Charging station based on integration of source network load and storage

Abstract

The invention relates to the technical field of charging stations, and provides a charging station based on source-grid-load storage integration, which comprises a source-grid-load storage system, the source-grid-load storage system comprises a power grid unit, an energy storage unit, a photovoltaic unit, a charging pile, an edge calculation unit, a collaborative scheduling unit, an execution module and a feedback module, the energy storage unit is connected with the power grid unit through a circuit, and the photovoltaic unit is connected with the charging pile through a circuit. The photovoltaic unit is connected with the energy storage unit through a circuit, the electric energy input end of the charging pile is connected with the power grid unit and the energy storage unit through circuits, the collaborative scheduling unit is in communication connection with the edge computing unit through an internet communication platform, and the execution module is in signal connection with the collaborative scheduling unit. The signal input end of the feedback module is in signal connection with the execution module. According to the invention, fine power metering and an ordered charging strategy are carried out through the edge calculation unit, and effective management and optimal scheduling of the distributed charging station are realized through cooperation with the cooperative scheduling unit, so that the waste of electric energy is reduced, and finally the revenue maximization of the charging station is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of charging stations, and in particular to a charging station based on source-grid-load-storage integration. Background Art

[0002] Renewable energy generation is characterized by randomness and intermittency. With the continuous growth of the installed capacity of new energy, it has brought severe challenges to the scheduling and arrangement of power grid dispatching, safe and economical dispatching and operation. The random problems on both sides of the power system supply and demand are becoming increasingly prominent. The traditional distribution network operation control does not take into account the active support capability of distributed photovoltaic power sources to the power grid. Distributed photovoltaic power sources currently do not have control performance such as voltage ride-through control and grid adaptability under fault conditions. Therefore, the traditional "source follows load" dispatching and control method is far from meeting the development needs of the power system, and is gradually shifting to a coordinated interactive dispatching method of source, grid, load and storage.

[0003] After searching, the Chinese patent publication number CN116187702A discloses "a source-grid-load-storage collaborative interactive optimization scheduling system, characterized in that it includes an edge computing module, a state evaluation module and a collaborative scheduling module; the edge computing module is used to perform data calculations on the new energy power stations and electric vehicle charging stations in the power grid, and transmit the calculation results to the state evaluation module; the state evaluation module evaluates the state of the power grid and sends the evaluation results to the collaborative scheduling module; the collaborative scheduling module performs collaborative scheduling of the source-grid-load-storage units in the power grid according to the evaluation results; the source-grid-load-storage units include the new energy power stations, electric vehicle charging stations, thermal power stations and energy storage units in the power grid".

[0004] This type of patent evaluates the overall state of the power grid and implements different source-grid-load-storage collaborative interactive optimization scheduling for different power grid states. However, in the scheduling process of source-grid-load-storage, the income of the charging station is also particularly important. Although the safe operation of the charging station can be maintained during scheduling, if the electricity sales cost is not considered, it will not only waste electricity but also cause losses to the charging station. Therefore, this type of patent cannot guarantee that the charging station can maximize its profits while balancing the load. In view of this, the present invention proposes a charging station based on the integration of source-grid-load-storage. Summary of the invention

[0005] The present invention proposes a charging station based on source-grid-load-storage integration, which solves the problem of being unable to maximize the benefits of the charging station.

