Dynamic reactive compensation optimal configuration method for weak power grid

Abstract

The invention relates to the technical field of weak power grids, and discloses a weak power grid dynamic reactive power compensation optimal configuration method, which comprises the following steps: S1, acquiring operation parameters such as power grid node voltage and current, and constructing a dynamic reactive power demand space-time decomposition model considering node space load heterogeneity, load time dynamic fluctuation and voltage-frequency coupling characteristics; s2, in combination with the characteristics of low inertia and easy voltage fluctuation of a weak power grid, screening adaptive compensation devices based on a decomposition result, and determining core parameter constraints; s3, taking improvement of voltage stability and minimization of network loss as a core, integrating full life cycle cost constraint and transient stability requirements, and constructing a multi-objective optimization configuration model; s4, solving an optimal installation position, configuration capacity and a switching threshold value by adopting an algorithm adaptive to the nonlinearity and topology dynamic change of the weak power grid; and S5, monitoring the state of the power grid in real time, dynamically updating the reactive power demand predicted value, and performing closed-loop adjustment on the operation parameters of the compensation device. And the operation stability and economy of the weak power grid are ensured.

Description

Technical Field

[0001] This invention relates to the field of weak grid technology, specifically to a method for optimizing the dynamic reactive power compensation configuration of weak grids. Background Technology

[0002] Weak power grids are characterized by high distributed generation penetration, weak grid structure, and low inertia. Poor voltage stability and difficulty in controlling grid losses are the core issues restricting their safe and economical operation. Reactive power compensation technology, as a key means to improve grid voltage quality and reduce grid losses, plays an important role in the operation and regulation of weak power grids. With the rapid development of new energy power generation technologies, the spatial differences and temporal fluctuations of load distribution in weak power grids are becoming increasingly prominent. In addition, the coupling effect between voltage and frequency is becoming more and more obvious. Traditional reactive power compensation configuration methods suitable for strong power grids are gradually becoming unable to meet the dynamic operation requirements of weak power grids.

[0003] Most existing reactive power compensation configuration methods fail to adequately consider the spatial load heterogeneity of different nodes in weak power grids and the dynamic fluctuation characteristics of load over time. They also lack a refined reactive power demand model that incorporates voltage-frequency coupling characteristics, resulting in insufficient accuracy in reactive power demand prediction. Consequently, the selection of compensation device types and the determination of core parameter constraints to adapt to the low inertia and voltage fluctuation characteristics of weak power grids lack a scientific basis, making it difficult to achieve accurate matching between compensation devices and grid operating characteristics. At the same time, traditional multi-objective optimization configuration models often fail to fully integrate the device's life-cycle cost and the grid's transient stability requirements. The solution algorithms used are also difficult to adapt to the nonlinear characteristics and dynamic topology changes of weak power grids. This results in the optimization results of the installation location, configuration capacity, and switching threshold of compensation devices lacking global optimality and scenario adaptability. Furthermore, they lack a closed-loop dynamic adjustment mechanism based on real-time operating status, making it impossible to respond promptly to changes in reactive power demand under dynamic scenarios such as excessive voltage fluctuations and sudden load changes. Ultimately, this limits the effectiveness of reactive power compensation and makes it difficult to simultaneously ensure the stability and economy of weak power grid operation. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a dynamic reactive power compensation optimization configuration method for weak power grids, thereby solving the problem mentioned in the background technology that the reactive power compensation effect is limited and it is difficult to simultaneously ensure the stability and economy of weak power grid operation.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the dynamic reactive power compensation configuration in a weak power grid, comprising the following steps:

[0006] S1 Reactive Power Demand Spatiotemporal Refined Decomposition Modeling

[0007] The voltage, current, load power, distributed power output, and grid topology operation status parameters of each node in the weak grid are collected. A dynamic reactive power demand spatiotemporal decomposition model based on the voltage-frequency coupling characteristics of the weak grid is constructed. The spatiotemporal decomposition model takes into account both the spatial load heterogeneity of different nodes in the grid and the dynamic fluctuation characteristics of the load over time.

[0008] S2 Compensation Device Type Selection and Parameter Constraint Determination

[0009] Based on the reactive power demand decomposition results, and combined with the core characteristics of low inertia and voltage fluctuation in weak power grids, suitable reactive power compensation device types are selected, and the constraints of the core parameters of device response speed and capacity adjustment range are clarified to ensure that the device matches the operating characteristics of the power grid.

[0010] S3 Multi-Objective Optimization Configuration Model Construction

[0011] With the core objectives of improving grid voltage stability and minimizing grid losses, and taking into account the cost constraints of the entire life cycle of equipment purchase, installation and operation and maintenance, and incorporating the transient stability requirements of weak grids, a multi-objective optimization configuration model is established. The priority of each objective is adjusted by dynamic weight coefficients to adapt to different operating scenarios.

