Method for analyzing potential and risks of grid load regulation based on v2g

Abstract

The application discloses a potential and risk analysis method of power grid load regulation based on V2G, comprising the following steps: determining the power grid load, power grid load peak value and power grid load valley value of all electric vehicles in a region at different time points after participating in V2G based on the total charging load time sequence curve, total discharge power and total charging power, and determining the peak shaving rate and valley filling rate of each region; determining the original peak valley difference of the power grid before the electric vehicles discharge to the power grid according to the original power grid load peak value and original power grid valley load; determining the target peak valley difference of the power grid after the electric vehicles in the region discharge to the power grid according to the power grid load peak value and power grid load valley value; and determining the peak valley difference change rate of the region and the potential and risk of power grid load regulation according to the original peak valley difference of the power grid and the target peak valley difference of the power grid. The application constructs the V2G charging and discharging scenario driven by the user behavior characteristics of electric vehicles and a simulation framework to evaluate the potential influence and risk of large-scale charging and discharging behavior on the operation of the power grid under uncertain conditions in the future.

Description

Technical Field

[0001] This invention relates to the field of V2G-based power grid load regulation technology, and in particular to a method for analyzing the potential and risks of V2G-based power grid load regulation. Background Technology

[0002] While V2G technology holds significant value in improving grid reliability and promoting renewable energy integration, its practical application in complex energy economic systems still faces considerable uncertainties and challenges. First, dynamic adjustments to future electricity pricing policies and differences in user participation mechanisms will directly impact the economic feasibility and effectiveness of V2G adoption. Second, heterogeneity in user behavior may diminish expected benefits; for example, some vehicle owners may immediately recharge after discharging, resulting in actual peak-shaving effects falling short of theoretical expectations. Furthermore, regional heterogeneity makes a uniform V2G strategy difficult to apply universally. Significant differences in grid load characteristics, electric vehicle penetration rates, and user charging habits across different regions can lead to inconsistent dispatching effects and even exacerbate grid fluctuations in certain scenarios.

[0003] Therefore, it is necessary to conduct in-depth research on the implementation effect of incorporating electric vehicles into the power grid dispatch, based on the current status of my country's power supply and demand, the development trend of electric vehicles, and the characteristics of user behavior, and to estimate the potential impact and risks of large-scale charging and discharging behavior on power grid operation under uncertain conditions in the future. Summary of the Invention

[0004] Therefore, it is necessary to propose a potential and risk analysis method for V2G-based power grid load regulation to address the above problems.

[0005] A method for analyzing the potential and risks of power grid load regulation based on V2G, the method comprising:

[0006] Obtain the time-series curves of total charging load, total discharge power, and total charging power of electric vehicles in different regions;

[0007] Based on the total charging load time-series curve, the total discharge power, and the total charging power, determine the grid load at different times for all electric vehicles in the region after participating in V2G;

[0008] The maximum value of the grid load is taken as the peak grid load of each region after the electric vehicles discharge to the grid, and the minimum value of the grid load is taken as the valley grid load of each region before the electric vehicles discharge to the grid.

[0009] Obtain the original peak and valley load of the power grid before the electric vehicle discharges into the grid; determine the peak reduction rate for each region based on the original peak and valley load; determine the valley filling rate for each region based on the original valley load and valley load.

[0010] The original peak-to-valley difference of the power grid before the electric vehicle discharges into the grid is determined based on the original peak load and the original valley load of the power grid; the target peak-to-valley difference of the power grid after the electric vehicle discharges into the grid within the region is determined based on the original peak load and the original valley load of the power grid.

[0011] The rate of change of peak-valley difference within the region is determined based on the original peak-valley difference of the power grid and the target peak-valley difference of the power grid.

[0012] The potential and risks of power grid load regulation are determined based on the peak shaving rate, the valley filling rate, and the peak-valley difference change rate.

[0013] This invention takes demand-side management as its starting point, and, based on the current state of my country's power supply and demand, the development trend of electric vehicles, and user behavior characteristics, delves into the implementation effects of incorporating electric vehicles into power grid dispatch. It constructs a V2G charging and discharging scenario and simulation framework driven by electric vehicle user behavior characteristics to assess the potential impact and risks of large-scale charging and discharging behavior on power grid operation under uncertain conditions. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] in:

[0016] Figure 1 This is an application environment diagram of a V2G-based power grid load regulation potential and risk analysis method in one embodiment;

[0017] Figure 2 This is a flowchart of a method for analyzing the potential and risks of V2G-based power grid load regulation in one embodiment;

[0018] Figure 3 A comparison chart of the kernel density distribution function of charging start time in one embodiment and historical real data;

[0019] Figure 4 This is a comparison chart of the initial SOC kernel density distribution function and historical real data in one embodiment;

[0020] Figure 5 This is a structural block diagram of a computer device in one embodiment. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] With the global energy structure transitioning towards low-carbon development and the rapid growth of the new energy vehicle industry, vehicle-to-grid (V2G) technology is gradually becoming an important way to optimize the balance of energy supply and demand. As of 2024, my country's pure electric vehicle fleet had exceeded 15.5 million vehicles. Based on an average bidirectional charging and discharging capacity of 15kW, new energy vehicles can provide the power grid with 150-180 million kilowatts of flexible regulation capacity, equivalent to 1.7 times the current total installed capacity of my country's power storage, and contributing more than 10% of peak load support capacity during peak grid periods. If the new energy vehicle market continues to grow, the fleet is expected to reach 300 million vehicles by 2040, with an average battery capacity of over 65kWh per vehicle. At that time, the total onboard energy storage capacity will exceed 20 billion kWh, approaching my country's total daily electricity consumption in 2023, demonstrating enormous demand-side regulation potential. Although V2G technology has significant value in improving grid reliability and promoting renewable energy consumption, its practical application in complex energy economic systems still faces significant uncertainties and real-world challenges. First, the dynamic adjustments to future electricity pricing policies and the differences in the design of user participation mechanisms will directly affect the economic feasibility and promotion effectiveness of V2G. Second, the heterogeneity of user behavior may weaken the expected benefits. For example, some car owners may immediately recharge after discharging, resulting in actual peak shaving effects lower than theoretically expected. Furthermore, regional heterogeneity makes a uniform V2G strategy difficult to apply universally. Significant differences in grid load characteristics, electric vehicle penetration rates, and user charging habits across different regions may lead to inconsistent dispatching effects and even exacerbate grid fluctuations in some scenarios. Therefore, it is necessary to conduct in-depth research on the implementation effects of incorporating electric vehicles into grid dispatching, considering my country's current electricity supply and demand situation, electric vehicle development trends, and user behavior characteristics, and to assess the potential impact and risks of large-scale charging and discharging behavior on grid operation under uncertain conditions.

