Scheduling method and device for virtual power plant in case of battery energy storage aging in commercial scene

Abstract

The invention provides a method and a device for scheduling a virtual power plant during battery energy storage aging in a business scene, and the method comprises the steps: taking the efficiency benefit maximization of the virtual power plant in the business scene as a target, and taking the energy balance established according to the flexible power resources in the virtual power plant and the aging state of the flexible power resources as constraints; establishing an electric power system efficiency model of the virtual power plant, and solving the electric power system efficiency model to obtain a scheduling strategy of the electric power flexible resources and an energy efficiency income of the virtual power plant; wherein the virtual power plant in the commercial scene comprises a typical commercial building, a charging station and a battery swap station. According to the method, flexible resource scheduling of the virtual power plant in a typical business scene is realized, the income of the virtual power plant is increased by coordinating different flexible resources, the balance of a power system is facilitated, and effective utilization of distributed resources is realized.

Description

Technical Field

[0001] The present application relates to the technical field of virtual power plant dispatching optimization, and particularly to a dispatching method and device for a virtual power plant when battery energy storage ages in a commercial scenario. Background Art

[0002] As a flexible dispatching platform integrating distributed energy resources, a virtual power plant (VPP) has gradually become an important part of the power system.

[0003] In a commercial scenario, the flexible resource dispatching of a virtual power plant faces many challenges. Resources such as distributed photovoltaics and energy storage systems in typical commercial buildings, electric vehicles and backup battery echelons in charging and swapping stations have high dynamics and uncertainties. Moreover, energy storage systems and batteries will face aging problems during long-term operation, and their capacity attenuation and performance degradation will have a significant impact on the dispatching flexibility and economy of the virtual power plant.

[0004] The application of existing virtual power plant dispatching technologies in commercial scenarios has deficiencies. On the one hand, traditional dispatching models often ignore the impact of energy storage system aging on dispatching flexibility and economy, resulting in dispatching strategies that cannot accurately reflect the actual available capacity and performance changes of the energy storage system. On the other hand, existing technologies lack comprehensive optimized dispatching of flexible resources in commercial scenarios and fail to fully explore the collaborative potential of resources such as distributed photovoltaics, energy storage, and electric vehicles, thus limiting the maximization of the virtual power plant's revenue in market bidding.

[0005] Therefore, there is an urgent need for a virtual power plant dispatching technology that can comprehensively consider the impact of energy storage system aging to improve the flexible resource dispatching ability and market revenue of the virtual power plant in commercial scenarios. Summary of the Invention

[0006] The present application provides a dispatching method and device for a virtual power plant when battery energy storage ages in a commercial scenario to improve the flexible resource dispatching ability of the virtual power plant in commercial scenarios and accurately evaluate the efficiency benefits of the virtual power plant.

[0007] In a first aspect, an embodiment of the present application provides a dispatching method for a virtual power plant when battery energy storage ages in a commercial scenario. The dispatching method for a virtual power plant when battery energy storage ages in a commercial scenario includes:

[0008] Taking the maximization of the efficiency benefit of the virtual power plant in the commercial scenario as the goal, and taking the energy balance established according to the power flexible resources in the virtual power plant and the aging state of the power flexible resources as the constraints, establish the power system efficiency model of the virtual power plant, and by solving the power system efficiency model, obtain the dispatching strategy of the power flexible resources and the energy efficiency benefit of the virtual power plant;

[0009] Among them, the virtual power plant in the commercial scenario includes: typical commercial buildings, charging stations, and battery swapping stations.

[0010] In a second aspect, an embodiment of the present application provides a scheduling device for a virtual power plant when the battery energy storage in a commercial scenario ages. The scheduling device for a virtual power plant when the battery energy storage in a commercial scenario ages includes:

[0011] A calculation module, which is used to maximize the efficiency benefit of the virtual power plant in the commercial scenario, establish a power system efficiency model of the virtual power plant with the energy balance established according to the flexible power resources in the virtual power plant and the aging state of the flexible power resources as constraints, and obtain the scheduling strategy of the flexible power resources and the energy efficiency benefit of the virtual power plant by solving the power system efficiency model;

[0012] Among them, the virtual power plant in the commercial scenario includes: typical commercial buildings, charging stations, and battery swapping stations.

[0013] The beneficial effects brought by the technical solution provided by the embodiment of the present application include:

[0014] By maximizing the efficiency benefit of the virtual power plant in the commercial scenario, establishing a power system efficiency model of the virtual power plant with the energy balance established according to the flexible power resources in the virtual power plant and the aging state of the flexible power resources as constraints, and obtaining the scheduling strategy of the flexible power resources and the energy efficiency benefit of the virtual power plant by solving the power system efficiency model; among them, the virtual power plant in the commercial scenario includes: typical commercial buildings, charging stations, and battery swapping stations. The flexible resource scheduling of the virtual power plant in the typical commercial scenario is realized. By coordinating different flexible resources, the income of the virtual power plant is increased, which helps to balance the power system and realizes the effective utilization of distributed resources. Description of the Drawings

[0015] Figure 1 It is a schematic flowchart of an embodiment of the scheduling method for a virtual power plant when the battery energy storage in the commercial scenario of the present application ages;

[0016] Figure 2 It is a schematic diagram of the functional modules of an embodiment of the scheduling device for a virtual power plant when the battery energy storage in the commercial scenario of the present application ages. Detailed Embodiments

[0017] To enable those skilled in the art to better understand the solution of this application, the following will clearly and completely describe the technical solution in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts belong to the scope of protection of this application.

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following will further describe the embodiments of this application in detail with reference to the accompanying drawings.

[0019] In a first aspect, an embodiment of this application provides a scheduling method for a virtual power plant when the battery energy storage ages in a commercial scenario.

[0020] In one embodiment, referring to Figure 1 , Figure 1 is a schematic flowchart of the first embodiment of the scheduling method for a virtual power plant when the battery energy storage ages in the commercial scenario of this application. The scheduling method for a virtual power plant when the battery energy storage ages in the commercial scenario of this application includes:

[0021] Taking the maximum efficiency benefit of the virtual power plant in the commercial scenario as the objective, and taking the energy balance established according to the power flexible resources in the virtual power plant and the aging state of the power flexible resources as constraints, establish the power system efficiency model of the virtual power plant, and by solving the power system efficiency model, obtain the scheduling strategy of the power flexible resources and the energy efficiency benefit of the virtual power plant.

[0022] It is worth noting that the power system efficiency model of the virtual power plant is based on the influence mechanism of the aging of power flexible resources on the power system scheduling. At the micro-mechanism level, it provides a system model for the virtual power plant scheduling based on the aging of power flexible resources for the power system, in order to improve the benefits of the virtual power plant in typical commercial scenarios. It defines a quantifiable evaluation system for the value of power flexible resources in the virtual power plant in typical commercial scenarios. The benefit of the energy storage system considering the aging of power flexible resources is defined as the value of the aging of power flexible resources in the virtual power plant. By analyzing the aging losses of power flexible resources in different typical commercial scenarios, the benefits of the virtual power plant are evaluated, and reasonable decisions are made accordingly.

