Power balance control method and device for source-load matching of active power distribution network

Abstract

The invention provides a power balance control method and device for source-load matching of an active power distribution network, and relates to the technical field of intelligent power grids, and the method comprises the steps: obtaining first output data and output probability of an intermittent distributed power supply through multi-state modeling based on the historical output data of the distributed power supply in the power distribution network, determining second output data of the non-intermittent distributed power supply through the load curve model; for the destination charging demand of the electric vehicle, an ordered charging and V2G bidirectional charging and discharging mode is adopted, and the charging and discharging strategy of the electric vehicle is formulated in combination with the charging demand prediction of the electric vehicle; according to the emergency charging requirement of the electric vehicle, an energy storage system is adopted for auxiliary adjustment, and a charging adjustment strategy is generated; the source load state of the power distribution network is monitored in real time, and a power balance scheduling algorithm is adopted to optimize and adjust the charging and discharging strategy and the charging adjustment strategy. According to the invention, power balance can be regulated when the electric vehicle is connected to the active power distribution network.

Description

Technical Field

[0001] This invention belongs to the technical field of smart grids, specifically relating to a power balance control method and device for source-load matching in active distribution networks. Background Technology

[0002] Distributed power sources connected to the power grid are mainly divided into two categories: intermittent distributed power sources and non-intermittent distributed power sources. Intermittent distributed power sources are characterized by strong fluctuations and randomness, with wind power and photovoltaic power generation being the most representative examples. Due to the uncontrollable nature of wind speed, wind power generation varies significantly over time; due to the Earth's rotation and revolution, photovoltaic power generation exhibits relatively obvious daily and seasonal characteristics. Non-intermittent distributed power sources generally have lower fluctuations and uncertainties in output, and are considered controllable power sources. Common examples include small hydropower, small thermal power, and cooling / heating units. Among these, small hydropower typically only needs to consider two states: output during the wet season and output during the dry season; small thermal power has characteristics similar to conventional thermal power units and is usually considered analogously to conventional units; the power fluctuation components of cooling / heating units are generally small in the short term and can be treated using a typical daily power curve similar to that of a similar load.

[0003] Based on the location and urgency of the charging need, electric vehicle (EV) charging needs can generally be divided into two categories: destination charging needs and emergency charging needs. Destination charging needs typically occur at the EV's destination, such as parking lots in residential areas, workplaces, or shopping malls. Since EVs are usually parked for a relatively long time at their destination, there is ample time to charge, and slow charging is generally used. Destination charging needs can be met by private charging stations or public charging stations in public parking areas. Emergency charging needs generally occur during the EV's driving process. When the remaining battery power drops to a certain safety threshold, or when the battery power is insufficient for the remaining driving needs, the EV needs to be temporarily charged nearby. This type of need requires meeting the charging requirement as quickly as possible, and is generally met by fast charging stations or battery swapping stations. DC fast charging or even battery replacement may be used. Emergency charging needs are generally met by centralized fast charging stations or battery swapping stations.

[0004] With the large-scale integration of electric vehicles (EVs) into the grid for charging, the power system faces significant challenges and transformations, particularly in terms of load fluctuations, power balance, and grid stability. Due to the fluctuating and random nature of intermittent distributed power generation output, a simultaneous surge in EV charging demand can place a significant burden on the grid, especially during peak hours, posing greater challenges to grid dispatching and stability. Therefore, a method is needed to regulate the power balance when EVs are integrated into the active distribution network. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned problems in the prior art by providing a power balance control method and apparatus for source-load matching in an active power distribution network, capable of regulating the power balance when an electric vehicle is connected to an active power distribution network.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] In a first aspect, a power balance control method for source-load matching in an active distribution network is provided, the method comprising:

[0008] The method includes:

[0009] Obtain historical output data of distributed generation in the power distribution network; the distributed generation includes intermittent distributed generation and non-intermittent distributed generation;

[0010] Based on the historical output data of the intermittent distributed power source, the first output data and output probability of the intermittent distributed power source under different weather conditions are obtained through multi-state statistical analysis.

[0011] Based on the historical output data of the non-intermittent distributed power source, the second output data of the non-intermittent distributed power source is determined through a load curve model;

[0012] For the destination charging demand of electric vehicles, an orderly charging and V2G bidirectional charging and discharging mode is adopted. Combined with the charging demand forecast of electric vehicles, a charging and discharging strategy for electric vehicles is formulated. The charging and discharging strategy includes optimizing the electricity pricing mechanism to control the orderly charging of electric vehicles when the grid load is lower than the preset load, and using V2G technology, combined with the charging and discharging status of electric vehicles, to release electricity as a distributed energy storage unit when the grid load is higher than the preset load, so as to balance load fluctuations.

[0013] For the emergency charging needs of electric vehicles, in response to load fluctuations at fast charging stations and battery swapping stations, an energy storage system connected to the power distribution network is used for auxiliary regulation to generate a charging regulation strategy. The charging regulation strategy includes scheduling power flow according to the power grid load status to reduce the impact of sudden loads on the power distribution network.

[0014] By combining the first output data, the output probability, and the second output data, the source and load status of the distribution network is monitored in real time, and the charging and discharging strategy and the charging regulation strategy are optimized and adjusted using a power balance scheduling algorithm.

[0015] Based on the above technical solutions, preferably, the step of performing multi-state statistical analysis on the intermittent distributed power source based on its historical output data to obtain the first output data and output probability of the intermittent distributed power source under different weather conditions includes:

[0016] Identify the meteorological conditions that affect the output of intermittent distributed power sources and obtain historical output data of intermittent distributed power sources under different meteorological conditions;

[0017] Based on the historical output data of intermittent distributed power sources under each meteorological condition, a probability distribution model is used to calculate the probability distribution of different output data of intermittent distributed power sources under each meteorological condition.

[0018] Based on the probability distribution of different power output data of intermittent distributed power sources under each meteorological condition, the first power output data and output probability of intermittent distributed power sources in different time periods in the future are predicted by time series model.

[0019] Based on the above technical solutions, preferably, the destination charging demand of electric vehicles adopts an ordered charging and V2G bidirectional charging and discharging mode, combined with the charging demand prediction of electric vehicles, to formulate a charging and discharging strategy for electric vehicles, specifically including:

[0020] Acquire historical charging data for electric vehicles and establish a destination charging demand prediction model for electric vehicles.

[0021] Based on the load conditions of the distribution network, a dynamic pricing model is used to set electricity price levels for different time periods;

[0022] Based on the charging demand forecast model, the predicted charging demand of electric vehicles is used to determine the grid load status and electricity pricing mechanism for the corresponding time period.

[0023] The charging time periods for electric vehicles are adjusted based on the predicted charging demand, the grid load status for the corresponding time period, and the electricity pricing mechanism.

[0024] Get the grid load after adjusting the charging period of electric vehicles, determine whether the grid load is higher than the preset load, and if so, get the real-time power of electric vehicles.

[0025] Based on the real-time battery level of the electric vehicle, determine whether V2G discharge mode needs to be activated, and instruct the electric vehicle to release power to the power distribution network.

[0026] Based on the above technical solutions, preferably, the step of determining whether to activate the V2G discharge mode based on the real-time battery level of the electric vehicle includes:

[0027] Calculate the average real-time battery level of all electric vehicles and use it as the preset battery level;

[0028] Determine a first real-time energy level that is greater than a preset energy level among multiple real-time energy levels, and a second real-time energy level that is less than or equal to the preset energy level among multiple real-time energy levels;

[0029] Calculate the difference between each of the first real-time power consumption and the preset power consumption, and sum the differences to obtain the first difference power consumption; and calculate the difference between each of the second real-time power consumption and the preset power consumption, and sum the differences to obtain the second difference power consumption.

[0030] If it is determined that the first difference in electricity is greater than or equal to the second difference in electricity, then it is determined that the V2G discharge mode needs to be activated and the feedback electricity released by the electric vehicle to the power distribution network needs to be calculated.

[0031] Based on the above technical solutions, preferably, the step of determining the electric vehicle's feedback power based on real-time power consumption further includes:

[0032] The system identifies a first electric vehicle among multiple electric vehicles that is fully charged, and uses the sum of the charges of the multiple first electric vehicles as a first charge. It also identifies a second electric vehicle among multiple electric vehicles whose real-time charge is less than or equal to a preset charge, and uses the sum of the charges of the multiple second electric vehicles as a second charge.

[0033] The average discharge efficiency of multiple first electric vehicles is obtained by averaging the discharge efficiency of multiple first electric vehicles, the average charging efficiency of multiple first electric vehicles is obtained by averaging the charging efficiency of multiple second electric vehicles, and the average charging efficiency of multiple second electric vehicles is obtained by averaging the charging efficiency of multiple second electric vehicles.

[0034] Calculate the feedback power using the following formula:

[0035] ;

[0036] In the above formula, Indicates the amount of electricity returned; This represents the average discharge efficiency. Preset battery level; This is the first charge level; This is the second charge; This represents the average of the first charging efficiency. This represents the average of the second charging efficiency.