[0023] To address the aforementioned technical issues, this application provides a method for analyzing the potential and risks of V2G-based power grid load regulation.

[0024] Figure 1This is an application environment diagram for a V2G-based power grid load regulation potential and risk analysis method in one embodiment. (Refer to...) Figure 1 This method for analyzing the potential and risks of V2G-based grid load regulation is applied to a system for analyzing the potential and risks of V2G-based grid load regulation. The system includes a terminal 110 and a server 120. The terminal 110 and server 120 are connected via a network. The terminal 110 can be a desktop terminal or a mobile terminal, specifically a mobile phone, tablet, or laptop. The server 120 can be a standalone server or a server cluster. The terminal 110 is used to acquire the total charging load time-series curve, total discharge power, and total charging power of electric vehicles in different regions; based on the total charging load time-series curve, the total discharge power, and the total charging power, it determines the grid load at different times after all electric vehicles in the region participate in V2G; the server 120 is used to take the maximum value of the grid load as the peak grid load after electric vehicles discharge to the grid in each region, and take the minimum value of the grid load as the valley grid load before electric vehicles discharge to the grid in each region; it acquires the original peak grid load and the original valley grid load before electric vehicles discharge to the grid; and based on the original... The peak shaving rate for each region is determined based on the initial peak load and the peak load of the power grid; the valley filling rate for each region is determined based on the original valley load and the original valley load of the power grid; the original peak-valley difference of the power grid before electric vehicles discharge to the grid is determined based on the original peak load and the original valley load of the power grid; the target peak-valley difference of the power grid after electric vehicles discharge to the grid in the region is determined based on the peak load and the valley load of the power grid; the rate of change of the peak-valley difference in the region is determined based on the original peak-valley difference and the target peak-valley difference of the power grid; and the potential and risk of power grid load regulation are determined based on the peak shaving rate, the valley filling rate, and the rate of change of the peak-valley difference.

[0025] like Figure 2 As shown, in one embodiment, a method for potential and risk analysis of V2G-based power grid load regulation is provided. This method can be applied to both terminals and servers; this embodiment illustrates its application to terminals. The specific steps of this V2G-based power grid load regulation potential and risk analysis method are as follows:

[0026] S10: Obtain the time-series curve of total charging load, total discharge power, and total charging power of electric vehicles in different regions;

[0027] S20: Based on the total charging load time-series curve, the total discharge power, and the total charging power, determine the grid load at different times for all electric vehicles in the region after participating in V2G;

[0028] S30: Take the maximum value of the grid load as the peak grid load after the electric vehicles in each region discharge to the grid, and take the minimum value of the grid load as the valley value of the grid load before the electric vehicles in each region discharge to the grid;

[0029] S40: Obtain the original peak load and valley load of the power grid before the electric vehicle discharges into the grid; determine the peak reduction rate for each region based on the original peak load and the peak load of the power grid; determine the valley filling rate for each region based on the original valley load and the valley load of the power grid;

[0030] S50: Determine the original peak-to-valley difference of the power grid before the electric vehicle discharges into the grid based on the original peak load and the original valley load; determine the target peak-to-valley difference of the power grid after the electric vehicle discharges into the grid within the region based on the original peak load and the original valley load;

[0031] S60: Determine the rate of change of peak-valley difference within the region based on the original peak-valley difference of the power grid and the target peak-valley difference of the power grid;

[0032] S70: Determine the potential and risk of power grid load regulation based on the peak shaving rate, the valley filling rate, and the peak-valley difference change rate.

[0033] In one embodiment, the total charging load timing curve is obtained through the following steps:

[0034] S101: Determine the first proportion of fast-charging vehicle users and the second proportion of slow-charging vehicle users in different regions; for each region Each car inside Generate random variables that follow a uniform distribution. and allocate;

[0035] S102: Determine the charging start time and initial state of charge of the electric vehicle, and obtain the charging duration based on the initial state of charge; determine the charging completion time based on the charging duration and the charging start time; and determine the total charging load time series curve based on the charging completion time.

[0036] The total discharge power Obtained through the following steps:

[0037] S103: If the discharge method and charging method of an electric vehicle are the same, then for the region... Each car inside Generate random variables that follow a uniform distribution. Determine the actual discharge power ;

[0038] S104: Determine the discharge start time of the electric vehicle ;

[0039] S105: Obtain the battery capacity of the electric vehicle and discharge level To obtain the area The Middle The discharge capacity of an electric vehicle And according to the dischargeable amount and the actual discharge power Determine the duration of the discharge and remaining battery percentage ;

[0040] S106: Based on each electric vehicle, according to the discharge start time point and the duration of the discharge Construct its single-vehicle discharge load and total discharge power .

[0041] In one embodiment, the total charging power Obtained through the following steps:

[0042] S107: Based on the remaining battery percentage Determine the region after discharge is completed No. Energy required for charging an electric vehicle ;

[0043] S108: Based on the required charging energy and the actual charging power Determine the region after discharge is completed The Charging time required for an electric vehicle ;

[0044] S109: Based on the actual charging power and the required charging time Determine the charging load of a single vehicle and total charging power .

