V2G bidirectional energy interaction moment determination method and system

Abstract

The invention provides a V2G bidirectional energy interaction moment determination method and system, and the key point of the technical scheme comprises the steps: constructing a layered distributed system architecture between an electric vehicle cluster and a microgrid, and configuring the component layout of the system architecture; a sensing detection device is deployed in a battery management system of the electric vehicle, and a battery health state evaluation model is established; acquiring multi-source data on one side of the micro-grid in real time and preprocessing the multi-source data, and establishing a real-time dynamic model to judge the current energy interaction moment; electricity market price information and real-time electricity demand data of a user are obtained in real time, and a real-time dynamic model is established; based on feedback information of the real-time dynamic model, efficient cooperation between the micro-grid and the electric vehicle is realized; efficient, safe and economical two-way energy interaction between the electric vehicle and the micro-grid can be achieved, battery safety and power grid stability are guaranteed, the energy utilization efficiency is effectively improved, carbon emission is reduced, and the overall economic benefit of the micro-grid is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems and electric vehicles, and in particular to a method and system for determining the moment of V2G bidirectional energy interaction. Background Art

[0002] With the vigorous development of the electric vehicle industry, the application prospects of V2G technology in power systems are becoming increasingly broad. However, the current V2G technology still faces many problems in its actual promotion. In terms of the two-way energy interaction framework, the existing system connection method and component layout are difficult to ensure the efficiency and safety of interaction when facing complex power grid environments and large-scale electric vehicle access; the energy flow control and optimization algorithm is not flexible enough when dealing with energy allocation problems in specific scenarios, and the constraints need to be further improved; battery management and safety strategies rely only on conventional data for analysis and cannot comprehensively and accurately assess the battery status; coordinated optimization scheduling strategies do not fully consider dynamic factors such as electricity market price fluctuations and changes in users' real-time electricity demand, resulting in difficulty in achieving optimal overall system benefits. Therefore, it is urgent to develop a more advanced V2G two-way energy interaction moment determination method and system. Summary of the Invention

[0003] In order to improve the efficiency of bidirectional energy interaction between electric vehicles and microgrids and enhance the safety and economy of bidirectional energy interaction, the present invention provides a V2G bidirectional energy interaction moment determination method and system.

[0004] In a first aspect, the present invention provides a method for determining the time of V2G bidirectional energy interaction, the technical solution of which is as follows:

[0005] A method for determining the moment of V2G bidirectional energy interaction comprises the following steps: constructing a hierarchical distributed system architecture between an electric vehicle cluster and a microgrid, and configuring the component layout of the system architecture; deploying sensor detection devices in the battery management system of the electric vehicle, and establishing a battery health status assessment model; acquiring multi-source data on the microgrid side in real time and preprocessing it, and establishing an energy flow control algorithm model; acquiring electricity market price information and users' real-time electricity demand data in real time, and establishing a real-time dynamic model; and achieving efficient collaboration between the microgrid and the electric vehicles based on feedback information from the real-time dynamic model.

[0006] Preferably, according to the electric vehicle distribution density and microgrid capacity, a hierarchical distributed connection architecture is adopted to divide the electric vehicle cluster into multiple subgrids, and an independent bidirectional charging and discharging device is deployed for each subgrid and connected to the microgrid. The bidirectional charging and discharging device is connected to the electric vehicle in the corresponding subgrid via the CAN bus; a distributed power supply, energy storage unit and load node are configured on the microgrid side, and an intelligent power router is configured and installed in each bidirectional charging and discharging device. The path planning algorithm of the power router is used to establish the shortest energy transmission path between the bidirectional charging and discharging device, the power source and the load; wherein, the number of subgrids is set to n, n≥2, and the number of electric vehicles in the i-th subgrid is N i , then the number of electric vehicle clusters is N,

[0007] Preferably, an encryption mechanism is established in the communication control system between the electric vehicle and the microgrid, a communication protocol is deployed to perform end-to-end encryption on the V2G communication data, and the key is periodically and dynamically updated, an IPSec tunnel is integrated in the communication gateway, and external network access is isolated; a fault monitoring and rapid isolation device is configured and installed on the transmission line between the electric vehicle and the microgrid, and the current, voltage and power parameters of the transmission line in the bidirectional charging and discharging device are sampled and monitored in real time, and an abnormal threshold is set. When the parameters sampled for three consecutive times exceed the set threshold, the rapid isolation device is started to cut off the power supply and isolate the subnet area corresponding to the bidirectional charging and discharging device.