[0037] Based on the above technical solutions, preferably, the method of integrating the first output data, output probability, and second output data to monitor the source-load status of the distribution network in real time, and using a power balance scheduling algorithm to optimize and adjust the charging and discharging strategy and the charging regulation strategy, specifically includes:

[0038] Based on the first power output data, the power output probability, and the second power output data, the predicted power generation within a preset time period is calculated by weighted average.

[0039] Historical electricity consumption data is analyzed to determine the predicted electricity consumption of the power grid load within a preset time period, where the power grid load does not include the destination charging demand of electric vehicles and the emergency charging demand.

[0040] Based on the predicted power generation and power consumption, determine the source-load difference of the distribution network;

[0041] Based on the source-load difference, the charging and discharging of electric vehicles are optimized, and the optimization target is determined.

[0042] The charging and discharging strategy and the charging regulation strategy are optimized and adjusted according to the optimization objectives. The reverse discharge regulation of the electric vehicle is carried out through V2G technology, the charging period of the electric vehicle is adjusted, and the charging and discharging is combined with the energy storage system.

[0043] Based on the above technical solutions, preferably, the step of determining the second output data of the non-intermittent distributed power source using a load curve model based on the historical output data of the non-intermittent distributed power source specifically includes:

[0044] Obtain historical output data of the non-intermittent distributed power source under different operating conditions;

[0045] By statistically analyzing the historical output data under different operating conditions, the typical load fluctuation pattern of the non-intermittent distributed power source is determined.

[0046] Based on the typical load fluctuation pattern, a typical load curve for the non-intermittent distributed power source is established, which includes the fluctuation characteristics of daily, seasonal, and annual variations.

[0047] A mathematical model is constructed to fit the typical load curve to obtain a dynamic function describing the change of power output over time.

[0048] Based on the established dynamic function, the second output data of the non-intermittent distributed power source is predicted for different time periods in the future.

[0049] In a second aspect, the present invention provides a power balance control device for source-load matching in an active power distribution network, the device comprising an acquisition module, a processing module, and an output module, wherein:

[0050] The acquisition module is used to acquire historical output data of distributed power sources in the distribution network; the distributed power sources include intermittent distributed power sources and non-intermittent distributed power sources.

[0051] The processing module is used to obtain the first output data and output probability of the intermittent distributed power source under different weather conditions by multi-state statistical analysis based on the historical output data of the intermittent distributed power source.

[0052] The processing module is further configured to determine the second output data of the non-intermittent distributed power source based on the historical output data of the non-intermittent distributed power source through a load curve model.

[0053] The processing module is also used to formulate a charging and discharging strategy for electric vehicles based on the destination charging demand of electric vehicles, using orderly charging and V2G bidirectional charging and discharging modes, combined with the charging demand prediction of electric vehicles. The charging and discharging strategy includes optimizing the electricity pricing mechanism to control the orderly charging of electric vehicles during periods when the grid load is lower than the preset load, and using V2G technology, combined with the charging and discharging status of electric vehicles, to release electricity as a distributed energy storage unit when the grid load is higher than the preset load, so as to balance load fluctuations.

[0054] The processing module is also used to address the emergency charging needs of electric vehicles and to assist in the adjustment of load fluctuations at fast charging stations and battery swapping stations by using an energy storage system connected to the power distribution network. The charging adjustment strategy includes scheduling power flow according to the power grid load status to reduce the impact of sudden loads on the power distribution network.

[0055] The output module is used to integrate the first output data, the output probability, and the second output data to monitor the source and load status of the distribution network in real time, and to optimize and adjust the charging and discharging strategy and the charging regulation strategy using a power balance scheduling algorithm.

[0056] Based on the above technical solutions, preferably, the acquisition module is also used to identify meteorological conditions that affect the output of intermittent distributed power sources and acquire historical output data of intermittent distributed power sources under different meteorological conditions;

[0057] The processing module is also used to calculate the probability distribution of different output data of the intermittent distributed power source under each meteorological condition based on the historical output data of the intermittent distributed power source under each meteorological condition using a probability distribution model.

[0058] The processing module is also used to predict the first output data and output probability of the intermittent distributed power source in different time periods in the future, based on the probability distribution of different output data of the intermittent distributed power source under each meteorological condition and through a time series model.

[0059] Based on the above technical solutions, preferably, the acquisition module is also used to acquire historical charging data of electric vehicles and establish a destination charging demand prediction model for electric vehicles.

[0060] The processing module is also used to set electricity price levels for different time periods based on the load conditions of the distribution network using a dynamic pricing model;

[0061] The processing module is also used to determine the grid load status and electricity pricing mechanism for the corresponding time period based on the charging demand forecasting model for electric vehicles.

[0062] The processing module is also used to adjust the charging time of electric vehicles based on the predicted charging demand and the grid load status and electricity pricing mechanism for the corresponding time period.

[0063] The processing module is also used to obtain the grid load after adjusting the charging period of the electric vehicle, determine whether the grid load is higher than the preset load, and if so, obtain the real-time power of the electric vehicle.

[0064] The processing module is also used to determine whether V2G discharge mode needs to be activated based on the real-time battery level of the electric vehicle, and to instruct the electric vehicle to release power to the power distribution network.

[0065] Based on the above technical solutions, preferably, the processing module is also used to calculate the average real-time battery level of all electric vehicles and use it as the preset battery level;

[0066] The processing module is also used to determine a first real-time energy level that is greater than a preset energy level among a plurality of real-time energy levels, and a second real-time energy level that is less than or equal to a preset energy level among a plurality of real-time energy levels.

[0067] The processing module is also used to calculate the difference between each of the first real-time power consumption and the preset power consumption and sum the differences to obtain the first difference power consumption, and to calculate the difference between each of the second real-time power consumption and the preset power consumption and sum the differences to obtain the second difference power consumption.

[0068] The processing module is also used to determine that if the first difference in power is greater than or equal to the second difference in power, it needs to start the V2G discharge mode and calculate the feedback power released by the electric vehicle to the power distribution network.

[0069] Based on the above technical solutions, preferably, the processing module is further configured to determine the first electric vehicle in a fully charged state among a plurality of electric vehicles, and take the sum of the power of the plurality of first electric vehicles as the first power, and determine the second electric vehicle in a plurality of electric vehicles whose real-time power is less than or equal to the preset power, and take the sum of the power of the plurality of second electric vehicles as the second power.

[0070] The acquisition module is further configured to acquire the average of the discharge efficiencies of multiple first electric vehicles to obtain the average discharge efficiency, acquire the average of the charging efficiencies of multiple first electric vehicles to obtain the average first charging efficiency, and acquire the average of the charging efficiencies of multiple second electric vehicles to obtain the average second charging efficiency.

[0071] The processing module is also used to calculate the feedback power according to the following formula:

[0072] ;

[0073] In the above formula, Indicates the amount of electricity returned; This represents the average discharge efficiency. Preset battery level; This is the first charge level; This is the second charge; This represents the average of the first charging efficiency. This represents the average of the second charging efficiency.

[0074] Based on the above technical solutions, preferably, the processing module is further used to calculate the predicted power generation within a preset time period by weighted average based on the first power output data, the power output probability and the second power output data.

[0075] The processing module is also used to analyze historical electricity consumption data to determine the predicted electricity consumption of the power grid load within a preset time period, wherein the power grid load does not include the destination charging demand of electric vehicles and the emergency charging demand.

[0076] The processing module is also used to determine the source-load difference of the distribution network based on the power generation forecast and the power consumption forecast.

[0077] The processing module is also used to optimize the charging and discharging of the electric vehicle based on the source-load difference and determine the optimization target;

[0078] The output module is also used to optimize and adjust the charging and discharging strategy and the charging regulation strategy according to the optimization target, regulate the reverse discharge of the electric vehicle through V2G technology, adjust the charging period of the electric vehicle, and combine the energy storage system for charging and discharging.

[0079] Based on the above technical solutions, preferably, the acquisition module is further used to acquire historical output data of the non-intermittent distributed power source under different operating conditions;

[0080] The processing module is also used to determine the typical load fluctuation pattern of the non-intermittent distributed power source by performing statistical analysis on the historical output data under different operating conditions.

[0081] The processing module is also used to establish a typical load curve of the non-intermittent distributed power source based on the typical load fluctuation pattern. The typical load curve includes the fluctuation characteristics of daily variation, seasonal variation and annual variation.

[0082] The processing module is also used to construct a mathematical model to fit the typical load curve and obtain a dynamic function describing the change of power output over time.

[0083] The processing module is also used to predict the second output data of the non-intermittent distributed power source in different time periods in the future, based on the established dynamic function.

[0084] In a third aspect, the present invention provides an electronic device including a processor, a communication bus, a user interface, a network interface, and a memory. The memory is used to store instructions. The user interface and the network interface are both used to communicate with other devices. The communication bus is used to realize the connection and communication between various components within the electronic device. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the aforementioned method.

[0085] In a fourth aspect, the present invention provides a computer-readable storage medium storing instructions that, when executed, perform the aforementioned method.

