A shopping mall discharge system based on V2G technology

Abstract

This invention relates to a shopping mall discharge system based on V2G technology, comprising: a flow analysis module for real-time prediction of the electricity load of the commercial area within a future time period T based on historical electricity data; a data acquisition module for calculating the range anxiety of electric vehicle users; a behavior analysis module for calculating the probability of electric vehicle users being guided; a behavior guidance module for guiding electric vehicle users to the corresponding load node in the commercial area upon their arrival, in response to their charging / discharging requests; and a discharge regulation module for calculating the discharge power and discharge time of electric vehicle users at the load node in the commercial area based on the electricity load of the commercial area within a future time period T, and prompting electric vehicle users to discharge. This invention reduces the management difficulty and operating costs of the power grid, ensuring the stable operation of the power grid.

Description

Technical Field

[0001] This invention belongs to the field of power system and electric vehicle technology, and in particular relates to a shopping mall discharge system based on V2G technology. Background Technology

[0002] With the rapid increase in the number of electric vehicles (EVs), power systems are facing increasing load fluctuations and management pressures. Traditional grid architectures struggle to cope with the peak load issues caused by the simultaneous charging and discharging of large numbers of EVs. The introduction of Vehicle-to-Grid (V2G) technology offers a new approach to solving this problem. V2G technology allows EVs to feed electrical energy back to the grid when not in use, thereby providing additional power support to the grid during peak demand periods and balancing power supply and demand. However, current V2G technology still faces the following problems and challenges in practical applications: existing technologies struggle to effectively predict and manage the charging and discharging behavior of EVs, leading to significant grid load fluctuations and increasing the difficulty of grid management and operating costs. Summary of the Invention

[0003] To address the problems existing in the background technology, this invention provides a shopping mall discharge system based on V2G technology. By monitoring the state of charge of electric vehicles and the power load of the commercial area in real time, and utilizing user profiling technology and discharge control algorithms, it can effectively manage and guide the discharge behavior of electric vehicles, thereby ensuring the stable operation of the power grid.

[0004] To achieve the above technical objectives, the present invention provides a shopping mall discharge system based on V2G technology, comprising:

[0005] The load monitoring terminal is installed in the power distribution room of the commercial area to collect real-time power data of the commercial area.

[0006] The traffic analysis module is used to predict the electricity load of the commercial area in real time within a future time period T based on the historical electricity data of the commercial area.

[0007] The data acquisition module is used to acquire facial image data of electric vehicle users and calculate their range anxiety.

[0008] The behavior analysis module is used to calculate the probability that electric vehicle users will be guided based on their range anxiety.

[0009] The behavior guidance module sends a discharge request to the electric vehicle user based on the electricity load of the commercial area within a future time period T when the probability of the electric vehicle user being guided is greater than a set threshold a; and in response to the electric vehicle user's reply to the charging and discharging request, guides the electric vehicle user to the corresponding commercial area load node when the electric vehicle user arrives at the commercial area.

[0010] The discharge regulation module is used to calculate the discharge power and discharge time of electric vehicle users at the load nodes in the commercial area based on the electricity load situation in the commercial area in the future time period T.

[0011] The present invention has at least the following beneficial effects

[0012] This invention collects real-time power data from commercial areas through a load monitoring terminal, providing a rich and accurate data foundation for the flow analysis module. Utilizing historical power data, the flow analysis module can predict the power load of the commercial area within a future time period T, significantly improving the accuracy and timeliness of load forecasting. The data acquisition module calculates the range anxiety of electric vehicle users by acquiring their facial image data, helping to understand their charging needs and psychological state, providing important reference for subsequent guidance strategies. The behavior analysis module calculates the probability of electric vehicle users being guided to the commercial area for discharge and responds to their charging / discharging requests, guiding them to the corresponding commercial area load node upon arrival. The system intelligently guides electric vehicle users to the commercial area load node for discharge. The discharge regulation module calculates the discharge power and discharge time of electric vehicle users at the commercial area load node. Together, the load monitoring terminal, flow analysis module, data acquisition module, behavior analysis module, behavior guidance module, and discharge regulation module constitute a complete shopping mall discharge system based on V2G technology. This system can collect and analyze power data in real time, predict future power load, and provide intelligent guidance based on the range anxiety and behavioral characteristics of electric vehicle users. It enables precise management of power load in commercial areas and effective utilization of electric vehicle energy storage resources, reducing the management difficulty and operating costs of the power grid and ensuring the stable operation of the power grid. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the system framework of the present invention;