[0008] Preferably, the current and voltage data of the electric vehicle battery are collected and monitored in real time, and the temperature sensor, gas sensor and pressure sensor are deployed in the battery management system of the electric vehicle to collect the temperature data, gas composition and pressure data inside the battery in real time; let the current of the electric vehicle battery be I, the voltage be V, the temperature be T, and the internal gas composition content vector be The pressure is P, and the battery health status SOH evaluation model expression formula is: The neural network model conversion expression formula is:

[0009]

[0010] Among them, x m Input parameters include current I, voltage V, temperature T, gas composition content vector Parameter data of pressure P, w l and w lm is the weight, b l and b are biases, and σ is the activation function.

[0011] Preferably, the load curve, distributed power output, and real-time electricity price data of the microgrid are obtained, the sliding window method is used to process missing values, the window length is set to 5 minutes, the time error of each node is ensured to be less than 10ms through the NTP protocol, and the multi-source data are aligned in time and space; the electric vehicle charging waiting time constraint, the ramp rate constraint of the distributed power supply in the microgrid, and the power market price constraint are added, and the adaptive fuzzy control and model predictive control algorithms are integrated to calculate the energy flow in the current state of the microgrid. The calculation formula is:

[0012]

[0013] Among them, W is the price weight matrix, p market (t+k|t) is the electricity market price predicted at time t+k, p target (t+k|t) is the target price;

[0014] The constraint formula for electric vehicle charging waiting time is:

[0015]

[0016] in, is the charging waiting time of the jth electric vehicle, is the maximum waiting time allowed for the jth electric vehicle;

[0017] The ramp rate constraint formula of distributed power generation in the microgrid is:

[0018]

[0019] Among them, P DG (t) is the output power of the distributed power source at time t, Δt is the time interval, R up and R down They represent the maximum ramp-up rate and maximum ramp-down rate of the distributed power supply respectively.

[0020] Preferably, a real-time dynamic model is established using a fitness function, and big data analysis and machine learning techniques are used to obtain real-time electricity market price information, users' real-time electricity demand data, and historical travel time distribution data. The weight coefficient in the fitness function is dynamically adjusted, and the formula of the fitness function is updated to:

[0021]

[0022] Among them, w1(t), w2(t), w3(t), w4(t) are weight coefficients that change dynamically with time, Δp market is the price fluctuation range of electricity market;

[0023] The dynamic adjustment formula of the weight coefficient is:

[0024]

[0025] Among them, α represents the learning rate;

[0026] A fitness threshold is set. If the fitness at the current moment is greater than or equal to the set fitness threshold, the current moment is determined to be a suitable energy interaction moment.

[0027] Preferably, a particle swarm optimization algorithm (PSO) is used to regulate the energy interaction and transmission between the microgrid and the electric vehicles in each subgrid. The particle swarm optimization algorithm (PSO) is improved according to the current charging and discharging plan and power generation plan of the microgrid and the feedback information of the real-time dynamic model to update the position vector and velocity vector of the particles.

[0028] The particle position update formula is:

[0029] x ij (t+1)=x ij (t)+v ij (t+1),

[0030] The particle velocity update formula is:

[0031] v ij (t+1)=ωv ij (t)+c1r1(p ij -x ij (t))+c2r2(p gj -x ij (t))+βΔF ij ,

[0032] Among them, v ij (t) is the velocity of the i-th particle in the j-th dimension, ω is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, p ij is the individual optimal position of the i-th particle, p gj is the global optimal position, x ij (t) is the position of the i-th particle in the j-th dimension at time t, β is the feedback coefficient, ΔF ij Fitness changes for real-time dynamic model feedback.

