifting parameters according to the installation position, capacity and operation parameters of the synchronous phase-shifting, so as to determine the optimal configuration parameters of the phase-shifting. On the one hand, the present invention can fully consider the operating parameters of the phase-shifting and the flow constraints, voltage constraints and numerous physical constraints of the AC / DC hybrid receiving-end power grid by constructing a converter station dynamic model and a converter station steady-state model, establishing an optimal planning model of an AC / DC hybrid power grid, establishing a new scenario set representation including generator power and system load loss, and establishing a corresponding optimal configuration model of phase-shifting parameters according to the installation position, capacity and operation parameters of the synchronous phase-shifting, so as to achieve the system operation stability. On the other hand, the present invention establishes an optimal planning model for an AC / DC hybrid power grid with the goal of minimizing the amount of load loss within a planning period, calculates the system load loss under each operating section and establishes a new scenario set representation including generator power and system load loss, and then clusters the new scenario set representation to screen typical scenarios, so that the system load shedding amount within the planning period can be effectively reduced, thereby improving the economy and safety of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0101] Figure 1 It is a logic block diagram of the phase regulator parameter optimization method for the AC / DC hybrid receiving-end power grid;

[0102] Figure 2 This is the control block diagram of the inverter side of the converter station;

[0103] Figure 3 It is a schematic diagram of improving the 118 test system in the embodiment;

[0104] Figure 4 This is a schematic diagram for comparing monthly load shedding under different planning schemes;

[0105] Figure 5 Schematic diagram of load shedding probability under different planning schemes;

[0106] Figure 6Schematic diagram of the average daily load shedding in December under different planning schemes. DETAILED DESCRIPTION

[0107] The following is a further detailed description through specific implementation methods:

[0108] Example:

[0109] This embodiment discloses a method for optimizing phase regulator parameters for an AC / DC hybrid receiving-end power grid.

[0110] like Figure 1 As shown, the method for optimizing phase modulation parameters of a phase shifter for an AC / DC hybrid receiving-end power grid includes the following steps:

[0111] S1: Construct the dynamic model and steady-state model of the converter station of the AC / DC hybrid receiving-end power grid;

[0112] S2: Set the planning period and operation section of the converter station dynamic model and the converter station steady-state model, and establish the optimal planning model of the AC / DC hybrid power grid with the goal of minimizing the load loss within the planning period;

[0113] S3: Based on the optimal planning model of AC / DC hybrid power grid, the system load loss under each operating section is obtained, and a new scenario set representation including generator power and system load loss is established;

[0114] S4: Cluster the new scene set representation to screen out the corresponding typical scenes;

[0115] S5: Under the typical scenarios screened, the corresponding phase condenser parameter optimal configuration model is established according to the installation location, capacity and operating parameters of the synchronous phase condenser; then, in the phase condenser parameter optimal configuration model, the goal is to minimize the sum of the total operating and planning costs within the planning period, and based on the impact of dynamic reactive equipment capacity and parameter changes on the system load, the optimal configuration parameters of the phase condenser within the planning period are determined.

[0116] The present invention first constructs a converter station dynamic model and a converter station steady-state model, then sets the planning cycle and operation section of the model and establishes an optimal planning model of an AC / DC hybrid power grid with the goal of minimizing the amount of load loss within the planning cycle, and then obtains the amount of system load loss under each operation section and establishes a new scenario set representation including generator power and system load loss, and finally selects typical scenarios and establishes a corresponding optimal configuration model of phase-shifting parameters according to the installation position, capacity and operation parameters of the synchronous phase-shifting, so as to determine the optimal configuration parameters of the phase-shifting. On the one hand, the present invention can fully consider the operating parameters of the phase-shifting and the flow constraints, voltage constraints and numerous physical constraints of the AC / DC hybrid receiving-end power grid by constructing a converter station dynamic model and a converter station steady-state model, establishing an optimal planning model of an AC / DC hybrid power grid, establishing a new scenario set representation including generator power and system load loss, and establishing a corresponding optimal configuration model of phase-shifting parameters according to the installation position, capacity and operation parameters of the synchronous phase-shifting, so as to achieve the system operation stability. On the other hand, the present invention establishes an optimal planning model for an AC / DC hybrid power grid with the goal of minimizing the amount of load loss within a planning period, calculates the system load loss under each operating section and establishes a new scenario set representation including generator power and system load loss, and then clusters the new scenario set representation to screen typical scenarios, so that the system load shedding amount within the planning period can be effectively reduced, thereby improving the economy and safety of the system.

