Electric vehicle network interaction control method and system based on quick charging right

Abstract

The invention discloses an electric vehicle network interaction control method and system based on a fast charging right, and belongs to the technical field of vehicle charging, and the method comprises the steps: building an electric vehicle fast charging right integral database; initializing an electricity price coefficient krice and excitation intensity SEn; monitoring the state of the power grid; judging whether the power grid is in a demand response state at the current moment; determining a power limit value PSC according to the power demand of the power grid at the current moment; according to the power limit value PSC and the charging power and state at the end of the previous moment, determining an electricity price coefficient Krice and excitation force SEn at the current moment; the charging mode and the charging power are determined according to the electricity price coefficient kPrice and the excitation force SEn of the current power grid and the state of the electric vehicle, the step of monitoring the state of the power grid is executed after the current moment is ended, and monitoring and adjustment are continued; according to the electric vehicle network interaction control method and system based on the quick charging right, the power grid adjustment cost is effectively reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of automobile charging, and in particular relates to a method and system for controlling an electric vehicle's vehicle network interaction based on fast charging rights. Background Art

[0002] With the advancement of policies and technological development, the interaction between electric vehicles (EVs) and the grid (V2G) has become the focus of power system research. V2G interaction refers to the two-way energy and information exchange between electric vehicles and the grid. Through V2G interaction, electric vehicles can be used as mobile distributed energy storage units to balance the grid load through dynamic charging and discharging, thereby effectively reducing the peak-to-valley difference of the grid load, reducing dependence on traditional power generation facilities, and promoting the integration of renewable energy. The existing V2G interaction incentive methods mainly include the following:

[0003] Electricity price fluctuation incentives: Grid companies indirectly regulate the charging and discharging power of electric vehicles on the grid by adjusting electricity prices. Based on the principle of minimizing charging costs, electric vehicle users charge when electricity prices are low and discharge when electricity prices are high to obtain price difference benefits. Therefore, grid companies increase charging prices during peak load periods and reduce charging prices during low electricity consumption periods, thereby indirectly regulating the charging power of electric vehicles.

[0004] Grid subsidies: When the grid load is peak, the grid company provides subsidies to electric vehicle users who participate in demand response, such as charging fee reduction or cash rewards, to attract electric vehicle users to suspend charging or reverse charge the grid.

[0005] Defects and shortcomings of the existing technology:

[0006] High grid regulation costs: Both electricity price fluctuation incentives and grid subsidies will bring high economic burdens to grid companies; grid subsidies require a lot of financial support, and electricity price fluctuation incentives require complex market mechanisms and efficient management;

[0007] Poor user experience: The existing mechanism fails to fully consider the actual travel needs of electric vehicle users, resulting in users not being able to meet their normal travel needs after participating in demand response, affecting user experience. For example, during peak hours, users may need to travel, but are required to discharge in order to respond to demand, which affects the user's convenience;

[0008] Limited incentive effect: When participating in demand response, electric vehicles need to charge and discharge frequently, which will have a significant impact on battery life. Current incentives mainly incentivize users through electricity price fluctuations or subsidies, but the benefits of these incentives are small compared to the loss of battery life, making it difficult to fully incentivize users to participate in demand response. This makes users choose not to participate or reduce the frequency of participation when weighing the benefits and battery loss, reducing the overall effect of demand response;

[0009] Therefore, given that it is currently impossible to reduce the grid incentive cost and meet users' basic travel needs, it is necessary to develop a new electric vehicle grid interaction control method and system based on fast charging rights to solve the existing problems. Summary of the invention

[0010] The purpose of the present invention is to provide an electric vehicle-grid interaction control method and system based on fast charging rights to solve the problem of high grid incentive costs.

[0011] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a vehicle-grid interactive control method for electric vehicles based on fast charging rights, comprising:

[0012] Establish a database of electric vehicle fast charging points;

[0013] Initialize the electricity price coefficient k price and incentive strength S En ;

[0014] Entering time t = 0, that is, when the system starts to operate, the state of the power grid is monitored;

[0015] Determine whether the power grid is in a demand response state at the current moment;

[0016] According to the current power demand of the power grid, determine the power limit value P SC ;

[0017] According to the power limit value P SC As well as the charging power and status at the end of the previous moment, determine the current electricity price coefficient k price and incentive strength S En ;

[0018] According to the electricity price coefficient k of the current power grid price and incentive strength S En And the electric vehicle's own status, determine the charging mode and charging power:

[0019] The current moment ends, and the process returns to the step of monitoring the power grid status to continue monitoring and adjusting.

[0020] Preferably, the determining whether the power grid is in a demand response state at the current moment includes:

[0021] If the power grid is not in a demand response state at the current moment, the electricity price coefficient k price and incentive strength S En Set to 0;

[0022] Demand response refers to the grid's behavior in any form, such as requiring, calling on or guiding users to reduce electricity consumption, stop electricity consumption and reverse discharge.

