An intelligent scheduling control method of a light storage and charging vehicle network interaction system

Abstract

This invention relates to microgrid dispatching and renewable energy grid integration technology, aiming to provide an intelligent dispatching and control method for a photovoltaic-storage-charging-vehicle-grid interactive system. It includes: constructing a multi-agent interaction model to quantify the mobile energy storage characteristics and flexible adjustment capabilities of electric vehicle clusters; constructing an optimized dispatching model considering time-series characteristics and uncertainties, with the core objectives of maximizing renewable energy absorption and minimizing grid power purchase costs; formulating real-time coordinated control strategies based on the predictions of the optimized dispatching model; smoothing photovoltaic output fluctuations and reducing peak-valley load differences through coordinated operation sub-strategies; and updating the real-time coordinated control strategy in real time to ensure system operational stability and continuity. This invention is applicable to multi-energy coordinated dispatching in scenarios such as public charging stations, industrial park microgrids, and distributed photovoltaic-storage-charging integrated projects, enabling efficient coordinated operation of photovoltaics, energy storage, electric vehicles, and the grid.

Description

Technical Field

[0001] This invention relates to the field of microgrid dispatching and new energy grid connection technology, specifically to an intelligent dispatching and control method for a photovoltaic-storage-charging-vehicle-grid interactive system. Background Technology

[0002] As the global energy structure accelerates its transition to clean and low-carbon energy, the large-scale deployment of distributed renewable energy and the explosive growth of the electric vehicle industry have become important forces driving high-quality energy development and ensuring energy security. Integrated photovoltaic, energy storage, and charging systems, as a key carrier connecting renewable energy consumption with the electrification of the transportation sector, can effectively alleviate the pressure on power distribution networks caused by the intermittency and volatility of photovoltaic power output and the uncertainty of electric vehicle charging loads.

[0003] However, existing photovoltaic-storage-charging-vehicle-grid interactive systems still face many technical bottlenecks in actual operation: First, the multi-energy flow coupling characteristics are complex. Photovoltaic output is significantly affected by environmental factors such as irradiance and temperature. Electric vehicle charging time, power, and initial SOC are highly random. Energy storage needs to balance energy supply and demand in real time. The energy interaction logic between the three and the grid is complex, and traditional single scheduling strategies are difficult to achieve multi-entity collaborative optimization. Second, the intelligence level of energy management systems is insufficient. Most of them adopt fixed thresholds or simple rule scheduling, which cannot adapt to the dynamic changes of different meteorological conditions and load scenarios. The system has poor operational stability and weak anti-interference ability. Third, the renewable energy absorption rate is low. When photovoltaic output is excessive, there is a lack of effective ways to absorb curtailed photovoltaic power. When output is insufficient, there is an over-reliance on purchasing electricity from the grid, which increases operating costs and fails to fully utilize the value of local clean energy. Fourth, existing technologies are mostly optimized for single entities and have not formed a complete multi-entity collaborative scheduling system. They also lack flexible strategy update mechanisms. Frequent calculations can easily increase the system burden, and calculation lag can affect the control effect, making it difficult to balance scheduling accuracy and operating efficiency.

[0004] Among the existing publicly available research results, some solutions attempt to optimize the scheduling of photovoltaic-storage-charging systems: for example, the droop control strategy with electricity price priority, but this can easily lead to distribution network overload caused by concentrated user charging; some solutions use improved state-space models to optimize operation, but the uncertainty scenarios under extreme conditions are not accurately set, resulting in a decrease in strategy performance; and some solutions optimize the charging and discharging scheduling of electric vehicles through metaheuristic algorithms, but do not consider the supporting role of local distributed power sources for charging piles, thus limiting their versatility.

