Scheduling method and system for power grid comprising electric automobile and wind power

Abstract

The invention provides a scheduling method and system for a power grid containing an electric vehicle and wind power, and the method comprises the steps: obtaining basic data of a power system, and carrying out the preprocessing of the basic data; processing the preprocessed basic data through a preset low-carbon scheduling model to obtain a corresponding scheduling strategy; the low-carbon scheduling model solves the target function through a preset algorithm, and determines a corresponding electric vehicle charging power value by taking the minimum comprehensive cost of wind power generation as a target, and the comprehensive cost of the wind power generation at least comprises the total load variance of a power grid, the total charging cost of an electric vehicle user and the carbon transaction cost; and performing scheduling control on the power grid according to the scheduling strategy to realize load adjustment of the power grid. According to the invention, flexible adjustment can be carried out according to actual conditions, a scheduling strategy is ensured to always meet actual requirements of power system operation, and the stability and safety of the system are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system automation, and particularly to a dispatching method and system for a power grid including electric vehicles and wind power. Background Art

[0002] The coordinated dispatching of electric vehicles and wind power refers to coordinating and optimizing the charging behavior of electric vehicles and the power generation process of wind power to achieve the stable operation of the power system, the efficient utilization of resources, and the sustainable development of the environment. This coordinated dispatching technology is mainly applied to occasions with rich wind resources and a large number of electric vehicles, such as cities around wind power bases and regions with a high penetration rate of electric vehicles.

[0003] During the coordinated dispatching process, electric vehicles can serve as distributed energy storage elements. Through reasonable planning and dispatching, they can play the role of "peak shaving and valley filling", that is, charging during high wind power output periods to store excess electric energy; when the wind power output is insufficient or during the peak load of the power grid, electric vehicles can discharge to supplement the power demand of the power grid. This method can not only improve the access capacity and consumption level of wind power, but also reduce the load fluctuation of the power grid and improve the stability of the power system.

[0004] However, there are still some deficiencies in the existing technology for the coordinated dispatching of electric vehicles and wind power. First, the charging behavior of electric vehicle users is random and uncertain, which increases the difficulty of coordinated dispatching. Although the charging load of electric vehicles can be simulated through prediction and modeling, the accuracy of the prediction results is still affected by various factors, such as user habits and weather conditions. Second, during the coordinated dispatching process, the intermittency and volatility of wind power need to be considered, which increases the uncertainty and complexity of the operation of the power system. In addition, the existing energy storage technologies and charging facilities still have bottlenecks in terms of scale, efficiency, and economy, restricting the potential of electric vehicles in coordinated dispatching. Summary of the Invention

[0005] The purpose of the present invention is to propose a dispatching method and system for a power grid including electric vehicles and wind power to solve the technical problem of how to adjust the charging and discharging strategies of electric vehicles and the power generation strategy of wind power to reduce the load fluctuation of the power grid.

[0006] On the one hand, a dispatching method for a power grid including electric vehicles and wind power is provided, including:

[0007] Obtain the basic data of the power system and preprocess the basic data; wherein, the basic data at least includes thermal power unit carbon emission data, thermal power unit output data, wind power output data, and electric vehicle charging data;

[0008] Process the preprocessed basic data through a preset low-carbon scheduling model to obtain corresponding scheduling strategies; wherein, the low-carbon scheduling model solves the objective function through a preset algorithm, with the goal of minimizing the comprehensive cost of wind power generation cost, and determines the corresponding electric vehicle charging power value, where the comprehensive cost of wind power generation cost at least includes the variance of the total grid load, the total charging cost of electric vehicle users, and the carbon trading cost;

[0009] Dispatch and control the power grid according to the scheduling strategy to achieve load adjustment of the power grid.

[0010] Preferably, the preprocessing of the basic data includes,

[0011] Detect the processed basic data according to preset data standards. If abnormal basic data is detected, delete the abnormal basic data; if missing values are detected in the basic data, fill in the basic data; if non-standardization is detected in the basic data, perform standardization processing;

[0012] Extract the feature information of the basic data, and determine the corresponding feature items according to the feature information. The feature information at least includes time features, meteorological features, and historical load features.

[0013] Preferably, the low-carbon scheduling model includes a function of the variance of the total grid load:

[0014]

[0015] Wherein, σ2 represents the variance of the total grid load, P(t) represents the load power at time t, and T represents the inspection time period;

[0016] The total charging cost of electric vehicle users is the product of the unit electricity price and the charging amount during the corresponding charging period plus other costs;

[0017] The carbon trading cost function is the product of the carbon emission and the carbon trading market price.