[0023] Preferably, the charging mode includes:

[0024] Weak response mode: used for charging at a lower power, with the charging price kept at the base level;

[0025] Forced fast charging mode: used for users to consume fast charging points or pay higher electricity prices for fast charging;

[0026] Strong response mode: It allows users to obtain fast charging points by discharging to the grid and accumulate opportunities for fast charging in the future.

[0027] Preferably, in the weak response mode, charging is performed at a slow charging power, and the charging cost is maintained at a base level, that is, the electricity price of the power grid when there is no demand response; as shown in formula (1):

[0028] a price =a common (1)

[0029] Among them, a price represents the charging price of electric vehicles at time t, a common Indicates the electricity price of the power grid under normal conditions.

[0030] Preferably, the forced fast charging mode includes: consuming the user's fast charging right points to obtain fast charging rights with low electricity prices, and the charging fee is as shown in formula (1), but the user's fast charging right points begin to decrease, as shown in formula (2):

[0031]

[0032] in, Represents the fast charging points obtained by the electric vehicle at time t.

[0033] Preferably, the forced fast charging mode includes:

[0034] Pay a higher charging price to obtain fast charging rights; when the user does not have fast charging rights points, pay a higher charging price to obtain a higher charging power. The relationship between charging cost and charging power is shown in formula (3):

[0035] aprice =k price (t)·(P EV (t)-P SC )+a common (3)

[0036] Among them, k price (t) represents the electricity price coefficient, P EV (t) represents the charging power of the electric vehicle at time t, P SC Indicates the charging power limit.

[0037] Preferably, in the strong response mode, the power grid recovers the electric energy discharged by the user at the benchmark electricity price, and rewards the user with fast charging points according to the total amount of electric energy discharged. The relationship between the reward of fast charging points and the user's discharge power is shown in formula (4):

[0038]

[0039] Among them, S En (t) represents the excitation coefficient; k price (t) and S En (t) is determined by the time-sharing decision-making mechanism on the grid side, that is, in each time period, it is adjusted according to the current load of the grid. The cumulative relationship of the fast charging weight points is shown in formula (5):

[0040]

[0041] Preferably, the method further comprises a step of verifying the control method: verifying through an electric vehicle charging model and a power grid dispatching model;

[0042] The electric vehicle charging model includes setting the uncompleted charging amount as V slack , to characterize the user's vehicle experience, the calculation formula is shown in formula (6):

[0043]

[0044] Assume that each user sets the desired end time and target SOC when starting charging If the EV does not reach the target SOC at the end of charging, the owner’s experience will be negatively affected; V slack To quantify this effect, V slack The larger the value, Indicates the SOC value of incomplete charging. Indicates the SOC value of the electric vehicle when charging is completed, V EV Indicates the battery capacity of electric vehicles; the greater the negative impact on user experience;

[0045] The electric vehicle charging optimization model is expressed as shown in formulas (7) and (8):

[0046]

[0047] Among them, t refers to the time t, D I n represents the sensitivity coefficient of each electric vehicle user to the fast charging right points; P EV (t) represents the charging power of the electric vehicle at time t, P FCM Represents the maximum charging power of electric vehicles, t DR Represents the demand response time, P DCM Represents the maximum discharge power of the electric vehicle, α SOC (t) represents the battery SOC of the electric vehicle at time t, Represents the remaining FCR points of the electric vehicle user at the beginning of this charging. represents the FCR points obtained by the electric vehicle at time t, Represents the remaining FCR points of the electric vehicle user at the start of the next charging. Indicates the SOC value of incomplete charging. Indicates the charging target SOC set by the user. Represents the battery SOC at the end of this charge, F Res represents the frequency of its participation in demand response in the past month, D punish Represents the penalty coefficient for the user's failure to complete charging, V EV Represents the battery capacity of the electric car user, V slack Represents the amount of power that has not been fully charged;

[0048] The objective function of the electric vehicle charging optimization model is to minimize the charging cost of the electric vehicle. The charging cost includes the charging power P EV With electricity price price The sum of the products of slack and the owner's remaining FCR points The equivalent cost and the penalty coefficient D for the incomplete charging amount punish , the penalty coefficient is related to the owner's F Res Inversely proportional to and determined by an empirical function; the incomplete charge capacity V slack Used to quantify negative impact;

[0049] The power grid dispatching model includes formula (9) and formula (10):

[0050]

[0051] in, represents the charging power of the i-th electric vehicle in the grid at time t, represents the electricity price of the i-th electric car at time t, D FC represents the weight of the total FCR points issued in the grid comprehensive income, D slack Represents the weight of the total amount of unfinished charging in the comprehensive income of the power grid, represents the FCR points obtained by the i-th electric vehicle at time t, represents the SOC value of the uncharged amount of the i-th electric vehicle, P Grid (t) represents the total charging power of the electric vehicle at time t, P DR (t) represents the total charging power limit of electric vehicles during grid demand response at time t; represents the battery capacity of the i-th electric car;

[0052] The objective function is to maximize the EVs The total revenue is adjusted by deducting the equivalent cost of the unfinished charging volume and the equivalent cost of the FCR points issued as an incentive. The grid adjusts the weight coefficient D of the equivalent cost of the FCR points. FC and the weight coefficient D of the uncompleted charging amount slack To optimize its scheduling strategy.

