New energy voltage ride-through parameter optimization method considering polymorphic voltage stability constraint, storage medium and equipment

Abstract

The invention discloses a new energy voltage ride-through parameter optimization method considering polymorphic voltage stability constraint, a storage medium and equipment, and belongs to the technical field of power system safety and stability improvement. In order to solve the problem that an existing new energy voltage ride-through parameter optimization method is only suitable for a single fault scene and only aims at a voltage stable form to optimize a new energy voltage ride-through parameter, the invention provides a new energy voltage ride-through parameter optimization method. Based on the dynamic characteristics of the new energy during the power grid fault period, the new energy general control model is established so as to determine the dominant control parameters influencing the dynamic characteristics of the new energy during the high / low voltage penetration period, the influence of the dominant control parameters on the voltage stability under different faults is considered, and the voltage regulation capability and the power regulation cost of the new energy are considered. A new energy voltage ride-through parameter optimization model considering polymorphic voltage stability constraints is established, and the optimization model is solved by adopting a particle swarm optimization algorithm. The method is used for voltage ride-through parameter optimization of new energy.

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 basic energy sources for human production and life, and can provide a strong driving force for social development and scientific and technological progress. However, it has also gradually exposed the inherent shortcomings of fossil energy, such as non-renewable and highly polluting. Therefore, it is urgent to develop and utilize clean energy more widely, deeply, efficiently and safely. However, the large-scale grid connection of new energy will make the voltage support capacity face serious shortages. The interaction between new energy and AC main grid and UHV DC, the system transient overvoltage, low voltage, continuous low voltage after fault and other safety and stability risks continue to increase, becoming the main factor restricting the absorption and transmission capacity of new energy. In terms of voltage form, the generation mechanism of voltage problems such as transient overvoltage, low voltage and continuous low voltage after fault is different. The influence of new energy voltage ride-through parameters on different forms of voltage is different. The unreasonable new energy voltage ride-through parameters cause the transmission capacity of new energy to be seriously limited, and the operation and control of the power grid face great challenges.

[0003] At present, there are some solutions for optimizing the control characteristics of new energy, 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. Starting from the mechanism of transient overvoltage in the system after a DC fault disturbance, this article 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 UHV DC transmission project, the sensitivity analysis of the unit fault ride-through control parameters to the voltage transient characteristics was carried out based on time domain simulation, and optimization suggestions for wind turbine control strategies and model parameters were proposed.

[0005] 2. Lin Weifang et al. published “Optimization of Control Parameters of Voltage Ride Through for Renewable Energy 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 renewable energy units during high / low voltage ride through on transient overvoltage at the DC sending end based on power system analysis comprehensive program simulation, proposed the optimization principles and ideas for renewable energy unit parameters, and verified their rationality through simulation based on actual power 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. Starting from the reactive power characteristics of the system, this article studies the mechanism by which the control parameters of the converters on the DC rectifier side and the wind turbine rotor side affect transient overvoltage, and constructs an optimization model with the goal of minimizing the peak value of transient overvoltage in 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, the current research on optimizing the control characteristics of renewable energy sources to improve voltage safety and stability ignores 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 voltage safety and stability in different forms under multiple fault types. Summary of the invention

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

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

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

[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 are the changes in active 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 in the steady state 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 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, and the standardized cost is 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 the AC fault is cleared, and the transient overvoltage improvement target;

