Method and device for optimizing operation of virtual power plant aggregating multiple flexible resources

Abstract

The application discloses a virtual power plant optimization operation method and device for polymerizing multiple flexible resources, and comprises the following steps: constructing an output model, a first charge-discharge model and a second charge-discharge model for the power supply side, the energy storage side and the demand side flexible resources in the virtual power plant; generating an available output curve according to the output model, determining the discharge power or the first charge load according to the first charge-discharge model, and determining the second charge load according to the second charge-discharge model; obtaining the next-day output plan of the virtual power plant according to the available output curve, the discharge power, the first and second charge loads and reporting the next-day output plan to the main grid; constructing an optimization operation model with the minimum total operation cost of the virtual power plant as the target according to the total output plan returned by the main grid and the operation constraint conditions of the virtual power plant, adjusting the next-day output plan according to the optimization operation model, and obtaining the optimization operation result of the virtual power plant. The application improves the flexibility of the virtual power plant and guarantees the real-time dynamic balance and the safe and stable operation of the system.

Description

Technical Field

[0001] This invention relates to the field of energy internet technology, and in particular to a method and apparatus for optimizing the operation of a virtual power plant that integrates multiple flexible resources. Background Technology

[0002] In recent years, distributed energy sources such as distributed photovoltaic and distributed wind power have grown rapidly in my country. New energy power generation is heavily dependent on natural factors such as sunlight intensity and wind intensity, and is generally characterized by randomness, intermittency and volatility. Its large-scale and high-proportion integration has brought a series of challenges to the balance of the power system and the safe operation of the power grid, and has placed increasingly higher demands on the power system's flexible adjustment capabilities.

[0003] The flexible resources of a new power system refer to various resources that have flexible adjustment capabilities and can maintain a dynamic supply and demand balance in the system. Virtual power plants rely on the Internet and modern information and communication technologies to aggregate various resources scattered across the power grid, such as distributed power sources, energy storage, and loads, and carry out collaborative and optimized operation and control. This enables multi-energy complementarity on the power source side and flexible interaction on the load side, providing ancillary services such as peak shaving, frequency regulation, and backup to the power grid.

[0004] Because virtual power plants aggregate various distributed and flexible resources, their internal regulation and operation are quite complex. Furthermore, many current virtual power plant optimization operations consider the aggregation and optimization of distributed resources but fail to account for the operational arrangements of virtual power plants after the introduction of a large number of flexible resources. This results in a lack of operational methods and technologies for virtual power plants with multiple flexible resources, impacting the safety and stability of the power system. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, equipment, and storage medium for optimizing the operation of a virtual power plant that integrates multiple flexible resources, in order to solve the technical problem of the impact of introducing multiple flexible resources into a virtual power plant on the safe and stable operation of the power system.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] Option 1 is a virtual power plant optimization operation method that integrates multiple flexible resources, including:

[0008] A power output model is constructed for the power supply-side flexibility resources in the virtual power plant, a first charge-discharge model is constructed for the energy storage-side flexibility resources, and a second charge-discharge model is constructed for the demand-side flexibility resources; the power supply-side flexibility resources include at least thermal power units, wind power units, and photovoltaic units, the energy storage-side flexibility resources include at least energy storage systems, and the demand-side flexibility resources include at least electric vehicles.

[0009] The available output curve of the power supply side flexibility resource is generated according to the output model. The discharge power or first charging load of the energy storage side flexibility resource is determined according to the first charging and discharging model. The second charging load of the demand side flexibility resource is determined according to the second charging and discharging model. The second charging load is determined based on the real-time charging price and the power corresponding to the user demand response.

[0010] The next day's power output plan of the virtual power plant is obtained based on the available output curve, the discharge power or the first charging load, and the second charging load, and then reported to the main power grid.

[0011] Based on the total output plan for the next day returned by the main power grid and the operating constraints of the virtual power plant, an optimized operation model is constructed with the goal of minimizing the total operating cost of the virtual power plant.

[0012] The daily power generation plan for the next day is adjusted according to the optimized operation model to obtain the optimized operation results of the virtual power plant.

[0013] Optionally, the construction of the optimized operation model with the objective of minimizing the total operating cost of the virtual power plant includes:

[0014] Construct an optimized operating model with the following objective function:

[0015]

[0016] Among them, F cost p represents the total operating cost of the virtual power plant. EG,t G EG,t Let C represent the purchased electricity volume and the purchased electricity price of the virtual power plant at time t, respectively. Thr,t C WPP,t C PV,t Let C represent the power generation costs of thermal power units, wind power units, and photovoltaic units at time t, respectively. ESS,t C represents the operating cost of the energy storage system at time t. EV,t This represents the operating cost of the electric vehicle at time t.

[0017] Optionally, the process of constructing an optimized operation model aimed at minimizing the total operating cost of the virtual power plant further includes:

[0018] The power generation cost of a thermal power unit at time t is calculated using the following formula;

[0019]

[0020]

[0021]

[0022] Among them, CThr,t This represents the power generation cost of a thermal power unit at time t. Let a represent the fuel cost and start-up / shutdown cost of the thermal power unit at time t, respectively; Thr b Thr c Thr The power generation energy consumption coefficient of the thermal power unit; g Thr,t u represents the power generation capacity of the thermal power unit at time t. Thr,t Let D be the power generation state variable of the thermal power unit at time t. It is a 0-1 variable, where 1 indicates that the thermal power unit is in operation and 0 indicates that the thermal power unit is in shutdown. Thr,t This represents the startup cost of a thermal power unit at time t.