[0053] The present invention further provides an electric vehicle-grid interactive control system based on fast charging rights, comprising: a grid decision layer: used to monitor the real-time charging status of electric vehicles in the grid, and adjust the electricity price coefficient and incentive coefficient based on demand response;

[0054] Electric vehicle charging layer: used to adjust its own charging and discharging power according to demand and the current status of the electric vehicle and the electricity price coefficient and incentive coefficient output by the power grid decision-making layer.

[0055] Preferably, the power grid decision layer includes:

[0056] Demand response monitoring module: used by the power grid decision-making layer to monitor the demand response of the power grid in real time and obtain the demand response information at the next moment;

[0057] State parameter generation module: used to generate the electricity price coefficient k at the next moment using a deep reinforcement learning algorithm based on the demand response status monitored by the demand response monitoring module and the charging status of electric vehicles in the power grid. price (t) and the excitation coefficient S En (t);

[0058] Parameter transfer module: used to transfer the generated coefficients to the electric vehicle charging layer to guide it to adjust the charging strategy.

[0059] Preferably, the electric vehicle charging layer includes:

[0060] Charging status monitoring module: used for the electric vehicle charging layer to monitor the charging status of each electric vehicle and obtain the electric vehicle status parameters after completing the charging activity at the last moment;

[0061] State parameter receiving module: used to receive the electricity price coefficient and incentive coefficient transmitted by the power grid decision-making layer;

[0062] Charging power adjustment module: used to adjust the charging power of each electric vehicle according to the received status parameters and the actual needs of the electric vehicle.

[0063] The technical effects and advantages of the present invention are as follows: the electric vehicle network interactive control method and system based on fast charging rights effectively reduces the grid regulation cost by introducing an incentive mechanism based on fast charging rights; users obtain fast charging rights points by participating in demand response, which can be used for future fast charging, thereby reducing dependence on direct economic subsidies and reducing the economic burden of power grid companies; there are three charging modes, and users can flexibly choose a suitable mode according to their own needs; the actual travel needs of users are fully considered, and by adjusting the electricity price coefficient and the incentive coefficient, users are forced to strike a balance between discharging profit and car use needs, thereby ensuring the car use needs of users after participating in demand response; through the fast charging rights points reward mechanism, users can obtain fast charging rights points by discharging during the demand response period, which can be used for future fast charging; compared with only motivating users through electricity price fluctuations or subsidies, the fast charging rights incentive is more rigid and attractive to users, significantly improving the user's willingness to participate and improving the overall effect of demand response. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a structural schematic diagram of a schematic diagram of a vehicle-network interaction incentive mechanism based on fast charging rights of the present invention;

[0065] Figure 2 This is a flow chart of the vehicle-network interaction incentive mechanism based on fast charging rights of the present invention;

[0066] Figure 3 This is a schematic diagram of the probability distribution of the charging start time of the present invention;

[0067] Figure 4 This is a schematic diagram of the probability distribution of charging time of the present invention;

[0068] Figure 5 A comparison diagram of electric vehicle charging power distribution without events and under three different incentive schemes during nine demand response periods of the present invention;

[0069] Figure 6 It is a schematic diagram of the number of times that the three incentive schemes of the present invention successfully perform DR power adjustment;

[0070] Figure 7A schematic diagram comparing the cost and uncompleted charging amount of three incentive schemes of the present invention;

[0071] Figure 8 It is a schematic diagram of the change of the overall charging power of an electric vehicle under the online and offline verification modes of the present invention;

[0072] Fig. 9 It is a schematic diagram comparing the economic efficiency and the unfinished charging amount under online and offline scheduling of the present invention. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] The present invention provides Figure 1 , Figure 2 The electric vehicle grid interaction control method based on fast charging rights is shown in the figure. The purpose is to use fast charging rights as an incentive for users to participate in demand response, so as to achieve efficient regulation of grid load power at low cost and ensure the travel experience of electric vehicle users.

[0075] The following steps are involved:

[0076] Step 1: Establish an electric vehicle fast charging rights points database to record the fast charging rights points of each electric vehicle;

[0077] Step 2: Initialize the electricity price coefficient k price and incentive strength S En ;

[0078] Step 3: At time t=0, the power grid decision-making layer starts to monitor the power grid status;

[0079] Step 4: Determine whether the power grid is in a demand response (DR) state at the current moment:

[0080] If not, the electricity price coefficient k price and incentive strength S En Set to 0 and go to step 7;

[0081] If yes, go to step 5;

[0082] Step 5: Determine the power limit value P according to the current grid power demand SC ;

[0083] Step 6: According to the power limit value P SCAs well as the charging power of the electric vehicle at the end of the previous moment and the relevant status of the electric vehicle, the grid decision-making layer determines the current electricity price coefficient k price and incentive strength S En :

[0084] Step 7: Output the current k price and S En to the electric vehicle charging floor;

[0085] Step 8: Electric vehicle charging layer: each user receives the k provided by the current power grid. price and S En , as well as the electric vehicle’s own status, determine the charging mode and charging power:

[0086] Weak response mode: charging at a lower power, and the charging electricity price remains at the benchmark level;

[0087] Forced fast charging mode: users can consume fast charging points or pay higher electricity prices for fast charging; Strong response mode: users obtain fast charging points by discharging to the grid, accumulating opportunities for future fast charging;

[0088] Step 9: Each electric vehicle is charged or discharged according to the selected charging mode and charging power;

[0089] Step 10: Enter the next time t=t+1, return to step 4, and continue monitoring and adjusting.