[0045] In one embodiment, the peak reduction rate is used to determine the peak reduction rate. and the valley filling rate and the rate of change of the peak-valley difference Determining the potential and risks of power grid load regulation includes:

[0046] S701: The peak reduction rate The higher the value, the more significant the support role of electric vehicles in the power grid during peak hours;

[0047] S702: The valley filling rate The higher the value, the stronger the V2G's effect on boosting off-peak loads;

[0048] S703: The rate of change of peak-valley difference A value greater than 0 indicates that V2G effectively reduces the peak-valley difference, helping to smooth grid load fluctuations. Furthermore, a larger peak-valley difference change rate indicates a more significant peak-shaving and valley-filling effect. The peak-valley difference change rate... When the value is less than 0, Table V2G not only fails to reduce the peak-to-valley difference but also causes the peak-to-valley difference to further expand.

[0049] This invention takes demand-side management as its starting point, and, based on the current state of my country's power supply and demand, the development trend of electric vehicles, and user behavior characteristics, delves into the implementation effects of incorporating electric vehicles into power grid dispatch. It constructs a V2G charging and discharging scenario and simulation framework driven by electric vehicle user behavior characteristics to assess the potential impact and risks of large-scale charging and discharging behavior on power grid operation under uncertain conditions.

[0050] Define area The percentage of users who use fast charging is [percentage missing]. The percentage of users who use slow charging is 1- Assume that vehicles use only the rated power of their category when charging. The specific allocation method is as follows: for each region... Each car inside Generate random variables that follow a uniform distribution. And allocate the actual charging power according to the following formula:

[0051] (1)

[0052] in, Indicates the region Inner The charging power of an electric vehicle; Indicates the region Inner The fast charging power of an electric vehicle; Indicates the region Inner The slow charging power of an electric vehicle Let be a random variable that follows a uniform distribution.

[0053] The charging start time is the moment when an electric vehicle user plugs into a charging station to begin charging. Its distribution is influenced by user travel patterns, exhibiting significant time-of-day variations. To accurately model the spatiotemporal characteristics of charging load, this invention employs the kernel density estimation (KDE) method to construct a regionalized charging time probability model. The specific steps are as follows:

[0054] First, the collection area Initial charging time points of n electric vehicle users (samples) Multiple initial charging time points The initial charging time point set T is formed as follows:

[0055] Secondly, the probability density function is constructed using the Gaussian kernel function from nonparametric estimation, and its form is:

[0056] (2)

[0057] Where t is the initial charging time point to be estimated; Indicates the first The initial charging time point of each sample; It represents the bandwidth of the time variable and controls the smoothness of the distribution.

[0058] Hypothetical region Total The first historical charging record sample, recorded as the first The initial charging time point for each sample is ,area The probability density function of the kernel density estimate is written as:

[0059] (3)

[0060] After substituting the Gaussian kernel function:

[0061] (4)

[0062] in, Indicates the region At the initial charging time point to be estimated The estimated probability density; The bandwidth represents the time variable and controls the smoothness of the distribution; No. One initial charging time point; Indicates the region The total number of internal samples; The variable to be estimated is the time variable.

[0063] Due to bandwidth This is a key parameter affecting the quality of KDE estimation; if it is too large, the distribution becomes too smooth, and if it is too small, overfitting occurs. The Silverman rule is used to select the optimal bandwidth, and its calculation formula is as follows:

[0064] (5)

[0065] in, The bandwidth represents the time variable and controls the smoothness of the distribution; It is the number of samples; Historical charging time samples The standard deviation of is calculated using the following formula:

[0066] (6)

[0067] (7)

[0068] in, Historical charging time samples Standard deviation; No. One initial charging time point; Indicates the total number of samples; Indicates multiple initial charging time points The mean;

[0069] Then, using a random sampling method, from Central region Every electric car in China Randomly select an initial charging time point Hours. This is the charging start time. This will directly determine the starting point for electric vehicle loads to be connected to the power grid.

[0070] (8)

[0071] in, For the region The Middle The simulated charging start time for electric vehicles is based on... The distribution is randomly generated. For example... Figure 3 The figure shows a comparison between the kernel density distribution function of charging start time and historical real data.

[0072] Initial State of Charge (SOC) is the remaining battery power of an electric vehicle user at the start of charging. Initial SOC directly impacts total charging demand, charging duration, and the grid load. The distribution characteristics of initial SOC are influenced by users' driving habits and travel needs, resulting in significant differences among users. Considering this high degree of individual variability, a statistical analysis of initial SOC was conducted based on historical charging data, and a probability distribution model of initial SOC was constructed using kernel density estimation.

[0073] First, obtain the region. Initial state of charge for each electric vehicle user Multiple initial states of charge The initial charging state set X is formed as follows:

[0074] (9)

[0075] in, For the first The initial history of the vehicle value.

[0076] Secondly, construct the probability density function of the initial charging state:

[0077] (10)

[0078] (11)

[0079] (12)

[0080] in, Indicates the region In the initial state of charge to be estimated The estimated probability density; This is the initial state of charge. The initial state of charge to be estimated; The bandwidth for the charged state; Indicates the region The total number of internal samples;

[0081] (13)

[0082] in, For the bandwidth of the charged state; to control the smoothness of the distribution; It is the number of samples; This is an initial charging state-of-charge sample. The standard deviation of is calculated using the following formula:

[0083] (14)

[0084] (15)

[0085] in, The average of multiple initial states of charge;

[0086] Subsequently, using a random sampling method, the initial values ​​of each electric vehicle were sampled from the aforementioned density function. :

[0087] (16)

[0088] in, For the region The Middle The initial state of charge of a vehicle when it begins charging.

[0089] Simulation results are as follows Figure 4 As shown.