[0005] Therefore, there is an urgent need for an intelligent scheduling and control method that can coordinate multiple stakeholders, adapt to complex working conditions, and balance economy and safety, in order to address the shortcomings of existing technologies. Summary of the Invention

[0006] This invention aims to overcome the shortcomings of existing photovoltaic-storage-charging-vehicle-grid interactive systems, such as insufficient intelligent scheduling, poor multi-entity coordination, weak adaptability to operating conditions, low renewable energy absorption rate, and high dependence on the large power grid, and provides an intelligent scheduling and control method for photovoltaic-storage-charging-vehicle-grid interactive systems.

[0007] To solve the technical problem, the solution of the present invention is:

[0008] A method for intelligent scheduling and control of a photovoltaic-storage-charging-vehicle-grid (PV-SG) interactive system is provided. The PV-storage-charging-vehicle-grid interactive system includes a public power grid, photovoltaic modules, a battery energy storage system, an electric vehicle cluster, and V2G charging piles. The intelligent scheduling and control method includes:

[0009] A multi-agent interactive model was constructed, which includes a photovoltaic power output model, an energy storage charging and discharging process model, and an electric vehicle load model, to quantify the mobile energy storage characteristics and flexible regulation capabilities of electric vehicle clusters.

[0010] With the core objectives of maximizing renewable energy absorption rate and minimizing grid power purchase cost, an optimized scheduling model considering time-series characteristics and uncertainties is constructed to achieve bidirectional energy interaction between vehicles and the grid under the premise of meeting user charging needs, equipment physical constraints and safe operation constraints.

[0011] Based on the predictions of the optimized scheduling model, a real-time coordinated control strategy is formulated. Through three sub-strategies—coordinated operation of V2G scheduling, hierarchical energy storage management strategy, and improved particle swarm optimization power allocation—the photovoltaic output fluctuations are smoothed out, and the load peak-valley difference is reduced.

[0012] An update mode combining periodic updates and event triggering is adopted to update the real-time coordination and control strategy in real time, ensuring the stability and continuity of system operation.

[0013] Compared with the prior art, the beneficial effects of the present invention are:

[0014] This invention constructs a multi-entity interaction model, an optimized scheduling model, and a collaborative control strategy, combined with an event triggering mechanism, to achieve coordinated and optimized operation of multiple energy entities, improve the local consumption rate of renewable energy, reduce the cost of purchasing electricity from the grid, ensure the stable operation of the system under different operating conditions, and meet the charging needs of electric vehicle users. It is applicable to multi-energy collaborative scheduling in scenarios such as public charging stations, park microgrids, and distributed photovoltaic-storage-charging integrated projects, and can realize the efficient collaborative operation of photovoltaics, energy storage, electric vehicles, and the power grid.

[0015] 1. This invention constructs a complete multi-entity interaction model and optimized scheduling system, which coordinates the operation of photovoltaic, energy storage, electric vehicles and power grid, solves the limitations of traditional single-entity scheduling, and significantly improves the overall operating efficiency of the system;

[0016] 2. By prioritizing energy management strategies and using V2G curtailment absorption mechanisms, the utilization of local photovoltaic energy is maximized. Simulation results show that the photovoltaic absorption rate is increased by more than 30%, effectively reducing curtailment losses.

[0017] 3. By optimizing power distribution and peak shaving and valley filling, the cost of purchasing electricity from the power grid can be reduced by more than 25%, while reducing overcharging and over-discharging of energy storage, extending the service life of equipment, and reducing the total life cycle cost. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall photovoltaic energy storage and charging system in this invention.

[0019] Figure 2 This is a flowchart of the improved particle swarm algorithm in this invention. Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0021] The photovoltaic-storage-charging-vehicle-grid interactive system in this implementation case is as follows: Figure 1 As shown, the system includes a public power grid, photovoltaic modules, a lithium battery energy storage system, an electric vehicle cluster, and V2G charging piles. For this system, this invention proposes a novel intelligent scheduling and control method, including the following steps:

[0022] Step 1: Construct a multi-agent interactive model that includes a photovoltaic power output model, an energy storage charging and discharging process model, and an electric vehicle load model to quantify the mobile energy storage characteristics and flexible regulation capabilities of electric vehicle clusters.