[0090] The maximum charging power of electric vehicles is getting higher and higher, which gradually becomes a shock to the power grid, especially during the period of power grid demand response, when high-power fast charging is particularly fatal. Therefore, from the perspective of limiting the fast charging power of users, fast charging of users is regarded as a special right and restricted during the period of demand response, and this is used as an incentive to attract users to participate in demand response;

[0091] When the grid is not in demand response, there is no limit on the charging power of electric vehicles (EVs). During grid demand response, the behaviors of all EVs will be divided into three categories: forced fast charging mode, weak response mode, and strong response mode.

[0092] When the grid enters demand response mode, all EVs should switch to weak response mode and start charging at a slow charging power. In weak response mode, the charging cost remains at the baseline level, that is, the electricity price of the grid when there is no demand response; as shown in formula (1):

[0093] a price =a common (1)

[0094] Among them, a price represents the charging price of electric vehicles at time t, acommon Indicates the electricity price of the power grid under normal conditions. In this mode, the fast charging points will not change;

[0095] During the demand response period, when the owner urgently needs the vehicle and needs to quickly recharge in a short period of time, he can choose to enter the forced fast charging mode. The user can obtain fast charging rights in two ways: the first method is to consume the user's fast charging rights points to obtain fast charging rights at low electricity prices. The charging fee is still as shown in formula (1), but the user's fast charging rights points begin to decrease. A number of fast charging rights points are consumed per unit time. Here, 1 point is consumed per unit time as an example, as shown in formula (2):

[0096]

[0097] in, Represents the fast charging points obtained by the electric vehicle at time t.

[0098] The second method is to pay a higher charging price to obtain fast charging rights. When the user does not have fast charging rights points, he can get a higher charging power by paying a higher charging price. The relationship between charging cost and charging power is shown in formula (3):

[0099] a price =k price (t)·(P EV (t)-P SC )+a common (3)

[0100] Among them, k price (t) represents the electricity price coefficient, P EV (t) represents the charging power of the electric vehicle at time t, P SC Indicates the limit of charging power. When the charging power of an electric vehicle exceeds the charging power limit, the charging fee will increase linearly with the excess power, encouraging users to strike a balance between charging speed and charging cost. At the same time, the purpose of retaining the high price of fast charging rights in the entire fast charging rights mechanism is to ensure that users do have urgent energy replenishment needs when they do not have fast charging rights points, and to ensure the travel experience of users when the grid responds to demand.

[0101] In addition to limiting the user's charging power through fast charging points, the fast charging mechanism also encourages users to discharge to the grid during the demand response period to obtain fast charging points as a strong response mode. The grid recovers the user's discharged energy at the benchmark electricity price and rewards the user with fast charging points based on the total amount of discharged energy. The relationship between the reward of fast charging points and the user's discharge power is shown in formula (4):

[0102]

[0103] Among them, S En (t) represents the excitation coefficient. k price (t) and S En (t) is determined by the time-sharing decision-making mechanism on the grid side, that is, in each time period, these two parameters will be adjusted according to the current load of the grid; the cumulative relationship of the fast charging weight points is shown in formula (5):

[0104]

[0105] Through the above mechanism, the power grid can attract electric vehicle users to participate in demand response at an extremely low regulation cost, while also retaining a channel for users to quickly replenish energy in emergency situations; and users can obtain fast charging rights points by releasing points to the power grid in their spare time, in exchange for the right to low-cost charging in emergency situations, taking into account the user's charging costs and vehicle use needs during the demand response period.

[0106] The present invention further provides an electric vehicle network interactive control system based on fast charging rights, the system comprising the following modules:

[0107] Grid decision-making layer: responsible for monitoring the real-time charging status of electric vehicles in the grid and adjusting the electricity price coefficient and incentive coefficient based on demand response (DR);

[0108] Electric vehicle charging layer: It is composed of several electric vehicle users. Each user adjusts his or her own charging and discharging power according to his or her own needs and the current status of the electric vehicle, as well as the electricity price coefficient and incentive coefficient given by the power grid decision-making layer.

[0109] Grid decision-making level:

[0110] Demand response monitoring: The grid decision-making layer monitors the grid’s demand response in real time and obtains demand response information at the next moment.

[0111] State parameter generation: Based on the monitored demand response and the charging status of electric vehicles in the power grid, the power grid decision-making layer uses a deep reinforcement learning algorithm (SAC algorithm) to generate the electricity price coefficient k for the next moment. price (t) and the excitation coefficient S En (t).

[0112] Parameter transfer: The generated coefficients are passed to the electric vehicle charging layer to guide it to adjust the charging strategy.