[0014] Figure 2 This is a schematic diagram of anxiety detection based on facial feature points;

[0015] Figure 3 This is a comparison of the load curves before and after discharge and the peak reduction effect of the present invention. Detailed Implementation

[0016] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0017] Please see Figure 1 This invention provides a shopping mall discharge system based on V2G technology, comprising:

[0018] The load monitoring terminal is installed in the power distribution room of the commercial area to collect real-time power data of the commercial area.

[0019] In this embodiment, the load monitoring terminal is mainly used to collect current data, voltage data and power data of the power distribution network; it is mainly monitored through relevant power monitoring sensors, such as current sensors and voltage sensors.

[0020] The traffic analysis module is used to predict the electricity load of the commercial area in real time within a future time period T based on the historical electricity data of the commercial area.

[0021] The data acquisition module is used to acquire facial image data of electric vehicle users and calculate their range anxiety.

[0022] The behavior analysis module is used to calculate the probability that electric vehicle users will be guided based on their range anxiety.

[0023] The behavior guidance module sends a discharge request to the electric vehicle user based on the electricity load of the commercial area within a future time period T when the probability of the electric vehicle user being guided is greater than a set threshold a; and in response to the electric vehicle user's reply to the charging and discharging request, guides the electric vehicle user to the corresponding commercial area load node when the electric vehicle user arrives at the commercial area.

[0024] The discharge regulation module is used to calculate the discharge power and discharge time of electric vehicle users at the load nodes in the commercial area based on the electricity load situation in the commercial area in the future time period T, and prompt electric vehicle users to discharge.

[0025] Preferably, the predicted electricity load of the commercial area within the future time period T includes:

[0026] X t =[P(tn),P(t-n+1),…,P(t-1),P(t)]

[0027] L(t)=[P(t+1),P(t+2),…,P(t+T)]=LSTM(X t )

[0028] Where t represents the current time, P(tn) represents the power data of the commercial area at time (tn); n represents the number of historical power data samples; LSTM represents Long Short-Term Memory Network; P(t+k) represents the power load of the commercial area at the future time t+k, k={1,2,..T}; L(t) represents the power load of the commercial area within the future time period T.

[0029] This embodiment introduces a Long Short-Term Memory (LSTM) network as the core prediction algorithm, further improving the accuracy and reliability of load forecasting. Combined with modules such as behavior analysis, behavior guidance, and discharge regulation, a shopping mall discharge system based on V2G technology is constructed. This system not only improves the accuracy and reliability of power load forecasting but also achieves optimized allocation and efficient utilization of electric vehicle energy storage resources, providing strong support for building a green, low-carbon, and intelligent urban energy system.

[0030] Preferably, calculating the range anxiety status of electric vehicle users includes:

[0031] When the distance between the electric vehicle user and the commercial area is less than a set threshold d, facial images of the electric vehicle user are collected, and the range anxiety status of the electric vehicle user is calculated in real time.

[0032] The calculation of range anxiety status for electric vehicle users includes:

[0033] Based on the facial images of electric vehicle users, facial feature vectors are extracted using the facial feature point detection library Dlib.