[0012] S4 Optimization Configuration Parameter Solution

[0013] An algorithm adapted to the nonlinear characteristics and dynamic changes of the weak power grid is adopted to solve for the optimal installation location, configuration capacity and switching threshold of the compensation device, ensuring the global optimality and dynamic adaptability of the optimization results;

[0014] S5 Compensation Operating Parameter Closed-Loop Dynamic Adjustment

[0015] Real-time monitoring of the power grid's operating status is conducted. When scenarios such as excessive voltage fluctuations, sudden load changes, or excessive fluctuations in the output of distributed power sources occur, the reactive power demand forecast is updated based on the spatiotemporal decomposition model. The operating parameters of the compensation device are then dynamically adjusted in a closed loop to ensure that the compensation effect dynamically matches the power grid's operating status.

[0016] Preferably, in step S1, the spatial dimension of the dynamic reactive power demand spatiotemporal decomposition model is weighted by the voltage sensitivity of each node to divide high, medium and low priority reactive power demand regions, and the time dimension is divided into three types of demand according to the load fluctuation frequency: transient impact type, steady state continuous type and intermittent fluctuation type. The definition of reactive power demand under different scenarios is realized through cross-mapping of spatiotemporal dimensions.

[0017] The calculation of voltage sensitivity weights in the spatial dimension needs to be comprehensively quantified by combining the grid topology correlation and the importance of node loads to ensure that the priority area division can accurately reflect the reactive power supply demand of weak links in the grid. The three types of demand classification in the time dimension need to be synchronously associated with the time scale requirements of the compensation response. Transient impact demand corresponds to millisecond-level response adaptation, steady-state continuous demand focuses on long-term stable regulation capability matching, and intermittent fluctuation demand emphasizes dynamic tracking adaptability. In the process of spatiotemporal cross-mapping, the correlation analysis between node load characteristics and distributed power generation output patterns needs to be incorporated to achieve accurate anchoring of reactive power demand in the spatiotemporal dimension, providing a refined decision-making basis for the differentiated configuration of subsequent compensation devices.

[0018] Preferably, in step S2, the selected reactive power compensation device adopts a hybrid configuration of SVG and STATCOM that integrates the characteristics of virtual synchronous machine. SVG is adapted to steady-state reactive power compensation and voltage support requirements, while STATCOM is adapted to transient reactive power surge suppression requirements. The core parameter constraints include that the device response speed is not lower than the response requirements of grid transient fluctuations and that the capacity adjustment range covers the maximum reactive power deficit of the corresponding area.

[0019] Hybrid configuration achieves seamless timing of response between the two types of devices through a unified collaborative control strategy, avoiding compensation gaps or overcompensation. The steady-state regulation characteristics of SVG can be adaptively optimized in combination with the power flow distribution of the grid, and the equivalent inertia of the grid can be enhanced through the characteristics of virtual synchronous machines, thereby improving the stability of steady-state operation. The transient response capability of STATCOM can be further enhanced through a fast current control algorithm to ensure rapid suppression of sudden reactive power impacts. The core parameter constraints need to take into account the balance between the economy and reliability of device operation, reduce device operating losses and failure risks while meeting compensation requirements, and adapt to the dynamic operation requirements of weak grid and low inertia environments.

[0020] Preferably, in step S3, the voltage deviation constraint is set based on the allowable fluctuation range of the rated voltage of the weak grid, the network loss constraint fully considers the distribution characteristics of reactive power flow in the grid branches, the full life cycle cost covers the various costs of equipment purchase, on-site installation, daily operation and maintenance and later replacement, and the dynamic weight coefficient is determined according to the current load level of the grid and the proportion of distributed power source access.

[0021] Voltage deviation constraints need to be tailored to different node types to ensure priority voltage quality at critical load nodes. Network loss constraints need to incorporate the distance attenuation characteristics of reactive power transmission and the influence of branch impedance, while also considering the optimization effect of reactive power compensation configuration on power flow distribution to achieve accurate network loss calculations. Lifecycle costs need to include the impact of the device's operating environment on its service life, as well as the cost optimization space under different operation and maintenance strategies, to establish a cost-benefit linkage evaluation mechanism. The determination of dynamic weight coefficients requires the construction of a multi-factor coupled evaluation model, which integrates the real-time operating status of the power grid, long-term planning goals, and changes in the external environment to achieve scientific and flexible weight adjustment.

[0022] Preferably, in step S4, the adapted solution algorithm is an improved adaptive genetic algorithm based on dynamic updating of weak grid topology. The solution algorithm dynamically adjusts the crossover probability and mutation probability by introducing a node correlation factor, and adapts in real time to the impact of grid topology changes on the configuration results.

[0023] The calculation of the node correlation factor needs to integrate the electrical distance between power grid nodes, the power flow interaction strength, and the topological connection relationship to ensure that the adjustment of the crossover probability and mutation probability can accurately match the characteristics of the power grid structure. The algorithm needs to incorporate an adaptive verification mechanism for the constraints, and eliminate solutions that do not meet the parameter constraints in real time during the iteration process to improve the solution efficiency and feasibility. Through integration and optimization with the local search algorithm, the convergence accuracy of the optimal solution can be further improved to avoid the algorithm getting stuck in local optima. In view of the dynamic changes in the topology of weak power grids, the algorithm needs to have the ability to quickly recalculate and update the optimized configuration results in a timely manner after the power grid structure is adjusted to ensure the dynamic adaptability of the configuration parameters.

