Intelligent charging and discharging system based on V2G

Abstract

The present disclosure provides a V2G-based intelligent charging and discharging system, which belongs to the field of intelligent charging and discharging technology. The V2G-based intelligent charging and discharging system includes: a charging pile module, a monitoring module, a control module and a communication module; the charging pile module is used to connect to the power grid and the vehicle respectively, and is used to support two-way charging between the power grid and the vehicle; the monitoring module is connected to the charging pile module, and is used to collect vehicle data, power grid data and charging and discharging data in the first area; the control module is connected to the monitoring module and the charging pile module respectively, and is used to control the charging and discharging mode of the charging pile module in the first area based on vehicle data, power grid data and particle swarm algorithm; the communication module is connected to the monitoring module and the control module respectively, and is used to connect to the control center, and is used to send the charging and discharging data and the charging and discharging mode to the control center. The present disclosure can optimize power dispatching.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent charging and discharging technology, and in particular to a V2G-based intelligent charging and discharging system. Background Art

[0002] With the continuous growth of electric vehicles and the increasingly urgent need for renewable energy integration and load balancing in the power grid, vehicle-to-grid (V2G) technology has emerged. When faced with a large number of electric vehicles randomly connected for charging, the traditional power grid management model is prone to cause peak loads in local periods, increasing the peak-shaving pressure on the power grid and affecting the stability and reliability of power supply. Due to the lack of precise charging and discharging control strategies, it is difficult to meet the balance needs of the power grid and users, which leads to problems in power dispatching.

[0003] From the above, it can be concluded that there is an urgent need for an intelligent and reliable V2G-based intelligent charging and discharging system. Summary of the invention

[0004] The disclosed embodiment provides a V2G-based intelligent charging and discharging system to solve the problem of poor power dispatching effect.

[0005] The embodiment of the present disclosure provides a V2G-based intelligent charging and discharging system, including: a charging pile module, a monitoring module, a control module and a communication module; The charging pile module is used to connect to the power grid and the vehicle respectively, and is used to support bidirectional charging between the power grid and the vehicle; A monitoring module, connected to the charging pile module, for collecting vehicle data, power grid data, and charging and discharging data in the first area; A control module, connected to the monitoring module and the charging pile module respectively, for controlling the charging and discharging mode of the charging pile module in the first area based on vehicle data, power grid data and a particle swarm algorithm; The communication module is connected to the monitoring module and the control module respectively, and is used to connect to the control center and send the charging and discharging data and the charging and discharging mode to the control center.

[0006] In an exemplary embodiment of the present disclosure, the control module is further configured to: Determine the fitness function of the particle swarm algorithm based on multiple influencing factors; Determine an initialization range of a particle swarm algorithm in the second area based on the power characteristics of the second area; determining a solution to the fitness function of the first region based on the solution to the fitness function of each second region; Controlling the charging and discharging mode of the charging pile module in the first area based on the solution of the fitness function in the first area; The second area is an area within the first area.

[0007] In an exemplary embodiment of the present disclosure, the control module is further configured to: Determine the number of particles of the particle swarm algorithm based on vehicle data; Determine the inertia weight of the particle swarm algorithm in the second area based on the vehicle data and the power grid data in the second area; Determine a learning factor of a particle swarm algorithm in the second area based on vehicle data and power grid data in the second area; determining a speed limit in a particle swarm algorithm for a second area based on vehicle data in the area; Iterative calculation is performed based on the number of particles, inertia weight, learning factor and speed limit until a preset number of iterations is reached and / or the difference between multiple consecutive fitness values ​​is less than a first fitness threshold.

[0008] In an exemplary embodiment of the present disclosure, the fitness function is: ,in, represents fitness, A quantitative value representing the stability of the power grid, represents the quantified value of the user fee, A quantitative value representing the charging demand, represents the weight of grid stability, represents the weight of user fees, Represents the weight of charging demand.

[0009] In an exemplary embodiment of the present disclosure, the control module is further configured to: determining a weight of grid stability in the fitness function based on grid characteristics of the second region; Determining a weight of the user fee in the fitness function based on the electricity fee standard of the second area; The weight of the charging demand in the fitness function is determined based on the regional characteristics of the second area.

[0010] In an exemplary embodiment of the present disclosure, the control module is further configured to: Determine the charging and discharging strategy based on multiple influencing factors; The charging and discharging strategy in the first region is used as the position vector in the particle swarm algorithm; Based on the position vector, a quantitative value of grid stability, a quantitative value of user costs and a quantitative value of charging demand are obtained.

[0011] In an exemplary embodiment of the present disclosure, the monitoring module includes: Multiple vehicle monitoring units, multiple power grid monitoring units, multiple charge monitoring units, multiple temperature monitoring units and a data central control unit; Each vehicle monitoring unit is used to monitor vehicle data in the second area; Each power grid monitoring unit is used to monitor power grid data of the second area; Each charge monitoring unit is used to monitor the charge and discharge data of the second area; Each temperature monitoring unit is used to monitor the charging and discharging temperature of the second area; Multiple vehicle monitoring units, multiple power grid monitoring units, multiple charge monitoring units and multiple temperature monitoring units are all connected to the data central control unit; Both the communication module and the charging pile module are connected to the data central control unit.