[0034] Please see Figure 2In this embodiment, facial images of electric vehicle users are captured by an in-vehicle camera. Using the facial feature point detection library Dlib, five feature points are automatically extracted from each eyebrow. The feature points of the left and right eyebrows are labeled as 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10, respectively. Six feature points are collected from each eye. The feature points of the left and right eyes are labeled as 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, and 22, respectively. Seven feature points are collected from the nose and labeled as 23, 24, 25, 26, 27, 28, and 29, respectively. Twenty feature points are collected from the mouth and labeled as 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, and 49, respectively. By constructing a facial coordinate system for the driver with the center of the nose as the origin, the line connecting the nostrils as the x-axis, and the line connecting the bridge of the nose as the y-axis, the geometric feature set h of the i-th facial image of the electric vehicle user can be obtained. i =(x1,y1,…,x 49 ,y 49 Let (x1, y1) represent the coordinates of the first feature point in the driver's facial coordinate system, and the remaining feature points are similarly represented; then the facial feature vector corresponding to the electric vehicle user is H = (h1, h2, ..., h...). n1 ) T n1 represents the acquisition of n1 facial images.

[0035] Based on the facial feature vectors of electric vehicle users, a range anxiety state determination model for electric vehicle users is constructed using the FACS coding system. The range anxiety state of electric vehicle users is then calculated based on the constructed range anxiety state determination model.

[0036] In this embodiment, after extracting the facial feature vectors of electric vehicle users, a range anxiety state determination model based on eyebrows (u), eyes (v), and mouth (w) is constructed using the FACS coding system; t1 and t2 are defined as representing the initial and next moments of facial image acquisition, respectively, and x m y m Represents the x and y coordinates of the m-th feature point at time t1; The expression for the user's anxious facial state, representing the x and y coordinates of the m-th feature point at time t2, is as follows:

[0037]

[0038] Where u represents eyebrow features, v represents eye features, and w represents mouth features.

[0039] The model for determining the range anxiety status of electric vehicle users is as follows:

[0040] g = α n u+βn v+γ n w

[0041] In the formula, g represents the range anxiety of electric vehicle users, which is a dimensionless unit with a value of 0-1; α n β n γ n This is for adjusting the coefficient.

[0042] Preferably, the probability that the electric vehicle user is guided includes:

[0043]

[0044] Where c represents the probability that an electric vehicle user is guided, exp() represents the natural exponential function; α and β are adjustment coefficients; a0∈(0,1) means that the average electricity consumption of an electric vehicle user per trip is no more than a0 times the battery capacity; B cap The battery capacity is represented by ε; the average energy consumption of an electric vehicle user is represented by d; and the distance traveled by an electric vehicle user from their current location to the commercial area is represented by S. SOC S represents the remaining battery power for electric vehicle users. th θ1 and θ2 represent the minimum safe battery level for electric vehicle users; g represents the range anxiety level of electric vehicle users.

[0045] In this embodiment, by calculating range anxiety, the system can assess the psychological state of electric vehicle users in real time, providing the system with accurate user emotion data. This enables the system to intelligently determine whether users are willing to participate in discharging and optimize the discharging guidance strategy, thereby improving user experience, reducing range anxiety, and ultimately helping the power grid to manage electric vehicle discharging behavior more efficiently, balance load demand, and improve the stability and efficiency of system operation.

[0046] In this embodiment, the average power consumption per trip is defined as not exceeding 60% of the battery capacity.

[0047] Preferably, sending a discharge request to electric vehicle users based on the electricity load of the commercial area within a future time period T includes:

[0048] Calculate the peak load index of the commercial area's electricity load based on the electricity load situation of the commercial area within the future time period T;

[0049]

[0050] Wherein, PLI represents the peak load index of the commercial area's electricity load, t0 represents the time point at which the detection begins, t0∈[0,T]; P(t0+k) represents the electricity load at time t0+k; N is the total number of times within the time period;

[0051] Calculate the time when the electricity load of a commercial area enters its peak period based on the peak load index of the commercial area's electricity load;

[0052] t peak =t0(PLI>P th )

[0053] Among them, t peak This refers to the time point when the load peak begins; P th It is a preset peak load threshold;

[0054] The timing of sending the discharge request is calculated based on the peak electricity load in the commercial area:

[0055]

[0056] Among them, t m The time to send a discharge request to the electric vehicle user; t a0 A fixed value for the time required to arrive at the business district in advance; d i v represents the distance traveled by electric vehicle user i from the commercial area. i The average driving speed of electric vehicle user i;

[0057] At the time t of sending the discharge request m Send a discharge request to electric vehicle users.