[0023] Optionally, the process of constructing an optimized operation model aimed at minimizing the total operating cost of the virtual power plant further includes:

[0024] The operating cost of the electric vehicle at time t is calculated based on the real-time charging price of the electric vehicle at time t and the second charging load at time t.

[0025] Optionally, the calculation of the operating cost of the electric vehicle at time t further includes:

[0026] The second charging load of the electric vehicle at time t is determined according to the following formula;

[0027]

[0028] In the formula, L EV,t L is the second charging load of the electric vehicle at time t. t0 For the charging load of electric vehicles before demand response is implemented, ε t,k For price-driven demand response, F t Let F be the charging electricity price for electric vehicles at time t after the implementation of demand response. t0 F represents the charging electricity price of electric vehicles at time t before demand response is implemented; k and t are the scheduling periods, k, t = 1, 2, 3…24; k Let F be the charging electricity price for electric vehicles at time k after the implementation of demand response. k0 The charging price of electric vehicles at time k before the implementation of demand response.

[0029] Optionally, obtaining the next day's output plan of the virtual power plant based on the available output curve, the discharge power or the first charging load, and the second charging load includes:

[0030] The next day's power output plan of the virtual power plant is obtained based on the available output curve, the discharge power or the first charging load, and the second charging load, as shown in the following formula.

[0031]

[0032] In the formula, P VPP,t For the output of the virtual power plant at time t, g Thr,t g WPP,t g PV,t and These represent the power outputs of the thermal power unit, wind power unit, photovoltaic unit, and energy storage system at time t, respectively. Let L be the charging load of the energy storage system at time t, i.e., the first charging load. EV,t The charging load of the electric vehicle at time t is the second charging load.

[0033] Optionally, the operational constraints include at least:

[0034] Constraints on power supply and demand balance, constraints on the operation of thermal power units, constraints on the operation of energy storage systems, and constraints on the operation of electric vehicles.

[0035] Option 2, a virtual power plant optimization operation device that integrates multiple flexible resources, including:

[0036] The model building module is used to build an output model for the power supply side flexibility resources in the virtual power plant, a first charge-discharge model for the energy storage side flexibility resources, and a second charge-discharge model for the demand side flexibility resources; the power supply side flexibility resources include at least thermal power units, wind power units, and photovoltaic units, the energy storage side flexibility resources include at least energy storage systems, and the demand side flexibility resources include at least electric vehicles.

[0037] The output or load calculation module is used to generate the available output curve of the power supply side flexibility resource according to the output model, determine the discharge power or first charging load of the energy storage side flexibility resource according to the first charging and discharging model, and determine the second charging load of the demand side flexibility resource according to the second charging and discharging model. The second charging load is determined based on the real-time charging price and the power corresponding to the user demand response.

[0038] The power output planning module is used to obtain the next day's power output plan of the virtual power plant based on the available power output curve, the discharge power or the first charging load, and the second charging load, and report it to the main power grid.

[0039] The optimization model construction module is used to construct an optimization operation model with the goal of minimizing the total operating cost of the virtual power plant, based on the total output plan for the next day returned by the main power grid and the operating constraints of the virtual power plant.

[0040] An optimized operation module is used to adjust the daily power generation plan for the next day according to the optimized operation model, so as to obtain the optimized operation results of the virtual power plant.

[0041] Option 3, an electronic device, comprising: a processor and a memory;

[0042] The memory stores a computer program, and the processor executes the computer program to implement the steps of Scheme 1.

[0043] Option 4: A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of Option 1.

[0044] This invention provides a method and apparatus for optimizing the operation of a virtual power plant that aggregates multiple flexible resources. The method includes: constructing an output model for the power supply-side flexible resources in the virtual power plant; constructing a first charge-discharge model for the energy storage-side flexible resources; and constructing a second charge-discharge model for the demand-side flexible resources. The power supply-side flexible resources include at least thermal power units, wind power units, and photovoltaic units; the energy storage-side flexible resources include at least energy storage systems; and the demand-side flexible resources include at least electric vehicles. The method generates available output curves for the power supply-side flexible resources based on the output models; and determines the discharge power of the energy storage-side flexible resources based on the first charge-discharge model or the second charge-discharge model. A charging load is determined based on the second charging and discharging model, which determines the second charging load of the demand-side flexibility resource. The second charging load is determined based on the real-time charging price and the power corresponding to the user demand response. The next-day power output plan of the virtual power plant is obtained based on the available output curve, the discharge power or the first charging load, and the second charging load, and reported to the main grid. Based on the next-day total power output plan returned by the main grid and the operating constraints of the virtual power plant, an optimized operation model is constructed with the goal of minimizing the total operating cost of the virtual power plant. The next-day power output plan is adjusted based on the optimized operation model to obtain the optimized operation result of the virtual power plant.

[0045] Based on the above technical solution, the beneficial effects of this invention are:

[0046] By constructing corresponding output models for various generator units in the virtual power plant, the available output curves of each generator unit are obtained based on the output models. Similarly, charging and discharging models are constructed for energy storage systems and electric vehicles, and the charging loads corresponding to these systems are obtained. Furthermore, the next-day power generation plan for the virtual power plant is generated based on the available output curves and charging loads. Building upon the traditional multi-distributed power sources in virtual power plants, this approach integrates various flexible resources such as demand response, energy storage, and electric vehicles, further enhancing the flexibility of the virtual power plant and promoting its integrated development across "source, grid, load, and storage." By connecting to multiple flexible resources and establishing an optimized operation model for the virtual power plant based on the output plan and operational constraints returned from the main grid, the next-day power generation plan is adjusted according to the optimized operation model, resulting in optimized operation of the virtual power plant. This enables collaborative optimized operation control, ensuring real-time dynamic supply and demand balance and safe and stable operation of the power system. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating an embodiment of the virtual power plant optimization operation method that integrates multiple flexible resources according to the present invention.