[0086] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0087] 1. The method described in this invention models and analyzes historical output data of distributed power sources, combines demand forecasting for electric vehicle charging and discharging with grid load status, and comprehensively considers the orderly charging and V2G bidirectional charging and discharging functions of electric vehicles. It flexibly adjusts the charging period and discharging strategy of electric vehicles, effectively regulating the power balance when electric vehicles are connected to the active distribution network. During high-load periods, the energy storage function of electric vehicles is used for reverse discharge to alleviate load fluctuations. Under emergency charging demand, the power flow is assisted by the distribution network energy storage system to reduce the impact of sudden loads on the grid. Compared with last year, through real-time optimization of the power balance scheduling algorithm, the power source and load are fully coordinated to ensure the stable operation and load balance of the grid under the large-scale connection of electric vehicles.

[0088] 2. The method described in this invention combines historical meteorological data with the output data of distributed power sources, which can accurately identify key meteorological factors affecting the output of intermittent distributed power sources. Through multi-state modeling and probability distribution analysis, it can effectively quantify the output volatility and uncertainty of power sources under different weather conditions, thereby enabling real-time dynamic updates of the probability distribution of power source output status. Furthermore, based on time series models, it provides accurate power generation forecast data and volatility probabilities for grid dispatch, thereby improving the power system's ability to cope with intermittent power source output fluctuations.

[0089] 3. The method described in this invention combines electric vehicle charging demand forecasting, dynamic electricity pricing mechanisms, and V2G bidirectional charging and discharging modes to intelligently adjust the charging time of electric vehicles. This allows for orderly charging when the grid load is low and the release of electricity through feedback when the grid load is high, thus balancing grid load fluctuations. By optimizing the charging and discharging strategy, electric vehicles can not only meet users' charging needs but also serve as distributed energy storage units to provide flexible load regulation support for the grid, improving grid stability and efficiency, reducing the burden on the power system, and enhancing the overall energy utilization efficiency of electric vehicles.

[0090] 4. The method described in this invention comprehensively considers the output data and output probability of distributed power sources and grid load forecasts, monitors the source-load status of the distribution network in real time, and optimizes the charging and discharging strategies and charging regulation strategies of electric vehicles through power balance scheduling algorithms. When optimizing the charging and discharging strategies, it calculates the source-load difference by accurately predicting power generation and consumption, thereby optimizing the charging and discharging timing of electric vehicles, reducing grid load fluctuations, and improving the flexibility and stability of grid scheduling. Through the cooperation of V2G technology and energy storage systems, it effectively balances grid load, reduces grid pressure during peak hours, and achieves energy optimization during the electric vehicle charging process, enhancing the operating efficiency and sustainability of the power system. Attached Figure Description

[0091] Figure 1 This is a schematic flowchart of the power balance control method for source-load matching in an active distribution network disclosed in an embodiment of the present invention.

[0092] Figure 2 This is a schematic diagram of the power balance control device for source-load matching in an active power distribution network disclosed in an embodiment of the present invention.

[0093] Figure 3 This is a schematic diagram of the structure of the electronic device disclosed in an embodiment of the present invention.

[0094] In the diagram above, 201 is the acquisition module; 202 is the processing module; 203 is the output module; 301 is the processor; 302 is the communication bus; 303 is the user interface; 304 is the network interface; and 305 is the memory. Detailed Implementation

[0095] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0096] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0097] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0098] Distributed power sources connected to the distribution network can be divided into two categories: intermittent and non-intermittent. Intermittent sources, such as wind and solar power, exhibit strong volatility and randomness, while non-intermittent sources, such as small hydropower, small thermal power, and cooling / heating units, are less volatile and controllable. Meanwhile, the charging demand of electric vehicles can be divided into destination charging demand and emergency charging demand. The former is typically slow charging, while the latter is met through fast charging stations or battery swapping stations. With the large-scale integration of electric vehicles, the power system faces challenges in load fluctuation and power balance, especially during peak hours, testing the grid's dispatching and stability. Therefore, effective power balance control methods are needed to address the pressure from distributed power sources and the charging demand of electric vehicles.

[0099] This embodiment discloses a power balance control method for source-load matching in an active distribution network, referring to... Figure 1 This includes the following steps S110-S160:

[0100] S110, Obtain historical output data of distributed power sources in the distribution network; the distributed power sources include intermittent distributed power sources and non-intermittent distributed power sources.

[0101] Historical output data of distributed generation sources in a distribution network is typically obtained through intelligent measurement systems and data acquisition equipment. These devices are installed at the power source end and key nodes of the distribution network, enabling real-time monitoring and recording of power source output. First, all distributed generation sources within the distribution network need to be categorized to determine which are intermittent sources (e.g., wind power, solar power) and non-intermittent sources (e.g., small hydropower, small thermal power). Then, intelligent metering devices in the distribution network, such as smart meters and data acquisition terminals, are used to periodically or in real-time collect output data from each power source, including parameters such as voltage, current, and power. This data is transmitted to a data management platform via a communication network. During data acquisition, the equipment should be calibrated and maintained to ensure data accuracy and integrity. Finally, the collected historical output data is stored and backed up, and analyzed and processed at a specific time frequency to provide fundamental data support for subsequent power balance scheduling and load forecasting.

[0102] S120: Based on the historical output data of intermittent distributed power sources, multi-state modeling of intermittent distributed power sources is performed, and the first output data and output probability of intermittent distributed power sources under different weather conditions are obtained through statistical analysis.

[0103] In one possible implementation, based on historical output data of intermittent distributed power sources, multi-state modeling of the intermittent distributed power sources is performed. Statistical analysis is used to obtain the first output data and output probability of the intermittent distributed power sources under different weather conditions. Specifically, this includes: collecting and organizing historical output data of intermittent distributed power sources under different meteorological conditions; using historical meteorological data as a basis to identify key factors affecting the power output of intermittent distributed power sources, thereby classifying the historical output data according to meteorological conditions; performing regression analysis on the classified historical output data to find the output characteristics of intermittent distributed power sources under different meteorological conditions; for the historical output data under each meteorological condition, using a probability distribution model to model the output fluctuations of the intermittent distributed power sources, calculating the probability of different output data under each meteorological condition; combining the classification and probability distribution of historical output data, multi-state modeling of the output of intermittent distributed power sources under each meteorological condition is performed, forming multiple discrete states of power output, and dynamically updating the probabilities of multiple discrete states over time. By establishing a time series model, the first output data and output probability of the intermittent distributed power sources in different time periods are predicted.

[0104] Specifically, the collection and organization of historical output data of the intermittent distributed power sources under different meteorological conditions includes: by setting up meteorological stations and combining them with monitoring equipment of various distributed power sources in the power distribution network, collecting meteorological data such as wind speed, light intensity, temperature, humidity, etc., as well as historical output data of the power sources within the corresponding time period, forming a time series dataset containing meteorological conditions and power output, ensuring that the data is representative and covers changes under different meteorological scenarios.

[0105] The process involves using historical meteorological data as a basis to identify key factors affecting the power output of the intermittent distributed power source, and then classifying the historical output data according to meteorological conditions. This includes: analyzing historical meteorological data to identify meteorological factors that have a significant impact on power output, such as the impact of wind speed on wind power and the impact of solar intensity on photovoltaic power; and then classifying the historical output data according to meteorological conditions such as sunny days, cloudy days, strong winds, and weak winds, with each category representing the power output characteristics under specific meteorological conditions.

[0106] The process involves performing regression analysis on the classified historical output data to identify the output characteristics of the intermittent distributed power source under different meteorological conditions. This includes: for the historical output data under each type of meteorological condition, applying regression analysis methods such as linear regression, nonlinear regression, or multinomial regression to identify the relationship between meteorological factors and power output, and then extracting the typical output characteristics of the power source under different meteorological conditions, including the fluctuation range, fluctuation frequency, and trend of the output.

[0107] For each weather condition's historical power output data, a probability distribution model is used to model the output fluctuations of the intermittent distributed power source. This involves calculating the probability of different power output data under each weather condition, including: using probabilistic statistical methods such as normal distribution, Poisson distribution, and gamma distribution to model the power output fluctuations obtained from regression analysis; calculating the probability and fluctuation amplitude of power output under different weather conditions; and obtaining the probability value corresponding to each historical power output data. These probability values ​​reflect the probability distribution of the power source outputting different power levels under given weather conditions.

[0108] The process combines the classification and probability distribution of historical power output data to perform multi-state modeling of power output under each meteorological condition, forming multiple discrete states of power output. The probabilities of these discrete states are dynamically updated over time. By establishing a time series model, the initial power output data and output probability of the intermittent distributed power source are predicted in different time periods. This includes: based on the aforementioned classification and probability analysis results, using a multi-state modeling method to discretize the power source output into multiple states such as high, medium, and low output states; and using time series analysis methods such as ARIMA and LSTM to dynamically predict the power source output state and its probability in different time periods, enabling real-time updates of the probability values ​​for each state and providing a basis for grid dispatch based on the prediction results. This process ensures that the power output characteristics of the power source in different time periods are reflected in power dispatch and load balancing strategies.

[0109] S130, based on the historical output data of the non-intermittent distributed power source, the second output data of the non-intermittent distributed power source is determined through the load curve model.