[0023] The virtual power plant in the commercial scenario of this application includes: typical commercial buildings, charging stations, and battery swapping stations. The following will be described in combination with specific commercial scenarios.

[0024] Typical commercial buildings such as office buildings and government office buildings are generally equipped with distributed photovoltaic, distributed energy storage, central air conditioning and other distributed dispatchable flexibility resources. When a typical commercial building participates in the energy market as a virtual power plant, it is crucial to accurately model the distributed dispatchable power flexibility resources therein.

[0025] When the virtual power plant is a typical commercial building, aiming at maximizing the efficiency benefit of the typical commercial building, and taking the internal energy balance of the typical commercial building established according to the power flexibility resources of the typical commercial building and the aging state of the power flexibility resources of the typical commercial building as constraints, a typical commercial building power system efficiency model is established, and by solving the typical commercial building power system efficiency model, the dispatch strategy of the power flexibility resources of the typical commercial building and the energy efficiency benefit of the typical commercial building are obtained; wherein, the power flexibility resources of the typical commercial building include: distributed photovoltaic, distributed energy storage and central air conditioning.

[0026] For the distributed photovoltaic in a typical commercial building, its output follows a Beta distribution. An output model of the distributed photovoltaic in the typical commercial building is established:

[0027]

[0028]

[0029] Wherein, is the output of the distributed photovoltaic at time t, A PV is the effective area of the distributed photovoltaic, η PV is the efficiency of the distributed photovoltaic, is the maximum output of the distributed photovoltaic, α and β are the historical output distribution parameters of the distributed photovoltaic, μ PV is the average value of the historical output of the distributed photovoltaic, σ PV is the variance of the historical output of the distributed photovoltaic. The uncertainty of the photovoltaic panel output can be characterized by simulating different weather scenarios.

[0030] A distributed energy storage model in the typical commercial building is established:

[0031]

[0032] Wherein, is the energy storage level of the distributed energy storage i at time t, is the charging power of the distributed energy storage i at time t, is the discharging power of the distributed energy storage i at time t, is the efficiency of the distributed energy storage i.

[0033] The charging and discharging power of the distributed energy storage is constrained within the preset power upper and lower limits:

[0034]

[0035] The energy storage level of the constrained distributed energy storage is within the upper and lower limits of the preset energy storage level:

[0036]

[0037] Among them, is the upper limit of the charge and discharge power of distributed energy storage i, is the lower limit of the energy storage level of distributed energy storage i, is the upper limit of the energy storage level of distributed energy storage i.

[0038] For central air conditioners, taking summer air conditioning refrigeration as an example, when the air conditioner cluster is operating stably, the indoor temperature T in can be regarded as stable and unchanged, that is, the air conditioning refrigeration rate is equivalent to the building heat dissipation rate. Considering the human thermal comfort in typical commercial buildings, the set temperature in the room is T set , and the temperature control margin is set as T Δ , then the range of the room temperature is in [T set - T Δ , T set + T Δ .

[0039] Establish a central air conditioning model in a typical commercial building:

[0040]

[0041] And constrain the power of the central air conditioner to be within the upper and lower limits of the air conditioner power:

[0042]

[0043] Among them, is the power of the central air conditioner at time t, is the indoor set temperature at time t, is the outdoor temperature at time t, η AC is the energy efficiency of the central air conditioner, is the temperature control margin at time t, is the upper limit of the air conditioner power, is the lower limit of the air conditioner power. Among them, can be learned from historical data.

[0044] The loads in a typical commercial building also include power and lighting loads (including elevator operation and lighting). Based on the output of distributed photovoltaics, the charging power and discharging power of distributed energy storage, the power of central air conditioning, the power and lighting loads of a typical commercial building, and the power interaction between a typical commercial building and the power grid, an internal energy balance of the typical commercial building is established as a constraint for the power system efficiency model of the typical commercial building:

[0045]

[0046] Among them, is the power interaction between the typical commercial building and the power grid at time t, is the power and lighting load of the typical commercial building at time t. Among them can be either positive or negative. When it is positive, it represents that the typical commercial building purchases electricity from the power grid. When it is negative, it represents that the typical commercial building releases electricity to the power grid.

[0047] During the use process of the energy storage in a typical commercial building, there is an aging phenomenon, and its energy capacity will gradually decrease with use, thus unable to provide the expected benefits for the typical commercial building. If distributed energy storage is not considered, there is an optimistic estimate of the benefits of the typical commercial building, and it cannot truly reflect the benefits of the typical commercial building participating in the energy market as a virtual power plant.

[0048] Therefore, this application takes the aging state of distributed energy storage as a constraint for the power system efficiency model of the typical commercial building:

[0049]

[0050] Among them, is the upper limit of the energy storage level at time t when the distributed energy storage i ages, and σ Building is the aging coefficient of the energy storage system, and E i is the initial energy storage level of the distributed energy storage i.

[0051] The objective function of the power system efficiency model of the typical commercial building is:

[0052]

[0053] In the formula:

[0054]

[0055] Among them, Q is the efficiency benefit of the typical commercial building, is the total internal electricity load of the typical commercial building, p t is the electricity price, and ω and b are efficiency benefit coefficients.

[0056] By maximizing the objective function of the power system efficiency model of a typical commercial building, solving the power system efficiency model of the typical commercial building can obtain the output of distributed photovoltaics, the charge and discharge power of distributed energy storage, the power of central air conditioning, and the first efficiency benefit Q of the typical commercial building when considering the aging of distributed energy storage. B1 。

[0057] It should be noted that in this embodiment, by taking the aging state of distributed energy storage as a constraint of the power system efficiency model of a typical commercial building, solving the power system efficiency model of the typical commercial building can obtain an accurate dispatching strategy for distributed photovoltaics, distributed energy storage, and central air conditioning when the goal is to maximize the energy efficiency benefit of the typical commercial building, thereby increasing the efficiency benefit of the virtual power plant of the typical commercial building.

[0058] In one embodiment, the method further includes: not considering the constraint of the aging state of distributed energy storage in the power system efficiency model of a typical commercial building:

[0059]

[0060] That is, deleting this constraint, solving the power system efficiency model of the typical commercial building to obtain the output of distributed photovoltaics, the charge and discharge power of distributed energy storage, the power of central air conditioning, and the second efficiency benefit Q of the typical commercial building when not considering the aging of distributed energy storage. B2 ;

[0061] Taking the difference between the second efficiency benefit and the first efficiency benefit of the typical commercial building to obtain the benefit loss caused by the aging of distributed energy storage;

[0062] Calculating the operation aging loss of the distributed energy storage per unit according to the second efficiency benefit and the first efficiency benefit of the typical commercial building:

[0063]

[0064] Among them, ε1 is the operation aging loss of the distributed energy storage per unit, Q B1 is the first efficiency benefit of the typical commercial building, Q B2 is the second efficiency benefit of the typical commercial building.