[0023] The multi-agent interaction model includes a photovoltaic power output model, an energy storage charging and discharging process model, and an electric vehicle load model. The specific construction of each model is as follows:

[0024] (1) The expression for the photovoltaic power output model is:

[0025]

[0026] In the formula, , , These are the output power, light intensity, and solar radiation temperature of the photovoltaic array, respectively. , , These represent the rated output power, reference illuminance, and solar radiation temperature under standard test conditions, respectively; K is the temperature coefficient; under standard test conditions, the reference illuminance is 1000 W / m². 2 The solar radiation temperature is 25℃.

[0027] (2) The expression for the energy storage charging process model is:

[0028]

[0029] The expression for the energy storage and discharge process model is as follows:

[0030]

[0031] In the formula, Let SOC be the SOC value of the battery at time t in the battery energy storage system; The previous moment; Power for charging the battery; This refers to the battery discharge power. The difference between t and t0; This refers to the total capacity of the energy storage battery. Improve battery charging efficiency; For battery discharge efficiency;

[0032] (3) The probability of electric vehicle charging has a significant impact on the system load in many ways, including: total load (charging demand scale and cumulative effect), load distribution (temporal distribution and spatial distribution), load peak and valley characteristics (peak load and valley load), and load uncertainty (random fluctuation). Therefore, this invention uses the probability of electric vehicle charging to measure the impact of electric vehicle charging behavior on the load, and constructs an electric vehicle load model based on Monte Carlo simulation.

[0033] The specific expression for the probability of electric vehicle charging is as follows:

[0034]

[0035] In the formula, f(t) arr ) represents the charging start time t arr The probability density function value represents the probability density function value at time t. arr The probability density of charging occurring within a unit of time in the vicinity; σ arr t represents the standard deviation of the charging start time, used to measure the dispersion of the time distribution; arr Electric vehicle charging start time; μ arr This represents the average charging start time, indicating the average charging start time for all vehicles.

[0036] As an example, the charging start time follows a normal distribution with a mean of 11:00 and a standard deviation of 2; the initial SOC follows a normal distribution with a mean of 0.5, a standard deviation of 0.15, and a value range of [0.2, 0.9]; the charging power does not exceed the rated power of the charging pile, and the discharging power does not exceed the maximum allowable discharging power of the vehicle.

[0037] Step 2: With the core objectives of maximizing renewable energy absorption rate and minimizing grid power purchase cost, construct an optimized scheduling model that considers time-series characteristics and uncertainties, and realize bidirectional energy interaction between vehicles and the grid under the premise of meeting user charging needs, equipment physical constraints and safe operation constraints.

[0038] The functional expression of the optimized scheduling model is as follows:

[0039]

[0040] In the formula, F is the total objective function value, and the system reaches optimal control when F is minimized; α and β are weighting coefficients (for example, α is 0.4 and β is 0.6); F1 is the total cost of purchasing electricity from the grid; F2 is the percentage of renewable energy used in the total power generation.

[0041] The constraints of the optimized scheduling model include physical constraints and security constraints. Physical constraints include energy storage system constraints, charging constraints, and power constraints, while security constraints include SOC constraints. Specifically,

[0042] The constraints of the energy storage system are:

[0043]

[0044] In the formula, This represents the maximum charging power of the energy storage system. Real-time power of the energy storage system; This represents the maximum discharge power of the energy storage system.

[0045] Corresponding SOC constraints:

[0046]

[0047] In the formula, This represents the upper limit of the energy storage system's capacity as a percentage. This represents the lower limit of the energy storage system's capacity percentage.

[0048] Charging constraints:

[0049]

[0050] In the formula, Current charging power for all vehicles. This is the minimum power supply required by the system. This is the maximum power supply for the system.

[0051] Power balance constraints:

[0052]

[0053] In the formula, Current charging power; Input power to the power grid; This refers to the photovoltaic power generation required by the system.

