Time-of-use electricity price-based charging method, device, equipment, medium and product

Abstract

The application discloses a charging method and device based on time-of-use electricity price, equipment, medium and product, and relates to the field of coordinated charging scheduling. The method comprises the following steps: acquiring information data; the information data comprises charging information data of a target electric vehicle in a schedulable unit; the schedulable unit is obtained based on a charging activity chain; the charging activity chain is an activity sequence used to represent the start and end of a driving event, which is obtained by sorting all the electric vehicles in a target region according to time stamps; a charging scheme is determined based on a mixed integer linear programming model; the mixed integer linear programming model is a mathematical model obtained by using a charging control simulation method, identifying a charging mode based on the charging activity chain, and statistically analyzing charging characteristic data in each charging mode after identification. The application aims to realize flexible scheduling of charging, shift charging peak load, and realize peak load shifting.

Description

Technical Field

[0001] This application relates to the field of coordinated charging scheduling, and in particular to a charging method, apparatus, equipment, medium and product based on time-of-use pricing. Background Technology

[0002] Driven by the coordinated development of transportation electrification and decarbonized power generation, the large-scale adoption of electric vehicles (EVs) offers a promising solution to the problems of fossil fuel depletion and greenhouse gas emissions. As an important demand-side flexible resource, EVs can alleviate peak loads through smart charging, achieving efficient integration of renewable energy and realizing EV-Grid Integration (EGI). Existing smart charging solutions show great potential in reshaping the electricity load profile by shifting EV charging to off-peak hours or using low-rate charging during peak hours.

[0003] Achieving efficient Energy-Driven Integration (EGI) by fully leveraging the charging flexibility of electric vehicles (EVs) relies not only on developing enabling control algorithms but also on accurately analyzing the travel activities of EV users. Therefore, it is essential to fully understand the inherent flexibility of EV charging behavior and how to effectively influence these behaviors to maximize EGI benefits. In summary, achieving flexible charging scheduling to shift peak charging loads and realize peak shaving and valley filling is crucial. Summary of the Invention

[0004] The purpose of this application is to provide a charging method, device, equipment, medium and product based on time-of-use pricing, which can realize flexible scheduling of charging to transfer peak charging load and achieve peak shaving and valley filling.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] In a first aspect, this application provides a charging method based on time-of-use pricing, comprising:

[0007] Acquire information data; the information data includes charging information data of the target electric vehicle within the schedulable unit; the schedulable unit is extracted based on the charging activity chain; the schedulable unit includes: a charging phase and an idle parking phase; the charging activity chain is an activity sequence that is used to characterize the start and end of driving events, obtained by sorting the operation data of all electric vehicles in the target area according to timestamps.

[0008] Based on the information data, a charging scheme is determined using a mixed-integer linear programming model. The charging scheme is determined based on time-of-use pricing to minimize the charging coefficient for each charging period. The charging scheme is used to transfer peak charging loads to achieve peak shaving and valley filling. The mixed-integer linear programming model is a mathematical model obtained by using a charging control simulation method, identifying charging modes based on the charging activity chain, and statistically analyzing the charging characteristic data under each identified charging mode. The charging characteristic data includes: charging power, charging time, and battery SOC.

[0009] Secondly, this application provides a charging device based on time-of-use pricing, comprising:

[0010] An information data acquisition module is used to acquire information data, including charging information data of the target electric vehicle within a schedulable unit. The schedulable unit is extracted based on a charging activity chain. The schedulable unit includes a charging phase and an idle parking phase. The charging activity chain is an activity sequence that represents the start and end of a driving event, obtained by sorting the operation data of all electric vehicles in the target area according to timestamps.

[0011] The charging scheme determination module is used to determine a charging scheme based on the information data and a mixed-integer linear programming model. The charging scheme is determined based on time-of-use pricing to minimize the charging coefficient for each charging period. The charging scheme is used to transfer peak charging loads to achieve peak shaving and valley filling. The mixed-integer linear programming model is a mathematical model obtained by using a charging control simulation method, identifying charging modes based on the charging activity chain, and statistically analyzing the charging characteristic data under each identified charging mode. The charging characteristic data includes: charging power, charging time, and battery SOC.

[0012] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the charging method based on time-of-use pricing described above.

[0013] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the charging method based on time-of-use pricing described above.

[0014] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the charging method based on time-of-use pricing described above.

[0015] According to the specific embodiments provided in this application, this application has the following technical effects:

[0016] This application provides a charging method, apparatus, equipment, medium, and product based on time-of-use pricing. It acquires information data and then determines a charging scheme based on a mixed-integer linear programming model. The charging scheme is determined based on time-of-use pricing to minimize the charging coefficient for each charging period. The information data includes charging information data of the target electric vehicle within a schedulable unit. The schedulable unit is extracted based on a charging activity chain. The charging activity chain is a sequence of activities representing the start and end of driving events, obtained by sorting the operating data of all electric vehicles within the target area according to timestamps. The mixed-integer linear programming model is a mathematical model obtained by using a charging control simulation method, identifying charging patterns based on the charging activity chain, and statistically analyzing the charging characteristic data under each identified charging mode. This application evaluates the flexibility of electric vehicles under different charging control strategies through rule-based charging pattern recognition, extracting key categories of electric vehicle charging activity chains characterized by the sequence of parking and charging activities between adjacent trips from actual electric vehicle operating data. Simulations are conducted by switching charging modes to design intelligent charging based on time-of-use pricing, thereby achieving flexible charging scheduling to transfer peak charging loads and achieve peak shaving and valley filling. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a charging method based on time-of-use pricing.

