New energy voltage ride-through parameter optimization method, storage medium and device considering multi-form voltage stability constraints

Abstract

The invention relates to a method, storage medium and device for optimizing the voltage crossing parameters of new energy sources taking into account multi-form voltage stability constraints, and belongs to the technical field of improving the safety and stability of power systems. In order to solve the problem that the existing method for optimizing the voltage crossing parameters of new energy sources is only applicable to a single fault scenario and only optimizes the voltage crossing parameters of new energy sources for one voltage stability form. Based on the dynamic characteristics of new energy sources during power grid faults, the present invention establishes a general control model for new energy sources to determine the dominant control parameters that affect the dynamic characteristics of new energy sources during high / low crossing periods, considers the influence of the dominant control parameters on voltage stability under different faults, takes into account the voltage regulation capability and power regulation cost of new energy sources, establishes a new energy voltage crossing parameter optimization model taking into account multi-form voltage stability constraints, and adopts a particle swarm optimization algorithm to solve the optimization model. The present invention is used for optimizing the voltage crossing parameters of new energy sources.

Description

Technical Field

[0001] The present invention belongs to the technical field of improving the safety and stability of power systems, and relates to a method, storage medium and device for optimizing voltage ride-through parameters taking into account new energy sources. Background Art

[0002] Fossil energy is one of the fundamental energy sources for human production and life, providing a powerful driving force for social development and scientific and technological progress. However, its inherent shortcomings, such as its non-renewable nature and high pollution levels, have gradually been exposed. Therefore, there is an urgent need for the wider, more in-depth, more efficient, and safer development and utilization of clean energy. However, the large-scale integration of renewable energy into the grid will severely limit voltage support capabilities. The interaction between renewable energy and the AC main grid and ultra-high voltage direct current (UHVDC) systems is increasing safety and stability risks such as transient overvoltage, low voltage, and sustained low voltage after faults, becoming a major factor restricting the absorption and transmission capacity of renewable energy. In terms of voltage profiles, transient overvoltage, low voltage, and sustained low voltage after faults have different mechanisms of generation. The voltage ride-through parameters of renewable energy systems have different effects on different voltage profiles. Improper voltage ride-through parameters for renewable energy systems can severely limit the transmission capacity of renewable energy systems, posing significant challenges to grid operation and control.

[0003] Currently, there are some solutions for optimizing the control characteristics of new energy sources, such as:

[0004] 1. "The Impact of Wind Turbine Fault Ride-Through Characteristics on Transient Overvoltage in Large-Scale Wind Power DC Transmission Systems and Parameter Optimization" published by Wang Xichun et al. Power System Technology: 2021, 45(12): 4612-4621. This article starts from the mechanism of transient overvoltage in the system after a DC fault disturbance, and introduces in detail the low-voltage ride-through characteristics and related variable parameters of wind turbines. Through the example of a direct-drive permanent magnet wind turbine in a UHVDC transmission project, the sensitivity analysis of the unit fault ride-through control parameters to the voltage transient characteristics is carried out based on time domain simulation, and optimization suggestions for wind turbine control strategies and model parameters are proposed.

[0005] 2. Lin Weifang et al. published “Optimization of Control Parameters of New Energy Voltage Ride-Through Considering Power Angle Stability and Transient Overvoltage”, Power System Technology: 2023, 47(4): 1323-1330. This article qualitatively studied the influence of control parameters of new energy units during high / low voltage ride-through on transient overvoltage at the DC sending end based on simulation of a comprehensive program for power system analysis, proposed the optimization principles and ideas for new energy unit parameters, and verified their rationality through simulation based on actual grid conditions.

[0006] 3. Chen Houhe et al. published "Coordinated Optimization of DC and Wind Power Control Parameters for Suppressing Transient Overvoltage in DC Sending-End Systems", Electric Power Automation Equipment: 2020, 40(10): 46-55. This article studies the mechanism by which the control parameters of the DC rectifier and wind turbine rotor converters affect transient overvoltages based on the reactive characteristics of the system, and constructs an optimization model with the goal of minimizing the peak transient overvoltage of the DC sending-end system. Based on the joint call of the theoretical model and the electromagnetic simulation model, an improved particle swarm optimization algorithm is used to coordinately optimize the DC and wind power control parameters.

[0007] In summary, current research on optimizing renewable energy control characteristics to improve voltage safety and stability has ignored the impact of renewable energy voltage ride-through parameters on grid voltage safety and stability in other fault scenarios. The optimized renewable energy voltage ride-through parameters only perform well in a single fault scenario, but it is difficult to take into account the different forms of voltage safety and stability under multiple fault types. Summary of the Invention

[0008] The present invention aims to solve the problem that the existing renewable energy voltage ride-through parameter optimization method is only applicable to a single fault scenario and optimizes the renewable energy voltage ride-through parameters for only one voltage stability form, and ignores the impact of the renewable energy voltage ride-through parameters on the grid voltage safety and stability in other fault scenarios.

[0009] A method for optimizing parameters of renewable energy voltage ride-through considering multi-mode voltage stability constraints, comprising:

[0010] According to the new energy voltage ride-through process, the power regulation cost of new energy during the low voltage ride-through period, the steady-state period after the AC fault is cleared, and the high voltage ride-through period is expressed as follows:

[0011]

[0012] Among them, TC L,ij TC clear,ij and TC H,ij are the power regulation costs of the i-th renewable energy during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period under the j-th fault scenario, respectively. L,l,i 、C clear,l,i and C H,l,i are the power regulation cost coefficients of the i-th renewable energy during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period, ΔP L,l,ij and ΔQ L,l,ij They are the changes in active power and reactive power of the i-th renewable energy during low voltage ride-through under the j-th fault scenario, ΔP clear,l,ij and ΔQ clear,l,ijare the changes in active power and reactive power of the i-th renewable energy source during the steady-state period after the AC fault is cleared under the j-th fault scenario, ΔP H,l,ij and ΔQ H,l,ij are the changes in active power and reactive power of the i-th renewable energy during the high voltage ride-through period under the j-th fault scenario;

[0013] The transient voltage offset is standardized to obtain the offset of the low voltage, steady-state voltage and overvoltage at the i-th renewable energy grid-connected terminal in the j-th fault scenario after standardization, that is, and The power regulation cost of renewable energy is standardized to obtain the power regulation cost of the i-th renewable energy during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period under the j-th fault scenario, that is, and

[0014] Then determine the objective function:

[0015]

[0016] Where F is the objective function of the new energy voltage ride-through parameter optimization model, n is the number of new energy sources; α i , β i and γ i are the weights of the transient low voltage improvement target of the i-th renewable energy source, the steady-state voltage improvement target after AC fault clearance, and the transient overvoltage improvement target;

[0017] Based on the objective function, the low-pass active current coefficient K will be included pLV , low-throughput reactive current coefficient K qLV 、The recovery coefficient of new energy low-power active power is K rec and high-throughput reactive current coefficient K qHV The optimization object is used as the particle in the particle swarm optimization algorithm. The particle swarm optimization algorithm is used to optimize the optimization object to obtain the optimization result.