[0054] Step 3: Based on the predictions of the optimized scheduling model, formulate a real-time coordinated control strategy; through three sub-strategies—coordinated operation of V2G scheduling, hierarchical energy storage management strategy, and improved particle swarm optimization power allocation—smooth photovoltaic power output fluctuations and reduce load peak-valley differences.

[0055] 1. The V2G scheduling strategy is used for the scheduling and control of V2G bidirectional charging piles. It should be accurately controlled according to the actual model, following the principles of prioritizing user charging needs and maximizing the grid's peak-shaving contribution. The specific execution process is as follows:

[0056] (1) Vehicle status filtering:

[0057] Real-time collection of electric vehicle status information (e.g., current SOC, estimated dwell time, user charging demand type) is used to filter vehicles that meet preset conditions. Example filtering conditions are: completed access authentication, estimated dwell time ≥ 1 hour, current SOC ≥ 35%, and user has not selected emergency charging mode.

[0058] (2) Scene recognition:

[0059] Identify the current operating scenario based on real-time load levels, photovoltaic output, and grid electricity price periods; examples include: power deficit scenario, peak load scenario, photovoltaic curtailment scenario, and flat-period operation scenario.

[0060] (3) Execution scheduled according to different scenarios:

[0061] In scenarios where system power is insufficient, and the system load exceeds the combined output of photovoltaic power and energy storage discharge capacity: V2G vehicles that meet the conditions are selected and sorted by their State of Charge (SOC) from high to low, and discharged sequentially to compensate for the supply and demand gap, but the discharge power does not exceed the rated power of the charging pile; grid power is only activated when the available discharge power of V2G vehicles is insufficient.

[0062] Peak load scenario (total power generation of new energy is less than the load and energy storage capacity is insufficient): Based on the actual gap of the system load peak, the discharge power of each vehicle is dynamically allocated to ensure that the discharge power does not exceed the maximum allowable discharge power of the vehicle and the rated power of the charging pile. At the same time, the vehicle charge status is monitored in real time to ensure that the vehicle charge status after discharge is not lower than the set threshold (such as 35%) until the system load drops below the peak threshold or there are no available V2G vehicles.

[0063] Solar curtailment scenarios (total power generation of new energy sources exceeds the load and energy storage is fully charged): guide V2G vehicles to charge in reverse to absorb curtailed solar power and improve the renewable energy consumption rate;

[0064] In a flat-segment operation scenario (where the amount of electricity generated by new energy sources exceeds the load and the energy storage is not fully charged): some vehicles with a set SOC margin are reserved as backups, and V2G vehicles are not actively called upon to discharge, thus ensuring the user's subsequent charging needs.

[0065] 2. Based on the optimized scheduling model and V2G scheduling strategy, the hierarchical energy storage management strategy is divided into three layers: charging control layer, discharging control layer, and safety protection layer. It adopts a hierarchical control and safety-first management logic to dynamically adjust energy storage charging and discharging commands, enabling each layer to operate independently yet coordinate with each other; specifically as follows:

[0066] Charging control layer: When the photovoltaic output is greater than the system load and the energy storage SOC is lower than the minimum threshold (e.g., 8%), the energy storage will be charged first. The charging power will be the smaller value between "the difference between the photovoltaic output and the system load" and "the rated maximum charging power of the energy storage". After charging is completed, the energy storage SOC will not exceed the maximum threshold (e.g., 80%). When the photovoltaic output is less than or equal to the system load, or the energy storage SOC is higher than the set value (e.g., 75%), the energy storage charging will be stopped.

[0067] Discharge control layer: When the system experiences a power deficit (i.e., the photovoltaic output is less than the system load) and the energy storage SOC is higher than the set value (e.g., 25%), the energy storage starts the discharge compensation mode. The discharge power is the smaller value between "the difference between the system load and the photovoltaic output" and "the rated maximum discharge power of the energy storage". After the system's power deficit is partially compensated by the discharge of V2G vehicles, the energy storage discharge power is adjusted according to the remaining power deficit to avoid over-discharge of the energy storage.

