Power grid load-based super charger dynamic power distribution system and distribution method

Abstract

The invention is suitable for the technical field of charging piles, and provides a power grid load-based dynamic power distribution system and method for a super charger, and the system comprises a V2G power cabinet of the super charger, a super charging terminal, and a fast charging terminal. The power grid load monitoring module is used for sending power grid load information to the vehicle pile network communication module; the vehicle pile network communication module is used for forwarding power grid load information to the intelligent control module; the intelligent control module is used for selecting the power distribution strategy represented by the particle with the highest fitness value as a dynamic power distribution strategy and sending the dynamic power distribution strategy to the intelligent power distribution module; and the intelligent power distribution module is used for receiving the dynamic power distribution strategy sent by the intelligent control module, and distributing the plurality of V2G power modules to the super charging terminal and the fast charging terminal according to the dynamic power distribution strategy. The invention aims to optimize the power grid stability and the charging and discharging benefits, and provides an efficient and intelligent charging and discharging scheme for the electric vehicle.

Description

Technical Field

[0001] The present invention belongs to the technical field of charging piles, and in particular relates to a dynamic power distribution system and method for a super charger based on grid load. Background Art

[0002] With the increasing awareness of environmental protection and the government's strong support for the new energy vehicle industry, the market share of electric vehicles has steadily increased year by year, and their ownership has shown explosive growth. More and more consumers tend to choose electric vehicles.

[0003] This trend has led to a sharp increase in demand for electric vehicle charging, placing enormous pressure on the power grid. To meet this demand, various regions have accelerated the construction of charging facilities, and superchargers with fast charging capabilities have become widely used. Superchargers are a type of charging pile. Their use may impact the power grid, further affecting its stable operation. To alleviate this pressure on the grid, dynamic power allocation strategies are needed.

[0004] However, existing technologies lack dynamic power allocation strategies, making it difficult to dynamically adjust superchargers. Consequently, this approach fails to alleviate pressure on the power grid and can even exacerbate power shortages during peak hours. Therefore, to alleviate this pressure on the power grid, a dynamic power allocation system for superchargers is urgently needed. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a supercharger dynamic power distribution system based on grid load, aiming to solve the technical problem of how to construct a supercharger dynamic power distribution system.

[0006] In a first aspect, a supercharger dynamic power distribution system includes a V2G power cabinet of the supercharger, a supercharging terminal, and a fast charging terminal;

[0007] The V2G power cabinet includes multiple V2G power modules, grid load monitoring module, intelligent control module, intelligent power distribution module, and vehicle-to-grid communication module;

[0008] The grid load monitoring module is used to obtain grid load information and send it to the vehicle-pile network communication module;

[0009] The vehicle-pile network communication module is used to receive grid load information, vehicle information, and user charging and discharging demand information, and forward the grid load information, vehicle information, and user charging and discharging demand information to the intelligent control module;

[0010] The intelligent control module is used to generate multiple particles based on grid load information, vehicle information, and user charging and discharging demand information. Each particle represents a power allocation strategy. Based on the particle swarm optimization algorithm and the target optimization model, the power allocation strategy represented by the particle that meets multiple constraints and has the highest fitness value is selected as the dynamic power allocation strategy, and the dynamic power allocation strategy is sent to the intelligent power allocation module.

[0011] The intelligent power allocation module is used to receive the dynamic power allocation strategy sent by the intelligent control module and allocate multiple V2G power modules to the super charging terminal and fast charging terminal according to the dynamic power allocation strategy.

[0012] In a possible implementation of the first aspect, the V2G power module is used to convert the AC power of the power grid into DC power that is compatible with the electric vehicle battery in charging mode, and to convert the DC power of the electric vehicle battery into AC power that meets the requirements of the power grid in discharging mode.

[0013] In a possible implementation of the first aspect, the power grid load monitoring module is specifically used to obtain real-time collection of the current, voltage, and frequency of the power grid, transmit the current, voltage, and frequency of the power grid to a data processing unit, and clean, filter, and feature extract the current, voltage, and frequency of the power grid through the data processing unit to obtain power grid load information, and send the power grid load information to the intelligent control module.

[0014] In a possible implementation of the first aspect, the V2G power cabinet further includes an AC switch protection module, which is configured to perform overcurrent protection, short circuit protection, and leakage protection.

[0015] In a possible implementation manner of the first aspect, the multi-objective optimization model consists of a first objective function and a second objective function, the first objective function is used to minimize the peak-to-valley difference of the power grid, and the second objective function is used to maximize energy utilization efficiency.

[0016] Among them, the first objective function is:

[0017]

[0018] P c : represents the power allocation vector, which is composed of the allocated power of the super charging terminal and the allocated power of the fast charging terminal;

[0019] P max : Indicates the maximum load power of the power grid in a certain period of time;

[0020] P min : Indicates the minimum load power of the power grid within a certain period of time.

[0021] Among them, the second objective function is:

[0022]

[0023] P c : represents the power allocation vector, which is composed of the allocated power of the super charging terminal and the allocated power of the fast charging terminal; η: represents the energy utilization efficiency;

[0024] E grid,t : represents the energy supply (electricity output) of the grid at time t;

[0025] η grid,t : Indicates the energy supply efficiency of the power grid at time t, indicating how much of the power output of the power grid is effectively utilized;

[0026] E EV,t : Indicates the charge and discharge amount of the electric vehicle battery at time t. When the electric vehicle is charging, it is a positive value; when the electric vehicle battery is discharging to the grid, it is a negative value;

[0027] η EV,t : represents the charge and discharge efficiency of the electric vehicle battery at time t. The charge and discharge efficiency includes charging efficiency and discharging efficiency. The charging efficiency represents the efficiency of converting the electric energy of the grid into the electric energy of the electric vehicle battery, and the discharging efficiency represents the efficiency of converting the electric energy of the electric vehicle battery into the electric energy of the grid.

[0028] E total,t : represents the total energy input of the system at time t, including the power supply of the grid and the discharge of the electric vehicle battery.

[0029] In a possible implementation of the first aspect, the multiple constraints include power constraints, battery status constraints of the electric vehicle, load constraints of the power grid, charging demand constraints, and power module quantity constraints.

[0030] (1) The power constraint is:

[0031]

[0032] P j : Indicates the actual output power of the super charging terminal or fast charging terminal;

[0033] Indicates the maximum allowable power of a Supercharger or Fast Charger terminal.

[0034] (2) The battery state constraints of electric vehicles are:

[0035] SOC min ≤SOC G ≤SOC max;

[0036] SOC min : Indicates the minimum allowable state of charge of the battery, which is usually a threshold set to avoid damage to the battery caused by excessive discharge;

[0037] SOC max : Indicates the maximum allowable state of charge of the battery, mainly used to prevent overcharging to extend battery life and ensure safety;

[0038] SOC G : Indicates the actual charge state of the power grid.