[0058] Preferably, guiding electric vehicle users to the corresponding commercial area load node when they arrive at the commercial area includes:

[0059] S101: When an electric vehicle user arrives at a commercial area, calculate the distance between the electric vehicle user and the load nodes of each commercial area and the discharge waiting time of the electric vehicle user at each load node of the commercial area.

[0060] S102: Traverse each commercial area load node and check whether the commercial area load node is occupied by other electric vehicle users. If so, proceed to step S103; otherwise, proceed to step S105.

[0061] S103: Determine whether the discharge waiting time of electric vehicle users at the load node in the commercial area is less than the set threshold τ. If so, proceed to step S104.

[0062] S104: Determine whether the distance from the electric vehicle user to the load node in the commercial area is less than the set threshold θ. If so, proceed to step S105:

[0063] S105: If the difference between the scheduling potential of electric vehicle users and the scheduling demand of commercial area load nodes is less than a set threshold δ, then the commercial area load nodes are assigned to electric vehicle users and the traversal stops.

[0064] In this embodiment, through steps S101 to S105, the system can intelligently guide electric vehicle users to select suitable discharge nodes, ensuring that users choose the optimal node for discharge based on distance and waiting time. This not only reduces user waiting time and improves user experience, but also optimizes power load distribution, avoids resource waste, balances power demand in the shopping mall area, and improves the overall system's scheduling efficiency and grid stability.

[0065] In this embodiment, the state of each commercial area load node is defined using S. j This indicates that, for each load node, the scheduling potential R of electric vehicle users in the commercial area load node is calculated. i,j ;

[0066]

[0067] R i,j =min(C j,dis ,η×SOC i ×P i )

[0068] Among them, R i,j C represents the scheduling potential of electric vehicle users at load node j in the commercial area. j,dis Represents the maximum discharge capacity of load node j in the commercial area; η is the discharge efficiency; SOC i P represents the remaining battery capacity (expressed as a percentage) of electric vehicle user i; i The maximum discharge power for electric vehicle user i.

[0069] For each electric vehicle user i, calculate the distance they travel from their current location to each business district load node j;

[0070] The discharge waiting time for each electric vehicle user i after arriving at node i is estimated. The discharge waiting time for electric vehicle user i at the commercial area load node j is:

[0071]

[0072] In the formula, Q i P represents the remaining discharge capacity of the i-th vehicle; i T represents the maximum discharge power of the i-th vehicle; K represents the number of vehicles currently in the queue; wait,avg Let i be the discharge waiting time of electric vehicle user i at load node j in the commercial area.

[0073] Based on the electricity load curve of the commercial area, the scheduling demand for each node j is calculated using the following formula:

[0074]

[0075] C(t) = L u (t)-L tg

[0076] Among them, C j This represents the scheduling demand of load node j in the commercial area. ω represents the maximum load capacity of load node j in the commercial area; m represents the total number of load nodes in the commercial area; C(t) represents the discharge demand of the commercial area power grid; j L represents the adjustment factor for load node j in the commercial area; u (t) represents the real-time electricity load of the commercial area; L tg This indicates the target control power of the commercial area's power grid.

[0077] The greedy algorithm is used to assign the optimal commercial area load node j to electric vehicle user i. The specific steps are as follows:

[0078] S101: When electric vehicle user i (initial value 1) arrives at the commercial area, the system begins to guide the user to select a commercial area load node (charging pile node) and prepares to allocate a suitable commercial area load node for the user to provide discharge service.

