Power supply ratio optimization method of wind-solar-thermal storage integrated bundling delivery system

Abstract

The invention provides a power supply ratio optimization method for a wind-solar-thermal storage integrated bundling delivery system, and the method comprises the steps: building a target function of a stability margin optimization model of a new energy station delivery system based on a maximum peak Nyquist stability criterion; when the system has an oscillation risk, a particle swarm optimization algorithm is adopted to adjust the power supply ratio of the wind power plant, the photovoltaic power station and the energy storage unit, and the system impedance is optimized to enable the system impedance to have the optimal stability margin; therefore, the risk of broadband oscillation of the sending-out system can be reduced, on the premise that safe and stable operation of a power grid is guaranteed, the new energy permeability is improved, rapid and stable development of new energy is powerfully guaranteed, and a technical basis is provided for constructing a novel power system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system analysis and control, and in particular relates to a power supply ratio optimization method for a wind, solar, thermal and energy storage integrated bundling transmission system. Background Art

[0002] In order to accelerate the construction of a new power system dominated by renewable energy, on January 29, 2021, Shaanxi Province issued the "Outline of the 14th Five-Year Plan for National Economic and Social Development of the Province and the Long-Term Goals for 2035", which clearly proposed "according to the integrated development model of wind, solar, thermal and storage and source, grid, load and storage, optimize the scale ratio of various power sources and expand the scale of power transmission. By 2025, the total installed capacity of electricity will exceed 136 million kilowatts, of which 65 million kilowatts will be renewable energy." At the same time, as an important power supply base for "West-to-East Power Transmission", Shaanxi Power Grid vigorously promotes the construction of ultra-high voltage direct current new energy transmission channels, and strives to build a "one AC and six DC" ultra-high voltage external interconnection channel. With the latest planned "Shaanxi Power into Henan" and "Shaanxi Power into Anhui" cross-regional transmission channels about to be put into construction, the three ultra-high voltage direct current lines of Shaanxi-Wu, Shaanxi-Henan and Shaanxi-Anhui will have a far-reaching impact on the power grid in northern Shaanxi after completion.

[0003] However, as wind power, photovoltaic power, thermal power, energy storage and other types of power sources are transmitted through AC / DC hybrid power grids, the proportion of renewable energy power generation is gradually increasing, and UHV DC transmission lines are continuously connected, which poses a great challenge to the safe and stable operation of the Shaanxi North power grid. The longer transmission distance and large-scale centralized grid connection of new energy have weakened the strength of local power grids. The extensive use of devices such as converters has led to complex dynamic characteristics of the power system. The interaction between new energy units and UHV DC, and the interaction between thermal power units and UHV DC may cause oscillation risks, which have become the focus of attention. The stability and safety of the power system have been challenged unprecedentedly. The risk of broadband oscillation may cause large-scale new energy units to be disconnected from the grid, equipment damage, torsional vibration of thermal power units and other accidents, causing significant economic losses, seriously threatening the safe and stable operation of the power grid, and restricting the efficient absorption of new energy.

[0004] Therefore, it is urgent to conduct research on the broadband oscillation risk of the integrated wind, solar, thermal and storage bundled transmission system in northern Shaanxi, and further reduce the risk of broadband oscillation in the system by optimizing the system power supply ratio. On the premise of ensuring the safe and stable operation of the power grid, the penetration rate of new energy can be improved, effectively guaranteeing the rapid and stable development of new energy, and providing a technical foundation for building a new power system. Summary of the invention

[0005] To solve the above problems, the present invention provides a method for optimizing the power ratio of an integrated wind, solar, thermal and storage bundled transmission system. By optimizing the system power ratio in the early planning, the risk of wide-band oscillation in the system is reduced. While ensuring the safe and stable operation of the power grid, the penetration rate of new energy is improved, which effectively guarantees the rapid and stable development of new energy and provides a technical basis for building a new power system.