[0039] (3) The load constraints of the power grid are:

[0040]

[0041] Indicates the lower limit of grid load, which is the minimum power requirement to ensure stable operation of the grid;

[0042] Indicates the upper limit of the grid load, which is the maximum power limit to prevent grid overload and voltage fluctuation;

[0043] P grid : Indicates the actual load power of the power grid.

[0044] (4) The constraints of charging demand are:

[0045] E demand ≤E EV ;

[0046] E demand : Indicates the charging requirements set by the user;

[0047] E EV : Indicates the actual amount of electricity charged.

[0048] (5) The constraints on the number of power modules are:

[0049] N min ≤N modules ≤N max ;

[0050] N min : Indicates the minimum value of the V2G power module in the active state;

[0051] N modules : Indicates the current value of the V2G power module in the active state;

[0052] N max : Indicates the maximum value of the V2G power module in the active state.

[0053] In a possible implementation of the first aspect, the network mode of the vehicle-pile network communication module includes one or a combination of 4G, 5G, Wi-Fi, Ethernet, and power line carrier communication.

[0054] In a possible implementation of the first aspect, the super charging terminal consists of a first user interaction module, a first charging gun, and a first cooling module, and the fast charging terminal consists of a second user interaction module, a second charging gun, and a second cooling module.

[0055] In a possible implementation of the first aspect, the first user interaction module is used to display a first graphical user interface, and the information interaction process between the user and the super charging terminal is completed through the first graphical user interface; the second user interaction module is used to display a second graphical user interface, and the information interaction process between the user and the fast charging terminal is completed through the second graphical user interface.

[0056] In a second aspect, an embodiment of the present application provides a distribution method based on a super charger dynamic power distribution system, characterized by comprising:

[0057] The grid load monitoring module obtains grid load information and sends it to the vehicle-pile network communication module;

[0058] The vehicle-pile network communication module receives grid load information, vehicle information, and user charging and discharging demand information, and forwards the grid load information, vehicle information, and user charging and discharging demand information to the intelligent control module;

[0059] The intelligent control module generates multiple particles based on grid load information, vehicle information, and user charging and discharging information. Each particle represents a power allocation strategy. Based on the particle swarm optimization algorithm and the target optimization model, the power allocation strategy represented by the particle that meets multiple constraints and has the highest fitness value is selected as the dynamic power allocation strategy and sent to the intelligent power allocation module.

[0060] The intelligent power allocation module receives the dynamic power allocation strategy sent by the intelligent control module, allocates multiple V2G power modules to the super charging terminal and fast charging terminal according to the dynamic power allocation strategy, and uploads the dynamic power allocation strategy to the server.

[0061] In the embodiment of the present invention, the beneficial effects are in two aspects. On the one hand, the super charger dynamic power distribution system includes a V2G power cabinet of the super charger, a super charging terminal and a fast charging terminal. The V2G power cabinet includes multiple V2G power modules, a grid load monitoring module, an intelligent control module, an intelligent power distribution module, and a vehicle-pile network communication module, which solves the technical problem of how to construct a dynamic power distribution system for a super charger. On the other hand, according to the dynamic power distribution strategy, multiple V2G power modules are allocated to the super charging terminal and the fast charging terminal. In this way, the power superposition of the super charging terminal and the fast charging terminal is smoother, which reduces the peak load pressure of the power grid, thereby alleviating the pressure on the power grid and avoiding the super charging terminal or the fast charging terminal from excessively occupying power grid resources during peak hours. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a structural block diagram of a supercharger dynamic power distribution system based on grid load provided by an embodiment of the present invention;

[0063] Figure 2 This is a flow chart of the implementation of the allocation method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0065] refer to Figure 1 , Figure 1 This is a structural block diagram of a super charger dynamic power distribution system based on grid load provided by an embodiment of the present invention. Figure 1 As shown, the supercharger dynamic power distribution system includes a supercharger V2G power cabinet, a super charging terminal and a fast charging terminal;

[0066] The V2G power cabinet includes multiple V2G power modules, grid load monitoring module, intelligent control module, intelligent power distribution module, and vehicle-to-grid communication module;

[0067] The grid load monitoring module is used to obtain grid load information and send it to the vehicle-pile network communication module;

[0068] The vehicle-pile network communication module is used to receive grid load information, vehicle information, and user charging and discharging demand information, and forward the grid load information, vehicle information, and user charging and discharging demand information to the intelligent control module;

[0069] The intelligent control module is used to generate multiple particles based on grid load information, vehicle information, and user charging and discharging demand information. Each particle represents a power allocation strategy. Based on the particle swarm optimization algorithm and the target optimization model, the power allocation strategy represented by the particle that meets multiple constraints and has the highest fitness value is selected as the dynamic power allocation strategy, and the dynamic power allocation strategy is sent to the intelligent power allocation module.

[0070] The intelligent power allocation module is used to receive the dynamic power allocation strategy sent by the intelligent control module and allocate multiple V2G power modules to the super charging terminal and fast charging terminal according to the dynamic power allocation strategy.

[0071] The user's charging and discharging demand information is information about the user's management of the vehicle's battery charging and discharging. The user's charging and discharging demand information includes but is not limited to the charging and discharging time period, target power, and power level.

[0072] Among them, the super charger is a high-power DC fast charging device that usually uses liquid cooling technology and supports a charging power of up to 600kW. It can replenish hundreds of kilometers of range for electric vehicles within 5-10 minutes, greatly improving charging efficiency.

[0073] Among them, the V2G power cabinet is a key device supporting vehicle-to-grid technology, and the V2G power cabinet contains multiple V2G power modules.

[0074] Among them, the Chinese definition of V2G in the V2G power module is: Vehicle to Grid, and the English definition is: Vehicle-to-Grid.

[0075] Among them, the V2G power module is used to convert the AC power of the power grid into DC power suitable for electric vehicle batteries in charging mode, and to convert the DC power of electric vehicle batteries into AC power that meets the requirements of the power grid in discharging mode.

[0076] Among them, the power grid load monitoring module is specifically used to obtain the real-time collection of the current, voltage and frequency of the power grid, transmit the current, voltage and frequency of the power grid to the data processing unit, clean, filter and extract the features of the current, voltage and frequency of the power grid through the data processing unit, obtain the power grid load information, and send the power grid load information to the intelligent control module.