[0012] In an exemplary embodiment of the present disclosure, the control module is further configured to: In response to the actual power level of the vehicle monitored by the vehicle monitoring unit being less than a first power level threshold, not performing a discharge operation on the vehicle; In response to the temperature of the vehicle monitored by the temperature monitoring unit being greater than a first temperature threshold, the charging operation and the discharging operation are not performed on the vehicle.

[0013] In an exemplary embodiment of the present disclosure, the V2G-based intelligent charging and discharging system further includes: a storage module; The storage module is connected to the monitoring module; The storage module is used to store the charge and discharge data monitored by the monitoring module.

[0014] In an exemplary embodiment of the present disclosure, the V2G-based intelligent charging and discharging system further includes: an alarm module; The alarm module is connected with the monitoring module; The alarm module is used to generate an alarm in response to the temperature monitored by the monitoring module being greater than a second temperature threshold.

[0015] The beneficial effects of the V2G-based intelligent charging and discharging system provided by the embodiments of the present disclosure are: The present disclosure uses the vehicle data, power grid data and charging and discharging data collected by the monitoring module, and the control module can formulate a more optimized charging strategy based on the data monitored by the monitoring module. The control module adopts a particle swarm algorithm, which can dynamically adjust the charging and discharging mode according to real-time data, which helps to balance the supply and demand of the power grid, reduce power grid fluctuations, and thus improve the stability and reliability of the power grid. The communication module is responsible for sending the charging and discharging data and charging and discharging mode to the control center in real time, ensuring that the control center can understand the operating status of the system in a timely manner and perform necessary remote monitoring and scheduling. At the same time, the communication module also supports two-way communication with the control center, so that the control center can flexibly adjust and optimize the system according to actual needs, optimize power scheduling, and reduce the occurrence of power problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] Figure 1 is a structural schematic diagram of a V2G-based intelligent charging and discharging system provided in an embodiment of the present disclosure; Figure 2 It is a structural diagram of a second V2G-based intelligent charging and discharging system provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0018] In order to enable people in the technical field to better understand the present solution, the technical solution in the embodiment of the present solution will be clearly described below in conjunction with the drawings in the embodiment of the present solution. Obviously, the described embodiment is an embodiment of a part of the present solution, not all of the embodiments. Based on the embodiments in the present solution, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present solution.

[0019] The term "including" and any other variations in the specification and claims of this solution and the above drawings mean "including but not limited to", and is intended to cover non-exclusive inclusions and is not limited to the examples listed in the text. In addition, the terms "first" and "second" are used to distinguish different objects, not to describe a specific order.

[0020] The following is a detailed description of the implementation of the present disclosure in conjunction with the specific drawings: Figure 1 A schematic diagram of a V2G-based intelligent charging and discharging system provided in an embodiment of the present disclosure. Figure 1 , the V2G-based intelligent charging and discharging system includes: a charging pile module 10, a monitoring module 11, a control module 12 and a communication module 13; The charging pile module 10 is used to connect to the power grid and the vehicle respectively, and is used to support bidirectional charging between the power grid and the vehicle; The monitoring module 11 is connected to the charging pile module 10 and is used to collect vehicle data, power grid data and charging and discharging data in the first area; The control module 12 is connected to the monitoring module 11 and the charging pile module 10 respectively, and is used to control the charging and discharging mode of the charging pile module 10 in the first area based on the vehicle data, the power grid data and the particle swarm algorithm; The communication module 13 is connected to the monitoring module 11 and the control module 12 respectively, and is used to connect to the control center and send the charging and discharging data and the charging and discharging mode to the control center.

[0021] In this embodiment, the charging pile module 10 is a physical interface that realizes the bidirectional flow of electric energy, so that electric energy can be transmitted bidirectionally between the vehicle and the power grid. On the one hand, it can transmit the electric energy of the power grid to the vehicle like a conventional charging pile to complete the charging operation of the vehicle; on the other hand, under appropriate conditions, it can also receive the electric energy stored in the vehicle battery and feed it back to the power grid to realize the discharge of the vehicle to the power grid.

[0022] For example, a parking lot in a large commercial center is equipped with a charging pile module 10 that supports V2G function. During the low electricity price period at night, the charging pile module 10 will transmit the power of the power grid to the electric vehicles parked here according to the normal charging process to charge the battery of the vehicle. During the peak period of power consumption during the day, when the power grid load is high and some electric vehicles are idle, these vehicles discharge to the power grid through the charging pile module 10, helping to alleviate the power supply pressure of the power grid and realize the two-way flexible allocation of power.

[0023] The monitoring module 11 can monitor vehicle data, power grid data and charging and discharging data. Vehicle data may include: the remaining power of the vehicle and the health status of the battery, etc., power grid data may include: power grid load, power grid voltage and power grid stability, etc., and charging and discharging data may include: charging start time, charging end time, discharging start time, discharging end time and charging and discharging power, etc.

[0024] The control module 12 can receive the data collected from the monitoring module 11, and control the charging and discharging mode of the charging pile module 10 in the first area in combination with the particle swarm algorithm. The control module 12 can analyze vehicle data, power grid data, etc., and use the particle swarm algorithm to search for the optimal solution among many possible charging and discharging strategy combinations, determine when each vehicle should be charged, when it should be discharged, and the corresponding charging and discharging power, etc., so as to achieve effective management of the charging and discharging behavior of electric vehicles in the entire area, optimize the power grid load and meet the charging needs of users.