[0017] Based on the objective function, the low-wear active current coefficient K will be included pLV , low wear reactive current coefficient K qLV 、The active power recovery coefficient of new energy low wear 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, in the process of optimizing the optimization object using the particle swarm optimization algorithm, 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, It is the low voltage improvement index, U 0,i is the initial steady-state voltage of the ith renewable energy grid-connected terminal, 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 end 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 ith renewable energy source when its terminal voltage recovers to a 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 are the lower limits of low voltage improvement index, steady-state voltage improvement index and overvoltage improvement index, respectively. Gi is the output active power of the ith thermal power plant, P Gilim_low is the lower limit of active output of the ith thermal power plant, P Gilim_up is the upper limit of active output of the ith thermal power plant, U busi is the grid-connected bus voltage of the ith thermal power plant, U busilim_low is the lower limit of the grid-connected bus voltage of the ith 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 They are the lower and upper limits of the low-running active current coefficient, and are the lower and upper limits of the low-running reactive current coefficient, and are the lower and upper limits of the low-breakdown active power recovery coefficient, respectively. 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, and 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 the AC fault is cleared, and the transient overvoltage improvement target; ΔU L,max,i is the low voltage offset of the grid-connected end 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 steady-state voltage after the recovery and the initial steady-state voltage of the ith renewable energy source when its terminal voltage recovers to the steady state under the worst working condition after the AC fault is cleared; H,max,i is the overvoltage offset of the grid-connected end of the i-th renewable energy under the most serious DC fault scenario.

[0038] Furthermore, the transient voltage offset is normalized as follows:

[0039]

[0040] in, and are the offsets of low voltage, steady-state voltage and overvoltage at the i-th renewable energy grid-connected terminal in 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 in 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 jth 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 standardization of the new energy power regulation cost is as follows:

[0042]

[0043] in, and After standardization, they 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 values ​​of the 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 LVRT period.

[0044] A computer storage medium stores at least one instruction, and the at least one instruction 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 parameters of voltage ride-through of a new energy source taking into account multi-form voltage stability constraints. The device comprises 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 parameters of voltage ride-through 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 influence of the voltage crossing parameters of new energy sources on different forms of voltage in different fault scenarios, and realizes a method for optimizing the voltage crossing parameters of new energy sources that can take into account the constraints of voltage stability in multiple forms. The present invention is not only applicable to the optimization of voltage crossing parameters of new energy sources in multiple fault scenarios, but also can optimize the voltage crossing parameters of new energy sources for multiple voltage stability forms. The present invention comprehensively considers the voltage stability of the high-proportion new energy ultra-high voltage direct current sending-end power grid under multiple faults, and adopts the particle swarm optimization algorithm for optimization. The optimization result obtained can ensure the voltage stability of the high-proportion new energy ultra-high voltage direct current sending-end power grid under multiple faults. The present invention can fully mobilize the voltage regulation capability of new energy sources to adapt to the voltage regulation requirements under different faults of the power grid, and provide guidance for the safe and stable operation of the high-proportion new energy ultra-high voltage direct current sending-end power grid. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

[0051] Figure 4 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 a DC bipolar blocking fault;

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

[0053] Figure 6 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 fault of AC line No. 1 N-2;

[0054] Figure 7 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 fault of AC line No. 2 N-2;

[0055] Figure 8 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 fault of No. 3 AC line N-2;

[0056] Fig. 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 fault of No. 4 AC line N-2. DETAILED DESCRIPTION

[0057] The existing method for optimizing the voltage ride-through parameters of renewable energy is only applicable to a single fault scenario and optimizes the voltage ride-through parameters of renewable energy for only one voltage stability form, ignoring the impact of the voltage ride-through parameters of renewable energy on the voltage safety and stability of the power grid in other fault scenarios. At the same time, the voltage regulation cost of renewable energy is not considered. In order to take into account the voltage safety and stability of different forms under various fault types, the present invention proposes a method for optimizing the voltage ride-through parameters of renewable energy taking into account multi-form voltage stability constraints on the basis of considering the voltage regulation cost of renewable energy. The present invention is described below in conjunction with a specific implementation method. Specific implementation method one:

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

[0060] The process of optimizing the parameters of new energy voltage ride-through 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-through control strategy of new energy during transient overvoltage, the low-through control strategy during transient low voltage, and the active power recovery strategy after fault clearing are determined. Based on the control strategy during the high / low-through period of new energy, the calculation formulas for active and reactive power during the high / low-through period of new energy are determined, and the dominant control parameters affecting the dynamic characteristics of new energy during the high / low-through period are determined to establish a general control model for new energy.

[0062] A typical fault scenario set for the UHV DC 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 timing voltage of the new energy grid-connected end under each fault scenario.