[0018] Furthermore, when using the particle swarm optimization algorithm to optimize the optimization object, the following constraints need to be met:

[0019]

[0020] P Gilim_low ≤P Gi ≤P Gilim_up

[0021] U busilim_low ≤U busi ≤U busilim_up

[0022]

[0023] in, is the low voltage improvement index, U 0,i is the initial steady-state voltage of the grid-connected terminal of the i-th renewable energy source, U L,ij is the transient low voltage at the grid-connected terminal of the i-th renewable energy source under the j-th fault scenario, ΔU L,max,i is the low voltage offset of the grid-connected terminal of the i-th renewable energy in the most serious AC fault scenario; is the steady-state voltage improvement index, U clear,ij is the steady-state voltage of the i-th renewable energy grid-connected terminal after the AC fault is cleared in the j-th fault scenario, ΔU clear,max,i is the offset between the recovered steady-state voltage and the initial steady-state voltage of the i-th renewable energy source when its terminal voltage recovers to steady-state under the worst working condition after the AC fault is cleared; is the overvoltage improvement index, U H,ij is the transient overvoltage at the grid-connected terminal of the i-th renewable energy source under the j-th fault scenario, ΔU H,max,i is the overvoltage offset of the grid-connected terminal of the i-th renewable energy in the most serious DC fault scenario; IF L_low IF clear_low and IF H_low They are the lower limits of low voltage improvement index, steady-state voltage improvement index and overvoltage improvement index, P Gi is the output active power of the i-th thermal power plant, P Gilim_low is the lower limit of active power output of the i-th thermal power plant, P Gilim_up is the upper limit of active power output of the i-th thermal power plant, U busi is the grid-connected bus voltage of the i-th thermal power station, U busilim_low is the lower limit of the grid-connected bus voltage of the i-th thermal power plant, U busilim_up is the upper limit of the grid-connected bus voltage of the i-th thermal power plant, P REj is the jth new energy source, P Lj is the active power of the jth load, N G is the number of thermal power plants, N RE is the amount of new energy, and are the lower and upper limits of the low-running active current coefficient, and are the lower and upper limits of the low-through reactive current coefficient, and are the lower and upper limits of the low-power active recovery coefficient, and They are the lower and upper limits of the high-throughput reactive current coefficient respectively.

[0024] Furthermore, in the process of optimizing the optimization object using the particle swarm optimization algorithm, when the particles in the population are screened for the kth time, the updated speed and position of each particle are as follows:

[0025] v i (k) = w i (k)v i +c1r1(p i -x i )+c2r2(p g -x i ) (17)

[0026] x i (k) = x i +v i (k) (18)

[0027] Among them, v i (k) is the updated speed of particle i after the kth iteration, w i (k) is the inertia weight of particle i at the kth iteration, x i 、v i is the original position and velocity of particle i, c1 and c2 are two learning factors at the kth iteration, r1 and r2 are random constants; p i is the original position of the individual optimal particle before this iteration, p g is the original position of the global optimal particle before this iteration; x i (k) is the updated position of particle i after the kth iteration.

[0028] Furthermore, the inertia weight of particle i at the kth iteration is as follows:

[0029]

[0030] Among them, w max and w min are the maximum and minimum values that the weight can take, respectively, f i (k) is the fitness function of particle i at the kth iteration; f min The lower limit of the particle's fitness, f a is the average fitness of the particles.

[0031] Furthermore, the fitness function of particle i at the kth iteration is as follows:

[0032] The expression of the fitness function is:

[0033]

[0034] Among them, λ1 and λ2 are weight factors, is the difference between the actual value and the expected value of the transient voltage at the grid-connected end of the i-th renewable energy at the k-th iteration, and t is the time.

[0035] Furthermore, the weights of the i-th new energy transient low voltage improvement target, the AC fault clearing steady-state voltage improvement target, and the transient overvoltage improvement target are as follows:

[0036]

[0037] Among them, α i , β i and γ i are the weights of the transient low voltage improvement target of the i-th renewable energy source, the steady-state voltage improvement target after AC fault clearance, and the transient overvoltage improvement target; ΔU L,max,i is the low voltage offset of the grid-connected terminal of the i-th renewable energy in the most serious AC fault scenario, U 0,i is the initial steady-state voltage of the grid-connected terminal of the i-th renewable energy source; ΔU clear,max,i ΔU is the offset between the recovered steady-state voltage and the initial steady-state voltage when the terminal voltage of the i-th renewable energy source recovers to steady-state under the worst working condition after the AC fault is cleared; H,max,i is the overvoltage offset of the grid-connected terminal of the i-th renewable energy source under the most serious DC fault scenario.

[0038] Furthermore, the normalization process of the transient voltage offset is as follows:

[0039]

[0040] in, and They are the offsets of low voltage, steady-state voltage and overvoltage at the i-th renewable energy grid-connected terminal under the j-th fault scenario after standardization, ΔU L,ij , ΔU clear,ij and ΔU H,ij are the offsets of low voltage, steady-state voltage and overvoltage at the i-th renewable energy grid-connected terminal under the j-th fault scenario before standardization, η j (ΔU L ), η j (ΔU clear ) and η j (ΔU H ) are the mean values of low voltage, steady-state voltage and overvoltage offset of all renewable energy grid-connected terminals under the j-th fault scenario, σ j (ΔU H ),σ j (ΔU L ) and σ j (ΔU clear) are the standard deviations of low voltage, steady-state voltage and overvoltage offset of all renewable energy grid-connected terminals.