[0006] A method for optimizing the power ratio of a wind-solar-thermal-storage integrated bundling transmission system comprises the following steps:

[0007] The objective function of the stability margin optimization model of the LCC-HVDC transmission system for new energy stations is as follows:

[0008]

[0009] Among them, J is the stability margin of the LCC-HVDC transmission system of the new energy station, is the positive sequence impedance of the sending-end AC grid, is the negative sequence impedance of the AC power grid at the sending end, is the positive sequence impedance of the LCC-HVDC transmission system, is the negative sequence impedance of the LCC-HVDC transmission system, s is the impedance variable, N wt is the number of wind turbines, N pv is the number of photovoltaic units, N es is the number of energy storage units;

[0010] Based on the set constraints, an intelligent particle swarm optimization algorithm is used to solve the objective function, and the ratio of the number of wind turbines, the number of photovoltaic units, and the number of energy storage units that enables the LCC-HVDC transmission system to have an optimal stability margin under the maximum peak Nyquist stability criterion is obtained.

[0011] Furthermore, the constraints include unit installation planning constraints, upper and lower limit constraints on the output power of a single unit, and the total wind and solar output power meeting the wind and solar output power demand of the area where the LCC-HVDC transmission system is located;

[0012] Among them, the machine assembly planning constraints are:

[0013] 0 <N wt ≤N wt_max ,0 <N pv ≤N pv_max ,0 <N es ≤N es_max ,N wt 、N pv 、N es ∈N

[0014] Among them, N wt_maxis the upper limit of the number of wind turbines, N pv_max is the upper limit of the number of photovoltaic units, N es_max is the upper limit of the number of energy storage units, N is a positive integer;

[0015] The upper and lower limits of the output power of a single unit are:

[0016] P wt_min ≤P wt ≤P wt_rated ,P pv_min ≤P pv ≤P pv_rated ,P es_min ≤P es ≤P es_rated

[0017] Among them, P wt is the actual output power of a single wind turbine, P wt_min is the lower limit of the output power of a single wind turbine, P wt_rated is the upper limit of the output power of a single wind turbine, P pv is the actual output power of a single photovoltaic unit, P pv_min is the lower limit of the output power of a single photovoltaic unit, P pv_rated is the upper limit of the output power of a single photovoltaic unit, P es is the actual output power value of a single energy storage unit, P es_min is the lower limit of the output power of a single energy storage unit, P es_rated Set the upper limit of the output power of a single energy storage unit.

[0018] Furthermore, the positive sequence impedance of the sending-end AC grid and the negative sequence impedance of the AC power grid at the sending end They are obtained respectively by the positive-sequence impedance and the negative-sequence impedance of the voltage source type parallel subsystem impedance, wherein the voltage source type parallel subsystem impedance is obtained by connecting the equivalent grid impedance and the thermal power unit impedance in parallel.

[0019] Furthermore, the construction method of the voltage source type parallel subsystem is:

[0020] Taking the AC busbar connected by the new energy station, LCC-HVDC transmission system, thermal power units, and the sending-end AC power grid as the PCC point, the thermal power units and the sending-end AC power grid are connected in parallel to form a voltage source type parallel subsystem impedance. At the same time, the new energy station and the LCC-HVDC transmission system are connected in parallel to form a current source type parallel subsystem impedance.

[0021] Furthermore, the maximum peak Nyquist stability criterion is:

[0022] The amplitude margin and phase margin requirements of the LCC-HVDC system are represented as a circular prohibited working area with (-1, j0) as the center and Rset as the radius; if the impedance ratio curve enters the circular prohibited working area, it means that the amplitude margin or phase margin is insufficient, and the LCC-HVDC system is at risk of oscillation; if the impedance ratio curve does not enter the circular prohibited working area, it means that the LCC-HVDC system meets the requirements of the amplitude margin and phase margin, and the LCC-HVDC system is stable.

[0023] Furthermore, the calculation method of the forbidden area radius Rset is:

[0024]

[0025] Among them, the amplitude margin GM of the LCC-HVDC system is the inverse of the distance between the intersection of the impedance ratio curve and the real axis of the Nyquist negative half plane and the origin; the phase margin PM of the LCC-HVDC system is the angle between the intersection of the impedance ratio curve and the unit circle and the real axis of the Nyquist negative half plane.