[0077] The intelligent control module can also coordinate the operation of each power unit and related equipment according to logical rules to achieve optimized scheduling. It can also exchange information with the upper-level system and other equipment through multiple communication interfaces to achieve remote monitoring and coordinated control, ensuring the efficient and stable operation of the entire V2G power cabinet. It has built-in advanced intelligent algorithms and optimization models. After receiving grid load information, it comprehensively evaluates the grid status and electric vehicle charging and discharging needs. Based on preset objective functions such as minimizing the peak-to-valley difference of the grid and maximizing energy utilization efficiency, it uses optimization algorithms to calculate the optimal power distribution plan. The calculation will take into account multiple factors such as the battery status of the electric vehicle, charging priority, and the real-time electricity price of the grid. Finally, the power distribution command is sent to the intelligent power distribution module to achieve precise control of the charging pile power. The charging and discharging power distribution error can be controlled within ±1%, effectively balancing the supply and demand relationship of the grid and reducing the grid operation cost.

[0078] Among them, the V2G power cabinet also includes an AC switch protection module, which is used to perform overcurrent protection, short circuit protection, and leakage protection actions.

[0079] Among them, the multi-objective optimization model consists of a first objective function and a second objective function. The first objective function is used to minimize the peak-to-valley difference of the power grid, and the second objective function is used to maximize energy utilization efficiency.

[0080] Among them, the first objective function is:

[0081]

[0082] P c : represents the power allocation vector, which is composed of the allocated power of the super charging terminal and the allocated power of the fast charging terminal;

[0083] P max : Indicates the maximum load power of the power grid in a certain period of time;

[0084] P min : Indicates the minimum load power of the power grid within a certain period of time.

[0085] Among them, the second objective function is:

[0086]

[0087] P c : represents the power allocation vector, which is composed of the allocated power of the super charging terminal and the allocated power of the fast charging terminal; η: represents the energy utilization efficiency;

[0088] E grid,t : represents the energy supply (electricity output) of the grid at time t;

[0089] ηgrid,t : Indicates the energy supply efficiency of the power grid at time t, indicating how much of the power output of the power grid is effectively utilized;

[0090] E EV,t : Indicates the charge and discharge amount of the electric vehicle battery at time t. When the electric vehicle is charging, it is a positive value; when the electric vehicle battery is discharging to the grid, it is a negative value;

[0091] η EV,t : represents the charge and discharge efficiency of the electric vehicle battery at time t. The charge and discharge efficiency includes charging efficiency and discharging efficiency. The charging efficiency represents the efficiency of converting the electric energy of the grid into the electric energy of the electric vehicle battery, and the discharging efficiency represents the efficiency of converting the electric energy of the electric vehicle battery into the electric energy of the grid.

[0092] E total,t : represents the total energy input of the system at time t, including the power supply of the grid and the discharge of the electric vehicle battery.

[0093] Among them, multiple constraints include power constraints, electric vehicle battery status constraints, power grid load constraints, charging demand constraints, and power module quantity constraints.

[0094] (1) The power constraint is:

[0095]

[0096] P j : Indicates the actual output power of the super charging terminal or fast charging terminal;

[0097] Indicates the maximum allowable power of a Supercharger or Fast Charger terminal.

[0098] (2) The battery state constraints of electric vehicles are:

[0099] SOC min ≤SOC G ≤SOC max ;

[0100] SOC min : Indicates the minimum allowable state of charge of the battery, which is usually a threshold set to avoid damage to the battery caused by excessive discharge;

[0101] SOC max : Indicates the maximum allowable state of charge of the battery, mainly used to prevent overcharging to extend battery life and ensure safety;

[0102] SOC G : Indicates the actual charge state of the power grid.

[0103] (3) The load constraints of the power grid are:

[0104]

[0105] Indicates the lower limit of grid load, which is the minimum power requirement to ensure stable operation of the grid;

[0106] Indicates the upper limit of the grid load, which is the maximum power limit to prevent grid overload and voltage fluctuation;

[0107] P grid : Indicates the actual load power of the power grid.

[0108] (4) The constraints of charging demand are:

[0109] E demand ≤E EV ;

[0110] E demand : Indicates the charging requirements set by the user;

[0111] E EV : Indicates the actual amount of electricity charged.

[0112] (5) The constraints on the number of power modules are:

[0113] N min ≤N modules ≤N max ;

[0114] N min : Indicates the minimum value of the V2G power module in the active state;

[0115] N modules : Indicates the current value of the V2G power module in the active state;

[0116] N max : Indicates the maximum value of the V2G power module in the active state.

[0117] Among them, the network mode of the vehicle-pile network communication module includes one or a combination of 4G, 5G, Wi-Fi, Ethernet, and power line carrier communication.

[0118] Among them, 4G is the network model of the fourth generation of mobile communication technology.

[0119] Among them, 5G is the network model of the fifth generation of mobile communication technology.

[0120] Among them, Wi-Fi is a network mode of wireless local area network.

[0121] The vehicle-pile network communication unit is a communication unit that connects vehicles, charging piles, and the backend management system for information exchange, and plays an important role in the vehicle-pile network collaborative system. The vehicle-pile network communication unit has both hardware and software functions. The hardware part includes an information acquisition unit, a control system, and a communication module, while the software part is responsible for data processing and transmission control. For example, through built-in sensors and communication interfaces, the unit can collect vehicle battery status, operating parameters, and grid load information in real time, and transmit the data to the backend management system via 4G, 5G, Wi-Fi, Ethernet, and power line carrier communications.

[0122] Among them, the super charging terminal consists of a first user interaction module, a first charging gun and a first cooling module, and the fast charging terminal consists of a second user interaction module, a second charging gun and a second cooling module.

[0123] Among them, the first user interaction module is used to display the first graphical user interface, and the information interaction process between the user and the super charging terminal is completed through the first graphical user interface; the second user interaction module is used to display the second graphical user interface, and the information interaction process between the user and the fast charging terminal is completed through the second graphical user interface.

[0124] Among them, users can view real-time grid load information, electric vehicle charging status (such as power, charging progress, remaining charging time, etc.), charging costs, and other content on the first or second graphical user interface. They can also set charging parameters (such as charging time and charging power), make charging reservations, and query historical charging records. Its practical effect is significant, greatly improving user experience and participation. Users can flexibly adjust charging strategies according to their own needs and actual conditions and enjoy personalized charging services. For example, they can make charging reservations when electricity prices are low to reduce costs, and they can also understand the charging status of electric vehicles in real time to plan their trips rationally.

[0125] Among them, the first charging gun is responsible for connecting with the first electric vehicle to realize power and information transmission; the first charging cooling module ensures that the temperature of the super charging terminal is within a safe range during the charging process, ensuring the stable operation of the super charging terminal.

[0126] Among them, the second charging gun is responsible for connecting with the second electric vehicle to realize power and information transmission; the second charging cooling module ensures that the temperature of the super charging terminal is within a safe range during the charging process, ensuring the stable operation of the super charging terminal.