[0048] Figure 2 This is a schematic diagram of the model structure of the virtual power plant of this invention;

[0049] Figure 3 This is a schematic diagram illustrating the process by which an electric vehicle participates in the demand response of a virtual power plant according to the present invention.

[0050] Figure 4 This is a schematic diagram of an embodiment of the virtual power plant optimized operation device that integrates multiple flexible resources according to the present invention. Detailed Implementation

[0051] Terminology Explanation:

[0052] Virtual power plant: A power coordination and management system that uses advanced information and communication technologies and software systems to aggregate and coordinate distributed energy sources such as distributed power sources, energy storage systems, controllable loads, and electric vehicles, so as to participate in the electricity market and grid operation as a special power plant.

[0053] This invention provides a method, apparatus, equipment, and storage medium for optimizing the operation of a virtual power plant that integrates multiple flexible resources, in order to solve the technical problem of the impact of introducing multiple flexible resources into a virtual power plant on the safe and stable operation of the power system.

[0054] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0056] Driven by the "dual carbon" goals, my country's power industry is undergoing a profound transformation, building a robust and intelligent power grid as a hub platform, diversifying market players, and continuously advancing the integration of power generation, grid, load, and storage with multi-energy complementarity. The aim is to construct a new type of power system that is cleaner, lower-carbon, safer, more controllable, more flexible and efficient, smarter, and more interactive, with new energy sources as the mainstay. In this transformation process, virtual power plants, by aggregating clean energy sources, loads, and flexibility resources, will play a crucial role in solving the challenges of clean energy consumption and facilitating the green energy transition.

[0057] Because virtual power plants aggregate various distributed and flexible resources, their internal regulation and operation are quite complex. Furthermore, many current virtual power plant optimization operations consider the aggregation and optimization of distributed resources but fail to account for the operational arrangements of virtual power plants after the introduction of a large number of flexible resources. This results in a lack of operational methods and technologies for virtual power plants with multiple flexible resource access.

[0058] To address the aforementioned issues, this invention provides a virtual power plant operation method applicable to the aggregation of multiple flexible resources. Based on wind power, photovoltaic, and thermal power units, it introduces flexible resources such as energy storage, electric vehicles, and demand response, further enhancing the flexibility of the virtual power plant. This is an optimized operation method for virtual power plants that aggregates multiple types of flexible resources.

[0059] The virtual power plant optimization operation method that integrates multiple flexible resources provided in this invention can be applied to the dispatch control center of a virtual power plant. The dispatch control center can communicate with electric vehicle load agents and the main power grid. The dispatch control center collects the available output curves of thermal power, wind power, and photovoltaic units, integrates them to form the next day's output plan, and reports it to the main power grid. The main power grid returns the next day's total output curve to the virtual power plant. Electric vehicle load agents collect the load information reported by electric vehicles and report it to the dispatch control center. The dispatch control center sends charging commands to guide electric vehicles to perform load transfer. The virtual power plant, combined with the output plan returned by the main grid, accesses flexible resources and constructs an optimized operation model for the virtual power plant with the lowest total operating cost. Based on the optimized operation model, the next day's output plan is adjusted to obtain the optimized operation result of the virtual power plant.

[0060] Please see Figure 1 The virtual power plant optimization operation method that aggregates multiple flexible resources provided in Embodiment 1 of the present invention includes:

[0061] S100: Construct an output model for the power supply side flexibility resources in the virtual power plant, construct a first charge-discharge model for the energy storage side flexibility resources, and construct a second charge-discharge model for the demand side flexibility resources; the power supply side flexibility resources include at least thermal power units, wind power units, and photovoltaic units, the energy storage side flexibility resources include at least energy storage systems, and the demand side flexibility resources include at least electric vehicles.

[0062] S200: Generate the available output curve of the power supply side flexibility resource according to the output model, determine the discharge power or first charging load of the energy storage side flexibility resource according to the first charging and discharging model, and determine the second charging load of the demand side flexibility resource according to the second charging and discharging model. The second charging load is determined based on the real-time charging price and the power corresponding to the user demand response.

[0063] S300: Based on the available output curve, the discharge power or the first charging load, and the second charging load, obtain the next day's output plan of the virtual power plant and report it to the main power grid;

[0064] S400: Based on the total output plan for the next day returned by the main power grid and the operating constraints of the virtual power plant, construct an optimized operation model with the goal of minimizing the total operating cost of the virtual power plant;

[0065] S500: Adjust the daily power generation plan according to the optimized operation model to obtain the optimized operation results of the virtual power plant.

[0066] In this invention, flexible resources refer to various resources with flexible adjustment capabilities that maintain the dynamic supply and demand balance of the system. Traditional power systems primarily rely on distributed power sources such as thermal power and pumped storage power plants. However, with the development of emerging technologies such as renewable energy and energy storage, and the continuous improvement of demand response mechanisms, a multi-faceted flexible resource pool encompassing source, grid, load, and storage should be gradually formed to ensure the real-time dynamic supply and demand balance and the safe and stable operation of the power system with a wider range of types and stronger adjustment capabilities.