[0068] Safety Protection Layer: Real-time monitoring of energy storage SOC, battery operating temperature, and charging / discharging current; continuous monitoring of the energy storage device's operating status to prevent damage from overcharging, over-discharging, or overheating; when the energy storage SOC falls below a set threshold (e.g., 21%), the energy storage discharge operation is immediately stopped, and a low charge alarm is triggered; when the energy storage SOC exceeds a set threshold (e.g., 80%), the charging power is reduced proportionally to prevent overcharging from causing battery life degradation; when the battery operating temperature exceeds a set threshold (e.g., 55℃), the energy storage charging / discharging operation is immediately stopped, and a cooling protection mechanism is activated; ensuring the safe and stable operation of the energy storage device and preventing equipment failure from affecting the normal scheduling of the entire photovoltaic-storage-charging-vehicle-grid interactive system.

[0069] 3. Optimize power allocation strategies using an improved particle swarm optimization algorithm to optimize the power allocation ratio between energy storage, V2G, and the power grid. The flowchart is shown below. Figure 2 As shown. This strategy specifically includes a velocity update equation, a position update equation, and a fitness function; among which,

[0070] The expression for the velocity update equation is:

[0071]

[0072] In the formula, v i t+1 ν represents the particle's velocity at iteration t+1; w is the inertia coefficient, which determines the degree to which the particle's current velocity affects its next motion. A larger w value helps to expand the search range and enhance global exploration capabilities, while a smaller w value helps to refine the local search and improve convergence accuracy. i t represents the particle's velocity at iteration t; c1 and c2 are learning rate parameters, controlling the weights of individual and group experience respectively; r1 and r2 are random variables in the interval 0 to 1, used to maintain population diversity; p best This represents the particle's own historical best position; g best x represents the current global optimal position of the group; i t This represents the spatial position of the particle at the t-th iteration; This allows for the customization of base particle velocities to avoid excessively low optimization rates.

[0073] The expression for the position update equation is:

[0074]

[0075] In the formula, x i t+1 This represents the spatial position of the particle at the (t+1)th iteration; v i t+1 Let be the particle's velocity at the (t+1)th iteration;

[0076] To balance charging service quality and grid dependence, a fitness function is introduced to optimize the charging power of newly allocated charging piles. The core objective is to minimize the deviation between grid-purchased electricity and SOC while meeting power constraints.

[0077] The fitness function adopts a multi-constraint penalty type; the smaller the value, the better the optimization effect, ensuring maximum photovoltaic power consumption. Its expression is shown below:

[0078]

[0079] In the formula, P1 is the power over-limit penalty; P2 is the low power penalty; P3 is the grid usage penalty; P4 is the SOC deviation penalty; and P5 is the total photovoltaic waste power during the simulation time.

[0080] Examples of the meanings of each item are as follows:

[0081]

[0082] P1: Power over-limit penalty. When the total allocated power exceeds the available power, a penalty of 1000 times the squared value is applied. This represents the actual photovoltaic power generation. The photovoltaic power required by the system;

[0083]

[0084] P2: Low power penalty. When the charging power is lower than 52.8kW (48kW×1.1), a square penalty is applied to the total power consumption. For electric vehicle load; The time interval between the execution of the current algorithm by the host computer and the next algorithm call; Total charging time for the car;

[0085]

[0086] P3: Grid usage penalty, the cube of the grid-purchased electricity volume multiplied by 10, to strengthen the goal of low grid dependence; Input power to the power grid;

[0087]

[0088] P4: SOC deviation penalty. A penalty of 999 times is applied when the SOC deviates from the set threshold.

[0089]

[0090] P5: Simulation of all photovoltaic waste electricity over time.