[0063] In the process of establishing the optimization model of the voltage ride-through parameters of renewable energy, it is necessary to consider the impact of the grid-connected location and capacity of renewable energy on the voltage regulation capability, and analyze the voltage regulation capability of renewable energy in different forms. The objective function is established with the goal of minimizing the transient overvoltage at the grid-connected end of renewable energy under the DC fault scenario set and achieving the strongest voltage stability under the AC fault scenario set. The constraints include the power constraint of the thermal power unit, the voltage constraint of the grid-connected busbar of the thermal power unit, the power balance constraint of the power grid, the range constraint of the dominant parameters of the high / low ride-through dynamic characteristics of renewable energy, and the current constraint of renewable energy.

[0064] In the process of solving the optimization model with 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, including establishing an iterative formula for updating the velocity and position of each particle and calculating the inertia weight.

[0065] More specifically,

[0066] At present, most of the new energy grid-connected converters connected to the power grid are grid-following type. If an AC short circuit occurs in the power grid and the voltage at the grid-connected end of the new energy drops below 0.9pu, the control strategy of the new energy 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 new energy can be uniformly expressed as:

[0067]

[0068] Among them, I pLV K is the active current of new energy low wear. pLV is the low wear active current coefficient, I p0 is the steady-state current of the new energy source, I max is the upper limit of the output current of new energy, I qLV K is the reactive current of new energy low-throughput qLV is the low-power 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 cross-threshold.

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

[0070]

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

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

[0073] If a DC fault occurs in the power grid and the voltage at the grid-connected end of the renewable energy source rises to above 1.1pu, the control strategy of the renewable energy source 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 source 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 cross threshold.

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

[0077]

[0078] Among them, P HV Output active power during the high penetration period 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 is the low wear active current coefficient K pLV , low reactive current coefficient K qLV And the active recovery coefficient K rec The dominant parameter affecting the dynamic characteristics of new energy during high-throughput is the high-throughput reactive current coefficient K qHV .

[0080] In order to ensure that the optimized voltage ride-through parameters of renewable energy can fully mobilize the regulation ability of renewable energy to different forms of voltage in different fault scenarios, it is necessary to construct a fault scenario set to analyze the influence of renewable energy voltage ride-through parameters on transient overvoltage, transient low voltage and steady-state voltage after fault in various fault scenarios. The fault scenario set includes 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. The active power, reactive power and grid-connected voltage of renewable energy output in various fault scenarios are collected.

[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 source during the AC fault and the initial steady-state voltage 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 end 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 grid-connected end of renewable energy when an AC fault occurs in the 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 renewable energy source 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 respectively to evaluate the improvement effects of transient low voltage, steady-state voltage after AC fault clearance and transient overvoltage. The expression of 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 ith renewable energy grid-connected terminal, 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 steady-state voltage after the recovery and the initial steady-state voltage of the ith renewable energy source when its terminal voltage recovers to the 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 evaluate the change trend of the voltage at the grid-connected terminal of renewable energy 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 source under the most serious DC fault scenario, ΔU H,max,i It can be the historical maximum value of DC faults in the actual power grid, or through transient simulation technology, evaluate the change trend of the voltage at the grid-connected terminal of renewable energy during DC faults, ΔU H,max,i Take the maximum deviation value.

[0092] In addition to the main indicator of voltage improvement effect at the grid-connected end of new energy, considering that changes in new energy output will cause equipment loss, the new energy regulation cost item is added as a secondary indicator to ensure the economy of new energy in the voltage regulation process. The type of new energy and the location where new energy is connected to the grid will affect the voltage regulation ability of new energy, which leads to different voltage regulation costs for each new energy in the grid. Since new energy achieves voltage regulation by changing the output power characteristics, the voltage regulation cost of new energy can be reflected by the power regulation cost of new energy. The expression of the power regulation cost of new energy during low voltage ride-through, steady state after AC fault clearance, and high voltage ride-through 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 are the changes in active 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 in the steady state after the AC fault is cleared under the j-th fault scenario, ΔP H,l,ij and ΔQ H,l,ij They are respectively the changes in active and reactive power of the ith renewable energy during the high voltage ride-through period under the jth 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 is different. Considering the difference in support demand of renewable energy grid-connected end voltage at different stages after grid fault, and taking into account the improvement effect of transient voltage caused by the change of renewable energy output power characteristics, the corresponding voltage weight is constructed so that the weights of different forms of voltage in the control target can be automatically adjusted according to their severity, so as to achieve the effect of dynamic adjustment of different forms of voltage regulation priority. The calculation expression of the target weight is:

[0096]

[0097] Among them, α i , β i and γi are the weights of the i-th renewable energy transient low voltage improvement target, the steady-state voltage improvement target after AC fault clearance, 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 They are respectively the offset of low voltage, steady-state voltage and overvoltage at the i-th renewable energy grid-connected terminal under the j-th fault scenario, 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 and 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 the two 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 new energy power regulation cost respectively. The standardized calculation of the transient voltage offset is:

[0105]

[0106] in, and are the offsets of low voltage, steady-state voltage and overvoltage at the i-th renewable energy grid-connected terminal in 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 in 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 jth 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 After standardization, they 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 values ​​of the 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 LVRT period.

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

[0111]

[0112] Among them, 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 are the lower limits of low voltage improvement index, steady-state voltage improvement index and overvoltage improvement index, respectively. Gi is the output active power of the ith thermal power plant, P Gilim_low is the lower limit of active output of the ith thermal power plant, P Gilim_up is the upper limit of active output of the ith thermal power plant, U busi is the grid-connected bus voltage of the ith thermal power plant, U busilim_low is the lower limit of the grid-connected bus voltage of the ith 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 They are the lower and upper limits of the low-running active current coefficient, and are the lower and upper limits of the low-running reactive current coefficient, and are the lower and upper limits of the low-breakdown active power recovery coefficient, respectively. 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 pLV , K qLV , K rec and K qHV Abstracted as massless and volumeless particles, these particles then form a population. In the population, the particles are screened by judging their fitness for the optimization target, and finally the optimal particle in the space is obtained, which is recorded as the optimal solution, including the establishment of an iterative formula for updating the speed and position of each particle and the calculation of the inertia weight. When the particles in the population are screened for the kth time, the updated speed 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 speed of particle i update 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 inheritance of the original velocity of the particle. The larger the inertia weight, the more the particle can maintain the original route of action, which makes the particle more likely to explore unknown areas. The smaller the inertia weight, the more willing the particle is to stick to the excellent route of itself and the population, which allows the particle to perform a more detailed search in a better position. In order to improve the solution speed of the algorithm while ensuring the solution accuracy, the algorithm allows the particles to have a larger inertia weight at the beginning of the iteration, so as to better perform global search and explore unknown areas. The algorithm allows the particles to have a smaller inertia weight at the end of the iteration, so as to better perform detailed local search to obtain a more accurate solution. 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, and 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 to measure the quality of the voltage at the grid-connected end of renewable energy. In order to improve the transient low voltage at the grid-connected end of renewable energy under AC faults and the steady-state voltage after fault clearance, and reduce the transient overvoltage at the grid-connected end of renewable energy under DC faults, the absolute error integral criterion and the time square error integral criterion are considered simultaneously 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 that the steady-state voltage evaluation index feedback is not obvious after the AC fault is cleared.

[0126] The time-squared error integral criterion takes the error in the later stage of response as the main performance evaluation index. 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 low voltage and transient overvoltage is not obvious.

[0127] Therefore, when designing the fitness function, considering both the absolute error integral criterion and the time square error integral criterion can take into account the improvement effect 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 end 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] In order to verify the effectiveness of the proposed parameter optimization method for renewable energy voltage ride-through considering multi-form voltage stability constraints, the renewable energy voltage ride-through parameters are optimized based on a model in which renewable energy accounts for 80% of a regional power grid in 2025 and the DC power grid is operating under heavy load. The new 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 and AC N-2 fault as examples, the voltage results before and after the optimization of the new energy low / high wear control parameters are compared in Table 2 and Table 3. It can be seen that after the optimization of the new energy low / high wear 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, which verifies the effectiveness of the method.