[0024] Preferably, in step S5, the voltage fluctuation exceeding the standard scenario refers to the node voltage deviating from the rated value and exceeding the allowable range; the load change scenario includes the start and stop of industrial loads and the peak change of residential electricity consumption; the distributed power output fluctuation scenario covers the intermittent fluctuation of wind power and photovoltaic power output; and the adjustment process simultaneously optimizes the output capacity and response timing of the compensation device.

[0025] Identifying various fluctuation scenarios requires multi-dimensional feature extraction from real-time monitoring data, combined with the prediction results of the spatiotemporal decomposition model to achieve accurate judgment. During the adjustment process, a real-time linkage mechanism between compensation response and reactive power demand changes needs to be established. The optimization of output capacity needs to take into account both the current utilization rate of compensation resources and the reserve for subsequent fluctuations, avoiding over-adjustment or under-adjustment. The optimization of response timing needs to avoid action conflicts with other grid regulation equipment, ensuring the coordination and stability of the entire regulation process. At the same time, the adjustment strategy needs to incorporate transient stability constraints to ensure the transient operation safety of the power grid while optimizing the compensation effect.

[0026] Preferably, in step S1, during the process of collecting operating status parameters, the parameter collection coverage of wind power and photovoltaic centralized access areas and industrial load-dense areas is more comprehensive, including node three-phase voltage imbalance and load type characteristic information, to ensure that the input data of the spatiotemporal decomposition model can reflect the actual operating differences of the power grid.

[0027] The core purpose of targeted data collection is to capture the differences in operating characteristics of key areas of the power grid. Information on three-phase voltage imbalance can support the formulation of three-phase phase-by-phase regulation strategies for reactive power compensation, avoiding the attenuation of compensation effects or the increase in power grid operation risks due to three-phase imbalance. Load type characteristic information needs to distinguish the operating characteristics of continuous loads, intermittent loads, and impulsive loads, providing accurate input of load dynamic characteristics for the spatiotemporal decomposition model. The optimization of the collection range needs to be combined with the identification results of weak links in the power grid, and intensified collection should be carried out in areas with poor voltage stability and severe reactive power fluctuations. At the same time, a dynamic adjustment mechanism for the collection frequency should be established to adapt the collection density according to the intensity of power grid operation fluctuations, ensuring the relevance, comprehensiveness, and real-time nature of data input.

[0028] Preferably, in step S3, the adjustment logic of the dynamic weight coefficient is as follows: when the power grid is in steady state, the target weight for minimizing network losses is increased; when the power grid faces transient disturbance risks, the target weight for transient stability requirements is prioritized; and the weight for device configuration cost is dynamically adapted according to the power grid investment budget.

[0029] Weight adjustment requires the establishment of a real-time response mechanism. The determination of the steady-state operation of the power grid needs to be based on a comprehensive evaluation of multiple indicators such as voltage fluctuation amplitude, load fluctuation intensity, and distributed power generation output stability to ensure the rationality of weight increase for minimizing network losses. The identification of transient disturbance risks needs to integrate fault early warning information, load change prediction, and early warning of large fluctuations in distributed power generation output to achieve timely priority switching of transient stability target weights. The dynamic adaptation of device configuration cost weights requires the establishment of an investment benefit evaluation model to balance short-term investment costs and long-term operating benefits under budget constraints, while also considering the reserved space for technological upgrades and iterations to avoid the compensation configuration being unable to adapt to future power grid development needs due to cost constraints.

[0030] Preferably, in step S5, the parameter adjustment adopts a graded adjustment mechanism: in the case of mild fluctuations, only the output capacity of the compensation device is finely adjusted; in the case of moderate fluctuations, the output capacity and response speed are adjusted simultaneously; and in the case of severe fluctuations, the backup compensation resources are activated for coordinated adjustment.

[0031] The formulation of classification standards should be based on a comprehensive assessment of multiple dimensions, including voltage deviation, load mutation intensity, distributed power output fluctuation range, and grid transient stability margin, to ensure the scientific and accurate nature of the classification. Fine-tuning in mild fluctuation scenarios employs a smooth adjustment strategy to avoid frequent actions that could increase device losses or cause grid operational fluctuations. Synchronous adjustments in moderate fluctuation scenarios require the establishment of a collaborative optimization model for capacity and response speed, adapting the optimal adjustment combination based on fluctuation characteristics. Collaborative adjustment of backup compensation resources in severe fluctuation scenarios requires the formulation of a unified resource scheduling strategy, clearly defining the action sequence and capacity allocation ratio of primary and backup compensation devices to avoid resource conflicts. Simultaneously, it should incorporate grid topology and power flow distribution optimization schemes to quickly restore grid operational stability.

