Power system voltage out-of-limit governance method and system based on comprehensive sensitivity

Abstract

The application discloses a power system voltage out-of-limit treatment method and system based on comprehensive sensitivity, relates to the technical field of power system operation and control, and acquires power grid node power and line parameter data; based on the power grid node power and the line parameter data, a power flow equation is solved through an improved polar coordinate Newton-Raphson power flow algorithm to obtain a voltage correction amount, and power flow is iteratively calculated based on the voltage correction amount; based on a Jacobian matrix obtained in the power flow calculation, active power sensitivity and reactive power sensitivity of nodes are determined, a device response time factor and an adjustment cost factor are introduced, and a comprehensive sensitivity index is obtained; nodes are sorted based on the comprehensive sensitivity index, and problem nodes are screened; a multi-objective optimization function is constructed based on the problem nodes, an optimal treatment strategy is solved, and adjustment is performed based on the optimal treatment strategy. The application breaks through the limitations of low efficiency, poor precision, high cost and weak adaptability of traditional voltage treatment, and guarantees stable operation of a power grid.

Description

Technical Field

[0001] This invention relates to the field of power system operation and control technology, and in particular to a method and system for managing voltage over-limit in power systems based on comprehensive sensitivity. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] The increasing penetration of distributed energy sources such as photovoltaics and wind power in power distribution networks presents both challenges to the safe and stable operation of these networks, particularly the issue of voltage exceeding limits. Voltage stability, a core element for the safe, reliable, and economical operation of power systems, is of paramount importance. From a macro perspective, stable voltage ensures the efficient operation of various electrical equipment under rated conditions, maintains coordination and balance among different parts of the power system, and guarantees reliable power supply for the entire system.

[0004] Traditional voltage control methods primarily rely on local control devices such as capacitor bank switching and on-load tap changer adjustment. These devices typically operate based on local voltage measurements, lacking a holistic system perspective. In complex scenarios with a high proportion of distributed power supply, the unidirectional nature of power flow is disrupted, potentially leading to repeated adjustments or even conflicting actions among multiple local controllers. This not only reduces equipment lifespan but also jeopardizes system stability. Once voltage limits are exceeded, a series of serious consequences can ensue.

[0005] To overcome these problems, academia and industry have proposed various centralized optimization algorithms, such as optimal power flow. While optimal power flow is theoretically a perfect solution, its complex models, high computational cost, and long solution time place an excessive burden on the computation of distribution network voltage control with high real-time requirements. Furthermore, although heuristic optimization methods can avoid local optima, the convergence speed and consistency of the calculation results are difficult to guarantee, making them unsuitable for online real-time control. Other studies have attempted to use simple sensitivity methods to guide voltage control, but these often only consider a single type of sensitivity, failing to comprehensively consider the synergistic effects of active and reactive power regulation resources, and lack a complete control process from accurate problem identification to equipment optimization decisions and closed-loop effect verification.

[0006] The aforementioned methods are even less effective when faced with complex and ever-changing power grid structures and load characteristics. With the widespread integration of distributed energy resources, the power flow distribution of the power grid has become more complex, and traditional voltage regulation methods cannot effectively address the impact of the randomness and fluctuations in distributed energy output on voltage. Moreover, traditional sensitivity methods often only consider a single type of sensitivity and lack performance optimization for basic power flow algorithms, making it difficult to balance control accuracy and economic benefits in practical applications. These limitations restrict the application effectiveness of traditional voltage limit mitigation methods. Summary of the Invention

[0007] To overcome the shortcomings of the prior art, this invention provides a power system voltage over-limit control method and system based on comprehensive sensitivity, aiming to achieve efficient and accurate voltage over-limit control to ensure the voltage stability of the power system.

[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0009] In a first aspect, the present invention provides a power system voltage over-limit mitigation method based on comprehensive sensitivity, comprising:

[0010] Acquire power and line parameter data of power grid nodes;

[0011] Based on power grid node power and line parameter data, the power flow equation is solved by an improved polar coordinate Newton-Raphson power flow algorithm to obtain voltage correction, and the power flow is iteratively calculated based on the voltage correction.

[0012] Based on the Jacobian matrix obtained from power flow calculation, the active and reactive power sensitivities of nodes are determined. Equipment response time factors and adjustment cost factors are introduced to obtain a comprehensive sensitivity index. Based on the comprehensive sensitivity index, nodes are ranked and problem nodes are screened.

