Transmitting-end power grid planning method based on generalized short-circuit ratio and transient overvoltage

Abstract

The invention discloses a sending-end power grid planning method based on a generalized short-circuit ratio and transient overvoltage, and belongs to the technical field of power systems. The method comprises the following steps: constructing a generalized short-circuit ratio index to quantify the electrical coupling strength among multiple new energy stations; establishing a quantitative relation model of the short-circuit ratio and the transient overvoltage peak value; evaluating the spatial-temporal distribution change of the short-circuit ratio before and after the planning scheme is adjusted, and identifying the risk through clustering analysis and probability evaluation; adopting modal sensitivity analysis and participation factor calculation to identify dominant factors and a core area of voltage stability; and constructing a multi-objective optimization model, taking investment cost, line constraint and voltage deviation as constraints, and adopting an improved MOEAD / DE algorithm to solve an optimal planning scheme. The system comprises the corresponding module, the storage medium and the electronic equipment. According to the method, the limitation of a traditional short-circuit ratio index is overcome, and the power grid voltage stability evaluation precision and the new energy consumption capability are improved.

Description

Technical Field

[0001] This invention belongs to the field of power system and its automation technology, specifically relating to a method for voltage stability analysis and planning of the sending-end power grid suitable for high proportion of new energy access, and particularly to a sending-end power grid planning method based on generalized short-circuit ratio and transient overvoltage. Background Technology

[0002] With the deepening implementation of the "dual-carbon" strategy, the proportion of new energy sources, represented by wind power and photovoltaics, in the power system continues to increase. Shanxi, as an important national energy base and a key sending-end power grid of the "West-to-East Power Transmission" northern corridor, is undergoing a transformation from a traditional thermal power base to a comprehensive new energy demonstration base. This transformation is accompanied by significant adjustments to the structure of the "West-to-East Power Transmission" corridor and large-scale, clustered integration of new energy sources within the province. This has profoundly changed the operating characteristics of the power grid. Voltage stability issues, especially the transient overvoltage risk of new energy generator sets, have become prominent bottlenecks restricting the safe and stable operation of the sending-end power grid and the efficient consumption of new energy.

[0003] Existing technologies, such as CN120855353A, disclose a multi-regional collaborative power grid planning system and method based on an improved multi-objective particle swarm optimization algorithm. This system solves the problems of multi-objective coupling and cross-regional coordination in traditional power grid planning by constructing multi-dimensional objective functions of economy, environmental protection, and reliability, and introducing game theory to quantify multi-objective constraints. The system includes a data acquisition module, a multi-objective optimization model construction module, an improved particle swarm optimization algorithm execution module, a collaborative decision-making module, and a result output module. The improved particle swarm optimization algorithm employs dynamic adaptive inertia weights, time-varying acceleration coefficients, and differential mutation operations, significantly improving convergence speed and Pareto front distribution quality. The collaborative decision-making module achieves cross-regional parameter interaction and scheme optimization through a hierarchical collaborative mechanism and fuzzy entropy theory. CN113381445A discloses a method and system for optimizing the configuration of synchronous condensers to suppress transient overvoltages in renewable energy sources. The method includes: calculating the short-circuit ratio of each renewable energy source connected to the target AC / DC hybrid power grid and the transient overvoltage of each renewable energy source during a fault in the target AC / DC hybrid power grid; when the transient overvoltage of a renewable energy source exceeds a threshold value, determining the threshold value for the short-circuit ratio of each renewable energy source based on its short-circuit ratio and transient overvoltage; configuring a synchronous condenser on the low-voltage side of the grid connection point of the renewable energy source with the smallest short-circuit ratio, and recalculating the short-circuit ratio of each renewable energy source; when the short-circuit ratio of each renewable energy source is greater than or equal to the threshold value, recalculating the transient overvoltage of each renewable energy source; and when the transient overvoltage of each renewable energy source is less than or equal to the threshold value, determining the current synchronous condenser configuration as the optimal configuration.

[0004] However, current grid strength and stability analyses still primarily rely on the conventional short-circuit ratio. This indicator has significant limitations when analyzing complex systems with multiple renewable energy power plants operating in parallel: First, the traditional short-circuit ratio cannot accurately quantify the electrical coupling and mutual influence between power plants, potentially overestimating the actual support strength of the grid; second, it lacks a clear theoretical model for the quantitative relationship between the short-circuit ratio and transient overvoltages, leading to inaccurate risk assessments; and third, at the planning level, a closed-loop decision-making method integrating strength assessment, mechanism analysis, factor identification, and scheme optimization has not yet been established. Therefore, current technologies are insufficient to accurately address the voltage stability challenges faced by the Shanxi power grid under the new circumstances. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention discloses a sending-end power grid planning method based on generalized short-circuit ratio and transient overvoltage, the technical solution of which is as follows:

[0006] A sending-end power grid planning method based on generalized short-circuit ratio and transient overvoltage is characterized by the following steps: S1. Constructing a generalized short-circuit ratio index.