[0041] Furthermore, the normalization of the renewable energy power regulation cost is as follows:

[0042]

[0043] in, and The normalized values are the power regulation cost of the i-th renewable energy during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period under the j-th fault scenario, TC L,ij TC clear,ij and TC H,ij are the power regulation costs of the i-th renewable energy during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period under the j-th fault scenario before normalization, η j (TC L ), η j (TC clear ) and η j (TC H ) are the mean power regulation costs of all renewable energy sources during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period under the j-th fault scenario, σ j (TC L ),σ j (TC clear ) and σ j (TC H ) are the standard deviations of the power regulation costs of all renewable energy sources during the LVRT period, the steady-state period after the AC fault is cleared, and the HVRT period, respectively.

[0044] A computer storage medium stores at least one instruction, which is loaded and executed by a processor to implement a new energy voltage ride-through parameter optimization method taking into account multi-form voltage stability constraints.

[0045] A device for optimizing voltage ride-through parameters of a new energy source taking into account multi-form voltage stability constraints. The device includes a processor and a memory. The memory stores at least one instruction. The at least one instruction is loaded and executed by the processor to implement a method for optimizing voltage ride-through parameters of a new energy source taking into account multi-form voltage stability constraints.

[0046] The present invention has the following beneficial effects:

[0047] The present invention considers the impact of renewable energy voltage ride-through parameters on different voltage forms in different fault scenarios, and realizes a renewable energy voltage ride-through parameter optimization method that can take into account multi-form voltage stability constraints. The present invention is not only applicable to renewable energy voltage ride-through parameter optimization in multiple fault scenarios, but can also optimize renewable energy voltage ride-through parameters for multiple voltage stability forms. The present invention comprehensively considers the voltage stability of a high-proportion renewable energy ultra-high voltage direct current (UHVDC) power grid under multiple faults, and uses a particle swarm optimization algorithm for optimization. The obtained optimization results can ensure the voltage stability of the high-proportion renewable energy power grid under multiple faults. The present invention can fully mobilize the voltage regulation capability of renewable energy to adapt to the voltage regulation requirements under different faults of the power grid, and provide guidance for the safe and stable operation of a high-proportion renewable energy ultra-high voltage direct current (UHVDC) power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Flowchart of the parameter optimization method for renewable energy voltage ride-through considering multi-state voltage stability constraints;

[0049] Figure 2 This is a schematic diagram for calculating the low voltage offset at the grid-connected renewable energy terminal when an AC fault occurs in the grid and the steady-state voltage offset after the fault is cleared;

[0050] Figure 3 This is a schematic diagram for calculating the overvoltage offset at the grid-connected renewable energy terminal when a DC fault occurs in the grid;

[0051] Figure 4 This is a comparison diagram of the transient voltage at the grid-connected terminal of a new energy source before and after parameter optimization under a DC bipolar blocking fault;

[0052] Figure 5 This is a comparison chart of the transient voltage at the grid-connected terminal of a new energy source before and after parameter optimization under a bipolar blocking fault after four DC commutation failures;

[0053] Figure 6 This is a comparison diagram of the transient voltage at the grid-connected terminal of a new energy source before and after parameter optimization under the fault of AC line N-2 on No. 1;

[0054] Figure 7 This is a comparison diagram of the transient voltage at the grid-connected terminal of a new energy source before and after parameter optimization under the fault of AC line N-2 on No. 2;

[0055] Figure 8 This is a comparison chart of the transient voltage at the grid-connected terminal of a new energy source before and after parameter optimization under the fault of AC line N-2 on No. 3;

[0056] Figure 9 This is a comparison diagram of the transient voltage at the grid-connected end of a new energy source before and after parameter optimization under the N-2 fault of AC line No. 4. DETAILED DESCRIPTION

[0057] Existing methods for optimizing renewable energy voltage ride-through parameters are only applicable to single fault scenarios and optimize renewable energy voltage ride-through parameters for only one voltage stability form. This method ignores the impact of renewable energy voltage ride-through parameters on grid voltage safety and stability in other fault scenarios and does not consider the voltage regulation cost of renewable energy. To balance voltage safety and stability in different forms under various fault types, the present invention proposes a renewable energy voltage ride-through parameter optimization method that takes into account multi-form voltage stability constraints, based on the consideration of renewable energy voltage regulation costs. The present invention is described below in conjunction with specific implementation methods. Specific implementation method one:

[0059] This implementation method is a parameter optimization method for voltage ride-through of renewable energy taking into account multi-form voltage stability constraints. Starting from the electromechanical modeling of the ultra-high voltage direct current (UHVDC) sending-end power grid with a high proportion of renewable energy, a general modeling method for renewable energy, synchronous machine and ultra-high voltage direct current is studied. An electromechanical model of the ultra-high voltage direct current (UHVDC) sending-end power grid with a high proportion of renewable energy, including direct-drive wind farms, doubly-fed wind farms, photovoltaics, synchronous machines and ultra-high voltage direct current, is constructed on a comprehensive power system analysis program simulation platform. The dominant control parameters of the dynamic characteristics of renewable energy during high / low ride-through are analyzed. On this basis, the impact of the dominant control parameters of renewable energy on the safety and stability of the grid voltage under different fault scenarios is analyzed, and the objective function and constraint conditions for improving the safety and stability of the grid are constructed. On this basis, a parameter optimization model for voltage ride-through of renewable energy is established, and an optimization model solution method based on the particle swarm optimization algorithm is established. The voltage ride-through parameters of renewable energy are optimized to improve the safety and stability of the voltage of the ultra-high voltage direct current sending-end power grid with a high proportion of renewable energy under different fault scenarios.

[0060] The process of the new energy voltage ride-through parameter optimization method considering multi-form voltage stability constraints is as follows: Figure 1 The specific process of the new energy voltage ride-through parameter optimization method considering multi-form voltage stability constraints described in this embodiment is as follows:

[0061] Starting from the general control strategy and control parameters of new energy, the high-throughput control strategy of new energy during transient overvoltage, the low-throughput control strategy during transient low voltage, and the active power recovery strategy after fault clearing are determined. Based on the control strategy of new energy during high / low through-put, the calculation formulas for active and reactive power during high / low through-put of new energy are determined, and the dominant control parameters affecting the dynamic characteristics of new energy during high / low through-put are determined, and a general control model for new energy is established.