[0033] In a second aspect, the present invention provides a V2G bidirectional energy interaction time determination system, the technical solution of which is as follows:

[0034] A V2G bidirectional energy interaction time determination system is used to implement the above-mentioned V2G bidirectional energy interaction time determination method, including a microgrid for running energy flow control and optimization solutions, an electric vehicle cluster for running battery management and safety strategies, and a bidirectional energy interaction framework and energy transmission network for running a coordinated optimization scheduling strategy for V2G-based smart microgrids and electric vehicles.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. The present invention provides a method for determining the time of V2G bidirectional energy interaction. By constructing a layered distributed system architecture between an electric vehicle cluster and a microgrid, the method reduces the line congestion and failure risks brought about by the existing centralized connection architecture, makes the energy transmission path more reasonable, and reduces transmission loss. By expanding constraint conditions and improving the energy flow control algorithm model and prediction model, the method addresses the shortcomings of existing algorithms in dealing with the intermittent nature of renewable energy and the uncertainty of electric vehicle charging demand. In combination with a battery health status assessment model, the method determines whether the energy interaction between the electric vehicle subgrid and the microgrid is appropriate at the current moment. Finally, an improved particle swarm optimization algorithm is used to achieve efficient, safe, and economical bidirectional energy interaction between the electric vehicle and the microgrid. At the same time, it ensures battery safety and grid stability, improves energy utilization efficiency, reduces carbon emissions, and enhances the overall economic benefits of the microgrid.

[0037] 2. The V2G bidirectional energy interaction time determination system of the present invention can realize efficient collaboration between smart microgrids and electric vehicles under different market environments and user needs, ensuring maximum energy utilization efficiency, minimization of system operating costs and maximization of user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is an operation flow chart of the method for determining the moment of V2G bidirectional energy interaction;

[0039] Figure 2 This is a structural block diagram of the V2G two-way energy interaction time determination system; DETAILED DESCRIPTION

[0040] The technical features of the present invention are further described in detail below with reference to the accompanying drawings so that those skilled in the art can understand them.

[0041] A V2G bidirectional energy interaction time determination method, the operation process is as follows Figure 1 The specific implementation steps are as follows:

[0042] Step S1: Build a hierarchical distributed system architecture between the electric vehicle cluster and the microgrid, and configure the component layout of the system architecture;

[0043] Specifically, based on the electric vehicle distribution density and microgrid capacity, a hierarchical distributed connection architecture is adopted to divide the electric vehicle cluster into multiple subgrids. An independent bidirectional charging and discharging device is deployed for each subgrid and connected to the microgrid. Each subgrid is connected to the microgrid through an independent bidirectional charging and discharging device, and the bidirectional charging and discharging device communicates with the electric vehicles in the corresponding subgrid through the CAN bus. The hierarchical distributed connection architecture can reduce the line congestion and failure risks brought by the existing centralized connection architecture, and improve the reliability and scalability of the system. Distributed power sources, energy storage units and load nodes are configured on the microgrid side. Distributed power sources include photovoltaic and wind power.

[0044] An intelligent power router is installed in each bidirectional charging and discharging device, and the path planning algorithm of the power router is used to establish the shortest energy transmission path between the bidirectional charging and discharging device, the power source, and the load. In terms of component layout, an intelligent power router is introduced to optimize the connection layout between the bidirectional charging and discharging device, the distributed power source in the microgrid, and the load, which can optimize the energy transmission path and make the energy transmission path more reasonable, thereby reducing the line impedance and the transmission loss. In which, the number of subnets is set to n, n ≥ 2, and the number of electric vehicles in the i-th subnet is N i , then the number of electric vehicle clusters is N, The energy transmission loss rate of the microgrid is η loss , P in is the input power of the microgrid, P out For the output power of the microgrid, the path planning algorithm of the power router is used to dynamically adjust the connection topology between the subgrid and the microgrid, which can ensure that the loss rate η_loss ≤ 5%.

[0045] Furthermore, to enhance interactive security, operational information transmitted between electric vehicles and microgrids is encrypted to prevent data leakage and malicious attacks. Furthermore, fault monitoring and rapid isolation devices are installed to monitor system parameters such as current, voltage, and power in real time. Once an anomaly is detected, the faulty area is immediately isolated to ensure the normal operation of the rest of the system.