[0025] For example, the first area is an industrial park, in which 50 electric vehicles are connected to the V2G system. The control module 12 receives information from the monitoring module 11. The current power load of the power grid is high, and it is in the peak load period in the morning. The remaining power of some vehicles is still sufficient. The control module 12 uses the particle swarm algorithm to take the charging start time, charging end time, discharge start time, discharge end time, charging and discharging power of each vehicle as the position vector of the particle, and constructs a fitness function with the goal of minimizing the fluctuation of the power grid load, reasonable user charging cost, and small battery life loss. After multiple iterative calculations, a better charging and discharging strategy is determined. For example, 20 of the vehicles are arranged to discharge to the power grid at a certain power in the next hour, and the other 30 vehicles are suspended from charging, waiting for the low load period of the power grid to continue charging, so as to smooth the load of the power grid and realize the overall intelligent charging and discharging control.

[0026] The communication module 13 ensures effective data interaction and communication between different modules in the system and with the external control center. On the one hand, the communication module 13 can transmit the charging and discharging data collected by the monitoring module 11 and the charging and discharging mode determined by the control module 12 to the control center, so that the control center can grasp the operating status of the system in real time, which is convenient for regulation and management; on the other hand, the communication module 13 can also receive instructions or other relevant information from the control center to achieve smooth communication of uplink and downlink information.

[0027] The first area is an area supporting V2G charging and discharging.

[0028] From the above, it can be concluded that the vehicle data, power grid data and charging and discharging data collected by the monitoring module 11 are used in the present disclosure, and the control module 12 can formulate a more optimized charging strategy based on the data monitored by the monitoring module 11. The control module 12 adopts a particle swarm algorithm, which can dynamically adjust the charging and discharging mode according to real-time data, which helps to balance the supply and demand of the power grid, reduce power grid fluctuations, and thus improve the stability and reliability of the power grid. The communication module 13 is responsible for sending the charging and discharging data and charging and discharging mode to the control center in real time, ensuring that the control center can understand the operating status of the system in a timely manner and perform necessary remote monitoring and scheduling. At the same time, the communication module 13 also supports two-way communication with the control center, so that the control center can flexibly adjust and optimize the system according to actual needs, optimize power scheduling, and reduce the occurrence of power problems.

[0029] Figure 1 A schematic diagram of a V2G-based intelligent charging and discharging system provided in an embodiment of the present disclosure. Figure 1 In one embodiment of the present disclosure, the control module 12 is further configured to: Determine the fitness function of the particle swarm algorithm based on multiple influencing factors; Determine an initialization range of a particle swarm algorithm in the second area based on the power characteristics of the second area; determining a solution to the fitness function of the first region based on the solution to the fitness function of each second region; Controlling the charging and discharging mode of the charging pile module 10 in the first area based on the solution of the fitness function in the first area; The second area is an area within the first area.

[0030] In one embodiment of the present disclosure, the fitness function is: ,in, represents fitness, A quantitative value representing the stability of the power grid, represents the quantified value of the user fee, A quantitative value representing the charging demand, represents the weight of grid stability, represents the weight of user fees, Represents the weight of charging demand.

[0031] In one embodiment of the present disclosure, the control module 12 is further configured to: Determine the charging and discharging strategy based on multiple influencing factors; The charging and discharging strategy in the first region is used as the position vector in the particle swarm algorithm; Based on the position vector, a quantitative value of grid stability, a quantitative value of user costs and a quantitative value of charging demand are obtained.

[0032] In this embodiment, the multiple influencing factors may be charging start time, charging end time, discharging start time, discharging end time, charging power and discharging power. The multiple influencing factors may be used as charging and discharging strategies, and these six values ​​may be used as position vectors of the particle swarm algorithm. Based on the charging start time, charging end time, charging power and discharging start time, discharging end time, and discharging power, combined with the initial load curve of the power grid, the net load change of the power grid caused by the charging and discharging behavior of the vehicle at each time point may be calculated to obtain a quantitative value of the stability of the power grid. The initial load curve of the power grid may be obtained through historical data or through real-time monitoring by the monitoring module 11.

[0033] For example, the grid load is , the charging power of a certain car is , charging starts at , the charging end time is , the discharge power is The discharge start time is The discharge end time is , then the contribution of the vehicle to the net load of the grid is The net load contributions of all vehicles are accumulated to obtain the total net load ,in is the total number of vehicles.

[0034] Thus, the quantitative value of the grid stability can be calculated. It should be noted that the above-mentioned charging start time, charging end time, discharging start time, discharging end time, charging power and discharging power are all obtained by continuous iterative calculation of the particle swarm algorithm, and are not pre-set. That is, in the continuous iterative process, the optimal solution for grid stability is continuously sought.

[0035] In this embodiment, the charging power of each vehicle can be calculated based on the charging power and the charging period, and the charging price can be calculated based on the electricity price information of the time period. Similarly, the discharge benefit can be calculated based on the discharge power, the discharge period and the electricity price information of the time period, and the quantitative value of the user's cost can be obtained comprehensively.

