New energy electric vehicle time-phased ordered charging management method

Abstract

The invention discloses a new energy electric vehicle time-phased ordered charging management method, which comprises six steps of data acquisition and preprocessing, user demand classification and parameter extraction, optimization model and constraint condition establishment, mode-divided scheduling strategy, dynamic adjustment and real-time control, and user interaction and feedback. According to the method, electric vehicle users are divided into interruptible and uninterruptible charging users for separate effective management, different charging priorities are distributed to different modes of users, and different scheduling strategies and different constraint conditions are adopted in a targeted manner, so that the load balance of a power grid and the stable operation of the power grid are ensured, and the charging efficiency is improved. Personalized charging services are provided for electric vehicle users with different requirements; the method has the advantages of being high in management level and good in charging service quality, and personalized charging services can be provided for electric vehicle users with different requirements while the load balance of the power grid and the stable operation of the power grid are guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of charging management of new energy electric vehicles, and in particular relates to a time-divided orderly charging management method for new energy electric vehicles. Background Art

[0002] New energy electric vehicles play a vital role in environmental protection and energy conservation. However, in some areas, the integration of large numbers of electric vehicles into distribution networks can cause a series of problems. For example, grid overload and voltage instability can occur. Electric vehicle charging requires a significant amount of electricity. When charging demand exceeds the distribution network's capacity, this can lead to grid overload and voltage instability, potentially causing equipment damage and power outages. Furthermore, when there are too many charging stations, charging demand may not always match supply. This large number of electric vehicles connected to distribution networks can create difficulties in demand-side management. Demand at existing charging stations is unpredictable and largely relies on user self-management, resulting in high randomness and difficulty in demand-side management, posing a challenge for distribution network operators. Furthermore, the aging of distribution equipment can be a problem. Excessive electric vehicle charging stations can exacerbate this degradation and increase operational and maintenance pressures. To address these issues, it is necessary to develop a method for managing the orderly, time-based charging of new energy electric vehicles. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a time-sharing orderly charging management method for new energy electric vehicles with high management level and good charging service quality. While ensuring the load balance and stable operation of the power grid, it can provide personalized charging services to electric vehicle users with different needs.

[0004] The object of the present invention is achieved as follows: a method for managing the orderly charging of a new energy electric vehicle by time period, comprising the following steps:

[0005] S1, data collection and preprocessing: collect and preprocess grid operation data and user charging demand data respectively;

[0006] S2, User Demand Classification and Parameter Extraction: Based on the collected user charging demand data, the interruptible mode parameters of users with interruptible charging are extracted, including total power demand, cutoff time, charging power, and number of allowed interruptions. For users with non-interruptible charging, the non-interruptible parameters are extracted, including total power demand, earliest start time, latest completion time, charging power, and continuous charging duration. Continuous charging duration = total power demand / charging power. Priority is assigned to vehicles with charging needs based on user willingness to pay or grid urgency.

[0007] S3, establish optimization model and constraints:

[0008] S31, objective function: min(α·load fluctuation + β·user cost); where α and β are corresponding weight coefficients, α+β=1;

[0009] in, Where T is the total number of time periods, L t is the base load of period t, N is the total number of electric vehicles dispatched in period t, P i,t is the charging power of the i-th vehicle in period t, L avg is the average total load for all periods of the day,

[0010] in, Where, π t The electricity price for the time period;

[0011] S32, constraints:

[0012] Grid security constraints: total charging power in each period ≤ transformer capacity limit;

[0013] User demand constraints: In the interruptible mode, the total charging capacity must meet user demand; in the non-interruptible mode, continuous charging time periods must be allocated;

[0014] S4, mode-based scheduling strategy:

[0015] S41, Interruptible Charging Scheduling Strategy: Using dynamic programming or heuristic algorithms, first divide the entire day into multiple time periods of 15 minutes to 1 hour, sort them from low to high by electricity price and load, and then allocate charging time periods to users in order of priority, giving priority to filling low-price time periods until the corresponding constraints are met;

[0016] S42, non-interruptible charging scheduling strategy: adopts time window sliding matching, first calculates the required continuous duration, and then searches for the continuous period with the lowest load within the user time window according to priority until the corresponding constraint conditions are met;

[0017] S5, Dynamic Adjustment and Real-Time Control: This includes grid status monitoring, interruptible mode rescheduling, and uninterruptible mode emergency handling. Grid status monitoring involves real-time detection of sudden load changes, triggering dynamic adjustment. Interruptible mode rescheduling involves suspending charging for low-priority users if the load exceeds the limit during a certain period, and reallocating their remaining power to subsequent low-priority periods. Uninterruptible mode emergency handling involves notifying users and providing compensation if a grid failure causes an outage.