[0046] The specific implementation method is as follows: establishing an encryption mechanism in the communication control system between the electric vehicle and the microgrid, deploying a communication protocol to perform end-to-end encryption on the V2G communication data, and periodically and dynamically updating the key, integrating an IPSec tunnel in the communication gateway to isolate external network access; configuring and installing a fault monitoring and rapid isolation device on the transmission line between the electric vehicle and the microgrid, sampling and monitoring the current, voltage and power parameters of the transmission line in the bidirectional charging and discharging device in real time, and setting an abnormal threshold. When the parameters sampled for three consecutive times exceed the set threshold, the rapid isolation device is activated to cut off the power supply and isolate the subnet area corresponding to the bidirectional charging and discharging device; in actual application, it is possible to choose to install a distributed fault monitoring device and a rapid isolation device on the transmission line within the bidirectional charging and discharging device, wherein the sampling frequency of the distributed fault monitoring device is set to 1kHz, and the setting thresholds of the monitoring parameters include: setting the current fluctuation threshold to ±10% of the rated value, the voltage deviation range threshold to ±5% of the nominal value, and the power mutation rate threshold to 20% per second.

[0047] Step S2: deploying a sensor detection device in the battery management system of the electric vehicle and establishing a battery health status assessment model;

[0048] Specifically, to more accurately assess the battery's health status, in addition to monitoring conventional data such as battery current, voltage, and temperature, it is also necessary to introduce data such as gas composition and pressure inside the battery for fusion analysis. Gas sensors and pressure sensors are added to the battery management system to collect real-time data on gas composition and pressure inside the battery, such as oxygen and hydrogen. By establishing a multi-parameter fusion battery health status assessment model, a comprehensive analysis and assessment of the battery's health status is conducted.

[0049] The specific implementation method is: real-time collection and monitoring of electric vehicle battery current and voltage data, deployment of temperature sensors, gas sensors and pressure sensors in the battery management system of electric vehicles, real-time collection of battery internal temperature data, gas composition and pressure data, gas composition such as oxygen, hydrogen, etc.; assuming that the current of the electric vehicle battery is I, the voltage is V, the temperature is T, and the internal gas composition content vector is The pressure is P, and the battery health status SOH evaluation model expression formula is: The neural network model conversion expression formula is:

[0050]

[0051] Among them, x m Input parameters include current I, voltage V, temperature T, gas composition content vector Parameter data of pressure P, w l and w lm is the weight, b land b are biases, and σ is the activation function.

[0052] In addition, when the hydrogen content inside the battery rises abnormally and the pressure increases, combined with the battery's charge and discharge current and voltage data, it is possible to determine whether the battery is at risk of thermal runaway. If there is a risk, the electric vehicle battery safety strategy will be immediately activated, including reducing the charge and discharge power, stopping the charge and discharge operations, or starting the battery cooling device. At the same time, based on multi-parameter data, the existing battery life prediction model can be optimized to provide a more accurate basis for battery maintenance and replacement.

[0053] Step S3: Acquire multi-source data on the microgrid side in real time and perform preprocessing to establish an energy flow control algorithm model;

[0054] Specifically, to address the shortcomings of existing algorithms in dealing with the intermittent nature of renewable energy and the uncertainty of electric vehicle charging demand, this method considers conventional information such as the electric vehicle's state of charge (SOC) and the power of the microgrid. It then introduces the predicted power fluctuations of renewable energy and the real-time charging demand changes of electric vehicles as input variables. By integrating the algorithmic strategies of adaptive fuzzy control and model predictive control, it can more accurately calculate the energy flow under the current state of the microgrid, thereby more accurately predicting the fluctuation state of the energy flow.