[0091] Step 4: Adopt an update mode that combines periodic updates and event triggering to update the real-time coordination and control strategy in real time, ensuring the stability and continuity of system operation.

[0092] The duration of the periodic update is set according to the actual operating scale and configuration of the system. For example, the regular update cycle is 7 minutes, and the update is executed immediately when an event is triggered.

[0093] To avoid meaningless repeated adjustments to the system, a policy update is triggered when any of the following preset event trigger conditions are met by the system's key state parameters:

[0094] (1) Photovoltaic power generation experiences short-term drastic fluctuations: the fluctuation range of photovoltaic output exceeds the preset threshold (e.g., 10%) within a specified time period.

[0095] (2) Energy storage device SOC reaches safety or operating limits: Energy storage SOC reaches the upper or lower limits of the safety threshold (such as 20% and 80%).

[0096] (3) Sudden change in electric vehicle charging load status: The load fluctuation exceeds the set proportion of the current load (e.g., 15%).

[0097] During the update process, the real-time coordination and control strategy is accurately matched with the current working conditions by re-collecting the status information of each subject, correcting and optimizing the parameters of the scheduling model, and adjusting the execution parameters of the three sub-strategies.

[0098] Based on the understanding of those skilled in the art, the intelligent scheduling and control method of the photovoltaic-storage-charging-vehicle-network interactive system described above is entirely based on computer technology. The entire implementation process includes data acquisition, processing, calculation, and result display.

[0099] The implementation of this technology can be embodied in a computing device, which includes: at least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores instructions that are executed by the at least one processor, and the instructions are executed by the at least one processor to cause the at least one processor to execute the aforementioned intelligent scheduling and control method of the optical storage charging vehicle network interactive system.

[0100] Therefore, it can also be understood that the technical implementation of the present invention can also be embodied in a computer-readable storage medium, which stores computer instructions for causing the computer to execute the aforementioned intelligent scheduling and control method of the optical storage charging vehicle network interactive system.

[0101] Finally, it should be noted that the above examples are merely some specific embodiments of the present invention. Obviously, the present invention is not limited to the above embodiments and many variations are possible. All variations that can be directly derived or conceived by those skilled in the art from the disclosure of the present invention should be considered within the scope of protection of the present invention.

Claims

1. An intelligent scheduling and control method for a photovoltaic-storage-charging-vehicle-grid interactive system, characterized in that, The photovoltaic-storage-charging-vehicle-grid interactive system includes a public power grid, photovoltaic modules, a battery energy storage system, an electric vehicle cluster, and V2G charging piles; the intelligent scheduling and control method includes: A multi-agent interactive model was constructed, which includes a photovoltaic power output model, an energy storage charging and discharging process model, and an electric vehicle load model, to quantify the mobile energy storage characteristics and flexible regulation capabilities of electric vehicle clusters. With the core objectives of maximizing renewable energy absorption and minimizing grid power purchase costs, an optimized scheduling model considering time-series characteristics and uncertainties is constructed to achieve bidirectional energy interaction between vehicles and the grid while meeting user charging needs, equipment physical constraints, and safe operation constraints. Based on the predictions of the optimized scheduling model, a real-time coordinated control strategy is formulated. Through three sub-strategies—coordinated operation of V2G scheduling, hierarchical energy storage management strategy, and improved particle swarm optimization power allocation—the photovoltaic output fluctuations are smoothed and the load peak-valley difference is reduced. An update mode combining periodic updates and event triggering is adopted to update the real-time coordination and control strategy in real time, ensuring the stability and continuity of system operation.