[0019] Figure 2 A schematic diagram of the electric vehicle charging activity chain being constructed;

[0020] Figure 3 A diagram illustrating the share of identified electric vehicle charging modes;

[0021] Figure 4 A schematic diagram showing the composition ratio of different charging modes among individual electric vehicle users;

[0022] Figure 5 This is a schematic diagram of the charging power distribution; where, Figure 5 (a) in the diagram is a schematic diagram of the charging power distribution in mode A; Figure 5 (b) in the diagram is a schematic diagram of the charging power distribution in mode B; Figure 5 (c) in the diagram is a schematic diagram of the charging power distribution in mode C; Figure 5 (d) in the diagram is a schematic diagram of the charging power distribution in mode D;

[0023] Figure 6 A schematic diagram showing the different charging power ranges for the four main charging modes;

[0024] Figure 7 This is a schematic diagram illustrating the charging power level analysis; where, Figure 7 (a) in the figure is a charging power distribution diagram using GMM; Figure 7 (b) in the figure is a percentage chart of the number of slow and fast charges in each mode;

[0025] Figure 8 This is a schematic diagram showing the distribution of charging duration; where, Figure 8 (a) in the figure is the duration distribution under slow charging; Figure 8 (b) in the figure is the duration distribution under fast charging; Figure 8 (c) in the figure is the distribution of parking duration during slow charging; Figure 8 (d) in the figure is the distribution of parking duration during fast charging;

[0026] Figure 9 A diagram illustrating the time distribution of charging activities; where, Figure 9 (a) in the figure is a time distribution diagram of slow charging activities; Figure 9 (b) in the figure is a time distribution diagram of fast charging activities;

[0027] Figure 10 This is a schematic diagram of the slow time distribution; where, Figure 10 (a) in the diagram is the slow charging time distribution for Mode A; Figure 10 (b) in the diagram is the slow charging time distribution for Mode B; Figure 10 (c) in the diagram is the slow charging time distribution for Mode B; Figure 10 (d) in the diagram is the slow charging time distribution for Mode B;

[0028] Figure 11 This is a schematic diagram of the rapid time distribution; Figure 11 (a) in the diagram is the fast charging time distribution for Mode A; Figure 11 (b) in the diagram is the fast charging time distribution for Mode B; Figure 11 (c) in the diagram is the fast charging time distribution for Mode B; Figure 11 (d) in the diagram is the fast charging time distribution for Mode B;

[0029] Figure 12 A schematic diagram of battery SOC distribution at the start and end of a charging activity; where, Figure 12 (a) in the diagram is a schematic diagram of the battery SOC distribution at the beginning and end of charging in Mode A under slow charging conditions; Figure 12(b) in the diagram is a schematic diagram of the battery SOC distribution at the beginning and end of charging in mode B under slow charging. Figure 12 (c) in the diagram is a schematic diagram of the battery SOC distribution at the beginning and end of charging in mode C under slow charging. Figure 12 (d) in the diagram is a schematic diagram of the battery SOC distribution at the beginning and end of charging in mode D under slow charging. Figure 12 (e) in the diagram is a schematic diagram of the battery SOC distribution at the start and end of charging in Mode A under fast charging. Figure 12 (f) in the diagram is a schematic diagram of the battery SOC distribution at the beginning and end of charging in mode B under fast charging. Figure 12 (g) in the diagram is a schematic diagram of the battery SOC distribution at the beginning and end of charging in mode C under fast charging. Figure 12 (h) in the diagram is a schematic diagram of the battery SOC distribution at the beginning and end of charging in mode D under fast charging.

[0030] Figure 13 This is a schematic diagram of the average daily charging load curve; where, Figure 13 (a) in the figure is a charging load curve for all electric vehicles; Figure 13 (b) in the figure is the charging load curve corresponding to slow charging and fast charging; Figure 13 (c) in the figure is the charging load curve for modes A, B, C and D under slow charging. Figure 13 (d) in the figure represents the charging load curves corresponding to modes A, B, C and D under fast charging.

[0031] Figure 14 This is a schematic diagram of the power load distribution; where, Figure 14 (a) in the diagram shows the changes in the charging load of electric vehicles in the network during peak and valley periods before and after coordinated charging control in the MinFee-S scenario. Figure 14 (b) in the diagram shows the changes in the charging load of electric vehicles in the network during peak and valley periods before and after coordinated charging control in the MinFee-F scenario. Figure 14 (c) in the diagram is a schematic diagram of the total load distribution during slow charging; Figure 14 (d) in the diagram is a schematic diagram of the total load distribution of fast charging. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0034] In one exemplary embodiment, such as Figure 1 As shown, a charging method based on time-of-use pricing is provided. The charging method based on time-of-use pricing includes:

[0035] Step 100: Acquire information data. The information data includes charging information data of the target electric vehicle within the schedulable unit; the schedulable unit is extracted based on the charging activity chain; the schedulable unit includes: the charging phase and the idle parking phase; the charging activity chain is an activity sequence that represents the start and end of driving events, obtained by sorting the operation data of all electric vehicles in the target area according to timestamps.

[0036] Step 200: Based on the information data, determine the charging scheme using a mixed-integer linear programming model. The charging scheme is determined based on time-of-use pricing to minimize the charging coefficient for each charging period. The charging scheme is used to transfer peak charging loads to achieve peak shaving and valley filling. The mixed-integer linear programming model is a mathematical model obtained by using a charging control simulation method, identifying charging modes based on the charging activity chain, and statistically analyzing the charging characteristic data under each identified charging mode. The charging characteristic data includes: charging power, charging time, and battery SOC.