[0036] In this embodiment, the quantitative value of the charging demand can be determined based on the charging time, charging power, discharging time and discharging power of each car. For example, the vehicle battery power is defined as greater than or equal to 85% (target charging amount) to meet the charging demand, or the target charging amount can be obtained based on the user input when the vehicle is parked, and the charging amount is used as the standard for meeting the charging demand. Whether the charging demand can be met can be calculated based on the charging time, charging power and the power data monitored by the monitoring module 11. The quantitative value of the charging demand can be the ratio of the actual power to the target charging amount. It should be noted that the actual power mentioned above means the final result of charging and discharging assuming this charging and discharging time and charging and discharging power, that is, it is obtained through calculation, not the current power of the vehicle.

[0037] The above calculation method can be used to determine the fitness function , and The corresponding weight can be determined based on the actual situation.

[0038] In the particle swarm algorithm, each particle represents a possible charging and discharging strategy, and the particle position vector includes parameters such as charging start time, charging end time, discharging start time, discharging end time, charging and discharging power, etc. The initialization range is to set a reasonable initial value range for these parameters.

[0039] The initialization range of the particle swarm algorithm in the second area can be determined based on the power characteristics of the second area. The second area refers to the area in the first area. The power characteristics can be the power consumption characteristics of the area. For example, for an underground garage in a shopping mall that is open at a fixed time, the parking and charging and discharging time of its vehicles is about 9:00-21:00. Then the initialization range of the particle swarm algorithm in this area can be 9:00-21:00, that is, for the particles in this area, their movement range will not exceed this area. The power characteristics of the second area can be obtained based on historical power consumption data.

[0040] In this embodiment, the fitness function of each second region will be different according to the characteristics of its own region. When each particle moves to a new position, a solution will be calculated according to its own fitness function. After each movement of each particle, a solution will be calculated at the new position and compared with the previous solution. If the solution at the new position is better than the solutions at all previous positions, the individual extreme value will be updated. If this solution is greater than the solutions of all particles, the solution will be used as the group extreme value of the particle group.

[0041] The group extreme value refers to the fitness value corresponding to the optimal position found by the entire particle group during the search process, which represents the optimal solution currently found by the entire group. The charging and discharging strategy of the first area can be determined based on this optimal solution, that is, when the optimal solution is obtained, the vehicle corresponding to the current position controller of each particle is charged and discharged.

[0042] The above-mentioned position refers to the position of the particle in the six-dimensional vector space composed of charging start time, charging end time, discharging start time, discharging end time, charging power and discharging power. It is a virtual position. Each position in this six-dimensional vector space corresponds to a combination of charging start time, charging end time, discharging start time, discharging end time, charging power and discharging power.

[0043] It can be concluded from the above that the control module 12 can intelligently determine the charging and discharging strategy according to multiple influencing factors and regional power characteristics by integrating the particle swarm algorithm, ensuring that the present disclosure can find the optimal charging and discharging solution under different circumstances, thereby improving the stability of the power grid and the economy of users. The present disclosure realizes regionalized management and optimization by dividing the second area (a sub-area within the first area) and determining the initialization range of the particle swarm algorithm based on the power characteristics of each area, which helps to better match the local power grid demand and vehicle charging and discharging behavior, reduce power grid fluctuations, and improve energy efficiency. The present disclosure comprehensively considers multiple dimensions such as power grid stability, user costs, and charging needs through the fitness function, ensuring the comprehensiveness and accuracy of the decision-making of the present disclosure and optimizing power dispatch.

[0044] Figure 1A schematic diagram of a V2G-based intelligent charging and discharging system provided in an embodiment of the present disclosure. Figure 1 In one embodiment of the present disclosure, the control module 12 is further configured to: Determine the number of particles of the particle swarm algorithm based on vehicle data; Determine the inertia weight of the particle swarm algorithm in the second area based on the vehicle data and the power grid data in the second area; Determine a learning factor of a particle swarm algorithm in the second area based on vehicle data and power grid data in the second area; determining a speed limit in a particle swarm algorithm for a second area based on vehicle data in the area; Iterative calculation is performed based on the number of particles, inertia weight, learning factor and speed limit until a preset number of iterations is reached and / or the difference between multiple consecutive fitness values ​​is less than a first fitness threshold.

[0045] In this embodiment, the vehicle data includes: the number of vehicles and vehicle battery information; the power grid data includes: power grid stability.

[0046] The speed update formula in the particle swarm algorithm is: ,in, Indicates The particle velocity at the iteration, represents the number of iterations, is the inertia weight, is the learning factor, is The random numbers uniformly distributed within the interval increase the randomness of particle search. It is a particle In the The optimal position of itself at the iteration Dimensional component, It is a particle In the The position of the Dimensional component, It is in The global optimal position of the entire particle swarm at the iteration Dimensional component.

[0047] In the particle swarm algorithm, particles represent different possible solutions, which in the present disclosure are different combinations of charging and discharging strategies. The number of particles refers to the total number of these representative individuals participating in the process of searching for the optimal solution. The number of vehicles can be used as the number of particles, that is, each particle corresponds to a vehicle.

