Optimized operation method of energy storage system of optical storage type charging station

Abstract

The present application relates to photovoltaic energy storage technical field, and disclose a kind of light storage type charging station energy storage system optimization operation method, the optimization method includes the following steps: step one, vehicle charging determination;Step two, charging allocation optimization;Step three, energy storage optimization;Step four, load optimization;Step five, multi-objective optimization model;Step six, real-time scheduling and feedback.The present application is by in the lower valley period of electricity price, system preferentially uses grid power and charges, improves the utilization of power grid, by reasonably allocating the charging power of grid and photovoltaic energy storage, reduces the pressure of grid in sharp period and peak period, in sharp period and peak period, by reducing the charging power of grid, system can effectively reduce the load pressure of grid, avoid the impact caused by excessive power consumption on power grid in peak period, by dynamically adjusting grid charging power ratio, smooth the load curve of grid, reach the beneficial effect of simultaneously improving the load of grid side and energy storage.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic energy storage, in particular to a method for optimizing operation of a light-storage charging station energy storage system. BACKGROUND

[0002] As a brand-new charging facility, the light-storage charging station can realize the on-site integration of renewable energy and electric vehicles and has been widely recognized. The energy storage system, as one of the most important components of the light-storage charging station, will directly affect the overall performance of the charging station in terms of the capacity involved in its operation within the dispatching period.

[0003] The prior art CN 112418605 A is a method for optimizing operation of a light-storage charging station energy storage system, which analyzes the system structure, operation strategy and load characteristics of the light-storage charging station; takes the minimum load variance of the grid side, the minimum operation and maintenance cost of the energy storage system and the minimum purchase cost of electricity from the grid as the objective function, takes the power, state of charge and power supply of the grid side as the constraint condition, establishes a multi-objective optimization operation model of the energy storage system, and effectively improves the load fluctuation level of the grid side.

[0004] Although the prior art improves the load fluctuation level of the grid side, the light-storage charging station is powered by a photovoltaic hybrid power grid, which converts solar energy into electrical energy through photovoltaic power generation technology, stores it in an energy storage device, and provides charging services for electric vehicles when needed. However, the prior art can only improve the load fluctuation of the grid side and cannot improve the load fluctuation of the energy storage system. At the same time, the mainstream of the prior art adopts separate charging, i.e. the grid mainly charges the vehicle and the energy storage is auxiliary, and the two are switched with each other and do not charge together. Moreover, because the domestic electricity price adopts segmented charging, the flat valley peak electricity price is inconsistent, and separate charging is adopted, even if the energy storage is used for power supply alone during the peak electricity price period, it cannot meet the demand of vehicles in the power station, which leads to that the photovoltaic power generation cannot well reduce the charging cost. SUMMARY

[0005] In view of the shortcomings of the prior art, the present application provides a method for optimizing operation of a light-storage charging station energy storage system, which can improve the load fluctuation of the grid side and the energy storage system at the same time, and the energy storage and the grid can participate in vehicle charging at the same time to reduce the charging cost of the vehicle owner, thereby solving the above-mentioned technical problems.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a method for optimizing operation of a light-storage charging station energy storage system, the light-storage charging station energy storage system comprising a photovoltaic assembly, an inverter, a bus box, a direct current distribution cabinet, an alternating current distribution cabinet, a photovoltaic storage battery and a bidirectional DC-DC converter, the optimization method comprising the following steps:

[0007] Step one, vehicle charging determination: the light-storage charging station detects whether the charging gun is connected to the vehicle for charging, if charging, step two is entered, if no vehicle charging, step three is entered;

[0008] Step two, charging distribution optimization: the system switches modes according to the current grid price, including valley, flat, sharp and peak, the price is in the valley, the grid power supply current ratio is 100%, the price is in the flat, the grid power supply current ratio is 65%-75%, the price is in the sharp, the grid power supply current ratio is 40%-50%, the price is in the peak, the grid power supply current ratio is 0%-10%, the rest is supplied by photovoltaic storage battery, the photovoltaic storage battery and the grid are used together. When using a bidirectional DC-DC converter to adjust the voltage;

