A method for assessing the renewable energy acceptance capacity of power systems based on short-circuit capacity ratio

Abstract

A method for assessing the renewable energy integration capacity of a power system based on the short-circuit capacity ratio is proposed. This method first constructs an assessment model of the renewable energy integration capacity of the power system considering the node-system short-circuit capacity ratio index. Then, an improved particle swarm optimization algorithm is used to solve the constructed assessment model to obtain the maximum renewable energy integration capacity of the system. This invention accurately assesses the renewable energy integration capacity of the system while ensuring better system security and stability.

Description

Technical Field

[0001] This invention belongs to the field of power grid technology, specifically relating to a method for assessing the renewable energy acceptance capacity of a power system based on short-circuit capacity ratio. Background Technology

[0002] Guided by the "dual carbon" goals, my country's new energy construction has developed rapidly in recent years, with a rapid increase in installed capacity. By the end of 2022, my country's newly installed wind and solar power capacity reached approximately 101 million kW, accounting for more than 50% of the global total, making it a mainstay of global renewable energy development. It is projected that by 2030, my country's total installed wind and solar power capacity will exceed 1.2 billion kW. In the future, my country's power system will evolve into a new type of power system with large-scale new energy sources as the main power source, characterized by green, clean, low-carbon, and environmentally friendly features.

[0003] Due to the intermittent and fluctuating nature of renewable energy output, large-scale grid integration of renewable energy brings a series of challenges, including increased grid peak-shaving pressure, frequency drops, and voltage exceedances. Simultaneously, large-scale renewable energy integration also reduces the short-circuit capacity of the power system, leading to a series of problems such as broadband system oscillations. Existing studies often use the short-circuit ratio index to measure changes in the power system's short-circuit capacity. However, existing short-circuit ratio indices do not account for the interaction effects between multiple renewable energy power plants, failing to measure the impact of large-scale renewable energy integration on the overall short-circuit capacity of the power system, and thus cannot accurately measure changes in the power system's short-circuit capacity. Therefore, there is an urgent need to propose a short-circuit ratio index that can accurately measure the impact of large-scale renewable energy integration on the power system's short-circuit capacity, and a method to reasonably assess the power system's renewable energy acceptance capacity. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in the existing technology by providing an accurate and reasonable method for assessing the renewable energy acceptance capacity of power systems based on short-circuit capacity ratio.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] A method for assessing the renewable energy acceptance capacity of a power system based on short-circuit capacity ratio includes the following steps:

[0007] Step A: Construct a power system renewable energy acceptance capacity assessment model, wherein the objective function F of the assessment model is:

[0008] F = max(p1F1 + p2F2)

[0009]

[0010] In the above formula, F1 and F2 are the optimization objectives, p1 and p2 are the weights of F1 and F2 respectively, and S RE,i S represents the grid-connected capacity of the renewable energy power station directly connected to the i-th load node, where i = 1, 2, ..., m. G,j Let m be the installed capacity of the j-th synchronous generator unit in the system, m be the number of load nodes in the system including renewable energy power plants connected to the grid, j = m+1, m+2, ..., n, and n be the total number of load nodes in the system. SCR system This is the system short-circuit capacity ratio;

[0011] Step B: The constructed evaluation model is solved using an improved particle swarm optimization algorithm to obtain the maximum new energy access capacity of the system.

[0012] The system short-circuit ratio SCR system The following formula is used for calculation:

[0013]

[0014]

[0015]

[0016] In the above formula, SCR node,i Let ω be the short-circuit capacity ratio of the i-th load node. i U represents the weight corresponding to the i-th load node. ac,i Let be the voltage of the i-th load node before the renewable energy power station is connected to the grid. This represents the short-circuit capacity provided by the power system to the i-th load node before the renewable energy power plant is connected to the grid. The dots above indicate complex numbers. This refers to the short-circuit capacity provided by the renewable energy power station to the i-th load node after grid connection. For load nodes without renewable energy grid connection, This represents the power required for the i-th load node.