[0006] The technical solution of the present invention is as follows: a charging station based on source-grid-load-storage integration includes a source-grid-load-storage system, which includes a power grid unit, an energy storage unit, a photovoltaic unit, a charging pile, an edge computing unit, a collaborative scheduling unit, an execution module and a feedback module; the power grid unit is used to provide a stable power supply; the energy storage unit is connected to the power grid unit through a circuit, and is used to access the power grid unit to store electric energy during low-peak power consumption; the photovoltaic unit is connected to the energy storage unit through a circuit, and is used to convert solar energy into electric energy to provide green electricity and input electric energy to the energy storage unit for energy storage; the power input end of the charging pile is connected to the power grid unit and the energy storage unit through a circuit The edge computing unit includes an edge server, a data acquisition module, a classification metering module, a load balancing calculation module, a charging and discharging strategy formulation module, a virtual power plant invitation judgment module and a revenue optimization calculation module connected to the edge server signal. The edge server is connected to the virtual power plant through an Internet communication platform. The signal input end of the data acquisition module is connected to the power grid unit, the energy storage unit, the photovoltaic unit and the charging pile signal, and is used to collect the operation data of the power grid unit, the energy storage unit, the photovoltaic unit and the charging pile infrastructure. The classification metering module is used to calculate the operation data of the power grid unit, the energy storage unit, the photovoltaic unit and the charging pile infrastructure through the corresponding mathematical model. The output power of the energy storage unit, photovoltaic unit and charging pile infrastructure is calculated and the total power is calculated. The load balancing calculation module is used to calculate the load limit of the power grid unit through the corresponding mathematical model. The charging and discharging strategy formulation module is used to formulate an orderly charging and discharging strategy for the energy storage unit through the corresponding mathematical model. The virtual power plant invitation judgment module is used to calculate whether the current charging station can participate in the invitation mechanism of the virtual power plant through the corresponding mathematical model. The revenue optimization calculation module is used to calculate the maximum revenue after the energy distribution of the current charging station power grid unit, energy storage unit, charging pile and photovoltaic unit through the corresponding mathematical model; the collaborative scheduling unit and The edge computing unit is connected through the Internet communication platform, and is used to optimize the scheduling through the operation data of the power grid unit, energy storage unit, photovoltaic unit and charging pile infrastructure and issue corresponding optimization instructions; the execution module is connected to the collaborative scheduling unit signal, and is used to execute the optimization instructions to adjust the operation status of the power grid unit, energy storage unit, photovoltaic unit and charging pile infrastructure; the signal input end of the feedback module is connected to the execution module signal, and the signal output end of the feedback module is connected to the collaborative scheduling unit signal, and is used to feed back the adjustment status of the power grid unit, energy storage unit, photovoltaic unit and charging pile infrastructure to the collaborative scheduling unit for further optimization scheduling.

[0007] Preferably, the mathematical model of the classification metering module is: assuming that there are N charging piles, M energy storage units, and K photovoltaic units in the charging station, for any time t, the power metering of each device can be expressed as the following mathematical formula:

[0008]

[0009] Where P ch,i (t) is the charging pile power, where i = 1, 2, ..., N; P es,j (t) is the power of the energy storage unit, where j = 1, 2, ..., M; P pv,k (t) is the output power of the photovoltaic unit, where k = 1, 2, ..., K.

[0010] Preferably, the mathematical model of the load balancing calculation module is: Assume that the load limit of the power grid unit on the charging station is P limit (t), the mathematical formula for load balancing calculation is:

[0011]

[0012] In the formula, by adjusting the charging pile power P ch,i (t) and energy storage unit power P es,j (t) makes the above equation valid to balance the load of the grid unit.

[0013] Preferably, the mathematical model of the charging and discharging strategy formulation module is: Assume that the charging demand of the charging pile is D i (t), the storage energy of the energy storage unit is S j (t), the mathematical formula for the orderly charging and discharging strategy is:

[0014]

[0015] P min,i ≤P ch,i (t)≤P max,i ;

[0016]

[0017] Where f and g are the cost functions of the charging pile and energy storage unit, respectively, P min,i and P max,i are the minimum and maximum power limits of the charging pile, S min,j and S max,j are the minimum and maximum capacity limits of the energy storage system, respectively.

[0018] Preferably, the mathematical model of the virtual power plant invitation judgment module is: Let C i is the classified metering load of the i-th distributed charging station, R i is the upper limit of the invitation capacity that the charging station can respond to, V t is the target capacity of the virtual power plant in period t, B i Indicates the Boolean variable that determines the charging station that participates in the invitation, where Bi The judgment logic is:

[0019]

[0020] Where n is the total number of distributed charging stations participating in the evaluation. The model first checks whether the load of each charging station is within its response capacity (C i ≤R i ), and then calculate whether the total load of all charging stations that may participate in the invitation does not exceed the invitation target capacity of the virtual power plant Charging stations that meet the conditions are given B i =1, indicating that he participates in the invitation; otherwise, B i =0.

[0021] Preferably, the mathematical model of the revenue optimization calculation module is: the profit of the charging station is set to J, wherein the calculation formula for the maximum profit of the charging station is;

[0022]

[0023] Where T is the total time step of the scheduling cycle (in hours), P sell,t and P buy,t are the power sold and purchased in the tth time step (in kW), π sell,t and π buy,t are the electricity prices for electricity sales and electricity purchases in the tth time step (in yuan / kWh), C op,t is the operating cost in the tth time step (in yuan).