[0110] In one possible implementation, determining the second output data of the non-intermittent distributed power source based on its historical output data using a load curve model includes: acquiring historical output data of the non-intermittent distributed power source under different operating conditions; determining typical load fluctuation patterns of the historical output data through statistical analysis; establishing a typical load curve for the non-intermittent distributed power source based on the typical load fluctuation patterns, the typical load curve including daily, seasonal, and annual variation fluctuation characteristics; constructing a mathematical model to fit the typical load curve to obtain a dynamic function describing the power output change over time; and predicting future output status based on the established dynamic function to obtain the second output data.

[0111] Specifically, the acquisition of historical output data of non-intermittent distributed power sources under different operating conditions includes: collecting historical output data of non-intermittent distributed power sources under different operating modes and environmental conditions through the distribution network monitoring system. This data includes the power output, load changes, operating status, equipment health status, and external environmental information such as temperature and humidity, ensuring that the collected data has sufficient representativeness and breadth.

[0112] The process of statistically analyzing historical power output data to determine typical load fluctuation patterns includes: statistically analyzing collected historical power output data to identify common power load fluctuation patterns, analyzing periodic, trend, and sudden fluctuations, and determining typical load change patterns of the power supply under different operating conditions, especially the impact of diurnal variations, seasonal fluctuations, annual variations, and special events such as equipment maintenance and extreme weather on power output.

[0113] Based on typical load fluctuation patterns, a typical load curve for non-intermittent distributed power sources is established. The typical load curve includes its daily variation, seasonal variation, and fluctuation characteristics under special events. This includes: converting the load fluctuation patterns obtained from statistical analysis into specific load curves, plotting the output change trends of the power source in different time periods such as day and night, incorporating seasonal fluctuations such as the load difference between summer and winter, and also considering the impact of special events to form a complete load curve diagram that reflects the typical fluctuation characteristics of the power source.

[0114] The construction of a mathematical model to fit a typical load curve and obtain a dynamic function describing the change of power output over time includes: selecting an appropriate mathematical model, such as a sine function, linear regression, or polynomial regression, to fit the law of power output change over time based on the obtained typical load curve; and establishing a dynamic mathematical function by fitting historical data to accurately describe the relationship between the change of power output and time. The expression of the dynamic function is as follows:

[0115] ;

[0116] In the above formula, express The power output of a non-intermittent distributed power source at any given time, expressed in power. The term describes the diurnal variation characteristics of non-intermittent distributed power sources, in which The first amplitude represents the maximum intraday fluctuation range of the power supply. It has a daily cycle, typically 24 hours; This is the first phase, indicating the start time of intraday power output fluctuations; The term describes the seasonal variation characteristics of non-intermittent distributed power sources, in which The second amplitude represents the maximum fluctuation range of seasonal variation; It has an annual cycle; The phase of seasonal change indicates the start time of seasonal fluctuations; The term describes the sudden fluctuations that occur in non-intermittent distributed power sources during specific periods, such as during faults or maintenance. The third amplitude indicates the magnitude of sudden fluctuations; The point in time when the emergency occurred; The time span of sudden fluctuations reflects the duration of the event. It is a random disturbance term used to simulate random fluctuations caused by external factors such as weather changes, equipment failures, and load fluctuations. It is usually represented by a white noise process. ,in Indicates the noise variance;

[0117] The aforementioned dynamic function integrates periodic fluctuations, seasonal variations, sudden fluctuations, and random disturbances. By adjusting the parameters, it can flexibly fit the output characteristics of different types of non-intermittent distributed power sources, adapting to various practical application scenarios.

[0118] The process of predicting future power output based on the established dynamic function to obtain second power output data includes: predicting future power output using the established dynamic function; and further refining the prediction using time series analysis methods such as ARIMA and LSTM, considering potential future changes such as load increases and changes in equipment operating status, to obtain the expected power output data for future time periods, i.e., the second power output data. This data provides a foundation for power system dispatching, ensuring that load allocation and grid optimization can be rationally performed based on the predicted power output.

[0119] S140, for the destination charging demand of electric vehicles, an orderly charging and V2G bidirectional charging and discharging mode is adopted. Combined with the charging demand prediction of electric vehicles, a charging and discharging strategy for electric vehicles is formulated. The charging and discharging strategy includes optimizing the electricity pricing mechanism to control the orderly charging of electric vehicles when the grid load is lower than the preset load, and using V2G technology, combined with the charging and discharging status of electric vehicles, to release electricity as a distributed energy storage unit when the grid load is higher than the preset load, so as to balance load fluctuations.

[0120] For destination charging loads, the control and utilization methods mainly include two types: orderly charging and V2G bidirectional charging and discharging modes. By controlling and utilizing the charging and discharging of electric vehicles, the peak load increase of the power grid caused by electric vehicle charging and discharging can be effectively reduced. Orderly charging involves effectively guiding and controlling electric vehicle charging, such as through electricity pricing mechanisms. This means that, while meeting the needs of electric vehicle users, effective technical and economic means are used to guide electric vehicles to avoid peak grid load periods for charging. The V2G bidirectional charging and discharging mode refers to electric vehicles acting as distributed mobile energy storage units, participating in grid regulation through charging and discharging, realizing bidirectional energy transfer between the grid and electric vehicles. This may transform the negative impact of electric vehicles on the grid into a positive one, similar to battery swapping. For emergency charging loads, the regulation effect on charging loads generated at fast charging stations is small; direct and effective energy storage systems can be used to achieve peak load reduction for the overall power grid.

[0121] In one possible implementation, the destination charging demand of electric vehicles (EVs) is addressed using a combination of ordered charging and V2G bidirectional charging / discharging modes, along with EV charging demand forecasting, to formulate an EV charging / discharging strategy. This includes: establishing an EV destination charging demand forecasting model by collecting and analyzing historical charging data for EVs; setting electricity price levels for different time periods using a dynamic pricing model based on the distribution network load; determining the grid load status and pricing mechanism for the corresponding time period based on the EV charging demand forecasting model; automatically adjusting the EV charging time period according to the charging demand forecast, the corresponding grid load status, and the pricing mechanism; acquiring the grid load after automatically adjusting the EV charging time period, determining whether the grid load is higher than a preset load, and if so, acquiring the real-time power consumption of each EV; determining whether to activate the V2G discharging mode based on the EV's real-time power consumption; and instructing multiple EVs to form a distributed energy storage unit to release power to the distribution network when activating the V2G discharging mode.

[0122] Specifically, the step of collecting and analyzing historical charging data for electric vehicles to establish a destination charging demand prediction model for electric vehicles includes: firstly, collecting data from charging piles, smart meters, and the electric vehicles themselves. Data from charging piles and smart meters includes charging duration, charging power, charging time points, and charging frequency, while data from the electric vehicles themselves includes driving data and remaining battery power. Then, by combining machine learning, regression analysis, and other methods, a destination charging demand prediction model for electric vehicles is established to predict the destination charging demand for electric vehicles in different time periods in the future, including factors such as charging amount and charging duration.

[0123] Furthermore, constructing a predictive model for electric vehicle destination charging demand requires, based on a thorough analysis of historical charging behavior patterns, a comprehensive consideration of the temporal dynamics and spatial differences of multi-source data. The specific implementation includes the following key steps:

[0124] First, multi-source data collection and preprocessing are required. This involves collecting detailed historical charging data from charging piles, vehicle terminals, smart meters, and management platforms. This data should include charging start and end times, duration, charging amount, peak and off-peak electricity pricing periods, vehicle arrival and departure times, remaining battery capacity, daily mileage, driving trajectory, and destination type (e.g., residential area, office area, commercial area). The preprocessing stage requires standardizing data formats, cleaning outliers, filling in missing data, and performing time synchronization to ensure data integrity and consistency.

[0125] Secondly, feature engineering is performed to construct charging behavior feature vectors, including time features (daytime, day of the week, holiday identifiers), spatial features (destination geographic location, type label), user behavior features (charging frequency, habitual time window), and vehicle status features (battery capacity, SOC level), etc.; feature selection is performed using methods such as principal component analysis (PCA) or Lasso regression to eliminate redundant variables and extract the feature set that is most explanatory to changes in charging demand.

[0126] Next, select an appropriate modeling method to characterize nonlinear charging behavior. Traditional statistical models such as multiple linear regression and ARIMA can be used, or machine learning algorithms such as support vector regression (SVR), random forest regression (RFR), or gradient boosting tree (GBRT) can be introduced to further consider the dynamics, periodicity, and heterogeneity of user charging behavior. For large-scale high-frequency datasets, time-series deep learning models based on long short-term memory networks (LSTM) or Transformer structures can be used to capture the long-term dependent evolution characteristics of charging demand.

[0127] Then, the model's fitting ability is evaluated through model training and validation. The historical dataset is divided into training and validation sets, and cross-validation is used to evaluate the model's prediction accuracy in different time intervals. Commonly used indicators include mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R²). The model can set time-segmented prediction windows and output the predicted total charging power of the electric vehicle group and its confidence interval for each time period.

[0128] Finally, the integrated prediction results and grid operating parameters are used for subsequent scheduling decisions. The model output will serve as input variables in the orderly charging strategy for electric vehicles and the V2G scheduling algorithm, supporting the grid's flexible control over electric vehicle loads and real-time source-load balance. Simultaneously, a feedback mechanism can be incorporated to dynamically correct prediction errors, enhancing the model's adaptability and robustness in actual operation.