[0032] Preferably, in step S1, a fusion processing method of historical operating data and real-time collected data is adopted. Data preprocessing is achieved by removing abnormal data and smoothing fluctuation interference. Furthermore, the core parameters of the spatiotemporal decomposition model are periodically calibrated based on the seasonal load characteristics of the power grid and the changes in the proportion of distributed power source access.

[0033] Data fusion employs a weighted fusion algorithm, dynamically adjusting weights based on the timeliness of historical data and the accuracy of real-time data to achieve complementary advantages between the two types of data. Anomaly removal requires establishing multi-dimensional anomaly judgment criteria, combining power grid operation patterns and data statistical characteristics to accurately identify and remove invalid information such as fault data and data collection error data. Fluctuation interference smoothing uses an adaptive filtering algorithm to effectively remove noise interference while preserving the true fluctuation characteristics of the data. Periodic calibration of the model's core parameters requires considering seasonal load change trends, the dynamic adjustment of the distributed power generation integration ratio, and power grid topology changes to establish calibration cycles and procedures, ensuring the model maintains high prediction accuracy and adapts to the dynamic changes in power grid operation.

[0034] Compared with existing technologies, this invention provides a method for optimizing the dynamic reactive power compensation configuration in weak power grids, which has the following advantages:

[0035] This dynamic reactive power compensation optimization configuration method for weak power grids constructs a dynamic reactive power demand spatiotemporal decomposition model that considers the spatial load heterogeneity and temporal dynamic fluctuation characteristics of different nodes in the weak power grid, while incorporating voltage-frequency coupling characteristics. It selects suitable reactive power compensation devices based on the core characteristics of low inertia and voltage volatility in weak power grids and clarifies core parameter constraints, ensuring a precise match between the compensation devices and the grid operating characteristics, effectively improving the targeting and adaptability of reactive power compensation. Based on the core objectives of improving grid voltage stability and minimizing network losses, a multi-objective optimization model is constructed incorporating full life-cycle cost constraints and transient stability requirements. A solution algorithm adapted to the nonlinear characteristics and dynamic topology changes of the weak power grid is adopted to ensure the global optimality and scenario adaptability of the installation location, configuration capacity, and switching threshold of the compensation devices. By monitoring the grid operating status in real time, in scenarios such as excessive voltage fluctuations, sudden load changes, or excessive fluctuations in distributed power output, the reactive power demand prediction value is updated based on the spatiotemporal decomposition model, and the operating parameters of the compensation devices are dynamically adjusted in a closed loop. This achieves dynamic matching between the compensation effect and the grid operating status, continuously ensuring the stability and economy of the weak power grid operation. Attached Figure Description

[0036] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] This invention provides a technical solution: a method for optimizing the dynamic reactive power compensation configuration in weak power grids. Please refer to [link / reference]. Figure 1 This includes the following steps:

[0039] S1 Reactive Power Demand Spatiotemporal Refined Decomposition Modeling

[0040] The voltage, current, load power, distributed power output, and grid topology operation status parameters of each node in the weak grid are collected. A dynamic reactive power demand spatiotemporal decomposition model based on the voltage-frequency coupling characteristics of the weak grid is constructed. The spatiotemporal decomposition model takes into account both the spatial load heterogeneity of different nodes in the grid and the dynamic fluctuation characteristics of the load over time.

[0041] By constructing a dynamic reactive power demand spatiotemporal decomposition model that takes into account the spatial load heterogeneity and load time dynamic fluctuation characteristics of different nodes in a weak power grid and incorporates voltage-frequency coupling characteristics, the pertinence and adaptability of reactive power compensation are effectively improved.

[0042] S2 Compensation Device Type Selection and Parameter Constraint Determination

[0043] Based on the reactive power demand decomposition results, and combined with the core characteristics of low inertia and voltage fluctuation in weak power grids, suitable reactive power compensation device types are selected, and the constraints of the core parameters of device response speed and capacity adjustment range are clarified to ensure that the device matches the operating characteristics of the power grid.

[0044] By combining the core characteristics of low inertia and voltage fluctuation in weak power grids, suitable reactive power compensation devices are selected and core parameter constraints are clarified, so that the compensation devices are precisely matched with the operating characteristics of the power grid, effectively improving the pertinence and adaptability of reactive power compensation.

[0045] S3 Multi-Objective Optimization Configuration Model Construction

[0046] With the core objectives of improving grid voltage stability and minimizing grid losses, and taking into account the cost constraints of the entire life cycle of equipment purchase, installation and operation and maintenance, and incorporating the transient stability requirements of weak grids, a multi-objective optimization configuration model is established. The priority of each objective is adjusted by dynamic weight coefficients to adapt to different operating scenarios.