[0079] For each electric vehicle user i, initialize the remaining battery capacity (SOC) of electric vehicle user i. i Maximum discharge power P i and average driving speed v i ;

[0080] S102: The system starts from the first commercial area load node j=1 and checks whether node j is currently occupied by other electric vehicle users. If the commercial area load node is occupied (i.e., S(j)=1), then proceed to the next step S103; if the node is not occupied (S(j)=0), then jump to step S105.

[0081] S103: Determine whether the discharge waiting time of electric vehicle users at the load node in the commercial area is less than the set threshold τ. If so, proceed to step S104.

[0082] S105: Determine whether the distance from the electric vehicle user to the load node in the commercial area is less than the set threshold θ. If so, proceed to step S105:

[0083] S106: If the difference between the scheduling potential of electric vehicle users and the scheduling demand of commercial area load nodes is less than a set threshold δ, then the commercial area load nodes are assigned to electric vehicle users and the traversal stops.

[0084] Preferably, the scheduling potential of the electric vehicle users at load nodes in commercial areas includes:

[0085] R i,j =min(C j,dis ,η×SOC i ×P i )

[0086] Among them, R i,j C represents the scheduling potential of electric vehicle users at load node j in the commercial area. j,dis Represents the maximum discharge capacity of load node j in the commercial area; η is the discharge efficiency; SOC i P represents the remaining battery capacity of electric vehicle user i; i The maximum discharge power for electric vehicle user i.

[0087] Preferably, the scheduling requirements of the load nodes in the commercial area include:

[0088]

[0089] C(t) = L i (t)-L tg

[0090] Among them, C j This represents the scheduling demand of load node j in the commercial area. ω represents the maximum load capacity of load node j in the commercial area; m represents the total number of load nodes in the commercial area; C(t) represents the discharge demand of the commercial area power grid; j L represents the adjustment factor for load node j in the commercial area; u (t) represents the real-time electricity load of the commercial area; L tg This indicates the target control power of the commercial area's power grid.

[0091] Preferably, the calculation of the discharge power and discharge time of electric vehicle users at load nodes in commercial areas includes:

[0092] The day is divided into 24 time periods by hour, and an objective function is constructed with the goal of smoothing out fluctuations in the electricity load curve of the commercial area:

[0093]

[0094] Where f represents the objective function, m represents the total number of load nodes in the commercial area, n represents the number of electric vehicle users discharging in the commercial area, and P Lt P represents the average electricity load of the commercial area during time period t. av P represents the average electricity load of a commercial area over a 24-hour period. it This represents the discharge power of electric vehicle user i during time period t;

[0095] With the discharge power P of electric vehicle users itUsing the electric vehicle user as the decision variable, the objective function is solved using the particle swarm optimization algorithm to obtain the optimal discharge power and discharge time. The discharge time of the electric vehicle user is determined by statistically analyzing the discharge power P of the electric vehicle user. it The number of time periods with a value greater than 0 is calculated.

[0096] In this embodiment, by dividing a day into 24 time periods, the system utilizes a particle swarm optimization (PSO) algorithm to optimize the discharge power and time for electric vehicle users, constructing an objective function to smooth the power load curve and reduce power fluctuations. The PSO algorithm can efficiently and accurately solve for the optimal discharge strategy while considering power and battery capacity constraints, ensuring the safety of user batteries and the rational allocation of grid load. This method not only improves the system's computational efficiency but also enhances the operational stability of the power grid and energy utilization efficiency.

[0097] In this embodiment, solving the objective function using the particle swarm optimization algorithm includes:

[0098] Let the dimension of the particle be n*24, and the position of particle I be represented as:

[0099] P1 = [P 1,1 ,P 1,2 ,…,P 1,24 ,P 2,1 ,…,P 2,24 ,…,P i,j ,…,P n,24 ]

[0100] The position of particle i in the D-dimensional solution space can be represented as vector x. i =[x i1 ,x i2 ,.…,x iD ], speed is v i =[v i1 ,v i2 ,v i3 ,..,v iD The particle updates its velocity and position according to the following formula.