[0026] Furthermore, the method of using intelligent particle swarm optimization algorithm to solve the objective function is:

[0027] Step 1: Randomly generate different ratios of wind turbine numbers, photovoltaic units, and energy storage units within a set range. Each ratio represents a particle, and all particles form an initial population.

[0028] Step 2: Obtain the positive-sequence impedance and negative-sequence impedance of the AC power grid at the sending end and the positive-sequence impedance and negative-sequence impedance of the LCC-HVDC transmission system corresponding to different particles;

[0029] Step 3: Obtain the stability margin J corresponding to each particle according to all impedances obtained in step 2, and take the particle corresponding to the maximum value of the stability margin J obtained in this iteration as the individual optimal solution; at the same time, determine whether the individual optimal solution of this iteration is greater than the global optimal solution of the historical iteration. If not, the global optimal solution remains unchanged; if yes, take the individual optimal solution of this iteration as the new global optimal solution;

[0030] Step 4: Determine whether one of the iteration stop conditions is met. If yes, the ratio represented by the particles corresponding to the global optimal solution at this time is the final ratio. If no, proceed to step 5.

[0031] Step 5: Based on the evolution equation of the intelligent particle swarm optimization algorithm, the velocity and inertia weight of the particles are updated in the next iteration, and new particles are generated to re-execute steps 2 to 4.

[0032] Furthermore, the iteration stop condition includes:

[0033] Reach the preset number of iterations;

[0034] The stability margin J corresponding to the global optimal solution does not change within a given number of iterations.

[0035] Beneficial effects:

[0036] 1. The present invention provides a method for optimizing the power ratio of a wind, solar, thermal and storage integrated bundled transmission system. By optimizing the system power ratio, the risk of broadband oscillation in the transmission system is reduced. Under the premise of ensuring the safe and stable operation of the power grid, the penetration rate of new energy is improved, and the rapid and stable development of new energy is effectively guaranteed, providing a technical basis for building a new power system.

[0037] 2. The present invention provides a method for optimizing the power ratio of an integrated wind, solar, thermal and storage bundled transmission system. Based on the maximum peak Nyquist stability criterion, the objective function of the stability margin optimization model of the new energy station transmission system is established; when there is an oscillation risk in the system, the particle swarm optimization algorithm is used to adjust the power ratio of the wind farm, photovoltaic power station, and energy storage unit, and the system impedance is optimized to have the optimal stability margin; wherein, the system stability margin is judged according to the minimum circle radius indicator, and then the power ratio of the wind farm, photovoltaic power station, and energy storage unit is optimized to effectively achieve the optimal stability margin, and then through planning, broadband oscillations are suppressed in advance, the penetration rate of new energy is improved, and the system stability is improved, which can promote the efficient consumption of multiple types of new energy and help achieve the "carbon peak and carbon neutrality" goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is the equivalent structural diagram of the wind, solar, thermal and energy storage integrated bundling and delivery system provided by the present invention.

[0039] Figure 2 This is a schematic diagram of the stability margin of the impedance ratio curve provided by the present invention.

[0040] Figure 3 This is a flow chart of the particle swarm optimization algorithm provided by the present invention. DETAILED DESCRIPTION

[0041] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0042] Figure 1 This is the equivalent structural diagram of the wind, solar, thermal and energy storage integrated bundling and delivery system provided by the present invention.

[0043] The AC busbar connecting the new energy station, LCC-HVDC transmission system, thermal power unit and the AC power grid at the sending end is taken as the PCC point. LCC-HVDC and new energy units can be equivalent to an ideal current source in parallel with their output admittance, while the thermal power unit can be equivalent to an ideal voltage source in series with its output admittance.

