Power distribution network response capability evaluation method based on dynamic simulation scene

Abstract

The invention relates to the field of power distribution network response capability evaluation and scheduling optimization, and discloses a power distribution network response capability evaluation method based on a dynamic simulation scene, and the method comprises the following steps: constructing a multi-dimensional model library which comprises an electrical characteristic model, a load fluctuation model, a renewable energy power generation model and an energy storage equipment model of a power grid; on the basis of the demand response plan, variables are extracted from the multi-dimensional model library, and a plurality of different test scenes are generated; and analyzing the relevance among the plurality of test scenes by using an association rule mining algorithm. And through dynamic simulation scene evaluation, association rule mining and optimization decision making, the response capability of the power grid under different loads and renewable energy fluctuations is accurately evaluated. Test scene selection is optimized, and simulation efficiency and calculation accuracy are improved. Meanwhile, the integrated technical scheme improves the operation efficiency of the power grid, reduces the operation cost, and ensures the efficient configuration and economical efficiency of resources.

Description

Technical Field

[0001] The present invention relates to the field of distribution network response capability evaluation and dispatch optimization, and specifically to a distribution network response capability evaluation method based on dynamic simulation scenarios. Background Art

[0002] As the intelligent distribution network continues to develop, the evaluation of the grid's response capability becomes particularly important when dealing with dynamic factors such as complex load fluctuations, climate change, and renewable energy fluctuations. Traditional grid response capability evaluation methods usually rely on static data and historical analysis, focusing on the impact of a single factor, such as load changes or weather conditions. This method fails to comprehensively consider the comprehensive response of the grid under the interaction of multiple factors, resulting in the evaluation results being unable to accurately reflect the performance of the grid in actual operation.

[0003] Current simulation technology also mainly relies on fixed scenarios for simulation, ignoring the correlation between scenarios and the actual response of the power grid under different scenarios. Static simulation not only has low computing resource utilization, but also fails to effectively screen scenarios that have a greater impact on power grid performance, resulting in low evaluation efficiency. Furthermore, traditional scheduling optimization strategies usually rely on simple rules or prediction models. These strategies cannot adapt to the frequently changing dynamic environment of power grid operation, resulting in slow response in complex situations and affecting the stability of the power grid.

[0004] In order to improve the stability, responsiveness and efficiency of the distribution network, a more comprehensive, dynamic and efficient evaluation and scheduling method is needed, which comprehensively considers multi-dimensional factors such as load fluctuations, renewable energy generation and climate change, so as to more accurately simulate the performance of the power grid in actual operation. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a distribution network responsiveness evaluation method based on dynamic simulation scenarios, which solves the problems that the existing distribution network responsiveness evaluation method cannot comprehensively and accurately simulate the performance of the power grid in a variety of complex environments, and the scheduling strategy cannot adapt to the dynamic changes of the power grid in real time.

[0006] To achieve the above objectives, the present invention is implemented by the following technical solutions: a distribution network response capability evaluation method based on a dynamic simulation scenario, comprising the following steps: S1. Build a multi-dimensional model library, which includes the electrical characteristics model of the power grid, the load fluctuation model, the renewable energy generation model and the energy storage equipment model; S2. Based on the demand response plan, extract variables from the multi-dimensional model library to generate multiple different test scenarios; S3. Use association rule mining algorithms to analyze the correlation between multiple test scenarios, and prioritize the simulation of key scenarios that have a greater impact on grid stability based on the correlation analysis results; S4. Perform dynamic simulation based on test scenarios to simulate the response behavior of the power grid in different scenarios and calculate the performance indicators of the power grid; S5. Evaluate the responsiveness of the distribution network based on performance indicators, including response time, maximum deviation rate, and recovery time; S6. Based on the performance evaluation results, an optimization algorithm is used to adjust the grid dispatch strategy to optimize the grid’s responsiveness and stability.

[0007] Preferably, the multidimensional model library includes the following models: A power grid electrical characteristic model is used to describe the electrical connection relationship between nodes in the power distribution network, and the model is represented by an admittance matrix; A load fluctuation model, which models load fluctuations through a random process, wherein the model includes a relationship between load power and time; Renewable energy generation model, which represents the power generation of renewable energy. The model calculates the output power of renewable energy based on climate data; The energy storage device model represents the charging and discharging mode of the energy storage device and its impact on the grid response capability, and the model includes the relationship between the energy storage charging and discharging power and the grid voltage.

[0008] Preferably, the association rule mining algorithm is an Apriori algorithm, which is used to analyze the associations between the multiple test scenarios, including support and confidence.

[0009] Preferably, the dynamic simulation step is based on the electrical characteristic model and load fluctuation model of the power grid, and uses partial differential equations to describe the dynamic changes of the voltage and current of the power grid, specifically: in, is the node voltage, is the inductance of the grid, is the load current, is the current generated by renewable energy, and the partial differential equation describes the variation of the voltage at the grid node with the load and renewable energy fluctuations.

[0010] Preferably, the dynamic simulation step further includes the following process: Prioritize simulation of key scenarios that have a significant impact on grid stability based on correlation analysis results; Through numerical simulation methods, the voltage, current and frequency parameters of the power grid are calculated in real time, and the stability and response time of the power grid are monitored.

[0011] Preferably, the performance indicators include response time, maximum deviation rate and recovery time, which are specifically defined as follows: Response time: the time it takes for the grid to recover from receiving a load or renewable energy change signal; Maximum deviation rate: the maximum deviation value of voltage and current parameters of the power grid during the response process, and the deviation rate relative to the steady-state value; Recovery time: The time required for a power grid to recover from a fault or disturbance to a stable state.