[0101]

[0102] In the formula: k represents the number of iterations; This represents the velocity of the D-dimensional vector of particle i in the k-th iteration; Let be the position vector corresponding to the current optimal position of particle i; Let ω be the position vector corresponding to the optimal position in the entire swarm. ω is the inertia weight, which is the influence factor of the particle's previous flight velocity on its current flight velocity; c1 and c2 are learning factors; rand1 and rand2 are random numbers between [0,1]. The search process for the inertia weight coefficients is as follows:

[0103] ω=ω max -k(ω max -ω min ) / k max

[0104] Control the velocity of each dimension of the particle within [-V] Dmax V Dmax ]between.

[0105] Maximum number of iterations k max This serves to terminate the algorithm; set it to 1000.

[0106] The PSO algorithm flow with constraints;

[0107] ① Initialize the particle swarm and determine the basic parameter values ​​of the particles;

[0108] Particle velocity updates are achieved by considering the current velocity, the individual optimal position, and the global optimal position; specifically, the velocity of each particle is updated. and location Based on the formula above, ensure that each particle follows the following constraints in terms of velocity and position.

[0109] 2) Power Constraint

[0110] The maximum charging current is half of the rated current, and the maximum discharging current is twice the rated current. Next, considering the line power capacity constraint, the charging and discharging power should not exceed 20kW.

[0111] P itmax =min(15,V) it ×2I iN )

[0112]

[0113] In the formula: P itmax P represents the maximum discharge power of electric vehicle user i at time t. itmin I represents the maximum discharge power of electric vehicle user i at time t; iN The rated current of the battery for electric vehicle users; V it The actual charging voltage value of the battery for electric vehicle users.

[0114] 3) Capacity constraints are available

[0115] Electric vehicle users must have at least 20% of their total battery capacity remaining.

[0116] SOC i ≥0.2

[0117] 4) User settings for minimum SOC upon leaving

[0118] SOC i ≥0.3

[0119] Where: SOC i The remaining battery capacity for electric vehicle users.

[0120] ③ Calculate the fitness function value of the particle swarm and track the optimal position of the individual particles and the optimal position of the swarm.

[0121] ④ Update the particle's velocity and position;

[0122] ⑤ Check whether the particle velocity and position exceed the limit value according to the constraint conditions. If they exceed the limit value range, limit them to the limit value and repeat step 2.

[0123] ⑥ Calculate the fitness function value of the particle swarm, update and record the optimal position of an individual in the particle swarm as the optimal position of the swarm.

[0124] ⑦ Determine if the maximum number of iterations has been reached. If yes, stop the calculation; otherwise, return to step 4.

[0125] Please see Figure 3 The attached figure uses Python simulation to show the power load curves of the shopping mall area under different load conditions, with particular emphasis on load changes during peak hours. The figure compares the mall's power load curves within a future time period T, the load curves after electric vehicles discharge, and the ideal load target set by the system, and analyzes their trends over 24 hours.

[0126] The yellow curve represents the mall's electricity load curve within the future time period T, reflecting the mall's electricity demand fluctuations before electric vehicle discharge regulation; the purple curve represents the load curve after electric vehicle discharge operation, showing the effectiveness of discharge in smoothing peak loads; the green dashed line represents the system's ideal load target, i.e., the balanced load state achieved by scheduling resources such as electric vehicles; the red cross ("X" mark) represents the peak point of the load curve; the gray shaded area represents the peak period of electricity load, reflecting the system's load management needs during these critical time periods.

[0127] As shown in the graph, the shopping mall's electricity load experienced a significant peak during peak hours. Through the discharge operation of electric vehicles, the peak load was significantly reduced, bringing the actual load closer to the ideal load. This indicates that electric vehicles based on V2G technology can provide effective load regulation support for the power grid, alleviate peak load pressure, optimize grid operation, and thus help balance power supply and demand, improving grid stability.