[0067] Power supply-side flexibility resources, also known as source-side flexibility resources, mainly include thermal power units, hydropower units, and wind power units. Energy storage-side flexibility resources, as flexible and rapid adjustment resources, currently include energy storage systems, battery energy storage, pumped hydro storage, flywheel energy storage, and compressed air energy storage. Energy storage-side flexibility resources can not only smooth load fluctuations but also operate in conjunction with new energy power plants, significantly improving the utilization efficiency of new energy sources. Demand-side flexibility resources mainly include small and distributed resources such as adjustable loads, electric vehicles, and user-side energy storage.

[0068] Virtual power plants offer an optimal solution for addressing grid connection issues related to distributed resources, energy storage, and load-side resources. During the real-time operation of a virtual power plant, the randomness of photovoltaic and wind power output, along with load fluctuations, necessitates real-time optimization and scheduling of the output of various components within the virtual power plant.

[0069] The development of virtual power plants enhances the flexibility of resource utilization on the user side, which helps ensure the safe and stable operation of the power grid, reduce the electricity and energy costs of industrial and commercial enterprises, alleviate the power supply and demand tension under extreme weather conditions, promote the improvement of the absorption of new energy sources, and save investment in power sources and power grids. It is of great significance for building a new type of power system with new energy sources as the main power source and achieving dynamic balance of the power system.

[0070] In step S100, an output model is constructed for the power supply-side flexibility resources in the virtual power plant, a first charging and discharging model is constructed for the energy storage-side flexibility resources, and a second charging and discharging model is constructed for the demand-side flexibility resources; wherein, the power supply-side flexibility resources include at least thermal power units, wind power units, and photovoltaic units, the energy storage-side flexibility resources include at least energy storage systems, and the demand-side flexibility resources include at least electric vehicles.

[0071] In this embodiment, each generator unit in the virtual power plant serves as the power output entity, and a corresponding power output model is constructed for each generator unit; the energy storage system and electric vehicle serve as the regulation entities, and corresponding charging and discharging models are constructed for the regulation entities, specifically including:

[0072] (1) Construct the corresponding output model for thermal power units based on the operating constraints of thermal power plants.

[0073] Specifically, the operating constraints of thermal power plants may include thermal power unit output constraints, thermal power unit ramping constraints, and thermal power unit start-up and shutdown constraints, as shown in equation (1):

[0074]

[0075] In the formula, and Indicates the lower and upper limits of the output of thermal power units, g Thr,t g represents the output of the thermal power unit at time t; Thr,t-1 u represents the output of the thermal power unit at time t-1; Thr,t u Thr,t-1 The power generation state variables of the thermal power unit at time t and time t-1 are 0-1 variables, where 1 indicates that the thermal power unit is in operation and 0 indicates that the thermal power unit is in shutdown. and Indicates the lower and upper limits of the slope for thermal power units; This indicates the time the thermal power unit has been running at time t; Indicates the shortest startup time for a thermal power unit; This indicates the time that the thermal power unit has been shut down at time t; This indicates the shortest downtime for a thermal power unit.

[0076] The output model of the thermal power unit is shown in equations (2) and (3):

[0077]

[0078]

[0079] In the formula, Let a represent the fuel cost and start-up / shutdown cost of the thermal power unit at time t, respectively; Thr b Thr c Thr The power generation energy consumption coefficient of the thermal power unit; g Thr,t D represents the power generation capacity of the thermal power unit at time t; Thr,t This represents the startup cost of a thermal power unit at time t.

[0080] It is understandable that startup costs will be incurred when a thermal power unit is started up, that is, when the state of the thermal power unit changes from shutdown to operation.

[0081] In one embodiment, the power generation cost C of the thermal power unit at time t is... Thr,t As shown in equation (4):

[0082]

[0083] It is understandable that the power generation cost of a thermal power unit at time t is equal to the sum of the fuel cost and start-up / shutdown cost of the thermal power unit at time t.

[0084] (2) Construct the corresponding power output model for the wind turbine.

[0085] Due to limitations in the technical parameters of wind turbine generators, the output of wind power exhibits a phased characteristic as wind speed changes, as shown in equation (5):

[0086]

[0087] In the formula, C W These are the performance parameters of the wind turbine; ρ is the air density; A WT The area swept by the wind turbine blades is the projection onto the plane perpendicular to the wind speed; v t This represents the real-time wind speed of the wind turbine at time t; v in v τ and v out These represent the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively; g WPP,t This represents the actual output power of the wind turbine at time t. This refers to the rated power of the wind turbine.

[0088] It should be noted that when the real-time wind speed v of the wind turbine is... t The wind turbine will stop operating if the wind speed is below the cut-in wind speed or above the cut-out wind speed. If the real-time wind speed v... t Between the rated wind speed v τ With cut-out wind speed ν out During this period, the wind turbine will output power at its rated capacity; otherwise, the wind turbine output will depend on the real-time wind speed v. t Size.

[0089] Due to the difference in wind speed at different heights, the wind speed at a specific height is converted into the actual wind speed at the height of the wind turbine tower, as shown in equation (6):

[0090]

[0091] In the formula, v' t To measure the wind speed at height h', f(v') t (v is the real-time wind speed) t The wind speed of the wind turbine is measured at a tower height h, and β is the calculation coefficient.

[0092] (3) Construct a corresponding power output model for the photovoltaic unit.