[0017] The The methods for determining this include:

[0018] Thevenin's theorem and the superposition theorem are used to divide the AC power system with multiple power plants connected to the grid and including power system models with and without multiple power plants connected to the grid.

[0019] For a power system model containing multiple renewable energy power plants connected to the grid, the voltage and current of each load node before the renewable energy power plants are connected to the grid are first determined based on the system's impedance parameters, and then the following calculations are performed.

[0020] For a power system model without the integration of multiple renewable energy power plants, the voltage change at load nodes caused by the integration of renewable energy power plants and the current injected from renewable energy power plants into load nodes are first determined based on the system's impedance parameters. Then, the following calculations are performed:

[0021] The It is calculated using the following formula:

[0022]

[0023]

[0024] In the above formula, Let be the voltage of the i-th load node. Let be the voltage of the i-th load node before the renewable energy power station is connected to the grid. Let be the equivalent complex impedance of the system to the i-th load node. Let U be the equivalent complex impedance between the i-th load node and the j-th load node, i,j=1,2,···,n. ac I represents the column vector of load node voltages before the grid connection of new energy sources. ac The column vector of current injected into the load nodes by the system before the new energy power station is connected to the grid;

[0025] The It is calculated using the following formula:

[0026]

[0027]

[0028]

[0029] In the above formula, This represents the voltage change caused by the connection of the new energy power station to the i-th load node. Let represent the grid-connected capacities of new energy power stations directly connected to the i-th and j-th load nodes, respectively. * indicates the conjugate operation of complex numbers. Let I represent the current injected by the renewable energy power station into the i-th and j-th load nodes, respectively, where i,j = 1, 2, ..., m. Let ΔU be the column vector of voltage changes at the load nodes caused by the connection of the renewable energy power station. RE This refers to the column vector of current injected from renewable energy power plants into load nodes.

[0030] The constraints of the power system renewable energy acceptance capacity assessment model include output limits for synchronous generators, output limits for renewable energy power plants, power balance constraints, and power limits for transmission lines.

[0031] The output limit of the synchronous generator unit is:

[0032] P G,imin ≤P G,i ≤P G,imax

[0033] In the above formula, P G,i To connect the synchronous machine output active power of the i-th load node, P G,imax P G,imin These are the maximum and minimum active power outputs of the synchronous machine connected to the i-th load node, respectively.

[0034] The power output limit of the aforementioned new energy power station is:

[0035] P RE,imin ≤P RE,i ≤P RE,imax

[0036] In the above formula, P RE,i To output active power for the renewable energy power station connected to the i-th load node, P RE,imax P RE,imin These are the maximum and minimum active power outputs of the renewable energy power station connected to the i-th load node, respectively.

[0037] The power balance constraint is:

[0038]

[0039] In the above formula, P load,i Q load,i Let Q be the active and reactive power required by the i-th load node. G,i Q RE,j These represent the reactive power output of the synchronous machine connected to the i-th load node and the reactive power output of the renewable energy power station connected to the j-th load node, respectively.

[0040] The power limit of the transmission line is:

[0041] P line,ij ≤P line,ijmax

[0042] In the above formula, P line,ij P line,ijmax These represent the transmission power and maximum value of the transmission lines connecting the i-th and j-th load nodes, respectively.

[0043] The power required by the i-th load node It is calculated using the following formula:

[0044]

[0045] In the above formula, P load,i Q load,iLet be the active and reactive power required by the i-th load node, respectively, and j be a complex number.