[0117] In the specific implementation process, Figure 2 As shown, the dynamic model of the converter station is expressed by the following formula:

[0118]

[0119] Where: γ represents the arc extinction angle; T I1 Represents the time constant of the first-order inertia link in constant current control; K I1 It represents the proportional coefficient of the first-order inertia link in constant current control; β′1 represents the trigger lead angle output after the first-order inertia link in constant current control; Indicates the DC current command value; Indicates the actual value of the DC current on the inverter side; T I2 Indicates the time constant of the first-order inertia link in the fixed arc extinction angle control; K I2 represents the proportional coefficient of the first-order inertia link in the fixed arc extinction angle control; β2 represents the trigger lead angle after the output of the first-order inertia link in the fixed arc extinction angle control; γ ref Indicates the arc extinction angle control value;

[0120] Since the inverter side converter station control link includes many piecewise linear links, such as the limit link and the maximum-minimum input and output link, the converter station dynamic model (converter station state equation) is discontinuous. In order to facilitate the subsequent solution, the present invention converts the converter station dynamic model into the corresponding continuous state equation by polynomial approximation;

[0121] The specific steps include:

[0122] S101: construct the following basis functions and linear combinations; the linear combination can approximately and continuously describe the dynamic characteristics of the limiting link and the maximum and minimum input and output links;

[0123] Basis functions:

[0124] Linear combinations:

[0125] Where: e λkx represents the basis of the λ order; z represents the input independent variable parameter; y represents the output dependent variable parameter; y min ,y max Respectively represent the minimum and maximum allowed values ​​of the output parameters; and They represent the best coefficients of approximation respectively; e λiz+jy represents the linear combination of the basis of the approximate limiting link; M represents the number of combined basis; l represents the linear combination function of the approximate limiting link; l max It represents the linear combination function of the minimum and maximum comparison input and output links; A and B represent the slope and intercept of the linear function of the non-limiting part in the limiting link;

[0126] S102: Calculate the approximation-related parameters based on the basis function and the linear combination in combination with the following formula;

[0127]

[0128]

[0129] Where: h(x) represents the linearized objective function, expressed as h(x)=Ax+B; a k The coefficients of the basis combination that approximates the limiting link; represents the coefficients of the basis combination of the approximate minimum and maximum comparison input and output links; arg represents the function of finding the parameters of the function;

[0130] S103: When a fault or disturbance occurs in the power system, the system dynamic characteristics are expressed by the following formula (6);

[0131]

[0132] Where: x, y, and u represent the state variable, algebraic variable, and control variable of the system respectively; i represents the i-th trigger event; t + and t - They represent the time before and after the trigger event respectively; the f function here represents the general form of the differential equations describing the system dynamics; the g function represents the general form of the algebraic equations describing the system state, where represents the set of algebraic equations describing the system dynamics at the moment before the i-th trigger event occurs; Represents the set of algebraic equations describing the dynamic characteristics of the system at one moment after the i-th trigger event occurs; and Respectively represent t - State variables and algebraic variables of the system at each moment; and Indicates t + State variables and algebraic variables of the system at each moment;

[0133] S104: Differentiate the time interval h of the system dynamic characteristics through a continuous differential algebraic equation to obtain the following partial differential equation (7) of the state variable and the algebraic variable with respect to the control variable;

[0134]

[0135] Where: h represents the step size of the calculation; I represents the unit matrix; represents the partial derivative of the g function with respect to the state variable at time t; represents the partial derivative of the g function with respect to the algebraic variable at time t; The partial derivative of the g function with respect to the control variable at time t; represents the partial derivative of function f with respect to the state variable at time t; represents the partial derivative of the function f with respect to the algebraic variable at time t; The partial derivative of the f function with respect to the control variable at time t; represents the partial derivative of the state variable with respect to the control variable at time t; represents the partial derivative of the algebraic variable with respect to the control variable at time t;

[0136] S105: For the fault occurrence and fault clearing moments, the trajectory sensitivity under the state change condition is calculated by differentiating the state variable and combining the following trajectory sensitivity solution formula;

[0137]

[0138]

[0139] Where: represents the partial derivative of the g function with respect to the algebraic variable after the i-th trigger event occurs; represents the partial derivative of the derivative variable with respect to the control variable after the i-th trigger event occurs; represents the partial derivative of the g function with respect to the control variable after the i-th trigger event occurs; represents the partial derivative of the state variable with respect to the control variable after the i-th trigger event occurs; represents the partial derivative of the state variable with respect to the control variable before the i-th trigger event occurs; represents the partial derivative of the g function with respect to the control variable after the i-th trigger event occurs;

[0140] S106: (Perform time domain simulation) Solve the system dynamic characteristics, partial differential equations of state variables and algebraic variables to control variables, and trajectory sensitivity solution formulas to obtain the full-process trajectory sensitivity of state variables and algebraic variables to control variables; then use the state variables and algebraic variables as the continuous state equations of the dynamic model in the form of Taylor series expansion as follows;

[0141]

[0142] In the formula: x represents the state variable; y represents the algebraic variable; x0 represents the initial point of the state variable; y0 represents the initial point of the algebraic variable; x u represents the partial derivative of the state variable with respect to the control variable; y u represents the partial derivative of the algebraic variable with respect to the control variable; Δu represents the increment of the control variable; o(Δu) 2 represents higher-order terms with respect to the control variables.