[0093] Photovoltaic power generation is based on the photovoltaic effect occurring on a semiconductor surface, directly converting absorbed solar energy into electrical energy. Assuming the photovoltaic device is equipped with maximum power point tracking, its output model can be expressed as equation (7):

[0094]

[0095] In the formula, g PV,t γ is the output power of the photovoltaic unit at time t, and γ is a temperature parameter representing the photoelectric conversion efficiency of the photovoltaic panel; T air For atmospheric ambient temperature, T n For normal operating temperature, T ref Reference temperature; η PV For reference efficiency, θ t Let S be the light radiation intensity at time t. PV N represents the area of ​​a single photovoltaic panel. PV This refers to the number of photovoltaic panels.

[0096] The output intensity of photovoltaic power generation is related to the intensity of solar radiation. The Beta distribution function is commonly used to describe the intensity of solar photovoltaic radiation, and the specific formula is shown in equation (8):

[0097]

[0098] In the formula, f(θ) represents the solar photovoltaic radiation intensity, θ represents the solar radiation, and α and β represent the shape parameters of the Beta distribution.

[0099] It should be noted that θ α-1 The Beta function is used to represent the intensity of solar radiation, and it has no special meaning.

[0100] Based on historical data of solar radiation intensity, after obtaining the expected value η and variance value δ of photovoltaic radiation intensity, the values ​​of β and α are shown in equations (9) and (10):

[0101]

[0102]

[0103] The photovoltaic radiation intensity distribution function is shown in equation (11):

[0104]

[0105] In the formula, θ max θ min These are the upper and lower limits of solar radiation intensity, respectively.

[0106] (4) Construct corresponding charging and discharging models for energy storage systems.

[0107] During the operation of the energy storage system, the energy storage device must meet the relevant technical constraints as shown in equation (12):

[0108]

[0109] In the formula, SOC(t) represents the state of charge of the energy storage device at time t. max SOC min These represent the upper and lower limits of the state of charge of the energy storage device at time t, respectively. This indicates the charging and discharging state of the energy storage device. It is a 0-1 variable, meaning that the charging and discharging states of the energy storage system cannot be in the same state at the same time. and Indicates the minimum and maximum power required to charge the energy storage device. This represents the charging power of the energy storage device at time t. and These represent the minimum and maximum power of energy storage discharge, respectively. f represents the discharge power of the energy storage device at time t.

[0110] The first charge-discharge model of the energy storage system in the virtual power plant can be expressed as equations (13) and (14):

[0111]

[0112]

[0113] Where SOC(t-1) represents the state of charge of the energy storage device at time t-1; η chr With η disc These represent the charging and discharging efficiencies of the energy storage system, respectively. and E represents the charging and discharging power of the energy storage system at time t. n This refers to the rated capacity of the energy storage system.

[0114] (5) Construct corresponding charging and discharging models for electric vehicles.

[0115] Electric vehicles can flexibly transfer some of the system load by charging during off-peak hours and discharging during peak hours. Research has found that the trip termination time of electric vehicles follows a piecewise logarithmic normal distribution, as shown in equation (15):

[0116]

[0117] The daily mileage of electric vehicles follows a piecewise logarithmic normal distribution, as shown in equation (16):

[0118]

[0119] In the formula, σ d It represents the standard deviation of the normal distribution of the load on daily mileage.

[0120] Specifically, the operating constraints of electric vehicles are shown in equation (17):

[0121]

[0122] In the formula, SOC i,min and SOC i,max These represent the lower and upper limits of the state of charge (SOC) of electric vehicles. i,t Let be the state of charge of electric vehicle i at time t; and These are the maximum charging and discharging power of electric vehicle i, respectively. The charging power for electric vehicle i; η is the discharge power of electric vehicle i. EV,i The charging and discharging efficiency of electric vehicle i.

[0123] In one embodiment, the second charge-discharge model of electric vehicle i is constructed as shown in equation (18):

[0124]

[0125] In the formula, E EV,i (t) represents the stored energy of electric vehicle i at time t. This refers to the remaining battery power of electric vehicle i after completing its journey the previous day. These represent the discharge and charging power of electric vehicle i, respectively, and η. EV The charging and discharging efficiency of electric vehicle i.

[0126] Given the charging power (charging load) of electric vehicle i, and based on the charging loads of all electric vehicles, the second charging load L of electric vehicle i can be obtained. EV,t ,in, N represents the number of electric vehicles.

[0127] Assuming the electric vehicle is connected to the power grid after its journey the previous day, it will be as shown in equation (19):

[0128]

[0129] In the formula, This refers to the battery capacity of the electric vehicle when it is connected to the grid after the previous day's journey. P represents the maximum and minimum battery capacity of the electric vehicle, respectively; D represents the daily mileage of the electric vehicle, and P is a random number following a log-normal distribution according to equation (16). EV The amount of electricity consumed per kilometer that an electric vehicle travels.

[0130] Due to issues such as the dispersion of demand-side flexibility resources (such as electric vehicles), significant differences in user energy consumption, and limited adjustable load capacity, demand-side flexibility testing is difficult to directly participate in the centralized market. Therefore, it is necessary to provide ancillary services through aggregator agents / virtual power plant agents to achieve unified management and scheduling of internal distributed resources using advanced communication technologies.

[0131] In one embodiment, such as Figure 3 As shown, each electric vehicle reports its charging and discharging plan to the electric vehicle load agent, taking into account information such as price-based demand response and usage time arrangements. The electric vehicle load agent collects usage data and load demand information of each electric vehicle, constructs a price impact model for electric vehicles, integrates the charging and discharging status of electric vehicles at different times, and reports it to the dispatch and control center of the virtual power plant.