[0012] Preferably, the response time is the time required for the power grid to stabilize from initial system changes under different load or energy fluctuations, and the response time is calculated through real-time monitoring data.

[0013] Preferably, the recovery time represents the time required for the power grid to recover to a stable power grid operation state after a disturbance or failure, and the recovery time is calculated through real-time simulation.

[0014] A distribution network response capability evaluation system, comprising: Multi-dimensional model library module, used to store power grid electrical characteristic models, load fluctuation models, renewable energy generation models and energy storage equipment models; A scenario generation module, for generating a plurality of different test scenarios based on a grid demand response plan, wherein the test scenarios include different load, climate and renewable energy conditions; An association rule mining module, used to analyze the associations between the multiple test scenarios and identify key scenario sequences; A dynamic simulation module, used to simulate the dynamic response of the power grid based on the test scenario and calculate the power grid performance index; A performance evaluation module is used to calculate the performance indicators of the response time, maximum deviation rate and recovery time of the power grid according to the simulation results, and to evaluate the response capability of the power grid; The optimization decision module is used to adjust the dispatching strategy of the power grid based on the evaluation results and optimize the responsiveness and stability of the power grid.

[0015] The present invention provides a method for evaluating the response capability of a distribution network based on a dynamic simulation scenario, which has the following beneficial effects: 1. The present invention adopts a distribution network response capability assessment method based on dynamic simulation scenarios, and achieves the technical effect of efficiently and accurately assessing the response capability of the power grid by combining a multi-dimensional model library with real-time simulation. Compared with the technical solutions in the prior art that only rely on static data or historical load fluctuations, the present invention can fully simulate the performance of the power grid under complex conditions such as different loads, climates, and renewable energy fluctuations, thereby avoiding the limitations of traditional methods in dealing with uncertainty and dynamic changes, and significantly improving the accuracy and adaptability of power grid assessment.

[0016] 2. The present invention uses an association rule mining algorithm to analyze the relevance of test scenarios, achieving the technical effect of optimizing test scenario selection and improving simulation efficiency. Compared with the prior art solution of blindly generating all possible scenarios for evaluation, the present invention reduces invalid simulation calculations by identifying key scenario combinations, effectively improving calculation efficiency and simulation accuracy. The application of this method makes the power grid evaluation process more refined, focusing on scenarios that have a greater impact on power grid stability, and improving the reliability of evaluation results.

[0017] 3. The present invention adopts a technical solution combining dynamic simulation with optimized decision-making, achieving the technical effect of optimizing the grid dispatching strategy and improving the response capability. Different from the static or partially optimized dispatching method in the prior art, the present invention continuously adjusts the dispatching strategy of the grid through real-time simulation and optimization algorithm, so that the grid can flexibly respond to load fluctuations, renewable energy generation fluctuations and other variable situations, reducing the instability risk of the grid in complex environments and achieving efficient resource allocation.

[0018] 4. The present invention achieves the technical effect of improving the overall operating efficiency of the distribution network and reducing operating costs by fully integrating multi-dimensional models, dynamic simulation, and optimization decision-making technologies. Compared with the existing single optimization strategy or local scheduling scheme, the present invention realizes a comprehensive consideration of various factors of the power grid, so that the power grid can be dynamically adjusted and optimized in different operating environments, which not only improves the stability and response speed of the power grid, but also effectively reduces the energy waste of the power system and improves the economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0021] Please refer to the attached Figure 1 The embodiment of the present invention provides a method for evaluating the response capability of a distribution network based on a dynamic simulation scenario, comprising the following steps: S1. Build a multi-dimensional model library, which includes the electrical characteristics model of the power grid, the load fluctuation model, the renewable energy generation model and the energy storage equipment model; In the present invention, building a multidimensional model library is the basic step of the entire evaluation method, which aims to provide accurate models and data support for the dynamic simulation and response capability evaluation of the distribution network. The multidimensional model library is not just a simple data set, but a system that integrates multiple key factors (such as electrical characteristics, load fluctuations, renewable energy generation and energy storage equipment models). These models can generate simulation data in different scenarios and simulate the behavior of the power grid under various uncertain conditions. The accuracy and comprehensiveness of building a multidimensional model library directly determines the reliability of subsequent evaluation results.

[0022] In this embodiment, the steps of building a multidimensional model library include: First, the electrical characteristic model of the power grid is established. This model is mainly used to describe the electrical connection relationship of each node in the distribution network and its impact on the stability of the power grid. In the present invention, the electrical characteristic model uses an admittance matrix to describe the electrical parameters of the power grid. The admittance matrix includes the parameters of all electrical components in the distribution network, such as the electrical characteristics of power supplies, loads, transformers and other equipment. Specifically, the admittance matrix is ​​defined as: ; in, Represents a grid node and nodes The admittance matrix reflects the electrical connection relationship between each node in the power grid and can accurately describe the electrical behavior of the power grid under different working conditions. In the specific implementation, a numerical solution method is used to solve the admittance matrix for application in dynamic simulation.

[0023] In some embodiments, the load fluctuation model is modeled by a random process, taking into account the load changes over time. Specifically, the load fluctuation model uses a Poisson process or other similar random process models to generate load fluctuation data over time through statistical analysis of historical load data. These fluctuation data can simulate the load fluctuation of the power grid in different seasons, time periods or weather conditions.