[0127] The first electric vehicle and the second electric vehicle are different electric vehicles.

[0128] Among them, the intelligent control module is specifically used to generate multiple particles based on grid load information, vehicle information and user charging and discharging demand information. Each particle represents each power allocation strategy. The position coordinates of each particle are the activation state vector in each power allocation strategy. The activation state vector is composed of the activation state values ​​of each power module group. Each power module group has two V2G power modules. Multiple particles are formed into a particle swarm. The particle swarm optimization algorithm is used to update the position coordinates and speed of each particle in the particle swarm. During the update process, the fitness value of each particle is calculated according to the multi-objective optimization model. When the termination condition is reached, the power allocation strategy represented by the particle that meets multiple constraints and has the highest fitness value is selected as the dynamic power allocation strategy, and the dynamic power allocation strategy is sent to the intelligent power allocation module.

[0129] The termination condition is that the current number of iterations reaches the maximum number of iterations or the fitness value of each particle converges to a preset range.

[0130] Among them, the power allocation strategy represented by the particle that meets multiple constraints and has the highest fitness value is the optimal power allocation solution. The optimal power allocation solution is used as the dynamic power allocation strategy. In this way, the dynamic power allocation strategy can not only meet the user's fast charging needs, but also control the grid load within the safety threshold, achieving efficient and stable energy utilization.

[0131] The particle swarm optimization algorithm, based on swarm intelligence, simulates the movement of particles in a search space to find the optimal solution. In generating dynamic power allocation strategies, the algorithm can effectively balance the multi-objective demands of the power grid and electric vehicles to find the optimal power allocation solution.

[0132] 1. Particle Representation

[0133] Each particle represents a power allocation scheme, and the particle's position is composed of multiple dimensions, each corresponding to the activation state (0 or 1) of a power module group. For example, a supercharger has 48 15kW power modules, divided into 24 groups (each group has 2 V2G power modules, totaling 30kW). The particle's position can be represented as a 24-dimensional binary vector, such as [1, 0, 1, ...], indicating that group 1, group 3, and so on are activated.

[0134] 2. Initialization

[0135] Randomly initialize the position and velocity of the particle swarm to ensure that the position of the particles meets the constraints.

[0136] 3. Fitness calculation

[0137] The fitness value of each particle is calculated according to the objective function, taking into account the peak-to-valley difference of the power grid, energy utilization efficiency, and the satisfaction degree of the constraint conditions.

[0138] 4. Update rules

[0139] Update the particle's velocity and position.

[0140]

[0141] v i : represents the velocity of the i-th particle;

[0142] x i : represents the position of the i-th particle;

[0143] represents the velocity of the i-th particle at time y+1;

[0144] w: Inertia weight, used to balance global and local search capabilities. A larger w helps particles explore globally, while a smaller w helps particles conduct detailed local searches.

[0145] represents the velocity of the i-th particle at time y;

[0146] c1 and c2: learning factors that control the impact of individual and global optima on speed updates. They are usually positive and determine the degree to which particles respond to individual and global optima.

[0147] r1 and r2: represent two independent random numbers, evenly distributed between [0, 1], used to increase the randomness and exploration ability of the algorithm;

[0148] P best,i : represents the individual optimal position of the i-th particle, that is, the optimal solution found by particle i so far;

[0149] represents the position of the i-th particle at time t;

[0150] g best : represents the global optimal position, that is, the optimal solution found by the entire particle swarm so far;

[0151] represents the position of the i-th particle at time y+1;

[0152] represents the velocity of the i-th particle at time y;

[0153] represents the velocity of the i-th particle at time y+1.

[0154] 5. Constraint processing

[0155] After updating the particle position, check whether multiple constraints are met and make corrections if they are not met.

[0156] 6. Termination Conditions

[0157] The termination condition is that the current number of iterations reaches the maximum number of iterations or the fitness value of each particle converges to a preset range.

[0158] For ease of explanation, the following examples are given:

[0159] An electric vehicle charging station is equipped with a power cabinet and six charging terminals, including two Supercharger terminals and four Fast Charger terminals. The power cabinet contains 48 15kW power modules, organized into 24 groups of two modules each, providing a combined power of 30kW per group. The Supercharger terminals have a maximum charging power of 360kW, while the Fast Charger terminals have a maximum charging power of 120kW.

[0160] (1) System initial state

[0161] Grid status: Currently in the level usage period, the real-time load of the grid is moderate, and the maximum allowable load Minimum allowable load

[0162] Electric vehicles:

[0163] Vehicle 1: Connected to Supercharger 1, battery capacity C1 = 100kWh, battery charge SOC1 = 25%, desired charging power P demand,1 = 320kW. Required charging capacity Q1 = 75kWh. If charging is performed at the desired power, the estimated charging time t1 is ≈ 1.4 minutes.

[0164] Vehicle 2: Connected to Supercharger 2, battery capacity C2 = 90 kWh, battery charge SOC2 = 30%, desired charging power P demand,2 = 280kW. Required charging capacity Q2 = 63kWh. If charging at the desired power, the estimated charging time t2 ≈ 13.5 minutes.

[0165] Vehicle 3: connected to fast charging terminal 1, battery capacity C3 = 60kWh, battery charge SOC3 = 15%, expected charging power P demand,3 = 100kW. Required charging capacity Q3 = 51kWh. If charging at the desired power, the estimated charging time t3 ≈ 30.6 minutes.

[0166] Vehicle 4: connected to fast charging terminal 2, battery capacity C4 = 70kWh, battery charge SOC4 = 20%, expected charging power P demand,4= 90kW. Required charging capacity Q4 = 56kWh. If charging at the desired power, the estimated charging time t4 ≈ 37.3 minutes.

[0167] Vehicle 5: connected to fast charging terminal 3, battery capacity C5 = 50kWh, battery charge SOC5 = 10%, expected charging power P demand,5 =80kW. Required charging capacity Q5 = 45kWh. If charging at the desired power, the estimated charging time t5 ≈ 33.8 minutes.

[0168] Vehicle 6: connected to fast charging terminal 4, battery capacity C6 = 80kWh, battery charge SOC6 = 22%, expected charging power P demand,6 =110kW. Required charging capacity Q6 = 62.4kWh. If charging at the desired power, the estimated charging time t6 ≈ 34 minutes.

[0169] Charging pile power constraint: Maximum charging power of super charging terminal Maximum charging power of fast charging terminal

[0170] Electric vehicle battery state constraint: the minimum allowable state of charge (SOC) of the battery min =5%, maximum allowable state of charge SOC max =100%.

[0171] Power module quantity constraint: Minimum number of module groups N min =1, the maximum number of module groups used is N max =24.

[0172] (2) Run the particle swarm optimization algorithm

[0173] Particle initialization: Randomly initialize a particle swarm containing 60 particles.