[0143] For the steady-state model of the converter station, the power flow constraints (11) and other physical operation constraints (12) can be met by coordinating the scheduling of filters, phase regulators and triggering / shutdown angles, including reactive power control capability, reactive power exchange between AC and DC grids, power transfer capacity and voltage amplitude constraints. In the receiving converter station, if the critical operating conditions are triggered, the constant current control / fixed shut-off angle control and fixed triggering angle / constant current control modes will be automatically switched. The dummy variable x is introduced to describe the converter control characteristics (13), where x=0 and x=1 represent the constant current control / fixed shut-off angle control and fixed triggering angle / constant current control modes in the receiving converter station, respectively.

[0144] The steady-state model of the converter station is expressed by the following formula:

[0145] stf acdc (p,q,v,θ,v dc ,i dc ,α,γ)=0 (11);

[0146]

[0147]

[0148] Where: p represents the active power in the system; q represents the reactive power in the system; v represents the voltage amplitude; θ represents the voltage phase angle; v dc Indicates the DC voltage value; i dc represents the DC current value; α represents the trigger delay angle; γ represents the turn-off angle; f acdc represents the power flow constraint; Represents reactive power exchange between AC and DC systems; ψ represents a dummy variable, where 0 represents constant current control on the rectifier side and constant arc extinction angle control on the inverter side; 1 represents constant trigger angle control on the rectifier side and constant current control on the inverter side; α Indicates the minimum value of the trigger delay angle; Indicates the maximum value of the trigger delay angle; Indicates the DC current on the rectifier side; Indicates the upper limit of DC current on the rectifier side, Indicates the lower limit of DC current on the rectifier side; γ indicates the arc extinction angle; Indicates the maximum value of the arc extinction angle; γ It represents the minimum value of the arc extinction angle; the g function represents the general form of the algebraic equation group describing the system state.

[0149] For the steady-state model of the converter station, since the external AC power grid often does not have sufficient reactive power control capability, it is impossible to control the voltage of the external power grid at a fixed value. Therefore, for the given value of the external equivalent power grid, the present invention solves the steady-state model through the trajectory sensitivity method and the step search algorithm to obtain the constraints of the converter station;

[0150] The specific steps include:

[0151] S111: Solve the following formula by a step search algorithm to obtain the maximum and minimum values ​​of reactive power exchange and AC bus voltage;

[0152]

[0153] Where: Indicates the reactive exchange value of AC / DC system; Indicates the minimum value of reactive exchange value of AC / DC system; Indicates the maximum value of the reactive power exchange value of the AC / DC system; v ac Indicates the AC side bus voltage value; v ac Indicates the minimum value of the AC side bus voltage; Indicates the maximum value of the AC side bus voltage;

[0154] S112: Perform piecewise linearization operation on the maximum and minimum values ​​of reactive power exchange and AC bus voltage to obtain the following (expressing and vac) linearized boundary set as the constraints of the converter station;

[0155]

[0156] Where: v ex Indicates the external grid voltage value; v ac Indicates the AC side bus voltage value; represents the reactive exchange value of the AC / DC system; A′, B′, C′, and D′ represent the coefficients obtained by linear fitting between the external grid voltage and the reactive exchange value.

[0157] The present invention constructs a dynamic model of a converter station and a steady-state model of a hybrid AC / DC receiving-end power grid, and processes the model through polynomial approximation, trajectory sensitivity method and step search algorithm, so as to reduce the difficulty of subsequent system load loss calculation and improve the calculation accuracy, thereby assisting in obtaining the optimal phase-modulating phase parameters to maximize the role of the phase-modulating phase.

[0158] During the specific implementation process, the planning period is set to one year, and four operating sections are selected every day within the planning period, totaling 1,460 operating sections. Then, combined with the constraints of the AC system, an optimal planning model for the AC / DC hybrid power grid is established with the goal of minimizing the load loss in each operating section.

[0159] The optimal planning model of AC / DC hybrid power grid is expressed by the following formula:

[0160]

[0161] stf acdc (p,q,v,θ,v dc ,i dc ,α,γ)=0(17);

[0162]

[0163]

[0164]

[0165]

[0166]

[0167]

[0168] v dc | t＝0.4s ≥0.85(24);

[0169] Where: Indicates the amount of system load shedding; vo indicates the AC bus voltage; v , They represent the minimum and maximum values ​​of the AC bus voltage respectively; r represents the reactive power output of the synchronous condenser in the AC system; r and Indicates the minimum and maximum reactive power output of the synchronous condenser in the flow system; p l It represents the active power transmitted by the lines in the AC system; p l and They represent the lower and upper limits of the active power transmitted by the lines in the AC system respectively; T represents the transformer tap ratio in the AC system, T and They represent the lower and upper limits of the transformer tap ratio adjustment in the AC system respectively; N represents the number of capacitor groups allowed to be switched in the AC system, N and Respectively represent the minimum and maximum number of capacitor groups allowed to be switched in the AC system; Indicates the area where the bus voltage drops after a fault; t ocr Indicates the time when the fault occurs; t end Indicates fault clearing time; v base Indicates the AC bus voltage reference value; v i Indicates the AC bus voltage value during the fault period; Indicates the maximum allowable AC bus voltage drop area; v dc | t＝0.4s represents the DC bus voltage value 0.4s after the fault is removed; p represents the active power in the system; θ represents the voltage phase angle; v dc Indicates the DC voltage value; i dc represents the DC current value; α represents the trigger delay angle; γ represents the turn-off angle; f acdc Represents power flow constraints.