[0065] It should be noted that the operation aging loss of the distributed energy storage calculated in this embodiment can provide data support for the system optimization decision of the virtual power plant of the typical commercial building.

[0066] Furthermore, during the operation of commercial charging stations, a large number of electric vehicle resources will be connected. After aggregating a large number of batteries in these electric vehicles, they can be used as flexibility resources for virtual power plant dispatching.

[0067] When the virtual power plant is a charging station, it includes: aiming at maximizing the charging station efficiency revenue, and taking the internal energy balance of the charging station established according to the flexible power resources of the charging station and the aging state of the flexible power resources of the charging station as constraints, establishing a charging station power system efficiency model, and by solving the charging station power system efficiency model, obtaining the scheduling strategy of the flexible power resources of the charging station and the energy efficiency revenue of the charging station; wherein, the flexible power resources of the charging station include: electric vehicles that charge by joining the power grid through the charging station.

[0068] Specifically, let t a and t d be the heating times when the electric vehicle joins and leaves the power grid. Existing research shows that the on-grid and off-grid times of electric vehicles both follow a normal distribution. Set the probability density of the electric vehicle joining and leaving the power grid through the charging station as:

[0069]

[0070] wherein, t a is the time when the electric vehicle joins the power grid, t d is the time when the electric vehicle leaves the power grid, μ is the average on-grid time of the electric vehicle, and σ is the variance of the on-grid time of the electric vehicle.

[0071] During the off-grid period of the electric vehicle, the charging station cannot dispatch the battery resources of the electric vehicle, and its charging and discharging power is zero:

[0072]

[0073] When the electric vehicle is off-grid, its energy storage level should reach the set value Generally, it is considered that it is fully charged at this time, that is:

[0074]

[0075] wherein, t a,i is the time when the electric vehicle i joins the power grid, t d,i is the time when the electric vehicle i leaves the power grid, is the battery charging and discharging power of the electric vehicle i at time t, is the battery energy storage level of the electric vehicle i at time t, is the target value of the battery energy storage level of the electric vehicle. i is the subscript of the electric vehicle.

[0076] Restrict the battery charging and discharging power of each electric vehicle within the preset upper and lower limits of the charging and discharging power:

[0077]

[0078] The charging and discharging scheduling of the battery of each electric vehicle is modeled as:

[0079]

[0080] Among them, is the battery charging power of electric vehicle i at time t, is the battery discharging power of electric vehicle i at time t, is the maximum charge-discharge power of the battery of electric vehicle i, is the charge-discharge efficiency of the battery of electric vehicle i, is the lower limit of the battery energy storage level of electric vehicle i, is the upper limit of the battery energy storage level of electric vehicle i.

[0081] Combined with the scheduling of the battery energy storage in electric vehicles in the charging station, an internal energy balance model of the charging station is established as a constraint of the charging station power system efficiency model:

[0082]

[0083] Among them, is the power interaction between the charging station and the power grid at time t, is the total load of the charging station at time t, is the battery charging power of electric vehicle i at time t, is the battery discharging power of electric vehicle i at time t, I t is the set of electric vehicles being charged in the charging station at time t. can be either positive or negative. When it is positive, it means the charging station purchases power from the power grid; when it is negative, it means the charging station releases power to the power grid.

[0084] The energy storage level of the electric vehicle battery should also be controlled within the upper and lower limits of the battery storage level. When considering the aging of the electric vehicle battery, the upper limit of the energy storage level of each schedulable electric vehicle is time-varying when making decisions in the charging station and is given by the battery life assessment algorithm.

[0085] When considering the aging of the electric vehicle battery, constrain the energy storage level of the electric vehicle battery:

[0086]

[0087] Among them, is the lower limit of the battery energy storage level of electric vehicle i, is the upper limit of the energy storage level of electric vehicle i at time t when considering the aging of the electric vehicle battery.

[0088] The objective function of the charging station power system efficiency model is:

[0089]

[0090] Among them, θi The unit price of charging service for the charging station is p t is the electricity price;

[0091] Solve the power system efficiency model of the charging station to obtain the charging and discharging power of the electric vehicle battery and the first energy efficiency benefit of the charging station when considering the aging of the electric vehicle battery.

[0092] It should be noted that in this embodiment, by using the energy storage level after the aging of the electric vehicle battery as a constraint of the power system efficiency model of the charging station, solving the power system efficiency model of the charging station can obtain an accurate scheduling strategy for the charging and discharging power of the electric vehicle battery with the goal of maximizing the energy efficiency benefit of the charging station, thereby improving the energy efficiency benefit of the virtual power plant of the charging station.

[0093] In one embodiment, remove the constraint of the upper limit of the energy storage level of the electric vehicle battery after the aging of the electric vehicle battery from the power system efficiency model of the charging station, that is, delete:

[0094]

[0095] And use the upper limit of the energy storage level of the electric vehicle battery when not considering the aging of the electric vehicle battery as a constraint of the power system efficiency model of the charging station:

[0096]

[0097] where is the lower limit of the battery energy storage level of electric vehicle i, is the upper limit of the energy storage level of electric vehicle i when not considering the aging of the electric vehicle battery;

[0098] Solve the power system efficiency model of the charging station to obtain the charging and discharging power, energy storage level of the electric vehicle battery and the second energy efficiency benefit of the charging station when not considering the aging of the electric vehicle battery;

[0099] Take the difference between the second energy efficiency benefit and the first energy efficiency benefit of the charging station to obtain the benefit loss caused by the aging of the electric vehicle battery;

[0100] Calculate the operating aging loss of the electric vehicle battery per unit according to the second energy efficiency benefit and the first energy efficiency benefit of the charging station:

[0101]

[0102] where ε2 is the operating aging loss of the electric vehicle battery per unit, Q E1 is the first energy efficiency benefit of the charging station, Q E2 is the second energy efficiency benefit of the charging station.

[0103] It should be noted that the operation aging loss of the electric vehicle calculated in this embodiment can provide data support for the system optimization decision of the charging station virtual power plant.

[0104] Furthermore, in one embodiment, a large number of electric vehicle resources are also accessed during the operation of a commercial battery swapping station. Different from commercial charging stations, the battery resources in commercial battery swapping stations can be separated from the vehicle body and, after aggregation, used as flexible resources for virtual power plant dispatching.

[0105] When the virtual power plant is a battery swapping station, it includes:

[0106] Taking the maximization of the battery swapping station's efficiency revenue as the goal, and taking the internal energy balance of the battery swapping station established based on the flexible power resources of the battery swapping station and the aging state of the flexible power resources of the battery swapping station as constraints, a battery swapping station power system efficiency model is established, and by solving the battery swapping station power system efficiency model, the dispatching strategy of the flexible power resources of the battery swapping station and the energy efficiency revenue of the battery swapping station are obtained;

[0107] Among them, the flexible power resources of the battery swapping station include: the batteries in the battery swapping station that are charged by connecting to the power grid through the battery swapping station.