[0174] The position of each particle is represented by a 24-dimensional binary vector.

[0175] For example: ([1,0,1,0,1,1,0,0,1,0,0,1,0,0,1,1,0,0,1,0,1,1,0,0,1]) means that the 1st, 3rd, 5th, 6th, 9th, 11th, 14th, 15th, 18th, 20th, 21st, and 24th groups of modules are activated. At the same time, the velocity of each particle is initialized.

[0176] Fitness calculation: Calculate the fitness value of each particle based on the objective function of the multi-objective optimization model (minimizing the peak-to-valley difference of the power grid and maximizing energy utilization efficiency) and various constraints.

[0177] For example, for the power allocation plan corresponding to a certain particle, the changes in the peak-to-valley difference of the power grid and the energy utilization efficiency are calculated while meeting the charging needs of each electric vehicle, and the fitness value is comprehensively obtained.

[0178] Iterative update: Update the particle's velocity and position according to the update rules. The inertia weight (w = 0.65) and the learning factor (c1 = c2 = 1.45) are used. After the update, check whether the particle's position meets the constraints. If not, make corrections.

[0179] Convergence judgment: Continue iteration. When the maximum number of iterations is reached (assuming it is 300) or the fitness value converges (such as the fitness value changes by less than 0.025% after 30 consecutive iterations), the algorithm stops.

[0180] (3) Obtain the optimal solution

[0181] Assume that after the algorithm converges, the global optimal position (g best =[1,1,1,1,1,1,1,1,1,1,1,1,1,

[0182] Since the current period is at a high level of demand and the available grid load is sufficient, all 24 module groups can be activated to meet the expected charging power of each vehicle when allocating power.

[0183] Power allocation and module activation calculation:

[0184] Supercharger Terminal 1: After the power module is activated, considering the expected charging power P of vehicle 1 demand,1 = 320kW, but since other vehicles are also charging, power allocation space needs to be left for them. Therefore, after comprehensive calculation, the charging power is allocated to 210kW, activating 7 modules, each with a power of 30kW. This is close to the vehicle's expected power while leaving power allocation space for other terminals.

[0185] Supercharger 2: Similarly, consider the desired charging power P for vehicle 2. demand,2 =280kW, allocated power P2 = 180kW, 6 groups of modules are activated.

[0186] Fast charging terminal 1: Similarly, consider the expected charging power P of vehicle 3 demand,3 =100kW, allocated power P3 = 90kW, 3 groups of modules are activated.

[0187] Fast charging terminal 2: Similarly, consider the expected power P of vehicle 4 demand,4 =90kW, allocated power P4 = 90kW, activating 3 groups of modules.

[0188] Fast charging terminal 3: Similarly, consider the expected power P of vehicle 5demand,5 =80kW, allocated power P5 = 60kW, 2 groups of modules are activated.

[0189] Fast charging terminal 3: Similarly, consider the expected power P of vehicle 6 demand,6 =110kW, allocated power P6 = 90kW, 3 groups of modules are activated.

[0190] The total number of activated module groups is 7+6+3+3+2+3=24 groups, which meets the module group number limit requirement.

[0191] (4) Execute charge and discharge control

[0192] The intelligent control module generates a charging and discharging strategy according to the above-mentioned allocated power, and sends it to the intelligent power distribution module to control the charging and discharging of each electric vehicle.

[0193] The vehicle-to-charging network communication unit continuously monitors grid load, EV battery status, and other information, providing real-time feedback to the system. The user interaction unit provides users with information such as charging status, estimated time to full charge, and charging costs. Based on real-time changes in grid load, EV battery status, and new user needs, the system further adjusts and optimizes power allocation strategies to ensure stable grid operation and efficient energy utilization while meeting user charging needs.

[0194] The beneficial effects of the embodiments of the present invention are as follows:

[0195] 1. Optimization effect on power grid operation

[0196] (1) Alleviate the pressure on power grid supply: With the vigorous development of the electric vehicle market, the demand for charging has increased dramatically, bringing huge pressure on the power grid. The power grid load monitoring module of the present invention can capture the operating status of the power grid in real time and accurately. The intelligent power distribution module dynamically allocates power to each V2G super charging pile based on the monitoring information and the preset optimization strategy. During peak power consumption periods, the system can quickly detect the sharp increase in load and adjust the power output of the V2G super charging pile in time to avoid the impact of the concentrated use of a large number of super chargers on the power grid, effectively alleviate the power supply pressure of the power grid, and ensure the stable operation of the power grid.

[0197] (2) Balancing the peak-valley difference of the power grid: The power grid has obvious peak-valley load differences in different time periods. If the charging behavior of electric vehicles is concentrated in peak hours when electric vehicles are widely popularized, the peak-valley difference will be further widened. The present invention uses V2G technology to achieve two-way energy flow between electric vehicles and the power grid. During the off-peak period of electricity consumption, the system can control the charging of electric vehicles and store excess electricity; during the peak period of electricity consumption, electric vehicles act as distributed energy storage units and feed the electricity in the battery back to the power grid, effectively balancing the peak-valley difference of the power grid and reducing the operating cost and management difficulty of the power grid.

[0198] (3) Improve grid stability and power quality: The high-power charging characteristics of super chargers may cause voltage fluctuations, frequency offsets and other problems, threatening grid stability and power quality. The V2G bidirectional charging module of the present invention consists of a bidirectional power converter and a charge and discharge control unit. It accurately regulates the charging current and voltage during charging and accurately controls the energy flow and power during discharge, with a power regulation accuracy of ±1%. This helps reduce the impact of the charging process on the grid, maintains the stability of the grid voltage and frequency, improves power quality, and reduces the risk of power outages in local areas caused by the rapid charging of a large number of electric vehicles at the same time.

[0199] (4) Enhance the grid's ability to absorb renewable energy: As the proportion of renewable energy such as solar and wind energy in the power system continues to increase, their intermittent and fluctuating characteristics pose challenges to the stable operation of the power grid. The V2G technology of the present invention can be used as a flexible energy storage method. When there is excess renewable energy generation, the system controls electric vehicles to store excess electricity; when there is insufficient power generation, the battery power is released back to the grid, effectively improving the renewable energy absorption capacity and strongly promoting the sustainable development of energy.

[0200] 2. Improve the efficiency and rationality of charging management

[0201] (1) Realize dynamic power distribution: Most existing charging methods are based on one-way charging of electric vehicles based on their power demand, which makes it difficult to dynamically adjust according to the real-time load of the power grid. The intelligent power distribution module of the present invention serves as the decision-making core of the system. It has built-in advanced intelligent algorithms and optimization models to comprehensively evaluate the power grid status and the charging and discharging needs of electric vehicles. Based on preset objective functions such as minimizing the peak-to-valley difference of the power grid and maximizing energy utilization efficiency, it uses optimization algorithms to calculate the optimal power distribution plan. The error in charging and discharging power distribution can be controlled within ±1%. This enables the system to dynamically adjust the power of the charging pile according to the real-time load of the power grid, effectively alleviating the pressure on the power grid and improving energy utilization efficiency.