[0009] Step three, energy storage optimization: the system reads the remaining capacity of the photovoltaic storage battery, calculates the full charging time according to the current photovoltaic module power generation, and supplements according to the current grid price. When the photovoltaic storage battery charging time period overlaps with the valley price and flat price time period, the grid charges the photovoltaic storage battery. When the grid charges, the price is in the valley, and the grid power supply current ratio is 75-90%. The price is in the flat, and the grid power supply current ratio is 10%-20%. The system does not use the grid to supplement the photovoltaic storage battery in the sharp and peak time period;

[0010] Step four, load optimization: real-time acquisition of photovoltaic module power generation, state of charge SOC of energy storage system, power supply capacity of grid side, and charging demand of electric vehicle, short-term load prediction model: LSTM neural network is used to predict photovoltaic power generation, electric vehicle charging demand and grid price in the future. According to the prediction result, the power supply proportion of the grid and the energy storage system is dynamically adjusted to ensure optimal load distribution in different price periods: valley, flat, sharp, peak;

[0011] Step five, multi-objective optimization model: a multi-objective optimization model is established, the objective function includes: the minimum variance of grid side load: by optimizing the power supply proportion of the grid and the energy storage system, the load fluctuation of the grid side is reduced; The minimum maintenance cost of the energy storage system: optimize the charging and discharging strategy of the energy storage system, prolong the battery life and reduce the maintenance cost; The minimum charging cost: combined with the segmented price mechanism, the grid power and photovoltaic power in the valley price period are used to charge the electric vehicle, the constraint conditions include: power limit and state of charge SOC limit of the energy storage system, power limit of the grid side, fluctuation limit of photovoltaic power generation, NSGA-III algorithm is used to solve the multi-objective optimization model, and the Pareto optimal solution set is obtained. The optimal compromise scheme is selected by fuzzy clustering method;

[0012] Step six, real-time scheduling and feedback: The system monitors the changes in real time photovoltaic power generation, energy storage system state of charge, power grid price and electric vehicle charging demand, dynamically adjusts the power supply proportion of power grid and energy storage system, optimizes the charging and discharging strategy according to the actual operation effect, and if the discharging capacity of the energy storage system is insufficient during the peak electricity price period, the system adjusts the charging strategy during the flat valley period to increase the charging capacity of the energy storage system for subsequent use.

[0013] During the low-price valley period, the system preferentially uses grid power for charging, and the vehicle owner can charge at the lowest electricity price, thereby greatly reducing the charging cost.

[0014] During the high-price peak period and the peak period, the energy storage system can release the previously stored electric energy to supplement the power grid, thereby avoiding direct grid charging during the high-price period.

[0015] The charging system intelligently adjusts the charging method according to the real-time electricity price and load condition, and selects the optimal charging time and method (grid or energy storage). This flexibility allows the vehicle owner to charge at a lower electricity price, further reducing charging costs.

[0016] The system can flexibly determine whether to charge the electric vehicle through the grid or the energy storage system according to the grid load condition, ensuring optimization in charging costs.

[0017] The energy storage system stores electric energy during the non-peak electricity price period, especially during periods of low charging demand (such as at night), when transportation and grid load are reduced, and cheap electricity is utilized, which can effectively reduce charging costs.

[0018] When the electric vehicle charging demand peaks (electricity price peak), the stored electric energy is released through the energy storage system, allowing the vehicle owner to avoid direct grid charging during periods of high electricity price.

[0019] Charging with renewable energy such as photovoltaic power can, in the long run, allow the vehicle owner to enjoy lower charging costs and reduce dependence on traditional power grids.

[0020] Charging with clean energy not only helps reduce costs, but also enhances the vehicle owner's environmental image and social responsibility.