[0018] Preferably, the low-carbon scheduling model further includes,

[0019] Electric vehicle constraints, which are used to keep the charging power and charging time of electric vehicles within a preset range, and the state of charge when arriving at the charging station is greater than a preset lower threshold;

[0020] Electric power balance constraints, which are used to limit the power generated by the power grid to be greater than or equal to the sum of the power consumed by the load and the transmission loss.

[0021] Preferably, the scheduling strategy at least includes the charging time, charging power, and charging amount of electric vehicles.

[0022] On the other hand, a dispatching system for a power grid including electric vehicles and wind power is also provided, which is used to implement the dispatching method for the power grid including electric vehicles and wind power, including,

[0023] A data acquisition module, which is used to acquire the basic data of the power system and preprocess the basic data; wherein, the basic data at least includes thermal power unit carbon emission data, thermal power unit output data, wind power output data, and electric vehicle charging data;

[0024] A strategy generation module, which is used to process the preprocessed basic data through a preset low-carbon dispatching model to obtain corresponding dispatching strategies; wherein, the low-carbon dispatching model solves the objective function through a preset algorithm, with the goal of minimizing the comprehensive cost of wind power generation cost, and determines the corresponding electric vehicle charging power value, wherein the comprehensive cost of wind power generation cost at least includes the variance of the total grid load, the total charging cost of electric vehicle users, and the carbon trading cost;

[0025] A dispatching module, which is used to perform dispatching control on the power grid according to the dispatching strategy to realize the load adjustment of the power grid.

[0026] Preferably, the data acquisition module is specifically used to detect the processed basic data according to a preset data standard. If abnormal basic data is detected, the abnormal basic data is deleted; if missing values are detected in the basic data, the basic data is filled; if non-standardization is detected in the basic data, standardization processing is performed;

[0027] Extract the characteristic information of the basic data, and determine the corresponding characteristic items according to the characteristic information. The characteristic information at least includes time characteristics, meteorological characteristics, and historical load characteristics.

[0028] Preferably, the strategy generation module is specifically used for the variance function of the total grid load:

[0029]

[0030] wherein, σ2 represents the variance of the total grid load, P(t) represents the load power at time t, and T represents the inspection time period;

[0031] The total charging cost of electric vehicle users is the product of the unit electricity price and the charging amount during the corresponding charging period plus other costs;

[0032] The carbon trading cost function is the product of the carbon emission amount and the carbon trading market price.

[0033] Preferably, the policy generation module is further configured for electric vehicle constraints, where the charging power and charging time of the electric vehicle are within a preset range, and the state of charge when arriving at the charging station is greater than a preset lower threshold;

[0034] Electric power balance constraint, which is used to limit the power generated by the power grid to be greater than or equal to the sum of the power consumed by the load and the transmission loss.

[0035] In summary, implementing the embodiments of the present invention has the following beneficial effects:

[0036] The scheduling method and system for a power grid including electric vehicles and wind power provided by the present invention. Obtaining the basic data of the power system is the prerequisite for accurate scheduling, ensuring the comprehensiveness and timeliness of information, and providing a solid foundation for subsequent analysis; by preprocessing the basic data, outliers can be effectively removed and missing data can be filled, improving the accuracy and reliability of the data and laying a good foundation for model construction; when constructing a low-carbon scheduling model with wind power coordination, the intermittency of wind power and the flexibility of electric vehicle charging are fully considered, realizing the complementarity and synergy between the two, and effectively improving the low-carbon operation level of the system; using an optimization algorithm to solve the objective function can quickly find the comprehensive optimal charging power value of the electric vehicle for the total load variance of the power grid, the total charging cost of the electric vehicle, the carbon trading cost, and the wind power generation cost in a complex and changeable power system environment. This not only improves the scheduling efficiency but also significantly reduces the system operation cost; the electric vehicle scheduling strategy generated according to the optimization solution results has high operability and adaptability. During implementation, it can be flexibly adjusted according to the actual situation to ensure that the scheduling strategy always meets the actual needs of the power system operation, further improving the stability and security of the system. Description of the Drawings

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, obtaining other drawings without creative efforts still belongs to the scope of the present invention.