[0090] Charging time refers to the duration from the start to the end of charging an electric vehicle, and it is one of the key parameters for assessing charging behavior characteristics and the impact on grid load. Based on the initial charging phase of an electric vehicle... The battery capacity and charging power of the electric vehicle, combined with the time when the electric vehicle is fully charged. The charging time of the electric vehicle is calculated using the following formula:

[0091] (17)

[0092] in, For the region No. The charging energy required for an electric vehicle, measured in kWh; (80%) is the region No. The target SOC set for a single electric vehicle; (20%) area No. The initial state of charge of a vehicle when it begins charging; Representing the Battery capacity of an electric vehicle.

[0093] Secondly, based on the vehicle's charging power and efficiency Calculate its charging duration :

[0094] (18)

[0095] in, Indicates the region The Middle The charging time of an electric vehicle; For the region No. The target SOC set for a single electric vehicle; area No. The initial charge state of an electric vehicle when it begins charging; Representing the Battery capacity of an electric vehicle; Indicates the region No. Charging power of electric vehicles; This indicates charging efficiency.

[0096] After obtaining the start time and charging duration for each vehicle, its charging end time can be directly calculated. The charging end time for an electric vehicle can be expressed as:

[0097] (19)

[0098] in, Indicates the region No. The charging completion time of an electric vehicle; Indicates the region No. The start time of charging for the electric vehicle; Indicates the region No. The charging time of an electric vehicle.

[0099] This time is used to determine the vehicle at any given moment. Whether it is in a charging state is a key parameter for constructing the load curve.

[0100] Establish a discrete time series (in hours, for example) For each time point It determines whether each vehicle is charging and accumulates the power of all vehicles currently charging to form the total charging load.

[0101] First, define an indicator function:

[0102] (20)

[0103] in, For the region The first in electric vehicles at all times An indicator variable indicating whether the device is in a charging state. This indicates that the vehicle is charging. This indicates that the vehicle is not charging. Indicates the region The first in The charging completion time of an electric vehicle; Indicates the region The first in Electric vehicle charging start time

[0104] Then calculate the total charging load at each time point. :

[0105] (twenty one)

[0106] That is, at every point in time All are in a charging state ( The sum of the power of the electric vehicles () is the total load at that moment. This is achieved by iterating through... This will provide a complete load curve for electric vehicles, which can then be used for subsequent peak shaving and valley filling analysis, load forecasting, and other tasks.

[0107] Based on the above modeling logic, the simulated region is obtained. Total charging load curve of electric vehicles over 24 hours without considering the impact of V2G discharge. , denoted as:

[0108] (twenty two)

[0109] After modeling the regular charging load of electric vehicles (EVs), we were able to obtain typical charging load distribution curves for EV users at different times of the day without V2G participation. This simulation process fully considers the spatiotemporal uncertainties and individual differences in user charging behavior, providing data support for identifying periods of concentrated EV load and trends in grid pressure changes. However, in practical applications, with the development of V2G technology, EVs can not only serve as a source of load growth but also have the potential to participate in grid regulation through reverse discharge. Therefore, to further evaluate the role of EVs in regulating grid load fluctuations, modeling the charging load alone is far from sufficient. It is necessary to build upon this foundation, comprehensively considering the differences in user behavior during V2G discharge, discharge response mechanisms, and their power distribution characteristics, to establish a scientifically sound V2G protocol logic.

[0110] Based on this, after completing the simulation of conventional charging load in the first stage, the present invention further enters the second stage, focusing on the design of V2G protocol based on behavioral characteristics, and modeling and analyzing the diversity and regional heterogeneity of user discharge behavior.

[0111] Specifically, in the second-stage modeling process, the charging start time generated in the first stage ( Parameters such as power output, battery capacity, and charging method will serve as key inputs for inferring the feasibility of the discharge strategy. Furthermore, the design of the discharge protocol not only relies on the technical parameters of the electric vehicle (such as maximum discharge power, battery capacity, and charging method), but must also fully consider users' travel security needs, compensation incentive expectations, and time response preferences. To this end, this invention introduces three typical behavioral scenarios and constructs a diversified and parameter-controllable V2G response mechanism by setting different discharge decision rules, timing logic, and power scheduling strategies to comprehensively simulate the possible response effects of different user behavior patterns.

[0112] Therefore, this section first analyzes users' potential charging behavior preferences and sets up representative charging and discharging scenarios. Based on this, it compares the changes in grid load before and after the implementation of different strategies, focusing on analyzing the potential risks of V2G behavior to grid operation and the regional differences, ultimately clarifying the specific effects of different V2G charging and discharging strategies on grid load regulation. Specifically, it includes the following steps:

[0113] The charging and discharging behavior of electric vehicle users is influenced by multiple factors, including psychological preferences, economic incentives, and rigid travel needs, exhibiting strong randomness and heterogeneity. These behavioral differences significantly affect the disturbance effect of V2G charging and discharging on the power grid load, thus impacting its ability to reduce peak-valley differences. To systematically evaluate the implementation effect of V2G under different behavioral patterns, this invention sets up various V2G operating scenarios based on behavioral economics theory, focusing on analyzing the impact of user behavioral preferences on changes in the power grid peak-valley difference.

[0114] Furthermore, considering that electric vehicles still need to be recharged after discharging to maintain basic travel functions, the study uniformly sets the discharging behavior to only occur during peak electricity consumption periods to maximize its potential contribution to suppressing grid load fluctuations. Charging behavior, based on user behavior response patterns, is constructed into three typical scenarios: "time priority," "habit preference," and "economic benefit priority," to characterize the impact mechanism of behavioral preference differences on grid fluctuations.

[0115] Scenario 1: "Time-Priority" Scenario. In this scenario, user charging behavior exhibits a strong "convenience-oriented" characteristic. This invention assumes that after completing V2G discharge, users will immediately charge to ensure the vehicle remains in a "ready-to-charge" state. Their decision-making logic leans towards ensuring travel reliability, reflecting a strong "availability demand preference." From a behavioral economics perspective, this scenario reflects the "loss aversion" characteristic in prospect theory: users are more inclined to avoid the risk of their vehicle becoming unusable due to delayed charging, rather than pursuing the additional benefits brought by electricity price incentives. Furthermore, this scenario also reflects the "safety-first" strategy in actual user decision-making; that is, when faced with uncertain future travel needs, users will choose the safest current behavioral path.