[0047] Based on the core objectives of improving grid voltage stability and minimizing grid losses, a multi-objective optimization model is constructed by incorporating full life cycle cost constraints and transient stability requirements, which ensures the global optimality and scenario adaptability of the installation location, configuration capacity and switching threshold of the compensation device;

[0048] S4 Optimization Configuration Parameter Solution

[0049] An algorithm adapted to the nonlinear characteristics and dynamic changes of the weak power grid is adopted to solve for the optimal installation location, configuration capacity and switching threshold of the compensation device, ensuring the global optimality and dynamic adaptability of the optimization results;

[0050] By adopting a solution algorithm adapted to the nonlinear characteristics and dynamic changes in topology of weak power grids, the global optimality and scenario adaptability of the installation location, configuration capacity and switching threshold of the compensation device are further ensured.

[0051] S5 Compensation Operating Parameter Closed-Loop Dynamic Adjustment

[0052] Real-time monitoring of the power grid operation status; when scenarios such as excessive voltage fluctuations, sudden load changes, or excessive fluctuations in the output of distributed power sources occur, the reactive power demand prediction value is updated based on the spatiotemporal decomposition model, and the operating parameters of the compensation device are dynamically adjusted in a closed loop to ensure that the compensation effect is dynamically matched with the power grid operation status.

[0053] By monitoring the grid operation status in real time, the reactive power demand forecast is updated based on the spatiotemporal decomposition model in scenarios such as excessive voltage fluctuations, sudden load changes, or excessive fluctuations in distributed power output. The operating parameters of the compensation device are then dynamically adjusted in a closed loop, achieving dynamic matching between the compensation effect and the grid operation status, and continuously ensuring the stability and economy of the weak grid operation.

[0054] In step S1, the spatial dimension of the dynamic reactive power demand spatiotemporal decomposition model is weighted by the voltage sensitivity of each node to divide high, medium and low priority reactive power demand regions. The time dimension is divided into three types of demand according to the load fluctuation frequency: transient impact type, steady state continuous type and intermittent fluctuation type. The definition of reactive power demand under different scenarios is realized through cross-mapping of spatiotemporal dimensions.

[0055] The calculation of voltage sensitivity weights in the spatial dimension needs to be comprehensively quantified by combining the grid topology correlation and the importance of node loads to ensure that the priority area division can accurately reflect the reactive power supply demand of weak links in the grid. The three types of demand classification in the time dimension need to be synchronously associated with the time scale requirements of the compensation response. Transient impact demand corresponds to millisecond-level response adaptation, steady-state continuous demand focuses on long-term stable regulation capability matching, and intermittent fluctuation demand emphasizes dynamic tracking adaptability. In the process of spatiotemporal cross-mapping, the correlation analysis between node load characteristics and distributed power generation output patterns needs to be incorporated to achieve accurate anchoring of reactive power demand in the spatiotemporal dimension, providing a refined decision-making basis for the differentiated configuration of subsequent compensation devices.

[0056] In step S2, the selected reactive power compensation device adopts a hybrid configuration of SVG and STATCOM that integrates the characteristics of virtual synchronous machine. SVG is adapted to steady-state reactive power compensation and voltage support requirements, while STATCOM is adapted to transient reactive power surge suppression requirements. The core parameter constraints include that the device response speed is not lower than the response requirements of grid transient fluctuations and the capacity regulation range covers the maximum reactive power deficit of the corresponding area.

[0057] Hybrid configuration achieves seamless timing of response between the two types of devices through a unified collaborative control strategy, avoiding compensation gaps or overcompensation. The steady-state regulation characteristics of SVG can be adaptively optimized in combination with the power flow distribution of the grid, and the equivalent inertia of the grid can be enhanced through the characteristics of virtual synchronous machines, thereby improving the stability of steady-state operation. The transient response capability of STATCOM can be further enhanced through a fast current control algorithm to ensure rapid suppression of sudden reactive power impacts. The core parameter constraints need to take into account the balance between the economy and reliability of device operation, reduce device operating losses and failure risks while meeting compensation requirements, and adapt to the dynamic operation requirements of weak grid and low inertia environments.

[0058] In step S3, the voltage deviation constraint is set based on the allowable fluctuation range of the rated voltage of the weak grid, the network loss constraint fully considers the distribution characteristics of reactive power flow in the grid branches, the full life cycle cost covers the various costs of equipment purchase, on-site installation, daily operation and maintenance and later replacement, and the dynamic weight coefficient is determined according to the current load level of the grid and the proportion of distributed power source access.

[0059] Voltage deviation constraints need to be tailored to different node types to ensure priority voltage quality at critical load nodes. Network loss constraints need to incorporate the distance attenuation characteristics of reactive power transmission and the influence of branch impedance, while also considering the optimization effect of reactive power compensation configuration on power flow distribution to achieve accurate network loss calculations. Lifecycle costs need to include the impact of the device's operating environment on its service life, as well as the cost optimization space under different operation and maintenance strategies, to establish a cost-benefit linkage evaluation mechanism. The determination of dynamic weight coefficients requires the construction of a multi-factor coupled evaluation model, which integrates the real-time operating status of the power grid, long-term planning goals, and changes in the external environment to achieve scientific and flexible weight adjustment.

