Vehicle network interaction load control system based on peak regulation demand

Abstract

The invention discloses a vehicle-network interaction load control system based on a peak regulation demand, and the system employs the following method to schedule a charging load: S1, obtaining the load data of a power grid and the combination data of a starting unit, and determining the peak regulation demand of the power grid; s2, acquiring historical charging load data of the charging station, and generating a load proportion curve of ordered charging; s3, constructing a vehicle network interaction master-slave game model according to the peak regulation demand of the power grid and the user behavior; and S4, scheduling the charging load of the charging station based on an output result of the vehicle network interaction master-slave game model. According to the control system, by coordinating the charging load of the electric vehicle, peak clipping and valley filling are achieved, the peak-valley difference of the power grid is reduced, and the peak shaving pressure of the power generation side is relieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging control, and in particular to a vehicle-grid interactive load control system based on peak load regulation demand. Background Art

[0002] With the rapid popularization of electric vehicles, the impact of electric vehicle charging load on the power grid is becoming increasingly significant. Disorderly charging behavior may lead to an increase in the peak load of the power grid, increase the peak load regulation pressure of the power grid, and affect the safe and stable operation of the power grid. At the same time, the charging and discharging flexibility of electric vehicles provides a new means of regulation for the power grid. How to use electric vehicles to participate in the peak load regulation of the power grid while meeting the charging needs of users has become an urgent problem to be solved. In view of this, this application is specially proposed. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a vehicle-grid interactive load control system to solve the problem that disordered charging behavior may cause an increase in the peak load of the power grid, increase the peak-shaving pressure of the power grid, and affect the safe and stable operation of the power grid.

[0004] In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is as follows: a vehicle-grid interactive load control system based on peak-shaving demand, characterized in that the vehicle-grid interactive load control system adopts the following method to schedule the charging load: S1: obtain the power grid load data and the combination data of the started units to determine the power grid peak-shaving demand; S2: obtain the historical charging load data of the charging station to generate a load proportion curve for orderly charging; S3: construct a vehicle-grid interactive master-slave game model based on the peak-shaving demand of the power grid and user behavior; S4: based on the output result of the vehicle-grid interactive master-slave game model, schedule the charging load of the charging station.

[0005] Preferably, step S1 includes: S11: extracting historical load data and real-time load data of the power grid, and analyzing load change characteristics; S12: predicting future power grid load based on historical load data and real-time load data of the power grid; S13: obtaining combination information and regulation capabilities of the started units, and determining the peak-shaving demand of the power grid in different time periods.

[0006] Preferably, the step S2 includes: S21: collecting information of charging stations in a set area; S22: obtaining historical charging data of the charging stations; S23: analyzing user charging behavior and dividing user types based on the historical charging data; S24: evaluating user willingness to participate in orderly charging based on the analysis results of user charging behavior and generating an orderly charging load ratio curve.

[0007] Preferably, the step S3 includes: S31: establishing the participants of the vehicle-grid interactive master-slave game model, the master being the power grid dispatching organization, and the slave being the electric vehicle user; S32: defining the strategy space of the master and slave, and establishing the benefit functions of the master and slave; S33: establishing the constraints of the vehicle-grid interactive master-slave game model; S34: solving the game equilibrium solution of the vehicle-grid interactive master-slave game model, and determining the strategy space of the master and the response strategy of the slave.

[0008] Preferably, step S4 comprises: S41: formulating a charging scheduling plan; S42: notifying users of relevant policies and implementing charging control, and monitoring load adjustment effects in real time; S43: adjusting the scheduling plan according to user feedback, evaluating peak-shaving effects and summarizing experience.

[0009] Preferably, in step S12, time series analysis, artificial neural network or support vector machine algorithm are used to predict the trend of future power grid load changes.

[0010] Preferably, the step S23, analyzing the user's charging behavior includes statistical analysis of the average daily charging times, charging time distribution, and average charging duration, and classifying the user types into fixed-time charging users, random charging users, and high-frequency charging users.

[0011] Preferably, in step S34, the strategy space of the master party includes electricity price adjustment strategy, incentive strategy, and charging power control strategy, and the response strategy of the slave party includes charging time selection, charging duration, and whether to participate in incentives.

[0012] Preferably, the charging scheduling plan remotely controls the charging piles of the charging station through a charging management system to dynamically adjust the charging power or delay the charging start time.