[0024] Preferably, the P sell,t The constraint mathematical model is:

[0025] P grid,t +P pv,t +P storage,t =P load,t +P charging,t +P sell,t ;

[0026] Where P grid,t is the power purchased from the grid in the tth time step, P pv,t is the photovoltaic power generation power in the tth time step, P storage,t is the power of the energy storage system charged and discharged in the tth time step, P load,t is the load power of the charging station itself in the tth time step, P charging,t is the charging power of the charging pile in the tth time step.

[0027] Preferably, the collaborative dispatching unit includes a load monitoring module, a distributed dispatching module, a load benefit management module and an optimization instruction generation module; the load monitoring module is used to monitor the operating status of the power grid unit, the energy storage unit, the photovoltaic unit and the charging pile; the distributed dispatching module is signal-connected to the load monitoring module, and is used to analyze the actual status and demand of the power grid unit, the energy storage unit, the photovoltaic unit and the charging pile according to the load monitoring results, and perform dispatching and optimization; the load benefit management module is signal-connected to the distributed dispatching module, and is used to calculate the economic benefit of the charging station through the benefit optimization calculation module to provide economic optimization support for the distributed dispatching module; the optimization instruction generation module is signal-connected to the distributed dispatching module, and is used to issue optimization instructions to the execution module according to the optimization results of the distributed dispatching module.

[0028] The working principle and beneficial effects of the present invention are:

[0029] 1. A close energy complementary relationship is formed between photovoltaic units, power grid units, charging piles and energy storage units. Photovoltaic units provide green electricity for charging stations, power grid units ensure stable power supply, energy storage units balance the load of the power grid, and charging piles serve as hubs for charging electric vehicles, converting electrical energy into actual use value. The four work together, which not only improves energy utilization efficiency and reduces operating costs, but also enhances user experience and promotes environmental protection and sustainable development of charging stations. This integrated model promotes efficient use of energy and low carbon emissions, and is an important direction for future energy development;

[0030] 2. Through the edge computing unit, precise power metering and orderly charging strategies are carried out for the power grid unit, energy storage unit, photovoltaic unit and charging pile, and then the coordinated scheduling unit is cooperated to realize the effective management and optimized scheduling of distributed charging stations, which not only reduces the waste of electric energy, but also ultimately maximizes the revenue of charging stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0032] Figure 1 This is a system block diagram of a charging station based on source-grid-load-storage integration of the present invention;

[0033] Figure 2 is a system block diagram of a charging station of the present invention;

[0034] Figure 3 It is a system block diagram of the collaborative scheduling unit of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] like Figure 1 to Figure 3 As shown, this embodiment proposes a charging station based on source-grid-load-storage integration, including a source-grid-load-storage system, which includes a power grid unit, an energy storage unit, a photovoltaic unit, a charging pile, an edge computing unit, a collaborative scheduling unit, an execution module, and a feedback module;

[0037] The grid unit is used to provide a stable power supply. When the photovoltaic unit generates insufficient power or during peak power consumption periods, the grid unit can quickly replenish power to ensure that the charging station continues to provide charging services for electric vehicles;

[0038] The energy storage unit is connected to the grid unit through a circuit and is used to access the grid unit to store electric energy during low-peak electricity consumption. It can release electric energy when electricity prices are high or photovoltaic power generation is insufficient. This not only balances the grid load and improves energy utilization efficiency, but also reduces the operating cost of the charging station, making the charging station more resilient in the face of power fluctuations and ensuring the stability and reliability of power supply.

[0039] The photovoltaic unit and the energy storage unit are connected through a circuit, which is used to convert solar energy into electrical energy to provide green electricity and input electrical energy into the energy storage unit for storage. This not only reduces dependence on fossil energy, but also improves the environmental protection and sustainability of the charging station by reducing carbon emissions. Photovoltaic power generation effectively reduces the power supply pressure of the power grid unit and reduces the operating cost of the charging station.

[0040] The power input end of the charging pile is connected to the grid unit and the energy storage unit through a circuit, which is used to convert electrical energy into current suitable for electric vehicles, meeting the charging needs of electric vehicle users.

[0041] A close energy complementary relationship has been formed between photovoltaic units, power grid units, charging piles and energy storage units. Photovoltaic units provide green electricity for charging stations, power grid units ensure stable power supply, energy storage units balance the power grid load, and charging piles serve as hubs for charging electric vehicles, converting electrical energy into actual use value. The four work together to not only improve energy utilization efficiency and reduce operating costs, but also enhance user experience and promote the environmental protection and sustainable development of charging stations. This integrated model promotes efficient energy utilization and low-carbon emissions, and is an important direction for future energy development.