[0170] In this embodiment, the objective function (16) means that the system load loss is minimized at each section, constraints (17)-(22) represent N-0 constraints, and equations (23)-(24) represent N-1 constraints. Specifically, equation (17) represents the power flow constraint, equation (18) represents the bus voltage amplitude constraint, equation (19) represents the synchronous phase condenser / capacitor group reactive power constraint, equation (20) represents the line capacity constraint, equation (21) represents the transformer tap constraint, equation (22) represents the capacitor switching group number constraint, and equation (23) represents that the AC bus voltage drop area under fault cannot be too large; equation (24) represents that the AC bus voltage value at 0.4s after the fault is cleared should be greater than 0.85pu to avoid the occurrence of continuous commutation failure.

[0171] The AC / DC hybrid power grid optimal planning model established by the present invention can take the minimum load loss within the planning period as the goal, so that the system load shedding within the planning period can be effectively reduced, thereby improving the economy and safety of the system.

[0172] Step S3 specifically includes the following steps:

[0173] S301: Converting the optimal planning model of the AC / DC hybrid power grid into the following general form;

[0174]

[0175] Where: h(u) is the general form of the objective function of the optimal planning model of the initial AC / DC hybrid power grid, indicating that the system load shedding is minimized; x and u represent the state variable and control variable of the system, respectively; f(x,u)=0 represents the equality constraint in the optimal planning model of the initial AC / DC hybrid power grid; g(x,u)≥0 represents the inequality constraint in the optimal planning model of the initial AC / DC hybrid power grid;

[0176] S302: Add variable b to the general form to obtain the following equality constraint form;

[0177]

[0178] Where: b is a newly added variable to facilitate the transformation of the inequality constraint in the original general form into an equality constraint;

[0179] S303: by a=[a B a N ], Decompose the coefficients a and u of the equality constraint form and obtain the following decomposition formula;

[0180]

[0181] Where: a represents the coefficient of the relevant control parameter before the equation constraint, a B represents the basic variable matrix after the coefficient splitting before the relevant control parameter equality constraint; a N represents the non-basic variable matrix after the coefficient splitting before the relevant control parameter equality constraint; u B represents the basic variable matrix about the control variable; u N represents the non-basic variable matrix about the control variables;

[0182] S304: When the variable b increases to b+Δb, the objective function h increases to h+Δh, and then Δh is calculated based on the decomposition formula combined with the following update formula;

[0183]

[0184] Where: Δh represents the increment of the objective function; represents the feasible solution of the basic variable of the control variable split; Δb represents the increment of the newly added variable; Represents the Hessian matrix obtained for the objective function; Δu represents the change in the control variable.

[0185] In the specific implementation process, in step S3, the new scenario set including the generator power and the system load loss is expressed by the following formula:

[0186]

[0187]

[0188] Where: Ω p Represents all scenarios of the power system within the planning period; N Total Indicates the total number of scenes; n g and n l Respectively represent vector p g and p l The dimension of p g and p l Indicates the generator output and load power in each scenario; T Represents the transpose of a matrix.

[0189] Through the above steps, the present invention can establish a new scenario set representation including generator power and system load loss, so that the operating parameters of the phase regulator and the flow constraints, voltage constraints and numerous physical constraints of the AC / DC hybrid receiving-end power grid can be fully considered, and then the optimal phase regulator parameters can be obtained to maximize the role of the phase regulator, thereby ensuring the operating stability of the system.

[0190] Step S4 specifically includes the following steps:

[0191] S401: constructing the following mapping relationship based on the new scene set representation;

[0192]

[0193] Where: Indicates the amount of load loss in the system under different generator output scenarios; Ω p Represents all scenarios of the power system within the planning period; p g represents the generator output in each scenario; p represents the active power in the system;

[0194] S402: Setting the initial scene set and Set the probability μ of each scene i →1 / N total, set the following scene filtering conditions; represents the set of scenes selected from the initial scene p; represents the empty set;

[0195] Scene filter conditions:

[0196] Where: ρ represents the scene screening condition; Δh N represents the deviation of the non-basis matrix of the objective function term under different conditions; Δb represents the change of the generator output power and the load power in the system under different scenarios; a B Represents the basis variable matrix after coefficient splitting before the equality constraint of related control parameters;

[0197] For p in the initial scene set i and p j , if the condition ρ(p i ,p j )>0.01, the system load loss is calculated by the optimal planning model of the AC / DC hybrid power grid (i.e., equations (16)-(24)); otherwise, the system load loss is calculated by updating the formula (equation (29)), and then the typical scenario set is screened and updated.

[0198] S403: When the updated number of scenes is still greater than the required value N require When , the distance between any two scenes in the filtered scene set is calculated according to the following formula And let the probability of each scenario be

[0199]

[0200] Where: P i and P j represents the i-th and j-th scenarios containing generator output information and system load loss information;

[0201] S404: Select the minimum scene probability value from the calculated scene probabilities m j =min{μ j |j=1,2,...,N Ω}, update the scene probability m j =min{μ j |j=1,2,...,N Ω} and remove scene j from the typical dataset

[0202] S405: Repeat steps S401 to S404 until the number of remaining typical scenes is equal to N. require When selecting a typical scenario, complete the screening.