[0132] This invention provides a method for regulating the operation of electric vehicles in a virtual power plant based on price-based demand response, specifically including the following steps:

[0133] (1) Electric vehicle load agents collect information such as the electricity demand and travel time of each electric vehicle and upload this information to the dispatch and control center of the virtual power plant.

[0134] (2) The dispatch and control center of the virtual power plant generates specific charging instructions based on the daily electricity price to guide electric vehicle users to transfer their load;

[0135] (3) Match the peak-shaving demand of electric vehicles to the system based on the output of the source-side flexibility resources (power supply-side flexibility resources) of the virtual power plant.

[0136] In price-based demand response, the charging load of electric vehicles is affected by real-time electricity prices and changes in electricity prices during adjacent time periods. The impact of real-time electricity prices on the charging load of electric vehicles can be expressed by equation (20):

[0137]

[0138] Where, ΔL t =L t -L t0 ΔF t =F t -F t0 ε represents the price elasticity of demand response, ΔL t Let ΔF be the change in electricity demand during time period t. t L represents the change in real-time electricity price during time period t; t0 For the charging load of electric vehicles before demand response is implemented, F t0 L represents the electricity price for charging electric vehicles at time t before demand response is implemented. tF represents the charging load of electric vehicles at time t after the implementation of demand response; t Let be the charging price of electric vehicles at time t after the implementation of demand response.

[0139] Furthermore, the electricity demand of electric vehicles is also related to the electricity price in adjacent time periods, and the elasticity coefficient between them is shown in equation (21):

[0140]

[0141] In the formula, ε t,k The price elasticity of electric vehicles is represented by k and t, where k and t are the scheduling periods, and k and t = 1, 2, 3…24; ΔL k Let ΔF be the change in electricity demand during time period k. t0 L represents the change in real-time electricity price before the implementation of demand response in time period t. k0 The charging load of electric vehicles before the implementation of demand response in time period k.

[0142] It should be noted that when k and t are equal, it indicates ε t,k This is the self-elasticity coefficient (generally negative); when k and t are not equal, it represents ε. t,k This is the mutual elasticity coefficient (usually a positive value).

[0143] In one embodiment, the elasticity matrix E consists of self-elasticity coefficients and mutual elasticity coefficients, as shown below:

[0144]

[0145] Therefore, the charging load of the electric vehicle at time t is as shown in equation (22):

[0146]

[0147] In the formula, L EV,t L is the second charging load of the electric vehicle at time t. t0 For the charging load of electric vehicles before demand response is implemented, ε t,k For price-driven demand response, F t Let F be the charging electricity price for electric vehicles at time t after the implementation of demand response. t0 F represents the charging electricity price of electric vehicles at time t before demand response is implemented; k and t are the scheduling periods, k, t = 1, 2, 3…24; k Let F be the charging electricity price for electric vehicles at time k after the implementation of demand response. k0 The charging price of electric vehicles at time k before the implementation of demand response.

[0148] The real-time charging price of electric vehicles at time t is shown in equations (23) and (24):

[0149] p EV,t =L EV,t ·Δt,Δt=1;(23)

[0150]

[0151] In the formula, p EV,0 (t) represents the electric vehicle charging price at the initial time point.

[0152] It should be noted that Δt is the change in time scale t, Δt = 1 represents a time scale of t, and p represents the change in time scale t. EV (t) represents the real-time charging price for electric vehicles. ε t,k Price elasticity of demand is a response to price-driven demand. Essentially, it is an elasticity coefficient between price and load, indicating that there is a mutual influence between price and load. Therefore, the real-time charging price of electric vehicles can be kept at the same rate of change as the charging load.

[0153] In step S200, the available output curve of the power supply side flexibility resource is generated according to the output model, the discharge power or first charging load of the energy storage side flexibility resource is determined according to the first charge-discharge model, and the second charging load of the demand side flexibility resource is determined according to the second charge-discharge model. The second charging load is determined based on the real-time charging price and the power corresponding to the user demand response.

[0154] In one embodiment, the power generation g of the thermal power unit at time t can be obtained based on the power output model of the thermal power unit. Thr,t g Thr,t This also represents the output of the thermal power unit at time t. Based on the output of the thermal power unit at each time point, the available output curve of the thermal power unit can be obtained. Based on the output model of the wind turbine unit, the actual output power g of the wind turbine unit at time t can be obtained. WPP,t This yields the wind turbine's output at time t, thus providing the available output curve of the wind turbine. Based on the photovoltaic (PV) turbine's output model, the actual output power g of the PV turbine at time t can be obtained. PV,t This gives the output of the photovoltaic unit at time t, and thus the available output curve of the photovoltaic unit can be obtained.

[0155] Based on the first charge-discharge model of the energy storage system and its flexible resources, the charging power P of the energy storage system at time t can be obtained. chr (t) or discharge power P disc (t), that is, the output of the energy storage system. Or the first charging load It should be noted that the charging and discharging states of an energy storage system cannot exist simultaneously; that is, the output power cannot be in a state of simultaneous charging and discharging. With the first charging load One of them must be 0.

[0156] Based on demand-side flexibility resources, such as the second charging and discharging model of electric vehicles, the second charging load L of electric vehicles can be obtained. EV,t And the real-time charging price p of electric vehicles at time t. EV,t We can obtain the operating cost (charging cost) C of the electric vehicle at time t. EV,t C EV,t =p EV,t ×L EV,t .

[0157] In one embodiment, before constructing an optimized operation model aimed at minimizing the total operating cost of the virtual power plant, the method further includes: calculating the operating cost of the electric vehicle at time t based on the real-time charging price of the electric vehicle at time t and the charging load at time t.