[0044] exist Figure 1 Middle, Y LCC (s) and I LCC They represent the output admittance and equivalent current source of LCC-HVDC, Y NG (s) and I NG Respectively represent the output admittance and equivalent current source of the new energy unit; Y SG (s) and V SG They represent the output admittance and equivalent voltage source of the thermal power unit, respectively, and Y g (s) and V g is the output admittance and equivalent voltage source of the AC power grid.

[0045] Figure 1 Middle, Y s (s)=Y SG (s)+Y g (s) represents the impedance of the voltage source type parallel subsystem, and Y o (s)=Y LCC (s)+Y NG (s) represents the impedance of the current source type parallel subsystem. According to automatic control theory, if the impedance ratio is Y o (s) / Y s (s) satisfies the Nyquist stability criterion, then the system is stable, and the stability margin depends on the distance of the Nyquist curve of the impedance ratio from the point (-1, j0).

[0046] Based on this, the present invention proposes a power supply ratio optimization method for a wind-solar-thermal-storage integrated bundling transmission system based on the maximum peak Nyquist stability criterion, including the following steps:

[0047] S1: The objective function of the stability margin optimization model of the LCC-HVDC transmission system for new energy stations is as follows:

[0048]

[0049] Among them, J is the stability margin of the LCC-HVDC transmission system of the new energy station, is the positive sequence impedance of the sending-end AC grid, is the negative sequence impedance of the AC power grid at the sending end, is the positive sequence impedance of the LCC-HVDC transmission system, is the negative sequence impedance of the LCC-HVDC transmission system, s is the impedance variable, N wt is the number of wind turbines, N pv is the number of photovoltaic units, N es is the number of energy storage units; They are the positive-sequence impedance ratio and negative-sequence impedance ratio of the grid impedance and the renewable energy transmission system respectively.

[0050] It can be seen that the smaller one of the minimum circle radius Rmin of the equivalent positive sequence impedance ratio and the negative sequence impedance ratio can describe the system stability margin, that is, the transmission system stability margin J can be described as:

[0051]

[0052] It should be noted that the positive sequence impedance of the AC power grid at the sending end is and the negative sequence impedance of the AC power grid at the sending end They are obtained respectively by the positive-sequence impedance and the negative-sequence impedance of the voltage source type parallel subsystem impedance, wherein the voltage source type parallel subsystem impedance is obtained by connecting the equivalent grid impedance and the thermal power unit impedance in parallel.

[0053] Furthermore, the construction method of the voltage source type parallel subsystem is:

[0054] Taking the AC busbar connected by the new energy station, LCC-HVDC transmission system, thermal power units, and the sending-end AC power grid as the PCC point, the thermal power units and the sending-end AC power grid are connected in parallel to form a voltage source type parallel subsystem impedance. At the same time, the new energy station and the LCC-HVDC transmission system are connected in parallel to form a current source type parallel subsystem impedance.

[0055] It should be noted that wind turbines, photovoltaic units, and energy storage units are current source impedances, and thermal power units and the sending-end AC grid impedance are voltage source impedances. The present invention only optimizes the ratio of units with current source impedances.

[0056] S2: Based on the set constraints, the objective function is solved by using an intelligent particle swarm optimization algorithm to obtain the ratio of the number of wind turbines, the number of photovoltaic units, and the number of energy storage units that enables the LCC-HVDC transmission system to have an optimal stability margin under the maximum peak Nyquist stability criterion.

[0057] That is to say, the present invention aims to meet the output power demand of new energy stations in northern Shaanxi by changing the number of new energy units connected to the grid and the output power of each unit to reshape the impedance of the new energy delivery system, thereby improving the system stability margin. Then, the intelligent particle swarm optimization (PSO) algorithm is used to solve the problem to obtain the power supply ratio of wind turbines, photovoltaic units, and energy storage units with the optimal stability margin.