[0055] The specific calculation process is as follows: obtain the load curve, distributed power output, and real-time electricity price data of the microgrid, use the sliding window method to handle missing values, set the window length to 5 minutes, ensure that the time error of each node is less than 10ms through the NTP protocol, and align the multi-source data in time and space; add electric vehicle charging waiting time constraints, distributed power ramp rate constraints in the microgrid, and electricity market price constraints; among them, adding electric vehicle charging waiting time constraints can avoid users waiting too long and improve user experience; considering the ramp rate constraints of distributed power sources in the microgrid, ensure the smooth change of distributed power output power to prevent impact on the power grid; introduce electricity market price constraints to ensure that energy flow control can meet system operation requirements while reducing costs as much as possible; integrate adaptive fuzzy control and model predictive control algorithms to calculate the energy flow in the current state of the microgrid. The calculation formula is:

[0056]

[0057] Among them, W is the price weight matrix, p market (t+k|t) is the electricity market price predicted at time t+k, p target (t+k|t) is the target price;

[0058] The constraint formula for electric vehicle charging waiting time is:

[0059]

[0060] in, is the charging waiting time of the jth electric vehicle, is the maximum waiting time allowed for the jth electric vehicle;

[0061] The ramp rate constraint formula of distributed power generation in the microgrid is:

[0062]

[0063] Among them, P DG (t) is the output power of the distributed power source at time t, Δt is the time interval, R up and R down They represent the maximum ramp-up rate and maximum ramp-down rate of the distributed power supply respectively.

[0064] Step S4: Obtaining electricity market price information and users' real-time electricity demand data in real time, and establishing a real-time dynamic model to determine the current energy interaction moment;

[0065] Specifically, a real-time dynamic model is established using a fitness function. Big data analysis and machine learning techniques are used to obtain real-time electricity market price information, users' real-time electricity demand data, and historical travel time distribution data. The weight coefficients in the fitness function are dynamically adjusted, and the fitness function formula is updated to:

[0066]

[0067] Among them, w1(t), w2(t), w3(t), w4(t) are weight coefficients that change dynamically with time, Δp market is the price fluctuation range of electricity market;

[0068] The dynamic adjustment formula of the weight coefficient is:

[0069]

[0070] Among them, α represents the learning rate;

[0071] Set a fitness threshold. If the fitness at the current moment is greater than or equal to the set fitness threshold, the current moment is determined to be a suitable energy interaction moment. The fitness threshold is generally set to 0.7.

[0072] Step S5: Based on the feedback information of the real-time dynamic model, efficient collaboration between the microgrid and the electric vehicle is achieved;

[0073] Specifically, a particle swarm optimization algorithm (PSO) is used to regulate the energy exchange and transmission between the microgrid and electric vehicles in each subgrid. The particle swarm optimization algorithm is improved based on the microgrid's current charging and discharging plan and power generation plan, as well as feedback information from the real-time dynamic model, to update the position vector and velocity vector of the particles.

[0074] The particle position update formula is:

[0075] x ij (t+1)=x ij (t)+v ij (t+1),

[0076] The particle velocity update formula is:

[0077] v ij (t+1)=ωv ij (t)+c1r1(p ij -x ij (t))+c2r2(p gj -x ij (t))+βΔF ij ,

[0078] Among them, v ij (t) is the velocity of the i-th particle in the j-th dimension, ω is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, p ij is the individual optimal position of the i-th particle, p gj is the global optimal position, x ij (t) is the position of the i-th particle in the j-th dimension at time t, β is the feedback coefficient, ΔF ij Fitness changes for real-time dynamic model feedback.

[0079] In addition, through continuous iterative optimization of the system, efficient collaboration between smart microgrids and electric vehicles can be achieved under different market environments and user needs, ensuring maximum energy utilization efficiency, minimization of system operating costs and maximization of user satisfaction.

[0080] A V2G bidirectional energy interaction time determination system, such as Figure 2 As shown, the method for implementing the above-mentioned V2G bidirectional energy interaction time determination includes a microgrid for running energy flow control and optimization solutions, an electric vehicle cluster for running battery management and safety strategies, and a bidirectional energy interaction framework and energy transmission network for running a coordinated optimization scheduling strategy for V2G-based smart microgrids and electric vehicles.