[0129] The aforementioned prediction model can not only reflect the load demand of individual electric vehicles and groups under different spatial scenarios and temporal conditions, but also provide a priori information basis for refined load scheduling and energy storage synergistic optimization in active power distribution networks.

[0130] Based on the load conditions of the distribution network, a dynamic pricing model is used to set electricity price levels for different time periods. Load data from the power grid is analyzed to establish a load forecasting model for real-time monitoring and prediction of load fluctuations. Combining the power grid load conditions, the dynamic pricing model sets corresponding electricity price levels based on peak and off-peak load conditions, increasing prices during high-load periods and decreasing them during low-load periods to guide electric vehicle charging behavior and optimize power grid load.

[0131] Based on the predicted charging demand of electric vehicles, the required charging power and charging time within a specific time period are obtained. The grid load status and electricity pricing mechanism corresponding to the predicted charging demand are determined. The predicted charging demand is compared with the distribution network load, and the grid load status and the established electricity pricing mechanism within this time period are analyzed to formulate the optimal charging strategy to balance the grid load and the charging demand of electric vehicles.

[0132] Based on charging demand forecasts, grid load, and electricity pricing mechanisms, intelligent algorithms such as linear programming and optimized scheduling are used to automatically adjust the charging times for electric vehicles. For example, charging can be initiated during periods of low grid load or lower electricity prices, avoiding charging during peak grid load times, reducing pressure on the grid, and saving users charging costs. Specifically, this includes:

[0133] Specifically, a mathematical model of the charging scheduling problem is first constructed. The optimization variable is set as the charging power of the electric vehicle. Or charging status ,in Indicates the first a car, Indicates a time period; parameters include the predicted total grid load. Predicted charging demand for each vehicle Available charging time slots And the corresponding electricity prices for each time period. .

[0134] Secondly, define the objective function. The objective can be set as minimizing the weighted comprehensive cost function, such as:

[0135] ;

[0136] In the above formula, the first term represents the total electricity cost, the second term represents the system load fluctuation penalty term, and the weighting coefficient is... Controlling the balance between the two reflects the optimization tendency for load shaving.

[0137] Next, constraints are constructed to satisfy various operational limitations:

[0138] The total charging capacity for each vehicle must meet its projected demand:

[0139] ;

[0140] Each vehicle can only be charged during the available hours of the charging station it is connected to; during non-available hours, its charging power is zero. The charging power of each vehicle must not exceed its maximum charging capacity. :

[0141] ;

[0142] The total grid load should not exceed the distribution system limit. :

[0143] ;

[0144] After the model is built, a suitable solution method is selected. For linear constraints and convex objective functions, linear programming (LP) or quadratic programming (QP) algorithms can be used for fast solutions. For cases with integer decision variables (such as some scenarios that require discrete charging time period decisions), mixed integer linear programming (MILP) algorithms can be used. If the system is large in scale and has strong non-convexity, heuristic or metaheuristic algorithms (such as genetic algorithms, particle swarm optimization, simulated annealing, etc.) or reinforcement learning strategies can be further adopted.

[0145] In practical deployment, the scheduling system runs a scheduling program according to the prediction cycle (such as every hour or every 15 minutes), and sends the optimal scheduling results to the vehicle management system or smart charging pile in real time to realize the dynamic allocation of charging time slots. The system should also integrate a feedback mechanism to respond to prediction errors and emergencies (such as temporary travel or price changes), update model parameters and optimization results in real time, and maintain the robustness and flexibility of the charging strategy.

[0146] The above scheduling algorithm can ensure that the charging demand of electric vehicles is met, while achieving the economy of charging periods, the stability of system load, and the efficient use of grid resources, thereby supporting the operation safety and energy efficiency improvement of active distribution networks in scenarios with high penetration of electric vehicles.

[0147] After automatically adjusting the charging period of electric vehicles, if it is determined that the grid load is higher than the preset load, the real-time power of each electric vehicle is obtained. When the grid load reaches the preset high load threshold, the server will automatically obtain the real-time power of each electric vehicle, monitor its remaining power and current charging status in real time, and ensure that the electric power of electric vehicles can be released through V2G technology during the peak grid load period to alleviate the grid load pressure.

[0148] In one possible implementation, determining whether to activate the V2G discharge mode based on the real-time battery level of the electric vehicle specifically includes: calculating the average real-time battery level of all electric vehicles and using it as a preset battery level; determining a first real-time battery level greater than the preset battery level among multiple real-time battery levels, and a second real-time battery level less than or equal to the preset battery level among multiple real-time battery levels; calculating the difference between each of the first real-time battery levels and the preset battery level and summing the differences to obtain a first difference battery level; and calculating the difference between each of the second real-time battery levels and the preset battery level and summing the differences to obtain a second difference battery level; if it is determined that the first difference battery level is greater than or equal to the second difference battery level, then it is determined that the V2G discharge mode needs to be activated and the feedback battery level released by the electric vehicle to the power distribution network is calculated.

[0149] Specifically, a first real-time energy level greater than a preset energy level is determined from among multiple real-time energy levels, and a second real-time energy level less than or equal to the preset energy level is determined from among multiple real-time energy levels. The preset energy level is determined based on the average real-time energy level of all electric vehicles. First, data is collected from the real-time energy levels of all electric vehicles, and the average energy level is calculated as the preset energy level. Then, the real-time energy levels of each electric vehicle are classified: those exceeding the preset energy level are classified as the first real-time energy level, and those less than or equal to the preset energy level are classified as the second real-time energy level. This step aims to differentiate based on the current energy distribution of the electric vehicles to facilitate subsequent difference calculations and determination of feedback energy levels.

[0150] For each electric vehicle in the first real-time energy level, the difference between each of the first real-time energy levels and the preset energy level is calculated, and the differences are summed to obtain the first difference energy level. Similarly, for each electric vehicle in the second real-time energy level, the difference between each of the second real-time energy levels and the preset energy level is calculated, and the differences are summed to obtain the second difference energy level. This step, by calculating the differences, assesses whether each electric vehicle can feed energy back to the grid within a given time period and provides data support for subsequent calculations of the fed-back energy level.

[0151] Based on the calculated difference, if the first difference in charge is greater than or equal to the second difference in charge, then it is determined that the V2G discharge mode needs to be activated and the amount of charge returned from the electric vehicle to the power grid needs to be calculated. In this case, the system assumes that the electric vehicle's battery has sufficient remaining charge for discharge during the charging process, and can safely release the first difference in charge to support the grid load. If the first difference in charge is less than the second difference in charge, then no charge needs to be returned to the grid.

[0152] In one possible implementation, the calculation steps for the regenerative braking power of the electric vehicle specifically include:

[0153] The process involves identifying a first electric vehicle (EV) among multiple EVs that is fully charged, and recording the sum of its charge level as the first charge level. It also involves identifying a second electric vehicle among multiple EVs whose real-time charge level is less than or equal to a preset charge level, and recording the sum of its charge level as the second charge level. This step aims to distinguish between vehicles with sufficient charge recovery and those requiring additional charge, in order to calculate the recovery charge level subsequently.

[0154] The average discharge efficiency of multiple first electric vehicles is obtained, the average charging efficiency of multiple first electric vehicles is obtained, and the average charging efficiency of multiple second electric vehicles is obtained, resulting in a second average charging efficiency. In this step, for a fully charged first electric vehicle, its discharge efficiency and charging efficiency are obtained and averaged. For the second electric vehicle, its charging efficiency is obtained and averaged. Discharge efficiency reflects the degree of energy loss when an electric vehicle releases energy from the battery, while charging efficiency represents the energy absorption efficiency of the battery during charging. By collecting and evaluating the charge and discharge efficiencies of electric vehicles, key data is provided for subsequent calculation of the feedback power.

[0155] Calculate the feedback power using the following formula:

[0156] ;

[0157] In the above formula, Indicates the amount of electricity returned; This represents the average discharge efficiency. Preset battery level; This is the first charge level; This is the second charge; This represents the average of the first charging efficiency. The average of the second charging efficiency; , These two ratios represent the time-related factors of electric vehicle discharge and charging, respectively. The first ratio is used to calculate the energy loss of the first electric vehicle during discharge, and the second ratio is used to calculate the energy loss of the second electric vehicle during charging, thus reasonably balancing discharge and charging efficiency. By multiplying by the discharge efficiency, the energy loss during discharge is taken into account, ensuring that the calculated amount is the effective amount of energy that can actually be fed back to the grid. This further limits the upper limit of the rechargeable electricity, meaning the rechargeable electricity cannot exceed the available electricity of the electric vehicle (i.e., from the...). arrive The difference) or the remaining power after a full charge (i.e. and The difference between them.

[0158] Finally, the server initiates the V2G discharge mode, instructing multiple electric vehicles to act as distributed energy storage units and release power to the power distribution network. The maximum amount of power released by each electric vehicle is the feedback power. Specifically, when the grid load exceeds a preset value, the server activates the V2G function, sends a discharge command to the electric vehicles via the communication network, initiates the discharge mode of the electric vehicles, calculates the released electrical energy based on the feedback power of the electric vehicles, and multiple electric vehicles will collectively act as distributed energy storage units to feed power back to the grid, optimizing grid load balance while ensuring that the electric vehicle batteries are not over-discharged, thus ensuring battery life.