[0048] Inertia weight is used to balance the global and local search capabilities of particles. A larger inertia weight makes particles more inclined to maintain their original direction and speed in the search space, which helps particles to conduct global search and explore a wider area to find possible optimal solutions, but will lead to slower convergence; a smaller inertia weight makes particles more susceptible to their own historical optimal position (i.e., individual extreme value) and the group's historical optimal position (i.e., group extreme value), and focuses more on local search in the better area that has been found, speeding up convergence, but there is also a risk of falling into the local optimal solution too early. Therefore, the inertia weight can be determined based on the number of vehicles and the stability of the current power grid.

[0049] In this embodiment, the control module 12 is further configured to: Determine the initial inertia weight; In response to the vehicle data satisfying a first quantity condition and / or the grid data satisfying a first stability condition, the initial inertia weight is reduced by a first inertia step length to obtain an inertia weight.

[0050] The vehicle data may be the number of vehicles, the grid data may be the stability of the current grid, the first quantity condition may be greater than the first quantity, and the first stability condition may be less than the first stability threshold. The initial inertia weight, the first quantity, the first stability threshold, and the first inertia step may be determined empirically.

[0051] The present disclosure takes into account that when the number of vehicles is large or the stability of the current power grid is low, it indicates that rapid power dispatch is needed. At this time, the initial inertia weight can be appropriately reduced so that the particles can focus more on local search in the better area that has been found, accelerate the convergence speed, improve the speed and stability of power dispatch, and ensure the safety of electricity use.

[0052] The particle swarm algorithm has two learning factors, namely individual learning factor and group learning factor The individual learning factor reflects the degree of dependence of the particle on its own past experience (i.e., the individual extreme value), which determines the tendency of the particle to approach its own historical optimal position; the social learning factor reflects the degree to which the particle is influenced by the optimal experience of other particles in the group (i.e., the group extreme value), that is, the tendency of the particle to learn and move closer to the optimal position of the group.

[0053] In this embodiment, the learning factors include: individual learning factors and group learning factors; The control module 12 is further specifically used for: Determine the initial individual learning factor and the initial group learning factor; In response to the vehicle data satisfying a second quantity condition, increasing the initial individual learning factor by a first individual step length to obtain an individual learning factor; In response to the power grid data satisfying a second stability condition, the initial group learning factor is increased with a first group step size to obtain a group learning factor.

[0054] The vehicle data may be the number of vehicles, the power grid data may be the stability of the current power grid, the second number condition may be less than the second number, and the second stability condition may be less than the second stability threshold. The initial individual learning factor, the initial group learning factor, the second number, the second stability threshold, the first individual step length, and the first group step length may be determined empirically.

[0055] The present disclosure takes into account that when the number of vehicles is small, because the total vehicle charging demand is small, the individual learning factor can be appropriately increased to allow more vehicles to meet the charging demand. When the grid stability is low, the group learning factor can be appropriately increased to make the particle swarm algorithm more inclined to maintain the stability of the grid and balance power dispatch.

[0056] The speed limit of the particle swarm algorithm is used to constrain the speed range of particles moving in the search space, including the maximum and minimum speeds of each dimension. Setting the speed limit can prevent particles from skipping the potential optimal solution area due to excessive speed during the search process, or from being trapped in the local optimal solution due to limited search range due to too low speed. For the present disclosure, the number of dimensions is 6, namely the above-mentioned charging start time, charging end time, discharging start time, discharging end time, charging power and discharging power.

[0057] For example, the monitoring module 11 monitors the speed limit range of the charging power in the vehicle battery information. If the vehicle battery capacity is small and the charging equipment power is low, then the adjustment amplitude of each iteration of the charging power cannot be too large, otherwise unreasonable charging and discharging that exceeds the battery and equipment's tolerance may occur.

[0058] Specifically, in a parking lot of an old community, the battery capacity of electric vehicles in the community is mostly 30-40 kWh, and the charging piles are mostly ordinary AC charging piles with a power of 3-7 kW. For the speed limit of the charging power parameter in the particle swarm algorithm, considering the safe charging rate of the battery (generally between 0.3C-1C, C is the battery capacity) and the power range of the charging pile, the control module 12 can set the charging power dimension to 1, indicating that the maximum step size of the charging power adjustment in each iteration is 1 kW, and set it to -1, so that when the particles search for charging and discharging strategies, the adjustment of the charging power will not exceed the reasonable range of the device and the battery.

[0059] In this embodiment, the speed limit includes: a maximum speed and a minimum speed; The control module 12 is further specifically used for: Determine the initial maximum speed value and the initial minimum speed value; In response to the vehicle information satisfying the first power condition, the initial maximum speed value is reduced based on the first power step to obtain the maximum speed; and the initial minimum speed value is increased based on the first power step to obtain the minimum speed.

[0060] The vehicle information may be vehicle battery information, and the first power condition may be that the power value is less than a first power threshold. The first power step, the first power threshold, the initial maximum speed value, and the initial minimum speed value may be determined empirically.

[0061] The present disclosure can limit the speed according to the charging and discharging power of the vehicle battery, so that the iterative charging and discharging strategy is in line with reality.

[0062] The particle swarm algorithm gradually searches for the optimal solution through multiple iterations. In each iteration, the particle speed and position are updated according to the set parameters such as the number of particles, inertia weight, learning factor and speed limit, and then the fitness value of each particle in the new position is calculated. Then, the fitness value of each particle is compared with its own historical optimal value and the group optimal value (group extreme value), and the individual extreme value and group extreme value are updated according to the comparison results, so as to continuously optimize the position of the particle and move it towards a better charging and discharging strategy.