[0024] The load fluctuation model can also be based on time series analysis, using methods such as ARIMA (autoregressive integrated moving average) to predict load changes. This method can generate more accurate load forecasts by analyzing the trend, seasonality and periodicity of historical load data, providing more targeted load data for dynamic simulation of the power grid.

[0025] In an embodiment of the present invention, the renewable energy generation model is modeled based on meteorological data to simulate the fluctuating generation characteristics of renewable energy sources such as wind power and solar energy. Specifically, the model predicts the output power of wind power and photovoltaic power generation by inputting meteorological data (such as temperature, humidity, wind speed, light intensity, etc.). The wind power generation power can be calculated by the following formula: ; in, is the power of wind power generation, is the air density, is the swept area of ​​the wind turbine, is the power coefficient, By inputting different wind speed data, the output power of wind power can be dynamically calculated and used as part of the distribution network simulation input.

[0026] For photovoltaic power generation, the calculation formula is: ; in, represents photovoltaic power generation power, is the light intensity, is the area of ​​the photovoltaic panel, The efficiency of the photovoltaic panel. By acquiring the light intensity and temperature data in real time, the power output of photovoltaic power generation can be dynamically predicted.

[0027] When building a multidimensional model library, the role of energy storage devices must also be considered. Energy storage devices play an important role in the stability of the power grid, especially when renewable energy fluctuates greatly. The charging and discharging strategy of energy storage devices needs to consider the load fluctuations of the power grid and the changes in renewable energy. The charging and discharging power of energy storage devices can be described by the following model: ; ; in and Respectively represent the charging power and discharging power of the energy storage device, is the maximum power of the energy storage device, is the grid power, is the grid load, is the amount of electricity currently stored in the energy storage device, is the minimum discharge power of the energy storage device.

[0028] Through the above energy storage device models, we can simulate the dynamic response of energy storage devices in the power grid and calculate the impact of their charging and discharging processes on the stability of the power grid. These models will be integrated into the multidimensional model library and used as input parameters in the subsequent scenario generation and simulation process.

[0029] In summary, in this embodiment, the process of building a multidimensional model library involves multiple aspects such as power grid electrical characteristics, load fluctuations, renewable energy generation, and energy storage equipment. Each model is accurately modeled and calculated through appropriate algorithms and formulas to ensure that the actual operating status of the power grid can be accurately reflected during the dynamic simulation process. The comprehensive application of these models ensures that the distribution network can be effectively evaluated under different load, climate and energy conditions, and provides a scientific basis for the dispatch and optimization of the power grid.

[0030] By establishing a multi-dimensional model library, the present invention can provide comprehensive support for subsequent dynamic simulation, performance evaluation and optimization decision-making, and provide accurate model data for the response capability evaluation of the distribution network.

[0031] S2. Based on the demand response plan, extract variables from the multi-dimensional model library to generate multiple different test scenarios; In the specific implementation of the present invention, generating test scenarios based on demand response plans is one of the key steps in evaluating the responsiveness of distribution networks. Demand response plans are usually scheduling plans set by power grids to cope with different load demands and changes in power supply conditions. According to the demand response plan, multiple test scenarios that conform to actual operating conditions can be generated. These test scenarios not only simulate common load changes, but also take into account factors such as climate change, fluctuations in renewable energy, and the role of energy storage equipment. By reasonably generating and selecting test scenarios, it can help us comprehensively evaluate the responsiveness of distribution networks under different circumstances.

[0032] In this embodiment, the specific steps of generating a test scenario based on a demand response plan are as follows: First, different types of load fluctuations, climate conditions, distributed power output and other factors are extracted from the multi-dimensional model library through the demand response plan. These factors are not only static load changes, but also dynamically changing environmental conditions, such as climate data such as temperature and humidity, and renewable energy output data such as wind power and light intensity. In some embodiments, these data are derived from meteorological forecast models or historical meteorological data. The demand response plan combines these data into multiple different test scenarios to simulate various working conditions that the power grid may encounter.

[0033] Specifically, the generated test scenarios include but are not limited to the following typical situations: High-load scenarios: In summer or winter, especially during peak load periods, load demand surges. At this time, the grid needs to maintain a high load level and quickly dispatch renewable energy or energy storage equipment to cope with load fluctuations.

[0034] Extreme weather scenarios: Such as snowstorms or high temperatures, these weather conditions not only affect the load demand on the grid, but also the generation capacity of renewable energy. For example, during a snowstorm, wind power generation may be suppressed, while solar power generation may fluctuate under cloud cover. Test scenarios simulate these weather changes to help evaluate the grid's ability to respond to such extreme conditions.

[0035] High electricity price incentive scenario: In the electricity market, high electricity prices may prompt large-scale load response or mobilize energy storage equipment for charging and discharging. The test scenario considers the impact of electricity price fluctuations on grid load regulation and evaluates the grid's responsiveness under high electricity prices.

[0036] Renewable energy fluctuation scenario: Wind power generation and photovoltaic power generation have strong fluctuations. The present invention predicts the output of these energy sources through meteorological data and introduces them into the test scenario. This scenario simulates how the power grid responds to the impact of renewable energy generation fluctuations under unstable light and wind speed conditions.

[0037] When generating test scenarios, you can also optimize scenarios based on historical data and prediction models using simulated annealing algorithms, genetic algorithms, and other methods. Through the optimization algorithm, you can prioritize the generation of scenario combinations that have a greater impact on the grid's response capabilities. This process can reduce invalid scenario tests and improve test efficiency.