[0159] For the emergency charging needs of electric vehicles, S150 uses an energy storage system connected to the power distribution network to assist in regulating the load fluctuations of fast charging stations and battery swapping stations, generating a charging regulation strategy.

[0160] To address the urgent charging needs of electric vehicles, especially during load fluctuations at fast charging stations and battery swapping stations, energy storage systems connected to the power distribution network can be used for auxiliary regulation. Specifically, this includes:

[0161] First, it is necessary to monitor the load status of the distribution network in real time, especially the load fluctuations of fast charging stations and battery swapping stations. Due to the instantaneous changes in the charging demand of electric vehicles, these charging facilities may cause rapid fluctuations in the load of the distribution network, which in turn affects the stability of the entire power grid. Therefore, by installing intelligent monitoring equipment in the distribution network and collecting charging data from various charging stations, battery swapping stations and electric vehicles, real-time monitoring and early warning of load fluctuations can be achieved.

[0162] Then, by analyzing the load status of the distribution network and combining it with the status of the energy storage system, a charging regulation strategy is generated. Specifically, load regulation targets can be set according to the real-time changes in the grid load. For example, during peak grid load periods, the energy storage system can prioritize providing power support to mitigate the impact of emergency charging of electric vehicles on the grid. The energy storage system can charge during off-peak grid load periods and release power during peak load periods, thereby balancing the grid load.

[0163] Next, the dispatching system uses intelligent optimization algorithms to schedule power flow based on real-time grid load conditions, coordinating power flow between energy storage systems, charging stations, and battery swapping stations. Specifically, based on predicted load fluctuations, the system plans power flow in advance to ensure that energy storage systems can be effectively charged and provide power support during peak load periods. For example, if a sharp increase in power demand is detected at a fast charging station, while the grid load is approaching or exceeding its capacity, the system can reduce the impact on the grid by allocating power from energy storage batteries to the charging station.

[0164] Furthermore, the charging regulation strategy should be dynamically adjusted, taking into account the urgency of electric vehicle charging needs and battery status. When emergency charging demands occur, the system should prioritize power supply to critical charging stations and battery swapping stations to avoid charging service interruptions or grid failures due to overload. To this end, high-priority scheduling rules can be set to ensure that emergency charging demands are met promptly while maintaining grid stability.

[0165] By taking the above steps, and combining the load status of the distribution network, the charging and discharging capacity of the energy storage system, and the demand information of the charging station, the power flow can be effectively regulated, the impact of sudden loads on the distribution network can be reduced, the emergency charging needs of electric vehicles can be responded to quickly, and the stable operation of the power grid can be guaranteed.

[0166] S160 integrates the first output data, output probability, and second output data to monitor the source and load status of the distribution network in real time, and uses a power balance scheduling algorithm to optimize and adjust the charging and discharging strategy and the charging regulation strategy.

[0167] In one possible implementation, the source-load status of the distribution network is monitored in real time by integrating the first output data, output probability, and second output data. A power balance scheduling algorithm is used to optimize and adjust the charging / discharging strategy and the charging regulation strategy. Specifically, this includes: calculating the predicted power generation within a preset time period using a weighted average based on the first output data, output probability, and second output data; analyzing historical electricity consumption data to determine the predicted electricity consumption of the grid load within the preset time period, where the grid load does not include destination charging demand and emergency charging demand for electric vehicles; determining the source-load difference of the distribution network based on the predicted power generation and electricity consumption; optimizing the charging and discharging of electric vehicles based on the source-load difference and determining the optimization target; optimizing and adjusting the charging / discharging strategy and the charging regulation strategy according to the optimization target, using V2G technology to regulate the reverse discharge of electric vehicles, adjusting the charging time of electric vehicles, and combining the charging and discharging of the energy storage system.

[0168] Weighted average calculation involves weighting the historical output of different types of power sources based on their output data and corresponding output probabilities, thereby estimating the total power generation over different time periods. This process yields a relatively accurate power generation forecast, providing a basis for subsequent source-load balance scheduling. The output of non-intermittent distributed power sources is only dynamically quantified and predicted using load curve models, without providing their probability distribution. Therefore, when calculating the total power generation forecast, the weighted average used should be a direct sum of the probabilistic weighting for intermittent power sources and the deterministic prediction for non-intermittent power sources. Specifically, this includes:

[0169] First, for intermittent distributed power sources, since their output is greatly affected by factors such as weather, a multi-state discrete model was established by statistically analyzing historical data, with each output state... Corresponding to a probability In each time period The expected output can be calculated using the following weighted average:

[0170] ;

[0171] In the above formula, Indicates time period Expected output; The number of intermittent power sources. For the first Number of power supply states;

[0172] Secondly, for non-intermittent distributed power sources, their output is a continuous function or typical curve fitted based on factors such as daily variation, seasonal variation, and sudden disturbances, denoted as... This part does not require probability weighting because of its deterministic nature.

[0173] Ultimately, the total power generation forecast is as follows: for:

[0174] ;

[0175] By using the above formula for comprehensive total power generation forecast, we can make full use of the output uncertainty information of intermittent power sources and combine it with the stable forecasting capability of non-intermittent power sources to form a hybrid power generation forecasting model that is more in line with the actual operating scenario, thereby improving the accuracy and robustness of source-load balance control.

[0176] Historical electricity consumption data is analyzed to determine the predicted electricity demand for the power grid within a preset time period. This process utilizes historical load data, combined with climate, seasonal, and daily electricity consumption patterns, to predict the power grid's demand. It is important to note that the predicted power grid load does not include destination charging demand for electric vehicles or emergency charging demand; its purpose is to provide a baseline load forecast to facilitate the assessment of the power grid's basic load condition.

[0177] Based on the aforementioned comprehensive total power generation forecast and power consumption forecast, the source-load difference of the distribution network is further determined. The source-load difference is the difference between the comprehensive total power generation forecast and the power consumption forecast. If the source-load difference is positive, it means that the power generation of the grid is greater than the power consumption; otherwise, it is negative, indicating that there is a power shortage in the grid. This step is crucial for power balance dispatching, ensuring the supply and demand balance of the grid.

[0178] Based on the source-load difference, the charging and discharging of electric vehicles (EVs) is optimized to determine the optimization objective. When the source-load difference is negative, the optimization objective can be to adjust the charging period of EVs through reverse discharge regulation using V2G technology, or to provide supplementary power through energy storage systems. The core of the optimization objective is to balance the load fluctuations of the power grid and ensure its stable operation. Setting up a power grid load balancing model, the optimization objective for EV charging and discharging can be expressed as:

[0179] ;

[0180] In the above formula, Preset time period The projected power generation within the region This represents the predicted electricity consumption within a preset time period t. For the first Electric vehicles in a preset time period The internal charging capacity, The discharge capacity of electric vehicles. This indicates the total number of electric vehicles;

[0181] The above optimization model adjusts the power flow to achieve load balance and ensure the charging needs of electric vehicles are met. This optimization model achieves grid balance by minimizing grid load fluctuations and deviations generated during electric vehicle charging and discharging. Specifically, the objective function for each time period... The calculation calculates the source-load difference between the total power generation and the predicted power consumption of the power grid, and adds the impact of the charging and discharging of all electric vehicles during the specified period. The minimization operation aims to minimize the deviation of the power grid load, that is, by adjusting the charging and discharging strategies of electric vehicles to minimize grid load fluctuations, thereby achieving load balance and grid stability. This regulation of power consumption during the charging and discharging of electric vehicles optimizes grid operation and improves the stability of the power system.

[0182] Finally, the charging and discharging strategies and charging regulation strategies are adjusted according to the optimization objectives. During this process, V2G technology is used to regulate the discharging behavior of electric vehicles, treating them as distributed energy storage units for power feedback. Through intelligent scheduling strategies, the system can dynamically adjust the charging periods and charging power of electric vehicles to achieve grid load balance. Combined with the charging and discharging functions of the energy storage system, the grid's regulation capability is further enhanced. Through this series of optimizations, the pressure on the grid during peak load periods can be effectively reduced, improving the grid's operational stability.

[0183] Specifically, considering the adjustable charging and discharging capabilities of electric vehicles, a minimization approach is adopted to balance grid load fluctuations. Introducing an energy storage system and further including its adjustment term in the objective function expands the result to:

[0184] ;

[0185] In the above formula, where and For energy storage systems during different time periods The amount of charge and discharge.

[0186] Since both energy storage systems and electric vehicles are essentially controllable bidirectional power regulation units, their regulation behaviors can be integrated into a single optimization objective and jointly optimized as control variables in the scheduling algorithm. Therefore, even if the energy storage parameters are not explicitly included in the initial optimization function, as long as the energy storage system is considered as a compensation resource participating in regulation during the algorithm design phase, joint optimization of the charging regulation strategy can be achieved by adding constraints and variables.