[0081] Specifically, the two-way energy interaction framework between electric vehicles and microgrids uses bidirectional charging and discharging devices and communication and control systems to regulate the charging and discharging of electric vehicles and maintain the energy balance of the microgrid; the energy flow control and optimization scheme set up on the microgrid side adopts a model predictive control algorithm to optimize the mobilization and control of energy flow with the goal of minimizing the microgrid operating cost and maximizing user satisfaction; the battery management and safety strategy set up on the electric vehicle side uses the ampere-hour integral method and the open-circuit voltage method to evaluate the battery health status, and sets safety strategies to protect the battery life; the coordinated optimization scheduling strategy set up in the two-way energy interaction framework between the microgrid and electric vehicles and the energy transmission network uses an improved particle swarm optimization algorithm to perform energy scheduling with the comprehensive goals of minimizing system operating costs, maximizing renewable energy absorption rate and maximizing user satisfaction, so as to achieve efficient collaboration between smart microgrids and electric vehicles in different market environments and user needs, and ensure maximum energy utilization efficiency, minimizing system operating costs and maximizing user satisfaction.

[0082] The embodiments described in the present invention are merely descriptions of preferred implementations of the present invention and are not limited to the precise structures described above and shown in the accompanying drawings. Various modifications and changes can be made without departing from the scope of protection thereof. Without departing from the design concept of the present invention, various variations and improvements made to the technical solutions of the present invention by engineers and technicians in this field should fall within the scope of protection of the present invention.

Claims

1. A method for determining the time of bidirectional energy interaction between V2G networks, characterized by: Build a layered distributed system architecture between the electric vehicle cluster and the microgrid, and configure the component layout of the system architecture; deploy sensor detection devices in the electric vehicle battery management system and establish a battery health status assessment model; obtain multi-source data from the microgrid in real time and pre-process it to establish an energy flow control algorithm model; obtain electricity market price information and users' real-time electricity demand data in real time, and establish a real-time dynamic model to determine the current energy interaction moment; Based on the feedback information of the real-time dynamic model, efficient collaboration between microgrids and electric vehicles is achieved.

2. The V2G bidirectional energy interaction time determination method according to claim 1, characterized in that: The method of constructing a hierarchical distributed system architecture between an electric vehicle cluster and a microgrid and configuring a component layout of the system architecture includes: using a hierarchical distributed connection architecture to divide the electric vehicle cluster into multiple subgrids according to the electric vehicle distribution density and the microgrid capacity, deploying an independent bidirectional charging and discharging device for each subgrid and connecting it to the microgrid, wherein the bidirectional charging and discharging device is connected to the electric vehicle in the corresponding subgrid via a CAN bus; configuring a distributed power supply, an energy storage unit, and a load node on the microgrid side, configuring and installing an intelligent power router in each bidirectional charging and discharging device, and using the path planning algorithm of the power router to establish the shortest energy transmission path between the bidirectional charging and discharging device, the power source, and the load; wherein the number of subgrids is set to n, n≥2, and the number of electric vehicles in the i-th subgrid is N. i , then the number of electric vehicle clusters is N, 3. The V2G bidirectional energy interaction time determination method according to claim 2, characterized in that: The method of constructing a layered distributed system architecture between the electric vehicle cluster and the microgrid and configuring the component layout of the system architecture also includes: establishing an encryption mechanism in the communication control system between the electric vehicle and the microgrid, deploying a communication protocol to perform end-to-end encryption on V2G communication data, and periodically and dynamically updating the key, integrating an IPSec tunnel in the communication gateway to isolate external network access; configuring and installing a fault monitoring and rapid isolation device on the transmission line between the electric vehicle and the microgrid, sampling and monitoring the current, voltage and power parameters of the transmission line in the bidirectional charging and discharging device in real time, and setting an abnormal threshold. When the parameters sampled for three consecutive times exceed the set threshold, the rapid isolation device is activated to power off and isolate the subnet area corresponding to the bidirectional charging and discharging device.