[0021] When the grid load is too high, the system can reduce the charging amount or limit charging from the grid to help balance the grid load, which helps reduce overall grid operating costs and ultimately passes on the savings to the vehicle owner.

[0022] Under the demand response mechanism, the vehicle owner can choose to charge the vehicle with energy storage during periods of high grid load to obtain corresponding subsidies or incentives, thereby further reducing costs.

[0023] Preferably, the step one charging determination calculation is:

[0024]

[0025] wherein: is a Boolean variable, representing the charging state; represents that there is no car charging, and the system enters step three; otherwise represents that the charging gun has been connected to the car for charging.

[0026] Preferably, the charging distribution optimization calculation in step two is:

[0027] Let: represent the total power supply current; represent the photovoltaic storage battery power supply current;

[0028] According to the electricity price period, the power supply current proportion of the power grid is The value range of is as follows: valley price: ; flat price: ; peak price: ;

[0029] The power supply current proportion of the photovoltaic storage battery is ;

[0030]

[0031]

[0032] wherein, The value of is determined according to the electricity price period :

[0033]

[0034] wherein: is the power supply current of the power grid, obtained by multiplying the total current by the power supply proportion of the power grid ; The value of is dynamically adjusted according to the electricity price period , to ensure optimal power supply distribution in different electricity price periods.

[0035]

[0036]

[0037] The power supply current proportion of the power grid in step three is calculated as:

[0038]

[0039] wherein: represents the remaining power of the photovoltaic storage battery, unit: kWh; represents the charging power of the power grid, unit: kW; represents the charging power ratio of the power grid; represents the current power grid price period: valley, flat, peak, peak; valley price: ; flat price: ; peak price and peak price: ; the charging power ratio of the photovoltaic storage battery is .

[0040] Preferably, the charging power ratio of the photovoltaic storage battery in step three is calculated as:

[0041]

[0042] wherein: is the charging power of the photovoltaic storage battery, unit: kilowatt; is the total charging power;

[0043] The charging power ratio of the power grid in step three is calculated as:

[0044]

[0045] wherein: is the charging power ratio of the power grid.

[0046] Preferably, the total power supply power in step four is calculated as:

[0047]

[0048] The power supply power ratio of the power grid is calculated as: ;

[0049] The power supply power ratio of the energy storage system is calculated as: ;

[0050] wherein: represents the power generation of the photovoltaic module at time , unit: kW; represents the power supply capacity of the power grid at time , unit: kW; represents the power supply power ratio of the power grid; represents the total power supply power, unit: kW.

[0051] Preferably, the dynamic adjustment of the power supply power ratio of the power grid in step four is:

[0052]

[0053] The proportion of power supply of the energy storage system in step four is dynamically adjusted as:

[0054]

[0055] Wherein: represents the proportion of power supply of the photovoltaic storage battery at time to the total power supply;

[0056] The charging power of the electric vehicle in step four is calculated as:

[0057]

[0058] Wherein: represents the charging power of the electric vehicle at time , unit: kW.

[0059] Preferably, the minimum load variance of the grid side in step five is calculated as:

[0060]

[0061] Wherein, represents the variance of the time series of the grid power supply;

[0062] The minimum operation and maintenance cost of the energy storage system is calculated as:

[0063]

[0064] Wherein: represents the unit maintenance cost of the energy storage system at time , unit: $ / kWh; represents the discharging power of the energy storage system at time , unit: kW; represents the charging power of the energy storage system at time , unit: kW;

[0065] The minimum charging cost is calculated as:

[0066]

[0067] Wherein: represents the electricity price of the grid at time , unit: $ / kWh; represents the power supply of the grid at time , unit: kW.

[0068] Preferably, the constraint conditions in step five include:

[0069] The power limit of the energy storage system:

[0070]

[0071]

[0072] wherein, is the maximum discharging power of the energy storage system, is the maximum charging power of the energy storage system;

[0073] State of Charge (SOC) limit: ;

[0074] wherein, and are the minimum and maximum state of charge of the energy storage system, respectively;

[0075] Grid-side power supply limit:

[0076] wherein, is the maximum power supply of the grid

[0077] Photovoltaic power fluctuation limit:

[0078] wherein, is the maximum power generation of the photovoltaic system.