[0187] In actual system execution, the regulation logic of the energy storage system can participate in the following two ways: as a generalized supplement to the V2G logic of electric vehicles: when V2G is insufficient to meet the source-load difference, the energy storage system is called to provide redundant support, thereby completing the load regulation; as a constraint boundary response mechanism: when the grid load or electricity price reaches the critical value, the dispatching system automatically activates the charging and discharging of the energy storage system according to the electricity market signal or operating boundary conditions, and works together to achieve the minimum deviation.

[0188] This invention discloses a power balance control method for source-load matching in an active power distribution network, which is applied to a server. The server includes, but is not limited to, electronic devices such as mobile phones, tablets, wearable devices, and PCs (Personal Computers), and can also be a backend server running the power balance control method for source-load matching in an active power distribution network. The server can be implemented using a standalone server or a server cluster composed of multiple servers.

[0189] This invention also discloses a power balance control device for source-load matching in an active distribution network, referring to... Figure 2 The device includes an acquisition module 201, a processing module 202, and an output module 203, wherein:

[0190] The acquisition module 201 is used to acquire historical output data of distributed power sources in the distribution network; the distributed power sources include intermittent distributed power sources and non-intermittent distributed power sources.

[0191] The processing module 202 is used to obtain the first output data and output probability of the intermittent distributed power source under different weather conditions by multi-state statistical analysis based on the historical output data of the intermittent distributed power source.

[0192] The processing module 202 is further configured to determine the second output data of the non-intermittent distributed power source based on the historical output data of the non-intermittent distributed power source through a load curve model.

[0193] The processing module 202 is also used to formulate a charging and discharging strategy for electric vehicles based on the destination charging demand of electric vehicles, using orderly charging and V2G bidirectional charging and discharging modes, combined with the charging demand prediction of electric vehicles. The charging and discharging strategy includes optimizing the electricity pricing mechanism to control the orderly charging of electric vehicles during periods when the grid load is lower than the preset load, and using V2G technology, combined with the charging and discharging status of electric vehicles, to release electricity as a distributed energy storage unit during periods when the grid load is higher than the preset load, so as to balance load fluctuations.

[0194] The processing module 202 is also used to address the emergency charging needs of electric vehicles. In response to load fluctuations at fast charging stations and battery swapping stations, it employs an energy storage system connected to the power distribution network for auxiliary regulation, generating a charging regulation strategy. This strategy includes scheduling power flow based on the grid load status to reduce the impact of sudden loads on the power distribution network.

[0195] The output module 203 is also used to integrate the first output data, output probability and second output data to monitor the source and load status of the distribution network in real time, and to optimize and adjust the charging and discharging strategy and the charging regulation strategy by using the power balance scheduling algorithm.

[0196] In one possible implementation, the acquisition module 201 is used to collect and organize historical output data of intermittent distributed power sources under different meteorological conditions.

[0197] The processing module 202 is used to identify key factors affecting the power output of intermittent distributed power sources based on historical meteorological data, and then classify the historical power output data according to meteorological conditions.

[0198] The processing module 202 is used to identify the output characteristics of intermittent distributed power sources under different meteorological conditions by performing regression analysis on the classified historical output data.

[0199] The processing module 202 is used to model the output fluctuation of the intermittent distributed power source using a probability distribution model for the historical output data under each meteorological condition, and to calculate the probability of the intermittent distributed power source outputting different historical output data under each meteorological condition.

[0200] The processing module 202 is used to combine the classification and probability distribution of historical power output data to perform multi-state modeling of power output under each meteorological condition, forming multiple discrete states of power output, and dynamically updating the probabilities of multiple discrete states over time. By establishing a time series model, it predicts the first power output data and output probability of intermittent distributed power sources in different time periods.

[0201] In one possible implementation, the acquisition module 201 is used to establish a predictive model of electric vehicle charging demand by collecting and analyzing historical charging data for electric vehicles.

[0202] The processing module 202 is used to set the electricity price level for different time periods based on the load conditions of the distribution network using a dynamic pricing model.

[0203] The processing module 202 is used to determine the grid load status and electricity pricing mechanism for the corresponding time period based on the predicted charging demand of electric vehicles predicted by the prediction model.

[0204] The processing module 202 is used to automatically adjust the charging time of electric vehicles based on the charging demand forecast, grid load status and electricity pricing mechanism.

[0205] The acquisition module 201 is used to acquire the real-time power of each electric vehicle if it is determined that the grid load is higher than the preset load after automatically adjusting the charging period of the electric vehicle.

[0206] The processing module 202 is used to determine whether V2G discharge mode needs to be activated based on the real-time battery level of the electric vehicle.

[0207] The output module 203 is used to activate the V2G discharge mode when the processing module 202 determines that the V2G discharge mode needs to be activated, and to instruct the distributed energy storage unit composed of multiple electric vehicles to release power to the distribution network.

[0208] In one possible implementation, the processing module 202 is used to calculate the average real-time battery level of all electric vehicles and use it as a preset battery level; determine a first real-time battery level that is greater than the preset battery level among a plurality of real-time battery levels, and a second real-time battery level that is less than or equal to the preset battery level among a plurality of real-time battery levels.

[0209] The processing module 202 is used to calculate the difference between each of the first real-time power consumption and the preset power consumption and sum the differences to obtain the first difference power consumption, and to calculate the difference between each of the second real-time power consumption and the preset power consumption and sum the differences to obtain the second difference power consumption.

[0210] The processing module 202 is used to determine that V2G discharge mode needs to be started and to calculate the feedback power released by the electric vehicle to the power distribution network when it is determined that the first difference power is greater than or equal to the second difference power.

[0211] In one possible implementation, the processing module 202 is configured to determine a first electric vehicle among a plurality of electric vehicles that is in a fully charged state, and to use the sum of the charges of the plurality of first electric vehicles as a first charge; and to determine a second electric vehicle among a plurality of electric vehicles whose real-time charge is less than or equal to a preset charge, and to use the sum of the charges of the plurality of second electric vehicles as a second charge.

[0212] The acquisition module 201 is used to acquire the average discharge efficiency of multiple first electric vehicles to obtain the average discharge efficiency, acquire the average charging efficiency of multiple first electric vehicles to obtain the average charging efficiency, and acquire the average charging efficiency of multiple second electric vehicles to obtain the average charging efficiency.

[0213] Processing module 202 is used to calculate the feedback power based on the average discharge efficiency, the average first charging efficiency, the average second charging efficiency, the first power level, and the second power level. The specific calculation formula is as follows:

[0214] ;

[0215] In the above formula, Indicates the amount of electricity returned; This represents the average discharge efficiency. Preset battery level; This is the first charge level; This is the second charge; This represents the average of the first charging efficiency. This represents the average of the second charging efficiency.

[0216] In one possible implementation, the processing module 202 is further configured to calculate the predicted power generation within a preset time period by weighted averaging based on the first power output data, the power output probability, and the second power output data.

[0217] The processing module 202 is also used to analyze historical electricity consumption data and determine the predicted electricity consumption of the power grid load within a preset time period, wherein the power grid load does not include the destination charging demand of electric vehicles and the emergency charging demand.

[0218] The processing module 202 is also used to determine the source-load difference of the distribution network based on the power generation forecast and the power consumption forecast.

[0219] The processing module 202 is also used to optimize the charging and discharging of electric vehicles based on the source-load difference and determine the optimization target.

[0220] The output module 203 is also used to optimize and adjust the charging and discharging strategy and the charging regulation strategy according to the optimization target, regulate the reverse discharge of electric vehicles through V2G technology, adjust the charging period of electric vehicles, and combine the charging and discharging of the energy storage system.

[0221] In one possible implementation, the processing module 202 is also used to acquire historical output data of non-intermittent distributed power sources under different operating conditions.

[0222] The processing module 202 is also used to determine the typical load fluctuation pattern of historical power output data by performing statistical analysis on historical power output data.

[0223] The processing module 202 is used to establish a typical load curve for non-intermittent distributed power sources based on typical load fluctuation patterns. The typical load curve includes the fluctuation characteristics of daily, seasonal, and annual variations.

[0224] The processing module 202 is used to construct a mathematical model to fit a typical load curve and obtain a dynamic function describing the change of power output over time.

[0225] The output module 203 is used to predict the future output state based on the established dynamic function and obtain the second output data.

[0226] It should be noted that the apparatus provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0227] This embodiment of the invention also discloses an electronic device, with reference to... Figure 3 The electronic device may include: at least one processor 301, at least one communication bus 302, user interface 303, network interface 304, and at least one memory 305.

[0228] The communication bus 302 is used to enable communication between these components.

[0229] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0230] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0231] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and executes various functions and output data of the server by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU mainly handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem is used for wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0232] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. As a computer storage medium, the memory 305 may include an operating system, a network communication module, a user interface 303 module, and an application program for a power balance control method for source-load matching in an active power distribution network.

[0233] exist Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 301 can be used to call the application program of a power balance control method for source-load matching of an active power distribution network stored in the memory 305. When executed by one or more processors 301, the electronic device executes one or more methods as described in the above embodiments.

[0234] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0235] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0236] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0237] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0238] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0239] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 305 and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory 305 includes various media capable of storing program code, such as a USB flash drive, external hard drive, magnetic disk, or optical disk.

[0240] This application also discloses a computer-readable storage medium storing instructions. When executed by one or more processors 301, these instructions cause an electronic device to perform one or more of the methods described in the above embodiments.