[0013] A multi-objective optimization function is constructed based on the problem nodes, and the optimal governance strategy is obtained by solving the problem. Adjustment is then carried out based on the optimal governance strategy.

[0014] A further technical solution is that the improved polar coordinate Newton-Raphson power flow algorithm introduces an adaptive step size factor based on the polar coordinate Newton-Raphson power flow algorithm.

[0015] A further technical solution is that the adaptive step size factor is dynamically adjusted based on the convergence of the previous iteration, expressed as:

[0016]

[0017] in, For the first The adaptive step size factor in the next iteration. For the first The power imbalance vector at the next iteration For the first The adaptive step size factor in the next iteration. For the first The power imbalance vector at the next iteration.

[0018] A further technical solution is that the comprehensive sensitivity index is expressed as:

[0019]

[0020] in, For comprehensive sensitivity index, For active sensitivity, For reactive power sensitivity, For installation on the node Adjust the response time of the device. For installation on the node The unit adjustment cost of the adjustment equipment , , , These are the weighting coefficients for active sensitivity, reactive sensitivity, response speed factor, and economic factor, respectively.

[0021] A further technical solution is that the multi-objective optimization function is expressed as:

[0022]

[0023] in, Let voltage quality be the objective function. For the number of nodes, For nodes voltage amplitude, For nodes The expected voltage reference value, To adjust the economic objective function, To adjust the number of devices, For installation on the node The unit adjustment cost of the adjustment equipment For equipment The absolute value of the planned adjustment amount, Let the system efficiency objective function be... This represents the total active power loss of the system.

[0024] A further technical solution employs an improved adaptive NSGA-II algorithm to solve the multi-objective optimization function, specifically as follows:

[0025] An initial population is generated based on comprehensive sensitivity indicators;

[0026] For each individual in the population, the improved polar coordinate Newton-Raphson power flow algorithm is invoked to calculate the system power flow;

[0027] The penalty function method is used to handle voltage over-limit constraints, transforming the constraint violation degree into a penalty term of the voltage quality objective function, and constructing an augmented voltage quality objective function.

[0028] The fast nondominated sorting method of NSGA-II is adopted, and the population individuals are divided into multiple nondominated fronts according to the objective functions of augmented voltage quality, regulation economy and system efficiency.

[0029] Individuals adapt and evolve to form a new generation of population, and select the optimal governance strategy based on fuzzy satisfaction decision-making.

[0030] A further technical solution involves comparing the adjusted node voltage with a preset voltage threshold to assess whether the adjustment effect has achieved the expected goal. If the adjusted voltage still exceeds the limit, the capacity of the connected equipment is adjusted until the voltage returns to the normal range.

[0031] Secondly, the present invention provides a power system voltage over-limit control system based on comprehensive sensitivity, comprising:

[0032] The data acquisition module is configured to acquire power grid node and line parameter data.

[0033] The power flow calculation module is configured to: solve the power flow equations using an improved polar coordinate Newton-Raphson power flow algorithm based on grid node power and line parameter data, obtain voltage corrections, and iteratively calculate the power flow based on the voltage corrections;

[0034] The sensitivity ranking module is configured to: determine the active and reactive sensitivities of nodes based on the Jacobian matrix obtained from power flow calculations, introduce equipment response time factors and regulation cost factors to obtain a comprehensive sensitivity index; rank the nodes based on the comprehensive sensitivity index and filter out problematic nodes.

[0035] The limit violation management module is configured to: construct a multi-objective optimization function based on the problem node, solve for the optimal management strategy, and make adjustments based on the optimal management strategy.

[0036] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the power system voltage over-limit management method based on comprehensive sensitivity as described in the first aspect.

[0037] Fourthly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the power system voltage over-limit management method based on comprehensive sensitivity as described in the first aspect.

[0038] The above one or more technical solutions have the following beneficial effects:

[0039] This invention iteratively solves power flow in the power grid by introducing an improved polar coordinate Newton-Raphson power flow algorithm. It also innovatively constructs a comprehensive sensitivity index that integrates equipment response time and regulation costs. Based on this comprehensive sensitivity index, problematic nodes are identified, significantly improving the accuracy and economy of voltage management. Traditional methods typically only consider electrical sensitivity, while this invention incorporates regulation costs and response speed from practical engineering scenarios into the decision-making system, effectively reducing power grid operating costs and improving regulation efficiency. Furthermore, a multi-objective optimization function is constructed to simultaneously optimize the voltage exceedance management effect, regulation equipment operating costs, and system network losses, maximizing the comprehensive management benefits and breaking the limitations of traditional single-objective optimization.