[0108] Specifically, let t a be the time when the electric vehicle enters the battery swapping station. Existing research shows that the access of electric vehicles to the power grid follows a normal distribution. The probability density of the time when the electric vehicle enters the battery swapping station is set as:

[0109]

[0110] The time when the electric vehicle leaves the battery swapping station is mainly determined by the time when it enters the battery swapping station. When the reserve battery in the battery swapping station is sufficient, the time for each electric vehicle to swap batteries follows a distribution Δt ∼ F(Δt). Then the time when the electric vehicle leaves the battery swapping station is:

[0111] t d = t a + Δt

[0112] where t a is the time when the electric vehicle enters the battery swapping station, t d is the time when the electric vehicle leaves the battery swapping station, and Δt is the time for the electric vehicle to swap batteries.

[0113] Restrict the charging and discharging power of each reserve battery within the preset upper and lower limits of the charging and discharging power:

[0114]

[0115] The charging and discharging scheduling of each reserve battery is modeled as:

[0116]

[0117] Among them, is the upper limit of the charging power of the backup battery j, is the charging power of the backup battery j at time t, is the discharging power of the backup battery j at time t, is the efficiency of the backup battery j, is the energy storage level of the backup battery j at time t, and j is the subscript of the backup battery.

[0118] The energy storage level of the backup battery should also be controlled within the upper and lower limits of the battery. When considering the aging of the backup battery, the energy storage level of the backup battery is restricted:

[0119]

[0120] Among them, is the lower limit of the energy storage level of the backup battery j, is the upper limit of the energy storage level of the backup battery j at time t when considering the aging of the backup battery.

[0121] The number of batteries in the battery swapping station is affected by the arrival and departure of electric vehicles at the battery swapping station. The constraint relationship between the number of backup batteries in the battery swapping station and the electric vehicle queue is:

[0122]

[0123] Among them, is the number of backup batteries existing in the battery swapping station at time t, is the number of electric vehicles arriving at the battery swapping station at time t, is the number of electric vehicles leaving the battery swapping station at time t.

[0124] The number of batteries in the battery swapping station is affected by the battery storage bin in the battery swapping station, and there is an upper limit. The upper limit of the number of backup batteries in the battery swapping station is restricted as:

[0125]

[0126] Among them, is the upper limit of the number of backup batteries in the battery swapping station;

[0127] Combined with the scheduling of the battery energy storage in the battery swapping station, an internal energy balance model of the battery swapping station is established as a constraint of the battery swapping station power system efficiency model:

[0128]

[0129] Among them, is the power interaction between the battery swapping station and the power grid at time t, is the total load of the battery swapping station at time t, The charging power of the backup battery j at time t The discharging power of the backup battery j at time t, J t is the set of backup batteries in the swapping station at time t

[0130] In this embodiment, the situation where the swapping station cannot provide fully charged batteries for swapping out is not considered. The objective function of the power system efficiency model of the swapping station is as follows:

[0131]

[0132] where θ j is the unit price of the charging service of the swapping station, and p t is the electricity price

[0133] Solve the power system efficiency model of the swapping station to obtain the charging and discharging powers of the backup batteries and the first energy efficiency revenue of the swapping station when considering the aging of the backup batteries

[0134] It should be noted that in this embodiment, by taking the energy storage level of the backup batteries in the swapping station after aging as the constraint of the power system efficiency model of the swapping station, solving the power system efficiency model of the swapping station can obtain an accurate scheduling strategy for the charging and discharging powers of the backup batteries with the maximum energy efficiency revenue of the swapping station as the goal, thereby improving the energy efficiency revenue of the virtual power plant of the swapping station

[0135] In one embodiment, remove the constraint on the upper limit of the energy storage level of the backup batteries after aging in the power system efficiency model of the swapping station, that is, delete:

[0136]

[0137] And take the upper limit of the energy storage level of the backup batteries when not considering the aging of the backup batteries as the constraint of the power system efficiency model of the swapping station:

[0138]

[0139] where is the lower limit of the energy storage level of the backup battery j, is the upper limit of the energy storage level of the backup battery j when not considering the aging of the backup battery

[0140] Solve the power system efficiency model of the swapping station to obtain the charging and discharging powers of the backup batteries and the second energy efficiency revenue of the swapping station when not considering the aging of the backup batteries

[0141] Take the difference between the second energy efficiency revenue and the first energy efficiency revenue of the swapping station to obtain the loss of energy efficiency revenue caused by the aging of the backup batteries

[0142] Calculate the operating aging loss of a single unit of backup battery according to the second energy efficiency revenue and the first energy efficiency revenue of the swapping station

[0143]

[0144] Among them, ε3 is the operating aging loss of the backup battery in units, and Q H2 is the first efficiency benefit of the battery swapping station, and Q H1 is the second efficiency benefit of the battery swapping station.

[0145] It should be noted that the calculated operating aging loss of the backup battery in this embodiment can provide data support for the system optimization decision of the virtual power plant of the battery swapping station.

[0146] The dispatching method of the virtual power plant when the battery energy storage ages in the commercial scenario provided by the embodiment of the present application aims at the operation optimization problem of the virtual power plant in the typical commercial scenario, with the goal of maximizing the market bidding revenue of the virtual power plant in the commercial scenario. Considering the system energy balance constraint, renewable resource dispatching constraint, energy storage system constraint, electric vehicle charging and swapping travel demand constraint, and combining the impact of battery energy storage aging, a power system model that combines energy storage system aging and virtual power plant dispatching is constructed. Through advanced dispatching and planning, the revenue of the virtual power plant is maximized. This includes the flexible resource dispatching of typical commercial buildings and battery swapping stations in typical commercial scenarios; and focuses on the impact of battery energy storage system aging on the revenue of the virtual power plant. The results of this model effectively solve the flexible resource dispatching of the virtual power plant in the typical commercial scenario. By coordinating different flexible resources, the revenue of the virtual power plant is increased, which helps to balance the power system and realizes the effective utilization of distributed resources.

[0147] In a second aspect, the embodiment of the present application also provides a dispatching device for a virtual power plant when the battery energy storage ages in a commercial scenario.

[0148] In one embodiment, referring to Figure 2 , Figure 2 is a schematic diagram of the functional modules of an embodiment of the dispatching device for a virtual power plant when the battery energy storage ages in the commercial scenario of the present application. As Figure 2 shown, the dispatching device for a virtual power plant when the battery energy storage ages in a commercial scenario includes:

[0149] A calculation module, which is used to aim at maximizing the efficiency benefit of the virtual power plant in the commercial scenario, and establish a power system efficiency model of the virtual power plant with the energy balance established according to the flexible power resources in the virtual power plant and the aging state of the flexible power resources as constraints, and obtain the dispatching strategy of the flexible power resources and the energy efficiency benefit of the virtual power plant by solving the power system efficiency model;

[0150] Among them, the virtual power plant in the commercial scenario includes: typical commercial buildings, charging stations, and battery swapping stations.