[0203] Through the above steps, the present invention can cluster the new scene set representation to screen typical scenes, so as to effectively reduce the system load shedding amount within the planning period, thereby improving the economy and safety of the system.

[0204] In the specific implementation process, the optimal configuration model of phase-shifting parameters is expressed by the following formula;

[0205]

[0206]

[0207]

[0208]

[0209]

[0210] Where: β represents the load shedding penalty cost; η Q It represents the configuration cost of unit capacity of the condenser; Indicates the cost coefficient for adjusting the phase regulator parameters; It represents the system load loss under the ith triggering event in the nth scenario; Indicates the change in the amount of system load loss; The linear relationship coefficient that represents the change in system load loss and dynamic reactive capacity; The linear relationship coefficient that represents the change in system load loss and the change in dynamic reactive equipment parameters; ΔQ C Indicates the dynamic reactive capacity change; ΔQ I Indicates the change in dynamic reactive equipment parameters; Indicates the upper limit of the dynamic reactive capacity change; Q allocat e represents the capacity of dynamic reactive equipment installed in the system; Indicates the maximum value of the dynamic reactive power equipment capacity allowed to be installed in the system; ΔQ I Indicates the minimum value of the dynamic reactive equipment parameter change; Indicates the maximum value of the dynamic reactive equipment parameter change; N select Indicates the number of selected scenes.

[0211] In this embodiment, (36) is an approximate linear expression between the load shedding amount and the reactive output of the phase shifter and the phase shifter parameter adjustment amount. For each operation scenario, when a certain number of new reactive devices are allocated to the system, the reactive control range of the system increases, as shown in (37). (38) is the maximum capacity limit of the installed synchronous phase shifter. (39) represents the allowable range of the phase shifter operation parameters.

[0212] At the same time, the above-mentioned phase-modulator parameter optimal configuration model established by the present invention can effectively consider the installation position, capacity and operating parameters of the synchronous phase-modulator to establish the corresponding phase-modulator parameter optimal configuration model, so that the operating parameters of the phase-modulator and the flow constraints, voltage constraints and numerous physical constraints of the AC / DC hybrid receiving-end power grid can be fully considered, and then the optimal phase-modulator parameters can be obtained to maximize the role of the phase-modulator, thereby ensuring the operating stability of the system.

[0213] In order to better illustrate the advantages of the technical solution of the present invention, the following experiments are disclosed in this embodiment.

[0214] First, Figure 3 As shown in Figure 1, an improved IEEE-118 test system was built, and three DC lines were added to the initial test system. The detailed information of the DC system is shown in Table 1.

[0215] Table 1 - DC data in the test system

[0216]

[0217] First, in order to compare the performance of different power system solutions, a total of 4 cases were designed for comparison. The details are as follows:

[0218] Case 1: The model proposed in this invention takes into account the capacity, installation location and operating parameters of all phase modulators in the system.

[0219] Case 2: Compared with Case 1, the installation location and capacity of the phase regulator are not considered; it only optimizes the internal parameters of the equipment.

[0220] Case 3: Compared with Case 1, internal parameter optimization is not considered, and only the equipment capacity and installation location of the phase regulator are considered.

[0221] Case 4: Compared with Case 1, the phase regulator is installed at a fixed location, i.e., on the busbar of the converter station.

[0222] Assume that the planning period of the power system is 1 year, and the initial scenario set contains 1460 operating sections (four sections exist every day). Figure 4 The total load shedding of the system under different planning schemes in different months of the year is recorded.

[0223] Combination Figure 4 It can be seen that the method proposed in the present invention (Case 1) is the best solution because the average load shedding of the system in one year is the lowest, and the average load shedding is 5.82MW.

[0224] Although the performance of Case 1 is the best in the whole year, it should be noted that in March and April, the load shedding of the proposed method is higher than that of Cases 3 and 4. The reason for this is that in these two months, the grid operation pressure is relatively high, which may cause problems related to steady-state voltage operation. In these two months, the equipment planning results of Cases 3 and 4 focus more on the optimization of steady-state related parameters compared with the proposed method.

[0225] Figure 5 The load shedding probability under different planning schemes is shown. In each case, 1460 operating sections are divided into four types of load shedding situations, and the load shedding range width of each situation is 2.5MW. Figure 5 , it can be concluded that, compared with the other three cases, the model method proposed in the present invention has the largest number of cases where the load shedding amount is less than 5.0MW (550 times), and the smallest number of cases where the load shedding amount is greater than 10.0MW (325 times).

[0226] Finally, select the average load shedding records for each day in December. Figure 6 The average load shedding in the four cases is 5.99MW, 7.22MW, 7.03MW and 6.70MW respectively. It can also be concluded from the figure that in most cases, the load shedding of model 1 of the method proposed by the present invention is the least.