[0042] The edge computing unit includes an edge server, a data acquisition module, a classification metering module, a load balancing calculation module, a charging and discharging strategy formulation module, a virtual power plant invitation judgment module and a revenue optimization calculation module connected to the edge server signal. The edge server is connected to the virtual power plant through an Internet communication platform; the signal input end of the data acquisition module is connected to the power grid unit, the energy storage unit, the photovoltaic unit and the charging pile signal, and is used to collect the operation data of the power grid unit, the energy storage unit, the photovoltaic unit and the charging pile infrastructure. The classification metering module is used to calculate the output power of the energy storage unit, the photovoltaic unit and the charging pile infrastructure respectively through the corresponding mathematical model and calculate the total power. Among them, the mathematical model of the classification metering module is: suppose there are N charging piles, M energy storage units and K photovoltaic units in the charging station. For any time t, the power metering of each device can be expressed as the following mathematical formula:

[0043]

[0044] Where P ch,i (t) is the charging pile power, where i = 1, 2, ..., N; P es,j (t) is the power of the energy storage unit, where j = 1, 2, ..., M; P pv,k (t) is the output power of the photovoltaic unit, where k = 1, 2, ..., K.

[0045] The load balancing calculation module is used to calculate the load limit of the power grid unit through the corresponding mathematical model, wherein the mathematical model of the load balancing calculation module is: Assume that the load limit of the power grid unit on the charging station is P limit (t), the mathematical formula for load balancing calculation is:

[0046]

[0047] In the formula, by adjusting the charging pile power P ch,i (t) and energy storage unit power P es,j (t) makes the above equation valid to balance the load of the grid unit.

[0048] The charging and discharging strategy formulation module is used to formulate an orderly charging and discharging strategy for the energy storage unit through a corresponding mathematical model. The mathematical model of the charging and discharging strategy formulation module is: Assume that the charging demand of the charging pile is D i (t), the storage energy of the energy storage unit is S j (t), the mathematical formula for the orderly charging and discharging strategy is:

[0049]

[0050] P min,i ≤P ch,i (t)≤Pmax,i ;

[0051]

[0052] Where f and g are the cost functions of the charging pile and energy storage unit, respectively, P min,i and P max,i are the minimum and maximum power limits of the charging pile, S min,j and S max,j are the minimum and maximum capacity limits of the energy storage system, respectively.

[0053] The virtual power plant invitation judgment module is used to calculate whether the current charging station can participate in the virtual power plant invitation mechanism through the corresponding mathematical model. The mathematical model of the virtual power plant invitation judgment module is: Let C i is the classified metering load of the i-th distributed charging station, R i is the upper limit of the invitation capacity that the charging station can respond to, V t is the target capacity of the virtual power plant in period t, B i Indicates the Boolean variable that determines the charging station that participates in the invitation, where B i The judgment logic is:

[0054]

[0055] Where n is the total number of distributed charging stations participating in the evaluation. The model first checks whether the load of each charging station is within its response capacity (C i ≤R i ), and then calculate whether the total load of all charging stations that may participate in the invitation does not exceed the invitation target capacity of the virtual power plant Charging stations that meet the conditions are given B i =1, indicating that he participates in the invitation; otherwise, B i =0.

[0056] The revenue optimization calculation module is used to calculate the maximum revenue after the energy distribution of the current charging station grid unit, energy storage unit, charging pile, and photovoltaic unit through the corresponding mathematical model. The mathematical model of the revenue optimization calculation module is: the profit of the charging station is set to J, and the calculation formula for the maximum profit of the charging station is;

[0057]

[0058] Where T is the total time step of the scheduling cycle (in hours), P sell,t and P buy,t are the power sold and purchased in the tth time step (in kW), π sell,t and π buy,tare the electricity prices for electricity sales and electricity purchases in the tth time step (in yuan / kWh), C op,t is the operating cost in the tth time step (in yuan).

[0059] Preferably, P sell,t The constraint mathematical model is:

[0060] P grid,t +P pv,t +P storage,t =P load,t +P charging,t +P sell,t ;

[0061] Where P grid,t is the power purchased from the grid in the tth time step, P pv,t is the photovoltaic power generation power in the tth time step, P storage,t is the power of the energy storage system charged and discharged in the tth time step, P load,t is the load power of the charging station itself in the tth time step, P charging,t is the charging power of the charging pile in the tth time step.