[0151] Further, in one embodiment, the calculation module is further configured to: aiming at maximizing the efficiency benefit of a typical commercial building, establishing a typical commercial building power system efficiency model with the internal energy balance of the typical commercial building established based on the flexible power resources of the typical commercial building and the aging state of the flexible power resources of the typical commercial building as constraints, and obtaining the scheduling strategy of the flexible power resources of the typical commercial building and the energy efficiency benefit of the typical commercial building by solving the typical commercial building power system efficiency model;

[0152] Wherein, the flexible power resources of the typical commercial building include: distributed photovoltaic, distributed energy storage, and central air conditioning.

[0153] Further, in one embodiment, the calculation module is further configured to: establish an output model of the distributed photovoltaic in the typical commercial building:

[0154]

[0155]

[0156] Wherein, is the output of the distributed photovoltaic at time t, A PV is the effective area of the distributed photovoltaic, η PV is the efficiency of the distributed photovoltaic, is the maximum output of the distributed photovoltaic, α and β are the historical output distribution parameters of the distributed photovoltaic, μ PV is the average value of the historical output of the distributed photovoltaic, σ PV is the variance of the historical output of the distributed photovoltaic;

[0157] Establish a distributed energy storage model in the typical commercial building:

[0158]

[0159] Wherein, is the energy storage level of the distributed energy storage i at time t, is the charging power of the distributed energy storage i at time t, is the discharging power of the distributed energy storage i at time t,

[0160] is the efficiency of the distributed energy storage i;

[0161] Restrict the charging and discharging power of the distributed energy storage within the preset power upper and lower limits:

[0162]

[0163] Restrict the energy storage level of the distributed energy storage within the preset energy storage level upper and lower limits:

[0164]

[0165] Among them, is the upper limit of the charging and discharging power of distributed energy storage i, is the lower limit of the energy storage level of distributed energy storage i, is the upper limit of the energy storage level of distributed energy storage i;

[0166] Build a central air-conditioning model in a typical commercial building:

[0167]

[0168] And constrain the power of the central air-conditioning within the upper and lower limits of the air-conditioning power:

[0169]

[0170] Among them, is the power of the central air-conditioning at time t, is the indoor set temperature at time t, is the outdoor temperature at time t, η AC is the energy efficiency of the central air-conditioning, is the temperature control margin at time t, is the upper limit of the air-conditioning power, is the lower limit of the air-conditioning power;

[0171] Based on the output of distributed photovoltaics, the charging power and discharging power of distributed energy storage, the power of the central air-conditioning, the power and lighting load of a typical commercial building, and the power interaction between the typical commercial building and the power grid, establish the internal energy balance of the typical commercial building as a constraint of the power system efficiency model of the typical commercial building:

[0172]

[0173] Among them, is the power interaction between the typical commercial building and the power grid at time t, is the power and lighting load of the typical commercial building at time t;

[0174] Take the aging state of the distributed energy storage as a constraint of the power system efficiency model of the typical commercial building:

[0175]

[0176] Among them, is the upper limit of the energy storage level at time t when the distributed energy storage i ages, σ Building is the aging coefficient of the energy storage system, E i is the initial energy storage level of the distributed energy storage i;

[0177] The objective function of the power system efficiency model for a typical commercial building is as follows:

[0178]

[0179] In the formula:

[0180]

[0181]

[0182] Among them, Q is the efficiency benefit of the typical commercial building, is the total internal electricity load of the typical commercial building, p t is the electricity price, and ω and b are efficiency benefit coefficients;

[0183] Solve the power system efficiency model of the typical commercial building to obtain the output of distributed photovoltaics, the charge and discharge power of distributed energy storage, the power of the central air conditioner, and the first efficiency benefit of the typical commercial building when considering the aging of distributed energy storage.

[0184] Furthermore, in one embodiment, the calculation module is further configured to:

[0185] Without considering the constraint of the aging state of the distributed energy storage in the power system efficiency model of the typical commercial building, solve the power system efficiency model of the typical commercial building to obtain the output of distributed photovoltaics, the charge and discharge power of distributed energy storage, the power of the central air conditioner, and the second efficiency benefit of the typical commercial building when not considering the aging of distributed energy storage;

[0186] Subtract the second efficiency benefit of the typical commercial building from the first efficiency benefit to obtain the benefit loss caused by the aging of the distributed energy storage;

[0187] According to the second efficiency benefit and the first efficiency benefit of the typical commercial building, calculate the operation aging loss of the distributed energy storage per unit:

[0188]

[0189] Among them, ε1 is the operation aging loss of the distributed energy storage per unit, Q B1 is the first efficiency benefit of the typical commercial building, Q B2 is the second efficiency benefit of the typical commercial building.

[0190] Furthermore, in one embodiment, the calculation module is further configured to:

[0191] With the goal of maximizing the efficiency benefit of the charging station, taking the internal energy balance of the charging station established according to the flexible power resources of the charging station and the aging state of the flexible power resources of the charging station as constraints, a power system efficiency model of the charging station is established, and by solving the power system efficiency model of the charging station, the scheduling strategy of the flexible power resources of the charging station and the energy efficiency benefit of the charging station are obtained;

[0192] Among them, the flexible power resources of the charging station include: electric vehicles that charge by joining the power grid through the charging station.

[0193] Furthermore, in one embodiment, the calculation module is further configured to:

[0194] Set the probability density of electric vehicles joining and leaving the power grid through the charging station as:

[0195]

[0196] where t a is the time when the electric vehicle joins the power grid, t d is the time when the electric vehicle leaves the power grid, μ is the average grid connection time of the electric vehicle, and σ is the variance of the grid connection time of the electric vehicle;

[0197] And set the charge and discharge during the period when the electric vehicle leaves the power grid to zero:

[0198]

[0199] When the electric vehicle leaves the power grid, the stored energy of the battery of the electric vehicle reaches the set value:

[0200]

[0201] where t a,i is the time when the electric vehicle i joins the power grid, t d,i is the time when the electric vehicle i leaves the power grid, is the charge and discharge power of the battery of the electric vehicle i at time t, is the battery energy storage level of the electric vehicle i at time t, is the target value of the battery energy storage level of the electric vehicle;

[0202] Restrict the charge and discharge power of each electric vehicle battery within the preset upper and lower limits of the charge and discharge power:

[0203]

[0204] The charge and discharge scheduling of each electric vehicle battery is modeled as:

[0205]

[0206] Among them, The battery charging power of electric vehicle i at time t The battery discharging power of electric vehicle i at time t The maximum charge-discharge power of the battery of electric vehicle i The charge-discharge efficiency of the battery of electric vehicle i The lower limit of the battery energy storage level of electric vehicle i The upper limit of the battery energy storage level of electric vehicle i

[0207] Establish an internal energy balance model of the charging station as a constraint for the charging station power system efficiency model:

[0208]

[0209] Among them, The power interaction between the charging station and the power grid at time t The total load of the charging station at time t The battery charging power of electric vehicle i at time t The battery discharging power of electric vehicle i at time t, I t The set of electric vehicles being charged in the charging station at time t

[0210] When considering the aging of the electric vehicle battery, constrain the energy storage level of the electric vehicle battery:

[0211]

[0212] Among them, The lower limit of the battery energy storage level of electric vehicle i The upper limit of the energy storage level of electric vehicle i at time t when considering the aging of the electric vehicle battery

[0213] The objective function of the charging station power system efficiency model is:

[0214]

[0215] Among them, θ i The charging service unit price of the charging station, p t The electricity price

[0216] Solve the charging station power system efficiency model to obtain the charge-discharge power of the electric vehicle battery and the first energy efficiency benefit of the charging station when considering the aging of the electric vehicle battery.