[0007] S2. Establish a theoretical model to characterize the quantitative relationship between the short-circuit ratio index and the transient overvoltage of new energy power plants;

[0008] S3. Assess the spatiotemporal distribution changes of the short-circuit ratio of multiple new energy power plants in the province before and after the adjustment of the planning scheme;

[0009] S4. Identify the dominant factors affecting grid voltage stability;

[0010] S5: Algorithm for solving multi-objective programming models.

[0011] The present invention also discloses a non-volatile storage medium, characterized in that the non-volatile storage medium includes a stored program, wherein the program, when running, controls the device where the non-volatile storage medium is located to execute the above-described method.

[0012] The present invention also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The device is characterized in that, when the processor executes the computer program, it implements the steps of the above-described method by calling a pre-compiled power system analysis library and an optimization algorithm library. The power system analysis library includes at least power flow calculation, short-circuit calculation, and transient stability simulation modules, and the optimization algorithm library includes at least non-dominated sorting genetic algorithm and multi-attribute decision algorithm modules.

[0013] Beneficial effects

[0014] Constructing a generalized short-circuit ratio index

[0015] Technical means: Introduce dynamic coupling coefficient, calculate electrical distance based on node impedance matrix, and take into account the active power of multiple stations.

[0016] Beneficial effects: It more accurately reflects the actual grid support strength when multiple new energy power plants are operating in parallel, avoids overestimating grid strength, and provides an accurate data basis for planning.

[0017] Establish a quantitative model for transient overvoltage

[0018] Technical approach: Based on the power balance and node voltage equations at the moment of fault clearance, an explicit expression is derived, and the sensitivity coefficient K is determined through simulation fitting.

[0019] Beneficial effects: It clarifies the quantitative relationship between short-circuit ratio and transient overvoltage, improves the accuracy of risk assessment, and guides overvoltage suppression measures.

[0020] Assessing the spatiotemporal distribution changes of the short-circuit ratio

[0021] Technical approach: Ward hierarchical clustering and Latin hypercube sampling were used to analyze the cluster characteristics and probability distribution of short-circuit ratio.

[0022] Beneficial effects: Comprehensive understanding of the spatiotemporal evolution of power grid strength, identification of high-risk areas with low short-circuit ratios, and enhancement of the foresight and adaptability of planning.

[0023] Identify the dominant factors in voltage stability

[0024] Technical means: Modal sensitivity analysis is used to calculate eigenvalue sensitivity, and participation factors are used for geographic positioning.

[0025] Beneficial effects: Accurately identify key components (such as lines, transformers, reactive power compensation equipment) and core sites that affect voltage stability, guiding targeted upgrades and investments.

[0026] Solving multi-objective optimization models

[0027] Technical approach: With investment cost, thermal stability limit, and voltage deviation as constraints, the MOEAD / DE algorithm is used to obtain the Pareto solution set, and the TOPSIS method is combined to optimize the solution.

[0028] Beneficial effects: While ensuring economic efficiency, it optimizes the power grid structure and reactive power compensation configuration, improves voltage stability and renewable energy absorption capacity, and achieves multi-objective synergy in the planning scheme. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0030] A sending-end power grid planning method based on generalized short-circuit ratio and transient overvoltage includes the following steps:

[0031] S1. Construct a generalized short-circuit ratio index.

[0032] The generalized short-circuit ratio (GSCR) is for new energy multi-station systems, and its calculation formula is as follows:

[0033]

[0034] In the formula, Let be the generalized short-circuit ratio of the i-th node. Let i be the short-circuit capacity of node i; The active power injected into the new energy source at node i. The active power injected by the new energy source at the j-th node. To characterize the dynamic coupling coefficient of the electrical coupling strength between node j and node i, it is calculated using the normalized electrical distance based on the node impedance matrix z, as follows:

[0035]

[0036] In the formula, This indicates the electrical coupling strength between node i and node j; and These represent the impedance magnitudes of node i and node j, respectively.

[0037] S2. Establish a theoretical model to characterize the quantitative relationship between the short-circuit ratio index and the transient overvoltage of new energy power plants.