[0040] This invention employs a closed-loop iterative voltage over-limit management mechanism with strong adaptive and self-optimizing capabilities. This method does not perform a one-time calculation, but rather recalculates the power flow after adjustment to verify the effect. If the voltage still does not return to the normal range, the system automatically adjusts the strategy for further management until the problem is resolved. This dynamic feedback and continuous optimization design significantly improves the reliability and robustness of voltage control, ensuring the safe and stable operation of the power grid.

[0041] This invention constructs a multi-objective optimization function centered on voltage management effectiveness, regulating equipment operating costs, and system network losses. This maximizes the overall management benefits, breaking the limitations of traditional single-objective optimization and simultaneously balancing three key dimensions: voltage quality, economy, and system operating efficiency. Through this multi-objective optimization model, the optimal solution can be intelligently selected from numerous feasible regulation schemes, ensuring voltage compliance while minimizing regulation costs and reducing system network losses. This not only directly improves power quality but also achieves energy conservation and emission reduction by reducing network losses, extends equipment lifespan by minimizing unnecessary equipment operations, and creates comprehensive economic and safety benefits for the power grid.

[0042] This invention overcomes the limitations of traditional voltage regulation methods, which suffer from low efficiency, poor accuracy, high cost, and weak adaptability. Its core value lies in achieving accurate solutions to power flow equations using an improved polar coordinate Newton-Raphson power flow algorithm. It combines comprehensive sensitivity ranking to establish the access priority of regulating equipment and constructs a multi-objective optimization function centered on voltage regulation, operating costs, and network losses. Through automated processes, it improves regulation efficiency and response speed, while relying on quantitative analysis to ensure the accuracy of voltage regulation. Whether dealing with daily load fluctuations or adapting to changes brought about by grid structure upgrades and new energy access, it can provide reliable technical support for power system voltage stability and has significant practical implications for improving grid operation safety, power quality, and economy.

[0043] This invention employs an improved adaptive NSGA-II algorithm to solve multi-objective optimization functions. Based on the initialization of the comprehensive sensitivity index, the algorithm starts near the most effective regulating device for voltage problems, avoiding long-term lingering in the invalid solution space, significantly reducing the number of iterations and improving the solution speed. Attached Figure Description

[0044] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0045] Figure 1 This is a flowchart of a power system voltage over-limit mitigation method based on comprehensive sensitivity, according to an embodiment of the present invention.

[0046] Figure 2 This is a diagram illustrating the effectiveness of the voltage over-limit problem mitigation method in the test system of this invention. Detailed Implementation

[0047] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0048] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0049] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0050] Example 1

[0051] like Figure 1 As shown in the figure, this embodiment discloses a power system voltage over-limit mitigation method based on comprehensive sensitivity. The method includes the following steps:

[0052] S1: Obtain power and line parameter data of power grid nodes;

[0053] In this embodiment, power and line parameter data of power grid nodes are obtained from power system data. This data includes the active power, reactive power, voltage amplitude and phase angle of the nodes, as well as the resistance, reactance, conductance and susceptance of the lines, forming a node admittance matrix.

[0054] Calculate the nodal admittance matrix. It reflects the electrical connections between nodes in a power system. Node admittance matrix elements It can be calculated using the following formula:

[0055]

[0056] in, For nodes and nodes The impedance between them For nodes and nodes The impedance between them This represents the number of nodes.

[0057] The node admittance matrix clarifies the core parameter dimensions required for voltage management in power systems. It includes active and reactive power at the node side, which reflect the operating status, as well as resistance, reactance, conductance, and susceptance at the line side, which determine the topology and impedance characteristics of the power grid. This provides complete and reliable basic data support for subsequent power flow calculations and sensitivity analysis.