[0018] S6, user interaction and feedback: including charging plan push, user intervention mechanism and satisfaction feedback; charging plan push is to display the scheduling results to users through the APP; the user intervention mechanism is to allow users to manually adjust the charging mode or deadline, in which case rescheduling is required; and satisfaction feedback is to collect user ratings for optimizing the algorithm weight coefficients α and β.

[0019] Preferably, in step S1, the grid operation data includes real-time grid load, historical load curve, regional electricity price time-sharing data, transformer capacity limit and weather conditions, wherein the regional electricity price time-sharing data includes peak, flat and valley period electricity prices; the grid operation data preprocessing includes cleaning abnormal data, normalization processing and generating the grid load forecast curve for the next 24 hours.

[0020] Preferably, in step S1, the user charging demand data includes charging amount, expected completion time, charging mode selection, vehicle battery parameters and charging pile status, wherein the vehicle battery parameters include battery capacity and current SOC, and the charging pile status includes available power and geographical location; the user charging demand data preprocessing includes data format unification and calculation of total charging amount, total charging amount = battery capacity × (target SOC - current SOC).

[0021] Preferably, in step S2, the priority includes high priority and normal priority, wherein high priority users include appliances and emergency vehicles willing to pay premium electricity charges, and normal priority users include ordinary household users and commercial fleets.

[0022] Preferably, in step S5, focus is placed on real-time load surges caused by extreme weather.

[0023] Due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0024] Based on the collected data, the present invention divides electric vehicle users into those with interruptible charging and those without, and manages them separately and effectively. Different charging priorities are assigned to users with different modes. By adopting targeted scheduling strategies and different constraints, the present invention provides personalized charging services to electric vehicle users with different needs while ensuring grid load balance and stable operation. This ensures high management level and good charging service quality.

[0025] The present invention adopts dynamic adjustment and real-time monitoring to adapt to the real-time changes in grid load, so that the grid load maintains dynamic balance and further improves the level of grid management;

[0026] The present invention adopts user interaction and feedback to strengthen communication with users, ensure that user demands are delivered in a timely manner, enhance user experience and further improve the service level of the power grid;

[0027] In general, the present invention has the advantages of high management level and good charging service quality. DETAILED DESCRIPTION

[0028] The technical solution of the present invention is further specifically described below through examples.

[0029] The present invention provides a method for managing the orderly charging of new energy electric vehicles by time period, comprising the following steps:

[0030] S1, data collection and preprocessing: collect and preprocess the grid operation data and user charging demand data respectively.

[0031] Grid operation data includes real-time grid load, historical load curves, regional electricity price time-sharing data, transformer capacity limitations and weather conditions, etc., which are obtained through smart meters, power dispatching centers and weather forecast interfaces. Among them, regional electricity price time-sharing data refers to electricity prices during peak, flat and valley periods.

[0032] The preprocessing method of power grid operation data is: cleaning abnormal data (such as sudden load increase caused by sensor failure), normalizing it and generating the power grid load forecast curve for the next 24 hours.

[0033] User charging demand data includes charging amount, expected completion time, charging mode selection, vehicle battery parameters and charging pile status, etc., which are submitted through the user's mobile phone APP, vehicle terminal or charging pile interactive interface. The vehicle battery parameters include battery capacity and current SOC, and the charging pile status includes available power and geographical location.

[0034] The preprocessing method of user charging demand data is as follows: data format unification and calculation of total charging capacity, total charging capacity = battery capacity × (target SOC – current SOC).

[0035] S2, user demand classification and parameter extraction: Based on the collected user charging demand data, the interruptible mode parameters of users with interruptible charging are extracted, including total power demand, deadline, charging power, and number of allowed interruptions. For users with non-interruptible charging, the non-interruptible parameters are extracted, including total power demand, earliest start time, latest completion time, charging power, and continuous charging duration, where continuous charging duration = total power demand / charging power.

[0036] Priority is assigned to vehicles with charging needs based on the user's willingness to pay or the urgency of the power grid. The priorities are divided into high priority and normal priority. High priority users include appliances and emergency vehicles willing to pay premium electricity charges, and normal priority users include ordinary household users and commercial fleets.