[0038] Based on the power grid equivalent model (i.e., an electrical model that simplifies the representation of a multi-energy power plant system), the peak value of transient overvoltage is derived. The quantitative relationship is as follows. Its core derivation is based on the power balance and node voltage equations at the instant of fault clearing, and through linearization simplification, an explicit expression is obtained:

[0039]

[0040] In the formula, K represents the active power injected by the renewable energy source before the fault, and K is the comprehensive sensitivity coefficient. The value of K is determined through simulation fitting. For example, if M different grid operation modes and fault scenarios are set, M sets of... Data points, using the least squares method to apply to the expression Perform linear regression to fit the optimal K value.

[0041] An equivalent model is a simplified representation of the power grid used to derive transient overvoltage relationships.

[0042] S3: Based on the generalized short-circuit ratio (GSCR), evaluate the spatiotemporal distribution changes of the power grid intensity of multiple new energy power plants in the province before and after the adjustment of the planning scheme;

[0043] The assessment of the spatiotemporal distribution variation of the short-circuit ratio includes cluster analysis and probability assessment;

[0044] The cluster analysis employed Ward hierarchical clustering, based on the electrical distance between nodes. For measurement, The calculation is based on the nodal impedance matrix:

[0045]

[0046] Based on electrical distance, stations with tight electrical coupling are grouped into several clusters, and the equivalent short-circuit ratio of each cluster is calculated;

[0047] The probability assessment employs Latin hypercube sampling technology to jointly sample the output of new energy sources and the load level, generating N operating scenarios with equal or unequal probabilities, and then calculating the probability distribution, expected value, and risk probability of the short-circuit ratio falling below the critical value.

[0048] S4. Identify the dominant factors affecting grid voltage stability;

[0049] Modal sensitivity analysis was used to identify the dominant factors affecting grid voltage stability.

[0050] This method first calculates the power flow Jacobian matrix J of the system at a certain steady-state operating point, and then solves for its eigenvalues. and the corresponding right eigenvector and left eigenvector ;

[0051] Furthermore, calculate the voltage characteristic value. For any parameter of the system (including line reactance) Transformer turns ratio and reactive power compensation device output The sensitivity of ) is as follows:

[0052]

[0053] By analyzing the absolute values ​​of sensitivity, the key components and parameters that have the greatest impact on the system voltage stability margin are determined.

[0054] Calculate the participation factor of the node for geolocation; node i for the k-th voltage mode Participating factors Defined as:

[0055]

[0056] in, Right eigenvector The i-th component; Left eigenvector The i-th component is obtained by analyzing the participation factors of each node. The size of the voltage mode is used to identify the weakest voltage mode. The core areas and core sites that made the greatest contributions.

[0057] S5: Algorithm for solving multi-objective programming models.

[0058] In the multi-objective optimization model for constructing and solving the power grid capacity improvement scheme, the optimization model is defined as:

[0059]

[0060] in, Let be the peak transient overvoltage at node j; x is a vector of decision variables, including: a Boolean variable indicating whether the line should be constructed. Integer variable indicating the installation location of reactive power compensation equipment and its capacity continuous variables ; The inequality constraints include the upper limit of investment cost, the thermal stability limit of the line, and the voltage deviation range; These are equality constraints, i.e., power flow equations.

[0061] The multi-objective optimization model is solved using the MOEAD / DE genetic algorithm with an elitist strategy and non-dominated sorting, yielding the Pareto optimal solution set. The final implementation scheme is then selected from the Pareto solution set using a sorting method that approximates the ideal solution. The steps are as follows:

[0062] Step 1: Parameter Settings. Configure the collection of individuals. subgroups Based on the objective function Each target value of an individual is sorted, and an individual ranking vector is assigned. Then, the sum of each ranking vector is calculated. ,in Furthermore, set the selection probability as follows.

[0063]

[0064]

[0065] Potentially better individuals are selected through roulette wheel selection. And undergo new mutations.

[0066]

[0067] in and From the subpopulation Different individuals were selected.

[0068] An external elite set is set up for each weight vector to store individuals with smaller weight values, and individuals are selected from the elite set for mutation to help the algorithm converge.

[0069] (1) For each weight vector Set up an elite group To store individual values ​​and weights.

[0070] (2) Compare the weight values ​​of individuals within the neighborhood of the i-th weight vector. The weight values ​​of cross-individuals are used, and individuals with smaller weight values ​​are stored in the elite set.