[0202] (2) Optimizing the utilization of charging resources: Traditional charging facility management lacks a scientific and reasonable dynamic power allocation strategy. Each charging device usually operates independently, making it difficult to achieve coordinated control, resulting in low efficiency in charging resource utilization. The vehicle-pile network communication module of the present invention is responsible for building a real-time communication channel between the system and the power grid and electric vehicles to ensure accurate information transmission and interaction. Through this module, the system can grasp the status information of electric vehicles and power grids in real time, realize the coordinated work of various modules, reasonably allocate appropriate charging power, improve the utilization efficiency of charging resources, and better respond to the charging needs of large-scale electric vehicles.

[0203] 3. Improve user experience and engagement

[0204] (1) Provide a friendly and convenient operation interface: The user interaction module uses a graphical user interface to create a friendly and convenient operation interface for users through touch screens, mobile phone applications, etc. Users can view real-time grid load information, electric vehicle charging status, charging costs, etc. on the interface. They can also set charging parameters, make charging reservations, and query historical charging records, which greatly improves user experience and participation.

[0205] (2) Support for personalized charging services: Users can make reservations for charging when electricity prices are low to reduce costs based on their own needs and actual conditions. They can also monitor the charging status of their electric vehicles in real time to plan their trips. This personalized charging service allows users to arrange charging activities more flexibly and meet the diverse needs of different users.

[0206] refer to Figure 2 , Figure 2 This is a flow chart of the implementation of the allocation method provided by an embodiment of the present invention, which is detailed as follows:

[0207] S201, the grid load monitoring module obtains grid load information and sends the grid load information to the vehicle-pile network communication module;

[0208] S202: The vehicle-pile network communication module receives grid load information, vehicle information, and user charging and discharging demand information, and forwards the grid load information, vehicle information, and user charging and discharging demand information to the intelligent control module;

[0209] S203: The intelligent control module generates multiple particles based on grid load information, vehicle information, and user charging and discharging demand information. Each particle represents a power allocation strategy. Based on the particle swarm optimization algorithm and the target optimization model, the intelligent control module selects the power allocation strategy represented by the particle that meets multiple constraints and has the highest fitness value as the dynamic power allocation strategy, and sends the dynamic power allocation strategy to the intelligent power allocation module.

[0210] S204: The intelligent power allocation module receives the dynamic power allocation strategy sent by the intelligent control module, allocates multiple V2G power modules to the super charging terminal and the fast charging terminal according to the dynamic power allocation strategy, and uploads the dynamic power allocation strategy to the server.

[0211] Among them, multiple V2G power modules are allocated to super charging terminals and fast charging terminals, and dynamic power allocation strategies are uploaded to the server, making it convenient for developers to view the dynamic power allocation strategies through the server.

[0212] For ease of illustration, the following is a specific example that illustrates the dynamic power allocation process:

[0213] For ease of explanation, the dynamic power allocation process is described using the grid peak shaving scenario as an example. The details are as follows:

[0214] A supercharging station in a technology park is equipped with a 930kW split supercharger. The supercharger is the core equipment in the supercharging station and the key component that directly enables charging. With a total capacity of 930kW, it consists of 61 15kW V2G power modules, with power distributed at a granularity of two V2G power modules per group, for a total of 31 power module groups. The supercharger includes a 930kW power cabinet and 12 split charging terminals, including three 360kW supercharging terminals and nine 120kW fast charging terminals.

[0215] It was 11:00 PM to 1:00 PM on a weekday, peak electricity consumption for the park. The grid load reached 1800 kW, exceeding the set threshold of 1500 kW. The grid monitoring system quickly sounded an alarm, triggering a peak-shaving strategy. At that moment, the charging station welcomed 12 electric vehicles, three of which were connected to 360 kW Superchargers and nine to 120 kW Fast Chargers. Four of the owners of the fast-charging vehicles had agreed to discharge their power back into the grid when needed.

[0216] 1. Dynamic power allocation process:

[0217] 1. Information collection stage:

[0218] Grid Information: The V2G Supercharger dynamic power allocation system, based on grid load, uses a grid load monitoring module to obtain detailed information about the regional grid in real time, including real-time load data, voltage, and frequency. Monitoring revealed that the grid was at peak load, reaching 1800kW, exceeding the preset threshold of 1500kW. The load exceeded 1800kW - 1500kW = 300kW.

[0219] Vehicle Information: The system communicates with each EV connected to a charging terminal, collecting information such as the vehicle's battery state of charge (SOC), charging power requirements, and willingness to discharge. Statistics show that the SOCs of the three vehicles connected to the Supercharger terminal were 12%, 18%, and 22%, respectively, indicating a more urgent need for charging. Of the nine vehicles connected to the Fast Charger terminal, five had SOCs between 20% and 45%, while the remaining four vehicles willing to discharge had SOCs of 88%, 90%, 92%, and 95%, respectively.

[0220] Among them, the State of Charge (SOC) refers to the percentage of the battery's current remaining power to its total capacity. It is a key indicator for measuring the battery's power level and is of great significance for the use, management and maintenance of the battery.

[0221] 2. Strategy formulation stage

[0222] The intelligent control module receives charging demand information such as the battery capacity, expected charging power, and estimated charging time of each electric vehicle from the vehicle-to-pile network communication unit. At the same time, combined with the real-time load assessment results fed back by the power grid monitoring unit, with the goal of minimizing the peak-to-valley difference of the power grid and maximizing energy utilization efficiency, a multi-objective optimization model combined with the particle swarm optimization algorithm (PSO) is used to calculate and make decisions on power allocation, and a dynamic power allocation strategy is quickly generated.

[0223] 3. Power allocation execution phase

[0224] Charging vehicle power distribution and power cabinet module call:

[0225] Vehicles connected to Superchargers: Considering grid load pressure, the system draws 14 power modules (420 kW) from the power cabinet and distributes them to the three vehicles connected to the Supercharger. Vehicles with a 12% SOC receive 180 kW (approximately six power modules), vehicles with an 18% SOC receive 120 kW (approximately four power modules), and vehicles with a 22% SOC receive 120 kW (approximately four power modules).

[0226] Vehicles connected to the charging terminal: For the five vehicles connected to the fast charging terminal, the system allocates 150kW of power from the remaining power modules in the power cabinet, an average of 30kW per vehicle (calling part of the power from one group of power modules), and a total of five groups of power modules are called.