[0116] Scenario 2: "Habitual Preference" Scenario. In this scenario, users' charging behavior exhibits a clear inertial characteristic. Even after participating in V2G discharge, they tend to recharge their electric vehicles according to their daily charging habits, such as consistently charging during the nighttime hours of 11:00 PM to 7:00 AM. This scenario reflects the path dependence and habit formation theories in behavioral economics, meaning users are more likely to continue existing energy usage patterns rather than responding dynamically based on system incentives. The V2G response in this scenario has a certain degree of stability, but its lower responsiveness to system price signals may limit further improvements in peak-shaving capabilities.

[0117] Scenario 3: "Economic Gain Priority" Scenario. This scenario assumes that users follow the rational economic agent assumption in their charging and discharging behavior, prioritizing maximizing economic gains. Their decision-making is highly dependent on electricity price fluctuations. Users actively discharge electricity during peak electricity price periods to obtain compensatory gains, and schedule charging during off-peak electricity price periods to minimize energy costs. In other words, users make cost-effective rational decisions when they are aware of electricity price fluctuations.

[0118] To comprehensively evaluate the impact of electric vehicles participating in V2G technology under different user behavior patterns on grid load regulation, this invention introduces multiple key parameter dimensions for scenario combination simulation based on three typical user charging and discharging behavior scenarios. The parameter settings aim to characterize the impact of behavioral characteristics such as user participation level, operation frequency, and single discharge intensity on V2G effectiveness, and construct a representative simulation scheme by combining the regional grid load characteristics and user behavior differences in my country.

[0119] (1) Electric vehicle participation rate: 20%, 40%, 60%, 80%

[0120] Purpose of the setting: This parameter is used to simulate scenarios where user groups of different sizes participate in V2G scheduling. As the participation rate increases, the flexibility resources that electric vehicle groups can provide will also increase significantly, which will determine whether this can improve the peak-shaving capability of V2G at the system level.

[0121] Of these, 20% represents limited participation or a pilot phase, 40%-60% represents a moderate level of acceptance, and 80% simulates the ideal participation state under conditions of high acceptance or sufficient policy incentives. By analyzing the tiered changes in participation rates, the marginal effect of resource expansion on peak-shaving capacity can be determined.

[0122] (2) Discharge frequency (how many times per month): 4 times / month, 8 times / month, 12 times / month

[0123] This parameter measures the frequency of user participation in V2G dispatch, covering typical scenarios from low-frequency response to more frequent response, reflecting the activity level of their behavioral responses. Higher frequency indicates more frequent user participation in grid load regulation, and also corresponds to a higher degree of cooperation and discharge behavior density.

[0124] Four times / month corresponds to low response activity, such as users being called only during specific peak hours; eight times / month is a medium frequency, about twice a week, representing relatively regular discharge participation; 12 times / month is close to once every two to three days, reflecting high-frequency scheduling or highly cooperative user situations. This parameter setting covers the typical frequency range from low to high response, facilitating the analysis of the impact of different discharge frequencies on load curve fluctuations.

[0125] (3) Discharge level (how much electricity the electric vehicle battery releases): 20%, 40%, 60%, 80%

[0126] Discharge level measures the proportion of battery capacity released by an electric vehicle during each V2G discharge, and is an important parameter reflecting the depth of user acceptance of V2G. It directly affects users' range anxiety and the threshold for responding to incentive mechanisms.

[0127] Among them, 20% represents shallow discharge, which has the least impact on user range; 40%-60% represents medium discharge, which has a certain peak-shaving capability and is still relatively acceptable to users; 80% represents deep discharge, which can be achieved in scenarios with sufficient compensation or high dispatch pressure. By controlling the discharge depth, the impact of different energy release strategies on grid load redistribution and the formation of charging replenishment load can be analyzed.

[0128] The above parameters are cross-combined under three behavioral scenarios to construct a total of 48 typical simulation scenarios (4 discharge levels × 4 participation rates × 3 frequencies) to evaluate the V2G regulation effect, load fluctuation changes, and potential peak-valley difference improvement of electric vehicles under different response intensities. To highlight its peak-shaving effect, this invention uniformly sets all discharge behaviors to occur during the evening peak period, while charging behaviors are set separately according to each behavioral mode, and comprehensively analyzes their dynamic impact on the grid load curve.

[0129] Provided that the user's discharge method is consistent with their charging method, that is, fast-charging users discharge at the fast-charging power, and slow-charging users discharge at the slow-charging power. Specifically:

[0130] (twenty three)

[0131] in, Indicates the region Inner The actual discharge power of the electric vehicle; Indicates the region Inner The fast charging power of an electric vehicle; Indicates the region Inner The slow charging power of an electric vehicle.

[0132] Divide a day into 24 hours A configurable discharge time period, denoted as:

[0133] (twenty four)

[0134] in, For the region The allocatable discharge time period; For the region The Middle Discharge start time of the period (unit: hour); For the region The Middle The discharge end time of the period must meet the following requirements. And the time periods do not overlap; For the region electric vehicles in the first The discharge probability of the segment satisfies . This refers to the k-th discharge time period;

[0135] No. electric vehicles by probability Select time period And randomly generate the discharge start time point within this period. ;

[0136] (25)

[0137] in, For the region The first in electric vehicles in the first The point in time when discharge begins during the period; For the region No. Discharge start time for the period (unit: hours); For the region No. The discharge end time of the period.

[0138] (26)

[0139] in, Indicates the first The battery capacity of an electric vehicle; For the region The Middle The discharge level of an electric vehicle; For the region in the current state The Middle The amount of electricity a vehicle can discharge.