[0071] (3) Randomly select two different individuals from the neighborhood and the elite set, respectively. and , with individuals Generate offspring.

[0072] ;

[0073] Step 2: Crossover Inheritance. Considering the advantage of uniform design in reducing the number of experiments by selecting points in local regions, a heuristic crossover operator is established using uniform design. The crossover process between parent and offspring individuals is as follows:

[0074] (1) Using the Latin square Generate a 0-1 orthogonal matrix. and Representing individuals from the parent generation and mutated individuals The j-th component is selected as the component corresponding to the crossover individual, and the number of recombined individuals is M.

[0075] (2) Randomly select a weight vector from the neighborhood and calculate the weight value of the recombined individual.

[0076] (3) Select the dominant individuals after recombination based on their weight values ​​and form an individual set, then analyze the matrix. Select the corresponding row and store it in matrix A.

[0077] (4) Calculate the probability that the element in the i-th column of matrix A is 1. .if Choose the i-th component of the crossover individual from the parent individual. Conversely, choose the i-th component of the crossover individual from the mutated individual, where... .

[0078] Step 3: Factor Improvement. In the early stages of evolution, the value of the mutation factor needs to be increased so that the algorithm can find the best possible exploration region. In the later stages, the value of the mutation factor needs to be decreased so that the group converges to the global optimum as much as possible. This indicates that the mutation factor F is related to the decision space. Therefore, the norm of the vectors in the decision space is used to adjust the mutation factor. The process is as follows:

[0079] Randomly select an objective function, and find the sets of individuals corresponding to the maximum and minimum values ​​of the objective function, denoted as and respectively. and .

[0080] Further calculation and The Euclidean distance between individuals. Find the vector that satisfies the maximum Euclidean distance. and And randomly select individuals from the parent body. and .

[0081] Calculate vectors and The norm of is denoted as and Finally, set the mutation factor. :

[0082]

[0083]

[0084] and They are Upper and lower limits, control parameters The settings are as follows:

[0085]

[0086] In the formula, e represents a constant, and G is the number of iterations. This represents the maximum number of iterations set.

[0087] Step 4: Convergence check. Based on the number of iterations... and the maximum function evaluation number The stopping criteria are as follows: If MODE / D meets the above criteria, mutation is performed according to the local optimum strategy, and offspring are generated using a heuristic crossover strategy. If the criteria are not met... If the algorithm fails, it employs both the external elite set strategy and the classic crossover strategy. Finally, to improve the algorithm's mining capability, an adaptive adjustment strategy is used throughout the iteration process; if the stopping condition is met, the optimal value is output.

[0088] The present invention also discloses a sending-end power grid planning system based on generalized short-circuit ratio and transient overvoltage, which performs the above-described method.

[0089] The present invention also discloses a non-volatile storage medium, characterized in that the non-volatile storage medium includes a stored program, wherein the program, when running, controls the device where the non-volatile storage medium is located to execute the above-described method.

[0090] The present invention also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The device is characterized in that, when the processor executes the computer program, it implements the steps of the above-described method by calling a pre-compiled power system analysis library and an optimization algorithm library. The power system analysis library includes at least power flow calculation, short-circuit calculation, and transient stability simulation modules, and the optimization algorithm library includes at least non-dominated sorting genetic algorithm and multi-attribute decision algorithm modules.

[0091] This invention solves the problem that traditional planning methods cannot accurately quantify the coupling of multiple power plants and the risk of overvoltage by collaboratively analyzing the generalized short-circuit ratio and transient overvoltage. It forms a closed-loop decision-making method from assessment and analysis to optimization, which is particularly suitable for the planning of the sending-end power grid with a high proportion of renewable energy access, such as the Shanxi power grid. It helps to ensure the safe and stable operation of the power grid and promote the efficient consumption of renewable energy.

[0092] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A sending-end power grid planning method based on generalized short-circuit ratio and transient overvoltage, characterized in that, The steps include: S1. Constructing the Generalized Short-Circuit Ratio (GSCR) model index; S2. Establish a theoretical model to characterize the quantitative relationship between the short-circuit ratio index and the transient overvoltage of new energy power plants; S3. Assess the spatiotemporal distribution changes of the short-circuit ratio of multiple new energy power plants before and after the adjustment of the planning scheme for the target power grid; S4. Identify the dominant factors affecting grid voltage stability; S5: Algorithm for solving multi-objective programming models.