[0063] The preset number of iterations can be determined based on historical data or multiple consecutive fitness thresholds are all less than the first fitness threshold, the multiple can be 6, the first fitness threshold can be determined based on actual conditions, when the difference between multiple consecutive fitness thresholds is less than the first fitness threshold, it can be judged that the particle position at this time is the optimal position, and the charging and discharging strategy corresponding to the particle position is the optimal strategy.

[0064] It can be concluded from the above that the control module 12 can intelligently determine the number of particles of the particle swarm algorithm according to the number of vehicles, ensuring the balance between search efficiency and accuracy. When the number of vehicles increases, the number of particles also increases, thereby expanding the search space. The present disclosure determines the inertia weight based on the vehicle data and power grid data in the second area, and the control module 12 can flexibly balance the ability of global search and local search. When the power grid stability is low or the number of vehicles is large, reducing the inertia weight helps to speed up the convergence speed, quickly respond to power dispatching needs, and ensure power safety. The control module 12 can dynamically adjust the individual learning factor and the group learning factor according to the number of vehicles and the stability of the power grid, which helps to meet the charging needs of more vehicles when the number of vehicles is small, and maintain the stable operation of the power grid when the power grid stability is low, so as to achieve balance and efficiency of power dispatching. The present disclosure monitors the vehicle battery information, and the control module 12 can set a reasonable speed limit to prevent particles from skipping the potential optimal solution area or falling into the local optimal solution during the search process, ensuring that the iterative charging and discharging strategy meets both actual needs and has high feasibility. During the iteration process of the present disclosure, the control module 12 continuously compares the fitness value of each particle with its own historical optimal value and the group optimal value, and updates the individual extreme value and the group extreme value accordingly, ensuring that the system can find the optimal charging and discharging strategy and achieve comprehensive optimization of grid stability, user costs and charging demand.

[0065] Figure 1 A schematic diagram of a V2G-based intelligent charging and discharging system provided in an embodiment of the present disclosure. Figure 1 In one embodiment of the present disclosure, the control module 12 is further configured to: determining a weight of grid stability in the fitness function based on grid characteristics of the second region; Determining a weight of the user fee in the fitness function based on the electricity fee standard of the second area; The weight of the charging demand in the fitness function is determined based on the regional characteristics of the second area.

[0066] In this embodiment, the control module 12 is further configured to: Determining the weight of initial grid stability, the weight of initial user charges, and the weight of initial charging demand; In response to the grid characteristic satisfying a third stability condition, increasing the weight of the initial grid stability based on a third stability step to obtain a grid stability weight; In response to the electricity fee standard satisfying the first electricity fee condition, increasing the weight of the initial user fee based on the first electricity fee step to obtain the weight of the user fee; In response to the regional characteristic satisfying the first regional condition, the weight of the initial charging requirement is increased based on the first regional step size to obtain the weight of the charging requirement.

[0067] The third stability condition may be that the grid stability is less than the third stability threshold, the first electricity fee condition may be that the electricity fee is charged according to the charging and discharging period, and the first area condition may be that the area is a high-demand area for vehicles. Whether it is a high-demand area for vehicles can be obtained from a preset area table. For example, for a centralized charging place for a taxi or online car-hailing service, it belongs to a high-demand area for vehicles. When iterating this area, full consideration should be given to whether the charging demand can be met.

[0068] In this embodiment, the weight of the initial grid stability, the weight of the initial user fee, the weight of the initial charging demand, the third stability step, the first electricity fee step and the first area step are all determined based on actual conditions and experience.

[0069] It can be concluded from the above that the control module 12 can determine the weight of the grid stability according to the grid characteristics of the second area. When the grid stability is low, the control module 12 will increase the weight of the grid stability to ensure that when optimizing the charging and discharging strategy, the stable operation of the grid can be given priority, which is helpful to maintain the smooth operation of the grid under the condition of tight power supply and demand, and avoid damage to the grid caused by power fluctuations. Considering the economic impact of the electricity fee standard on the charging and discharging strategy, the control module 12 can determine the weight of the user fee according to the electricity fee standard of the second area. When the electricity fee is charged according to the charging and discharging period, the control module 12 will increase the weight of the user fee, so as to consider more electricity cost issues. The control module 12 can also determine the weight of the charging demand according to the regional characteristics of the second area. For high-demand areas for vehicles, the control module 12 will increase the weight of the charging demand to ensure that the charging demand in these areas can be fully met, which helps to balance the load of the grid, while meeting the charging needs of users, improving user experience, and optimizing power dispatching.

[0070] Figure 2 This is a schematic diagram of the structure of the second V2G-based intelligent charging and discharging system provided in the embodiment of the present disclosure. Figure 2 In one embodiment of the present disclosure, the monitoring module 11 includes: A plurality of vehicle monitoring units 111, a plurality of power grid monitoring units 112, a plurality of charge monitoring units 113, a plurality of temperature monitoring units 114 and a data central control unit 115; Each vehicle monitoring unit 111 is used to monitor vehicle data in the second area; Each power grid monitoring unit 112 is used to monitor power grid data of the second area; Each charge amount monitoring unit 113 is used to monitor the charge and discharge data of the second area; Each temperature monitoring unit 114 is used to monitor the charging and discharging temperature of the second region; Multiple vehicle monitoring units 111, multiple power grid monitoring units 112, multiple charge monitoring units 113 and multiple temperature monitoring units 114 are all connected to the data central control unit 115; The communication module 13 and the charging pile module 10 are both connected to the data central control unit 115 .