[0038] Demand response programs can also dynamically adjust test scenarios based on the grid operator's dispatch strategy and goals. For example, the grid may adjust its operation strategy based on real-time factors such as load demand, equipment status, and market demand. In this case, the generated test scenarios will be more in line with the actual operating environment and reflect the actual response of the grid in different situations.

[0039] Specifically, the process of generating a test scenario can be carried out through the following steps: Load extraction: Based on the demand response plan, specific load data is first extracted from the load fluctuation model. This data can be a time series generated based on historical load fluctuations, or future load forecast data generated based on a forecasting model (such as an ARIMA model). The load data is divided into multiple time periods and applied to the test scenario.

[0040] Climate and renewable energy output data extraction: Using meteorological forecast models or historical meteorological data, climate data for a specific time period is extracted, such as wind speed, temperature, light intensity, etc. These data are used to predict the power generation capacity of renewable energy (such as wind power and photovoltaics) and are used as input data together with load data to generate test scenarios.

[0041] Scenario generation: Combine load data and renewable energy output data to form multiple test scenarios. Each scenario represents a possible grid operating state, and the differences between scenarios are usually reflected in load demand, weather conditions, and renewable energy fluctuations.

[0042] Scenario prioritization and optimization: Through association rule mining or optimization algorithms (such as genetic algorithms), multiple test scenarios are prioritized to ensure that key scenarios that may have a greater impact on the power grid can be simulated first. This process helps improve simulation efficiency and avoid unnecessary simulation of low-impact scenarios.

[0043] The generated test scenarios can also be verified by historical data playback or real-time simulation. This process ensures that the generated test scenarios are representative of actual operations and can effectively reflect the response capabilities of the power grid in actual operation.

[0044] Through this series of steps, the test scenarios generated based on the demand response plan can provide rich simulation data for the evaluation of the distribution network's responsiveness. These test scenarios cover a variety of complex working conditions and can comprehensively evaluate the performance of the power grid in the face of different load fluctuations, climate change, and renewable energy fluctuations. On this basis, power grid managers can effectively optimize the grid's dispatch and improve the grid's stability and emergency response capabilities.

[0045] In general, the process of generating test scenarios based on demand response plans is an important part of the present invention. Through reasonable scenario generation and optimization, the accuracy and efficiency of distribution network response capability evaluation can be improved, providing strong support for subsequent dynamic simulation and performance evaluation.

[0046] S3. Use association rule mining algorithms to analyze the correlation between multiple test scenarios, and prioritize the simulation of key scenarios that have a greater impact on grid stability based on the correlation analysis results; In the above steps, by generating multiple test scenarios based on the demand response plan, rich data support is provided for the subsequent grid response capability evaluation. However, due to the complex relationships between scenarios, simply generating these scenarios is not enough to fully evaluate the response capability of the distribution network. In order to identify the most influential scenarios from the multiple generated test scenarios, it is necessary to use association rule mining algorithms to analyze the correlation between scenarios and optimize subsequent simulation work. This process can effectively reduce the amount of calculation while ensuring that the grid evaluation can focus on scenarios that have a greater impact on system stability.

[0047] In this embodiment, an association rule mining algorithm is used to analyze the relevance of the test scenarios. The specific steps are as follows: First, after generating the test scenarios, the relationship between different scenarios is analyzed through association rule mining algorithms (such as the Apriori algorithm). The core purpose of the association rule mining algorithm is to discover frequently occurring patterns or associations in the data. In the test scenario of the distribution network, the association rule algorithm can help identify the potential correlation between factors such as load fluctuations, changes in climate conditions, and fluctuations in renewable energy and grid performance.

[0048] Generally speaking, association rule mining focuses on two parameters: support and confidence. These two parameters can help us evaluate the strength of the relationship between scenarios and the possibility of other scenarios occurring when a certain scenario occurs.

[0049] Support is defined as the frequency of a scenario or a combination of scenarios in all test scenarios. Assume that the scenario set is , the support can be expressed as a combination of scenarios and The ratio of simultaneous occurrence is: ; in, Representation scene and scenes The number of times it appears at the same time, Indicates the total number of test scenarios. Support reflects the prevalence of a certain scenario combination in all test scenarios.

[0050] Confidence measures the scenario Scene when it happens The probability of occurrence is expressed as: ; in, Combination for scenes and Concurrent support, For the scene The higher the confidence, the more likely the scenario is. When it appears, the scene The higher the possibility of occurrence.

[0051] By calculating the support and confidence between different test scenarios, we can identify which scenarios often occur together under certain conditions. For example, if the volatility of renewable energy is strong when the load fluctuates greatly, the support and confidence of this scenario combination will be high. In this way, we can screen out those key scenario combinations that have a high probability of occurrence in actual operation and have a greater impact on grid stability.

[0052] The application of association rule mining algorithms can help prioritize test scenario combinations that have a greater impact on grid stability. In this way, we can focus on simulating these key scenarios in subsequent dynamic simulations to improve simulation efficiency and accuracy. For example, if certain high-load scenarios and extreme weather scenarios often appear together and have a greater impact on grid stability, then these scenarios will be prioritized for simulation.

[0053] As an option, association rule mining can also be used to identify the mutual influence of different components in the power grid. During the simulation process, some load fluctuations may be closely related to the charging and discharging strategies of energy storage devices, while other scenarios may be related to fluctuations in renewable energy generation. By analyzing these associations, the power grid dispatching system can better understand the working status of the power grid under different conditions, thereby formulating more accurate dispatching strategies.