[0128] In summary, this invention collects real-time power data from commercial areas through a load monitoring terminal, providing a rich and accurate data foundation for the flow analysis module. The flow analysis module, utilizing historical power data, can predict the power load of the commercial area within a future time period T, significantly improving the accuracy and timeliness of load forecasting. The data acquisition module calculates the range anxiety of electric vehicle users by acquiring their facial image data, helping to understand their charging needs and psychological state, providing important reference for subsequent guidance strategies. The behavior analysis module calculates the probability of electric vehicle users being guided to the commercial area for discharge and, in response to their charging / discharging requests, guides them to the corresponding load node in the commercial area upon arrival. The system intelligently guides electric vehicle users to the load node for discharge. The discharge regulation module calculates the discharge power and time of electric vehicle users at the load node. Together, the load monitoring terminal, flow analysis module, data acquisition module, behavior analysis module, behavior guidance module, and discharge regulation module constitute a complete shopping mall discharge system based on V2G technology. This system can collect and analyze power data in real time, predict future power load, and provide intelligent guidance based on the range anxiety and behavioral characteristics of electric vehicle users. It enables precise management of power load in commercial areas and effective utilization of electric vehicle energy storage resources, reducing the management difficulty and operating costs of the power grid and ensuring the stable operation of the power grid.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A shopping mall discharge system based on V2G technology, characterized in that, include: The load monitoring terminal is installed in the power distribution room of the commercial area to collect real-time power data of the commercial area. The traffic analysis module is used to predict the electricity load of the commercial area in real time within a future time period T based on the historical electricity data of the commercial area. The data acquisition module is used to acquire facial image data of electric vehicle users and calculate their range anxiety. The behavior analysis module is used to calculate the probability that electric vehicle users will be guided based on their range anxiety. The probability that the electric vehicle user is guided includes: in, This represents the probability that an electric vehicle user is being misled. Represents the natural exponential function; and This is the adjustment coefficient; This indicates that the average electricity consumption per trip for electric vehicle users does not exceed the battery capacity. times; Indicates battery capacity; The average energy consumption of electric vehicle users; This indicates the distance traveled by an electric vehicle user from their current location to the business district. Indicates the remaining battery power for electric vehicle users; This indicates the minimum safe battery level for electric vehicle users; and Indicates adjustment coefficient , This indicates the range anxiety experienced by electric vehicle users. This indicates the minimum remaining battery percentage when an electric vehicle user arrives at a commercial area. Indicates the user's anxiety relief coefficient; This represents the total energy consumption of an electric vehicle user from their current location to the business district. This indicates the level of range anxiety among electric vehicle users; The behavior guidance module sends a discharge request to the electric vehicle user based on the electricity load of the commercial area within a future time period T when the probability of the electric vehicle user being guided is greater than a set threshold a; and in response to the electric vehicle user's reply to the charging and discharging request, guides the electric vehicle user to the corresponding commercial area load node when the electric vehicle user arrives at the commercial area. The discharge regulation module is used to calculate the discharge power and discharge time of electric vehicle users at the load nodes in the commercial area based on the electricity load situation in the commercial area in the future time period T.

2. The shopping mall discharge system based on V2G technology according to claim 1, characterized in that, The predicted electricity load for the commercial area within the future time period T includes: in, Indicates the current moment. Indicates the commercial area is Power data at any given time; This indicates the number of samples in the historical electricity data. Represents a long short-term memory network; Indicating the future of the business district Power load at any given time ; This indicates the electricity load of the commercial area within a future time period T.

3. The shopping mall discharge system based on V2G technology according to claim 1, characterized in that, The calculation of range anxiety status for electric vehicle users includes: When the distance between electric vehicle users and commercial areas is less than a set threshold At the same time, facial images of electric vehicle users are collected, and their range anxiety is calculated in real time. The calculation of range anxiety status for electric vehicle users includes: Based on the facial images of electric vehicle users, facial feature vectors are extracted using the facial feature point detection library Dlib. Based on the facial feature vectors of electric vehicle users, a range anxiety state determination model for electric vehicle users is constructed using the FACS coding system. The range anxiety state of electric vehicle users is then calculated based on the constructed range anxiety state determination model.