[0217] Furthermore, in one embodiment, the calculation module is further configured to:

[0218] Remove the constraint on the upper limit of the energy storage level of the electric vehicle battery after battery aging in the charging station power system efficiency model, and use the upper limit of the energy storage level of the electric vehicle battery when not considering electric vehicle battery aging as the constraint of the charging station power system efficiency model:

[0219]

[0220] Among them, is the lower limit of the battery energy storage level of electric vehicle i, is the upper limit of the energy storage level of electric vehicle i when not considering electric vehicle battery aging;

[0221] Solve the charging station power system efficiency model to obtain the charging and discharging power, energy storage level of the electric vehicle battery, and the second efficiency benefit of the charging station when not considering electric vehicle battery aging;

[0222] Subtract the second efficiency benefit of the charging station from the first efficiency benefit to obtain the benefit loss caused by electric vehicle battery aging;

[0223] Calculate the operating aging loss of a unit of electric vehicle battery based on the second efficiency benefit and the first efficiency benefit of the charging station:

[0224]

[0225] Among them, ε2 is the operating aging loss of a unit of electric vehicle battery, Q E1 is the first efficiency benefit of the charging station, Q E2 is the second efficiency benefit of the charging station.

[0226] Furthermore, in one embodiment, the calculation module is further configured to:

[0227] Taking the maximization of the swapping station efficiency benefit as the goal, and using the internal energy balance of the swapping station established according to the flexible power resources of the swapping station and the aging state of the flexible power resources of the swapping station as constraints, establish a swapping station power system efficiency model, and by solving the swapping station power system efficiency model, obtain the scheduling strategy of the flexible power resources of the swapping station and the energy efficiency benefit of the swapping station;

[0228] Among them, the flexible power resources of the swapping station include: the batteries in the swapping station that are charged by the swapping station connecting to the power grid.

[0229] Furthermore, in one embodiment, the calculation module is further configured to:

[0230] Set the time probability density of electric vehicles entering the swapping station as:

[0231]

[0232] When the spare batteries in the battery swapping station are sufficient, the time for an electric vehicle to swap batteries follows a normal distribution Δt ∼ F(Δt), and the time for the electric vehicle to leave the battery swapping station is:

[0233] t d =t a +Δt

[0234] Wherein, t a is the time when the electric vehicle enters the battery swapping station, t d is the time when the electric vehicle leaves the battery swapping station, and Δt is the time for the electric vehicle to swap batteries;

[0235] Restrict the charging and discharging power of each spare battery within the preset upper and lower limits of the charging and discharging power:

[0236]

[0237] The charging and discharging scheduling of each spare battery is modeled as:

[0238]

[0239] Wherein, is the upper limit of the charging power of spare battery j, is the charging power of spare battery j at time t, is the discharging power of spare battery j at time t, is the efficiency of spare battery j, is the energy storage level of spare battery j at time t;

[0240] When considering the aging of the spare battery, restrict the energy storage level of the spare battery:

[0241]

[0242] Wherein, is the lower limit of the energy storage level of spare battery j, is the upper limit of the energy storage level of spare battery j at time t when considering the aging of the spare battery;

[0243] The relationship between the number of spare batteries in the battery swapping station and the electric vehicle queue is:

[0244]

[0245] Wherein, is the number of spare batteries existing in the battery swapping station at time t, is the number of electric vehicles arriving at the battery swapping station at time t, is the number of electric vehicles leaving the battery swapping station at time t;

[0246] Restrict the upper limit of the number of spare batteries in the battery swapping station to be:

[0247]

[0248] Among them, is the upper limit of the number of spare batteries in the battery swapping station;

[0249] An internal energy balance model of the battery swapping station is established as a constraint for the power system efficiency model of the battery swapping station:

[0250]

[0251] Among them, is the power interaction between the battery swapping station and the power grid at time t, is the total load of the battery swapping station at time t, is the battery charging power of spare battery j at time t, is the discharging power of spare battery j at time t, J t is the set of spare batteries in the battery swapping station at time t;

[0252] The objective function of the power system efficiency model of the battery swapping station is:

[0253]

[0254] Among them, θ j is the unit price of the charging service of the battery swapping station, p t is the electricity price;

[0255] The power system efficiency model of the battery swapping station is solved to obtain the charging and discharging power of the spare battery and the first energy efficiency benefit of the battery swapping station when considering the aging of the spare battery.

[0256] Among them, the function implementation of each module in the dispatching device of the virtual power plant when the battery energy storage ages in the above commercial scenario corresponds to each step in the embodiment of the dispatching method of the virtual power plant when the battery energy storage ages in the above commercial scenario, and its function and implementation process will not be elaborated here one by one.

[0257] It should be noted that the serial numbers of the embodiments of the present application above are only for description and do not represent the advantages and disadvantages of the embodiments.

[0258] The terms "including" and "having" and any variations thereof in the specification, claims and drawings of the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products or devices. The descriptions of the terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit that "first", "second" and "third" are different types.

[0259] In the description of the embodiments of the present application, words such as "exemplary", "for example", or "for illustration purposes" are used to indicate examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary", "for example", or "for illustration purposes" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary", "for example", or "for illustration purposes" is intended to present the relevant concepts in a specific manner.

[0260] In the description of the embodiments of the present application, unless otherwise specified, " / " means "or". For example, A / B may represent A or B; "and / or" in the text is merely a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present application, "a plurality of" means two or more than two.

[0261] In some of the processes described in the embodiments of the present application, a plurality of operations or steps appear in a specific order. However, it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or may be executed in parallel. The serial numbers of the operations are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in order or in parallel, and these operations or steps may be combined.

[0262] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases, the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium as described above (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal device to execute the methods described in the various embodiments of the present application.