[0058] Furthermore, the constraints include unit installation planning constraints, upper and lower limit constraints on the output power of a single unit, and the total wind and solar output power meeting the wind and solar output power demand of the area where the LCC-HVDC transmission system is located;

[0059] Among them, the machine assembly planning constraints are:

[0060] 0 <N wt ≤N wt_max ,0 <N pv ≤N pv_max ,0 <N es ≤N es_max ,N wt 、N pv 、N es ∈N

[0061] Among them, N wt_max is the upper limit of the number of wind turbines, N pv_max is the upper limit of the number of photovoltaic units, N es_max is the upper limit of the number of energy storage units, N is a positive integer;

[0062] The upper and lower limits of the output power of a single unit are:

[0063] P wt_min ≤P wt ≤P wt_rated ,P pv_min ≤P pv ≤P pv_rated ,P es_min ≤P es ≤P es_rated

[0064] Among them, P wt is the actual output power of a single wind turbine, P wt_min is the lower limit of the output power of a single wind turbine, P wt_rated is the upper limit of the output power of a single wind turbine, P pv is the actual output power of a single photovoltaic unit, P pv_min is the lower limit of the output power of a single photovoltaic unit, P pv_rated is the upper limit of the output power of a single photovoltaic unit, P es is the actual output power value of a single energy storage unit, P es_min is the lower limit of the output power of a single energy storage unit, P es_rated Set the upper limit of the output power of a single energy storage unit.

[0065] It should be noted that in the constraints on the upper and lower limits of the output power of a single unit, assuming that the output of each internal generator set is averaged, the impedance of the new energy transmission system can be reshaped by modifying the number of units and the output power of a single unit, that is, the power demand is evenly distributed to each unit. As the output power of a single unit changes, the number of units changes, and the equivalent impedance of the station will also change.

[0066] Furthermore, the Maximum Peak Criterion (MPC) is as follows:

[0067] As Figure 2 shown, the requirements for the amplitude margin and phase margin of the LCC-HVDC system are characterized as a circular prohibited operating area centered at the point (-1, j0) with a radius of Rset. If the impedance ratio curve enters the circular prohibited operating area, it means that the amplitude margin or phase margin is insufficient, and there is an oscillation risk in the LCC-HVDC system. If the impedance ratio curve does not enter the circular prohibited operating area, it means that the LCC-HVDC system meets the requirements for the amplitude margin and phase margin, and the LCC-HVDC system is stable.

[0068] To describe the stability margin of the system, the minimum circle radius Rmin is introduced, which is defined as the minimum distance from the point (-1, j0) to the impedance ratio curve on the premise that the system satisfies the Nyquist criterion. The larger the minimum circle radius, the greater the stability margin of the system.

[0069] Among them, the relationship between the radius Rset of the prohibited area and the amplitude margin GM and phase margin PM of the system is as follows:

[0070]

[0071] Among them, the amplitude margin GM of the LCC-HVDC system is the reciprocal of the distance from the intersection point of the impedance ratio curve and the real axis in the negative half-plane of the Nyquist diagram to the origin. The phase margin PM of the LCC-HVDC system is the angle between the intersection point of the impedance ratio curve and the unit circle and the real axis in the negative half-plane of the Nyquist diagram.

[0072] If Rmin < Rset, it means that the stability margin of the system is insufficient.

[0073] Different from the conventional Nyquist criterion, MPC unifies the system's amplitude margin and phase margin requirements into a circular prohibited working area on the complex plane (i.e., amplitude and phase stability margin), making the criterion representation more concise. At the same time, the criterion provides differentiated amplitude and phase stability margin values ​​for different grid-connected scenarios, further improving the flexibility and accuracy of the criterion. In addition, by defining a circular prohibited area, MPC effectively covers the "oscillation blind spot" caused by the ideal criterion that only considers the amplitude margin or phase margin. The criterion can accurately determine the range in which the system is prone to oscillation instability, thereby serving as the basic judgment condition for whether the system is in a stable operating range.

[0074] like Figure 3 As shown in the figure, the specific method of using particle swarm optimization algorithm to adjust the power ratio of the wind-solar-thermal-storage integrated bundled transmission system to achieve the optimal system stability margin is as follows:

[0075] Step 1: Randomly generate different ratios of wind turbine numbers, photovoltaic units, and energy storage units within a set range. Each ratio represents a particle, and all particles form an initial population.