[0071] In this embodiment, the monitoring module 11 can monitor vehicle data, power grid data, charging data and charging and discharging temperature, and integrate the monitored data through the data control unit 115, and send it to the external control center through the communication module 13. Relevant personnel can perform macro-control on the operation of the V2G system in the whole city based on these data. For example, if the power grid load in a certain area is too high, the communication module 13 can issue an instruction to adjust the charging and discharging strategy to the data control unit 115 of the parking lot in the area. After receiving the instruction, the data control unit 115 passes it to the corresponding charging pile module 10. If the instruction requires a certain vehicle to suspend charging, the charging pile module 10 will immediately stop the charging operation of the vehicle, and feed back the status information after execution to the data control unit 115 to ensure that the entire system operates in an orderly manner according to the control requirements.

[0072] It can be concluded from the above that the monitoring module 11 realizes comprehensive monitoring of the vehicle data, power grid data, charging and discharging data and charging and discharging temperature of the second area through multiple vehicle monitoring units 111, power grid monitoring units 112, charging amount monitoring units 113 and temperature monitoring units 114, so that the system can grasp the operating status of each key link in real time, and provide strong support for subsequent macro-control. The communication module 13 is not only responsible for data transmission, but also for issuing instructions. When the control center needs to adjust the charging and discharging strategy according to the monitoring data, it will issue instructions to the data central control unit 115 of the corresponding area through the communication module 13. After receiving the instruction, the data central control unit 115 will pass it to the corresponding control module 12 to realize the rapid execution of the instruction.

[0073] Figure 2 This is a schematic diagram of the structure of the second V2G-based intelligent charging and discharging system provided in the embodiment of the present disclosure. Figure 2 In one embodiment of the present disclosure, the control module 12 is further configured to: In response to the actual power level of the vehicle monitored by the vehicle monitoring unit 111 being less than the first power level threshold, not performing a discharge operation on the vehicle; In response to the temperature of the vehicle monitored by the temperature monitoring unit 114 being greater than the first temperature threshold, the charging operation and the discharging operation are not performed on the vehicle.

[0074] In one embodiment of the present disclosure, the V2G-based intelligent charging and discharging system further includes: a storage module 14; The storage module 14 is connected to the monitoring module 11; The storage module 14 is used to store the charge and discharge data monitored by the monitoring module 11 .

[0075] In one embodiment of the present disclosure, the V2G-based intelligent charging and discharging system further includes: an alarm module 15; The alarm module 15 is connected to the monitoring module 11; The alarm module 15 is configured to generate an alarm in response to the temperature monitored by the monitoring module 11 being greater than a second temperature threshold.

[0076] In this embodiment, the first power threshold and the first temperature threshold can be determined based on experiments. The present disclosure takes into account that when the power of the vehicle is less than a certain value, it is unreasonable to control it to discharge, and it also cannot meet the basic needs of users. Therefore, when the power of the vehicle is less than the preset first power threshold, the discharge operation is not performed on the vehicle. When the temperature of the vehicle exceeds the first temperature threshold during the charging and discharging process, it may be that the battery fails. At this time, continuing the charging or discharging operation will further aggravate the chemical reaction inside the battery, which may cause the battery to overheat, bulge, or even cause fire and other serious safety accidents. Therefore, once the temperature monitoring unit 114 detects that the vehicle temperature exceeds this safety limit value, the control module 12 will prohibit the vehicle from charging and discharging to ensure the safety of the vehicle and the entire system.

[0077] The storage module 14 can receive and save the various charging and discharging data monitored by the monitoring module 11, which can be specifically connected to the data central control unit 115, covering vehicle data, power grid data, charging and discharging data, temperature data and other aspects, which can be used for data analysis, helping to optimize charging and discharging strategies, evaluate system operation effects, troubleshoot faults, and prepare statistical reports.

[0078] The alarm module 15 is connected to the monitoring module 11, and can pay attention to various monitoring data transmitted by the monitoring module 11 in real time. Specifically, it can be connected to the data control unit 115, and an alarm prompt is issued when the temperature monitored by the monitoring module 11 is greater than the preset second temperature threshold. Compared with the first temperature threshold, the second temperature threshold is higher, indicating that the temperature is seriously abnormal. Once the temperature exceeds this value, it means that a relatively urgent safety hazard may have occurred, and it is necessary to promptly notify relevant personnel and take corresponding measures to avoid further expansion of the danger.