[0037] The mixed-integer linear programming model includes: an objective function and constraints; the constraints include: charging demand constraints and time constraints; wherein, the time constraints are determined based on the start and end times of each schedulable unit.

[0038] The objective function includes:

[0039]

[0040] Where F is the objective function; T i，end T represents the end time of the i-th schedulable unit. i,start P is the start time of the i-th schedulable unit; i，avg Let x(t) be the average charging power within the i-th schedulable unit; x(t) be a binary variable at time t; and C(t) be the electricity price at time t.

[0041] Charging demand constraints specifically include:

[0042]

[0043] Among them, T i，end T represents the end time of the i-th schedulable unit. i，startP is the start time of the i-th schedulable unit; i (t) represents the charging power at time t within the i-th schedulable unit; E i Let be the total energy billed within the i-th schedulable unit.

[0044] In one embodiment, based on information data and a mixed-integer linear programming model, a charging scheme is determined, specifically including:

[0045] Based on the information data, the objective function is solved according to the constraints to obtain the charging scheme; the information data includes: the battery capacity of the target electric vehicle, the initial SOC, the final SOC, the charging start time, and the charging end time.

[0046] As an optional implementation method, the charging method based on time-of-use pricing also includes:

[0047] According to the charging plan, the setting cycle and pricing mechanism of time-of-use electricity prices will be adjusted to incentivize off-peak charging.

[0048] This application evaluates the flexibility of electric vehicles under different charging control strategies based on rule-based charging pattern recognition. It extracts key categories of electric vehicle charging activity chains characterized by the sequence of parking and charging activities between adjacent trips from actual electric vehicle operation data. Through simulation of switching charging modes, a time-to-user (ToU) smart charging system is designed. The charging pattern recognition method can serve as a foundation for other subsequent research, and the designed time-to-user smart charging method can, to some extent, shift peak charging loads, achieving peak shaving and valley filling effects.

[0049] The design concept corresponding to the method provided in this application consists of the following steps.

[0050] 1. Build a charging activity chain.

[0051] The dataset used in this application contains real-world electric vehicle operation data from 45,093 light electric vehicles operating in a certain city in September 2021, including 326,998 charging sessions. The raw data was collected at a 30-second time resolution according to the Chinese national standard GB / T32960 and further processed into data segments including parking, charging, and driving periods. The information provided mainly includes vehicle model, battery capacity, timestamp, vehicle status, state of charge (SOC), and cumulative mileage.

[0052] To reduce the impact of data anomalies on further analysis, the collected raw data is decoded, abnormal sessions are eliminated, erroneous parameters are corrected, and missing session data is supplemented.

[0053] First, perform data cleaning to remove charging sessions with missing information, unreasonable energy or power values, or durations of less than 1 minute.

[0054] In addition, charging periods exceeding 24 hours were eliminated to ensure data integrity for accurate analysis. The data for each electric vehicle was then organized chronologically, and timestamps were checked. The differences in time and SOC between adjacent periods were then identified and added to the dataset.

[0055] Finally, the processed session data is arranged chronologically, with sessions showing the same vehicle status merged into one. This data cleaning and reconstruction process is repeated until all sessions are sorted by consecutive timestamps and the states of two adjacent segments are clearly different. The processed session data is then used to build the electric vehicle chain.

[0056] In this application, the charging activity chain of an electric vehicle user is defined as a sequence of activities that begins and ends with a driving event, with no intervening driving activities in between. The acquired continuous electric vehicle operation data is segmented into sessions labeled as "driving" events, such as... Figure 2 As shown, charging activity chains, also known as charging patterns, are extracted from activity chains containing at least one charging event. These charging patterns clearly demonstrate the correlation between parking and charging activities between two adjacent trips, revealing the charging flexibility of electric vehicles in the time dimension. Each time period within a specific charging pattern is considered a unified "schedulable unit." Each schedulable unit contains a specific sequence of activities, including charging and idle parking. A schedulable unit refers to the time from when the electric vehicle arrives at its departure point, essentially the interval between the end of one driving phase and the beginning of the next.

[0057] 2. Identify the charging modes of electric vehicle users.

[0058] Identifying charging modes helps quantify the potential for large-scale changes in electric vehicle behavior under coordinated charging strategies. Charging control simulations are based on switching charging modes by modifying charging time and power. The identified charging modes are shown in Table 1 and... Figure 3 As shown. Figure 3 As shown, the four main charging activity chain modes account for 97% of the total charging activity.

[0059] Table 1. Classification of different charging modes

[0060]

[0061] Specifically, the charging mode is identified as the following modes:

[0062] Pattern A is the most common pattern, defining a charging pattern where the electric vehicle continues to charge throughout the entire parking period between two trips. It accounts for approximately 46.6% of total charging activity, indicating that nearly half of the charging activity occurs when there is no available parking time. This could be due to electric vehicle user preferences or parking restrictions (such as high parking fees).

[0063] Pattern B accounted for 7.9%, which includes the period from charging to departure, with a free time before charging.

[0064] Pattern C accounted for 9.4%, which refers to charging during idle parking time before and after the two trips.

[0065] Pattern D accounted for 36%, which is defined as charging immediately upon arrival, followed by an extended parking period until departure.