[0113] Electric Vehicle Charging Layer:

[0114] Charging status monitoring: The electric vehicle charging layer monitors the charging status of each electric vehicle and obtains the electric vehicle status parameters after completing the charging activity at the last moment, including the EV's SOC, the charging power at the last moment, the remaining charging time, etc.

[0115] State parameter reception: receiving the electricity price coefficient and incentive coefficient transmitted by the power grid decision-making layer.

[0116] Charging power adjustment: Adjust the charging power of each electric vehicle based on the received status parameters and the actual needs of the electric vehicle.

[0117] The electric vehicle network interactive control method and system based on fast charging rights effectively reduces the grid regulation cost by introducing an incentive mechanism based on fast charging rights; users obtain fast charging rights points by participating in demand response, which can be used for future fast charging, thereby reducing dependence on direct economic subsidies and reducing the economic burden of power grid companies; there are three charging modes, and users can flexibly choose a suitable mode according to their own needs; the actual travel needs of users are fully considered, and by adjusting the electricity price coefficient and the incentive coefficient, users are forced to strike a balance between discharging profit and car use needs, thereby ensuring the car use needs of users after participating in demand response; through the fast charging rights points reward mechanism, users can obtain fast charging rights points by discharging during the demand response period, which can be used for future fast charging; compared with motivating users only through electricity price fluctuations or subsidies, the fast charging rights incentive is more rigid and attractive to users, significantly improving the user's willingness to participate and improving the overall effect of demand response.

[0118] In order to verify the effectiveness of the Fast Charging Right (FCR) incentive mechanism, the Fast Charging Right (FCR) is the fast charging right. First, it is necessary to model the electric vehicle charging model and the power grid dispatch model;

[0119] Electric car models;

[0120] For each electric vehicle, its charging behavior can be abstracted as an optimization problem, whose main goal is to minimize the comprehensive charging cost. The optimization goal is constrained by the intrinsic physical characteristics of the electric vehicle; these constraints include the battery capacity V EV , Maximum charging power P FCM and the maximum discharge power P DCM Secondly, it is also affected by the charging preferences of different car owners, including the fast charging score sensitivity coefficient D In and the owner’s previous frequency of participation in demand response (DR) F Res , D In The demand for fast charging points by different car owners is quantified. The higher the value, the greater the demand for fast charging, which makes car owners more inclined to participate in the strong response mode. Res Indicates the number of DR events the car owner has participated in in the past month; F ResHigher values ​​indicate a higher frequency of participation, suggesting that car owners have a more positive attitude toward DR; therefore, these car owners show a higher tolerance for the degradation of the car experience that may result from participating in DR activities.

[0121] The concept of uncompleted charge is expressed in V slack In order to characterize the user's vehicle experience, the calculation formula of this parameter is shown in formula (6):

[0122]

[0123] Assume that each user sets the desired end time and target SOC when starting charging. Indicates the SOC value of incomplete charging. Indicates the SOC value of the electric vehicle when charging is completed, V EV Indicates the battery capacity of the electric vehicle; in addition, the vehicle owner will abide by the scheduled time and will not terminate the charging process prematurely. If the electric vehicle does not reach the target SOC at the end of charging, the owner's experience will be negatively affected; V slack To quantify the negative impact, V slack The larger the value, the greater the negative impact on user experience; therefore, in order to ensure that users have a good experience when participating in demand response (DR), the V of each vehicle must be minimized. slack value.

[0124] Based on the above analysis, an electric vehicle charging optimization model is constructed. The model takes into account the physical characteristics of electric vehicles, the charging preferences of car owners, the amount of unfinished charging and other influencing factors. The mathematical expression of the model is shown in formulas (7) and (8):

[0125]

[0126] Where t refers to the time t, D In represents the sensitivity coefficient of each electric vehicle user to the fast charging right points; P EV (t) represents the charging power of the electric vehicle at time t, P FCM Represents the maximum charging power of electric vehicles, t DR Represents the demand response time, P DCM Represents the maximum discharge power of the electric vehicle, α SOC (t) represents the battery SOC of the electric vehicle at time t, Represents the remaining FCR points of the electric vehicle user at the beginning of this charging. represents the FCR points obtained by the electric vehicle at time t, Represents the remaining FCR points of the electric vehicle user at the start of the next charging. Indicates the SOC value of incomplete charging. Indicates the charging target SOC set by the user. Represents the battery SOC at the end of this charge, F Res represents the frequency of its participation in demand response in the past month, D punish Represents the penalty coefficient for the user's failure to complete charging, V EV Represents the battery capacity of the electric car user, V slack Represents the amount of power that has not been fully charged;

[0127] The objective function of the model is to minimize the charging cost of electric vehicles. The charging cost includes not only the charging power P EV With electricity price price The sum of the products of V and V slack and the owner's remaining FCR points The above factors are taken into account in the total cost. In addition, the cost also includes the penalty coefficient D for the incomplete charging amount. punish , the penalty coefficient is related to the owner's F Res Inversely proportional and determined by an empirical function.