[0058] S2: Based on the power grid node power and line parameter data, the power flow equation is solved by the improved polar coordinate Newton-Raphson power flow algorithm to obtain the voltage correction amount;

[0059] In this embodiment, an improved polar coordinate Newton-Raphson power flow algorithm is executed to calculate the voltage at each node. The polar coordinate Newton-Raphson power flow algorithm is an efficient iterative algorithm for solving power system power flow equations. Its core idea is to linearize the nonlinear power flow equations at the current iteration point, then solve the linearized equations to obtain the voltage correction, iterating continuously until convergence. The following is an explanation of the core formulas in the improved Newton-Raphson power flow algorithm:

[0060] (1) For a given A power system with n nodes, where the state variable is the voltage phase angle. and voltage amplitude ,node Power balance equation (active power) and reactive power ) can be represented as:

[0061]

[0062] in, and They are nodes and nodes voltage amplitude, Nodal admittance matrix The Middle Line number Column elements, For nodes and nodes The voltage phase angle difference between them For nodal susceptance matrix The Middle Line number The elements of the column.

[0063] Node susceptance matrix and node admittance matrix similar, It is composed of admittance. Composed of susceptance, therefore constructed Then, extract The imaginary part constitutes the nodal susceptance matrix. .

[0064] In polar coordinates, nodes The corrected equation (active power imbalance) and reactive power imbalance ) can be represented as:

[0065]

[0066] in, and They are nodes Given active power and reactive power, and To determine the current node voltage amplitude and phase angle The calculated active and reactive power.

[0067] (2) Constructing the Jacobian matrix. The Jacobian matrix is ​​a key matrix in the polar coordinate Newton-Raphson power flow algorithm. It contains the partial derivatives of the power imbalance with respect to voltage magnitude and phase angle. Jacobian matrix The elements can be represented as:

[0068]

[0069] in, This represents the partial derivative of the active power imbalance with respect to the voltage phase angle. This represents the partial derivative of the active power imbalance with respect to the voltage amplitude. This represents the partial derivative of reactive power imbalance with respect to the voltage phase angle. It represents the partial derivative of reactive power imbalance with respect to voltage amplitude.

[0070] The Jacobian matrix reflects the relationship between power imbalance and voltage magnitude and phase angle. By solving the Jacobian matrix, voltage corrections can be obtained, thus enabling iterative solutions to the power flow equations. An adaptive step size factor is introduced based on the traditional polar coordinate Newton-Raphson power flow algorithm. :

[0071]

[0072] Among them step size factor Dynamically adjust based on the convergence result of the previous iteration:

[0073]

[0074] in, Representing the The system state variable for the next iteration is the voltage amplitude. and phase angle The correction vector, Representing the The power imbalance vector at the next iteration, i.e. and .

[0075] According to the principle of the Newton-Raphson method, the voltage correction includes voltage amplitude correction and phase angle correction. and This can be obtained by solving the following system of linear equations:

[0076]

[0077] in, Jacobian matrix The inverse matrix of the given equations. By solving the above system of linear equations, the corrections for the voltage magnitude and phase angle can be obtained. These corrections are then added to the current voltage value to obtain a new voltage value, and the next round of iterative calculation is performed. The termination condition for the iterative calculation is set to a voltage correction less than 0.0001. pu .

[0078] Before the end of each algorithm, the initial voltage amplitude phase angle of this iteration is added to the correction amount to obtain a new voltage amplitude phase angle. The iteration continues until the change in voltage amplitude phase angle reaches below the preset threshold, at which point the iteration ends and the final voltage amplitude phase angle is the current power flow.

[0079] The above technical solution uses the improved Newton-Raphson algorithm in polar coordinates as the core of power flow solution. It dynamically adjusts the iteration step size to avoid oscillations, improves the convergence speed, and accurately solves the node voltage for the nonlinear characteristics of the power flow equation. It is also adaptable to power grids of different sizes and structures, providing accurate power flow calculation results for subsequent sensitivity analysis and ensuring the stability and applicability of the technical process.

[0080] S3: Based on the Jacobian matrix obtained from the power flow calculation, determine the active and reactive power sensitivities of the nodes, introduce the equipment response time factor and the regulation cost factor to obtain the comprehensive sensitivity index; sort the nodes based on the comprehensive sensitivity index and screen out problem nodes.

[0081] In this embodiment, based on the Jacobian matrix obtained during the power flow calculation, the sensitivity of each node voltage to changes in active and reactive power and the degree of influence of different regulation methods on these node voltages are determined. Equipment response time factor and regulation cost factor are introduced to obtain a comprehensive sensitivity index.

[0082] S301: Calculate the active power sensitivity and reactive power sensitivity of each node, expressed as:

[0083]

[0084] in, Active power sensitivity, i.e., node sensitivity voltage to node Sensitivity to changes in active power For nodes voltage, For nodes The active power.