[0038] Figure 1 It is a main flow schematic diagram of a scheduling method for a power grid including electric vehicles and wind power in an embodiment of the present invention. Detailed Embodiments

[0039] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings.

[0040] Such as Figure 1As shown in the figure, it is a schematic diagram of an embodiment of a scheduling method for a power grid including electric vehicles and wind power provided by the present invention. In this embodiment, it includes:

[0041] Step S1, obtain the basic data of the power system and preprocess the basic data; wherein, the basic data at least includes thermal power unit carbon emission data, thermal power unit output data, wind power output data, and electric vehicle charging data; it can be understood that the thermal power unit carbon emission data includes but is not limited to the carbon emission amount and carbon emission intensity of the thermal power unit, etc., and is used to evaluate the environmental protection performance data of the thermal power unit, etc. Energy consumption records can be obtained from the energy management system of the thermal power unit, especially the consumption of fossil fuels such as coal and fuel oil. The carbon emission coefficient in the power production process can also be referred to, and combined with the energy consumption to estimate the carbon emission amount. For thermal power units equipped with carbon emission monitoring equipment, real-time carbon dioxide and other greenhouse gas emission data can be directly obtained from the equipment. These data can accurately reflect the carbon emission intensity of the thermal power unit. The accuracy and reliability of the data are crucial, so the authority of the data source and the scientific nature of the data collection method should be ensured to provide strong data support for the coordinated scheduling of electric vehicles and wind power. The thermal power unit output data records the real-time output situation of the thermal power unit to understand its operating state in the power grid. The thermal power unit output data can accurately reflect the real-time output situation and operating state of the thermal power unit in the power grid, which plays a key role in realizing coordinated scheduling. Collecting the thermal power unit output data mainly relies on the data acquisition system. The data acquisition system converts the parameters of the unit into electrical signals in real time through sensors, and converts them into digital signals through data collectors for recording. These data collectors can be arranged in a centralized or distributed manner to ensure that all sensor signals are effectively received and processed. The data is uploaded to the cloud database through the monitor for real-time monitoring and analysis. Obtain the real-time output data of the wind farm, including wind speed, wind direction, wind power output power, etc., for evaluating the availability and stability of wind power. The wind speed and wind direction data can be collected in real time through anemometers and wind vanes installed on the wind turbines, usually recorded at a frequency of seconds or minutes to ensure the accuracy and timeliness of the data. The wind power output power data is collected in real time through the generator of the wind turbine, and the accuracy and integrity of the data also need to be ensured. The electric vehicle charging data includes the charging time, charging power, charging amount, etc. of the electric vehicle, and is used to evaluate the charging demand and charging behavior of the electric vehicle. The collection of charging data can be achieved through various channels. On the one hand, the intelligent system built into the electric vehicle can be used, which can record and transmit various data during the charging process in real time. On the other hand, public and private charging piles can also be important nodes for data collection. By installing sensors and data acquisition modules, the charging behavior of electric vehicles can be recorded in real time, and then using big data technology and cloud computing platforms, the data can be stored to provide a reliable data basis for subsequent analysis.

[0042] In one embodiment, the preprocessing of the basic data includes detecting the processed basic data according to a preset data standard. If abnormal basic data is detected, the abnormal basic data is deleted; if missing values are detected in the basic data, the basic data is filled; if non-standardization is detected in the basic data, standardization processing is performed; the feature information of the basic data is extracted, and the corresponding feature items are determined according to the feature information. The feature information includes at least time feature, meteorological feature, and historical load feature. It can be understood that data cleaning of the obtained basic data of the power system is the first step of preprocessing, aiming to remove outliers and noise data in the data and ensure the accuracy and reliability of the data. Specific methods include: Threshold method: Set a reasonable threshold range, and regard the data outside the range as outliers for deletion or replacement. Statistical method: Use statistical methods, such as mean, standard deviation, etc., to identify and process outliers. Model method: Establish a data distribution model, such as a normal distribution model, and identify outliers according to the model parameters. Remove errors and noise in the data and improve the accuracy of the data; provide a reliable basis for subsequent data analysis and modeling. Filling missing values in the obtained basic data of the power system. The purpose of filling missing values is to fill these missing values and ensure the integrity of the data. Specifically, it includes: Mean filling: Fill with the mean value of the column where the missing value is located. Interpolation method: Perform interpolation calculation according to the values of adjacent data points to fill the missing value. Regression method: Establish a regression model and predict the missing value according to the values of other variables. Hot deck imputation: Find data points similar to the missing value in the dataset and fill it with its value. The role of filling missing values: Ensure the integrity of the data and avoid information loss caused by missing values; improve the accuracy of subsequent data analysis and modeling. Performing data standardization on the obtained basic data of the power system. Data standardization is the process of converting data with different dimensions into the same dimension, aiming to eliminate the dimension differences between different features and improve the convergence speed and prediction accuracy of the model. Implementation methods: Min-max standardization: Linearly transform the data into the range [0, 1]. Z-score standardization: Convert the data into a normal distribution data with a mean of 0 and a standard deviation of 1. Decimal scaling standardization: Move the decimal point position to convert the data into the range [-1, 1]. The role of data standardization: Eliminate the dimension differences between different features and improve the convergence speed and prediction accuracy of the model; make the data comparable between different features and facilitate subsequent data analysis and modeling.