4. The V2G bidirectional energy interaction time determination method according to claim 2, characterized in that: The method of deploying a sensing detection device in the battery management system of an electric vehicle and establishing a battery health status assessment model includes: real-time acquisition and monitoring of the current and voltage data of the electric vehicle battery, deploying a temperature sensor, a gas sensor and a pressure sensor in the battery management system of the electric vehicle, and real-time acquisition of the temperature data, gas composition and pressure data inside the battery; assuming that the current of the electric vehicle battery is I, the voltage is V, the temperature is T, and the internal gas composition content vector is The pressure is P, and the battery health status SOH evaluation model expression formula is: The neural network model conversion expression formula is: Among them, x m Input parameters include current I, voltage V, temperature T, gas composition content vector Parameter data of pressure P, w l and w lm is the weight, b l and b are biases, and σ is the activation function.

5. The V2G bidirectional energy interaction time determination method according to claim 4, characterized in that: The method acquires multi-source data from the microgrid side in real time and performs preprocessing to establish an energy flow control algorithm model, including: acquiring the load curve of the microgrid, the output of distributed power sources, and real-time electricity price data; using the sliding window method to process missing values, setting the window length to 5 minutes, ensuring that the time error of each node is less than 10ms through the NTP protocol, and aligning the multi-source data in time and space; adding electric vehicle charging waiting time constraints, the ramp rate constraints of distributed power sources in the microgrid, and the power market price constraints, integrating adaptive fuzzy control and model predictive control algorithms, and calculating the energy flow in the current state of the microgrid. The calculation formula is: Among them, W is the price weight matrix, p market (t+k|t) is the electricity market price predicted at time t+k, p target (t+k|t) is the target price; The constraint formula for electric vehicle charging waiting time is: in, is the charging waiting time of the jth electric vehicle, is the maximum waiting time allowed for the jth electric vehicle; The ramp rate constraint formula of distributed power generation in the microgrid is: Among them, P DG (t) is the output power of the distributed power source at time t, Δt is the time interval, R up and R down They represent the maximum ramp-up rate and maximum ramp-down rate of the distributed power supply respectively.

6. The V2G bidirectional energy interaction time determination method according to claim 5, characterized in that: The method of obtaining electricity market price information and users' real-time electricity demand data in real time and establishing a real-time dynamic model to determine the current energy interaction moment includes: using a fitness function to establish a real-time dynamic model, utilizing big data analysis and machine learning technology to obtain electricity market price information, users' real-time electricity demand data and historical travel time distribution data in real time, dynamically adjusting the weight coefficient in the fitness function, and updating the fitness function formula to: Among them, w1(t), w2(t), w3(t), w4(t) are weight coefficients that change dynamically with time, Δp market is the price fluctuation range of electricity market; The dynamic adjustment formula of the weight coefficient is: Among them, α represents the learning rate; A fitness threshold is set. If the fitness at the current moment is greater than or equal to the set fitness threshold, the current moment is determined to be a suitable energy interaction moment.

7. The V2G bidirectional energy interaction time determination method according to claim 6, characterized in that: The feedback information based on the real-time dynamic model is used to achieve efficient collaboration between the microgrid and electric vehicles, including: using a particle swarm optimization algorithm (PSO) to regulate the energy exchange and transmission between the microgrid and electric vehicles in each subgrid, improving the particle swarm optimization algorithm (PSO) based on the microgrid's current charging and discharging plan and power generation plan, and the feedback information from the real-time dynamic model to update the position vector and velocity vector of the particles; The particle position update formula is: x ij (t+1)=x ij (t)+v ij (t+1), The particle velocity update formula is: v ij (t+1)=ωv ij (t)+c1r1(p ij -x ij (t))+c2r2(p gj -x ij (t))+βΔF ij , Among them, v ij (t) is the velocity of the i-th particle in the j-th dimension, ω is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, p ij is the individual optimal position of the i-th particle, p gj is the global optimal position, x ij (t) is the position of the i-th particle in the j-th dimension at time t, β is the feedback coefficient, ΔF ij Fitness changes for real-time dynamic model feedback.

8. A V2G bidirectional energy interaction time determination system, configured to implement the V2G bidirectional energy interaction time determination method according to any one of claims 1 to 7, characterized in that: It includes a microgrid for running energy flow control and optimization solutions, an electric vehicle cluster for running battery management and safety strategies, and a two-way energy interaction framework and energy transmission network for running V2G-based smart microgrids and electric vehicles' coordinated optimization scheduling strategies.

Images