[0085]

[0086] in, Reactive power sensitivity, i.e., node sensitivity. voltage to node Sensitivity to changes in reactive power For nodes The reactive power.

[0087] The sensitivity index is obtained by calculating the active and reactive power sensitivity of each node. .

[0088] S302: Based on active power sensitivity and reactive power sensitivity, the equipment response time factor and regulation cost factor are introduced as weighting coefficients to calculate the comprehensive sensitivity index. , is represented as:

[0089]

[0090] in, For installation on the node The response time of regulating devices, such as the typical response time of SVG, energy storage, and voltage regulating transformers, For installation on the node The unit adjustment cost of the adjustment equipment , , , The configurable weighting coefficients are the weighting coefficients for active power sensitivity, reactive power sensitivity, response speed factor, and economic factor, respectively, satisfying the following conditions: The system operator can dynamically adjust these weights according to actual operational needs, such as whether to prioritize rapid response or cost-effectiveness, in order to achieve different control objectives.

[0091] Based on the comprehensive sensitivity index values, the nodes are sorted from largest to smallest. The sorting results quickly determine which nodes are critical for voltage over-limit regulation, and which nodes' active or reactive power should be prioritized for regulation when voltage over-limit issues occur, in order to achieve the best voltage regulation effect.

[0092] The above technical solution, based on the Jacobian matrix obtained from power flow calculation, transforms the impact of node voltage on changes in active and reactive power into a quantifiable sensitivity index. By introducing equipment response time factor and regulation cost factor as weighting coefficients, it can not only accurately locate the nodes most sensitive to voltage changes, but also quantify the degree of influence of different regulation methods on the voltage of sensitive nodes, providing data basis for subsequent selection of optimal regulation equipment.

[0093] S4: Construct a multi-objective optimization function based on the problem nodes, solve for the optimal governance strategy, and adjust accordingly based on the optimal governance strategy.

[0094] In this embodiment, a multi-objective optimization function is constructed to simultaneously optimize the voltage over-limit mitigation effect, the operating cost of regulating equipment, and the system network loss. By comparing the voltage amplitude of each node with the preset upper and lower voltage limits, the problematic nodes with voltage over-limits are identified. Based on the sensitivity sequence and the externally input regulating equipment and its capacity currently available in the system, the regulating equipment with the most significant voltage regulation effect on the problematic nodes is selected. After the regulating equipment is connected, the power flow distribution of the power system is recalculated to obtain the adjusted node voltage. By comparing the node voltage before and after regulation, the regulation effect is evaluated to see if the expected goal has been achieved. If the voltage still exceeds the limit after regulation, the voltage over-limit mitigation strategy is adjusted until the voltage returns to the normal range.

[0095] Decision variables: Assume that the available regulating devices in the system, such as SVG, energy storage, and on-load tap changer taps, are shared. Each device. The adjustment quantities, such as reactive power output, active power, and transformer ratio, are denoted as... All adjustment variables constitute the decision variables:

[0096]

[0097] The multi-objective optimization function is:

[0098]

[0099] in, Let voltage quality be the objective function. For the number of nodes, For all in the system Voltage amplitude at each node Its corresponding reference voltage The sum of the absolute values ​​of the deviations; Represents a node The voltage amplitude; Represents a node The expected voltage reference value is usually the rated voltage or the optimal value within the safe range; This represents the objective function for regulating economic efficiency, which is the sum of all... Total adjustment cost of an adjustable device; For equipment The absolute value of the planned adjustment is expressed in Mvar for reactive power equipment and in MW for active power equipment. Let be the system efficiency objective function, where is the total active power loss of the system. Constraints include upper and lower voltage limits, equipment capacity limits, and power flow equation constraints.

[0100] The constraints include equipment capacity constraints and node voltage safety constraints:

[0101] Equipment capacity constraints:

[0102]

[0103] Node voltage safety constraints:

[0104]

[0105] in, This represents the number of nodes.

[0106] An improved adaptive NSGA-II algorithm is used to solve the multi-objective optimization function, specifically:

[0107] (1) Generate an initial population based on comprehensive sensitivity index.