[0062] A typical fault scenario set for the UHVDC sending-end power grid with a high proportion of new energy is constructed, including typical DC fault scenario sets such as DC commutation failure fault, DC single-machine lockout fault, DC bipolar lockout fault, and DC bipolar lockout fault after continuous commutation failure. It also includes typical AC fault scenario sets such as AC short circuit fault, AC N-1 fault, and AC N-2 fault, and collects the time series voltage of the new energy grid-connected end under various fault scenarios.

[0063] When developing a parameter optimization model for renewable energy voltage ride-through, it's important to consider the impact of renewable energy grid connection location and capacity on voltage regulation and analyze the renewable energy's ability to regulate voltage under different voltage conditions. The objective function is established to minimize transient overvoltage at the renewable energy grid connection under a set of DC fault scenarios while maximizing voltage stability under a set of AC fault scenarios. Constraints include the power constraints of the thermal power units, the voltage constraints of the grid-connected buses, the power balance constraints of the grid, the range constraints on the dominant parameters of the renewable energy high / low ride-through dynamic characteristics, and the current constraints of the renewable energy.

[0064] In the process of solving the optimization model using the particle swarm optimization algorithm, it is necessary to abstract the elements in the optimization object into massless and volumeless particles, and then form a population from these particles. In the population, the particles are screened by judging their fitness for the optimization target, and finally the optimal particles in the space are obtained, which is recorded as the optimal solution. This includes establishing an iterative formula for updating the velocity and position of each particle and calculating the inertia weight.

[0065] More specifically,

[0066] Currently, most grid-connected converters for renewable energy sources connected to the grid are grid-following type. If an AC short circuit occurs in the grid and the voltage at the grid-connected end of the renewable energy source drops below 0.9pu, the control strategy for the renewable energy source will switch to low voltage ride-through control. The low voltage ride-through active current and low voltage ride-through reactive current of each type of renewable energy source can be uniformly expressed as:

[0067]

[0068] Among them, I pLV K is the active current of new energy low-power consumption, pLV is the low wear active current coefficient, I p0 is the steady-state current of new energy, I max is the upper limit of the output current of new energy, I qLV K is the low reactive current of new energy, qLV is the low-throughput reactive current coefficient, U t is the voltage of the new energy port, I N is the rated current of new energy, U Lin To enter the low voltage ride-through threshold.

[0069] The active and reactive output of each type of renewable energy during the low-energy period can be expressed as:

[0070]

[0071] Among them, P LV Output active power during the period of new energy low penetration, Q LV Output reactive power during the period of new energy low penetration.

[0072] The active power recovery coefficient of new energy low-power is K rec When the new energy exits the low-voltage operation, the active power is calculated according to K rec The recovery rate (pu / s) returns to steady state.

[0073] If a DC fault occurs in the power grid and the voltage at the grid-connected renewable energy terminal rises above 1.1pu, the control strategy for the renewable energy will switch to high voltage ride-through control. The high voltage ride-through active current and high voltage ride-through reactive current of each type of renewable energy can be uniformly expressed as:

[0074]

[0075] Among them, I pHV is the high-power active current of new energy, P0 is the steady-state active current of new energy, I qHV K is the reactive current of new energy high voltage, qHV is the high-throughput reactive current coefficient, P0 is the steady-state active power of new energy, U Hin To enter the high voltage ride-through threshold.

[0076] The active and reactive output of each type of renewable energy during the high-peak period can be expressed as:

[0077]

[0078] Among them, P HV Output active power during the period of high penetration of new energy, Q HV Output reactive power during the period of high power penetration of new energy.

[0079] From the above analysis, it can be seen that the dominant control parameter affecting the dynamic characteristics of new energy during high / low wear-through is the low wear-through active current coefficient K pLV , low-pass reactive current coefficient K qLV And the active recovery coefficient K rec The dominant parameter affecting the dynamic characteristics of renewable energy during high-voltage power generation is the high-voltage power generation reactive current coefficient K. qHV .

[0080] To ensure that the optimized voltage ride-through parameters of renewable energy sources can fully mobilize the renewable energy sources' ability to regulate different voltage forms in different fault scenarios, it is necessary to construct a fault scenario set to analyze the impact of the renewable energy voltage ride-through parameters on transient overvoltage, transient undervoltage, and post-fault steady-state voltage under various fault scenarios. The fault scenario set includes typical DC fault scenarios such as DC commutation failure, DC single-machine lockout fault, DC bipolar lockout fault, and DC bipolar lockout fault after continuous commutation failure. It also includes typical AC fault scenarios such as AC short circuit fault, AC N-1 fault, and AC N-2 fault. The active power and reactive power output of the renewable energy sources and the time series voltage at the grid-connected end are collected under each fault scenario.

[0081] In order to quantify the severity of different voltage forms after a grid fault, the offset ΔU between the minimum voltage at the grid-connected renewable energy terminal and the initial steady-state voltage during the AC fault is used. L To evaluate the severity of transient low voltage, the offset ΔU between the voltage at the grid-connected terminal of renewable energy and the initial steady-state voltage during the steady-state period after the AC fault is cleared is used. clear To evaluate the voltage recovery effect, the offset ΔU between the maximum voltage at the renewable energy grid-connected terminal and the initial steady-state voltage during the DC fault is used. H To evaluate the severity of transient overvoltage, the offsets between different forms of voltage and the initial steady-state voltage are as follows: Figure 2 and Figure 3 As shown, Figure 2 In the figure, U0 is the initial steady-state voltage of the new energy source, U L is the transient low voltage at the renewable energy grid-connected terminal when an AC fault occurs in the power grid, ΔU L is the low voltage offset, ΔU clear is the steady-state voltage offset, Figure 3 Middle,U H is the transient overvoltage at the grid-connected terminal of renewable energy when a DC fault occurs in the grid, ΔU H is the overvoltage offset. The calculation expressions for low voltage offset, steady-state voltage offset and overvoltage offset are:

[0082]

[0083] In order to quantitatively evaluate the improvement of transient voltage after optimizing the voltage ride-through parameters of new energy sources, low voltage improvement index, steady-state voltage improvement index, and overvoltage improvement index are constructed to evaluate the improvement effects of transient low voltage, steady-state voltage after AC fault clearance, and transient overvoltage, respectively. The expression of the low voltage improvement index is:

[0084]

[0085] Among them, IF L,ijis the low voltage improvement index of the i-th new energy source under the j-th fault scenario, U 0,i is the initial steady-state voltage of the grid-connected terminal of the i-th renewable energy source, U L,ij is the transient low voltage at the grid-connected terminal of the i-th renewable energy source under the j-th fault scenario, ΔU L,max,i is the low voltage offset of the grid-connected terminal of the i-th renewable energy in the most serious AC fault scenario, ΔU L,max,i It can be the historical maximum value of AC faults in the actual power grid, or through transient simulation technology, the voltage change trend of the new energy grid-connected terminal can be evaluated according to the N-1 principle, ΔU L,max,i Take the maximum deviation value.