[0013] Preferably, the real-time monitoring includes collecting and analyzing the operating status of the charging piles, charging load data and user response of the charging station.

[0014] Due to the adoption of the above technical solution, the present invention has the following beneficial effects:

[0015] 1. Meet the peak load regulation demand of the power grid: By coordinating the charging load of electric vehicles, peak load shaving and valley filling can be achieved, the peak-to-valley difference of the power grid can be reduced, and the peak load regulation pressure on the power generation side can be alleviated.

[0016] 2. Improve the efficiency of power grid operation: optimize the operation mode of generator sets, reduce the number of unit starts and stops and deep adjustments, and improve the economy and reliability of power grid operation.

[0017] 3. Promote user participation: Design a reasonable incentive mechanism that takes into account user interests and comfort, encourages users to actively participate in orderly charging, and achieves a win-win situation for vehicles and the network.

[0018] 4. Improve the capacity to absorb new energy: During periods of surplus new energy output, guide electric vehicles to increase their charging load, improve the utilization rate of clean energy, and reduce the phenomenon of wind and solar power abandonment. DETAILED DESCRIPTION

[0019] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the present invention will be further described in detail below. Generally, the components in the embodiments of the present invention can be arranged and designed in various configurations. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] Some embodiments of the present invention are described in detail below. In the absence of conflict, the features of the following embodiments can be combined with each other.

[0021] The method of the vehicle-grid interactive load control system based on peak-shaving demand in the present application for scheduling the charging load of a charging station includes steps S1, S2, S3 and S4.

[0022] Among them, step S1 is to obtain the power grid load data and the combined data of the starting units to determine the peak-shaving demand of the power grid. Step S1 includes S11 to S13. S11: Extract the historical load data and real-time load data of the power grid and analyze the load change characteristics. The historical load data of the power grid can be extracted from the database of the power grid dispatching center for example, the power grid load data of the past period of time (such as the past year or the past month), including periodic load changes, seasonal changes, etc. The power dispatching system can be used to obtain the power grid load data at the current moment to understand the current power grid operation status. S12: Based on the historical load data and real-time load data of the power grid, the future power grid load is predicted. Time series analysis, artificial neural network or support vector machine algorithm can be used to predict the trend of future power grid load changes, such as predicting the trend of power grid load changes in the short term (such as within 24 hours) in the future. S13: Obtain the combined information and regulation capacity of the starting units to determine the peak-shaving demand of the power grid in different time periods. Obtain the information of the generator sets started in the current power grid, including the type of unit (thermal power, hydropower, wind power, photovoltaic, etc.), maximum output, minimum output, ramp rate, start-stop cost, etc. According to the output characteristics of the generator set, the upper and lower adjustment capabilities of the computer set are calculated to determine the adjustable capacity of the generator set in different time periods. The predicted grid load curve is compared with the adjustment capacity of the generator set to identify the peak load period and load gap that may occur in the grid, and determine the specific demand for peak load regulation.

[0023] Among them, step S2 obtains the historical charging load data of the charging station and generates a load ratio curve for orderly charging. Step S2 includes steps S21 to S24. S21: Collect information about charging stations in a set area. For example, obtain basic information such as the distribution of charging stations in the set area, the number of charging piles, the type (fast charging, slow charging), and the operation and maintenance unit. S22: Obtain historical charging data of the charging station. The historical charging records of the charging piles of the charging station can be extracted from the charging operator or the charging pile management system, including the charging start time, end time, charging power, user ID, etc. S23: Analyze the user's charging behavior and divide the user type according to the historical charging data. The analysis of the user's charging behavior includes statistical analysis of the average daily charging times, charging period distribution, and average charging time, and divides the user type into fixed-time charging users (charging at a specific time every day, such as commuting time), random charging users (charging time is not fixed, with greater randomness), and high-frequency charging users (charging frequency is high, and may be operating vehicles). S24: According to the analysis results of the user's charging behavior, evaluate the user's willingness to participate in orderly charging and generate an orderly charging load ratio curve. The possibility and willingness of different types of users to participate in orderly charging can be evaluated through questionnaire surveys and historical data analysis. Based on the evaluation results and combined with the peak load demand, the proportion of charging load that users can adjust under different incentives is calculated to form an orderly charging load proportion curve corresponding to the time period.