[0079] Preferably, the real-time scheduling model in step six is:

[0080]

[0081] wherein: is the time is the grid price at time is the discharging cost of the energy storage system at time

[0082] The adjustment calculation of insufficient discharging capacity in the peak price period in step six is:

[0083] If the discharging capacity of the energy storage system in the peak price period is insufficient, the system will adjust the charging strategy in the flat valley period to increase the charging amount of the energy storage system;

[0084] Increase the charging amount in the flat valley period:

[0085]

[0086] wherein: is the insufficient part of the discharging capacity of the energy storage system in the peak price period;

[0087] Charging power adjustment in the flat valley period:

[0088] ​

[0089] wherein: is the adjusted valley period charging power.

[0090] Preferably,

[0091] Compared with the prior art, the application provides a light storage type charging station energy storage system optimization operation method, which has the following beneficial effects:

[0092] 1. In the valley period with low electricity price, the system preferentially uses grid power for charging, thereby improving the utilization rate of the grid, balancing the load of the grid, and reducing the pressure of the grid in the peak period and the peak period by reasonably allocating the charging power of the grid and the photovoltaic energy storage, so that the system can effectively reduce the load pressure of the grid in the peak period and the peak period, avoid the impact of excessive power consumption in the peak period on the grid, and smooth the load curve of the grid by dynamically adjusting the proportion of the grid charging power , thereby reducing the power transmission of the grid in the peak load period, especially in the peak period and the peak period, using the self-power of the photovoltaic energy storage system, thereby reducing the load fluctuation of the grid, providing rapid response capability of the energy storage system in the demand peak period, and instantaneously supplementing the required grid, reducing the instantaneous fluctuation of the grid load, and improving the overall stability of the power system, thereby achieving the beneficial effects of simultaneously improving the load of the grid side and the energy storage.

[0093] 2. The charging system intelligently adjusts the charging mode according to the real-time electricity price and load condition, selects the optimal charging time and mode, and the flexibility enables the vehicle owner to charge at a lower electricity price, thereby further reducing the charging cost, and the system can flexibly determine whether to charge the electric vehicle through the grid or the energy storage according to the grid load condition, thereby ensuring optimization of the charging cost, storing electric energy in the non-peak electricity price period, especially in the period with low charging demand, reducing the transportation and grid burden, and utilizing cheap electricity, thereby effectively reducing the charging cost, and when the electric vehicle charging demand is at the peak, the stored electric energy is released through the energy storage system, thereby enabling the vehicle owner to avoid directly charging from the grid in the period with high electricity price, and achieving the beneficial effects of simultaneously participating in the charging of the vehicle by the energy storage and the grid to reduce the charging cost of the vehicle owner.

[0094] 3. The light storage type charging station is equipped with an intelligent management system, can realize real-time monitoring of the grid, electricity price and photovoltaic power generation condition, automatically selects the optimal charging and discharging strategy, when the photovoltaic power generation is insufficient, the system can preferentially utilize the electric energy provided by the energy storage battery to meet the charging demand, thereby reducing the dependence on the grid and realizing efficient utilization of energy, and achieving the beneficial effects of improving the waste of energy storage caused by the lack of power source of photovoltaic components at night. BRIEF DESCRIPTION OF DRAWINGS

[0095] Figure 1The flow chart of the steps of the present application is shown in the figure; DETAILED DESCRIPTION

[0096] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0097] Please refer to Figure 1 A method for optimizing operation of a light storage type charging station energy storage system, the light storage type charging station energy storage system comprising a photovoltaic assembly, an inverter, a busbar box, a direct current distribution cabinet, an alternating current distribution cabinet, a photovoltaic storage battery, and a bidirectional DC-DC converter, the optimization method comprising the following steps:

[0098] Step one, vehicle charging determination: the light storage type charging station detects whether a charging gun is connected to a car for charging. If charging is detected, step two is entered. If no car is charging, step three is entered.