[0043] Specific embodiments further include establishing a feature engineering for the obtained basic power system data. Feature engineering is to extract useful information from the original data and construct new features to improve the prediction ability of the model. For the basic power system data, feature engineering includes time features, meteorological features, historical load features, etc. Time feature extraction: Extract time information such as hour, day of the week, month, etc. as time features. Meteorological feature extraction: Extract meteorological features such as temperature, humidity, wind speed, etc. according to meteorological data. Historical load feature extraction: Use historical load data to extract lag features, sliding window features, etc. Feature selection: Select features useful for model prediction according to methods such as correlation analysis and importance evaluation. Feature transformation: Transform the features, such as logarithmic transformation, polynomial transformation, etc., to improve the prediction ability of the model.

[0044] The role of establishing feature engineering: Extract useful information from the original data, construct new features, and improve the prediction ability of the model; reduce redundant features and improve the generalization ability of the model through feature selection and transformation.

[0045] For example: Preprocess the carbon emission data of thermal power units. Data cleaning: Remove outliers and noise data, such as carbon emission values beyond the reasonable range. Missing value filling: Fill in the missing carbon emission data using mean filling or interpolation method. Data standardization: Convert the carbon emission data into dimensionless data for subsequent analysis. Feature engineering: Extract features such as the output situation and fuel type of thermal power units to construct a carbon emission prediction model. Role: Ensure the accuracy and integrity of carbon emission data and provide a reliable basis for the subsequent construction of environmental objective functions.

[0046] Preprocess the output data of thermal power units. Data cleaning: Remove outliers and noise data, such as outliers beyond the output range of the unit. Missing value filling: Fill in the missing output data using interpolation method or regression method. Data standardization: Convert the output data into dimensionless data for subsequent analysis. Feature engineering: Extract features such as unit type, capacity, operation status, etc. to construct an output prediction model. Role: Ensure the accuracy and integrity of output data and provide a reliable basis for the subsequent construction of economic objective functions.

[0047] Preprocess the wind power output data. Data cleaning: Remove outliers and noise data, such as abnormal output values caused by sudden changes in wind speed. Missing value filling: Fill in the missing output data using interpolation method or a filling method based on a wind speed prediction model. Data standardization: Convert the output data into dimensionless data for subsequent analysis. Feature engineering: Extract features such as wind speed, wind direction, wind turbine type, etc. to construct a wind power output prediction model. Role: Ensure the accuracy and integrity of wind power output data and provide a reliable basis for the subsequent coordinated scheduling of wind power and electric vehicles.

[0048] Preprocess the electric vehicle charging data, including data cleaning: remove outliers and noise data, such as charging power and charging time that exceed the reasonable range. Fill in missing values: use mean filling or filling methods based on user charging behavior characteristics to fill in the missing charging data. Data standardization: convert the charging data into dimensionless data for subsequent analysis. Feature engineering: extract features such as user charging behavior characteristics, electric vehicle types, and battery capacity, and construct an electric vehicle charging power prediction model.