[0086] The expression of steady-state voltage improvement index is:

[0087]

[0088] Among them, IF clear,ij is the steady-state voltage improvement index of the i-th renewable energy source under the j-th fault scenario, U clear,ij is the steady-state voltage of the i-th renewable energy grid-connected terminal after the AC fault is cleared in the j-th fault scenario, ΔU clear,max,i ΔU is the offset between the recovered steady-state voltage and the initial steady-state voltage when the terminal voltage of the i-th renewable energy source recovers to steady-state under the worst working condition after the AC fault is cleared. clear,max,i It can be the historical maximum value of the steady-state voltage offset after the AC fault is cleared in the actual power grid, or through transient simulation technology, it can be used to evaluate the change trend of the voltage at the renewable energy grid-connected terminal after the AC fault is restored, ΔU clear,max,i Take the maximum deviation value.

[0089] The expression of overvoltage improvement index is:

[0090]

[0091] Among them, IF H,ij is the overvoltage improvement index of the i-th renewable energy source under the j-th fault scenario, U H,ij is the transient overvoltage at the grid-connected terminal of the i-th renewable energy source under the j-th fault scenario, ΔU H,max,i is the overvoltage offset of the grid-connected terminal of the i-th renewable energy in the most serious DC fault scenario, ΔU H,max,i It can be the historical maximum value of DC faults in actual power grids, or through transient simulation technology, the change trend of the voltage at the grid-connected terminal of renewable energy during DC faults can be evaluated, ΔU H,max,i Take the maximum deviation value.

[0092] In addition to the primary indicator of voltage improvement at the grid-connected end of renewable energy, the renewable energy regulation cost is added as a secondary indicator, taking into account that changes in renewable energy output can cause equipment losses. This ensures the economic efficiency of renewable energy in the voltage regulation process. The type of renewable energy and the location where it is connected to the grid both affect its voltage regulation capability, resulting in different voltage regulation costs for each renewable energy source in the grid. Since renewable energy sources achieve voltage regulation by changing the output power characteristics, the voltage regulation cost of renewable energy can be reflected by its power regulation cost. The expression for the power regulation cost of renewable energy during low voltage ride-through, the steady-state period after the AC fault is cleared, and the high voltage ride-through period is:

[0093]

[0094] Among them, TC L,ij TC clear,ij and TC H,ij are the power regulation costs of the i-th renewable energy during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period under the j-th fault scenario, respectively. L,l,i 、C clear,l,i and C H,l,i are the power regulation cost coefficients of the i-th renewable energy during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period, ΔP L,l,ij and ΔQ L,l,ij They are the changes in active power and reactive power of the i-th renewable energy during low voltage ride-through under the j-th fault scenario, ΔP clear,l,ij and ΔQ clear,l,ij are the changes in active power and reactive power of the i-th renewable energy source during the steady-state period after the AC fault is cleared under the j-th fault scenario, ΔP H,l,ij and ΔQ H,l,ij are the changes in active power and reactive power of the i-th renewable energy during the high voltage ride-through period under the j-th fault scenario.

[0095] Due to different fault scenarios, the severity of transient low voltage at the renewable energy grid-connected end, steady-state voltage after AC fault clearance, and transient overvoltage varies. Considering the differences in voltage support requirements at the renewable energy grid-connected end at different stages after a grid fault, and taking into account the improvement effect of transient voltage due to changes in renewable energy output power characteristics, voltage corresponding weights are constructed so that the weights of different voltage forms in the control target can be automatically adjusted according to their severity, thereby achieving the effect of dynamic adjustment of voltage regulation priorities of different forms. The calculation expression of the target weight is:

[0096]

[0097] Among them, α i , β i and γi are the weights of the transient low voltage improvement target of the i-th new energy, the steady-state voltage improvement target after the AC fault is cleared, and the transient overvoltage improvement target.

[0098] The transient voltage offset of the i-th renewable energy grid-connected terminal under the j-th fault scenario is:

[0099]

[0100] Among them, F U,i is the transient voltage offset of the i-th renewable energy grid-connected terminal, and are the offsets of low voltage, steady-state voltage and overvoltage at the i-th renewable energy grid-connected terminal under the j-th fault scenario, respectively. N1 is the number of AC fault scenarios, and N2 is the number of DC fault scenarios.

[0101] The power regulation cost of the i-th renewable energy source under the j-th fault scenario is:

[0102]

[0103] Among them, F C,i is the power regulation cost of the i-th renewable energy source.

[0104] In order to ensure that the transient voltage offset of the renewable energy grid-connected terminal is minimized while the power regulation cost of renewable energy is also minimized, it is necessary to set F U,ij With F C,ij Linear summation, but both include multiple factors such as voltage change, power change, and cost change. The dimensions and orders of magnitude of these factors are different. In order to ensure the comparability and additivity of the characteristics of different factors and improve the accuracy of the model optimization results, it is necessary to standardize the transient voltage offset and the renewable energy power regulation cost respectively. The standardized calculation of transient voltage offset is:

[0105]

[0106] in, and They are the offsets of low voltage, steady-state voltage and overvoltage at the i-th renewable energy grid-connected terminal under the j-th fault scenario after standardization, ΔU L,ij , ΔU clear,ij and ΔU H,ij are the offsets of low voltage, steady-state voltage and overvoltage at the i-th renewable energy grid-connected terminal under the j-th fault scenario before standardization, η j (ΔU L ), η j (ΔU clear ) and η j (ΔU H) are the mean values of low voltage, steady-state voltage and overvoltage offset of all renewable energy grid-connected terminals under the j-th fault scenario, σ j (ΔU H ),σ j (ΔU L ) and σ j (ΔU clear ) are the standard deviations of low voltage, steady-state voltage and overvoltage offset of all renewable energy grid-connected terminals.