[0079] It can be concluded from the above that the control module 12 can respond to the actual power of the vehicle monitored by the vehicle monitoring unit 111. When the power is less than the preset first power threshold, the discharge operation of the vehicle is automatically prohibited, ensuring that the vehicle can retain enough power to meet the user's basic travel needs when the power is insufficient, and optimizing the customer experience. When the temperature monitoring unit 114 detects that the vehicle temperature exceeds the preset first temperature threshold, it will automatically prohibit the charging and discharging operations on the vehicle. This safety control mechanism effectively avoids the occurrence of serious safety accidents such as battery overheating, bulging and even fire, and ensures the safe and stable operation of the vehicle and the entire system. Through the data saved by the storage module 14, the present disclosure can realize the tracing and analysis of the historical charging and discharging process, which is helpful to find potential problems and improvement points, and provides data support for the further optimization and upgrading of the system. Through the real-time monitoring and alarm function of the alarm module 15, the present disclosure can timely discover and deal with potential faults and safety hazards, thereby improving the safety and stability of the entire system, and helping to ensure the safety of users and the normal operation of the system.

[0080] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A V2G-based intelligent charging and discharging system, characterized in that: include: Charging pile module, monitoring module, control module and communication module; The charging pile module is used to connect to the power grid and the vehicle respectively, and to support bidirectional charging between the power grid and the vehicle; The monitoring module is connected to the charging pile module and is used to collect vehicle data, power grid data and charging and discharging data in the first area; The control module is connected to the monitoring module and the charging pile module respectively, and is used to control the charging and discharging mode of the charging pile module in the first area based on the vehicle data, the power grid data and the particle swarm algorithm; The communication module is connected to the monitoring module and the control module respectively, and is used to connect to a control center, and is used to send the charge and discharge data and the charge and discharge mode to the control center.

2. The V2G-based intelligent charging and discharging system according to claim 1, characterized in that: The control module is further specifically used for: Determine the fitness function of the particle swarm algorithm based on multiple influencing factors; Determine an initialization range of a particle swarm algorithm in the second area based on the power characteristics of the second area; determining a solution to the fitness function of the first region based on the solution to the fitness function of each of the second regions; Controlling a charging and discharging mode of a charging pile module in the first area based on a solution of the fitness function of the first area; The second area is an area within the first area.

3. The V2G-based intelligent charging and discharging system according to claim 2, characterized in that: The control module is further specifically used for: Determining the number of particles of the particle swarm algorithm based on the vehicle data; Determine the inertia weight of the particle swarm algorithm in the second area based on the vehicle data and the power grid data in the second area; Determine a learning factor of a particle swarm algorithm in the second area based on vehicle data and power grid data in the second area; determining a speed limit in a particle swarm algorithm for the area based on vehicle data within the second area; Iterative calculation is performed based on the number of particles, the inertia weight, the learning factor and the speed limit until a preset number of iterations is reached and / or a difference between multiple consecutive fitness values ​​is less than a first fitness threshold.

4. The V2G-based intelligent charging and discharging system according to claim 2, characterized in that: The fitness function is: ,in, represents fitness, A quantitative value representing the stability of the power grid, represents the quantified value of the user fee, A quantitative value representing the charging demand, represents the weight of grid stability, represents the weight of user fees, Represents the weight of charging demand.

5. The V2G-based intelligent charging and discharging system according to claim 4, characterized in that: The control module is further specifically used for: determining a weight of grid stability in the fitness function based on the grid characteristics of the second area; Determining a weight of user fees in the fitness function based on the electricity fee standard of the second area; The weight of the charging demand in the fitness function is determined based on the regional characteristics of the second area.

6. The V2G-based intelligent charging and discharging system according to claim 4, characterized in that: The control module is further specifically used for: Determining a charge and discharge strategy based on the multiple influencing factors; Using the charging and discharging strategy in the first area as a position vector in the particle swarm algorithm; A quantified value of the grid stability, a quantified value of the user fee, and a quantified value of the charging demand are obtained based on the position vector.

7. The V2G-based intelligent charging and discharging system according to claim 2, characterized in that: The monitoring module comprises: Multiple vehicle monitoring units, multiple power grid monitoring units, multiple charge monitoring units, multiple temperature monitoring units and a data central control unit; Each of the vehicle monitoring units is used to monitor vehicle data in the second area; Each of the power grid monitoring units is used to monitor power grid data of the second area; Each of the charge amount monitoring units is used to monitor the charge and discharge data of the second area; Each of the temperature monitoring units is used to monitor the charging and discharging temperature of the second area; The multiple vehicle monitoring units, the multiple power grid monitoring units, the multiple charge amount monitoring units and the multiple temperature monitoring units are all connected to the data central control unit; The communication module and the charging pile module are both connected to the data central control unit.

8. The V2G-based intelligent charging and discharging system according to claim 7, characterized in that: The control module is further specifically used for: In response to the actual power level of the vehicle monitored by the vehicle monitoring unit being less than a first power level threshold, not performing a discharge operation on the vehicle; In response to the temperature of the vehicle monitored by the temperature monitoring unit being greater than a first temperature threshold, a charging operation and a discharging operation are not performed on the vehicle.

9. The V2G-based intelligent charging and discharging system according to claim 1, characterized in that: Also included: a storage module; The storage module is connected to the monitoring module; The storage module is used to store the charge and discharge data monitored by the monitoring module.

10. The V2G-based intelligent charging and discharging system according to claim 1, characterized in that: Also includes: Alarm module; The alarm module is connected to the monitoring module; The alarm module is used to generate an alarm in response to the temperature monitored by the monitoring module being greater than a second temperature threshold.

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