4. A shopping mall discharge system based on V2G technology according to claim 1, characterized in that, Sending discharge requests to electric vehicle users based on the electricity load of the commercial area within a future time period T includes: Calculate the peak load index of the commercial area's electricity load based on the electricity load situation of the commercial area within the future time period T; in, The peak load index represents the peak electricity load in the commercial area. Indicates the time point at which the detection began. [0,T]; Indicates time The electrical load; It is the total number of moments within the time period; Calculate the time when the electricity load of a commercial area enters its peak period based on the peak load index of the commercial area's electricity load; in, This refers to the time point when the load peaks; It is a preset peak load threshold; The timing of sending the discharge request is calculated based on the peak electricity load in the commercial area: in, The time when a discharge request is sent to an electric vehicle user; A fixed value for the time required to arrive in the business district ahead of schedule; Indicates electric vehicle users Distance from the business district For electric vehicle users The average driving speed; At the time of sending the discharge request Send a discharge request to electric vehicle users.

5. A shopping mall discharge system based on V2G technology according to claim 1, characterized in that, The step of guiding electric vehicle users to the corresponding commercial area load node when they arrive at the commercial area includes: S101: When an electric vehicle user arrives at a commercial area, calculate the distance between the electric vehicle user and the load nodes of each commercial area and the discharge waiting time of the electric vehicle user at each load node of the commercial area. S102: Traverse each commercial area load node and check whether the commercial area load node is occupied by other electric vehicle users. If so, proceed to step S103; otherwise, proceed to step S105. S103: Determine whether the discharge waiting time of electric vehicle users at load nodes in commercial areas is less than a set threshold. If so, proceed to step S104; S104: Determine whether the distance from the electric vehicle user to the load node in the commercial area is less than a set threshold. If so, proceed to step S105: S105: Based on whether the difference between the scheduling potential of electric vehicle users at commercial area load nodes and the scheduling demand of commercial area load nodes is less than a set threshold. If so, the commercial area load node will be assigned to the electric vehicle user, and the traversal will stop.

6. A shopping mall discharge system based on V2G technology according to claim 5, characterized in that, The scheduling potential of electric vehicle users at load nodes in commercial areas includes: in, Indicates the load node of electric vehicle users in the commercial area scheduling potential Indicates the load node of the commercial area Maximum discharge capacity; For discharge efficiency; Indicates electric vehicle users The remaining battery capacity; For electric vehicle users The maximum discharge power.

7. A shopping mall discharge system based on V2G technology according to claim 5, characterized in that, The scheduling requirements of the load nodes in the commercial area include: in, Indicates the load node of the commercial area scheduling requirements Indicates the load node of the commercial area Maximum load capacity; express The total quantity; This indicates the discharge demand of the power grid in the commercial area; Indicates the load node of the commercial area Adjustment coefficient; This indicates the real-time electricity load of the commercial area; This indicates the target control power of the commercial area's power grid.

8. A shopping mall discharge system based on V2G technology according to claim 1, characterized in that, The calculation of the discharge power and discharge time of electric vehicle users at load nodes in commercial areas includes: The day is divided into 24 time periods by hour, and an objective function is constructed with the goal of smoothing out fluctuations in the electricity load curve of the commercial area: in, Let m represent the objective function. Total quantity This indicates the number of electric vehicle users discharging their vehicles in the commercial area. Represented as time period Average electricity load in commercial areas; This represents the average electricity load of a commercial area over a 24-hour period. Indicates electric vehicle users During the period The discharge power; Discharge power of electric vehicle users Using the electric vehicle user as the decision variable, the objective function is solved using the particle swarm optimization algorithm to obtain the optimal discharge power and discharge time. The discharge time of the electric vehicle user is determined by statistically analyzing the discharge power of the electric vehicle user. The number of time periods is calculated.

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