[0062] The collaborative scheduling unit is communicatively connected to the edge computing unit through an Internet communication platform, and is used to optimize scheduling and issue corresponding optimization instructions through the operating data of the power grid unit, energy storage unit, photovoltaic unit and charging pile infrastructure. The execution module is signal-connected to the collaborative scheduling unit, and is used to execute the optimization instructions to adjust the operating status of the power grid unit, energy storage unit, photovoltaic unit and charging pile infrastructure; the signal input end of the feedback module is signal-connected to the execution module, and the signal output end of the feedback module is signal-connected to the collaborative scheduling unit, and is used to feed back the adjustment status of the power grid unit, energy storage unit, photovoltaic unit and charging pile infrastructure to the collaborative scheduling unit for further optimization scheduling.

[0063] Furthermore, the collaborative dispatching unit includes a load monitoring module, a distributed dispatching module, a load revenue management module and an optimization instruction generation module; the load monitoring module is used to monitor the operating status of the power grid unit, the energy storage unit, the photovoltaic unit and the charging pile; the distributed dispatching module is signal-connected to the load monitoring module, and is used to analyze the actual status and demand of the power grid unit, the energy storage unit, the photovoltaic unit and the charging pile according to the load monitoring results, and perform dispatching and optimization; the load revenue management module is signal-connected to the distributed dispatching module, and is used to calculate the economic benefit of the charging station through the revenue optimization calculation module to provide economic optimization support for the distributed dispatching module; the optimization instruction generation module is signal-connected to the distributed dispatching module, and is used to issue optimization instructions to the execution module according to the optimization results of the distributed dispatching module.

[0064] The edge computing unit is used to perform precise power metering and orderly charging strategies for the power grid unit, energy storage unit, photovoltaic unit and charging pile, and the coordinated scheduling unit is used to achieve effective management and optimized scheduling of distributed charging stations, which not only reduces the waste of electricity, but also ultimately maximizes the revenue of charging stations.

[0065] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A charging station based on the integration of source, grid, load and storage, including a source, grid, load and storage system, characterized in that: The source-grid-load-storage system comprises Grid units, which provide a stable supply of electricity; An energy storage unit, which is connected to the power grid unit through a circuit and is used to access the power grid unit to store electric energy during low-peak electricity consumption; A photovoltaic unit, wherein the photovoltaic unit is connected to the energy storage unit through a circuit, and is used to convert solar energy into electrical energy to provide green electricity and input electrical energy into the energy storage unit for energy storage; A charging pile, wherein the power input end of the charging pile is connected to the power grid unit and the energy storage unit through a circuit, and is used to convert the electric energy into a current suitable for use by the electric vehicle; An edge computing unit, wherein the edge computing unit includes an edge server, a data acquisition module, a classification metering module, a load balancing calculation module, a charging and discharging strategy formulation module, a virtual power plant invitation judgment module and a revenue optimization calculation module connected to the edge server signal, wherein the edge server is connected to the virtual power plant through an Internet communication platform; the signal input end of the data acquisition module is connected to the power grid unit, the energy storage unit, the photovoltaic unit and the charging pile signal, and is used to collect the operation data of the power grid unit, the energy storage unit, the photovoltaic unit and the charging pile infrastructure; the classification metering module is used to calculate the output power of the energy storage unit, the photovoltaic unit and the charging pile infrastructure respectively through the corresponding mathematical model and calculate the total power; the load balancing calculation module is used to calculate the load limit of the power grid unit through the corresponding mathematical model; the charging and discharging strategy formulation module is used to formulate an orderly charging and discharging strategy of the energy storage unit through the corresponding mathematical model; the virtual power plant invitation judgment module is used to calculate whether the current charging station can participate in the invitation mechanism of the virtual power plant through the corresponding mathematical model; the revenue optimization calculation module is used to calculate the maximum revenue after the energy distribution of the power grid unit, the energy storage unit, the charging pile and the photovoltaic unit of the current charging station through the corresponding mathematical model; A collaborative scheduling unit, which is connected to the edge computing unit through an Internet communication platform, and is used to optimize the scheduling and issue corresponding optimization instructions based on the operation data of the power grid unit, the energy storage unit, the photovoltaic unit and the charging pile infrastructure; An execution module, the execution module is connected to the coordinated scheduling unit signal and is used to execute optimization instructions to adjust the operating status of the power grid unit, the energy storage unit, the photovoltaic unit and the charging pile infrastructure; A feedback module, wherein the signal input end of the feedback module is connected to the execution module signal, and the signal output end of the feedback module is connected to the collaborative scheduling unit signal, and is used to feed back the adjustment status of the power grid unit, energy storage unit, photovoltaic unit and charging pile infrastructure to the collaborative scheduling unit for further optimization scheduling.