[0066] As can be seen from the definitions, Mode B represents delayed charging, while Mode D represents immediate charging to meet users' urgent charging needs for electric vehicles, similar to Mode A. Delayed charging (Mode B) or a combination of immediate and delayed charging (Mode C) is generally used when the charging point is unavailable upon arrival. For idle parking periods, Modes B, C, and D allow for a degree of charging flexibility in terms of charging time and power. The existence of these three modes also indicates that existing electric vehicle behavior models that assume immediate charging upon arrival to predict charging demand may produce significant errors. Less common modes, such as intermittent Mode E (accounting for less than 0.1%), are ignored in the charging modes of this application.

[0067] The charging behavior of electric vehicle (EV) users can consist of a single mode or multiple different modes. For example... Figure 4 As shown, different EV user groups are distinguished based on the complexity of their charging modes. Figure 4Categories composed of patterns, such as single-mode, dual-mode, triple-mode, and quad-mode, are shown in different colors. This proportion reflects the number of electric vehicle users whose charging behavior falls into a specific pattern category. Only categories with a proportion greater than 0.1% are displayed to highlight the most relevant information. These categories represent 98.6% of all users, with the largest group being dual-mode users (Modes A and D), accounting for approximately 23% of all EV users studied. Approximately 16% of users exhibit only Mode A, followed by multi-mode users (Modes A, C, and D), accounting for approximately 12.5%. The identification and analysis of charging patterns provides a basis for flexibly quantifying certain types of users and their numbers, and for incorporating them into coordinated charging schemes by modifying their behavior. This will help decision-makers identify target EV users for Smart Charging (EV-Grid Integration, EGI).

[0068] 3. Analysis of electric vehicle charging characteristics.

[0069] By statistically analyzing the charging power, charging time, and battery SOC of electric vehicle charging activities, and providing an overview of the charging load under each EV charging mode, we can better explore the distribution of EV charging time and energy usage patterns, infer the scenarios in which each charging mode occurs, and provide a statistical basis for assessing the potential flexibility of charging behavior.

[0070] Charging power is a key factor characterizing the charging scenario for electric vehicle users, as it determines the duration of a charging cycle and indicates the urgency of the user's need to charge. Charging power is calculated as average power, which is obtained by dividing the product of battery capacity and the difference between battery capacity and state of charge (SOC) by the charging time. Figure 5 and Figure 6 The charging power distribution of four primary charging modes with similar distribution shapes is shown. Among them, Figure 5 Part (a) in the diagram is a schematic diagram of the charging power distribution in Mode A; Figure 5 Part (b) in the diagram is a schematic diagram of the charging power distribution in mode B; Figure 5 Part (c) in the diagram is a schematic diagram of the charging power distribution in mode C; Figure 5 Part (d) in the diagram is a schematic diagram of the charging power distribution in mode D.

[0071] To further classify these patterns, a Gaussian Mixture Model (GMM) was used to identify different charging power levels based on the entire dataset, such as... Figure 7 As shown in the figure, the charging power exhibits a symmetrical distribution around 10.2kW. Therefore, slow charging and fast charging are defined as charging with an average power of less than 10.2kW and greater than 10.2kW, respectively.

[0072] in, Figure 7Part (a) in the diagram is a charging power distribution diagram using GMM; Figure 7 Part (b) in the chart is a percentage graph of the number of slow and fast charges in each mode.

[0073] like Figure 8 As shown, slow charging is more common among most EV users compared to fast charging. Modes B, C, and D overwhelmingly favor slow charging, while Mode A exhibits a more balanced distribution, with a 4:6 ratio of fast to slow charging. The higher prevalence of slow charging in Modes B, C, and D, particularly at power levels of 3kW and 7kW, indicates that these power levels correspond to the most common charging facilities, especially in residential areas. This also suggests that users charging at 7kW are more likely to have private charging stations or easy access to charging facilities, as 7kW is the mainstream charging power for slow charging stations in residential areas. Therefore, the higher incidence of slow charging in the dataset used may not necessarily indicate a preference among EV users. Fast charging power distribution is more dispersed, with an average power of around 30kW.

[0074] Charging modes with sufficient idle parking time can provide considerable flexibility for coordinated charging control. Figure 8 The charging time and total parking duration for each charging mode are shown. The total parking duration includes charging and potential idle parking. Relevant statistics are shown in Table 2.

[0075] in, Figure 8 (a) in the figure is the duration distribution under slow charging; Figure 8 (b) in the figure is the duration distribution under fast charging; Figure 8 (c) in the figure is the distribution of parking duration during slow charging; Figure 8 (d) in the figure is a distribution of parking duration during fast charging.

[0076] Analysis of the idle charging time ratio under different charging modes highlights the necessity of developing intelligent charging strategies tailored to the actual usage patterns of electric vehicles. By quantifying idle time relative to charging time, the charging mode that provides the greatest flexibility can be identified.

[0077] Table 2. Parking, charging, and idle schedules under different charging modes.

[0078]

[0079] To infer the scenarios in which these charging modes occur, the heatmap, depicting a binary distribution, uses charging power and start and end times as input features to visually represent charging activities occurring at different times and charging powers. For example... Figure 9 As shown, the timeline extends from 4 AM of the current day to 4 AM of the next day, where... Figure 9 (a) in the figure is a time distribution diagram of slow charging activities; Figure 9 (b) in the figure is a time distribution diagram of fast charging activities.