[0046] Step B includes the following steps in sequence:

[0047] Step B1: Initialize particle swarm parameters, where dimension D is the number of new energy power stations in the system;

[0048] Step B2: Using the objective function of the power system's new energy acceptance capacity assessment model as the fitness, calculate the fitness of the contemporary particles;

[0049] Step B3: Adjust the learning coefficients c1 and c2 of the particles according to their fitness:

[0050]

[0051]

[0052] In the above formula, c max c min These represent the maximum and minimum learning coefficients, respectively, and F is the fitness of a single particle. ave (k) represents the average fitness of contemporary particles;

[0053] Step B4: Update the particle's flight velocity and position based on the adjusted learning coefficients c1 and c2;

[0054] Step B5: Determine whether the stopping iteration condition is met. If not, return to step B2; if met, output the maximum new energy access capacity of the system.

[0055] In step B4, the particle's flight velocity is updated according to the following formula:

[0056] v(k+1)=ωv(k)+c1rand1[p best -p present (k)]

[0057] +c2rand2[g best -p present (k)]

[0058] In the above formula, v(k) and v(k+1) are the velocities of the particle in the k-th and k+1-th iterations, respectively, ω is the inertial coefficient of the particle, rand1 and rand2 are random numbers between [0,1], and p best g best These are the swarm's best particle and its own historical best particle, p present (k) represents the position of the particle at the k-th iteration, i.e., the current position of the particle;

[0059] The particle's flight position is updated according to the following formula:

[0060] p present (k+1)=p present (k)+v(k+1)

[0061] In the above formula, p present (k+1) represents the position of the particle in the (k+1)th iteration.

[0062] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0063] 1. The present invention provides a method for assessing the renewable energy acceptance capacity of a power system based on the short-circuit capacity ratio. This method constructs an assessment model for the renewable energy acceptance capacity of a power system that considers the node-system short-circuit capacity ratio index. This node-system short-circuit capacity ratio index can not only measure the impact of renewable energy plant access on the voltage intensity of load nodes and the interactive impact of multiple renewable energy plants, but also reflect the impact on the overall voltage intensity of the power system, making the assessment of the renewable energy acceptance capacity of the power system more accurate and reasonable.

[0064] 2. The present invention provides a method for assessing the renewable energy acceptance capacity of a power system based on short-circuit capacity ratio. This method employs an improved particle swarm optimization (PSO) algorithm to solve the constructed PSO assessment model. This improved PSO algorithm adaptively adjusts the learning coefficients of particles based on their fitness during the iteration process. When the fitness of a single particle is greater than the average fitness of the current generation of particles, it is considered that the particle should strengthen its self-learning. In this case, c2 = c max >c min =c1; When the fitness of a single particle is less than the average fitness of the current generation of particles, it is considered that the particle should strengthen its social learning, and in this case, c1 = c max >c min =c2 can not only evaluate the system's renewable energy acceptance capacity while ensuring a higher short-circuit capacity ratio (i.e., a higher load node voltage intensity), thus ensuring better system safety and stability, but also has better convergence results and performance compared to the traditional particle swarm optimization algorithm. Attached Figure Description

[0065] Figure 1 This is a thumbnail diagram of the IEEE-39 node system architecture used in Example 1.

[0066] Figure 2 This is an equivalent model diagram of a power system with multiple power stations connected to new energy sources.

[0067] Figure 3 This is an equivalent model diagram of the superposition theorem for multiple power stations with new energy access.

[0068] Figure 4This is an equivalent model diagram of the superposition theorem without the access of multiple power stations for new energy sources.

[0069] Figure 5 A flowchart for improving the particle swarm optimization algorithm.

[0070] Figure 6 The convergence figures for the improved particle swarm optimization algorithm and the traditional particle swarm optimization algorithm are shown. Detailed Implementation

[0071] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0072] This invention provides a method for evaluating the renewable energy access capacity of a power system based on the short-circuit capacity ratio. The method first proposes the node-to-system short-circuit capacity ratio index, then establishes an evaluation model that comprehensively considers the node-to-system short-circuit capacity ratio and the renewable energy access capacity, and uses an improved particle swarm optimization algorithm for fast solution. It can be used to evaluate the maximum renewable energy access capacity that takes into account the safe and stable operation of the power system.