[0140] The theoretical discharge duration is:

[0141] (27)

[0142] in, For the first electric vehicles in the area The duration of discharge, For the region in the current state The Middle The amount of electricity a vehicle can discharge; Indicates the region Inner The actual discharge power of the electric vehicle; .

[0143] For each electric vehicle Based on the discharge start time point and discharge duration Construct its single-vehicle discharge load :

[0144] (28)

[0145] in, For the region The Middle electric vehicles at all times The single-vehicle discharge load supplied to the power grid; the negative sign indicates that energy flows from the vehicle to the power grid. ; For the charging and discharging efficiency of electric vehicles; For the region The first in electric vehicles in the first The point in time at which discharge begins during the specified period.

[0146] After each round of discharge, the user status is updated. for:

[0147] (29)

[0148] in, For the first electric vehicles in the area The percentage of remaining battery power after discharge is complete; For the first The percentage of remaining battery power in an electric vehicle before it begins to discharge. For the region in the current state The Middle The discharge capacity of an electric vehicle; that is, the cumulative energy delivered to the grid during this discharge cycle. Indicates the first Battery capacity of an electric vehicle.

[0149] (30)

[0150] in, For the region At any moment The total discharge power provided to the grid by all electric vehicles participating in V2G; For the region Total number of electric vehicles in China; For the region The Middle electric vehicles at all times The single-vehicle discharge load supplied to the power grid.

[0151] This total load calculation can generate a 24-hour continuous total load curve and provide directly controllable load regulation resources for power grid dispatch.

[0152] To ensure that electric vehicles can promptly and effectively restore their charge after completing V2G discharge, thus meeting the needs of subsequent travel and system load balancing, this invention constructs three typical charging scenarios based on differences in user charging behavior and proposes corresponding charging logic. The charging behavior modeling logic includes the following four steps:

[0153] The user charging behavior scenarios and charging start times are defined as follows:

[0154] 1) Scenario 1: "Time Priority" Scenario

[0155] These users prioritize the immediate availability of the vehicle and initiate charging immediately after completing V2G discharge to ensure the vehicle maintains sufficient SOC before the next trip.

[0156] This behavioral logic can be modeled by setting the charging start time to be equal to the discharging end time, that is, the discharging end time is the charging start time:

[0157] (31)

[0158] in, Indicates the region The The charging start time for each electric vehicle; For the region The The final end time of the discharge behavior of an electric vehicle.

[0159] 2) Scenario Two: "Habitual Preference" Scenario

[0160] The charging behavior of this type of user is significantly influenced by their daily travel and work schedules. Although they participate in discharging activities, their recharging time still follows the time pattern formed by individual habits. To describe the charging initiation behavior of this type of user, this paper adopts the charging start time probability model based on kernel density estimation established above, and constructs a probability density function from historical behavior samples. And based on this, individual behavior simulations are conducted. Specifically, the first... The method for generating the charging start time for each user is as follows:

[0161] (32)

[0162] in, Indicates the region The The charging start time for a vehicle. The first one estimated by the KDE method The probability density function of the charging start time of each region Indicates the region The Charging start-up time obtained from a random sample of electric vehicles.

[0163] 3) Scenario Three: "Prioritizing Economic Gains"

[0164] These users are highly sensitive to electricity price signals and tend to charge during periods of low prices to minimize charging costs. Provided the vehicle has enough remaining battery power to last until the next trip, users will choose the time with the lowest electricity price within the next 24 hours after discharging to start charging.

[0165] Specifically, firstly, in the time period [ Search area within Based on the changes in electricity prices, determine the time t corresponding to the lowest electricity price:

[0166] (33)

[0167] in, Indicates the region The The charging start time for a vehicle. Indicates the region At any moment The electricity price level and the constraints ensure that the charging behavior begins after the discharging behavior is completed; This means "taking the value of the independent variable that minimizes the objective function"; limited The search range extends from the end of the discharge to within the next 24 hours.

[0168] Based on the user's current battery state of charge and target SOC setting, the first step is to calculate the... The charging energy required for one electric vehicle:

[0169] (34)

[0170] in, For the first electric vehicles in the area The percentage of remaining battery power after discharge is complete; (80%) is the region No. The target SOC set for a single electric vehicle; Representing the Battery capacity of an electric vehicle; Region after discharge is completed No. The energy required to charge an electric vehicle.

[0171] The theoretical charging time required by the user is:

[0172] (35)

[0173] in, ; This refers to charging efficiency parameters. For the region after discharge is completed The The charging demand for electric vehicles; Region after discharge is completed The The charging time required for an electric vehicle.

[0174] Regions set based on user behavior decisions The Charging start time of electric vehicles Compared with theoretical duration The power response function of an electric vehicle throughout the entire charging cycle can be constructed:

[0175] (36)

[0176] in, For the region The Middle electric vehicles at all times Single-vehicle charging load, For the region The electric vehicles , This refers to charging efficiency parameters. Indicates the region The The charging start time for each electric vehicle; Region after discharge is completed The The charging time required for an electric vehicle.

[0177] This power timing function provides the basic input data for subsequent system load summarization, load forecasting, and control strategy formulation.

[0178] After considering the power response of all connected vehicles, the system at any given time... The formula for calculating the total charging load is:

[0179] (37)

[0180] in, Indicates the region At any moment Total charging power, For electric vehicles In the region The charging load of a single bicycle For the region Number of electric vehicles in China. It can be used to construct an overall load curve, and then analyze the effect of load peak-valley shift and the effectiveness of time-of-use pricing adjustments.

[0181] When considering the comprehensive impact of electric vehicles participating in V2G charging and discharging on the power grid, the regional... At any moment The total load can be expressed as:

[0182] (38)

[0183] in, For the region Electric vehicles participating in V2G at a certain time The grid load (the sum of the demands of all vehicles). For the region At any moment The total discharge power provided to the grid by all electric vehicles participating in V2G; Indicates the region At any moment Total charging power, For the region Original grid load, Indicates the region before participating in V2G discharge. At any moment Total charging power.