[0054] Specifically, in this embodiment, the application of the association rule mining algorithm is not only used to select test scenarios, but also to provide some additional information for subsequent dynamic simulation. Through association rule mining, we can find which scenario combinations have higher risks in terms of power grid stability, which provides an important decision-making basis for the optimal dispatch of the power grid.

[0055] Generally speaking, in practical applications, the results of association rule mining are not unique. The support of some scenario combinations may be low, indicating that these scenarios are less likely to occur in actual operation and may have limited impact on the power grid. Therefore, when selecting scenarios, in addition to relying on support and confidence, it is also necessary to combine the power grid operation characteristics and dispatching goals in actual applications, and comprehensively consider various factors to finally determine the simulation scenario.

[0056] In addition to using support and confidence, other extended forms of association rule mining algorithms, such as lift value, chi-square test, etc., can also be used to further evaluate the correlation between scenarios. Through these methods, the accuracy of scenario selection can be further improved, providing more reliable data support for subsequent dynamic simulation and evaluation.

[0057] In summary, using association rule mining algorithms to analyze the correlation of test scenarios can effectively identify key scenario combinations that may affect power grid stability, and by optimizing the selection of test scenarios, reduce invalid simulations, and improve the efficiency and accuracy of power grid evaluation. This process provides accurate input data for subsequent dynamic simulation and performance evaluation, ensuring the scientificity and comprehensiveness of power grid response capability evaluation.

[0058] S4. Perform dynamic simulation based on test scenarios to simulate the response behavior of the power grid in different scenarios and calculate the performance indicators of the power grid; In the previous steps, multiple test scenarios were generated through the demand response plan, and the correlation between the scenarios was analyzed using the association rule mining algorithm. Based on these screened test scenarios, the next step is to conduct dynamic simulation to deeply evaluate the various situations that the distribution network may encounter in actual operation. By simulating the behavior of the power grid, dynamic simulation can predict the response characteristics of the power grid under different loads, climate conditions and renewable energy fluctuations. Through this simulation, data support can be further provided for the optimization of the grid's dispatch strategy and the improvement of its response capability.

[0059] In this embodiment, the steps of performing dynamic simulation based on the test scenario include: First, the system enters the dynamic simulation phase by inputting optimized and screened test scenarios. Each test scenario corresponds to a different grid operation state, including variables such as load demand, renewable energy generation, and climate conditions. During the simulation process, the changes in these variables will be calculated in real time through the grid's electrical characteristics model, load fluctuation model, and energy storage device model to simulate the dynamic response of the grid.

[0060] In general, the dynamic response of the power grid is described by partial differential equations based on the electrical characteristics of the grid and load fluctuations. The changes of electrical parameters such as grid voltage and current over time can be modeled by the following equations: ; in, represents the node voltage, is the inductance of the grid, is the load current, is the power generation current of renewable energy. This equation describes the dynamic change process of voltage in the power grid with load and renewable energy fluctuations. Specifically, the voltage response of the power grid is inversely proportional to the difference between the load current and the power generation current.

[0061] Specifically, during the simulation process, the electrical characteristic model of the power grid first calculates the voltage value of each node. Over time, these voltage values ​​will be adjusted according to factors such as load changes and the charging and discharging of energy storage devices. At the same time, the simulation system will calculate multiple electrical parameters such as current, voltage, and power in real time and compare them with the actual operating standards of the power grid. These calculation results can provide the key data required for subsequent performance evaluation.

[0062] As an option, more complex nonlinear models can be introduced during the power grid simulation process to take into account the possible nonlinear characteristics of certain devices in the power grid (such as transformers, circuit breakers, etc.). Through numerical solution methods, these nonlinear models can simulate the response behavior of the power grid under complex conditions, further improving the accuracy and reliability of the simulation results.

[0063] In practical applications, the simulation process also needs to take into account the energy storage devices in the grid. These devices play a vital role in grid stability, especially when dealing with load fluctuations and renewable energy fluctuations. The charging and discharging behavior of the energy storage device is modeled by the following formula: ; ; in, Indicates the charging power of the energy storage device, Indicates the discharge power, is the maximum power of the energy storage device, is the power of the grid, is the load of the grid, is the current power of the energy storage device, is the minimum discharge power of the energy storage device. Through these formulas, the contribution of energy storage devices to the power grid can be accurately simulated, and the operation strategy of the power grid can be further adjusted.

[0064] In the process of dynamic simulation, it is particularly important to consider the superposition effect of load fluctuations and renewable energy fluctuations. Specifically, when the load fluctuations are large, the power grid may need to rely on more renewable energy generation to maintain balance; at the same time, the scheduling strategy of energy storage equipment will also affect the stability of the power grid. The simulation system ensures that the power grid can remain stable under various external disturbances by continuously adjusting the charging and discharging mode of the energy storage equipment and adjusting the load distribution in real time.

[0065] In one possible implementation, dynamic simulation not only considers the stability of the power grid, but also includes the calculation of the power grid recovery time. That is, when a fault or disturbance occurs in the power grid, the simulation system will calculate the time from the fault to the restoration of stability. By real-time monitoring of the voltage, current and other parameters of the power grid, the simulation system can determine the progress of power grid recovery and adjust the dispatch strategy in a timely manner.