[0073] Example 1:

[0074] This embodiment uses a model built in Matlab / Simulink as an example. Figure 1 The IEEE-39 node system shown has nodes 0, 32, 33, 34, 35, 36, 37, 38, and 39 as PV nodes, with 10 synchronous generators connected to these 10 PV nodes. Node 31 is the balancing node, and three renewable energy power plants are connected to nodes 20, 23, and 27. The power factor of the renewable energy units is... The remaining nodes in the system are all load nodes. These are the evaluation objects. The power parameters of the system are shown in Table 1.

[0075] Table 1 Power parameters of the IEEE-39 Node system

[0076]

[0077] A method for assessing the renewable energy acceptance capacity of a power system based on short-circuit capacity ratio is proposed, which proceeds in the following steps:

[0078] 1. Using Thevenin's theorem and the superposition theorem, the AC power system with multiple power plants connected to the grid and renewable energy sources is divided into a power system model with multiple power plants connected to renewable energy sources and a power system model without multiple power plants connected to renewable energy sources. The equivalent model of the power system before using the superposition theorem is as follows: Figure 2 As shown, load nodes 1-m are load nodes with renewable energy power plants connected to the grid, and load nodes (m+1)-n are load nodes without renewable energy power plants connected to the grid. After applying the superposition theorem, the equivalent diagrams of the power system model with multiple renewable energy power plants connected to the grid and the power system model without multiple renewable energy power plants connected to the grid are respectively shown below. Figure 3 , Figure 4 As shown.

[0079] 2. For power system models with multiple renewable energy power plants connected to the grid, it is assumed that the load node injection current is provided only by synchronous generators. The voltage U of each load node before the renewable energy power plant is connected to the grid is first calculated based on the system impedance parameters and using the node voltage equation. ac and current I ac Then, calculate the short-circuit capacity provided by the power system to each load node before the new energy power station is connected to the grid.

[0080]

[0081]

[0082] In the above formula, Let be the voltage of the i-th load node. Let be the voltage of the i-th load node before the renewable energy power station is connected to the grid. Let be the equivalent complex impedance of the system to the i-th load node. Let be the equivalent complex impedance between the i-th load node and the j-th load node, where i,j = 1, 2, ..., n.

[0083] For a power system model without renewable energy grid connection, it is assumed that the load node injection current is provided only by the renewable energy grid connection. When the load node with renewable energy grid connection is electrically far from the other load nodes, the impact of renewable energy grid connection on the other load nodes can be ignored. First, based on the system impedance parameters, the load node voltage change ΔU caused by the renewable energy grid connection and the current I injected by the renewable energy grid connection into the load node are calculated using the node voltage equation. RE Then, calculate the short-circuit capacity provided by the renewable energy power station to the load node after grid connection.

[0084]

[0085]

[0086]

[0087] In the above formula, This represents the voltage change caused by the connection of the new energy power station to the i-th load node. Let represent the grid-connected capacities of new energy power stations directly connected to the i-th and j-th load nodes, respectively. * indicates the conjugate operation of complex numbers. This represents the complex power conversion factor between load nodes i and j, reflecting the phase and amplitude differences in electrical quantities between the grid connection points of various renewable energy power plants. Let i and j be the currents injected by the new energy power station into the i-th and j-th load nodes, respectively, where i,j = 1, 2, ..., m.

[0088] 3. Based on and Determine the short-circuit capacity ratio of each load node and the system short-circuit capacity ratio:

[0089]

[0090]

[0091]

[0092]

[0093] In the above formula, SCR system SCR is the system short-circuit capacity ratio. node,i Let ω be the short-circuit capacity ratio of the i-th load node. i U represents the weight corresponding to the i-th load node. ac,i Let be the voltage of the i-th load node before the renewable energy power station is connected to the grid. This represents the short-circuit capacity provided by the power system to the i-th load node before the renewable energy power plant is connected to the grid. The dots above indicate complex numbers. This refers to the short-circuit capacity provided by the renewable energy power station to the i-th load node after grid connection. For load nodes without renewable energy grid connection, P is the power required by the i-th load node. load,i Q load,i Let be the active and reactive power required by the i-th load node, respectively, and j be a complex number.