2. The sending-end power grid planning method based on generalized short-circuit ratio and transient overvoltage according to claim 1, characterized in that: step S1 further includes the following: The generalized short-circuit ratio (GSCR) model is designed for multi-station systems of new energy sources, and its calculation formula is as follows: In the formula, Let be the generalized short-circuit ratio of the i-th node. Let i be the short-circuit capacity of node i; The active power injected into the new energy source at node i. The active power injected by the new energy source at the j-th node. To characterize the dynamic coupling coefficient of the electrical coupling strength between node j and node i, it is calculated using the normalized electrical distance based on the node impedance matrix z, as follows: In the formula, This indicates the electrical coupling strength between node i and node j; and These represent the impedance magnitudes of node i and node j, respectively.

3. The sending-end power grid planning method based on generalized short-circuit ratio and transient overvoltage as described in claim 1, characterized in that: Step S2 further includes the following: deriving the peak value of transient overvoltage based on the GSCR model. Quantitative relationship; Its core derivation is based on the power balance and node voltage equations at the instant of fault clearing. Through linearization simplification, an explicit expression is obtained: In the formula, K represents the active power injected by the renewable energy source before the fault, and K is the comprehensive sensitivity coefficient. The value of K is determined through simulation fitting, setting M different grid operation modes and fault scenarios to obtain M sets of data. Data points, using the least squares method to apply to the expression Perform linear regression to fit the optimal K value.

4. The sending-end power grid planning method based on generalized short-circuit ratio and transient overvoltage according to claim 3, characterized in that: step S3 further includes the following: The assessment of the spatiotemporal distribution variation of the short-circuit ratio includes cluster analysis and probability assessment; The cluster analysis employed Ward hierarchical clustering, based on the electrical distance between nodes. For measurement, The calculation is based on the nodal impedance matrix: Based on electrical distance, stations with tight electrical coupling are grouped into several clusters, and the equivalent short-circuit ratio of each cluster is calculated; The probability assessment employs Latin hypercube sampling technology to jointly sample the output of new energy sources and the load level, generating N operating scenarios with equal or unequal probabilities, and then calculating the probability distribution, expected value, and risk probability of the short-circuit ratio falling below the critical value.

5. The sending-end power grid planning method based on generalized short-circuit ratio and transient overvoltage according to claim 1, characterized in that: step S4 further includes the following: The identification of the dominant factors affecting grid voltage stability adopts the modal sensitivity analysis method: firstly, the power flow Jacobian matrix J of the system at a certain steady-state operating point is calculated, and its eigenvalues ​​are solved. and the corresponding right eigenvector and left eigenvector ; Calculate voltage characteristic value For any parameter of the system The sensitivity is as follows: By analyzing the absolute values ​​of sensitivity, the key components and parameters that have the greatest impact on the system voltage stability margin are determined.

6. The sending-end power grid planning method based on generalized short-circuit ratio and transient overvoltage according to claim 5, characterized in that: step S4 further includes the following: calculating the participation factor of the node for geographical location; node i for the k-th voltage mode Participating factors Defined as: in, Right eigenvector The i-th component; Left eigenvector The i-th component is obtained by analyzing the participation factors of each node. The size of the voltage mode is used to identify the weakest voltage mode. The core areas and core sites that made the greatest contributions.

7. The sending-end power grid planning method based on generalized short-circuit ratio and transient overvoltage as described in claim 6, characterized in that: Step S5 further includes the following: In the multi-objective optimization model for constructing and solving the power grid capacity improvement scheme, the optimization model is defined as: in, Let be the peak transient overvoltage at node j; x is a vector of decision variables, including: a Boolean variable indicating whether the line should be constructed. Integer variable indicating the installation location of reactive power compensation equipment and its capacity continuous variables ; The inequality constraints include the upper limit of investment cost, the thermal stability limit of the line, and the voltage deviation range; These are equality constraints, i.e., power flow equations; The multi-objective optimization model is solved using the MOEAD / DE genetic algorithm with an elitist strategy to obtain the Pareto optimal solution set. The final implementation scheme is then selected from the Pareto solution set using the approximation ideal solution sorting method.

8. A power grid planning system based on generalized short-circuit ratio and transient overvoltage, characterized by: The system performs the method described in any one of claims 1-7.

9. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein the program, when executed, controls the device where the non-volatile storage medium is located to perform the method described in any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 7 by calling the pre-compiled power system analysis library and optimization algorithm library, wherein the power system analysis library includes at least power flow calculation, short circuit calculation and transient stability simulation modules, and the optimization algorithm library includes at least non-dominated sorting genetic algorithm and multi-attribute decision algorithm modules.

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