[0066] As an extension, dynamic simulation can also optimize the simulation results by using a variety of algorithms (such as genetic algorithms, particle swarm optimization, etc.). These algorithms can help the power grid automatically adjust its operating status, optimize the power grid dispatch strategy, reduce response time, and improve the operating efficiency of the power grid.

[0067] In short, dynamic simulation based on test scenarios can accurately simulate the response characteristics of the power grid under various working conditions and evaluate the performance of the power grid under load fluctuations, renewable energy fluctuations, extreme weather, etc. This step provides accurate data support for subsequent performance evaluation and scheduling optimization, ensuring that the distribution network can maintain good stability and responsiveness in various complex environments.

[0068] S5. Evaluate the responsiveness of the distribution network based on performance indicators, including response time, maximum deviation rate, and recovery time; In the previous steps, through the test scenarios and dynamic simulations generated based on the demand response plan, we have obtained the response data of the power grid under various conditions. The next step is to perform performance evaluation on the simulation results to measure the response capability of the power grid. This process comprehensively evaluates the stability and adjustment capability of the power grid in the face of load fluctuations, renewable energy fluctuations and external disturbances by calculating performance indicators such as the response time, maximum deviation rate and recovery time of the power grid. The performance evaluation provides a scientific basis for the subsequent optimization of power grid dispatch, ensuring that the power grid can operate efficiently and stably in various complex environments.

[0069] In this embodiment, the performance evaluation steps specifically include the following aspects: First, response time is one of the important indicators for evaluating power grid performance. Response time is defined as the time required for the power grid to recover to a steady state after receiving a signal of load change or renewable energy fluctuation. Generally, the response time of the power grid is closely related to the magnitude of load change, the rate of renewable energy fluctuation, and the regulation capacity of energy storage equipment. The shorter the response time, the faster the power grid can adjust to cope with sudden load fluctuations or renewable energy generation fluctuations.

[0070] In general, the response time can be calculated based on the voltage and current changes of the power grid during the simulation process, and the time series analysis method can be used to measure the process from receiving the disturbance signal to restoring stability of the power grid. Specifically, the response time It can be calculated by the following formula: ; in, It is the time it takes for the grid voltage or current to return to the set steady-state value. is the time when the disturbance occurs in the power grid. This formula helps us measure the speed of the power grid adjustment. The shorter the response time, the better the emergency response capability of the power grid.

[0071] Next, the maximum deviation rate is another important performance indicator. The maximum deviation rate refers to the maximum deviation value of parameters such as voltage or current during the adjustment process of the power grid, relative to the steady-state value. Usually, when a fault or disturbance occurs in the power grid, there will be certain fluctuations in voltage and current. The maximum deviation rate measures the stability of the power grid during the response process. Power grids with large deviation rates may experience problems such as voltage drops and current surges, affecting the stability and reliability of the power grid.

[0072] Specifically, the maximum deviation rate It can be calculated by the following formula: ; in, is the voltage value when the grid voltage reaches the maximum deviation during the simulation process. is the voltage value of the grid in a stable state. Through this formula, we can quantify the degree of voltage fluctuation of the grid during the simulation process and thus evaluate the stability of the grid.

[0073] If the grid experiences large load fluctuations or renewable energy generation fluctuations, the maximum deviation rate may increase. Therefore, during the simulation process, we also need to conduct a detailed analysis of load changes, climate change, and renewable energy generation fluctuations to ensure that the grid can remain stable under these external disturbances.

[0074] Finally, recovery time is another important indicator in performance evaluation. Recovery time refers to the time required for the power grid to recover to normal steady state after a fault or disturbance occurs. A power grid with a shorter recovery time can recover to normal operation in the shortest time, reducing the risk of power outage or system instability.

[0075] Specifically, the recovery time It can be calculated by the following formula: ; in, is the time required for the grid to return to a stable state, is the time point when the disturbance occurs in the power grid. Through this formula, we can measure the recovery ability of the power grid in different scenarios, and further evaluate the self-recovery ability and stability of the power grid.

[0076] In order to improve the accuracy and reliability of the evaluation, the three indicators (response time, maximum deviation rate, and recovery time) can be comprehensively analyzed. Through the weight distribution method, we can reasonably evaluate the overall response capability of the power grid according to the importance and complexity of different scenarios. During the simulation process, the system can adjust the scenario priority according to the importance of each indicator and optimize the dispatch strategy of the power grid.

[0077] For example, in the face of high load fluctuations and extreme weather scenarios, the grid may need more time to recover, so recovery time and response time may become key evaluation indicators. For these scenarios, more energy storage equipment or adjustments to the dispatch strategy may be needed to improve the grid's resilience.

[0078] As an extension, the performance evaluation results can not only provide the current response capability of the power grid, but also provide guidance for future power grid optimization. In the process of power grid optimization and dispatch, the evaluation results can be used to adjust the load dispatch strategy, optimize the charging and discharging strategy of energy storage equipment, and adjust the access strategy of renewable energy, so as to improve the stability and emergency response capability of the power grid.

[0079] In general, the performance evaluation step helps us comprehensively evaluate the responsiveness of the distribution network by calculating key indicators such as response time, maximum deviation rate, and recovery time. These indicators not only reflect the stability of the power grid in the face of disturbances, but also provide a basis for adjusting and optimizing the power grid. Through in-depth analysis of the simulation results, we can provide scientific support for power grid management and ensure that the power grid operates efficiently and safely in complex environments.

[0080] S6. Based on the performance evaluation results, an optimization algorithm is used to adjust the grid dispatch strategy to optimize the grid’s responsiveness and stability.