[0099] Step two, charging distribution optimization: the system switches modes according to the current grid price. The grid price includes valley, flat, peak, and peak. When the price is at the valley price, the grid power supply current ratio is 100%. When the price is at the flat price, the grid power supply current ratio is 65%-75%. When the price is at the peak price, the grid power supply current ratio is 40%-50%. When the price is at the peak price, the grid power supply current ratio is 0%-10%. The rest is supplied by the photovoltaic storage battery. When the photovoltaic storage battery and the grid supply power together, the bidirectional DC-DC converter is used to adjust the voltage.

[0100] Step three, energy storage optimization: the system reads the remaining power of the photovoltaic storage battery, calculates the full charging time according to the current photovoltaic assembly power generation, and supplements according to the current grid price. When the photovoltaic storage battery charging time period overlaps with the valley price and flat price time period, the grid charges the photovoltaic storage battery. When the grid charges, the price is at the valley price, and the grid power supply current ratio is 75-90%. When the price is at the flat price, the grid power supply current ratio is 10%-20%. The system does not use the grid to supplement the photovoltaic storage battery during the peak price and peak price time period.

[0101] Step four, load optimization: real-time acquisition of photovoltaic assembly power generation, state of charge SOC of energy storage system, power supply capacity of grid side, and charging demand of electric vehicles, short-term load forecasting model: LSTM neural network is used to predict photovoltaic power generation, electric vehicle charging demand, and grid price in a future period of time. According to the prediction result, the power supply proportion of the grid and the energy storage system is dynamically adjusted to ensure optimal load distribution during different price periods: valley, flat, peak, and peak.

[0102] Step five, multi-objective optimization model: a multi-objective optimization model is established, the objective functions include: minimum variance of grid-side load: by optimizing the power supply ratio of the grid and the energy storage system, the load fluctuation on the grid side is reduced; minimum energy storage system operation and maintenance cost: optimize the charging and discharging strategy of the energy storage system, prolong the battery life and reduce the maintenance cost; minimum charging cost: combined with the segmented electricity price mechanism, preferentially use the grid power and photovoltaic power in the valley price period for charging electric vehicles, the constraints include: power limit and state of charge SOC limit of the energy storage system, power limit of the grid side, fluctuation limit of photovoltaic power generation, NSGA-III algorithm is used to solve the multi-objective optimization model to obtain the Pareto optimal solution set, and the optimal compromise scheme is selected by fuzzy clustering method;

[0103] Step six, real-time scheduling and feedback: the system monitors the changes of photovoltaic power generation, state of charge of energy storage system, grid electricity price and charging demand of electric vehicles in real time, dynamically adjusts the power supply ratio of the grid and the energy storage system, optimizes the charging and discharging strategy according to the actual operation effect, and if the discharging capacity of the energy storage system is insufficient in the peak electricity price period, the system adjusts the charging strategy in the flat and valley period to increase the charging capacity of the energy storage system for subsequent use.

[0104] In the valley period with lower electricity price, the system preferentially uses grid power for charging, which improves the utilization rate of the grid and balances the grid load. By reasonably allocating the charging power of the grid and photovoltaic energy storage, the pressure on the grid in the peak period and peak period is reduced.

[0105] In the peak period and peak period, by reducing the charging power of the grid (for example, set to 0), the system can effectively reduce the load pressure of the grid and avoid the impact of excessive electricity consumption in the peak period on the grid.

[0106] The energy storage system releases stored energy when the grid load is high (such as the peak period), which not only reduces the burden of the energy storage system in the charging state, but also provides additional power supply for the grid, achieving load balancing.

[0107] The photovoltaic power generation system charges the energy storage system in the power generation peak period (usually during the day), and stores excess energy in the period with lower electricity price (valley price period), reducing the waste of electricity. This not only optimizes the efficiency of electricity use, but also provides a sustainable source of energy for grid supply.