[0037] Interruptible charging users can interrupt the charging process and only need to complete the total charge before a specified deadline, such as charging at night. Non-interruptible charging users require charging to be completed in one go, such as emergency charging.

[0038] S3, establish optimization model and constraints:

[0039] S31, objective function: min(α·load fluctuation + β·user cost); where α and β are corresponding weight coefficients, α+β=1;

[0040] in, Where T is the total number of time periods, L t is the base load of period t, N is the total number of electric vehicles dispatched in period t, P i,t is the charging power of the i-th vehicle in period t, L avg is the average total load for all periods of the day,

[0041] in, Where, π t The electricity price for the period.

[0042] S32, constraints:

[0043] Grid security constraints: total charging power in each period ≤ transformer capacity limit;

[0044] User demand constraints: In the interruptible mode, the total charging capacity must meet user demand, while in the non-interruptible mode, continuous charging time periods must be allocated.

[0045] S4, mode-based scheduling strategy:

[0046] S41, Interruptible Charging Scheduling Strategy: Using dynamic programming or heuristic algorithms, first divide the entire day into multiple time periods of 15 minutes to 1 hour, sort them from low to high according to electricity price and load, and then allocate user charging time periods in sequence according to priority, giving priority to filling low-price time periods until the corresponding constraints are met.

[0047] In interruptible charging mode, the power system divides the total charging demand time into multiple time periods and, through an advanced dispatch center, schedules these time periods to coincide with periods of low grid load and low prices. While the user's vehicle is charging, the charger communicates with the management system in real time to ensure that charging is conducted as far as possible during peak grid periods. This charging model helps balance the overall grid load, optimize resource allocation, and reduce grid pressure, making grid operation more efficient and economical. In this way, power companies can effectively manage energy consumption and reduce overloading of power facilities during peak periods.

[0048] S42, non-interruptible charging scheduling strategy: adopt time window sliding matching, first calculate the required continuous duration, and then slide the search for the continuous period with the lowest load within the user time window according to priority until the corresponding constraint conditions are met.

[0049] In continuous charging mode, the power system schedules a continuous charging schedule based on user-submitted charging requests until the vehicle is fully charged or reaches the user's set charging target. This mode eliminates the need for user adjustments and is suitable for scenarios requiring rapid replenishment, such as when a user needs to quickly obtain sufficient power for a short trip. The main advantages of continuous charging lie in its simple control method and ease of operation.

[0050] S5, dynamic adjustment and real-time control: including grid status monitoring, interruptible mode re-dispatch and uninterruptible mode emergency handling; grid status monitoring is to detect sudden changes in load in real time, trigger dynamic adjustment, and focus on real-time load surges caused by extreme weather; interruptible mode re-dispatch is to suspend charging of low-priority users if the load exceeds the limit in a certain period of time, and reallocate their remaining power to subsequent low-priority periods; uninterruptible mode emergency handling is to notify users and provide compensation if a grid failure causes an interruption.

[0051] S6, user interaction and feedback: including charging plan push, user intervention mechanism and satisfaction feedback; charging plan push is to display the scheduling results to users through the APP; the user intervention mechanism is to allow users to manually adjust the charging mode or deadline, in which case rescheduling is required; and satisfaction feedback is to collect user ratings for optimizing the algorithm weight coefficients α and β.

[0052] In addition, orderly charging management by time period can also be combined with mobile applications and data analysis platforms to improve user experience and management efficiency. Through mobile applications, users can view the grid load, electricity price fluctuations and charging status in real time, and flexibly arrange their charging time. Users can also choose the charging mode (such as intermittent or non-interruptible charging) according to their own needs, and set preference options, such as economic priority or fast charging priority. The system will automatically adjust the charging arrangement based on user selection and current grid load to ensure that users can enjoy the best charging experience while the grid load is effectively managed. The real-time data analysis platform can also use historical electricity consumption data and forecasting models to better formulate future charging strategies and load scheduling plans, thereby improving the adaptability and operational stability of the grid.

[0053] In summary, this invention, by integrating these strategies and technologies, manages the orderly charging of connected new energy electric vehicles by time period, achieving a win-win situation for both user experience and grid operation. Through intelligent and flexible charging scheduling, it not only meets the diverse charging needs of users but also maintains the stability and sustainability of the grid.