[0081] In the above steps, test scenarios were generated based on the multi-dimensional model library, and the responsiveness of the power grid under different working conditions was evaluated through dynamic simulation. Through these simulation results, we can obtain the performance indicators of the power grid under different load fluctuations, renewable energy fluctuations, climate change and other conditions, such as response time, maximum deviation rate and recovery time. These evaluation results provide the necessary reference for the optimal dispatch of the power grid. The next step is to make optimal decisions based on these evaluation results, so as to adjust the dispatch strategy of the power grid and improve the stability, responsiveness and efficiency of the power grid.

[0082] In this embodiment, the specific steps of optimizing decision making and adjusting the power grid dispatching strategy are as follows: First, based on the grid response capability evaluation results obtained in the dynamic simulation, such as response time, maximum deviation rate, and recovery time, the system will use optimization algorithms to adjust the grid dispatch strategy. Common optimization algorithms include particle swarm optimization (PSO) and genetic algorithms (GA), which can help find the optimal grid dispatch solution, so that the grid can maximize its response capability and maintain stability when facing challenges such as load fluctuations and renewable energy fluctuations.

[0083] In general, the optimization objectives mainly include: minimizing the response time of the power grid, reducing the maximum deviation rate during the operation of the power grid, and shortening the recovery time of the power grid after a fault or disturbance. By adjusting the dispatch strategy, the optimization algorithm can ensure that the power grid maintains good performance in various scenarios and meets the stability requirements of the power grid.

[0084] Specifically, assuming that the grid dispatch strategy involves the charging and discharging scheduling of energy storage equipment, load scheduling, and renewable energy generation scheduling, these decisions need to comprehensively consider various needs and constraints. In order to optimize the operation of the grid, the optimization algorithm is usually based on the following objective function Make adjustments: ; in, The response time refers to the time required for the grid to recover to a steady state after receiving a load or renewable energy change signal; It is the maximum deviation of the grid voltage, current and other parameters, and the deviation rate relative to the steady-state value; is the recovery time, which indicates the time required for the power grid to recover from a fault or disturbance to a normal state. , , The objective function is weighted and adjusted according to the operation objectives and priorities of the power grid. The objective function reflects the dispatching objectives of the power grid in different situations. At the same time, the objective function is solved by the optimization algorithm to achieve accurate optimization of the power grid dispatching.

[0085] As an option, in addition to the above objective function, it can also be adjusted according to the economic benefits of the power grid. For example, in some scenarios, it may be necessary to balance the stability and economic operation of the power grid, and further optimize the dispatch strategy by introducing economic cost models, such as electricity market prices, energy storage equipment operating costs and other factors. In this case, the objective function can be expanded to: ; in, Represents the grid operation cost, including the charging and discharging cost of energy storage equipment, electricity market transaction cost, etc. , , , Weighted adjustments are made based on the grid’s operational objectives and priorities.

[0086] During the optimization process, the algorithm will perform real-time calculations based on the actual operation data of the power grid (such as load demand, climate data, renewable energy generation, etc.), and gradually adjust the charging and discharging strategies of energy storage equipment, load scheduling plans, renewable energy generation ratio, etc. This process aims to achieve the optimal scheduling of the power grid at different times and under different conditions, so that the power grid has stronger response capabilities and stability.

[0087] In one possible implementation, the optimization algorithm gradually converges to the optimal solution through multiple iterations of the power grid dispatch strategy. In each iteration, the algorithm adjusts the next dispatch plan based on the current dispatch strategy and the response capability evaluation results of the power grid, thereby optimizing the dispatch plan of the power grid.

[0088] As an extension, in practical applications, real-time data and forecast information can be combined to make adaptive adjustments to grid dispatch. For example, as the grid operation status changes, the system can obtain new load forecasts, weather forecasts, and electricity price fluctuations in real time, and adjust the dispatch strategy based on this new information. This adaptive adjustment can further improve the response efficiency and stability of the grid.

[0089] In the process of optimizing decisions, we can also evaluate the advantages and disadvantages of different dispatch strategies by simulating the dispatch effects in different scenarios. This process can provide strong support for the dispatch optimization of the power grid and help power grid operators make more reasonable and efficient decisions.

[0090] Specifically, between dynamic simulation and optimization decision-making, the system can track the changes in various performance indicators during the simulation process, such as response time, maximum deviation rate and recovery time, and evaluate the effectiveness of the current scheduling strategy by comparing it with the optimization target in the objective function. If the current strategy cannot meet the set target, the system will automatically adjust the scheduling strategy through the optimization algorithm until the optimal scheduling solution is found.

[0091] Generally speaking, by optimizing decisions and adjusting grid dispatch strategies, the grid can always maintain a short response time, a small deviation rate, and a short recovery time when facing complex load changes, renewable energy fluctuations, and sudden failures. This process not only improves the stability and efficiency of the grid, but also reduces the operating costs of the grid, ensuring that the grid maintains good performance in long-term operations.

[0092] In summary, the steps of optimizing decision-making and adjusting grid dispatching strategies, by combining dynamic simulation results with optimization algorithms, can accurately adjust the grid dispatching strategies and ensure that the grid maintains the best operating state in a variety of environments and situations. This technical solution provides an effective means for the intelligent dispatching and operation management of the grid, and can cope with the ever-changing grid load and complex environmental factors.