[0128] Grid dispatch model:

[0129] The main goal of the power grid is to maximize the revenue from electricity sales. Therefore, during non-demand response (DR) periods, the power grid prohibits electric vehicles from reverse discharging. However, during DR periods, while maximizing the revenue from electricity sales, it is also necessary to ensure grid stability and the user experience of those participating in demand response, that is, to ensure that the total charging load of electric vehicles is within the load limit and to minimize the total uncompleted charging of electric vehicles in the grid. Therefore, in the FCR incentive scheme, the power grid balances these factors by dynamically adjusting the electricity price and incentive coefficient at each moment. Ensure the best balance between maximizing revenue and maintaining grid stability, while minimizing the uncompleted charging of electric vehicles during DR periods. Based on this, the power grid dispatch model is as follows, see formula (9) and formula (10):

[0130]

[0131]

[0132] in, represents the charging power of the i-th electric vehicle in the grid at time t, represents the electricity price of the i-th electric car at time t, D FC represents the weight of the total FCR points issued in the grid comprehensive income, D slack Represents the weight of the total amount of unfinished charging in the comprehensive income of the power grid, represents the FCR points obtained by the i-th electric vehicle at time t, represents the SOC value of the uncharged amount of the i-th electric vehicle, P Grid (t) represents the total charging power of the electric vehicle at time t, P DR (t) represents the total charging power limit of electric vehicles during grid demand response at time t; represents the battery capacity of the i-th electric car;

[0133] The objective function is to maximize the EVs The total revenue is adjusted by deducting the equivalent cost of the uncompleted charging volume and the equivalent cost of the FCR points issued as an incentive. The grid can adjust the weight coefficient D of the equivalent cost of the FCR points by adjusting the weight coefficient D of the equivalent cost of the FCR points. FC and the weight coefficient D of the uncompleted charging amount slack This adjustment can ensure that the grid's dispatching strategy achieves a balance between economic benefits and the user experience of electric vehicles.

[0134] Simulation verification:

[0135] Offline verification of the fast charging incentive mechanism:

[0136] The offline calculation assumes that the grid can accurately predict the demand response for each hour of the next day. The prediction includes the start and end time of the DR period and the overall EV power limit for each hour during this period. Based on this information, the grid dispatch layer sets the hourly electricity price and incentive coefficient for the next 24 hours. The EV charging layer determines the charging power per hour in its charging time period based on the known 24-hour electricity price coefficient and incentive coefficient. Using all FCR points spent on the day, the uncompleted charging data of all EVs, and the grid's electricity sales revenue, the grid's comprehensive revenue for the day can be calculated. A heuristic algorithm is used for solution.

[0137] (1) Verification scheme

[0138] To verify the effectiveness of the proposed incentive scheme, it is compared with the current pilot DR incentive schemes in Changzhou Grid and Suzhou Grid. 10,000 electric vehicles and their charging demand data for one month (31 days) are randomly generated. During these 31 days, nine DR events are randomly set, with corresponding time points and power limits specified. These events are mainly arranged during the period of high grid load, ensuring that the simulation covers the main scenarios of grid dispatch.

[0139] The evaluation indicators include the number of successful power regulation, the economic efficiency of the power grid, and the amount of uncompleted charging. Using these indicators, the performance of different incentive schemes is compared to evaluate their effectiveness.

[0140] (2) Simulation data generation method

[0141] The data for the electric vehicles includes several key parameters that are essential for a comprehensive analysis. These parameters include the start time, initial SOC, target SOC, and charging duration of each charging session. In addition, the data also includes the charging frequency, FCR score sensitivity factor, and accumulated FCR score in the past 30 days. The data set also contains the battery capacity, maximum charging power, and maximum discharge power of the electric vehicle.

[0142] The charging start time and charging duration of electric vehicles are based on Figure 4 and Figure 3 The probability distribution in is randomly generated.

[0143] The remaining parameters are generated as shown in Table 1:

[0144] Table 1. Random generation method for electric vehicle data

[0145]

[0146]

[0147] (3) Comparison plan

[0148] This embodiment compares the linear demand response incentive schemes of the Changzhou power grid and the Suzhou power grid vehicle-grid interaction pilot. The specific contents of the two are shown in Table 2:

[0149] Table 2. Contents of existing pilot demand response incentive programs

[0150]

[0151] (4) Comparison results

[0152] Figure 5 The total charging power of electric vehicles during DR is shown, and the charging power of electric vehicles in the grid without DR events, Suzhou scheme, Changzhou scheme and fast charging scheme are compared.

[0153] If the total charging power of the EVs remains below the power limit throughout the DR period, the DR regulation is considered successful. Figure 6 The number of successful instances of the three incentive schemes is shown. The results show that the fast charging scheme has the highest number of successes, followed by the Changzhou scheme, and the Suzhou scheme has the lowest number of successes.