2. The method according to claim 1, characterized in that, In the multi-agent interaction model, each model is constructed in the following manner: (1) The expression for the photovoltaic power output model is: ； In the formula, , , These are the output power, light intensity, and solar radiation temperature of the photovoltaic array, respectively. , , These represent the rated output power, reference illuminance, and solar radiation temperature under standard test conditions, respectively; K is the temperature coefficient. (2) The expression for the energy storage charging process model is: ； The expression for the energy storage and discharge process model is as follows: ； In the formula, Let SOC be the SOC value of the battery at time t in the battery energy storage system; The previous moment; Power for charging the battery; This refers to the battery discharge power. The difference between t and t0; This refers to the total capacity of the energy storage battery. Improve battery charging efficiency; For battery discharge efficiency; (3) Utilize the electric vehicle charging probability to measure the impact of electric vehicle charging behavior on system load, and construct an electric vehicle load model based on Monte Carlo simulation; ensure that the charging start time and initial SOC follow normal distributions, the charging power does not exceed the rated power of the charging pile, and the discharging power does not exceed the maximum allowable discharging power of the vehicle; The expression for the probability of charging an electric vehicle is as follows: ； In the formula, f(t) arr ) represents the charging start time t arr The probability density function value represents the probability density function value at time t. arr The probability density of charging occurring within a unit of time in the vicinity; σ arr t represents the standard deviation of the charging start time, used to measure the dispersion of the time distribution; arr Electric vehicle charging start time; μ arr This represents the average charging start time, indicating the average charging start time for all vehicles.

3. The method according to claim 1, characterized in that, The functional expression of the optimized scheduling model is as follows: ； In the formula, F is the overall objective function value, and the system reaches optimal control when F is minimized; α and β are weighting coefficients; F1 is the total cost of purchasing electricity from the grid; and F2 is the percentage of renewable energy used in the total power generation. The constraints of the optimized scheduling model include physical constraints and security constraints. Physical constraints include energy storage system constraints, charging constraints, and power constraints, while security constraints include SOC constraints. Specifically, The constraints of the energy storage system are: ； In the formula, This represents the maximum charging power of the energy storage system. Real-time power of the energy storage system; This represents the maximum discharge power of the energy storage system. Corresponding SOC constraints: ； In the formula, and These represent the upper and lower limits of the energy storage system's capacity percentage, respectively. Charging constraints: ； In the formula, Current charging power for all vehicles. and These are the minimum and maximum power supplies for the system, respectively. Power balance constraints: ； In the formula, Current charging power; Input power to the power grid; This refers to the photovoltaic power generation required by the system.

4. The method according to claim 1, characterized in that, The V2G scheduling strategy is used for the scheduling and control of V2G bidirectional charging piles, following the principles of prioritizing user charging needs and maximizing the grid's peak-shaving contribution. The specific execution process is as follows: (1) Vehicle status filtering: Real-time collection of electric vehicle status information from the system, and screening of vehicles that meet preset conditions; (2) Scene recognition: Identify the current operating scenario based on real-time load levels, photovoltaic output, and grid electricity price periods; (3) Execution scheduled according to different scenarios: In scenarios with insufficient system power: V2G vehicles that meet the conditions are called up in order of their SOC from high to low and discharged sequentially to compensate for the supply and demand gap, but the discharge power does not exceed the rated power of the charging pile; grid power is only activated when the available discharge power of the V2G vehicles is insufficient. Peak load scenario: Based on the actual gap of the system load peak, the discharge power of each vehicle is dynamically allocated to ensure that the discharge power does not exceed the maximum allowable discharge power of the vehicle and the rated power of the charging pile. At the same time, the vehicle's state of charge is monitored in real time to ensure that the vehicle's state of charge is not lower than the set threshold after discharge, until the system load drops below the peak threshold or there are no available V2G vehicles. Solar curtailment scenarios: Guide V2G vehicles to charge in reverse to absorb curtailed solar power and improve the renewable energy consumption rate; In a flat-segment operation scenario: some vehicles with a set SOC margin or higher are reserved as backups, and V2G vehicles are not actively used for discharge to ensure users' subsequent charging needs.