[0076] Step 2: Obtain the positive-sequence impedance and negative-sequence impedance of the AC power grid at the sending end and the positive-sequence impedance and negative-sequence impedance of the LCC-HVDC transmission system corresponding to different particles;

[0077] Step 3: Obtain the stability margin J corresponding to each particle according to all impedances obtained in step 2, and take the particle corresponding to the maximum value of the stability margin J obtained in this iteration as the individual optimal solution; at the same time, determine whether the individual optimal solution of this iteration is greater than the global optimal solution of the historical iteration. If not, the global optimal solution remains unchanged; if yes, take the individual optimal solution of this iteration as the new global optimal solution;

[0078] Step 4: Determine whether one of the iteration stop conditions is met. If yes, the ratio represented by the particles corresponding to the global optimal solution at this time is the final ratio. If no, proceed to step 5.

[0079] Wherein, the iteration stop condition includes:

[0080] Reach the preset number of iterations;

[0081] The stability margin J corresponding to the global optimal solution does not change within a given number of iterations;

[0082] Step 5: Based on the evolution equation of the intelligent particle swarm optimization algorithm, the velocity and inertia weight of the particles are updated in the next iteration, and new particles are generated to re-execute steps 2 to 4.

[0083] In summary, under the premise of meeting the output power demand of the new energy station, the present invention reshapes the impedance of the new energy transmission system by changing the number of new energy units connected to the grid and the output power of each unit, thereby improving the stability margin of the transmission system.

[0084] Of course, the present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may certainly make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for optimizing the power ratio of a wind-solar-thermal-storage integrated bundling and delivery system, characterized in that: The following steps are involved: The objective function of the stability margin optimization model of the LCC-HVDC transmission system for new energy stations is as follows: Among them, J is the stability margin of the LCC-HVDC transmission system of the new energy station, is the positive sequence impedance of the sending-end AC grid, is the negative sequence impedance of the AC power grid at the sending end, is the positive sequence impedance of the LCC-HVDC transmission system, is the negative sequence impedance of the LCC-HVDC transmission system, s is the impedance variable, N wt is the number of wind turbines, N pv is the number of photovoltaic units, N es is the number of energy storage units; Based on the set constraints, an intelligent particle swarm optimization algorithm is used to solve the objective function, and the ratio of the number of wind turbines, the number of photovoltaic units, and the number of energy storage units that enables the LCC-HVDC transmission system to have an optimal stability margin under the maximum peak Nyquist stability criterion is obtained.

2. The power supply ratio optimization method of the wind-solar-thermal-storage integrated bundling and delivery system according to claim 1 is characterized in that: The constraints include unit installation planning constraints, upper and lower limit constraints on the output power of a single unit, and the total wind and solar output power meeting the wind and solar output power demand of the area where the LCC-HVDC transmission system is located; Among them, the machine assembly planning constraints are: 0<N wt ≤N wt_max ,0<N pv ≤N pv_max ,0<N es ≤N es_max ,N wt 、N pv 、N es ∈N Among them, N wt_max is the upper limit of the number of wind turbines, N pv_max is the upper limit of the number of photovoltaic units, N es_max is the upper limit of the number of energy storage units, N is a positive integer; The upper and lower limits of the output power of a single unit are: P wt_min ≤P wt ≤P wt_rated ,P pv_min ≤P pv ≤P pv_rated ,P es_min ≤P es ≤P es_rated Among them, P wt is the actual output power of a single wind turbine, P wt_min is the lower limit of the output power of a single wind turbine, P wt_rated is the upper limit of the output power of a single wind turbine, P pv is the actual output power of a single photovoltaic unit, P pv_min is the lower limit of the output power of a single photovoltaic unit, P pv_rated is the upper limit of the output power of a single photovoltaic unit, P es is the actual output power value of a single energy storage unit, P es_min is the lower limit of the output power of a single energy storage unit, P es_rated Set the upper limit of the output power of a single energy storage unit.