[0108] When the grid electricity price is low, the energy storage system can charge, and then release the electricity in the high electricity price period (such as peak), improving the economic benefit. Providing electricity in the high electricity price period helps the grid accept less external power input, which helps to reduce the burden and power transmission loss of the grid.

[0109] By dynamically adjusting the proportion of grid charging power, the grid load curve is smoothed. During peak load periods, the grid reduces power transmission, especially during peak and peak periods, using photovoltaic energy storage system power, thereby reducing grid load fluctuations.

[0110] During peak demand, the energy storage system provides rapid response capability, instantly supplementing the grid's needs, reducing transient fluctuations in grid load, and improving overall power system stability.

[0111] Example 1: Valley price period

[0112] Set

[0113] Electricity price: Valley

[0114] Total power supply current : 100A

[0115] Photovoltaic power generation power : 50kW

[0116] Photovoltaic battery remaining power : 200kWh

[0117] Total charging power \(P_{\mathrm{total}}\) : 80kW

[0118] Grid power supply ratio \(\alpha\) : 1

[0119] Calculate

[0120] 1. Grid power supply current:

[0121]

[0122] 2. Photovoltaic battery power supply current:

[0123]

[0124] 3. Photovoltaic battery full charging time:

[0125]

[0126] 4. Grid charging power:

[0127]

[0128] 5. Photovoltaic battery charging power:

[0129]

[0130] Example 2: Flat price period

[0131] Set

[0132] Electricity price state: flat price

[0133] Total supply current : 100 A

[0134] Photovoltaic power generation power : 50 kW

[0135] Photovoltaic storage battery remaining power : 200 kWh

[0136] Total charging power : 80 kW

[0137] Grid power supply proportion : take the intermediate value 0.70

[0138] Calculation

[0139] 1. Grid supply current:

[0140]

[0141] 2. Photovoltaic storage battery supply current:

[0142]

[0143] 3. Photovoltaic storage battery full charging time:

[0144]

[0145] 4. Grid charging power:

[0146]

[0147] 5. Photovoltaic storage battery charging power:

[0148]

[0149] Example 3: peak price period

[0150] Set

[0151] Electricity price state: peak price

[0152] Total supply current : 100 A

[0153] Photovoltaic power generation power : 50 kW

[0154] Photovoltaic storage battery remaining power : 200 kWh

[0155] Total charging power : 80 kW

[0156] Grid power supply ratio : take the intermediate value 0.45

[0157] Calculation

[0158] 1. Grid power supply current:

[0159]

[0160] 2. Photovoltaic storage battery power supply current:

[0161]

[0162] 3. Photovoltaic storage battery full charging time:

[0163]

[0164] 4. Grid charging power:

[0165]

[0166] 5. Photovoltaic storage battery charging power:

[0167]

[0168] Example 4: Peak price period

[0169] Set

[0170] Electricity price state: peak price

[0171] Total power supply current : 100A

[0172] Photovoltaic power generation power : 50kW

[0173] Photovoltaic storage battery remaining capacity : 200kWh

[0174] Total charging power : 80kW

[0175] Grid power supply ratio : take value 0.05

[0176] 1. Grid power supply current:

[0177]

[0178] 2. Photovoltaic storage battery power supply current:

[0179]

[0180] 3. Photovoltaic storage battery full charging time:

[0181]

[0182] 4. Grid charging power:

[0183]

[0184] 5. Photovoltaic battery charging power:

[0185]

[0186] Example 1 (grain price):

[0187] Grid supply current: 100A

[0188] Photovoltaic battery supply current: 0A

[0189] Photovoltaic battery full charge time: 4 hours

[0190] Grid charging power: 80kW

[0191] Photovoltaic battery charging power: 0kW

[0192] Example 2 (parity):