[0227] Discharging vehicle power distribution and reverse utilization of power cabinet modules:

[0228] For the four vehicles willing to discharge, the system arranged for them to discharge energy to the grid based on grid voltage fluctuations and load demands. The power cabinet reversely utilized the energy fed back by these vehicles. Vehicles with a 95% SOC discharged at 120 kW (using the power cabinet's four power modules for reverse conversion), vehicles with 92% and 90% SOCs each discharged at 90 kW (using the power cabinet's six power modules for reverse conversion), and vehicles with an 88% SOC discharged at 60 kW (using the power cabinet's two power modules for reverse conversion). This totaled 360 kW of energy fed back to the grid, reducing the grid load threshold to below 1500 kW.

[0229] 4. Real-time adjustment stage

[0230] During power allocation, the system continuously monitors real-time grid load changes. If the grid load remains above a threshold, the system further reduces the power supplied to the charging vehicles, reducing the number of calls to the charging vehicle power modules from the power cabinets. If the grid load decreases significantly, approaching the threshold, the system appropriately increases the power supplied to the charging vehicles while reducing the discharge power of the discharging vehicles, achieving precise grid load regulation.

[0231] For ease of explanation, the dynamic power allocation process is described using the grid valley filling scenario as an example. The details are as follows:

[0232] Between 1:00 AM and 3:00 AM, the grid's power consumption is at its lowest. The current grid load is 500 kW, below the set load threshold of 800 kW. The grid monitoring system immediately triggers the "valley-filling" strategy. At this time, there are eight electric vehicles at the charging station, two of which are connected to 360 kW Superchargers and six to 120 kW Fast Chargers. All of them require charging.

[0233] Dynamic power allocation process:

[0234] 1. Information collection stage

[0235] Grid information: The system obtains real-time grid load data of 500kW, which is lower than the set grid load threshold of 800kW. The load needs to be increased.

[0236] Vehicle information: After communicating with 8 electric vehicles, we learned that the SOC of the two vehicles connected to the super charging terminal were 15% and 20% respectively, and they had a need for fast charging; the SOC of the six vehicles connected to the fast charging terminal was between 25% and 45%, and the degree of charging demand was different.

[0237] 2. Strategy formulation stage

[0238] The intelligent control module receives charging demand information such as the battery capacity, expected charging power, and estimated charging time of each electric vehicle from the vehicle-to-pile network communication unit. At the same time, combined with the real-time load assessment results fed back by the power grid monitoring unit, with the goal of minimizing the peak-to-valley difference of the power grid and maximizing energy utilization efficiency, a multi-objective optimization model combined with the particle swarm optimization algorithm (PSO) is used to calculate and make decisions on power allocation, and a dynamic power allocation strategy is quickly generated.

[0239] 3. Power allocation execution phase

[0240] Charging vehicle power distribution and power cabinet module call:

[0241] Vehicles connected to a Supercharger: The system deploys 19 power modules (570 kW) from the power cabinet to two vehicles connected to the Supercharger. The vehicle with a 15% SOC receives 300 kW (using 10 power modules), while the vehicle with a 20% SOC receives 270 kW (using 9 power modules).

[0242] Vehicles connected to the charging terminal: For the six vehicles connected to the fast charging terminal, the system allocates 360kW of power, an average of 60kW per vehicle (calling two groups of power modules, a total of 12 groups of power modules).

[0243] 4. Real-time adjustment stage

[0244] The system continuously monitors user demand, grid load, and vehicle charging status. If grid load approaches a threshold, the system appropriately reduces the power supplied to the charging vehicle. If load growth slows, the system increases charging power, ensuring that vehicle charging needs are met while keeping grid load within a reasonable range.

[0245] Through the dynamic power allocation process in the above different scenarios, the V2G super charger dynamic power allocation system based on grid load can effectively balance the grid load according to the grid load conditions, improve energy utilization efficiency, and meet the charging and discharging needs of electric vehicles.

[0246] The beneficial effects of the embodiments of the present invention are as follows:

[0247] 1. Optimization effect on power grid operation

[0248] (1) Alleviate the pressure on power grid supply: With the vigorous development of the electric vehicle market, the demand for charging has increased dramatically, bringing huge pressure on the power grid. The power grid load monitoring module of the present invention can capture the operating status of the power grid in real time and accurately. The intelligent power distribution module dynamically allocates power to each V2G super charging pile based on the monitoring information and the preset optimization strategy. During peak power consumption periods, the system can quickly detect the sharp increase in load and adjust the power output of the V2G super charging pile in time to avoid the impact of the concentrated use of a large number of super chargers on the power grid, effectively alleviate the power supply pressure of the power grid, and ensure the stable operation of the power grid.

[0249] (2) Balancing the peak-valley difference of the power grid: The power grid has obvious peak-valley load differences in different time periods. If the charging behavior of electric vehicles is concentrated in peak hours when electric vehicles are widely popularized, the peak-valley difference will be further widened. The present invention uses V2G technology to achieve two-way energy flow between electric vehicles and the power grid. During the off-peak period of electricity consumption, the system can control the charging of electric vehicles and store excess electricity; during the peak period of electricity consumption, electric vehicles act as distributed energy storage units and feed the electricity in the battery back to the power grid, effectively balancing the peak-valley difference of the power grid and reducing the operating cost and management difficulty of the power grid.

[0250] (3) Improve grid stability and power quality: The high-power charging characteristics of super chargers may cause voltage fluctuations, frequency offsets and other problems, threatening grid stability and power quality. The V2G bidirectional charging module of the present invention consists of a bidirectional power converter and a charge and discharge control unit. It accurately regulates the charging current and voltage during charging and accurately controls the energy flow and power during discharge, with a power regulation accuracy of ±1%. This helps reduce the impact of the charging process on the grid, maintains the stability of the grid voltage and frequency, improves power quality, and reduces the risk of power outages in local areas caused by the rapid charging of a large number of electric vehicles at the same time.

[0251] (4) Enhance the grid's ability to absorb renewable energy: As the proportion of renewable energy such as solar and wind energy in the power system continues to increase, their intermittent and fluctuating characteristics pose challenges to the stable operation of the power grid. The V2G technology of the present invention can be used as a flexible energy storage method. When there is excess renewable energy generation, the system controls electric vehicles to store excess electricity; when there is insufficient power generation, the battery power is released back to the grid, effectively improving the renewable energy absorption capacity and strongly promoting the sustainable development of energy.