[0241] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A power balance control method for source-load matching in an active distribution network, characterized in that: The method includes: Obtain historical output data of distributed generation in the power distribution network; the distributed generation includes intermittent distributed generation and non-intermittent distributed generation; Based on the historical output data of the intermittent distributed power source, the first output data and output probability of the intermittent distributed power source under different weather conditions are obtained through multi-state statistical analysis. Based on the historical output data of the non-intermittent distributed power source, the second output data of the non-intermittent distributed power source is determined through a load curve model; For the destination charging demand of electric vehicles, an orderly charging and V2G bidirectional charging and discharging mode is adopted. Combined with the charging demand forecast of electric vehicles, a charging and discharging strategy for electric vehicles is formulated. The charging and discharging strategy includes optimizing the electricity pricing mechanism to control the orderly charging of electric vehicles when the grid load is lower than the preset load, and using V2G technology, combined with the charging and discharging status of electric vehicles, to release electricity as a distributed energy storage unit when the grid load is higher than the preset load, so as to balance load fluctuations. For the emergency charging needs of electric vehicles, and in response to load fluctuations at fast charging stations and battery swapping stations, an energy storage system connected to the power distribution network is used for auxiliary regulation to generate a charging regulation strategy. The charging regulation strategy includes scheduling power flow according to the power grid load status to reduce the impact of sudden loads on the power distribution network. By combining the first output data, the output probability, and the second output data, the source and load status of the distribution network is monitored in real time, and the charging and discharging strategy and the charging regulation strategy are optimized and adjusted using a power balance scheduling algorithm.

2. The power balance control method for source-load matching in an active distribution network according to claim 1, characterized in that: The step involves performing multi-state statistical analysis on the intermittent distributed power source based on its historical output data to obtain the first output data and output probability of the intermittent distributed power source under different weather conditions, including: Identify the meteorological conditions that affect the output of intermittent distributed power sources and obtain historical output data of intermittent distributed power sources under different meteorological conditions; Based on the historical output data of intermittent distributed power sources under each meteorological condition, a probability distribution model is used to calculate the probability distribution of different output data of intermittent distributed power sources under each meteorological condition. Based on the probability distribution of different power output data of intermittent distributed power sources under each meteorological condition, the first power output data and output probability of intermittent distributed power sources in different time periods in the future are predicted by time series model.

3. The power balance control method for source-load matching in an active distribution network according to claim 1, characterized in that: Regarding the destination charging demand of electric vehicles, an orderly charging and V2G bidirectional charging and discharging mode is adopted. Combined with electric vehicle charging demand prediction, a charging and discharging strategy for electric vehicles is formulated, specifically including: Obtain historical charging data for electric vehicles and establish a charging demand prediction model for electric vehicles. Based on the load conditions of the distribution network, a dynamic pricing model is used to set electricity price levels for different time periods; Based on the charging demand forecast model, the predicted charging demand of electric vehicles is used to determine the grid load status and electricity pricing mechanism for the corresponding time period. The charging time periods for electric vehicles are adjusted based on the predicted charging demand, the grid load status for the corresponding time period, and the electricity pricing mechanism. Get the grid load after adjusting the charging period of electric vehicles, determine whether the grid load is higher than the preset load, and if so, get the real-time power of electric vehicles. Based on the real-time battery level of the electric vehicle, determine whether V2G discharge mode needs to be activated, and instruct the electric vehicle to release power to the power distribution network.

4. The power balance control method for source-load matching in an active distribution network according to claim 1, characterized in that: The process of determining whether to activate V2G discharge mode based on the real-time battery level of the electric vehicle includes: Calculate the average real-time battery level of all electric vehicles and use it as the preset battery level; Determine a first real-time energy level that is greater than a preset energy level among multiple real-time energy levels, and a second real-time energy level that is less than or equal to the preset energy level among multiple real-time energy levels; Calculate the difference between each of the first real-time power consumption and the preset power consumption, and sum the differences to obtain the first difference power consumption; and calculate the difference between each of the second real-time power consumption and the preset power consumption, and sum the differences to obtain the second difference power consumption. If it is determined that the first difference in electricity is greater than or equal to the second difference in electricity, then it is determined that the V2G discharge mode needs to be activated and the feedback electricity released by the electric vehicle to the power distribution network needs to be calculated.

5. The power balance control method for source-load matching in an active distribution network according to claim 4, characterized in that: The calculation steps for the regenerative braking power of the electric vehicle include: The system identifies a first electric vehicle among multiple electric vehicles that is fully charged, and uses the sum of the charges of the multiple first electric vehicles as a first charge. It also identifies a second electric vehicle among multiple electric vehicles whose real-time charge is less than or equal to a preset charge, and uses the sum of the charges of the multiple second electric vehicles as a second charge. The average discharge efficiency of multiple first electric vehicles is obtained by averaging the discharge efficiency of multiple first electric vehicles, the average charging efficiency of multiple first electric vehicles is obtained by averaging the charging efficiency of multiple second electric vehicles, and the average charging efficiency of multiple second electric vehicles is obtained by averaging the charging efficiency of multiple second electric vehicles. Calculate the feedback power using the following formula: ； In the above formula, Indicates the amount of electricity returned; This represents the average discharge efficiency. Preset battery level; This is the first charge level; This is the second charge; This represents the average of the first charging efficiency. This represents the average of the second charging efficiency.

6. The power balance control method for source-load matching in an active distribution network according to claim 1, characterized in that: The combined first output data, output probability, and second output data are used to monitor the source-load status of the distribution network in real time. A power balance scheduling algorithm is employed to optimize and adjust the charging and discharging strategy and the charging regulation strategy, specifically including: Based on the first power output data, the power output probability, and the second power output data, the predicted power generation within a preset time period is calculated by weighted average. Historical electricity consumption data is analyzed to determine the predicted electricity consumption of the power grid load within a preset time period, where the power grid load does not include the destination charging demand of electric vehicles and the emergency charging demand. Based on the predicted power generation and power consumption, determine the source-load difference of the distribution network; Based on the source-load difference, the charging and discharging of electric vehicles are optimized, and the optimization target is determined. The charging and discharging strategy and the charging regulation strategy are optimized and adjusted according to the optimization objectives. The reverse discharge regulation of the electric vehicle is carried out through V2G technology, the charging period of the electric vehicle is adjusted, and the charging and discharging is combined with the energy storage system.

7. The power balance control method for source-load matching in an active distribution network according to claim 1, characterized in that: The step of determining the second output data of the non-intermittent distributed power source based on its historical output data and through a load curve model specifically includes: Obtain historical output data of the non-intermittent distributed power source under different operating conditions; By statistically analyzing the historical output data under different operating conditions, the typical load fluctuation pattern of the non-intermittent distributed power source is determined. Based on the typical load fluctuation pattern, a typical load curve for the non-intermittent distributed power source is established, which includes the fluctuation characteristics of daily, seasonal, and annual variations. A mathematical model is constructed to fit the typical load curve to obtain a dynamic function describing the change of power output over time. Based on the established dynamic function, the second output data of the non-intermittent distributed power source is predicted for different time periods in the future.

8. A power balance control device for source-load matching in an active power distribution network, characterized in that: The device includes an acquisition module (201), a processing module (202), and an output module (203), wherein: The acquisition module (201) is used to acquire historical output data of distributed power sources in the distribution network; the distributed power sources include intermittent distributed power sources and non-intermittent distributed power sources. The processing module (202) is used to obtain the first output data and output probability of the intermittent distributed power source under different weather conditions by multi-state statistical analysis based on the historical output data of the intermittent distributed power source. The processing module (202) is also used to determine the second output data of the non-intermittent distributed power source based on the historical output data of the non-intermittent distributed power source through a load curve model; The processing module (202) is also used to formulate a charging and discharging strategy for electric vehicles based on the destination charging demand of electric vehicles, using orderly charging and V2G bidirectional charging and discharging modes, combined with the charging demand prediction of electric vehicles; wherein, the charging and discharging strategy includes optimizing the electricity price mechanism to control the electric vehicles to charge in an orderly manner during periods when the grid load is lower than the preset load, and using V2G technology, combined with the charging and discharging status of the electric vehicles, to release electricity as a distributed energy storage unit during periods when the grid load is higher than the preset load, so as to balance load fluctuations; The processing module (202) is also used to address the emergency charging needs of electric vehicles by using an energy storage system connected to the power distribution network to assist in adjusting the load fluctuations of fast charging stations and battery swapping stations, and to generate a charging adjustment strategy. The charging adjustment strategy includes scheduling the power flow according to the power grid load status to reduce the impact of sudden loads on the power distribution network. The output module (203) is used to integrate the first output data, the output probability and the second output data to monitor the source and load status of the distribution network in real time, and to optimize and adjust the charging and discharging strategy and the charging regulation strategy by using a power balance scheduling algorithm.

9. An electronic device, characterized in that: The device includes a processor (301), a communication bus (302), a user interface (303), a network interface (304), and a memory (305). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are both used to communicate with other devices. The communication bus (302) is used to realize the connection and communication between the components within the electronic device. The processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device performs the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-7.

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