[0094] 4. Construct the following power system renewable energy acceptance capacity assessment model:

[0095] F = max(p1F1 + p2F2)

[0096]

[0097] In the above formula, F is the objective function, F1 and F2 are the optimization objectives, p1 and p2 are the weights of F1 and F2 respectively, and S RE,i S represents the grid-connected capacity of the renewable energy power station directly connected to the i-th load node, where i = 1, 2, ..., m. G,j Let be the installed capacity of the j-th synchronous generator unit in the system, m be the number of load nodes in the system including new energy power stations connected to the grid, j = m+1, m+2, ..., n, and n be the total number of load nodes in the system.

[0098] The constraints of the power system renewable energy acceptance capacity assessment model include:

[0099] Output limitations of synchronous generator units:

[0100] P G,imin ≤P G,i ≤P G,imax

[0101] In the above formula, P G,i To connect the synchronous machine output active power of the i-th load node, P G,imax P G,imin These are the maximum and minimum active power outputs of the synchronous machine connected to the i-th load node, respectively.

[0102] Power output limitations of new energy power plants:

[0103] P RE,imin ≤P RE,i ≤P RE,imax

[0104] In the above formula, P RE,i To output active power for the renewable energy power station connected to the i-th load node, P RE,imax P RE,imin These are the maximum and minimum active power outputs of the renewable energy power station connected to the i-th load node, respectively.

[0105] Power balance constraints:

[0106]

[0107] In the above formula, P load,i Q load,i Let Q be the active and reactive power required by the i-th load node. G,i Q RE,j These represent the reactive power output of the synchronous machine connected to the i-th load node and the reactive power output of the renewable energy power station connected to the j-th load node, respectively.

[0108] Power limits for transmission lines:

[0109] P line,ij ≤P line,ijmax

[0110] In the above formula, P line,ij P line,ijmax These represent the transmission power and maximum value of the transmission lines connecting the i-th and j-th load nodes, respectively.

[0111] 5. See Figure 5 Initialize the particle swarm parameters. Each particle is defined as a D-dimensional space, where the dimension D is the number of new energy power stations in the system, i.e., D = m. Set the particle population size to 100, the maximum number of iterations to 100, and the maximum learning coefficient c. max =0.8, minimum value cmin =0.2, p1=p2=0.5, indicating that the capacity for accepting new energy sources is as important as the node-to-system short-circuit ratio.

[0112] 6. Using the objective function of the power system's new energy acceptance capacity assessment model as the fitness, calculate the fitness of the contemporary particle.

[0113] 7. Adjust the learning coefficients c1 and c2 based on the particle's fitness:

[0114]

[0115]

[0116] In the above formula, c max c min These represent the maximum and minimum learning coefficients, respectively, and F is the fitness of a single particle. ave (k) represents the average fitness of contemporary particles.

[0117] 8. Update the particle's velocity and position based on the adjusted learning coefficients c1 and c2, where the particle's velocity is updated according to the following formula:

[0118] v(k+1)=ωv(k)+c1rand1[p best -p present (k)]

[0119] +c2rand2[g best -p present (k)]

[0120] In the above formula, v(k) and v(k+1) are the velocities of the particle in the k-th and k+1-th iterations, respectively, ω is the inertial coefficient of the particle, rand1 and rand2 are random numbers between [0,1], and p best g best These are the swarm's best particle and its own historical best particle, p present (k) represents the position of the particle at the k-th iteration, i.e., the current position of the particle;

[0121] The particle's flight position is updated according to the following formula:

[0122] p present (k+1)=p present (k)+v(k+1)

[0123] In the above formula, p present (k+1) represents the position of the particle in the (k+1)th iteration.