[0024] Among them, step S3 is to construct a master-slave game model of vehicle-grid interaction according to the peak-shaving demand of the power grid and user behavior. Step S3 includes steps S31 to S34. S31: Establish the participants of the master-slave game model of vehicle-grid interaction, the master is the power grid dispatching organization, and the slave is the electric vehicle user. The power grid dispatching organization formulates incentive strategies to guide user charging behavior. S32: Define the strategy space of the master and the slave, and establish the benefit functions of the master and the slave. The strategy space of the master includes electricity price adjustment strategy (adjusting the peak-valley electricity price difference, setting dynamic electricity price), incentive strategy (points reward, cash back, coupons, etc.), charging power control strategy (limiting the maximum power of the charging pile), and the response strategy of the slave includes charging time selection, charging time, and whether to participate in incentives. The factors considered in the master's benefit function include peak-shaving benefits (cost savings or benefits brought by reducing peak load), incentive costs (incentive fees paid to users), and electricity sales revenue (income obtained from electricity sales). The goal is to maximize the overall benefit, that is, to control the incentive cost while meeting the peak-shaving demand. S34: Solve the game equilibrium solution of the vehicle-grid interactive master-slave game model to determine the strategy space of the master and the response strategy of the slave. The factors considered in the slave's benefit function include charging cost (electricity fee paid, affected by electricity price strategy), incentive benefit (reward obtained by participating in orderly charging), and comfort loss (inconvenience caused by adjusting charging time, quantified as cost). The goal is to maximize its own net benefit, that is, minimize the total charging cost. S33: Establish the constraint conditions of the vehicle-grid interactive master-slave game model. Constraints include power grid constraints (total load shall not exceed the safe operation limit of the power grid), charging service constraints (guarantee basic charging services, and shall not force restrictions on user charging), and user constraints (users must complete charging before the scheduled latest time to meet travel needs). The master-slave game (Stacke lberg Game) solution method can be adopted. The master first formulates a strategy, and the slave makes the best response according to the master's strategy. It can be solved by reverse induction, starting from the optimal response function of the slave, and obtaining the optimal strategy of the master. Obtain the electricity price and incentive strategy that the master should implement, as well as the charging behavior adjustment expectations of the slave. Estimate the overall charging load curve changes and evaluate the peak-shaving effect.

[0025] Among them, step S4 is to schedule the charging load of the charging station based on the output result of the vehicle-grid interactive master-slave game model. Step S4 includes steps S41 to S43. S41: Formulate a charging scheduling plan. According to the results of the game model, formulate a specific charging scheduling plan, including an electricity price adjustment plan (clarifying the electricity price in each time period), incentive measures (participation conditions, reward methods, reward amounts, etc.), and a charging power limit strategy (implementing power limits on some charging piles when necessary). S42: Notify users of relevant policies and implement charging control, and monitor the load adjustment effect in real time. Users can be notified of new electricity prices and incentive policies through APP, SMS, email, etc., publicize the benefits of participating in orderly charging, and guide users to actively participate. S43: Adjust the scheduling plan based on the feedback from the root user, evaluate the peak-shaving effect and summarize the experience. The charging scheduling plan remotely controls the charging piles of the charging station through the charging management system to dynamically adjust the charging power or delay the charging start time. Real-time monitoring includes the collection and analysis of the operating status of the charging piles of the charging station, charging load data and user response. According to the real-time data, it is compared with the plan to judge the execution effect of the scheduling plan. If deviations are found, timely adjustments will be made, such as adjusting incentives and optimizing control strategies. After the scheduling cycle is over, the peak-shaving effect and user responsiveness will be evaluated. User feedback will be analyzed, experience will be summarized, and improvement suggestions will be provided for subsequent scheduling.