[0107] The standardized calculation of the cost of renewable energy power regulation is:

[0108]

[0109] in, and The normalized values are the power regulation cost of the i-th renewable energy during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period under the j-th fault scenario, TC L,ij TC clear,ij and TC H,ij are the power regulation costs of the i-th renewable energy during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period under the j-th fault scenario before normalization, η j (TC L ), η j (TC clear ) and η j (TC H ) are the mean power regulation costs of all renewable energy sources during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period under the j-th fault scenario, σ j (TC L ),σ j (TC clear ) and σ j (TC H ) are the standard deviations of the power regulation costs of all renewable energy sources during the LVRT period, the steady-state period after the AC fault is cleared, and the HVRT period, respectively.

[0110] During the optimization of renewable energy voltage ride-through parameters, the regulation priority of different voltage forms is considered. After taking into account the target weight, an objective function is established with the goal of minimizing the transient voltage offset of all renewable energy grid-connected terminals under various fault conditions and minimizing the power regulation cost of renewable energy. The expression of the objective function is:

[0111]

[0112] Where F is the objective function of the new energy voltage ride-through parameter optimization model, and n is the number of new energy sources.

[0113] The constraints of the new energy voltage ride-through parameter optimization model are:

[0114]

[0115] Among them, IF L_low IF clear_low and IF H_low They are the lower limits of low voltage improvement index, steady-state voltage improvement index and overvoltage improvement index, P Gi is the output active power of the i-th thermal power plant, P Gilim_low is the lower limit of active power output of the i-th thermal power plant, P Gilim_up is the upper limit of active power output of the i-th thermal power plant, U busi is the grid-connected bus voltage of the i-th thermal power station, U busilim_low is the lower limit of the grid-connected bus voltage of the i-th thermal power plant, U busilim_up is the upper limit of the grid-connected bus voltage of the i-th thermal power plant, P REj is the jth new energy source, P Lj is the active power of the jth load, N G is the number of thermal power plants, N RE is the amount of new energy, and are the lower and upper limits of the low-running active current coefficient, and are the lower and upper limits of the low-through reactive current coefficient, and are the lower and upper limits of the low-power active recovery coefficient, and They are the lower and upper limits of the high-throughput reactive current coefficient respectively.

[0116] In the process of solving the optimization model using the particle swarm optimization algorithm, it is necessary to set the optimization object K pLV , K qLV , K rec and K qHV Abstracted as massless and volumeless particles, these particles then form a population. Within the population, particles are screened by judging their fitness for the optimization goal, and finally the optimal particle in the space is obtained, recorded as the optimal solution. This includes establishing an iterative formula for updating the velocity and position of each particle and calculating the inertia weight. When the particles in the population are screened for the kth time, the updated velocity and position of each particle are:

[0117] v i (k) = w i (k)v i +c1r1(p i -x i )+c2r2(p g -x i) (17)

[0118] Among them, v i (k) is the updated speed of particle i after the kth iteration, w is the inertia weight in the iterative screening process, x i 、v i is the original position and velocity of particle i, c1 and c2 are two learning factors at the kth iteration, r1 and r2 are random constants; p i is the original position of the individual optimal particle before this iteration, p g is the original position of the global optimal particle before this iteration.

[0119] x i (k) = x i +v i (k) (18)

[0120] Among them, x i (k) is the updated position of particle i after the kth iteration.

[0121] In the particle swarm optimization algorithm, the inertia weight represents the particle's inheritance of the original velocity. The larger the inertia weight, the more the particle can maintain its original route, which gives the particle more opportunities to explore unknown areas. The smaller the inertia weight, the more willing the particle is to stick to its own and the swarm's excellent route, which allows the particle to perform a more detailed search at a better location. In order to improve the algorithm's solution speed while ensuring solution accuracy, the algorithm gives particles a larger inertia weight at the beginning of the iteration to better perform global searches and explore unknown areas. The algorithm gives particles a smaller inertia weight at the end of the iteration to better perform detailed local searches and obtain more accurate solutions. The inertia weight calculation expression is:

[0122]

[0123] Among them, w i (k) is the inertia weight value of particle i at the kth iteration, w max and w min are the maximum and minimum values that the weight can take, respectively, f i (k) is the fitness function of particle i at the kth iteration; f min The lower limit of the particle's fitness, f a is the average fitness of the particles.

[0124] The fitness function is a metric that measures the quality of the voltage at the grid-connected terminal of renewable energy. To improve the transient low voltage at the grid-connected terminal of renewable energy under AC fault conditions and the steady-state voltage after the fault is cleared, and to reduce the transient overvoltage at the grid-connected terminal of renewable energy under DC fault conditions, both the absolute error integral criterion and the time squared error integral criterion are considered when designing the fitness function.

[0125] The voltage ride-through parameters of renewable energy are optimized based on the absolute error integral criterion. The optimized voltage at the renewable energy grid-connected terminal has a good transient response and can effectively improve the transient low voltage at the renewable energy grid-connected terminal under AC faults and the transient overvoltage under DC faults. However, there may be a problem of unclear feedback of steady-state voltage evaluation indicators after the AC fault is cleared.

[0126] The time-squared error integral criterion uses the error in the late response period as the main performance evaluation indicator. It can effectively improve the steady-state voltage of the renewable energy grid-connected terminal after the AC fault is cleared, but the error feedback for transient undervoltage and transient overvoltage is not obvious.

[0127] Therefore, when designing the fitness function, considering both the absolute error integral criterion and the time squared error integral criterion can simultaneously take into account the improvement effects of transient low voltage, transient overvoltage at the renewable energy grid-connected end, and steady-state voltage after AC fault clearance. The specific fitness function expression is:

[0128]

[0129] Among them, λ1 and λ2 are weight factors, is the difference between the actual value and the expected value of the transient voltage at the grid-connected terminal of the i-th renewable energy at the k-th iteration, The absolute error integral criterion that characterizes the fitness function, Characterizes the time-squared error integration criterion of the fitness function, where t is time.

[0130] Example

[0131] To verify the effectiveness of the proposed renewable energy voltage ride-through parameter optimization method considering multi-form voltage stability constraints, the renewable energy voltage ride-through parameters were optimized based on a model in which renewable energy accounts for 80% of a regional power grid in 2025 and the DC power grid operates under heavy load. The renewable energy low / high ride-through control parameters before and after optimization are shown in Table 1.