[0080] The charging activities are categorized into seven groups (four groups for slow charging and three groups for fast charging):

[0081] Morning-Slow (MS): Slow charging at the beginning and end of the morning.

[0082] Noon-Slow (NS): Slow charging that begins and ends at noon.

[0083] Afternoon-Slow (AS): Start slow charging in the afternoon and end slow charging in the evening.

[0084] Evening-Slow (ES): Slow charging that begins in the late afternoon or evening and ends in the early morning of the next day.

[0085] Morning-Fast (MF): Fast charging at the beginning and end of the morning.

[0086] Noon-Fast (NF): Fast charging that starts and ends at noon.

[0087] Afternoon-Fast (AF): Fast charging that begins and ends in the afternoon.

[0088] In constructing the charging activity chain, the data used primarily pertains to electric vehicles registered as private passenger cars. These are used by diverse groups of individuals for both personal and commercial purposes. These groups may include daily commuters, ride-hailing drivers, freelancers, remote workers, and other non-working individuals with flexible schedules. Specific charging patterns observed in the data indicate different user activities. For example, MS and MF charging patterns are more likely associated with daily commuters charging at their workplaces, while NS and NF patterns may be associated with ride-hailing drivers who tend to use public charging stations during their lunch breaks. The AS charging pattern, which runs from afternoon to evening, is common among daily commuters charging in residential areas after get off work. ES charging, which begins later in the day and continues into the evening, can be applicable to both commuters and ride-hailing drivers, while AF charging represents random, fast-charging demand during the day.

[0089] Figure 10 and Figure 11 The distribution of slow and fast charging times in different modes is shown. Among them, Figure 10 (a) in the diagram is the slow charging time distribution for Mode A; Figure 10(b) in the diagram is the slow charging time distribution for Mode B; Figure 10 (c) in the diagram is the slow charging time distribution for Mode B; Figure 10 (d) in the diagram is the slow charging time distribution for Mode B. Figure 11 (a) in the diagram is the fast charging time distribution for Mode A; Figure 11 (b) in the diagram is the fast charging time distribution for Mode B; Figure 11 (c) in the diagram is the fast charging time distribution for Mode B; Figure 11 (d) in the diagram is the fast charging time distribution diagram for Mode B.

[0090] It can be seen that Mode A generally occurs in the afternoon and evening, including AF and all slow charging categories, with AS and ES being the most frequent. Figure 10 (a) reveals that many EV users following Pattern A begin charging around 8 a.m. and 7 p.m., leaving approximately two hours later if no parking space is available. Since Pattern A is the most common pattern, it is assumed to reflect a variety of user types. While the charging times in Pattern A are consistent with commuting patterns, this pattern may occasionally reflect random or unique schedules, such as those of users with flexible work hours or non-commuting EV owners. These users may plan their charging around other daily activities, such as shopping or attending evening events, resulting in the charging behavior observed in Pattern A. For example, a user might charge their EV while shopping after get off work and then leave immediately afterward. Users with flexible schedules, such as freelancers, remote workers, shift workers, or part-time employees, may also start their workday in the morning, leading to charging peaks around 8 a.m. After charging, these individuals may leave immediately to continue their day, whether for work or other activities. For example, ride-hailing drivers might take a break after a busy evening rush hour, quickly charge, and then continue their shift, potentially all night. Other user groups, such as retirees or homebodies, may also exhibit charging behavior different from traditional commuting patterns. These users might use their electric vehicles for short trips or errands throughout the day, explaining charging activity around 10 a.m. and 9 p.m. The lack of available parking after charging suggests these users only charge when necessary and leave shortly afterward to continue their daily lives.

[0091] The availability of charging infrastructure may also influence these behaviors, as electric vehicle users typically utilize available charging points, even during short charging sessions. Similar analyses can be applied to other patterns. Pattern B, including AF, NS, and ES, represents charging behaviors likely performed by ride-hailing drivers who may face delays due to limited charging points. Pattern D, primarily including MF, MS, AS, and ES, tends to occur after morning and afternoon peak hours and may indicate commuter charging patterns. Pattern C is an intermediate pattern between Patterns B and D, including NF, MS, AS, and ES. Its slow charging pattern may reflect commuter charging habits, while its fast charging pattern suggests that ride-hailing drivers seeking quick charging during lunchtime may adopt this pattern. While these inferences have not yet been validated, they provide preliminary insights into billing behavior associated with different user types, which may help identify potential target users for coordinating billing controls.

[0092] The battery SOC at the start and end of a charging activity reflects the magnitude and urgency of the user's charging needs. For slow charging, in all modes, the median SOC at the start consistently remains between 40% and 50%, see details. Figure 12 .in, Figure 12 (a) in the diagram is a schematic diagram of the battery SOC distribution at the beginning and end of charging in Mode A under slow charging conditions; Figure 12 (b) in the diagram is a schematic diagram of the battery SOC distribution at the beginning and end of charging in mode B under slow charging. Figure 12 (c) in the diagram is a schematic diagram of the battery SOC distribution at the beginning and end of charging in mode C under slow charging. Figure 12 (d) in the diagram is a schematic diagram of the battery SOC distribution at the beginning and end of charging in mode D under slow charging. Figure 12 (e) in the diagram is a schematic diagram of the battery SOC distribution at the start and end of charging in Mode A under fast charging. Figure 12 (f) in the diagram is a schematic diagram of the battery SOC distribution at the beginning and end of charging in mode B under fast charging. Figure 12 (g) in the diagram is a schematic diagram of the battery SOC distribution at the beginning and end of charging in mode C under fast charging. Figure 12 (h) in the diagram represents the battery SOC distribution at the start and end of charging in mode D under fast charging conditions.