3. The power supply ratio optimization method of the wind-solar-thermal-storage integrated bundling and delivery system according to claim 1 is characterized in that: Positive sequence impedance of the AC power grid at the sending end and the negative sequence impedance of the AC power grid at the sending end They are obtained respectively by the positive-sequence impedance and the negative-sequence impedance of the voltage source type parallel subsystem impedance, wherein the voltage source type parallel subsystem impedance is obtained by connecting the equivalent grid impedance and the thermal power unit impedance in parallel.

4. The power supply ratio optimization method of the wind-solar-thermal-storage integrated bundling and delivery system as claimed in claim 3 is characterized in that: The construction method of the voltage source type parallel subsystem is: Taking the AC busbar connected by the new energy station, LCC-HVDC transmission system, thermal power units, and the sending-end AC power grid as the PCC point, the thermal power units and the sending-end AC power grid are connected in parallel to form a voltage source type parallel subsystem impedance. At the same time, the new energy station and the LCC-HVDC transmission system are connected in parallel to form a current source type parallel subsystem impedance.

5. The power supply ratio optimization method of the wind-solar-thermal-storage integrated bundling and delivery system according to claim 1 is characterized in that: The maximum peak Nyquist stability criterion is: The amplitude margin and phase margin requirements of the LCC-HVDC system are represented as a circular prohibited working area with (-1, j0) as the center and Rset as the radius; if the impedance ratio curve enters the circular prohibited working area, it means that the amplitude margin or phase margin is insufficient, and the LCC-HVDC system is at risk of oscillation; if the impedance ratio curve does not enter the circular prohibited working area, it means that the LCC-HVDC system meets the requirements of the amplitude margin and phase margin, and the LCC-HVDC system is stable.

6. The power supply ratio optimization method of the wind-solar-thermal-storage integrated bundling and delivery system as claimed in claim 5 is characterized in that: The calculation method of the prohibited area radius Rset is: Among them, the amplitude margin GM of the LCC-HVDC system is the inverse of the distance between the intersection of the impedance ratio curve and the real axis of the Nyquist negative half plane and the origin; the phase margin PM of the LCC-HVDC system is the angle between the intersection of the impedance ratio curve and the unit circle and the real axis of the Nyquist negative half plane.

7. The power supply ratio optimization method of the wind-solar-thermal-storage integrated bundling and delivery system according to claim 1 is characterized in that: The method of solving the objective function using intelligent particle swarm optimization algorithm is: Step 1: Randomly generate different ratios of wind turbine numbers, photovoltaic units, and energy storage units within a set range. Each ratio represents a particle, and all particles form an initial population. Step 2: Obtain the positive-sequence impedance and negative-sequence impedance of the AC power grid at the sending end and the positive-sequence impedance and negative-sequence impedance of the LCC-HVDC transmission system corresponding to different particles; Step 3: Obtain the stability margin J corresponding to each particle according to all impedances obtained in step 2, and take the particle corresponding to the maximum value of the stability margin J obtained in this iteration as the individual optimal solution; at the same time, determine whether the individual optimal solution of this iteration is greater than the global optimal solution of the historical iteration. If not, the global optimal solution remains unchanged; if yes, take the individual optimal solution of this iteration as the new global optimal solution; Step 4: Determine whether one of the iteration stop conditions is met. If yes, the ratio represented by the particles corresponding to the global optimal solution at this time is the final ratio. If no, proceed to step 5. Step 5: Based on the evolution equation of the intelligent particle swarm optimization algorithm, the velocity and inertia weight of the particles are updated in the next iteration, and new particles are generated to re-execute steps 2 to 4.

8. The power supply ratio optimization method of the wind-solar-thermal-storage integrated bundling and delivery system as claimed in claim 7 is characterized in that: The iteration stop condition includes: Reach the preset number of iterations; The stability margin J corresponding to the global optimal solution does not change within a given number of iterations.

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