[0184] The model uses a baseline load. Based on this, V2G discharge power is superimposed. With V2G post-charging power And deduct the original electric vehicle charging power This method enables an accurate characterization of the total system load under V2G operating scenarios. It can quantify the impact of different V2G strategies on regional load curves, providing theoretical support for power grid dispatch optimization and demand-side response strategy evaluation.

[0185] This section uses Monte Carlo simulation to quantitatively assess different regions ( The study examines the effect of V2G on grid load regulation under various scenarios. Based on the original regional power load and the total power load after V2G participation, the following indicators are defined to analyze the potential and risks of electric vehicles in peak shaving and valley filling.

[0186] Peak load refers to the highest electricity consumption reached by the power grid within a specific time period. When electric vehicles connect to the grid, they may overlap with the base load of the power system, affecting grid stability. In this invention, we use a 24-hour period as the main analysis period; therefore, peak load is the maximum electricity demand value within 24 hours. This indicator reflects the highest instantaneous power load that the grid can withstand and is a direct measure of the peak-shaving effect of V2G strategies.

[0187] Peak shaving rate is a core indicator used to quantify the degree to which V2G reduces peak load on the power grid. It is defined as the percentage decrease in peak load after V2G regulation compared to the original peak load. The calculation formula is as follows:

[0188] (39)

[0189] (40)

[0190] in, The peak value of the original grid load before the electric vehicle discharges into the grid. Let be the peak grid load after electric vehicles in region r discharge to the grid. Indicates the region The peak shaving rate. The higher the peak shaving rate, the more significant the support role of electric vehicles in the power grid during peak hours.

[0191] In contrast to peak load, valley load refers to the period of day with the lowest electricity demand. When the grid load is low, generating units may operate inefficiently or even be forced to reduce output due to insufficient load. In power systems with a high proportion of renewable energy, intermittent energy sources such as wind and solar power may be abandoned during off-peak hours due to insufficient demand. Therefore, increasing off-peak load (i.e., "valley filling") is of great significance for optimizing generation dispatch and improving grid operating efficiency.

[106] .

[0192] The valley filling rate is an important indicator used to quantify and evaluate the effect of V2G technology on improving the valley load level of the power grid. It is defined as the percentage of the difference between the valley load after V2G regulation and the original valley load before regulation, relative to the original valley load. The calculation formula is as follows:

[0193] (41)

[0194] = (42)

[0195] In the formula, The initial grid valley load within region r before electric vehicles discharge to the grid. Let $r$ be the grid load valley value before electric vehicles in region $r$ discharge to the grid. For the region The valley filling rate. The higher the valley filling rate, the stronger the effect of the regulation strategy on improving the off-peak load.

[0196] First, the peak-valley difference is a key indicator for measuring the degree of fluctuation in power grid load. It is defined as the difference between the peak load and the valley load over a 24-hour period. Its mathematical expression is:

[0197] (43)

[0198] (44)

[0199] In the formula, For the peak load of the power grid, This represents the off-peak load of the power grid. A large peak-to-valley difference means that the grid load fluctuates significantly throughout the day, requiring the power generation side to frequently adjust its output to match the supply and demand balance.

[0200] Under different V2G scenarios, electric vehicles can simultaneously affect the peak and valley values ​​of the load curve through bidirectional charging and discharging behavior, thereby changing the peak-valley difference of the power grid. To quantify the overall regulating effect of V2G on the load curve, this invention introduces the peak-valley difference change rate, defined as the percentage change in the power grid peak-valley difference before and after V2G regulation relative to the peak-valley difference before regulation. The calculation formula is as follows:

[0201] (45)

[0202] in, The original peak-to-valley difference in the power grid before the electric vehicle discharges into the grid. Let be the target peak-to-valley difference in the power grid after electric vehicles in region r discharge to the grid. Representative area The rate of change of peak-to-valley difference.

[0203] when The data shows that V2G effectively reduces the peak-valley difference, which helps to smooth out grid load fluctuations. At the same time, the greater the rate of change of the peak-valley difference, the more obvious the peak shaving and valley filling effect.

[0204] like This means that V2G not only failed to reduce the peak-to-valley difference, but actually caused the peak-to-valley difference to widen further.

[0205] Figure 5 An internal structural diagram of a computer device in one embodiment is shown. This computer device can specifically be a terminal or a server. Figure 5As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program enables the processor to implement a V2G-based method for analyzing the potential and risks of grid load regulation. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to implement a V2G-based method for analyzing the potential and risks of grid load regulation. Those skilled in the art will understand that... Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0206] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0207] 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.