5. The method according to claim 1, characterized in that, The hierarchical energy storage management strategy consists of three layers: a charging control layer, a discharging control layer, and a safety protection layer. It employs a hierarchical control and safety-first management logic, dynamically adjusting energy storage charging and discharging commands to ensure that each layer operates independently yet coordinates with the others. Specifically: Charging control layer: When the photovoltaic output is greater than the system load and the energy storage SOC is lower than the minimum threshold, the energy storage will be charged first. The charging power is the smaller value between "the difference between the photovoltaic output and the system load" and "the rated maximum charging power of the energy storage". After charging is completed, the energy storage SOC will not exceed the maximum threshold. When the photovoltaic output is less than or equal to the system load, or the energy storage SOC is higher than the set value, the energy storage charging will be stopped. Discharge control layer: When the photovoltaic output is less than the system load, resulting in a power deficit and the energy storage SOC is higher than the set value, the energy storage starts the discharge supplement mode. The discharge power is the smaller value between "the difference between the system load and the photovoltaic output" and "the rated maximum discharge power of the energy storage". When the power deficit of the system is partially supplemented by the discharge of V2G vehicles, the energy storage discharge power is adjusted according to the remaining power deficit to avoid excessive discharge of the energy storage. Safety protection layer: Real-time monitoring of energy storage SOC, battery operating temperature, and charging / discharging current; continuous monitoring of the energy storage device's operating status to prevent damage such as overcharging, over-discharging, and overheating; when the energy storage SOC is below a set threshold, the energy storage discharge operation is immediately stopped and a low charge alarm is triggered; when the energy storage SOC is above a set threshold, the charging power is reduced proportionally to avoid overcharging and battery life degradation; when the battery operating temperature exceeds a set threshold, the energy storage charging / discharging operation is immediately stopped, and a cooling protection mechanism is activated.

6. The method according to claim 1, characterized in that, The improved particle swarm optimization power allocation strategy specifically includes a velocity update equation, a position update equation, and a fitness function; wherein... The expression for the velocity update equation is: ； In the formula, v i t+1 The velocity of the particle at the (t+1)th iteration is v; w is the inertia coefficient; i t represents the particle's velocity at iteration t; c1 and c2 are learning rate parameters, controlling the weights of individual and group experience, respectively; r1 and r2 are random variables in the interval 0 to 1; p best This represents the particle's own historical best position; g best x represents the current global optimal position of the group; i t This represents the spatial position of the particle at the t-th iteration; Customize the base particle velocity; The expression for the position update equation is: ； In the formula, x i t+1 This represents the spatial position of the particle at the (t+1)th iteration; v i t+1 Let be the particle's velocity at the (t+1)th iteration; The fitness function is expressed as follows: ； In the formula, P1 is the power over-limit penalty; P2 is the low power penalty; P3 is the grid usage penalty; P4 is the SOC deviation penalty; and P5 is the total photovoltaic waste power during the simulation time.

7. The method according to claim 1, characterized in that, A policy update is triggered when any of the following preset event trigger conditions are met by the system's key status parameters: (1) Photovoltaic power generation experiences short-term drastic fluctuations: the fluctuation range of photovoltaic output exceeds the preset threshold within a specified time period; (2) Energy storage device SOC reaches safety or operating limits: The upper or lower limit of the safety threshold for energy storage SOC; (3) Sudden change in electric vehicle charging load status: The load fluctuation exceeds the set ratio of the current load.

8. The method according to claim 1, characterized in that, During the update process, the real-time coordination and control strategy is accurately matched with the current working conditions by re-collecting the status information of each subject, correcting and optimizing the parameters of the scheduling model, and adjusting the execution parameters of the three sub-strategies.

9. A computing device, characterized in that, include: At least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores instructions that are executed by the at least one processor to cause the at least one processor to perform the intelligent scheduling and control method of the photovoltaic-storage-charging-vehicle-grid interactive system as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing computer instructions for causing the computer to execute the intelligent scheduling and control method of the photovoltaic-storage-charging-vehicle-grid interactive system as described in any one of claims 8.

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