[0227] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing phase-converter parameters for an AC / DC hybrid receiving-end power grid, characterized in that: The following steps are involved: S1: Construct the dynamic model and steady-state model of the converter station of the AC / DC hybrid receiving-end power grid; S2: Set the planning period and operation section of the converter station dynamic model and the converter station steady-state model, and establish the optimal planning model of the AC / DC hybrid power grid with the goal of minimizing the load loss within the planning period; S3: Based on the optimal planning model of AC / DC hybrid power grid, the system load loss under each operating section is obtained, and a new scenario set representation including generator power and system load loss is established; S4: Cluster the new scene set representation to screen out the corresponding typical scenes; Step S4 specifically includes the following steps: S401: constructing the following mapping relationship based on the new scene set representation; Where: Indicates the amount of load loss in the system under different generator output scenarios; Ω p Represents all scenarios of the power system within the planning period; p g represents the generator output in each scenario; p represents the active power in the system; S402: Setting the initial scene set and Set the probability μ of each scene i →1 / N total , set the following scene filtering conditions; represents the set of scenes selected from the initial scene p; represents the empty set; Scene filter conditions: Where: ρ represents the scene screening condition; Δh N represents the deviation of the non-basis matrix of the objective function term under different circumstances; Δb represents the change of the generator output power and the load power in the system under different scenarios; a B Represents the basis variable matrix after coefficient splitting before the equality constraint of related control parameters; For pi and p in the initial scene set j , if the condition ρ(p i ,p j )>0.01, the system load loss is calculated by the optimal planning model of the AC / DC hybrid power grid; otherwise, the system load loss is calculated by updating the formula, and then the typical scenario set is screened and updated. S403: When the updated number of scenes is still greater than the required value N require When , the Kantorovich distance between any two scenes in the selected scene set is calculated according to the following formula And let the probability of each scenario be Where: P i and P j represents the i-th and j-th scenarios containing generator output information and system load loss information; S404: Select the minimum scene probability value from the calculated scene probabilities m j =min{μ j |j=1,2,...,N Ω }, update the scene probability m j =min{μ j |j=1,2,...,N Ω } and remove scene j from the typical dataset S405: Repeat steps S401 to S404 until the number of remaining typical scenes is equal to N. require When completing the screening of typical scenarios; S5: Under the typical scenarios screened, the corresponding phase condenser parameter optimal configuration model is established according to the installation location, capacity and operating parameters of the synchronous phase condenser; then, in the phase condenser parameter optimal configuration model, the goal is to minimize the sum of the total operating and planning costs within the planning period, and based on the impact of dynamic reactive equipment capacity and parameter changes on the system load, the optimal configuration parameters of the phase condenser within the planning period are determined.

2. The method for optimizing phase modulator parameters for an AC / DC hybrid receiving-end power grid according to claim 1, characterized in that: In step S1, the converter station dynamic model is expressed by the following formula: Where: γ represents the arc extinction angle; T I1 Represents the time constant of the first-order inertia link in constant current control; K I1 It represents the proportional coefficient of the first-order inertia link in constant current control; β1′ represents the trigger lead angle output after passing through the first-order inertia link in constant current control; Indicates the DC current command value; Indicates the actual value of the DC current on the inverter side; T I2 Indicates the time constant of the first-order inertia link in the fixed arc extinction angle control; K I2 represents the proportional coefficient of the first-order inertia link in the fixed arc extinction angle control; β2 represents the trigger lead angle after the output of the first-order inertia link in the fixed arc extinction angle control; γ ref Indicates the arc extinction angle control value; The steady-state model of the converter station is expressed by the following formula: stf acdc (p,q,v,θ,v dc ,i dc ,a,c)=0; Where: p represents the active power in the system; q represents the reactive power in the system; v represents the voltage amplitude; θ represents the voltage phase angle; v dc Indicates the DC voltage value; i dc represents the DC current value; α represents the trigger delay angle; γ represents the turn-off angle; f acdc represents the power flow constraint; represents reactive power exchange between AC and DC systems; ψ represents a dummy variable, where 0 represents constant current control on the rectifier side and constant arc extinction angle control on the inverter side; l1 represents constant trigger angle control on the rectifier side and constant current control on the inverter side; α represents the minimum value of the trigger delay angle; Indicates the maximum value of the trigger delay angle; Indicates the DC current on the rectifier side; Indicates the upper limit of DC current on the rectifier side, Indicates the lower limit of DC current on the rectifier side; γ indicates the arc extinction angle; Indicates the maximum value of the arc extinction angle; γ It represents the minimum value of the arc extinction angle; the g function represents the general form of the algebraic equation group describing the system state.