[0049] Step S2, process the preprocessed basic data through a preset low-carbon scheduling model to obtain the corresponding scheduling strategy; among them, the low-carbon scheduling model solves the objective function through a preset algorithm, with the goal of minimizing the comprehensive cost of wind power generation cost, and determines the corresponding electric vehicle charging power value, where the comprehensive cost of wind power generation cost at least includes the variance of the total grid load, the total charging cost of electric vehicle users, and the carbon trading cost; it can be understood that based on the obtained basic data, a low-carbon scheduling model under wind power coordination is constructed, including an objective function, electric vehicle constraints, and power system power balance constraints. On the basis of the constructed model, use an optimization algorithm to solve the objective function and generate the electric vehicle charging power value when the variance of the total grid load, the total charging cost of electric vehicle users, the carbon trading cost, and the comprehensive cost of wind power generation are minimized.

[0050] Specific embodiments, the optimization algorithms include genetic algorithms, particle swarm algorithms, simulated annealing algorithms, etc. According to the complexity of the problem and the requirements of the solution accuracy, select a suitable algorithm for solving. The algorithm parameters include population size, number of iterations, crossover probability, mutation probability, etc., to ensure that the algorithm has good convergence and stability during the solution process. Combine the constructed model and the optimization algorithm, and run the algorithm for solution. During the solution process, continuously iterate and update the solution set until the termination condition is met (such as reaching the maximum number of iterations or converging to the optimal solution). Output the solution results, including the charging power values of the electric vehicle at each time period, the variance of the total grid load, the total charging cost of electric vehicle users, the carbon trading cost, and the wind power generation cost, etc. The operation and solution of the optimization algorithm can be realized by using programming languages and mathematical optimization software. Through visualization technology and data analysis tools, display and analyze the solution results. Function: Optimization solution is the key link of the coordinated scheduling method. By solving, the optimal electric vehicle charging power value is obtained, realizing the balance of the grid load and the minimization of the cost. The objective function comprehensively considers the comprehensive minimization of the variance of the total grid load, the total charging cost of electric vehicle users, the carbon trading cost, and the wind power generation cost.

[0051] In one embodiment, the low-carbon scheduling model includes a total grid load variance function:

[0052]

[0053] Among them, σ2 represents the variance of the total grid load, P(t) represents the load power at time t, and T represents the time period under investigation; the total grid load variance function reflects the fluctuation of the grid load. The total grid load variance function is an important tool for quantifying the fluctuation of the grid load. In the power system, the load changes with time, and this change is usually random. The uncertainty of the load parameters will lead to the instability of the dynamic response in the power system. Therefore, in order to evaluate the degree of this load change, it is necessary to define a variance function to describe the fluctuation of the total grid load. The total grid load variance function is usually defined as the average of the squares of the deviations of the load power P(t) from its average value. The magnitude of the total grid load variance function has an important impact on both the operation of the power system and the lifespan of the equipment. When the load variance is large, it indicates that the change range of the load is large, and the uncertainty of the system operation is high, which may lead to a decrease in the stability and reliability of the power system. On the contrary, when the load variance is small, it indicates that the change range of the load is small, and the uncertainty of the system operation is low, and the stability and reliability of the power system are relatively high.

[0054] The total charging cost of electric vehicle users is the product of the unit electricity price and the charging amount during the corresponding charging period plus other costs; the total charging cost function takes into account the charging demand and charging cost of electric vehicles. Defining the total charging cost function of electric vehicle users is a comprehensive consideration process involving multiple factors, aiming to accurately reflect the dynamic relationship between the charging demand and charging cost of users. This function can be expressed as:

[0055] Total cost (TC) = f(charging amount Q, unit electricity price P, charging period T, other costs C_other)

[0056] Among them, the charging amount Q represents the amount of electricity that needs to be charged into the electric vehicle to meet the driving demand; the unit electricity price P is affected by factors such as the grid supply and demand relationship, time periods (such as peak hours, normal hours, valley hours), and policy subsidies; the charging period T not only affects the electricity price but may also be related to the congestion degree of the charging station and the service fee; other costs C_other cover possible parking fees, membership fees, or special service fees, etc., which are defined according to actual needs. The core of this function is to comprehensively consider the interaction between various variables, and through accurate measurement and reasonable pricing mechanisms, to achieve the transparency and optimization of the charging cost. For example, by introducing an intelligent charging management system, users can charge during periods with lower electricity costs to reduce the charging cost; at the same time, the system can also dynamically adjust the charging strategy according to the real-time electricity price to provide users with an economical and efficient charging plan;