[0108] Set population size Maximum number of iterations To accelerate convergence, the initial population is not generated completely randomly. For the problem node set... The relevant adjustment equipment, its initial adjustment amount Generation probability and comprehensive sensitivity index Proportional:

[0109]

[0110] in, For sensitivity weights, A random number in the range [0,1]. Using uniformly distributed random numbers, this strategy allows the algorithm to focus on devices most sensitive to voltage limits from the initial stage.

[0111] (2) For each individual in the population, the improved polar coordinate Newton-Raphson power flow algorithm is called to calculate the power flow of the system.

[0112] For each individual in the population, i.e., one decision variable The improved polar coordinate Newton-Raphson power flow algorithm is used to calculate the power flow of the system and obtain the voltage of each node. Total network loss .

[0113] (3) The penalty function method is used to handle voltage limit violations. The degree of constraint violation is transformed into a penalty term of the voltage quality objective function, and an augmented voltage quality objective function is constructed for comparison.

[0114]

[0115] in, To augment the voltage quality objective function, in the original voltage quality objective function The objective function is obtained by adding a penalty term for voltage over-limit constraints to the basic structure; The original voltage quality objective function is... For nodes The upper limit of the allowable voltage, For nodes The lower limit of the allowable voltage, It is a large positive penalty coefficient.

[0116] (4) The fast non-dominated sorting method of NSGA-II is adopted, based on Three target values ​​divide the population into multiple non-dominated frontiers.

[0117] Prioritize individuals with high non-dominant rankings and use simulated binary crossover, with crossover probabilities... and distribution index It adapts and adjusts with each generation of evolution to balance global exploration and local development capabilities:

[0118]

[0119]

[0120] in, This is the current iteration number. , , , Preset boundaries.

[0121] Mutation probability and distribution index Similarly, adaptive changes:

[0122]

[0123]

[0124] in, , , , Preset boundaries.

[0125] Adaptability ensures that the algorithm is biased towards exploration in the early stages and towards development in the later stages.

[0126] Merge the current parent population with the generated offspring population to form a population of size For a mixed population, non-dominated ranking and crowding calculation are performed, and the top [populations] are selected sequentially. Each individual forms a new generation of population, and the steps described in S4 are iteratively executed until the maximum number of iterations is reached. .

[0127] (5) After the algorithm terminates, the final implementation scheme is selected from the final Pareto optimal solution set. The fuzzy satisfaction decision method is adopted:

[0128] For each solution in the Pareto solution set Calculate its target values satisfaction :

[0129]

[0130] in, and For the current Pareto solution set, the first... The maximum and minimum values ​​of each target.

[0131] Calculate the overall satisfaction level for each solution. :

[0132]

[0133] in, The preference weights for each objective can be set by the operators. Select overall satisfaction. highest solution As the optimal governance strategy.

[0134] S401: For each problem node, extract the corresponding comprehensive sensitivity sequence (comprehensive sensitivity index sorting) based on the previously calculated comprehensive sensitivity index.

[0135] S402: Based on optimal governance strategy Adjustment values ​​of each device In conjunction with the external input of the available regulating devices and their capacity, after the regulating devices are connected, the improved Newton-Raphson power flow algorithm is used again to calculate the power flow distribution of the power system and obtain the regulated node voltage.

[0136] S403: By comparing the adjusted node voltage with the preset voltage threshold, evaluate whether the adjustment effect has achieved the expected goal. If the adjusted voltage still exceeds the limit, adjust the capacity of the connected equipment. Continue until the voltage returns to normal.

[0137] Through the above technical solution, a multi-objective optimization function is constructed to simultaneously optimize the voltage over-limit mitigation effect, the operating cost of regulating equipment, and the system network loss. Then, the problem node is identified by comparing the voltage amplitude with the preset upper and lower limits. The optimal regulating equipment is then selected by combining the sensitivity sequence and the capacity of available regulating equipment. After the regulating equipment is connected, the regulation effect is verified by recalculating the power flow distribution. If the voltage still exceeds the limit, the mitigation strategy is dynamically adjusted to form a closed-loop mitigation process, ensuring that the voltage of the final node is restored to the normal range, thus guaranteeing the reliability and thoroughness of the mitigation effect.