[0060] In step S4, the adaptation solution algorithm is an improved adaptive genetic algorithm based on the dynamic update of the weak power grid topology. The solution algorithm dynamically adjusts the crossover probability and mutation probability by introducing a node correlation factor, and adapts to the impact of power grid topology changes on the configuration results in real time.

[0061] The calculation of the node correlation factor needs to integrate the electrical distance between power grid nodes, the power flow interaction strength, and the topological connection relationship to ensure that the adjustment of the crossover probability and mutation probability can accurately match the characteristics of the power grid structure. The algorithm needs to incorporate an adaptive verification mechanism for the constraints, and eliminate solutions that do not meet the parameter constraints in real time during the iteration process to improve the solution efficiency and feasibility. Through integration and optimization with the local search algorithm, the convergence accuracy of the optimal solution can be further improved to avoid the algorithm getting stuck in local optima. In view of the dynamic changes in the topology of weak power grids, the algorithm needs to have the ability to quickly recalculate and update the optimized configuration results in a timely manner after the power grid structure is adjusted to ensure the dynamic adaptability of the configuration parameters.

[0062] In step S5, voltage fluctuation exceeding the standard scenario refers to the node voltage deviating from the rated value beyond the allowable range; load change scenario includes the start-up and shutdown of industrial loads and the sudden change of peak electricity consumption in residential areas; distributed power output fluctuation scenario covers the intermittent fluctuation of wind power and photovoltaic power output; the adjustment process simultaneously optimizes the output capacity and response timing of the compensation device.

[0063] Identifying various fluctuation scenarios requires multi-dimensional feature extraction from real-time monitoring data, combined with the prediction results of the spatiotemporal decomposition model to achieve accurate judgment. During the adjustment process, a real-time linkage mechanism between compensation response and reactive power demand changes needs to be established. The optimization of output capacity needs to take into account both the current utilization rate of compensation resources and the reserve for subsequent fluctuations, avoiding over-adjustment or under-adjustment. The optimization of response timing needs to avoid action conflicts with other grid regulation equipment, ensuring the coordination and stability of the entire regulation process. At the same time, the adjustment strategy needs to incorporate transient stability constraints to ensure the transient operation safety of the power grid while optimizing the compensation effect.

[0064] In step S1, during the process of collecting operating status parameters, the parameter collection coverage of concentrated wind power and photovoltaic access areas and dense industrial load areas is more comprehensive, including node three-phase voltage imbalance and load type characteristic information, to ensure that the input data of the spatiotemporal decomposition model can reflect the actual operating differences of the power grid.

[0065] The core purpose of targeted data collection is to capture the differences in operating characteristics of key areas of the power grid. Information on three-phase voltage imbalance can support the formulation of three-phase phase-by-phase regulation strategies for reactive power compensation, avoiding the attenuation of compensation effects or the increase in power grid operation risks due to three-phase imbalance. Load type characteristic information needs to distinguish the operating characteristics of continuous loads, intermittent loads, and impulsive loads, providing accurate input of load dynamic characteristics for the spatiotemporal decomposition model. The optimization of the collection range needs to be combined with the identification results of weak links in the power grid, and intensified collection should be carried out in areas with poor voltage stability and severe reactive power fluctuations. At the same time, a dynamic adjustment mechanism for the collection frequency should be established to adapt the collection density according to the intensity of power grid operation fluctuations, ensuring the relevance, comprehensiveness, and real-time nature of data input.

[0066] In step S3, the adjustment logic of the dynamic weight coefficient is as follows: when the power grid is in steady state, the target weight for minimizing network losses is increased; when the power grid faces transient disturbance risks, the target weight for transient stability requirements takes priority; the weight for device configuration cost is dynamically adapted according to the power grid investment budget.

[0067] Weight adjustment requires the establishment of a real-time response mechanism. The determination of the steady-state operation of the power grid needs to be based on a comprehensive evaluation of multiple indicators such as voltage fluctuation amplitude, load fluctuation intensity, and distributed power generation output stability to ensure the rationality of weight increase for minimizing network losses. The identification of transient disturbance risks needs to integrate fault early warning information, load change prediction, and early warning of large fluctuations in distributed power generation output to achieve timely priority switching of transient stability target weights. The dynamic adaptation of device configuration cost weights requires the establishment of an investment benefit evaluation model to balance short-term investment costs and long-term operating benefits under budget constraints, while also considering the reserved space for technological upgrades and iterations to avoid the compensation configuration being unable to adapt to future power grid development needs due to cost constraints.

[0068] In step S5, the parameter adjustment adopts a graded adjustment mechanism. In the case of mild fluctuation, only the output capacity of the compensation device is finely adjusted. In the case of moderate fluctuation, the output capacity and response speed are adjusted simultaneously. In the case of severe fluctuation, the backup compensation resources are activated for coordinated adjustment.