[0057] The carbon trading cost function is the product of carbon emissions and the price of the carbon trading market. The carbon trading cost and wind power generation cost function considers the carbon emission cost of thermal power units and the power generation cost of wind power. The carbon trading cost function can be expressed as the product of carbon emissions (usually in tons) and the carbon trading market price (usually in yuan / ton). When the company's emissions exceed its quota, the carbon trading cost is positive, otherwise it may be negative (that is, the company sells excess quotas to make a profit). The wind power generation cost function mainly covers factors such as the initial investment cost, operation and maintenance costs, and power generation efficiency of the wind farm. The initial investment cost includes wind turbine purchase costs, installation engineering costs, construction engineering costs, etc., and the operation and maintenance costs involve the daily operation and maintenance, insurance and management costs of the wind farm. The power generation efficiency determines the power generation of the wind farm at a given wind speed, thereby affecting its unit power generation cost.

[0058] When considering the carbon emission costs of thermal power units and the generation costs of wind power, a comprehensive cost function can be constructed. This function incorporates the carbon trading costs (based on the emissions of thermal power units) and the wind power generation costs (based on the various costs of wind farms), while taking into account factors such as the reliability and stability of wind power and the fuel costs of thermal power units. Such a cost function helps to optimize the dispatch and operation strategies of the power system to achieve a win-win situation in terms of economic and environmental benefits.

[0059] The low-carbon scheduling model also includes electric vehicle constraints, which are used to ensure that the charging power and charging time of electric vehicles are within a preset range, and the charge state when arriving at the charging station is greater than a preset lower threshold; and electric power balance constraints, which are used to limit the power emitted by the power grid to be greater than or equal to the sum of the power consumed by the load and the transmission loss.

[0060] Electric vehicle constraints include charging power constraints, charging time constraints, and charging demand constraints. Charging power constraints are important parameters in the charging process of electric vehicles. It is necessary to ensure that the charging power of electric vehicles is within the specified range, meeting the lower power limits of fast charging and slow charging, and not exceeding their upper limits. This helps protect charging equipment and electric vehicle batteries and avoids low charging efficiency or battery damage caused by excessive or insufficient power. The charging time of electric vehicles should be moderate and should not be too long or too short. Generally, charging for 8 to 10 hours can fully charge the battery. At the same time, frequent short-term charging and excessive charging time should be avoided to extend the battery life and reduce safety hazards. Charging demand constraints involve the actual usage needs of users. Electric vehicle users should reasonably arrange the charging time and charging amount according to their own driving plans and power conditions, ensure that the state of charge is greater than the lower limit threshold when arriving at the charging station, and complete sufficient charging during the stay time to meet the driving needs of the next stage.

[0061] Power balance constraints in the power system ensure power balance in the power grid during the dispatching process and avoid overload or underload phenomena. It is necessary to clarify the electric power balance constraint, that is, the power generated by the power source should be equal to the sum of the power consumed by the load and the transmission loss. Considering the uncertainty of renewable energy such as wind and light, its output range should be reasonably predicted and corresponding reserve capacity should be set to cope with load fluctuations and output changes. Thermal power balance constraints and hydrogen power balance constraints are equally important. For a combined heat and power system, it is necessary to ensure that the heat and power output match the load demand, and at the same time consider the ramp constraint to avoid the impact on the power grid caused by frequent start-stop of the units. For a hydrogen energy system, it is necessary to ensure the balance of hydrogen production, storage and consumption links.

[0062] In addition, it is also necessary to pay attention to unit constraints, including wind and light output constraints, operation equipment constraints of the integrated electricity-thermal-hydrogen energy system, etc., to ensure the stable operation of various units within the specified output range. Using mathematical modeling and simulation methods, the above objective function and constraint conditions are constructed. Through programming languages and simulation software, the establishment and solution of the model are realized. Function: Model construction is the core link of the coordinated dispatching method, providing a mathematical basis and theoretical basis for subsequent optimization and solution.

[0063] Step S3, perform dispatching control on the power grid according to the dispatching strategy to achieve load adjustment of the power grid. The dispatching strategy at least includes the charging time, charging power and charging amount of electric vehicles. It can be understood that according to the obtained charging power values of electric vehicles at each time period, a dispatching strategy for electric vehicles is generated. The dispatching strategy includes the charging time, charging power and charging amount of electric vehicles, etc. Apply the generated dispatching strategy to the actual power grid to conduct charging dispatching for electric vehicles. Through the remote monitoring system and intelligent charging equipment, the charging control and dispatching of electric vehicles are realized. During the actual operation process, continuously monitor the power grid load and the charging situation of electric vehicles. According to the monitoring results, adjust and optimize the dispatching strategy to ensure the stability of the power grid and meet the charging requirements of electric vehicles. Use the remote monitoring system and intelligent charging equipment to realize the implementation and monitoring of the dispatching strategy. Through data analysis and simulation technology, adjust and optimize the dispatching strategy. Function: The generation and implementation of the dispatching strategy are the final links of the coordinated dispatching method. By implementing and optimizing the dispatching strategy, the balance of the power grid load and the efficient management of electric vehicle charging are realized.