[0158] It should be noted that the cost of discharging an electric vehicle is not considered when calculating the operating cost of the electric vehicle at time t.

[0159] In step S300, the next day's power output plan of the virtual power plant is obtained based on the available output curve, discharge power or first charging load and second charging load, and reported to the main power grid.

[0160] In one embodiment, based on the available output curves of thermal power units, wind power units, and photovoltaic units, the discharge power of the energy storage system or the first charging load, and the second charging load of electric vehicles, a next-day power generation plan for a virtual power plant can be integrated and reported to the main power grid.

[0161] Specifically, the power generation plan reported by the virtual power plant for the next day is shown in equation (25):

[0162]

[0163] In the formula, P VPP,t For the output of the virtual power plant at time t, g Thr,t g WPP,t g PV,t and These represent the power outputs of the thermal power unit, wind power unit, photovoltaic unit, and energy storage system at time t, respectively. Let L be the charging load of the energy storage system at time t, i.e., the first charging load. EV,t The charging load of the electric vehicle at time t is the second charging load.

[0164] It is important to note that energy storage systems cannot charge and discharge simultaneously. At any given moment, the energy storage system must either be charging or discharging. and One of the values ​​must be 0. For example, at time t, when the energy storage system is charging, The value is 0; when the energy storage system is discharging, It is 0.

[0165] In step S400, based on the total output plan for the next day returned by the main power grid and the operating constraints of the virtual power plant, an optimized operation model is constructed with the goal of minimizing the total operating cost of the virtual power plant.

[0166] This invention combines price-based demand response, electric vehicles, energy storage, and other flexible resources, along with the purchase and sale relationship between the virtual power plant and the main grid, to construct an optimized operation model for the virtual power plant with the lowest total operating cost. It also aggregates multiple flexible resources to optimize the operation of the virtual power plant.

[0167] In one embodiment, the objective function for optimizing the running model is shown in equation (26):

[0168]

[0169] Among them, F cost p represents the total operating cost of the virtual power plant. EG,t G EG,t Let C represent the purchased electricity volume and the purchased electricity price of the virtual power plant at time t, respectively. Thr,t C WPP,t C PV,t Let C represent the power generation costs of thermal power units, wind power units, and photovoltaic units at time t, respectively. ESS,t C represents the operating cost of the energy storage system at time t. EV,t This represents the operating cost of the electric vehicle at time t.

[0170] Wherein, the operating cost C of the electric vehicle at time t EV,t The main consideration is charging cost, as shown in equation (27):

[0171]

[0172] In the formula, p VPP,t G represents the average cost electricity price of the virtual power plant at time t. VPP,t p represents the power generation of the virtual power plant at time t. EV,t L represents the real-time price of charging an electric vehicle. EV,t L represents the charging load of the electric vehicle at time t. EVi,t This represents the charging load of electric vehicle i at time t. If the grid electricity price is high, the charging price of electric vehicles is settled based on the average electricity price of the virtual power plant; if the grid electricity price is low, the charging price of electric vehicles can be calculated based on the electricity purchase price.

[0173] It should be noted that the unit power generation cost of wind turbines and photovoltaic units is related to the specific project. The unit power generation cost of wind turbines and photovoltaic units in different projects may have certain differences. The unit power generation cost of wind turbines and photovoltaic units in the embodiments of this invention is a constant. Therefore, as long as the output of wind turbines and photovoltaic units at time t is known, the power generation cost of wind turbines and photovoltaic units at time t can be obtained.

[0174] In one embodiment, the operating constraints of the virtual power plant include at least:

[0175] Constraints on power supply and demand balance, constraints on the operation of thermal power units, constraints on the operation of energy storage systems, and constraints on the operation of electric vehicles.

[0176] Specifically, the power supply and demand balance constraint can be expressed by equation (28):

[0177]

[0178] In the formula, g Thr,t g WPP,t and g PV,t These represent the power generation outputs of thermal power, wind power, and photovoltaic units at time t, respectively. and g represents the discharge and charge power of the energy storage system at time t. EG,t L represents the purchased electricity of the virtual power plant at time t; EV,t ΔL represents the charging load demand of electric vehicles at time t. EV,t This represents the charging load of the electric vehicle at time t. If the vehicle is in a discharging state, this load can be negative.

[0179] Among them, the operating constraints of thermal power units are shown in Equation (1), the operating constraints of energy storage systems are shown in Equation (12), and the operating constraints of electric vehicles are shown in Equation (17).

[0180] In step S500, the daily power generation plan for the next day is adjusted according to the optimized operation model to obtain the optimized operation result of the virtual power plant.

[0181] In one embodiment, after the virtual power plant reports its next-day output plan to the main grid, the main grid generates a total output curve for the next day and returns it to the virtual power plant. The virtual power plant then combines this total output plan with flexible resources such as energy storage systems and electric vehicles. By considering the power purchase and sale relationship between the virtual power plant and the main grid, an optimized operation model for the virtual power plant with the lowest total operating cost is constructed, resulting in an optimized operation method that integrates multiple flexible resources. By connecting to the main grid, the virtual power plant can obtain revenue from selling electricity and can also purchase electricity from the main grid, further enhancing its flexibility.

[0182] This invention combines flexible resources such as price-based demand response, electric vehicles, and energy storage systems, and integrates the purchase and sale relationship between the virtual power plant and the main grid to construct a virtual power plant operation model with the lowest total operating cost. It also proposes an optimized operation method for virtual power plants that aggregates multiple flexible resources.