[0069] The formulation of classification standards should be based on a comprehensive assessment of multiple dimensions, including voltage deviation, load mutation intensity, distributed power output fluctuation range, and grid transient stability margin, to ensure the scientific and accurate nature of the classification. Fine-tuning in mild fluctuation scenarios employs a smooth adjustment strategy to avoid frequent actions that could increase device losses or cause grid operational fluctuations. Synchronous adjustments in moderate fluctuation scenarios require the establishment of a collaborative optimization model for capacity and response speed, adapting the optimal adjustment combination based on fluctuation characteristics. Collaborative adjustment of backup compensation resources in severe fluctuation scenarios requires the formulation of a unified resource scheduling strategy, clearly defining the action sequence and capacity allocation ratio of primary and backup compensation devices to avoid resource conflicts. Simultaneously, it should incorporate grid topology and power flow distribution optimization schemes to quickly restore grid operational stability.

[0070] In step S1, a fusion processing method of historical operating data and real-time collected data is adopted. Data preprocessing is achieved by removing abnormal data and smoothing fluctuation interference. The core parameters of the spatiotemporal decomposition model are periodically calibrated based on the seasonal load characteristics of the power grid and the changes in the proportion of distributed power sources connected.

[0071] Data fusion employs a weighted fusion algorithm, dynamically adjusting weights based on the timeliness of historical data and the accuracy of real-time data to achieve complementary advantages between the two types of data. Anomaly removal requires establishing multi-dimensional anomaly judgment criteria, combining power grid operation patterns and data statistical characteristics to accurately identify and remove invalid information such as fault data and data collection error data. Fluctuation interference smoothing uses an adaptive filtering algorithm to effectively remove noise interference while preserving the true fluctuation characteristics of the data. Periodic calibration of the model's core parameters requires considering seasonal load change trends, the dynamic adjustment of the distributed power generation integration ratio, and power grid topology changes to establish calibration cycles and procedures, ensuring the model maintains high prediction accuracy and adapts to the dynamic changes in power grid operation.

[0072] The workflow for optimizing dynamic reactive power compensation in weak grids achieves precise compensation and stable operation through a progressive structural derivation: First, multi-dimensional operating parameters of the power grid are collected to construct a dynamic reactive power demand spatiotemporal decomposition model that takes into account spatial load heterogeneity, temporal dynamic fluctuations, and voltage-frequency coupling characteristics, providing a targeted demand basis for subsequent optimization; based on the reactive power demand decomposition results, and considering the core characteristics of weak grids such as low inertia and voltage volatility, suitable compensation device types are selected and core parameter constraints such as response speed and capacity adjustment range are defined to achieve precise matching between the device and the grid operating characteristics; with voltage stability improvement and network loss minimization as the core objectives, full life-cycle cost constraints and transient stability requirements are incorporated. A multi-objective optimization configuration model is constructed that can adapt to different scenarios through dynamic weight coefficients to ensure the global optimality of the configuration scheme. A solution algorithm adapted to the nonlinear characteristics and dynamic changes of the topology of the weak power grid is adopted to solve for the optimal installation location, configuration capacity and switching threshold of the compensation device, further enhancing the global optimality and dynamic adaptability of the optimization results. By monitoring the power grid operation status in real time, in scenarios such as excessive voltage fluctuations, sudden load changes or excessive fluctuations in the output of distributed power sources, the reactive power demand prediction value is updated based on the spatiotemporal decomposition model, and the operating parameters of the compensation device are dynamically adjusted in a closed loop. Ultimately, the compensation effect is dynamically matched with the power grid operation status, and the stability and economy of the weak power grid operation are continuously guaranteed.

[0073] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0074] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the dynamic reactive power compensation configuration in a weak power grid, characterized in that, Includes the following steps: S1 Reactive Power Demand Spatiotemporal Refined Decomposition Modeling The voltage, current, load power, distributed power output, and grid topology operation status parameters of each node in the weak grid are collected. A dynamic reactive power demand spatiotemporal decomposition model based on the voltage-frequency coupling characteristics of the weak grid is constructed. The spatiotemporal decomposition model takes into account both the spatial load heterogeneity of different nodes in the grid and the dynamic fluctuation characteristics of the load over time. S2 Compensation Device Type Selection and Parameter Constraint Determination Based on the reactive power demand decomposition results, and combined with the core characteristics of low inertia and voltage fluctuation in weak power grids, suitable reactive power compensation device types are selected, and the constraints of the core parameters of device response speed and capacity adjustment range are clarified to ensure that the device matches the operating characteristics of the power grid. S3 Multi-Objective Optimization Configuration Model Construction With the core objectives of improving grid voltage stability and minimizing grid losses, and taking into account the cost constraints of the entire life cycle of equipment purchase, installation and operation and maintenance, and incorporating the transient stability requirements of weak grids, a multi-objective optimization configuration model is established. The priority of each objective is adjusted by dynamic weight coefficients to adapt to different operating scenarios. S4 Optimization Configuration Parameter Solution An algorithm adapted to the nonlinear characteristics and dynamic changes of the weak power grid is adopted to solve for the optimal installation location, configuration capacity and switching threshold of the compensation device, ensuring the global optimality and dynamic adaptability of the optimization results; S5 Compensation Operating Parameter Closed-Loop Dynamic Adjustment Real-time monitoring of the power grid's operating status is conducted. When scenarios such as excessive voltage fluctuations, sudden load changes, or excessive fluctuations in the output of distributed power sources occur, the reactive power demand forecast is updated based on the spatiotemporal decomposition model. The operating parameters of the compensation device are then dynamically adjusted in a closed loop to ensure that the compensation effect dynamically matches the power grid's operating status.