[0154] Figure 7The graph shows the grid electricity sales revenue and incomplete charging volume under the three incentive schemes. Regarding DR costs, the Changzhou scheme has the highest cost, which has a significant impact on the grid's electricity sales revenue. The fast charging rights scheme slightly increased the grid's electricity sales revenue, that is, the DR cost was the lowest among the three schemes. For the incomplete charging volume, the Changzhou scheme led to a significant increase in the incomplete charging volume, which had a negative impact on the user experience of participating in DR. Similarly, the Suzhou scheme also led to a significant increase in the incomplete charging volume. In contrast, the fast charging rights scheme has the least impact on the incomplete charging volume, and tries to take into account the car use needs of electric vehicle users.

[0155] Combining the power regulation effects, grid electricity sales revenue, and unfinished charging volume of the three incentive schemes during DR, the fast charging scheme performs best in terms of overall charging power regulation for electric vehicles. This scheme minimizes the increase in unfinished charging volume while ensuring grid revenue.

[0156] Online verification of the fast charging incentive mechanism

[0157] For the power grid, the increase in the proportion of renewable energy has brought greater volatility and unpredictability to the power system. In addition, information such as the charging demand of electric vehicles is usually difficult for the power grid to accurately predict in advance. Therefore, in the online verification, it is assumed that the power grid can only accurately predict the power grid demand response in the current time period before the start of the current time period, and formulate the electricity price coefficient and incentive coefficient for the current time period based on this. Users can only determine their own charging power based on the two coefficients of the current time period announced by the power grid. Using the SAC (Soft Actor-critic) algorithm, an upper and lower optimization model is established, which is the power grid dispatch model and the electric vehicle charging model. A 24-hour simulation experiment was carried out using data from 100 electric vehicles. By comparing the online simulation results with the results obtained from the previous offline calculation, we verified the effectiveness of the fast charging weight scheme in power regulation during online calculation.

[0158] Figure 8 The overall EV charging power distribution is shown. Both online and offline scheduling methods successfully implement DR scheduling, ensuring that the total charging power of EVs remains below the power limit.

[0159] Fig. 9 The comparison results of online and offline dispatch in terms of grid revenue, unfinished charging volume, and FCR points given by the grid are shown. Online dispatch significantly increases grid revenue, which is significantly higher than offline dispatch. In addition, although the total charging power is slightly increased compared to the no-DR scenario, the unfinished charging volume is reduced compared to offline dispatch. In addition, the FCR points given by the grid are slightly increased.

[0160] According to the statistical results, the power grid completed the charging power dispatch of electric vehicles during the DR period through online dispatch, which significantly increased the power grid's electricity sales revenue. At the same time, the unfinished charging volume and paid FCR points remained relatively stable or slightly improved. Overall, online dispatch is as effective as offline dispatch in DR power regulation.

[0161] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for controlling electric vehicle-grid interaction based on fast charging rights, characterized in that: include: Establish a database of electric vehicle fast charging points; Initialize the electricity price coefficient k price and incentive strength S En ; Monitor the status of the power grid; Determine whether the power grid is in a demand response state at the current moment; According to the current power demand of the power grid, determine the power limit value P SC ; According to the power limit value P SC As well as the charging power and status at the end of the previous moment, determine the current electricity price coefficient k price and incentive strength S En ; According to the electricity price coefficient k of the current power grid price and incentive strength S En And the electric vehicle's own status, determine the charging mode and charging power: The current moment ends, and the process returns to the step of monitoring the power grid status to continue monitoring and adjusting.

2. The electric vehicle network interaction control method based on fast charging rights according to claim 1 is characterized by: The charging modes include: Weak response mode: used for charging at a lower power, with the charging price kept at the base level; Forced fast charging mode: used for users to consume fast charging points or pay higher electricity prices for fast charging; Strong response mode: It is used for users to obtain fast charging points by discharging to the grid and accumulate opportunities for fast charging in the future.

3. The electric vehicle network interaction control method based on fast charging rights according to claim 1 is characterized by: The weak response mode is charged at a slow charging power, and the charging cost is maintained at a base level, that is, the electricity price of the grid when there is no demand response; as shown in formula (1): a price =a common (1) Among them, a price represents the charging price of electric vehicles at time t, a common Indicates the electricity price of the power grid under normal conditions.

4. The electric vehicle network interaction control method based on fast charging rights according to claim 1 is characterized by: The forced fast charging mode includes: consuming the user's fast charging right points to obtain fast charging rights with low electricity prices. The charging fee is shown in formula (1), but the user's fast charging right points begin to decrease, as shown in formula (2): in, Represents the fast charging points obtained by the electric vehicle at time t.

5. The electric vehicle network interaction control method based on fast charging rights according to claim 1 is characterized by: The forced fast charging mode includes: Pay a higher charging price to obtain fast charging rights; when the user does not have fast charging rights points, pay a higher charging price to obtain a higher charging power. The relationship between charging cost and charging power is shown in formula (3): a price =k price (t)·(P EV (t)-P SC )+a common (3) Among them, k price (t) represents the electricity price coefficient, P EV (t) represents the charging power of the electric vehicle at time t, P SC Indicates the charging power limit.