[0064] An embodiment of the present invention also provides a dispatching system for a power grid including electric vehicles and wind power, which is used to implement the dispatching method for a power grid including electric vehicles and wind power, including

[0065] A data acquisition module, configured to acquire the basic data of the power system and preprocess the basic data; wherein, the basic data at least includes thermal power unit carbon emission data, thermal power unit output data, wind power output data, and electric vehicle charging data;

[0066] A strategy generation module, configured to process the preprocessed basic data through a preset low-carbon scheduling model to obtain corresponding scheduling strategies; wherein, the low-carbon scheduling model solves the objective function through a preset algorithm, with the goal of minimizing the comprehensive cost of wind power generation, and determines the corresponding electric vehicle charging power value, wherein the comprehensive cost of wind power generation at least includes the variance of the total grid load, the total charging cost of electric vehicle users, and the carbon trading cost;

[0067] A scheduling module, configured to perform scheduling control on the power grid according to the scheduling strategy to achieve load adjustment of the power grid.

[0068] In a specific embodiment, the data acquisition module is specifically configured to detect the processed basic data according to a preset data standard. If abnormal basic data is detected, the abnormal basic data is deleted; if missing values are detected in the basic data, the basic data is filled; if the basic data is detected to be unstandardized, standardization processing is performed; extract the feature information of the basic data, and determine the corresponding feature items according to the feature information, where the feature information at least includes time features, meteorological features, and historical load features.

[0069] The strategy generation module is specifically used for the function of the variance of the total grid load:

[0070]

[0071] Among them, σ2 represents the variance of the total grid load, P(t) represents the load power at time t, and T represents the investigated time period; the total charging cost of electric vehicle users is the product of the unit electricity price and the charging amount during the corresponding charging period plus other costs; the carbon trading cost function is the product of the carbon emission and the carbon trading market price.

[0072] The strategy generation module is further configured to perform electric vehicle constraints, where the charging power and charging time of the electric vehicle are within a preset range, and the state of charge when arriving at the charging station is greater than a preset lower threshold; and electric power balance constraints, which are used to limit the power generated by the power grid to be greater than or equal to the sum of the power consumed by the load and the transmission loss.

[0073] It should be noted that the system described in the above embodiment corresponds to the method described in the above embodiment. Therefore, the parts not detailed in the system described in the above embodiment can be obtained by referring to the content of the method described in the above embodiment, and will not be elaborated here.

[0074] In summary, implementing the embodiments of the present invention has the following beneficial effects:

[0075] For the dispatching method and system of a power grid including electric vehicles and wind power provided by the present invention, obtaining the basic data of the power system is a prerequisite for accurate dispatching, ensuring the comprehensiveness and timeliness of information and providing a solid foundation for subsequent analysis; by preprocessing the basic data, outliers can be effectively removed and missing data can be filled, improving the accuracy and reliability of the data and laying a good foundation for model construction; when constructing a low-carbon dispatching model with wind power coordination, the intermittency of wind power and the flexibility of electric vehicle charging are fully considered, realizing the complementarity and coordination between the two, and effectively improving the low-carbon operation level of the system; using an optimization algorithm to solve the objective function can quickly find the optimal electric vehicle charging power value that comprehensively optimizes the variance of the total grid load, the total cost of electric vehicle charging, the carbon trading cost, and the wind power generation cost in a complex and changeable power system environment, which not only improves the dispatching efficiency but also significantly reduces the system operation cost; the electric vehicle dispatching strategy generated according to the optimization solution results has high operability and adaptability. During implementation, it can be flexibly adjusted according to the actual situation to ensure that the dispatching strategy always meets the actual needs of power system operation, further enhancing the stability and security of the system.