2. The method for optimizing the dynamic reactive power compensation configuration of a weak power grid according to claim 1, characterized in that: In step S1, the spatial dimension of the dynamic reactive power demand spatiotemporal decomposition model is weighted by the voltage sensitivity of each node to divide high, medium and low priority reactive power demand regions. The time dimension is divided into three types of demand according to the load fluctuation frequency: transient impact type, steady state continuous type and intermittent fluctuation type. The reactive power demand under different scenarios is defined through cross-mapping of the spatiotemporal dimensions.

3. The method for optimizing the dynamic reactive power compensation configuration of a weak power grid according to claim 1, characterized in that: In step S2, the selected reactive power compensation device adopts a hybrid configuration of SVG and STATCOM that integrates the characteristics of virtual synchronous machine. SVG is adapted to steady-state reactive power compensation and voltage support requirements, while STATCOM is adapted to transient reactive power surge suppression requirements. The core parameter constraints include that the device response speed is not lower than the response requirements of grid transient fluctuations and the capacity adjustment range covers the maximum reactive power deficit of the corresponding area.

4. The method for optimizing the dynamic reactive power compensation configuration of a weak power grid according to claim 1, characterized in that: In step S3, the voltage deviation constraint is set based on the allowable fluctuation range of the rated voltage of the weak grid, the network loss constraint fully considers the distribution characteristics of reactive power flow in the grid branches, the full life cycle cost covers the various costs of equipment purchase, on-site installation, daily operation and maintenance and later replacement, and the dynamic weight coefficient is determined according to the current load level of the grid and the proportion of distributed power source access.

5. The method for optimizing the dynamic reactive power compensation configuration of a weak power grid according to claim 1, characterized in that: In step S4, the adaptation solution algorithm is an improved adaptive genetic algorithm based on the dynamic update of weak power grid topology. The solution algorithm dynamically adjusts the crossover probability and mutation probability by introducing a node correlation factor, and adapts in real time to the impact of power grid topology changes on the configuration results.

6. The method for optimizing the dynamic reactive power compensation configuration of a weak power grid according to claim 1, characterized in that: In step S5, voltage fluctuation exceeding the standard scenario refers to the node voltage deviating from the rated value beyond the allowable range, load change scenario includes industrial load start-up and shutdown and peak change of residential electricity consumption, distributed power output fluctuation scenario covers intermittent fluctuations of wind power and photovoltaic power output, and the adjustment process simultaneously optimizes the output capacity and response timing of the compensation device.

7. The method for optimizing the dynamic reactive power compensation configuration of a weak power grid according to claim 1, characterized in that: In step S1, during the acquisition of operating status parameters, the parameter acquisition coverage of concentrated wind power and photovoltaic access areas and dense industrial load areas is more comprehensive, including node three-phase voltage imbalance and load type characteristic information, to ensure that the input data of the spatiotemporal decomposition model can reflect the actual operating differences of the power grid.

8. The method for optimizing the dynamic reactive power compensation configuration of a weak power grid according to claim 1, characterized in that: In step S3, the adjustment logic of the dynamic weight coefficient is as follows: when the power grid is in steady state, the target weight of minimizing network loss is increased. When the power grid faces transient disturbance risks, the transient stability requirement takes precedence over the target weight; the equipment configuration cost weight is dynamically adapted according to the power grid investment budget.

9. The method for optimizing the dynamic reactive power compensation configuration of a weak power grid according to claim 1, characterized in that: In step S5, the parameter adjustment adopts a graded adjustment mechanism. In the case of mild fluctuation, only the output capacity of the compensation device is finely adjusted. In the case of moderate fluctuation, the output capacity and response speed are adjusted simultaneously. In the case of severe fluctuation, the backup compensation resources are activated for coordinated adjustment.

10. The method for optimizing the dynamic reactive power compensation configuration of a weak power grid according to claim 1, characterized in that: In step S1, a fusion processing method of historical operating data and real-time collected data is adopted. Data preprocessing is achieved by removing abnormal data and smoothing fluctuation interference. Furthermore, the core parameters of the spatiotemporal decomposition model are periodically calibrated based on the seasonal load characteristics of the power grid and the changes in the proportion of distributed power sources connected.

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