6. The electric vehicle network interaction control method based on fast charging rights according to claim 1 is characterized by: In the strong response mode, the power grid recovers the electric energy discharged by the user at the benchmark electricity price, and rewards the user with fast charging points according to the total amount of electric energy discharged. The relationship between the reward of fast charging points and the user's discharge power is shown in formula (4): Among them, S En (t) represents the excitation coefficient; k price (t) and S En (t) is determined by the time-sharing decision-making mechanism on the grid side, that is, in each time period, it is adjusted according to the current load of the grid. The cumulative relationship of the fast charging weight points is shown in formula (5):

7. The electric vehicle network interaction control method based on fast charging rights according to claim 1 is characterized by: The method further comprises a step of verifying the control method: verifying through an electric vehicle charging model and a power grid dispatching model; The electric vehicle charging model includes setting the uncompleted charging amount as V slack , to characterize the user's vehicle experience, the calculation formula is shown in formula (6): Assume that each user sets the desired end time and target SOC when starting charging If the EV does not reach the target SOC at the end of charging, the owner’s experience will be negatively affected; V slack To quantify the negative impact, V slack The larger the value, the greater the negative impact on user experience; the incomplete charging capacity V slack Used to quantify negative impacts; Indicates the SOC value of incomplete charging. Indicates the SOC value of the electric vehicle when charging is completed, V EV Indicates the battery capacity of electric vehicles; The electric vehicle charging optimization model is expressed as shown in formulas (7) and (8): Where t refers to the time t, D In represents the sensitivity coefficient of each electric vehicle user to the fast charging right points; P EV (t) represents the charging power of the electric vehicle at time t, P FCM Represents the maximum charging power of electric vehicles, t DR Represents the demand response time, P DCM Represents the maximum discharge power of the electric vehicle, α SOC (t) represents the battery SOC of the electric vehicle at time t, Represents the remaining FCR points of the electric vehicle user at the beginning of this charging. represents the FCR points obtained by the electric vehicle at time t, Represents the remaining FCR points of the electric vehicle user at the start of the next charging. Indicates the SOC value of incomplete charging. Indicates the charging target SOC set by the user. Represents the battery SOC at the end of this charge, F Res represents the frequency of its participation in demand response in the past month, D punish Represents the penalty coefficient for the user's failure to complete charging, V EV Represents the battery capacity of the electric car user, V slack Represents the amount of power that has not been fully charged; The objective function of the electric vehicle charging optimization model is to minimize the charging cost of the electric vehicle. The charging cost includes the charging power P EV With electricity price price The sum of the products of slack and the owner's remaining FCR points The equivalent cost and the penalty coefficient D for the incomplete charging amount punish , the penalty coefficient is related to the owner's F Res Inversely proportional and determined by empirical function; The power grid dispatching model includes formula (9) and formula (10): in, represents the charging power of the i-th electric vehicle in the grid at time t, represents the electricity price of the i-th electric car at time t, D FC represents the weight of the total FCR points issued in the grid comprehensive income, D slack Represents the weight of the total amount of unfinished charging in the comprehensive income of the power grid, represents the FCR points obtained by the i-th electric vehicle at time t, represents the SOC value of the uncharged amount of the i-th electric vehicle, P Grid (t) represents the total charging power of the electric vehicle at time t, P DR (t) represents the total charging power limit of electric vehicles during grid demand response at time t; represents the battery capacity of the i-th electric car; The objective function is to maximize the EVs The total revenue is adjusted by deducting the equivalent cost of the unfinished charging volume and the equivalent cost of the FCR points issued as an incentive. The grid adjusts the weight coefficient D of the equivalent cost of the FCR points. FC and the weight coefficient D of the uncompleted charging amount slack To optimize its scheduling strategy.

8. An electric vehicle network interactive control system based on fast charging rights, characterized in that: include: Grid decision-making layer: used to monitor the real-time charging status of electric vehicles in the grid and adjust the electricity price coefficient and incentive coefficient based on demand response; Electric vehicle charging layer: used to adjust its own charging and discharging power according to demand and the current status of the electric vehicle and the electricity price coefficient and incentive coefficient output by the power grid decision-making layer.

9. The electric vehicle network interactive control system based on fast charging rights according to claim 8, characterized in that: The power grid decision-making layer includes: Demand response monitoring module: used by the power grid decision-making layer to monitor the demand response of the power grid in real time and obtain the demand response information at the next moment; State parameter generation module: used to generate the electricity price coefficient k at the next moment using a deep reinforcement learning algorithm based on the demand response status monitored by the demand response monitoring module and the charging status of electric vehicles in the power grid. price (t) and the excitation coefficient S En (t); Parameter transfer module: used to transfer the generated coefficients to the electric vehicle charging layer to guide it to adjust the charging strategy.

10. The electric vehicle network interactive control system based on fast charging rights according to claim 8, characterized in that: The electric vehicle charging layer comprises: Charging status monitoring module: used for the electric vehicle charging layer to monitor the charging status of each electric vehicle and obtain the electric vehicle status parameters after completing the charging activity at the last moment; State parameter receiving module: used to receive the electricity price coefficient and incentive coefficient transmitted by the power grid decision-making layer; Charging power adjustment module: used to adjust the charging power of each electric vehicle according to the received status parameters and the actual needs of the electric vehicle.

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