3. The method for optimizing phase modulator parameters for an AC / DC hybrid receiving-end power grid according to claim 2, characterized in that: In step S1, the converter station dynamic model is converted into a corresponding continuous state equation by means of polynomial approximation; specifically, the following steps are included: S101: Construct the following basis functions and linear combinations; Basis function: Ξ λ (e λkx )=span(1,e λx ,e 2λx ,...,e λMx ); Linear combinations: Where: e λkx represents the basis of the λ order; z represents the input independent variable parameter; y represents the output dependent variable parameter; y min ,y max Respectively represent the minimum and maximum allowable values ​​of the output parameters; e λiz+jy represents the linear combination of the basis of the approximate limiting link; M represents the number of combined basis; l represents the linear combination function of the approximate limiting link; l max It represents the linear combination function of the minimum and maximum comparison input and output links; A and B represent the slope and intercept of the linear function of the non-limiting part in the limiting link; S102: Calculate the approximation-related parameters based on the basis function and the linear combination in combination with the following formula; Where: h(x) represents the linearized objective function, expressed as h(x)=Ax+B; a k The coefficients of the basis combination that approximates the limiting link; represents the coefficients of the basis combination of the approximate minimum and maximum comparison input and output links; arg represents the function of finding the parameters of the function; S103: When a fault or disturbance occurs in the power system, the dynamic characteristics of the system are expressed by the following formula; Where: x, y, and u represent the state variable, algebraic variable, and control variable of the system respectively; i represents the i-th trigger event; t + and t - They represent the time before and after the trigger event respectively; the f function here represents the general form of the differential equations describing the system dynamics; the g function represents the general form of the algebraic equations describing the system state, where represents the set of algebraic equations describing the system dynamics at the moment before the i-th trigger event occurs; Represents the set of algebraic equations describing the dynamic characteristics of the system at one moment after the i-th trigger event occurs; and Respectively represent t - State variables and algebraic variables of the system at each moment; and Indicates t + State variables and algebraic variables of the system at each moment; S104: Differentiate the time interval h of the system dynamic characteristics through a continuous differential algebraic equation to obtain the following partial differential expressions of the state variables and algebraic variables with respect to the control variables; Where: h represents the step size of the calculation; I represents the unit matrix; represents the partial derivative of the g function with respect to the state variable at time t; represents the partial derivative of the g function with respect to the algebraic variable at time t; The partial derivative of the g function with respect to the control variable at time t; represents the partial derivative of function f with respect to the state variable at time t; represents the partial derivative of the function f with respect to the algebraic variable at time t; The partial derivative of the f function with respect to the control variable at time t; represents the partial derivative of the state variable with respect to the control variable at time t; represents the partial derivative of the algebraic variable with respect to the control variable at time t; S105: For the fault occurrence and fault clearing moments, the trajectory sensitivity under the state change condition is calculated by differentiating the state variable and combining the following trajectory sensitivity solution formula; Where: represents the partial derivative of the g function with respect to the algebraic variable after the i-th trigger event occurs; represents the partial derivative of the derivative variable with respect to the control variable after the i-th trigger event occurs; represents the partial derivative of the g function with respect to the control variable after the i-th trigger event occurs; represents the partial derivative of the state variable with respect to the control variable after the i-th trigger event occurs; represents the partial derivative of the state variable with respect to the control variable before the i-th trigger event occurs; represents the partial derivative of the g function with respect to the control variable after the i-th trigger event occurs; S106: Solve the partial differential equations of the system dynamic characteristics, state variables and algebraic variables to the control variables, and the trajectory sensitivity solution formula to obtain the full-process trajectory sensitivity of the state variables and algebraic variables to the control variables; then use the state variables and algebraic variables as the continuous state equations of the dynamic model in the form of Taylor series expansion as follows; In the formula: x represents the state variable; y represents the algebraic variable; x0 represents the initial point of the state variable; y0 represents the initial point of the algebraic variable; x u represents the partial derivative of the state variable with respect to the control variable; y u represents the partial derivative of the algebraic variable with respect to the control variable; Δu represents the increment of the control variable; o(Δu) 2 represents higher-order terms with respect to the control variables.

4. The method for optimizing phase modulator parameters for an AC / DC hybrid receiving-end power grid according to claim 2, characterized in that: In step S1, the steady-state model is solved by a step search algorithm to obtain the constraints of the converter station; specifically, the following steps are included: S111: Solve the following formula by a step search algorithm to obtain the maximum and minimum values ​​of reactive power exchange and AC bus voltage; Where: Indicates the reactive exchange value of AC / DC system; Indicates the minimum value of reactive exchange value of AC / DC system; Indicates the maximum value of the reactive power exchange value of the AC / DC system; v ac Indicates the AC side bus voltage value; v ac Indicates the minimum value of the AC side bus voltage; Indicates the maximum value of the AC side bus voltage; S112: performing piecewise linearization operation on the maximum and minimum values ​​of reactive power exchange and AC bus voltage, and obtaining the following linearized boundary set as constraints of the converter station; Where: v ex Indicates the external grid voltage value; v ac Indicates the AC side bus voltage value; represents the reactive exchange value of the AC / DC system; A′, B′, C′, and D′ represent the coefficients obtained by linear fitting between the external grid voltage and the reactive exchange value.

5. The method for optimizing phase modulator parameters for an AC / DC hybrid receiving-end power grid according to claim 4, characterized in that: In step S2, the planning period is set to one year, and four operating sections are selected every day within the planning period, totaling 1460 operating sections; then, combined with the constraints of the AC system, an optimal planning model for the AC / DC hybrid power grid is established with the goal of minimizing the load loss in each operating section.