2. The charging station based on source-grid-load-storage integration according to claim 1 is characterized in that: The mathematical model of the classification metering module is: suppose there are N charging piles, M energy storage units, and K photovoltaic units in the charging station. For any time t, the power metering of each device can be expressed as the following mathematical formula: Where P ch,i (t) is the charging pile power, where i = 1, 2, ..., N; P es,j (t) is the power of the energy storage unit, where j = 1, 2, ..., M; P pv,k (t) is the output power of the photovoltaic unit, where k = 1, 2, ..., K.

3. The charging station based on source-grid-load-storage integration according to claim 2 is characterized in that: The mathematical model of the load balancing calculation module is: Assume that the load limit of the power grid unit on the charging station is P limit (t), the mathematical formula for load balancing calculation is: In the formula, by adjusting the charging pile power P ch,i (t) and energy storage unit power P es,j (t) makes the above equation valid to balance the load of the grid unit.

4. The charging station based on source-grid-load-storage integration according to claim 4 is characterized in that: The mathematical model of the charging and discharging strategy formulation module is: Assume that the charging demand of the charging pile is D i (t), the storage capacity of the energy storage unit is S j (t), the mathematical formula for the orderly charging and discharging strategy is: P min,i ≤P ch,i (t)≤P max,i ; Where f and g are the cost functions of the charging pile and energy storage unit, respectively, P min,i and P max,i are the minimum and maximum power limits of the charging pile, S min,j and S max,j are the minimum and maximum capacity limits of the energy storage system, respectively.

5. The charging station based on source-grid-load-storage integration according to claim 4 is characterized in that: The mathematical model of the virtual power plant invitation judgment module is: Let C i is the classified metering load of the i-th distributed charging station, R i is the upper limit of the invitation capacity that the charging station can respond to, V t is the target capacity of the virtual power plant in period t, B i Indicates the Boolean variable that determines the charging station that participates in the invitation, where B i The judgment logic is: Where n is the total number of distributed charging stations participating in the evaluation. The model first checks whether the load of each charging station is within its response capacity (C i ≤R i ), and then calculate whether the total load of all charging stations that may participate in the invitation does not exceed the invitation target capacity of the virtual power plant Charging stations that meet the conditions are given B i =1, indicating that he participates in the invitation; otherwise, B i =0.

6. The charging station based on source-grid-load-storage integration according to claim 1 is characterized in that: The mathematical model of the revenue optimization calculation module is: the profit of the charging station is set to J, wherein the calculation formula for the maximum profit of the charging station is; Where T is the total time step of the scheduling cycle (in hours), P sell,t and P buy,t are the power sold and purchased in the tth time step (in kW), π sell,t and π buy,t are the electricity prices for electricity sales and electricity purchases in the tth time step (in yuan / kWh), C op,t is the operating cost in the tth time step (in yuan).

7. The charging station based on source-grid-load-storage integration according to claim 6 is characterized in that: The P sell,t The constraint mathematical model is: P grid,t +P pv,t +P storage,t =P load,t +P charging,t +P sell,t ; Where P grid,t is the power purchased from the grid in the tth time step, P pv,t is the photovoltaic power generation power in the tth time step, P storage,t is the power of the energy storage system charged and discharged in the tth time step, P load,t is the load power of the charging station itself in the tth time step, P charging,t is the charging power of the charging pile in the tth time step.

8. The charging station based on source-grid-load-storage integration according to claim 1 is characterized in that: The collaborative scheduling unit includes Load monitoring module, used to monitor the operating status of the grid unit, energy storage unit, photovoltaic unit and charging pile; A distributed scheduling module, which is connected to the load monitoring module signal, is used to analyze the actual status and demand of the power grid unit, energy storage unit, photovoltaic unit and charging pile according to the load monitoring results, and perform scheduling and optimization; A load revenue management module, which is connected to the distributed scheduling module by signal, and is used to calculate the economic benefits of the charging station through the revenue optimization calculation module to provide economic optimization support for the distributed scheduling module; The optimization instruction generation module is connected to the distributed scheduling module by signal, and is used to issue optimization instructions to the execution module according to the optimization result of the distributed scheduling module.

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