[0054] In summary, the time-based, orderly charging management provided by this invention is a strategy that optimizes grid load and user charging experience by allocating charging time. Because charging stations are influenced by both user behavior and grid operating conditions when scheduling new energy vehicle charging times, implementing time-based, orderly charging management can help balance grid load and ensure stable system operation.

[0055] In the future, this charging management model will continue to develop, combined with more artificial intelligence and data optimization technologies to further improve charging efficiency and the utilization of power resources, and provide reliable guarantees for the coordinated development of new energy vehicles and power grids.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for managing the orderly charging of new energy electric vehicles by time period, characterized in that: The steps include: S1, data collection and preprocessing: collect and preprocess grid operation data and user charging demand data respectively; S2, User Demand Classification and Parameter Extraction: Based on the collected user charging demand data, the interruptible mode parameters of users with interruptible charging are extracted, including total power demand, cutoff time, charging power, and number of allowed interruptions. For users with non-interruptible charging, the non-interruptible parameters are extracted, including total power demand, earliest start time, latest completion time, charging power, and continuous charging duration. Continuous charging duration = total power demand / charging power. Priority is assigned to vehicles with charging needs based on user willingness to pay or grid urgency. S3, establish optimization model and constraints: S31, objective function: min(α·load fluctuation + β·user cost); where α and β are corresponding weight coefficients, α+β=1; in, Where T is the total number of time periods, L t is the base load of period t, N is the total number of electric vehicles dispatched in period t, P i,t is the charging power of the i-th vehicle in period t, L avg is the average total load for all periods of the day, in, Where, π t The electricity price for the time period; S32, constraints: Grid security constraints: total charging power in each period ≤ transformer capacity limit; User demand constraints: In the interruptible mode, the total charging capacity must meet user demand; in the non-interruptible mode, continuous charging time periods must be allocated; S4, mode-based scheduling strategy: S41, Interruptible Charging Scheduling Strategy: Using dynamic programming or heuristic algorithms, first divide the entire day into multiple time periods of 15 minutes to 1 hour, sort them from low to high by electricity price and load, and then allocate charging time periods to users in order of priority, giving priority to filling low-price time periods until the corresponding constraints are met; S42, non-interruptible charging scheduling strategy: adopts time window sliding matching, first calculates the required continuous duration, and then searches for the continuous period with the lowest load within the user time window according to priority until the corresponding constraint conditions are met; S5, Dynamic Adjustment and Real-Time Control: This includes grid status monitoring, interruptible mode rescheduling, and uninterruptible mode emergency handling. Grid status monitoring involves real-time detection of sudden load changes, triggering dynamic adjustment. Interruptible mode rescheduling involves suspending charging for low-priority users if the load exceeds the limit during a certain period, and reallocating their remaining power to subsequent low-priority periods. Uninterruptible mode emergency handling involves notifying users and providing compensation if a grid failure causes an outage. S6, user interaction and feedback: including charging plan push, user intervention mechanism and satisfaction feedback; charging plan push is to display the scheduling results to users through the APP; the user intervention mechanism is to allow users to manually adjust the charging mode or deadline, in which case rescheduling is required; and satisfaction feedback is to collect user ratings for optimizing the algorithm weight coefficients α and β.

2. The method for managing the orderly charging of new energy electric vehicles by time period according to claim 1 is characterized in that: In step S1, the grid operation data includes real-time grid load, historical load curve, regional electricity price time-sharing data, transformer capacity limit and weather conditions, wherein the regional electricity price time-sharing data includes peak, flat and valley period electricity prices; the grid operation data preprocessing includes cleaning abnormal data, normalization processing and generating the grid load forecast curve for the next 24 hours.

3. The method for managing the orderly charging of new energy electric vehicles by time period according to claim 1 is characterized in that: In step S1, the user charging demand data includes charging amount, expected completion time, charging mode selection, vehicle battery parameters and charging pile status, wherein the vehicle battery parameters include battery capacity and current SOC, and the charging pile status includes available power and geographical location; User charging demand data preprocessing includes data format unification and calculation of total charging capacity, where total charging capacity = battery capacity × (target SOC – current SOC).

4. The method for managing the orderly charging of new energy electric vehicles by time period according to claim 1 is characterized in that: In step S2, the priority includes high priority and normal priority, wherein high priority users include appliances and emergency vehicles willing to pay premium electricity charges, and normal priority users include ordinary household users and commercial fleets.

5. The method for managing the orderly charging of new energy electric vehicles by time period according to claim 1 is characterized in that: In step S5, the focus is on the real-time load surge caused by extreme weather.

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