[0093] The median starting SOC for fast charging ranges from 30% to 40% for each mode, suggesting that users opting for fast charging tend to start at a lower energy level compared to those using slow charging. This variation may indicate that fast-charging users are more comfortable operating their electric vehicles at lower SOC levels, possibly due to the availability of fast-charging infrastructure, which enhances their confidence in the reliability of electric vehicles. Alternatively, the decision to choose fast charging may be driven by a lower SOC level, reflecting a greater sense of urgency.

[0094] Whether slow or fast charging, Modes C and D are more likely to have high end-of-charge SOC levels, ranging from 90% to 100%. In contrast, Modes A and B show lower and more diverse end-of-charge SOC distributions. This suggests that Modes C and D may have sufficient parking time to support near-full or full charging, while charging activity in Modes A and B may be constrained by the driver's personal schedule or charging facility limitations. Mode B tends to start with a lower SOC but end with a moderate SOC, meaning users with frequent Mode B activity may face significant limitations in charging availability. In contrast, the high percentage of EV users with Modes C and D have a high degree of flexibility in coordinating charging control.

[0095] Furthermore, this application also calculates the total charging load for each charging mode, providing a baseline for inflexible charging load profiles. Total charging load represents the cumulative energy charged during each charging session. Figure 13 It shows the average daily total EV charging load over a one-month period, and Figure 13 The slow and fast charging scenarios for each charging mode are shown along their 95% envelope over 30 days. The proportion of charging load contributed by each mode is also indicated in each subplot. Figure 13 (a) in the figure is a charging load curve for all electric vehicles; Figure 13 (b) in the figure is the charging load curve corresponding to slow charging and fast charging; Figure 13 (c) in the figure is the charging load curve for modes A, B, C and D under slow charging. Figure 13 (d) in the figure represents the charging load curves corresponding to modes A, B, C and D under fast charging.

[0096] Generally, slow charging accounts for the majority of the total load of the private electric vehicle in this application, with a variance of 3100kW. 2The peak-to-valley difference was 10532kW. The morning peak was relatively stable, coinciding with the evening peak for residential electricity consumption, while the lowest point occurred around 7-8 AM. Modes A and D contributed the most to the total charging load distribution, with Mode A accounting for approximately 41% and Mode D approximately 40% of the daily slow charging load. For fast charging load, Modes A and D contributed approximately 68% and 22%, respectively. The slow charging load curves for all four modes showed peak periods overlapping with the evening peak of electricity demand, and secondary peaks during the day, especially for Mode D. The overall fast charging load curve was generally smoother, with less variation throughout the day, and a variance of 1728kW. 2 The peak-to-valley difference was 5735kW. However, it should be noted that during the daytime period from 7:00 AM to 6:00 PM, its variance was greater than that of the slow-charging load, with the daytime fast-charging load exceeding the fast-charging load at night and in the early morning. This indicates that fast charging, characterized by short durations and high power levels, is more frequent during the day.

[0097] As can be seen from the above, different charging models have varying degrees of impact on electricity load. Some models (such as Model D) may have a negative load impact on the power grid, but they also provide great flexibility for coordinating charging control.

[0098] 4. Coordinated charging scheduling during charging mode switching.

[0099] To quantify the effectiveness of proposed changes in charging behavior under different coordinated charging strategies, simulations were performed by switching between charging modes based on EV charging data. To evaluate the flexibility of electric vehicle charging, this application redefines the flexibility of demand-side resources. A shiftable load refers to a load that can be moved in a timely manner, but must be moved as a whole without interrupting the entire charging cycle. A transferable (shiftable) load allows for charging interruptions and reallocation of the charging load within the available scheduling time. A load that can be reduced is achieved by decreasing the charging power. From the definitions of charging modes, it can be inferred that the charging load in Mode A is more rigid and less controllable. In contrast, the charging loads in Modes B, C, and D exhibit greater scheduling flexibility due to idle parking time, thus allowing for shiftable, reducible, or switchable load adjustments. Detailed definitions are as follows:

[0100] Transposition: Due to parking before or after charging, the charging load can be transferred as a whole while maintaining the same average charging rate and charging session duration.

[0101] Reduced: By spreading the charging process throughout the parking period, the charging power can be reduced to a lower level.

[0102] Switchable: Charging can be rescheduled within the parking time window to take advantage of lower electricity rates, and charging can be paused during the entire charging process.

[0103] Modifying charging behavior to switch from one mode to another (e.g., from mode C or mode D to mode B, or from mode B, mode C, mode D to mode A) allows for the simulation of different grid management strategies. This is achieved by adjusting the original billing session data. Specifically:

[0104] Simulate a delayed charging scenario from Mode C or Mode D to Mode B, where charging is shifted to the last possible minute.

[0105] ToU rates strategically reallocate charging sessions to maximize cost-effectiveness by assessing the impact of reduced power during a charging session, from Mode B, Mode C, or Mode D to Mode A, or by evaluating the impact of smart charging using time-of-use rates.