[0026] The above-mentioned dispatching method is applied, for example, to the peak load regulation of the urban power grid. In a specific application scenario, the power grid load data of a certain city in the past month is first collected to confirm the peak period and peak load. The load forecasting model is used to predict the peak load in the next week, and it is estimated that 100MW of load needs to be reduced from 19:00 to 21:00. Further, the information of the current operating units is obtained to evaluate the peak load regulation capacity on the power generation side, which is not enough to fully meet the demand. It is determined that part of the load needs to be reduced through load side management, and the charging load of electric vehicles becomes an important regulation object. Further, the orderly charging load ratio is generated, and the historical charging records of the main charging stations in the city are obtained. Analysis shows that the charging load during the period of 19:00-21:00 is about 50MW. Through questionnaire surveys, the willingness of users to adjust the charging time is understood, and it is found that 50% of users are willing to postpone charging under incentives. It is estimated that 25MW of load can be reduced through orderly charging. A master-slave game model was constructed. The master strategy included the electricity price strategy (a 20% increase in electricity prices during peak hours and a 30% decrease in electricity prices during off-peak hours) and incentives (a 5 yuan reward for users who participated in orderly charging). The slave (user) strategy included whether to participate in orderly charging and postpone the charging time to the off-peak period (after 22:00). The model results show that under the above incentives, the city can reduce the peak charging load by 30MW.

[0027] The vehicle-grid interactive load control system based on peak-shaving demand of the present invention can achieve a positive interaction between the power grid and electric vehicle users, meet the peak-shaving demand of the power grid, improve the new energy consumption capacity, and take into account the interests and experience of users.

[0028] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A vehicle-grid interactive load control system based on peak load regulation demand, characterized in that: The vehicle-grid interactive load control system uses the following method to dispatch charging load: S1: Obtain grid load data and startup unit combination data to determine grid peak regulation requirements; S2: Obtain historical charging load data of the charging station and generate a load ratio curve for orderly charging; S3: Based on the peak load demand of the power grid and user behavior, a master-slave game model of vehicle-grid interaction is constructed; S4: Based on the output result of the vehicle-grid interactive master-slave game model, the charging load of the charging station is scheduled.

2. The vehicle-grid interactive load control system according to claim 1, characterized in that: The step S1 comprises: S11: extract historical load data and real-time load data of the power grid and analyze load change characteristics; S12: Predict future grid load based on historical grid load data and real-time grid load data; S13: Obtain the combination information and regulation capabilities of the powered-on units to determine the peak regulation requirements of the power grid in different time periods.

3. The vehicle-grid interactive load control system according to claim 1, characterized in that: The step S2 comprises: S21: Collect information about charging stations in a set area; S22: Obtain historical charging data of the charging station; S23: Analyze the user's charging behavior and classify the user types according to the historical charging data; S24: Based on the analysis results of the user's charging behavior, the user's willingness to participate in orderly charging is evaluated, and an orderly charging load ratio curve is generated.

4. The vehicle-grid interactive load control system according to claim 1, characterized in that: The step S3 comprises: S31: establishing the participants of the master-slave game model of vehicle-grid interaction, where the master party is the grid dispatching organization and the slave party is the electric vehicle user; S32: define the strategy space of the master and the slave, and establish the benefit functions of the master and the slave; S33: Establishing constraint conditions of the vehicle-grid interactive master-slave game model; S34: Solve the game equilibrium solution of the vehicle-grid interactive master-slave game model to determine the strategy space of the master and the response strategy of the slave.

5. The vehicle-grid interactive load control system according to claim 1, characterized in that: The step S4 comprises: S41: Formulate a charging scheduling plan; S42: Notify users of relevant policies and implement charging control, and monitor load adjustment effects in real time; S43: Adjust the scheduling plan based on the feedback from root users, evaluate the peak load effect and summarize the experience.

6. The vehicle-grid interactive load control system according to claim 2, characterized in that: The step S12 uses time series analysis, artificial neural network or support vector machine algorithm to predict the trend of future power grid load changes.

7. The vehicle-grid interactive load control system according to claim 3, characterized in that: The step S23, analyzing the user's charging behavior includes statistical analysis of the average daily charging times, charging time distribution, and average charging duration, and classifying the user types into fixed-time charging users, random charging users, and high-frequency charging users.

8. The vehicle-grid interactive load control system according to claim 4, characterized in that: In step S34, the strategy space of the master includes electricity price adjustment strategy, incentive strategy, and charging power control strategy, and the response strategy of the slave includes charging time selection, charging duration, and whether to participate in incentives.

9. The vehicle-grid interactive load control system according to claim 5, characterized in that: The charging scheduling plan remotely controls the charging piles of the charging station through the charging management system to dynamically adjust the charging power or delay the charging start time.

10. The vehicle-grid interactive load control system according to claim 5, characterized in that: The real-time monitoring includes the collection and analysis of the charging station's charging pile operating status, charging load data and user response.