[0132] Table 1 New energy low / high penetration control parameters before and after optimization

[0133]

[0134] Taking the DC bipolar lockout fault, DC four-time commutation failure fault, DC four-time commutation failure followed by bipolar lockout fault, and AC N-2 fault as examples, the voltage results before and after the optimization of the new energy low / high-speed ride-through control parameters are compared in Tables 2 and 3. It can be seen that after the optimization of the new energy low / high-speed ride-through control parameters, the maximum and average values of the transient overvoltage at the grid-connected end of the new energy in the DC fault scenario are reduced, and the minimum and average values of the steady-state voltage at the grid-connected end of the new energy in the N-2 fault scenario after the fault are improved, verifying the effectiveness of the method.

[0135] Table 2 Comparison of new energy terminal voltage before and after optimization under different DC fault conditions

[0136]

[0137] Table 3 Comparison of new energy terminal voltage before and after optimization under different N-2 faults

[0138]

[0139]

[0140] Taking the DC bipolar lockout fault, the DC bipolar lockout fault after four commutation failures, and the AC N-2 fault as examples, the transient voltage of the new energy grid-connected terminal under different faults before and after the optimization of the new energy low / high wear control parameters is as follows: Figures 4 to 9 As shown in the figure, compared with the control parameters before optimization, the optimized control parameters can improve the transient low voltage of the renewable energy grid-connected terminal during the AC N-2 fault and the steady-state voltage after the fault, and can also suppress the transient overvoltage peak of the renewable energy grid-connected terminal under the DC bipolar blocking fault and the bipolar blocking fault after four commutation failures. Specific implementation method 2:

[0142] This embodiment is a computer storage medium, which stores at least one instruction. The at least one instruction is loaded and executed by a processor to implement the new energy voltage ride-through parameter optimization method taking into account multi-form voltage stability constraints.

[0143] It should be understood that the instructions include computer program products, software, or computerized methods corresponding to any method described in the present invention; the instructions can be used to program a computer system or other electronic device. Computer storage media may include readable media on which instructions are stored, and may include but are not limited to magnetic storage media, optical storage media; magneto-optical storage media include read-only memory ROM, random access memory RAM, erasable programmable memory (e.g., EPROM and EEPROM) and flash memory layers, or other types of media suitable for storing electronic instructions. Specific implementation method three:

[0145] This embodiment provides a device for optimizing voltage ride-through parameters of renewable energy sources taking into account multi-mode voltage stability constraints. The device includes a processor and a memory. It should be understood that the device includes any device including a processor and a memory described in the present invention. The device may also include other units or modules that perform display, interaction, processing, control, and other functions through signals or instructions.

[0146] At least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the new energy voltage ride-through parameter optimization method taking into account multi-form voltage stability constraints.

[0147] Those skilled in the art will appreciate that at least one instruction stored is a computer program product corresponding to the method or system. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0148] The present application is described with reference to the flowcharts and / or block diagrams of the methods, systems, and computer program products according to the embodiments of the present application, and can also be used for corresponding devices. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0151] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0152] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

[0153] The above examples are merely illustrative of the calculation model and process of the present invention and are not intended to limit the embodiments of the present invention. Persons skilled in the art will readily appreciate that other variations or modifications based on the above description are possible. This list of embodiments is not exhaustive; however, any obvious variations or modifications derived from the technical solution of the present invention remain within the scope of protection of the present invention.

Claims

1. A method for optimizing parameters of renewable energy voltage ride-through considering multi-form voltage stability constraints, characterized in that: include: According to the new energy voltage ride-through process, the power regulation cost of new energy during the low voltage ride-through period, the steady-state period after the AC fault is cleared, and the high voltage ride-through period is expressed as follows: Among them, TC L,ij TC clear,ij and TC H,ij are the power regulation costs of the i-th renewable energy during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period under the j-th fault scenario, respectively. L,l,i 、C clear,l,i and C H,l,i are the power regulation cost coefficients of the i-th renewable energy during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period, ΔP L,l,ij and ΔQ L,l,ij They are the changes in active power and reactive power of the i-th renewable energy during low voltage ride-through under the j-th fault scenario, ΔP clear,l,ij and ΔQ clear,l,ij are the changes in active power and reactive power of the i-th renewable energy source during the steady-state period after the AC fault is cleared under the j-th fault scenario, ΔP H,l,ij and ΔQ H,l,ij are the changes in active power and reactive power of the i-th renewable energy during the high voltage ride-through period under the j-th fault scenario; The transient voltage offset is standardized to obtain the offset of the low voltage, steady-state voltage and overvoltage at the i-th renewable energy grid-connected terminal in the j-th fault scenario after standardization, that is, and The power regulation cost of renewable energy is standardized to obtain the power regulation cost of the i-th renewable energy during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period under the j-th fault scenario, that is, and Then determine the objective function: Where F is the objective function of the new energy voltage ride-through parameter optimization model, n is the number of new energy sources; α i , β i and γ i are the weights of the transient low voltage improvement target of the i-th renewable energy source, the steady-state voltage improvement target after AC fault clearance, and the transient overvoltage improvement target; Based on the objective function, the low-pass active current coefficient K will be included pLV , low-throughput reactive current coefficient K qLV , New energy low-power active power recovery coefficient K rec and high-throughput reactive current coefficient K qHV The optimization object is used as the particle in the particle swarm optimization algorithm. The particle swarm optimization algorithm is used to optimize the optimization object to obtain the optimization result.