[0135] Table 2 Comparison of voltage at the new energy terminal before and after optimization under different DC faults

[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 before optimization, the optimized control parameters can improve the transient low voltage at the renewable energy grid-connected end during the AC N-2 fault and the steady-state voltage after the fault, and can also suppress the transient overvoltage peak at the renewable energy grid-connected end under the DC bipolar locking fault and the bipolar locking fault after four commutation failures. Specific implementation method 2:

[0142] This embodiment is a computer storage medium, in which at least one instruction is stored. 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, which 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 is a device for optimizing voltage ride-through parameters of new energy sources taking into account multi-form 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 and 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 adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt 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 codes. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal 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 may 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 generate 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 capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A 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 operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0151] Although the preferred embodiments of the present application have been described, those skilled in the art may make other 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 falling within the scope of the present application.

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

[0153] The above calculation examples of the present invention are only used to explain the calculation model and calculation process of the present invention in detail, and are not intended to limit the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for optimizing parameters of renewable energy voltage ride-through taking into account multi-form voltage stability constraints, characterized in that: include: According to the new energy voltage ride-through process, the expression of the power regulation cost of new energy during low voltage ride-through, steady state after AC fault clearance, and high voltage ride-through is: 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 are the changes in active 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 in the steady state 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 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, and the standardized cost is 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 the AC fault is cleared, and the transient overvoltage improvement target; Based on the objective function, the low-wear active current coefficient K will be included pLV , low wear reactive current coefficient K qLV 、The active power recovery coefficient of new energy low wear 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.

2. According to claim 1, a new energy voltage ride-through parameter optimization method taking into account multi-form voltage stability constraints is characterized in that: When optimizing an object using the particle swarm optimization algorithm, the following constraints need to be met: P Gilim_low ≤P Gi ≤P Gilim_up IN busilim_low ≤U busi ≤U busilim_up in, It is the low voltage improvement index, U 0,i is the initial steady-state voltage of the ith renewable energy grid-connected terminal, 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 end 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 ith renewable energy source when its terminal voltage recovers to a 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 are the lower limits of low voltage improvement index, steady-state voltage improvement index and overvoltage improvement index, respectively. Gi is the output active power of the ith thermal power plant, P Gilim_low is the lower limit of active output of the ith thermal power plant, P Gilim_up is the upper limit of active output of the ith 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 ith 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 They are the lower and upper limits of the low-running active current coefficient, and They are the lower and upper limits of the low-running reactive current coefficient, and are the lower and upper limits of the low-breakdown active power recovery coefficient, respectively. and They are the lower and upper limits of the high-throughput reactive current coefficient respectively.

3. The method for optimizing parameters of new energy voltage ride-through taking into account multi-form voltage stability constraints according to claim 2, characterized in that: 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: 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 speed of particle i update 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 new energy voltage ride-through taking into account multi-form voltage stability constraints according to claim 3 is 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, and 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 new energy voltage ride-through taking into account 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 new 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 steady-state voltage improvement target after AC fault clearance, 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 the AC fault is cleared, and the transient overvoltage improvement target; ΔU L,max,i is the low voltage offset of the grid-connected end 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 steady-state voltage after the recovery and the initial steady-state voltage of the ith renewable energy source when its terminal voltage recovers to the steady state under the worst working condition after the AC fault is cleared; H,max,i is the overvoltage offset of the grid-connected end of the i-th renewable energy under the most serious DC fault scenario.

7. The method for optimizing parameters of new energy voltage ride-through taking into account multi-form voltage stability constraints according to claim 6, characterized in that: The normalization of transient voltage offset is as follows: in, and are the offsets of low voltage, steady-state voltage and overvoltage at the i-th renewable energy grid-connected terminal in 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 in 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 jth 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 new energy voltage ride-through taking into account multi-form voltage stability constraints according to claim 6, characterized in that: The normalization of the cost of renewable energy power regulation is as follows: in, and After standardization, they 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 values ​​of the 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 LVRT period.

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 crossing 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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