[0106] To fully utilize variable charging behavior, a smart charging strategy based on a mixed-integer linear programming (MILP) model was designed. This model optimizes charging plans in modes B, C, and D to reallocate transferable loads using time-of-use pricing. The algorithm is shown below:

[0107]

[0108]

[0109] Where C(t) is the electricity price at time t; T i，start T is the start time of the i-th schedulable unit; i，end P represents the end time of the i-th schedulable unit. i,avg Let P be the average charging power within the i-th schedulable unit; x(t) is a binary variable at time t, which is 1 if charging occurs at time t, and 0 otherwise; i (t) represents the charging power at time t within the i-th schedulable unit; E i T represents the total energy billed within the i-th schedulable unit; i,cs T represents the charging start time within the i-th schedulable unit; i,ce The charging end time within the i-th schedulable unit; SOC i,start Start the SOC for the i-th schedulable unit; SOC i,end End SOC for the i-th schedulable unit; Capacity i N represents the battery capacity of an electric vehicle. i The charging factor (electricity cost) calculated per ToU without the use of smart charging of the dispatchable unit; S i Electricity charges based on the (charging factor) for ToUs with dispatchable unit intelligent charging function; CS i,save To save costs through intelligent charging via a schedulable unit.

[0110] Based on the above analysis, a smart charging MinFee scenario with a ToU (ToU) rate was designed to fully utilize parking time between adjacent trips while meeting the charging needs of electric vehicle users. This includes an evaluation of the potential for shifting, reducing, and transferring EV charging loads.

[0111] In the time-of-use (TOU) smart charging scenario, assuming the total parking time and charging capacity remain constant, the MILP-based smart charging method is applied to modes B, C, and D to minimize the charging cost for each charging period under TOU pricing. The slow charging and fast charging scenarios are denoted as MinFee-S and MinFee-F, respectively. The MILP model is constrained by charging demand and the start and end times of each schedulable unit, and the objective function minimizes the cost of a single charging session. The maximum charging power is set to the average power in the original charging session data, and the minimum charging power is set to 0. The electricity pricing scheme references the TOU pricing for industrial and commercial users in Beijing.

[0112] To investigate whether changes in electric vehicle (EV) charging behavior can effectively shift overlapping peak charging loads and facilitate valley filling, a visualized net load-time flow model was constructed. This model illustrates the daily shift of EV charging loads during peak and off-peak periods through coordination. First, the daily curve of EV charging load and the load differences before and after coordination are established. Then, the amount of shifted charging load is calculated by analyzing whether the increased or decreased load in each time period flows into or out of adjacent time periods, and how much flows into or out of adjacent time periods. This process is performed iteratively for each scenario until the start and end points of the net load for each time period are determined.

[0113] To flatten the distribution of electricity load, the ToU pricing mechanism uses the price difference between peak and off-peak periods to encourage electric vehicle users to charge their vehicles during periods of low prices. Figure 14 This is a schematic diagram of the power load distribution. (For example...) Figure 14 As shown in (a), in the MinFee-S scenario, most of the charging load during the nighttime peak and flat periods is shifted to the off-peak hours when electricity prices are lowest. Some of the high-priced morning peak load is shifted to the adjacent afternoon flat period, and even to the off-peak hours of the following day. This shift in load results in a significant reduction in EV charging load during peak hours, with the morning peak load decreasing from 9.4% to 2.8% and the evening peak load decreasing from 28% to 3.4%, respectively. Meanwhile, the off-peak load increases sharply from 37% to 74.1%. This price-driven smart charging allows EV users to avoid peak charging times to reduce their charging costs.

[0114] Figure 14In scenario (b) of the MinFee-F model, some of the charging load during the morning-flat, evening-peak, and evening-flat periods is shifted to the off-peak hours of the following day, while more than one-third of the morning-peak load is shifted to the later afternoon-flat period. This results in a reduction in EV charging load from 17.2% to 9.6% during the morning peak and from 25.9% to 16.7% during the evening peak. It can be concluded that the smart charging approach with ToU tariffs is more effective for slow charging scenarios than for fast charging scenarios because fast charging activity mainly occurs during the daytime, when pricing incentives are less effective.

[0115] like Figure 14 (c)- Figure 14 As shown in (d), sharp load peaks in EV charging load are observed around 11 PM in both the MinFee-S and MinFee-F scenarios. This sudden surge can overload the local grid, as EV users typically begin charging during the only low-price period of the day. In the MinFee-F scenario, a new load peak is observed between 1 PM and 3 PM. This new peak may coincide with peak photovoltaic power generation, indicating that Time-of-Use (ToU) pricing has a positive impact on promoting the integration of renewable energy. Therefore, policymakers must design appropriate time-of-use pricing cycles and effective dynamic pricing mechanisms to incentivize off-peak charging while ensuring compatibility with renewable energy production models and preventing the formation of new peaks.

[0116] This application proposes a time-of-use (TOU) smart charging method based on charging pattern recognition, using charging data samples collected from an electric vehicle big data platform and combined with electric vehicle charging behavior. It emphasizes the importance of identifying idle parking time and its ratio to required charging time, a key indicator for identifying potential electric vehicle users who choose smart charging control. By examining the sequence and timing of parking and charging events, it can quantify the potential for electric vehicles to change their charging load. In the MinFee scheme, utilizing one hour of idle parking time, up to 0.45 kWh of net charging load can be transferred from peak hours, and 0.54 kWh can be transferred to off-peak hours for slow charging. For fast charging, the transferred load during peak and off-peak hours reaches 0.55 kWh and 0.38 kWh, respectively.

[0117] Based on the same inventive concept, this application also provides a time-of-use (TOU) charging device for implementing the above-mentioned TOU-based charging method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more TOU-based charging device embodiments provided below can be found in the limitations of the TOU-based charging method described above, and will not be repeated here.