[0093] As part of this application, the present invention also provides a distribution network responsiveness evaluation system, comprising: Multi-dimensional model library module, used to store power grid electrical characteristic models, load fluctuation models, renewable energy generation models and energy storage equipment models; A scenario generation module, for generating a plurality of different test scenarios based on a grid demand response plan, wherein the test scenarios include different load, climate and renewable energy conditions; An association rule mining module, used to analyze the associations between the multiple test scenarios and identify key scenario sequences; A dynamic simulation module, used to simulate the dynamic response of the power grid based on the test scenario and calculate the power grid performance index; A performance evaluation module is used to calculate the performance indicators of the response time, maximum deviation rate and recovery time of the power grid according to the simulation results, and to evaluate the response capability of the power grid; The optimization decision module is used to adjust the dispatching strategy of the power grid based on the evaluation results and optimize the responsiveness and stability of the power grid.

[0094] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A distribution network response capability evaluation method based on dynamic simulation scenarios, characterized in that: The following steps are involved: S1. Build a multi-dimensional model library, including the electrical characteristics model of the power grid, the load fluctuation model, the renewable energy generation model and the energy storage equipment model; S2. Based on the demand response plan, extract variables from the multi-dimensional model library to generate multiple different test scenarios; S3. Use association rule mining algorithms to analyze the correlation between multiple test scenarios, and prioritize simulation of key scenarios that have a greater impact on grid stability based on the correlation analysis results; S4. Perform dynamic simulation based on test scenarios to simulate the response behavior of the power grid in different scenarios and calculate the performance indicators of the power grid; S5. Evaluate the responsiveness of the distribution network based on performance indicators, including response time, maximum deviation rate, and recovery time; S6. Based on the performance evaluation results, an optimization algorithm is used to adjust the grid dispatch strategy to optimize the grid’s responsiveness and stability.

2. The method for evaluating the response capability of a distribution network based on a dynamic simulation scenario according to claim 1 is characterized in that: The multidimensional model library includes the following models: A power grid electrical characteristic model is used to describe the electrical connection relationship between nodes in the power distribution network, and the model is represented by an admittance matrix; A load fluctuation model, which models the load fluctuation through a random process, wherein the model includes the relationship between load power and time; Renewable energy generation model, which represents the power generation of renewable energy. The model calculates the output power of renewable energy based on climate data; The energy storage device model represents the charging and discharging mode of the energy storage device and its impact on the grid response capability, and the model includes the relationship between the energy storage charging and discharging power and the grid voltage.

3. The method for evaluating the response capability of a power distribution network based on a dynamic simulation scenario according to claim 1, characterized in that: The association rule mining algorithm is an Apriori algorithm, which is used to analyze the associations between the multiple test scenarios, including support and confidence.

4. The method for evaluating the response capability of a distribution network based on a dynamic simulation scenario according to claim 1, characterized in that: The dynamic simulation step is based on the electrical characteristic model and load fluctuation model of the power grid, and uses partial differential equations to describe the dynamic changes of the voltage and current of the power grid, specifically: in, is the node voltage, is the inductance of the grid, is the load current, is the current generated by renewable energy, and the partial differential equation describes the variation of the voltage at the grid node with the load and renewable energy fluctuations.

5. The method for evaluating the response capability of a distribution network based on a dynamic simulation scenario according to claim 1, characterized in that: The dynamic simulation step further includes the following process: Prioritize simulation of key scenarios that have a significant impact on grid stability based on correlation analysis results; Through numerical simulation methods, the voltage, current and frequency parameters of the power grid are calculated in real time, and the stability and response time of the power grid are monitored.

6. The method for evaluating the response capability of a distribution network based on a dynamic simulation scenario according to claim 1, characterized in that: The performance indicators include response time, maximum deviation rate and recovery time, which are specifically defined as follows: Response time: the time it takes for the grid to recover from receiving a load or renewable energy change signal; Maximum deviation rate: the maximum deviation value of voltage and current parameters of the power grid during the response process, and the deviation rate relative to the steady-state value; Recovery time: The time required for a power grid to recover from a fault or disturbance to a stable state.

7. The method for evaluating the response capability of a power distribution network based on a dynamic simulation scenario according to claim 1, characterized in that: The response time is the time required for the power grid to stabilize from initial system changes under different load or energy fluctuations, and the response time is calculated through real-time monitoring data.

8. The method for evaluating the response capability of a distribution network based on a dynamic simulation scenario according to claim 1, characterized in that: The recovery time refers to the time required for the power grid to recover to a stable power grid operation state after a disturbance or failure. The recovery time is calculated through real-time simulation.

9. A distribution network response capability evaluation system, characterized in that: The method for evaluating the response capability of a distribution network based on a dynamic simulation scenario as described in any one of claims 1 to 8 comprises: Multi-dimensional model library module, used to store power grid electrical characteristic models, load fluctuation models, renewable energy generation models and energy storage equipment models; A scenario generation module, used to generate a plurality of different test scenarios based on a grid demand response plan, wherein the test scenarios include different load, climate and renewable energy conditions; An association rule mining module, used to analyze the associations between the multiple test scenarios and identify key scenario sequences; A dynamic simulation module, used to simulate the dynamic response of the power grid based on the test scenario and calculate the power grid performance index; A performance evaluation module is used to calculate the performance indicators of the response time, maximum deviation rate and recovery time of the power grid according to the simulation results, and to evaluate the response capability of the power grid; The optimization decision module is used to adjust the dispatching strategy of the power grid based on the evaluation results and optimize the responsiveness and stability of the power grid.

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