[0252] 2. Improve the efficiency and rationality of charging management

[0253] (1) Realize dynamic power distribution: Most existing charging methods are based on one-way charging of electric vehicles based on their power demand, which makes it difficult to dynamically adjust according to the real-time load of the power grid. The intelligent power distribution module of the present invention serves as the decision-making core of the system. It has built-in advanced intelligent algorithms and optimization models to comprehensively evaluate the power grid status and the charging and discharging needs of electric vehicles. Based on preset objective functions such as minimizing the peak-to-valley difference of the power grid and maximizing energy utilization efficiency, it uses optimization algorithms to calculate the optimal power distribution plan. The error in charging and discharging power distribution can be controlled within ±1%. This enables the system to dynamically adjust the power of the charging pile according to the real-time load of the power grid, effectively alleviating the pressure on the power grid and improving energy utilization efficiency.

[0254] (2) Optimizing the utilization of charging resources: Traditional charging facility management lacks a scientific and reasonable dynamic power allocation strategy. Each charging device usually operates independently, making it difficult to achieve coordinated control, resulting in low efficiency in charging resource utilization. The vehicle-pile network communication module of the present invention is responsible for building a real-time communication channel between the system and the power grid and electric vehicles to ensure accurate information transmission and interaction. Through this module, the system can grasp the status information of electric vehicles and power grids in real time, realize the coordinated work of various modules, reasonably allocate appropriate charging power, improve the utilization efficiency of charging resources, and better respond to the charging needs of large-scale electric vehicles.

[0255] 3. Improve user experience and engagement

[0256] (1) Provide a friendly and convenient operation interface: The user interaction module uses a graphical user interface to create a friendly and convenient operation interface for users through touch screens, mobile phone applications, etc. Users can view real-time grid load information, electric vehicle charging status, charging costs, etc. on the interface. They can also set charging parameters, make charging reservations, and query historical charging records, which greatly improves user experience and participation.

[0257] (2) Support for personalized charging services: Users can make reservations for charging when electricity prices are low to reduce costs based on their own needs and actual conditions. They can also monitor the charging status of their electric vehicles in real time to plan their trips. This personalized charging service allows users to arrange charging activities more flexibly and meet the diverse needs of different users.

[0258] Through the above description of the embodiments, those skilled in the art will clearly understand that the present invention can be implemented using software plus necessary general-purpose hardware. The program can be stored in a readable storage medium, such as a random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), electrically erasable programmable memory (EEPM), registers, etc. The storage medium is located in a memory, and a processor reads the information in the memory and, in conjunction with its hardware, executes the methods described in the various embodiments of the present invention.

[0259] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0260] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0261] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0262] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0263] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0264] In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that ordinary technicians in this field can implement them. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0265] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0266] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A super charger dynamic power distribution system, characterized in that: The supercharger dynamic power distribution system includes a supercharger V2G power cabinet, a super charging terminal and a fast charging terminal; The V2G power cabinet includes multiple V2G power modules, grid load monitoring module, intelligent control module, intelligent power distribution module, and vehicle-to-grid communication module; The grid load monitoring module is used to obtain grid load information and send it to the vehicle-pile network communication module; The vehicle-pile network communication module is used to receive grid load information, vehicle information, and user charging and discharging demand information, and forward the grid load information, vehicle information, and user charging and discharging demand information to the intelligent control module; The intelligent control module is used to generate multiple particles based on grid load information, vehicle information, and user charging and discharging demand information. Each particle represents a power allocation strategy. Based on the particle swarm optimization algorithm and the target optimization model, the power allocation strategy represented by the particle that meets multiple constraints and has the highest fitness value is selected as the dynamic power allocation strategy, and the dynamic power allocation strategy is sent to the intelligent power allocation module. The intelligent power allocation module is used to receive the dynamic power allocation strategy sent by the intelligent control module and allocate multiple V2G power modules to the super charging terminal and fast charging terminal according to the dynamic power allocation strategy.

2. The super charger dynamic power distribution system according to claim 1, characterized in that: The V2G power module is used to convert the AC power of the power grid into DC power suitable for electric vehicle batteries in charging mode, and to convert the DC power of electric vehicle batteries into AC power that meets the requirements of the power grid in discharging mode.

3. The super charger dynamic power distribution system according to claim 1, characterized in that: The grid load monitoring module is specifically used to obtain real-time collection of the current, voltage, and frequency of the grid, transmit the current, voltage, and frequency of the grid to the data processing unit, clean, filter, and extract features of the current, voltage, and frequency of the grid through the data processing unit, obtain grid load information, and send the grid load information to the intelligent control module.

4. The super charger dynamic power distribution system according to claim 1, characterized in that: The V2G power cabinet also includes an AC switch protection module, which is used to perform overcurrent protection, short circuit protection, and leakage protection.

5. The super charger dynamic power distribution system according to claim 1, characterized in that: The multi-objective optimization model consists of a first objective function and a second objective function. The first objective function is used to minimize the peak-to-valley difference of the power grid, and the second objective function is used to maximize energy utilization efficiency.

6. The super charger dynamic power distribution system according to claim 1, characterized in that: The multiple constraints include power constraints, battery status constraints of electric vehicles, load constraints of the power grid, charging demand constraints, and constraints on the number of power modules.

7. The super charger dynamic power distribution system according to claim 1, characterized in that: The network mode of the vehicle-pile network communication module includes one or a combination of 4G, 5G, Wi-Fi, Ethernet, and power line carrier communication.

8. The super charger dynamic power distribution system according to claim 1, characterized in that: The super charging terminal consists of a first user interaction module, a first charging gun and a first cooling module, and the fast charging terminal consists of a second user interaction module, a second charging gun and a second cooling module.

9. The super charger dynamic power distribution system according to claim 8, characterized in that: The first user interaction module is used to display a first graphical user interface, and complete the information interaction process between the user and the super charging terminal through the first graphical user interface; the second user interaction module is used to display a second graphical user interface, and complete the information interaction process between the user and the fast charging terminal through the second graphical user interface.

10. A distribution method based on the super charger dynamic power distribution system according to any one of claims 1 to 9, characterized in that: include: The grid load monitoring module obtains grid load information and sends it to the vehicle-pile network communication module; The vehicle-pile network communication module receives grid load information, vehicle information, and user charging and discharging demand information, and forwards the grid load information, vehicle information, and user charging and discharging demand information to the intelligent control module; The intelligent control module generates multiple particles based on grid load information, vehicle information, and user charging and discharging demand information. Each particle represents a power allocation strategy. Based on the particle swarm optimization algorithm and the target optimization model, the power allocation strategy represented by the particle that meets multiple constraints and has the highest fitness value is selected as the dynamic power allocation strategy and sent to the intelligent power allocation module. The intelligent power allocation module receives the dynamic power allocation strategy sent by the intelligent control module, allocates multiple V2G power modules to the super charging terminal and fast charging terminal according to the dynamic power allocation strategy, and uploads the dynamic power allocation strategy to the server.

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