[0138] In summary, this invention first reads the current power and line parameter data of the power system nodes. Then, using an improved Newton-Raphson power flow algorithm in polar coordinates, it iteratively calculates the power flow based on the node power and line parameter data. Next, based on the Jacobian matrix obtained during the power flow calculation, it determines the sensitivity of each node voltage to changes in active and reactive power, and the impact of different regulation methods on these node voltages. A comprehensive sensitivity index is proposed, and equipment response time and regulation cost factors are introduced as weighting coefficients. Finally, voltage limit exceedance issues are identified and addressed. A multi-objective optimization function is constructed to simultaneously optimize the voltage limit exceedance management effect, the operating cost of regulating equipment, and system network losses. By comparing the voltage amplitude of each node with preset upper and lower voltage limits, problem nodes with voltage limits are identified. For each problem node, a corresponding sensitivity sequence is extracted based on the previously calculated comprehensive sensitivity index. Based on the sensitivity sequence and the externally input available regulating equipment and its capacity, the power flow distribution of the power system is recalculated after the regulating equipment is connected, resulting in the regulated node voltages. By comparing the node voltages before and after adjustment, the effect of the adjustment is evaluated to see if it meets the expected target. If the voltage still exceeds the limit after adjustment, the voltage limit control strategy is adjusted until the voltage returns to the normal range.

[0139] The current equipment connected to each node in the tested power system is as follows: a 20kVAR reactive power upper limit regulator is connected to node 8; a 20kW active power upper limit regulator is connected to node 12; a 20kVAR reactive power upper limit regulator is connected to node 12; a 60kW active power upper limit regulator is connected to node 19; a 30kVAR reactive power upper limit regulator is connected to node 32; a 30kW active power upper limit regulator is connected to node 36; a 30kVAR reactive power upper limit regulator is connected to node 36; a 60kW active power upper limit regulator is connected to node 42; and a 20kW active power upper limit regulator is connected to node 44. For example... Figure 2As shown, the current system's available regulating devices and their capacities are used as external inputs, with the device status of each node serving as the external input. An example of the regulation process is as follows: if no node's voltage exceeds the lower limit, regulation is skipped. Regulation begins at node 36, with a current voltage of 1.1136. Node 12 (20kW active power) is connected; Node 19 (60kW active power) is connected; Node 36 (30kW active power) is connected; Node 42 (60kW active power) is connected; Node 44 (20kW active power) is connected; Node 8 (20kVAR reactive power) is connected; Node 12 (20kVAR reactive power) is connected; Node 32 (30kVAR reactive power) is connected. The voltage at node 36 has reached the target of 1.0591. Figure 2 This invention demonstrates that it can effectively manage voltage deviations using regulating devices in power systems, based on sensitivity ranking.

[0140] Example 2

[0141] This embodiment discloses a power system voltage over-limit control system based on comprehensive sensitivity, including:

[0142] The data acquisition module is configured to acquire power grid node and line parameter data.

[0143] The power flow calculation module is configured to: solve the power flow equations using an improved polar coordinate Newton-Raphson power flow algorithm based on grid node power and line parameter data, obtain voltage corrections, and iteratively calculate the power flow based on the voltage corrections;

[0144] The sensitivity ranking module is configured to: determine the active and reactive sensitivities of nodes based on the Jacobian matrix obtained from power flow calculations, introduce equipment response time factors and regulation cost factors to obtain a comprehensive sensitivity index; rank the nodes based on the comprehensive sensitivity index and filter out problematic nodes.

[0145] The limit violation management module is configured to: construct a multi-objective optimization function based on the problem node, solve for the optimal management strategy, and make adjustments based on the optimal management strategy.

[0146] Example 3

[0147] The purpose of this embodiment is to provide a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method of Embodiment 1.

[0148] Example 4

[0149] The purpose of this embodiment is to provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the method of Embodiment 1.

[0150] The steps and methods involved in the apparatuses of Embodiments 3 and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0151] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0152] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0153] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A power system voltage over-limit mitigation method based on comprehensive sensitivity, characterized in that, include: Acquire power and line parameter data of power grid nodes; Based on power grid node data and line parameter data, an improved polar coordinate Newton-Raphson power flow algorithm is used to solve the power flow equations and obtain voltage correction values. Power flow is then iteratively calculated based on these voltage correction values. The improved polar coordinate Newton-Raphson power flow algorithm introduces an adaptive step size factor. This adaptive step size factor is dynamically adjusted based on the convergence of the previous iteration, and is expressed as: in, For the first The adaptive step size factor in the next iteration. For the first The power imbalance vector at the next iteration For the first The adaptive step size factor in the next iteration. For the first The power imbalance vector at the next iteration; Based on the Jacobian matrix obtained from power flow calculation, the active and reactive power sensitivities of nodes are determined. Equipment response time factors and regulation cost factors are introduced to obtain a comprehensive sensitivity index. Nodes are then ranked based on this comprehensive sensitivity index to identify problematic nodes. The comprehensive sensitivity index is expressed as follows: in, For comprehensive sensitivity index, For active sensitivity, For reactive power sensitivity, For installation on the node Adjust the response time of the device. For installation on the node The unit adjustment cost of the adjustment equipment , , , These are the weighting coefficients for active sensitivity, reactive sensitivity, response speed factor, and economic factor, respectively. A multi-objective optimization function is constructed based on the problem nodes, and the optimal governance strategy is obtained by solving the problem. Adjustment is then carried out based on the optimal governance strategy.