6. The method for optimizing phase modulator parameters for an AC / DC hybrid receiving-end power grid according to claim 5, characterized in that: In step S2, the optimal planning model of the AC / DC hybrid power grid is expressed by the following formula: stf acdc (p,q,v,θ,v dc ,i dc ,a,c)=0; v dc | t＝0.4s ≥0.85； Where: Indicates the amount of system load shedding; vo indicates the AC bus voltage; vo , They represent the minimum and maximum values ​​of the AC bus voltage respectively; r represents the reactive power output of the synchronous condenser in the AC system; r and Indicates the minimum and maximum reactive power output of the synchronous condenser in the flow system; p l It represents the active power transmitted by the lines in the AC system; p l and They represent the lower and upper limits of the active power transmitted by the lines in the AC system respectively; T represents the transformer tap ratio in the AC system, T and They represent the lower and upper limits of the transformer tap ratio adjustment in the AC system respectively; N represents the number of capacitor groups allowed to be switched in the AC system, N and Respectively represent the minimum and maximum number of capacitor groups allowed to be switched in the AC system; Indicates the area where the bus voltage drops after a fault; t ocr Indicates the time when the fault occurs; t end Indicates fault clearing time; v base Indicates the AC bus voltage reference value; v i Indicates the AC bus voltage value during the fault period; Indicates the maximum allowable AC bus voltage drop area; v dc | t＝0.4s represents the DC bus voltage value 0.4s after the fault is removed; p represents the active power in the system; θ represents the voltage phase angle; v dc Indicates the DC voltage value; i dc represents the DC current value; α represents the trigger delay angle; γ represents the turn-off angle; f acdc Represents power flow constraints.

7. The method for optimizing phase modulator parameters for an AC / DC hybrid receiving-end power grid according to claim 1, characterized in that: Step S3 specifically includes the following steps: S301: Converting the optimal planning model of the AC / DC hybrid power grid into the following general form; Where: h(u) is the general form of the objective function of the optimal planning model of the initial AC / DC hybrid power grid, indicating that the system load shedding is minimized; x and u represent the state variable and control variable of the system, respectively; f(x,u)=0 represents the equality constraint in the optimal planning model of the initial AC / DC hybrid power grid; g(x,u)≥0 represents the inequality constraint in the optimal planning model of the initial AC / DC hybrid power grid; S302: Add variable b to the general form to obtain the following equality constraint form; Where: b is a newly added variable to facilitate the transformation of the inequality constraint in the original general form into an equality constraint; S303: by a=[a B a N ], Decompose the coefficient a and control variable u in the form of equality constraints to obtain the following decomposition formula; Where: a represents the coefficient of the relevant control parameter before the equation constraint, a B represents the basic variable matrix after the coefficient splitting before the relevant control parameter equality constraint; a N represents the non-basic variable matrix after the coefficient splitting before the relevant control parameter equality constraint; u B represents the basic variable matrix about the control variable; u N represents the non-basic variable matrix about the control variables; S304: When the variable b increases to b+Δb, the objective function h increases to h+Δh, and then Δh is calculated based on the decomposition formula combined with the following update formula; Where: Δh represents the increment of the objective function; represents the feasible solution of the basic variable of the control variable split; Δb represents the increment of the newly added variable; Represents the Hessian matrix obtained for the objective function; Δu represents the change in the control variable.

8. The method for optimizing phase modulator parameters for an AC / DC hybrid receiving-end power grid according to claim 7, characterized in that: In step S3, a new scenario set including generator power and system load loss is expressed by the following formula: Where: Ω p Represents all scenarios of the power system within the planning period; N Total Indicates the total number of scenes; n g and n l Represents vector p g and p l The dimension of p g and p l Indicates the generator output and load power in each scenario; T Represents the transpose of a matrix.

9. The method for optimizing phase modulator parameters for an AC / DC hybrid receiving-end power grid according to claim 1, characterized in that: In step S5, the optimal configuration model of phase-shifting parameters is expressed by the following formula: Where: β represents the load shedding penalty cost; η Q It represents the configuration cost of the unit capacity of the condenser; Indicates the cost coefficient for adjusting the phase regulator parameters; It represents the system load loss under the ith triggering event in the nth scenario; Indicates the change in the amount of system load loss; The linear relationship coefficient that represents the change in system load loss and dynamic reactive capacity; The linear relationship coefficient that represents the change in system load loss and the change in dynamic reactive equipment parameters; ΔQ C Indicates the dynamic reactive capacity change; ΔQ I Indicates the change in dynamic reactive equipment parameters; Indicates the upper limit of the dynamic reactive capacity change; Q allocate Indicates the capacity of dynamic reactive equipment installed in the system; Indicates the maximum value of the dynamic reactive power equipment capacity allowed to be installed in the system; ΔQ I Indicates the minimum value of the dynamic reactive equipment parameter change; Indicates the maximum value of the dynamic reactive equipment parameter change; N select Indicates the number of selected scenes.

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