[0124] 9. Determine whether the stopping iteration condition is met. If not, return to step 6. If it is met, complete the solution of the power system new energy acceptance capacity assessment model and output the maximum new energy access capacity of the system.

[0125] To examine the performance of the improved particle swarm optimization algorithm used in this invention, the evaluation results of Example 1 are compared with those of the traditional particle swarm optimization algorithm (the power system renewable energy acceptance capacity evaluation model constructed is the same as that of Example 1). The results are shown in Table 2:

[0126] Table 2 Evaluation results of the improved particle swarm optimization algorithm and the traditional particle swarm optimization algorithm

[0127]

[0128] As can be seen from Table 2, the improved particle swarm optimization algorithm used in this invention evaluates the system's renewable energy acceptance capacity while ensuring a higher short-circuit capacity ratio (i.e., a higher load node voltage intensity), thus obtaining the renewable energy access capacity with the largest objective function, thereby improving the system's safety and stability.

[0129] Meanwhile, the convergence of the improved particle swarm optimization algorithm used in this invention and the traditional particle swarm optimization algorithm (mainly reflected in the convergence algebra and fitness) were examined, and the results are as follows: Figure 6 As shown in Table 3.

[0130] Table 3. Convergence algebra and fitness results of the improved particle swarm optimization algorithm and the traditional particle swarm optimization algorithm.

[0131] Convergent Algebra Convergence fitness Improved Particle Swarm Optimization Algorithm 11 1.6895 Traditional Particle Swarm Optimization Algorithm 10 1.6177

[0132] Depend on Figure 6 As shown in Table 3, compared with the traditional particle swarm optimization algorithm, the improved particle swarm optimization algorithm proposed in this invention has a significantly improved final convergence fitness, and the convergence algebra is not much different from that of the traditional particle swarm optimization algorithm. Therefore, the improved particle swarm optimization algorithm performs better in solving the evaluation model proposed in this invention.

Claims

1. A method for assessing the renewable energy acceptance capacity of a power system based on short-circuit capacity ratio, characterized in that: The evaluation method includes the following steps in sequence: Step A: Construct a power system renewable energy acceptance capacity assessment model, wherein the objective function of the assessment model is... for: ； ； In the above formula, , To optimize the objective, , They are respectively , The weight, To directly connect the grid-connected capacity of the new energy power station to the i-th load node, , Let m be the installed capacity of the j-th synchronous generator unit in the system, and m be the number of load nodes in the system that include renewable energy power plants connected to the grid. n is the total number of load nodes in the system. This is the system short-circuit capacity ratio; The system short-circuit ratio The following formula is used for calculation: ； ； ； In the above formula, Let be the short-circuit capacity ratio of the i-th load node. Let be the weight corresponding to the i-th load node. Let be the voltage of the i-th load node before the renewable energy power station is connected to the grid. This represents the short-circuit capacity provided by the power system to the i-th load node before the renewable energy power plant is connected to the grid. The dots above indicate complex numbers. This refers to the short-circuit capacity provided by the renewable energy power station to the i-th load node after grid connection. For load nodes without renewable energy grid connection, =0, The power required by the i-th load node; The , The methods for determining this include: Thevenin's theorem and the superposition theorem are used to divide the AC power system with multiple power plants connected to the grid and including power system models with and without multiple power plants connected to the grid. For a power system model containing multiple renewable energy power plants connected to the grid, the voltage and current of each load node before the renewable energy power plants are connected to the grid are first determined based on the system's impedance parameters, and then the following calculations are performed. ; For a power system model without the integration of multiple renewable energy power plants, the voltage change at load nodes caused by the integration of renewable energy power plants and the current injected from renewable energy power plants into load nodes are first determined based on the system's impedance parameters. Then, the following calculations are performed: ; Step B: The constructed evaluation model is solved using an improved particle swarm optimization algorithm to obtain the maximum new energy access capacity of the system.