2. The power system voltage over-limit mitigation method based on comprehensive sensitivity as described in claim 1, characterized in that, The multi-objective optimization function is expressed as: in, Let voltage quality be the objective function. For the number of nodes, For nodes voltage amplitude, For nodes The expected voltage reference value, To adjust the economic objective function, To adjust the number of devices, For installation on the node The unit adjustment cost of the adjustment equipment For equipment The absolute value of the planned adjustment amount, Let the system efficiency objective function be... This represents the total active power loss of the system.

3. The power system voltage over-limit control method based on comprehensive sensitivity as described in claim 2, characterized in that, An improved adaptive NSGA-II algorithm is used to solve the multi-objective optimization function, specifically: An initial population is generated based on comprehensive sensitivity indicators; For each individual in the population, the improved polar coordinate Newton-Raphson power flow algorithm is invoked to calculate the system power flow; The penalty function method is used to handle voltage over-limit constraints, transforming the constraint violation degree into a penalty term of the voltage quality objective function, and constructing an augmented voltage quality objective function. The fast nondominated sorting method of NSGA-II is adopted, and the population individuals are divided into multiple nondominated fronts according to the objective functions of augmented voltage quality, regulation economy and system efficiency. Individuals adapt and evolve to form a new generation of population, and select the optimal governance strategy based on fuzzy satisfaction decision-making.

4. The power system voltage over-limit control method based on comprehensive sensitivity as described in claim 3, characterized in that, By comparing the adjusted node voltage with the preset voltage threshold, the adjustment effect is evaluated to see if it meets the expected target. If the adjusted voltage still exceeds the limit, the capacity of the connected equipment is adjusted until the voltage returns to the normal range.

5. A power system voltage over-limit control system based on comprehensive sensitivity, characterized in that, include: The data acquisition module is configured to acquire power grid node and line parameter data. The power flow calculation module is configured to: solve the power flow equations using an improved polar coordinate Newton-Raphson power flow algorithm based on grid node power and line parameter data, obtain voltage correction values, and iteratively calculate the power flow based on these voltage correction values; the improved polar coordinate Newton-Raphson power flow algorithm introduces an adaptive step size factor based on the previous polar coordinate Newton-Raphson power flow algorithm; the adaptive step size factor is dynamically adjusted according to the convergence status of the previous iteration, and is expressed as: in, For the first The adaptive step size factor in the next iteration. For the first The power imbalance vector at the next iteration For the first The adaptive step size factor in the next iteration. For the first The power imbalance vector at the next iteration; The sensitivity ranking module is configured to: determine the active and reactive power sensitivities of nodes based on the Jacobian matrix obtained from power flow calculations; introduce equipment response time factors and regulation cost factors to obtain a comprehensive sensitivity index; rank nodes based on the comprehensive sensitivity index and filter out problematic nodes; the comprehensive sensitivity index is expressed as: in, For comprehensive sensitivity index, For active sensitivity, For reactive power sensitivity, For installation on the node Adjust the response time of the device. For installation on the node The unit adjustment cost of the adjustment equipment , , , These are the weighting coefficients for active sensitivity, reactive sensitivity, response speed factor, and economic factor, respectively. The limit violation management module is configured to: construct a multi-objective optimization function based on the problem node, solve for the optimal management strategy, and make adjustments based on the optimal management strategy.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the power system voltage over-limit control method based on comprehensive sensitivity as described in any one of claims 1-4.

7. A computer 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 program, it implements the steps in the power system voltage over-limit control method based on comprehensive sensitivity as described in any one of claims 1-4.

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