[0193] Grid supply current: 70A

[0194] Photovoltaic battery supply current: 30A

[0195] Photovoltaic battery full charge time: 4 hours

[0196] Grid charging power: 8kW

[0197] Photovoltaic battery charging power: 72kW

[0198] Example 3 (sharp price):

[0199] Grid supply current: 45A

[0200] Photovoltaic battery supply current: 55A

[0201] Photovoltaic battery full charge time: 4 hours

[0202] Grid charging power: 0kW

[0203] Photovoltaic battery charging power: 80kW

[0204] Example 4 (peak price):

[0205] Grid supply current: 5A

[0206] Photovoltaic battery supply current: 95A

[0207] Photovoltaic battery full charge time: 4 hours

[0208] Grid charging power: 0 kW

[0209] PV battery charging power: 80 kW

[0210] These embodiments demonstrate how the PV-battery charging station optimizes power distribution and charging efficiency under different electricity pricing periods.

[0211] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely divergences of the principles and spirit of the application and that numerous modifications, changes, substitutions, and alterations can be undertaken by one skilled in the art without departing from the principles and spirit of the application, which is defined by the claims and their equivalents.

Claims

1. A method for optimizing the operation of a photovoltaic charging station energy storage system, wherein the photovoltaic charging station energy storage system includes photovoltaic modules, an inverter, a combiner box, a DC distribution cabinet, an AC distribution cabinet, a photovoltaic storage battery, and a bidirectional DC-DC converter, characterized in that: The optimization operation method comprises the following steps: Step 1: Vehicle charging determination: The solar-powered charging station detects whether the charging gun is connected to the car for charging. If it is charging, it proceeds to step 2; if not, it proceeds to step 3; Step 2: Optimize charging distribution: The system switches modes based on the current grid electricity price, which includes valley, flat, peak, and peak electricity prices. When the electricity price is at the valley price, the grid-supplied current accounts for 100%; when the electricity price is at the flat price, the grid-supplied current accounts for 65%-75%; when the electricity price is at the peak price, the grid-supplied current accounts for 40%-50%; and when the electricity price is at the peak price, the grid-supplied current accounts for 0%-10%. The remainder is supplied by the photovoltaic battery. When the photovoltaic battery and the grid jointly supply power, a bidirectional DC-DC converter is used to regulate the voltage. Step 3: Energy storage optimization: The system reads the remaining power of the photovoltaic storage battery, calculates the full charge time based on the current power generation of the photovoltaic module, and supplements it based on the grid electricity price. If the photovoltaic storage battery charging period overlaps with the valley price and parity price period, the grid will charge the photovoltaic storage battery. When the grid is charging, the grid supply current accounts for 75%-90% when the electricity price is at valley price, and 10%-20% when the electricity price is at parity price. The system does not use the grid to supplement the photovoltaic storage battery during peak price and peak price periods; Step 4: Load Optimization: The system collects real-time data on PV panel power generation, the energy storage system's state of charge (SOC), the grid's power supply capacity, and EV charging requirements. It then uses a short-term load forecasting model, an LSTM neural network, to predict PV power generation, EV charging requirements, and grid electricity prices over a specific period. Based on these forecasts, the system dynamically adjusts the power supply ratio between the grid and the energy storage system to ensure optimal load distribution during different price periods: off-peak, flat, peak, and peak. Step 5. Multi-objective optimization model: A multi-objective optimization model is established. The objective functions include: Minimizing grid-side load variance: By optimizing the power supply ratio between the grid and the energy storage system, the load fluctuation on the grid side is reduced; Minimizing the operation and maintenance cost of the energy storage system: Optimizing the charging and discharging strategy of the energy storage system to extend battery life and reduce maintenance costs; Minimizing charging cost: In combination with the segmented electricity price mechanism, the grid electricity and photovoltaic power generation are preferentially used during the valley price period to charge electric vehicles. The constraints include: the power limit and state of charge (SOC) limit of the energy storage system, the power supply limit on the grid side, and the fluctuation limit of the photovoltaic power generation. The NSGA-III algorithm is used to solve the multi-objective optimization model to obtain the Pareto optimal solution set, and the fuzzy clustering method is used to screen the optimal compromise solution; Step 6: Real-time Scheduling and Feedback: The system monitors changes in PV power generation, the energy storage system's state of charge, grid electricity prices, and EV charging demand in real time, dynamically adjusting the power supply ratio between the grid and the energy storage system. It optimizes the charging and discharging strategy based on actual operating results. If the energy storage system's discharge capacity is insufficient during peak electricity price periods, the system adjusts the charging strategy during off-peak periods, increasing the energy storage system's charge capacity for subsequent use. The minimum grid load variance calculation in step 5 is: in, represents the time series variance of the power supplied by the power grid; The minimum operation and maintenance cost of the energy storage system is calculated as: in: Indicates time The unit maintenance cost of the energy storage system is Indicates time The energy storage system discharge power at Indicates time Energy storage system charging power at 100 ms; The minimum charging cost is calculated as: in: Indicates time The grid electricity price at Indicates time The grid power supply power at the time The constraints in step 5 include: Energy storage system power limit: in, is the maximum discharge power of the energy storage system, is the maximum charging power of the energy storage system; State of charge SOC limit: ; in, and are the minimum and maximum states of charge of the energy storage system, respectively; Power supply limit on the grid side: in, is the maximum power supply of the power grid Limitations on the fluctuation of photovoltaic power generation: in, is the maximum power generation capacity of the photovoltaic system; The real-time scheduling model in step 6 is: in: For time The grid electricity price at that time; For time The discharge cost of the energy storage system when The adjustment calculation for insufficient discharge capacity during peak electricity price period in step 6 is: If the energy storage system's discharge capacity is insufficient during peak electricity price periods, the system will adjust its charging strategy during off-peak periods to increase the amount of energy storage system charged. Increase the charging capacity during off-peak hours: in: This is the insufficient discharge capacity of the energy storage system during peak electricity price periods; Charging power adjustment during off-peak hours: in: It is the charging power during the adjusted off-peak period.