[0263] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structural or equivalent process transformation made by using the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A scheduling method for a virtual power plant when the battery energy storage ages in a business scenario, characterized in that, The scheduling method of the virtual power plant when the battery energy storage in the commercial scenario ages includes: Taking the maximization of the efficiency benefit of the virtual power plant in the commercial scenario as the goal, and taking the energy balance established according to the flexible power resources in the virtual power plant and the aging state of the flexible power resources as the constraints, establish the power system efficiency model of the virtual power plant, and by solving the power system efficiency model, obtain the scheduling strategy of the flexible power resources and the energy efficiency benefit of the virtual power plant; Among them, the virtual power plant in the commercial scenario includes: typical commercial buildings, charging stations, and battery swapping stations.

2. The scheduling method of the virtual power plant during the aging of battery energy storage in the business scenario according to claim 1, wherein When the virtual power plant is a typical commercial building, it includes: Taking the maximization of the efficiency benefit of the typical commercial building as the goal, and taking the internal energy balance of the typical commercial building established according to the flexible power resources of the typical commercial building and the aging state of the flexible power resources of the typical commercial building as the constraints, establish the power system efficiency model of the typical commercial building, and by solving the power system efficiency model of the typical commercial building, obtain the scheduling strategy of the flexible power resources of the typical commercial building and the energy efficiency benefit of the typical commercial building; Among them, the flexible power resources of the typical commercial building include: distributed photovoltaic, distributed energy storage, and central air conditioning.

3. The scheduling method of the virtual power plant during the aging of battery energy storage in the business scenario according to claim 2, wherein Taking the maximization of the efficiency benefit of the typical commercial building as the goal, and taking the internal energy balance of the typical commercial building established according to the flexible power resources of the typical commercial building and the aging state of the flexible power resources of the typical commercial building as the constraints, establish the power system efficiency model of the typical commercial building, and by solving the power system efficiency model of the typical commercial building, obtain the scheduling strategy of the flexible power resources of the typical commercial building and the energy efficiency benefit of the typical commercial building, including: Establish the output model of the distributed photovoltaic in the typical commercial building: Among them, is the output of distributed PV at time t, A PV is the effective area of distributed PV, η PV is the efficiency of distributed PV, is the maximum output of distributed PV, α and β are the historical output distribution parameters of distributed PV, μ PV is the average value of the historical output of distributed PV, σ PV is the variance of the historical output of distributed PV; Establish the distributed energy storage model in the typical commercial building: Among them, is the energy storage level of distributed energy storage i at time t, is the charging power of distributed energy storage i at time t, is the discharging power of distributed energy storage i at time t, is the efficiency of distributed energy storage i; Restrict the charge and discharge power of the distributed energy storage within the preset upper and lower power limits: Restrict the energy storage level of the distributed energy storage within the preset upper and lower energy storage level limits: Among them, is the upper limit of the charge and discharge power of distributed energy storage i, is the lower limit of the energy storage level of distributed energy storage i, is the upper limit of the energy storage level of distributed energy storage i; Establish the central air conditioning model in the typical commercial building: And restrict the power of the central air conditioning within the upper and lower limits of the air conditioning power: Among them, is the central air-conditioning power at time t, is the indoor set temperature at time t, is the outdoor temperature at time t, η AC is the energy efficiency of the central air-conditioning, is the temperature control margin at time t, is the upper limit of the air-conditioning power, is the lower limit of the air-conditioning power; According to the output of the distributed photovoltaic, the charging power and discharging power of the distributed energy storage, the power of the central air conditioning, the power and lighting load of the typical commercial building, and the power interaction between the typical commercial building and the power grid, establish the internal energy balance of the typical commercial building as the constraint of the power system efficiency model of the typical commercial building: Among them, is the power interaction between the typical commercial building and the power grid at time t, is the power load of the lighting and power of the typical commercial building at time t; Take the aging state of the distributed energy storage as the constraint of the power system efficiency model of the typical commercial building: Among them, is the upper limit of the energy storage level at time t when the distributed energy storage i ages, and σ Building is the aging coefficient of the energy storage system, and E i is the initial energy storage level of the distributed energy storage i; The objective function of the power system efficiency model of the typical commercial building is: In the formula: Among them, Q is the efficiency benefit of a typical commercial building, which is the total internal electricity load of a typical commercial building, p t is the electricity price, and ω and b are efficiency benefit coefficients; Solve the power system efficiency model of the typical commercial building to obtain the output of the distributed photovoltaic, the charge and discharge power of the distributed energy storage, the power of the central air conditioning, and the first efficiency benefit of the typical commercial building when considering the aging of the distributed energy storage.

4. The scheduling method of the virtual power plant during the aging of battery energy storage in the business scenario according to claim 3, wherein, It also includes: Without considering the constraints of the aging state of distributed energy storage in the power system efficiency model of a typical commercial building, solve the power system efficiency model of the typical commercial building to obtain the output of distributed photovoltaics, the charge and discharge power of distributed energy storage, the central air-conditioning power, and the second efficiency benefit of the typical commercial building when not considering the aging of distributed energy storage; Subtract the second efficiency benefit of the typical commercial building from the first efficiency benefit to obtain the benefit loss caused by the aging of distributed energy storage; Calculate the operation aging loss of the distributed energy storage per unit according to the second efficiency benefit and the first efficiency benefit of the typical commercial building: Among them, ε1 is the operating aging loss of the distributed energy storage per unit, Q B1 is the first efficiency benefit of the typical commercial building, Q B2 is the second efficiency benefit of the typical commercial building.

5. The scheduling method of the virtual power plant during the aging of battery energy storage in the business scenario according to claim 1, characterized in that, When the virtual power plant is a charging station, it includes: With the goal of maximizing the charging station efficiency benefit, and with the internal energy balance of the charging station established based on the flexible power resources of the charging station and the aging state of the flexible power resources of the charging station as constraints, establish a power system efficiency model of the charging station, and by solving the power system efficiency model of the charging station, obtain the scheduling strategy of the flexible power resources of the charging station and the energy efficiency benefit of the charging station; Among them, the flexible power resources of the charging station include: electric vehicles that charge by connecting to the grid through the charging station.