[0076] The above-disclosed are only the preferred embodiments of the present invention. Of course, the scope of the rights of the present invention cannot be limited thereby. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. A method for dispatching a power grid including electric vehicles and wind power, characterized in that: include: Obtaining basic data of the power system and preprocessing the basic data; wherein the basic data at least includes carbon emission data of thermal power units, output data of thermal power units, wind power output data and electric vehicle charging data; The pre-processed basic data is processed by a preset low-carbon dispatch model to obtain a corresponding dispatch strategy; wherein the low-carbon dispatch model solves the objective function by a preset algorithm, and determines the corresponding electric vehicle charging power value with the minimum comprehensive cost of wind power generation cost as the goal, wherein the comprehensive cost of wind power generation cost at least includes the total grid load variance, the total charging cost of electric vehicle users, and the carbon trading cost; The power grid is dispatched and controlled according to the dispatching strategy to achieve load adjustment of the power grid.

2. The method according to claim 1, characterized in that The preprocessing of the basic data comprises: The processed basic data is tested according to the preset data standards. If the basic data is detected to be abnormal, the abnormal basic data will be deleted; if the basic data is detected to have missing values, the basic data will be filled; if the basic data is detected to be unstandardized, it will be standardized; The characteristic information of the basic data is extracted, and corresponding characteristic items are determined according to the characteristic information, wherein the characteristic information at least includes time characteristics, meteorological characteristics, and historical load characteristics.

3. The method according to claim 2, characterized in that The low-carbon dispatch model includes a total grid load variance function: Among them, σ2 represents the variance of the total load of the power grid, P(t) represents the load power at time t, and T represents the time period of investigation; The total charging cost for electric vehicle users is the product of the unit electricity price and the charging amount during the corresponding charging period plus other costs; The carbon trading cost function is the product of carbon emissions and the carbon trading market price.

4. The method according to claim 3, characterized in that The low-carbon dispatch model also includes: Electric vehicle constraints, used to ensure that the charging power and charging time of the electric vehicle are within a preset range, and the state of charge when arriving at the charging station is greater than a preset lower threshold; The electric power balance constraint is used to limit the power output of the power grid to be greater than or equal to the sum of the power consumed by the load and the transmission loss.

5. The method according to claim 4, characterized in that The scheduling strategy includes at least the charging time, charging power and charging amount of the electric vehicle.

6. A dispatching system for a power grid including electric vehicles and wind power, used to implement the method according to any one of claims 1 to 5, characterized in that: include, A data acquisition module, used to acquire basic data of the power system and pre-process the basic data; wherein the basic data at least includes carbon emission data of thermal power units, output data of thermal power units, wind power output data and electric vehicle charging data; A strategy generation module is used to process the pre-processed basic data through a preset low-carbon dispatch model to obtain a corresponding dispatch strategy; wherein the low-carbon dispatch model solves the objective function through a preset algorithm, and determines the corresponding electric vehicle charging power value with the minimum comprehensive cost of wind power generation cost as the goal, wherein the comprehensive cost of wind power generation cost at least includes the total grid load variance, the total charging cost of electric vehicle users, and the carbon trading cost; The dispatching module is used to dispatch and control the power grid according to the dispatching strategy to achieve load adjustment of the power grid.

7. The system according to claim 6, characterized in that The data acquisition module is specifically used to detect the processed basic data according to the preset data standard, and if the basic data is detected to be abnormal, the basic data with the abnormality is deleted; if the basic data is detected to have missing values, the basic data is filled; if the basic data is detected to be unstandardized, standardization is performed; The characteristic information of the basic data is extracted, and corresponding characteristic items are determined according to the characteristic information, wherein the characteristic information at least includes time characteristics, meteorological characteristics, and historical load characteristics.

8. The system according to claim 7, characterized in that The strategy generation module is specifically used for the total load variance function of the power grid: Among them, σ2 represents the variance of the total load of the power grid, P(t) represents the load power at time t, and T represents the time period of investigation; The total charging cost for electric vehicle users is the product of the unit electricity price and the charging amount during the corresponding charging period plus other costs; The carbon trading cost function is the product of carbon emissions and the carbon trading market price.

9. The system according to claim 8, characterized in that The strategy generation module is also used for electric vehicle constraints, which are used to ensure that the charging power and charging time of the electric vehicle are within a preset range, and the state of charge when arriving at the charging station is greater than a preset lower threshold; The electric power balance constraint is used to limit the power output of the power grid to be greater than or equal to the sum of the power consumed by the load and the transmission loss.

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