2. The method for optimizing the operation of a photovoltaic charging station energy storage system according to claim 1, characterized in that: The calculation of step 1 charging determination is: in: is a Boolean variable indicating the charging status; Indicates that there is no car charging, the system enters step 3; otherwise It means that the charging gun has been connected to the car for charging.

3. The method for optimizing the operation of a photovoltaic charging station energy storage system according to claim 1, characterized in that: The charging distribution optimization calculation in step 2 is: set up: Indicates the total supply current; Indicates the photovoltaic battery supply current; According to the electricity price period, the proportion of grid power supply current The value range of is as follows: Grain price: (100%); parity: ; Tip price: Peak price: ; The proportion of power supply current of photovoltaic storage battery is ; in, The value is determined according to the electricity price period Sure: in: is the grid supply current, which is determined by the total current Multiply by the proportion of grid power supply get; The value is determined according to the electricity price period Dynamic adjustment ensures optimal power supply distribution during different electricity price periods.

4. The method for optimizing the operation of a photovoltaic charging station energy storage system according to claim 1, characterized in that: The proportion of grid charging power in step 3 is calculated as: in: Indicates the charging power of the grid, unit: kW; Indicates the proportion of grid charging power; Indicates the current power grid electricity price period: valley, flat, peak, peak; valley price: ;parity: ; Tip price and peak price: ; The proportion of photovoltaic charging power is .

5. The method for optimizing the operation of a photovoltaic charging station energy storage system according to claim 1, characterized in that: The total power supply power in step 4 is calculated as: The proportion of grid power supply is calculated as: ; The proportion of energy storage system power supply is calculated as: ; in: Indicates the photovoltaic module at time Power generation power, unit: kW; Indicates the power grid at time Power supply capacity, unit: kW; Indicates the proportion of power supplied by the power grid; Indicates the total power supply, in kW.

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