6. The scheduling method of the virtual power plant during the aging of battery energy storage in the business scenario according to claim 5, characterized in that, With the goal of maximizing the charging station efficiency benefit, and with the internal energy balance of the charging station established based on the flexible power resources of the charging station and the aging state of the flexible power resources of the charging station as constraints, establish a power system efficiency model of the charging station, and by solving the power system efficiency model of the charging station, obtain the scheduling strategy of the flexible power resources of the charging station and the energy efficiency benefit of the charging station, including: Set the probability density of electric vehicles connecting to the grid and leaving the grid through the charging station as: where, t a is the time when the electric vehicle connects to the grid, t d is the time when the electric vehicle disconnects from the grid, μ is the average grid connection time of the electric vehicle, and σ is the variance of the grid connection time of the electric vehicle; And set the charge and discharge during the period when the electric vehicle leaves the grid to zero: t < t a,i or t > t d,i When the electric vehicle leaves the grid, the stored energy of the battery of the electric vehicle reaches the set value: where, t a,i is the time when electric vehicle i connects to the grid, t d,i is the time when electric vehicle i disconnects from the grid, is the battery charging and discharging power of electric vehicle i at time t, is the battery energy storage level of electric vehicle i at time t, is the target value of the battery energy storage level of the electric vehicle; Restrict the charge and discharge power of each electric vehicle battery within the preset upper and lower limits of the charge and discharge power: Model the charge and discharge scheduling of each electric vehicle battery as: Among them, is the battery charging power of electric vehicle i at time t, is the battery discharging power of electric vehicle i at time t, is the maximum charge-discharge power of the battery of electric vehicle i, is the charge-discharge efficiency of the battery of electric vehicle i, is the lower limit of the battery energy storage level of electric vehicle i, is the upper limit of the battery energy storage level of electric vehicle i; Establish an internal energy balance model of the charging station as a constraint of the power system efficiency model of the charging station: Among them, is the power interaction between the charging station and the power grid at time t, is the total load of the charging station at time t, is the battery charging power of electric vehicle i at time t, is the battery discharging power of electric vehicle i at time t, I t is the set of electric vehicles being charged in the charging station at time t; When considering the aging of the electric vehicle battery, restrict the energy storage level of the electric vehicle battery: Among them, is the lower limit of the battery energy storage level of electric vehicle i, is the upper limit of the energy storage level of electric vehicle i at time t considering the battery aging of the electric vehicle. The objective function of the power system efficiency model of the charging station is: Among them, θ i is the unit price of the charging service at the charging station, and p t is the electricity price; Solve the power system efficiency model of the charging station to obtain the charge and discharge power of the electric vehicle battery and the first energy efficiency benefit of the charging station when considering the aging of the electric vehicle battery.

7. The scheduling method of the virtual power plant when the battery energy storage ages in the commercial scenario as described in claim 6, characterized in that: Remove the constraint on the upper limit of the energy storage level after the aging of the electric vehicle battery in the power system efficiency model of the charging station, and use the upper limit of the energy storage level of the electric vehicle battery when not considering the aging of the electric vehicle battery as a constraint of the power system efficiency model of the charging station: Among them, is the lower limit of the battery energy storage level of electric vehicle i, is the upper limit of the energy storage level of electric vehicle i without considering the battery aging of the electric vehicle. Solve the power system efficiency model of the charging station to obtain the charge and discharge power, energy storage level of the electric vehicle battery, and the second efficiency benefit of the charging station when not considering the aging of the electric vehicle battery; Subtract the second efficiency benefit of the charging station from the first efficiency benefit to obtain the benefit loss caused by the aging of the electric vehicle battery; Calculate the operating aging loss of the electric vehicle battery per unit according to the second efficiency benefit and the first efficiency benefit of the charging station: Among them, ε2 is the operating aging loss of the electric vehicle battery in units of Q E1 is the first efficiency benefit of the charging station, and Q E2 is the second efficiency benefit of the charging station.

8. The scheduling method of the virtual power plant during the aging of battery energy storage in the business scenario according to claim 1, characterized in that, When the virtual power plant is a battery swapping station, it includes: Taking the maximization of the battery swapping station's efficiency benefit as the goal, and taking the internal energy balance of the battery swapping station established based on the flexible power resources of the battery swapping station and the aging state of the flexible power resources of the battery swapping station as constraints, establish a power system efficiency model for the battery swapping station, and by solving the power system efficiency model for the battery swapping station, obtain the scheduling strategy of the flexible power resources of the battery swapping station and the energy efficiency benefit of the battery swapping station; Among them, the flexible power resources of the battery swapping station include: the batteries in the battery swapping station that are charged by connecting to the power grid through the battery swapping station.

9. The scheduling method of the virtual power plant during the aging of battery energy storage in the business scenario according to claim 8, characterized in that, Taking the maximization of the battery swapping station's efficiency benefit as the goal, and taking the energy balance of the battery swapping station established based on the flexible power resources of the battery swapping station and the aging state of the flexible power resources of the battery swapping station as constraints, establish a power system efficiency model for the battery swapping station, and by solving the power system efficiency model for the battery swapping station, obtain the scheduling strategy of the flexible power resources of the battery swapping station and the energy efficiency benefit of the battery swapping station, including: Set the time probability density of electric vehicles entering the battery swapping station as: When there are sufficient spare batteries in the battery swapping station, the time for electric vehicles to swap batteries follows a normal distribution Δt ∼ F(Δt), and the time for electric vehicles to leave the battery swapping station is: t d = t a + Δt where, t a is the time when the electric vehicle enters the battery swapping station, t d is the time when the electric vehicle leaves the battery swapping station, and Δt is the time for the electric vehicle to swap the battery; Restrict the charging and discharging power of each spare battery within the preset upper and lower limits of the charging and discharging power: Model the charging and discharging scheduling of each spare battery as: wherein, is the upper limit of the charging power of the backup battery j, is the charging power of the backup battery j at time t, is the discharging power of the backup battery j at time t, is the efficiency of the backup battery j, is the energy storage level of the backup battery j at time t; When considering the aging of spare batteries, restrict the energy storage level of spare batteries: Among them, is the lower limit of the energy storage level of the backup battery j, is the upper limit of the energy storage level of the backup battery j at time t considering the aging of the backup battery; The relationship between the number of spare batteries in the battery swapping station and the electric vehicle queue constraint is: Among them, is the number of spare batteries existing in the battery swapping station at time t, is the number of electric vehicles arriving at the battery swapping station at time t, is the number of electric vehicles leaving the battery swapping station at time t; Restrict the upper limit of the number of spare batteries in the battery swapping station to be: Among them, is the upper limit of the number of spare batteries in the battery swapping station; Establish an internal energy balance model for the battery swapping station as a constraint for the power system efficiency model of the battery swapping station: Among them, is the power interaction between the battery swapping station and the power grid at time t, is the total load of the battery swapping station at time t, is the battery charging power of the spare battery j at time t, is the discharging power of the spare battery j at time t, J t is the set of spare batteries in the battery swapping station at time t; The objective function of the power system efficiency model of the battery swapping station is: Among them, θ j is the unit price of the charging service of the battery swapping station, and p t is the electricity price; Solve the power system efficiency model for the battery swapping station to obtain the charging and discharging power of the spare batteries when considering the aging of the spare batteries and the first energy efficiency benefit of the battery swapping station.

10. A scheduling device for a virtual power plant when battery energy storage ages in a commercial scenario. The scheduling device for a virtual power plant when battery energy storage ages in the commercial scenario includes: A calculation module, which is used to take the maximization of the efficiency benefit of the virtual power plant in the commercial scenario as the goal, and take the energy balance established based on the flexible power resources in the virtual power plant and the aging state of the flexible power resources as constraints, establish a power system efficiency model for the virtual power plant, and by solving the power system efficiency model, obtain the scheduling strategy of the flexible power resources and the energy efficiency benefit of the virtual power plant; Among them, the virtual power plant in the commercial scenario includes: typical commercial buildings, charging stations, and battery swapping stations.

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