[0208] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for analyzing the potential and risks of power grid load regulation based on V2G, characterized in that, The method includes: Obtain the time-series curves of total charging load, total discharge power, and total charging power of electric vehicles in different regions; Based on the total charging load time-series curve, the total discharge power, and the total charging power, determine the grid load at different times for all electric vehicles in the region after participating in V2G; The maximum value of the grid load is taken as the peak grid load after electric vehicles discharge to the grid in each region, and the minimum value of the grid load is taken as the valley grid load after electric vehicles discharge to the grid in each region. Obtain the original peak and valley load of the power grid before the electric vehicle discharges into the grid; determine the peak shaving rate for each region based on the original peak and valley load; determine the valley filling rate for each region based on the original valley load and valley load. The original peak-to-valley difference of the power grid before the electric vehicle discharges into the grid is determined based on the original peak load and the original valley load of the power grid; the target peak-to-valley difference of the power grid after the electric vehicle discharges into the grid within the region is determined based on the original peak load and the original valley load of the power grid. The rate of change of peak-valley difference within the region is determined based on the original peak-valley difference of the power grid and the target peak-valley difference of the power grid. The potential and risks of power grid load regulation are determined based on the peak shaving rate, the valley filling rate, and the peak-valley difference change rate. The total charging load time-series curve is obtained in the following way: The system determines a first proportion of fast-charging vehicle users and a second proportion of slow-charging vehicle users in different regions; generates and assigns uniformly distributed random variables for each vehicle in each region; determines the charging start time and initial state of charge of the electric vehicle, and obtains the charging duration based on the initial state of charge; determines the charging completion time based on the charging duration and the charging start time; and determines the total charging load time series curve based on the charging completion time. This includes: Get area Initial state of charge for each electric vehicle user Multiple initial states of charge The initial charging state set X is formed as follows: in, For the first The initial history of the vehicle value; Construct the probability density function of the initial charging state: in, Indicates the region In the initial state of charge to be estimated The estimated probability density; This is the initial state of charge. The initial state of charge to be estimated; The bandwidth for the charged state; Indicates the region The total number of samples within the sample; in, For the bandwidth of the charged state; to control the smoothness of the distribution; It is the number of samples; This is an initial charging state-of-charge sample. The standard deviation of is calculated using the following formula: in, The average of multiple initial states of charge; Using a random sampling method, the initial values ​​of each electric vehicle are sampled from the above density function. : in, For the region The Middle The initial state of charge of a vehicle when it begins charging; Based on the initial charging of electric vehicles The battery capacity and charging power of the electric vehicle, combined with the time when the electric vehicle is fully charged. The charging time of an electric vehicle can be calculated using the following formula: in, For the region No. The charging energy required for an electric vehicle, measured in kWh; For the region No. The target SOC set for the electric vehicle is 80%; area No. The initial state of charge of the vehicle when charging begins, wherein the initial state of charge is 20%; Representing the Battery capacity of an electric vehicle; Secondly, based on the vehicle's charging power and efficiency Calculate its charging duration : in, Indicates the region The Middle The charging time of an electric vehicle; For the region No. The target SOC set for an electric vehicle; area No. The initial charge state of an electric vehicle when it begins charging; Representing the Battery capacity of an electric vehicle; Indicates the region No. Charging power of electric vehicles; Indicates charging efficiency; After obtaining the start time and charging duration for each vehicle, its charging end time can be directly calculated. The charging end time for an electric vehicle can be expressed as: in, Indicates the region No. The charging completion time of an electric vehicle; Indicates the region No. The start time of charging for the electric vehicle; Indicates the region No. The charging time of an electric vehicle; Establish a discrete time series , in hours, for each point in time It determines whether each vehicle is charging and accumulates the power of all vehicles currently charging to form the total charging load. First, define an indicator function: in, For the region The first in electric vehicles at all times An indicator variable indicating whether the device is in a charging state. This indicates that the vehicle is charging. This indicates that the vehicle is not charging. Indicates the region The first in The charging completion time of an electric vehicle; Indicates the region The first in The start time of charging for the electric vehicle; Calculate the total charging load at each time point : in, Total charging load; This refers to the actual charging power. For the region The first in electric vehicles at all times An indicator variable indicating whether the device is in a charging state.

2. The method for potential and risk analysis of V2G-based power grid load regulation according to claim 1, characterized in that, The total discharge power is obtained through the following steps: If the discharge method of electric vehicles is the same as the charging method, then a random variable following a uniform distribution is generated for each vehicle in each region to determine the actual discharge power. Determine the start time of discharge for the electric vehicle; The battery capacity and discharge level of electric vehicles are obtained to determine the discharge capacity of each electric vehicle in each area, and the discharge duration and remaining power percentage are determined based on the discharge capacity and the actual discharge power. For each electric vehicle, its single-vehicle discharge load and total discharge power are constructed based on the discharge start time and the discharge duration.

3. The method for potential and risk analysis of V2G-based power grid load regulation according to claim 2, characterized in that, The total charging power is obtained through the following steps: The region after discharge is determined based on the remaining battery percentage. No. The energy required to charge an electric vehicle; The required charging time for each electric vehicle in each area after discharge is determined based on the required charging energy and the actual charging power. The charging load and total charging power of a single vehicle are determined based on the actual charging power and the required charging time.

4. The method for potential and risk analysis of V2G-based power grid load regulation according to claim 1, characterized in that, The determination of the potential and risks of power grid load regulation based on the peak shaving rate, the valley filling rate, and the peak-valley difference change rate includes: The higher the peak reduction rate, the more significant the support role of electric vehicles in the power grid during peak hours; The higher the valley filling rate, the stronger the effect of V2G on improving the valley load. When the peak-valley difference change rate is greater than 0, it indicates that V2G effectively reduces the peak-valley difference and helps to smooth out grid load fluctuations. At the same time, the larger the peak-valley difference change rate, the more obvious the peak shaving and valley filling effect. When the peak-valley difference change rate is less than 0, it indicates that V2G not only fails to reduce the peak-valley difference but also causes the peak-valley difference to further expand.

5. The method for potential and risk analysis of V2G-based power grid load regulation according to claim 1, characterized in that, The total discharge power is achieved by the following expression: in, This represents the total discharge power. For the region The Middle electric vehicles at all times Single vehicle discharge load; For the region Total number of electric vehicles in China.

6. The method for potential and risk analysis of V2G-based power grid load regulation according to claim 1, characterized in that, The total charging power is achieved through the following expression: in, Indicates the region At any moment Total charging power, For electric vehicles In the region The charging load of a single bicycle For the region Number of electric vehicles in China.

7. The method for potential and risk analysis of V2G-based power grid load regulation according to claim 1, characterized in that, The region is determined based on the total charging load time-series curve, the total discharging power, and the total charging power. All electric vehicles participating in V2G at time The grid load is represented by the following expression: in, For the region Electric vehicles participating in V2G at a certain time The grid load, which is the sum of the demands of all vehicles, For the region At any moment The total discharge power provided to the grid by all electric vehicles participating in V2G; Indicates the region At any moment Total charging load, For the region Original grid load, Indicates the region before participating in V2G discharge. At any moment Total charging load.

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