[0118] In one exemplary embodiment, a charging device based on time-of-use pricing is provided, comprising:

[0119] The information data acquisition module is used to acquire information data, including charging information data of the target electric vehicle within the schedulable unit. The schedulable unit is extracted based on the charging activity chain. The schedulable unit includes a charging phase and an idle parking phase. The charging activity chain is an activity sequence that represents the start and end of driving events, obtained by sorting the operation data of all electric vehicles in the target area according to timestamps.

[0120] The charging scheme determination module is used to determine the charging scheme based on information data and a mixed-integer linear programming model. The charging scheme is determined based on time-of-use pricing to minimize the charging coefficient for each charging period. The charging scheme is used to transfer peak charging loads to achieve peak shaving and valley filling. The mixed-integer linear programming model is a mathematical model obtained by using a charging control simulation method, identifying charging modes based on the charging activity chain, and statistically analyzing the charging characteristic data of each identified charging mode. The charging characteristic data includes: charging power, charging time, and battery SOC.

[0121] In one exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement a charging method based on time-of-use pricing.

[0122] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0123] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0124] In this application, all actions involving the acquisition of signals, information, or data are carried out in compliance with the relevant data protection laws and regulations of the country where the application is located, and with the authorization granted by the owner of the relevant device. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with relevant regulations.

[0125] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A charging method based on time-of-use electricity pricing, characterized by, The charging method based on the time-of-use electricity price comprises the following steps: obtaining information data; the information data comprises charging information data of a target electric vehicle in a schedulable unit; the schedulable unit is extracted based on a charging activity chain; the schedulable unit comprises a charging phase and an idle parking phase; the charging activity chain is an activity sequence representing the start and end of a driving event, which is sorted according to a time stamp based on the operation data of all electric vehicles in a target region; determining a charging scheme based on a mixed integer linear programming model according to the information data; the charging scheme is determined based on the time-of-use electricity price to minimize the charging coefficient of each charging period; the charging scheme is used to shift the charging peak load to achieve peak load shifting; the mixed integer linear programming model is a mathematical model obtained by using a charging control simulation method, identifying the charging mode based on the charging activity chain, and statistically analyzing the charging characteristic data in each charging mode; the charging characteristic data comprises charging power, charging time and battery SOC; the charging activity chain is an activity sequence representing the start and end of a driving event without intervening driving activities; the charging mode shows the correlation between parking and charging activities between two adjacent trips; and the charging coefficient is the charging price.

2. The time-of-use electricity price-based charging method according to claim 1, wherein The mixed integer linear programming model comprises an objective function and a constraint condition; the constraint condition comprises a charging demand constraint and a time constraint; wherein the time constraint is determined based on the start time and end time of each schedulable unit.

3. The time-of-use electricity price-based charging method according to claim 2, wherein The objective function comprises: where F is the objective function; T i,end is the end time of the ith schedulable unit; T i,start is the start time of the ith schedulable unit; P i,avg is the average charging power in the ith schedulable unit; x(t) is a binary variable at the tth moment; and C(t) is the electricity price at the tth moment.

4. The time-of-use electricity price-based charging method according to claim 2, wherein The charging demand constraint specifically comprises: where T i，end is the end time of the ith schedulable unit; T i，start is the start time of the ith schedulable unit; P i (t) is the charging power at time t in the ith schedulable unit; E i is the total energy charged in the ith schedulable unit.

5. The time-of-use electricity price-based charging method according to claim 2, wherein determining a charging scheme based on a mixed integer linear programming model according to the information data, specifically comprising: solving the objective function based on the constraint condition to obtain the charging scheme according to the information data; the information data comprises the battery capacity, the initial SOC, the final SOC, the charging start time and the charging end time of the target electric vehicle.

6. The time-of-use electricity pricing based charging method of claim 1, wherein, The charging method based on the time-of-use electricity price further comprises: adjusting the setting period and pricing mechanism of the time-of-use electricity price according to the charging scheme to encourage off-peak charging.

7. A charging device based on time-of-use electricity pricing, characterized by, The charging device based on the time-of-use electricity price comprises: an information data acquisition module configured to obtain information data; the information data comprises charging information data of a target electric vehicle in a schedulable unit; the schedulable unit is extracted based on a charging activity chain; the schedulable unit comprises a charging phase and an idle parking phase; the charging activity chain is an activity sequence representing the start and end of a driving event, which is sorted according to a time stamp based on the operation data of all electric vehicles in a target region; The charging scheme determination module is configured to determine a charging scheme based on a mixed integer linear programming model according to the information data; the charging scheme is determined based on a time-of-use electricity price to minimize a charging coefficient of each charging period; the charging scheme is used to shift charging peak load to achieve peak load shifting; the mixed integer linear programming model is a mathematical model obtained by using a charging control simulation method, performing charging mode recognition based on the charging activity chain, and performing statistical analysis on charging characteristic data in each charging mode after the recognition; the charging characteristic data includes charging power, charging time, and battery SOC; the charging activity chain is a sequence of activities starting and ending with a driving event, without intervening driving activities; the charging mode shows the correlation between parking and charging activities between two adjacent trips; and the charging coefficient is a charging electricity price.

8. A computer device comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the time-of-use electricity price-based charging method of any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the time-of-use electricity price-based charging method of any one of claims 1-6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the time-of-use electricity price-based charging method of any one of claims 1-6. The computer program is executed by the processor to implement the time-of-use electricity price-based charging method of any one of claims 1-6.

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