2. The method for assessing the renewable energy acceptance capacity of a power system based on short-circuit capacity ratio according to claim 1, characterized in that: The It is calculated using the following formula: ； ； In the above formula, Let be the voltage of the i-th load node. Let be the voltage of the i-th load node before the renewable energy power station is connected to the grid. Let be the equivalent complex impedance of the system to the i-th load node. Let be the equivalent complex impedance between the i-th load node and the j-th load node. , This represents the column vector of load node voltages before the new energy source is connected to the grid. The column vector of current injected into the load nodes by the system before the new energy power station is connected to the grid; The It is calculated using the following formula: ； ； ； In the above formula, This represents the voltage change caused by the connection of the new energy power station to the i-th load node. , These represent the grid-connected capacities of new energy power plants directly connected to the i-th and j-th load nodes, respectively. Represents the conjugate operation for complex numbers. , These represent the current injected by the renewable energy power plant into the i-th and j-th load nodes, respectively. , This is a column vector representing the voltage changes at load nodes caused by the connection of new energy power plants. This refers to the column vector of current injected from renewable energy power plants into load nodes.

3. The method for assessing the renewable energy acceptance capacity of a power system based on short-circuit capacity ratio according to claim 1, characterized in that: The constraints of the power system renewable energy acceptance capacity assessment model include output limits for synchronous generators, output limits for renewable energy power plants, power balance constraints, and power limits for transmission lines. The output limit of the synchronous generator unit is: ； In the above formula, To output active power for the synchronous machine connected to the i-th load node, , These are the maximum and minimum active power outputs of the synchronous machine connected to the i-th load node, respectively. The power output limit of the aforementioned new energy power station is: ； In the above formula, To output active power to the renewable energy power station connected to the i-th load node, , These are the maximum and minimum active power outputs of the renewable energy power station connected to the i-th load node, respectively. The power balance constraint is: ； In the above formula, , Let be the active and reactive power required by the i-th load node, respectively. , These represent the reactive power output of the synchronous machine connected to the i-th load node and the reactive power output of the renewable energy power station connected to the j-th load node, respectively. The power limit of the transmission line is: ； In the above formula, , These represent the transmission power and maximum value of the transmission lines connecting the i-th and j-th load nodes, respectively.

4. The method for assessing the renewable energy acceptance capacity of a power system based on short-circuit capacity ratio according to claim 1, characterized in that: The power required by the i-th load node It is calculated using the following formula: ； In the above formula, , Let be the active and reactive power required by the i-th load node, respectively, and j be a complex number.

5. The method for assessing the renewable energy acceptance capacity of a power system based on short-circuit capacity ratio according to claim 1, characterized in that: Step B includes the following steps in sequence: Step B1: Initialize particle swarm parameters, where dimension D is the number of new energy power stations in the system; Step B2: Using the objective function of the power system's new energy acceptance capacity assessment model as the fitness, calculate the fitness of the contemporary particles; Step B3: Adjust the learning coefficient based on the particle's fitness. and Adjustments will be made: ； In the above formula, , These represent the maximum and minimum values ​​of the learning coefficient, respectively. For the fitness of a single particle, The average fitness of contemporary particles; Step B4: Based on the adjusted learning coefficient and Update the particle's flight speed and position; Step B5: Determine whether the stopping iteration condition is met. If not, return to step B2; if met, output the maximum new energy access capacity of the system.

6. The method for assessing the renewable energy acceptance capacity of a power system based on short-circuit capacity ratio according to claim 5, characterized in that: In step B4, the particle's flight velocity is updated according to the following formula: ； In the above formula, , These represent the particle velocities at the k-th and k+1-th iterations, respectively. The inertial coefficient of the particle's flight. , A random number between [0, 1] , These are the group's best particle and its own historical best particle, respectively. This represents the particle's position at the k-th iteration, i.e., the particle's current position. The particle's flight position is updated according to the following formula: ； In the above formula, This represents the position of the particle during the (k+1)th iteration.

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