2. The method for optimizing parameters of renewable energy voltage ride-through considering multi-form voltage stability constraints according to claim 1, characterized in that: When using the particle swarm optimization algorithm to optimize the object, the following constraints need to be met: P Gilim_low ≤P Gi ≤P Gilim_up IN busilim_low ≤U busi ≤U busilim_up in, is the low voltage improvement index, U 0,i is the initial steady-state voltage of the grid-connected terminal of the i-th renewable energy source, U L,ij is the transient low voltage at the grid-connected terminal of the i-th renewable energy source under the j-th fault scenario, ΔU L,max,i is the low voltage offset of the grid-connected terminal of the i-th renewable energy in the most serious AC fault scenario; is the steady-state voltage improvement index, U clear,ij is the steady-state voltage of the i-th renewable energy grid-connected terminal after the AC fault is cleared in the j-th fault scenario, ΔU clear,max,i is the offset between the recovered steady-state voltage and the initial steady-state voltage of the i-th renewable energy source when its terminal voltage recovers to steady-state under the worst working condition after the AC fault is cleared; is the overvoltage improvement index, U H,ij is the transient overvoltage at the grid-connected terminal of the i-th renewable energy source under the j-th fault scenario, ΔU H,max,i is the overvoltage offset of the grid-connected terminal of the i-th renewable energy in the most serious DC fault scenario; IF L_low IF clear_low and IF H_low They are the lower limits of low voltage improvement index, steady-state voltage improvement index and overvoltage improvement index, P Gi is the output active power of the i-th thermal power plant, P Gilim_low is the lower limit of active power output of the i-th thermal power plant, P Gilim_up is the upper limit of active power output of the i-th thermal power plant, U busi is the grid-connected bus voltage of the i-th thermal power plant, U busilim_low is the lower limit of the grid-connected bus voltage of the i-th thermal power plant, U busilim_up is the upper limit of the grid-connected bus voltage of the i-th thermal power plant, P REj is the jth new energy source, P Lj is the active power of the jth load, N G is the number of thermal power plants, N RE is the amount of new energy, and are the lower and upper limits of the low-running active current coefficient, and are the lower and upper limits of the low-through reactive current coefficient, and are the lower and upper limits of the low-power active recovery coefficient, and They are the lower and upper limits of the high-throughput reactive current coefficient respectively.

3. The method for optimizing parameters of renewable energy voltage ride-through considering multi-form voltage stability constraints according to claim 2, characterized in that: When the particle swarm optimization algorithm is used to optimize the object, when the particles in the population are screened for the kth time, the updated speed and position of each particle are as follows: v i (k)=w i (k)v i +c1r1(p i -x i )+c2r2(p g -x i ) (17) x i (k)=x i +v i (k) (18) Among them, v i (k) is the updated speed of particle i after the kth iteration, w i (k) is the inertia weight of particle i at the kth iteration, x i 、v i is the original position and velocity of particle i, c1 and c2 are two learning factors at the kth iteration, r1 and r2 are random constants; p i is the original position of the individual optimal particle before this iteration, p g is the original position of the global optimal particle before this iteration; x i (k) is the updated position of particle i after the kth iteration.

4. The method for optimizing parameters of renewable energy voltage ride-through considering multi-form voltage stability constraints according to claim 3, characterized in that: The inertia weight of particle i at the kth iteration is as follows: Among them, w max and w min are the maximum and minimum values that the weight can take, respectively, f i (k) is the fitness function of particle i at the kth iteration; f min The lower limit of the particle's fitness, f a is the average fitness of the particles.

5. The method for optimizing parameters of renewable energy voltage ride-through considering multi-form voltage stability constraints according to claim 4, characterized in that: The fitness function of particle i at the kth iteration is as follows: The expression of the fitness function is: Among them, λ1 and λ2 are weight factors, is the difference between the actual value and the expected value of the transient voltage at the grid-connected end of the i-th renewable energy at the k-th iteration, and t is the time.

6. A method for optimizing parameters of renewable energy voltage ride-through taking into account multi-form voltage stability constraints according to any one of claims 1 to 5, characterized in that: The weights of the i-th new energy transient low voltage improvement target, the AC fault clearing steady-state voltage improvement target, and the transient overvoltage improvement target are as follows: Among them, α i , β i and γ i are the weights of the transient low voltage improvement target of the i-th renewable energy source, the steady-state voltage improvement target after AC fault clearance, and the transient overvoltage improvement target; ΔU L,max,i is the low voltage offset of the grid-connected terminal of the i-th renewable energy in the most serious AC fault scenario, U 0,i is the initial steady-state voltage of the grid-connected terminal of the i-th renewable energy source; ΔU clear,max,i ΔU is the offset between the recovered steady-state voltage and the initial steady-state voltage when the terminal voltage of the i-th renewable energy source recovers to steady-state under the worst working condition after the AC fault is cleared; H,max,i is the overvoltage offset of the grid-connected terminal of the i-th renewable energy source under the most serious DC fault scenario.

7. The method for optimizing parameters of renewable energy voltage ride-through considering multi-form voltage stability constraints according to claim 6, characterized in that: The normalization of transient voltage offset is as follows: in, and They are the offsets of low voltage, steady-state voltage and overvoltage at the i-th renewable energy grid-connected terminal under the j-th fault scenario after standardization, ΔU L,ij , ΔU clear,ij and ΔU H,ij are the offsets of low voltage, steady-state voltage and overvoltage at the i-th renewable energy grid-connected terminal under the j-th fault scenario before standardization, η j (ΔU L ), η j (ΔU clear ) and η j (ΔU H ) are the mean values of low voltage, steady-state voltage and overvoltage offset of all renewable energy grid-connected terminals under the j-th fault scenario, σ j (ΔU H ),σ j (ΔU L ) and σ j (ΔU clear ) are the standard deviations of low voltage, steady-state voltage and overvoltage offset of all renewable energy grid-connected terminals.

8. The method for optimizing parameters of renewable energy voltage ride-through considering multi-form voltage stability constraints according to claim 6, characterized in that: The normalized treatment of the power regulation cost of new energy is as follows: in, and The normalized values are the power regulation cost of the i-th renewable energy during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period under the j-th fault scenario, TC L,ij TC clear,ij and TC H,ij are the power regulation costs of the i-th renewable energy during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period under the j-th fault scenario before normalization, η j (TC L ), η j (TC clear ) and η j (TC H ) are the mean power regulation costs of all renewable energy sources during the low voltage ride-through period, the steady state period after the AC fault is cleared, and the high voltage ride-through period under the j-th fault scenario, σ j (TC L ),σ j (TC clear ) and σ j (TC H ) are the standard deviations of the power regulation costs of all renewable energy sources during the LVRT period, the steady-state period after the AC fault is cleared, and the HVRT period, respectively.

9. A computer storage medium, characterized in that The storage medium stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement a new energy voltage ride-through parameter optimization method taking into account multi-form voltage stability constraints as described in any one of claims 1 to 8.

10. A new energy voltage ride-through parameter optimization device taking into account multi-form voltage stability constraints, characterized in that: The device includes a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement a new energy voltage ride-through parameter optimization method taking into account multi-form voltage stability constraints as described in any one of claims 1 to 8.

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