[0183] The virtual power plant optimization operation method provided in this invention, which integrates multiple flexible resources, constructs corresponding output models for various generator sets in the virtual power plant, and obtains the available output curves of each generator set based on the output models; it also constructs corresponding charging and discharging models for energy storage systems and electric vehicles, and obtains the charging loads corresponding to the energy storage systems and electric vehicles based on the charging and discharging models; and generates the next-day power generation plan of the virtual power plant based on the available output curves and charging loads. Building upon the traditional multi-distributed power sources in virtual power plants, this method integrates multiple flexible resources such as demand response, energy storage, and electric vehicles, further improving the flexibility of the virtual power plant and promoting the integrated development of "source, grid, load, and storage" in the virtual power plant. By accessing multiple flexible resources, establishing an optimized operation model for the virtual power plant based on the output plan and operating constraints returned by the main grid, and adjusting the next-day power generation plan based on the optimized operation model, the optimized operation result of the virtual power plant is obtained. This enables collaborative optimized operation control, ensuring the real-time dynamic supply and demand balance and safe and stable operation of the power system.

[0184] Please see Figure 4 The present invention also provides an embodiment of a virtual power plant optimized operation device that integrates multiple flexible resources, including:

[0185] The model building module 11 is used to build an output model for the power supply side flexibility resources in the virtual power plant, a first charge-discharge model for the energy storage side flexibility resources, and a second charge-discharge model for the demand side flexibility resources; the power supply side flexibility resources include at least thermal power units, wind power units, and photovoltaic units, the energy storage side flexibility resources include at least energy storage systems, and the demand side flexibility resources include at least electric vehicles.

[0186] The output or load calculation module 22 is used to generate the available output curve of the power supply side flexibility resource according to the output model, determine the discharge power or first charging load of the energy storage side flexibility resource according to the first charging and discharging model, and determine the second charging load of the demand side flexibility resource according to the second charging and discharging model. The second charging load is determined based on the real-time charging price and user demand response.

[0187] The power output planning module 33 is used to obtain the next day's power output plan of the virtual power plant based on the available power output curve, the discharge power or the first charging load, and the second charging load, and report it to the main power grid.

[0188] The optimization model construction module 44 is used to construct an optimization operation model with the goal of minimizing the total operating cost of the virtual power plant, based on the total output plan for the next day returned by the main power grid and the operating constraints of the virtual power plant.

[0189] The optimization operation module 55 is used to adjust the next day's power generation plan according to the optimization operation model to obtain the optimized operation result of the virtual power plant.

[0190] The virtual power plant optimization operation device provided in this invention integrates multiple flexible resources. It constructs corresponding output models for various generator sets within the virtual power plant, obtaining the available output curves for each generator set based on these models. It also constructs corresponding charging and discharging models for energy storage systems and electric vehicles, obtaining the charging loads for these systems. Finally, it generates the next-day power generation plan for the virtual power plant based on the available output curves and charging loads. Building upon the traditional multi-distributed power source model of virtual power plants, it integrates multiple flexible resources such as demand response, energy storage, and electric vehicles, further enhancing the flexibility of the virtual power plant and promoting the integrated development of "source, grid, load, and storage." By accessing multiple flexible resources and establishing an optimized operation model for the virtual power plant based on the output plan and operational constraints returned from the main grid, and adjusting the next-day power generation plan according to the optimized operation model, the optimized operation result of the virtual power plant is obtained. This enables collaborative optimized operation control, ensuring real-time dynamic supply and demand balance and safe and stable operation of the power system.

[0191] In addition, the present invention also provides an electronic device, including: a processor and a memory;

[0192] The memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0193] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described.

[0194] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0195] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0196] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0197] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0198] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0199] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the operation of a virtual power plant by aggregating multiple flexible resources, characterized in that: include: A power output model is constructed for the power supply-side flexibility resources in the virtual power plant, a first charge-discharge model is constructed for the energy storage-side flexibility resources, and a second charge-discharge model is constructed for the demand-side flexibility resources; the power supply-side flexibility resources include at least thermal power units, wind power units, and photovoltaic units, the energy storage-side flexibility resources include at least energy storage systems, and the demand-side flexibility resources include at least electric vehicles. The available output curve of the power supply side flexibility resource is generated according to the output model. The discharge power or first charging load of the energy storage side flexibility resource is determined according to the first charging and discharging model. The second charging load of the demand side flexibility resource is determined according to the second charging and discharging model. The second charging load is determined based on the real-time charging price and the power corresponding to the user demand response. The next day's power output plan of the virtual power plant is obtained based on the available output curve, the discharge power or the first charging load, and the second charging load, and then reported to the main power grid. Based on the next day's total power output plan returned by the main power grid and the operating constraints of the virtual power plant, an optimized operation model is constructed with the objective of minimizing the total operating cost of the virtual power plant, including: Construct an optimized operating model with the following objective function: ； in, This represents the total operating cost of the virtual power plant. , These represent virtual power plants in Real-time external electricity purchase volume and price , , These respectively represent thermal power units, wind power units, and photovoltaic units in The cost of generating electricity at any given time Indicates that the energy storage system is in Operating costs at any time Indicates electric vehicles The operating cost at any given moment; Before constructing the optimized operation model with the objective of minimizing the total operating cost of the virtual power plant, the following steps are also included: According to the electric vehicle Real-time charging prices and The second charging load calculation of the electric vehicle at time is performed. The operating cost at any given moment; The electric vehicle is determined according to the following formula. The second charging load at that moment: ； In the formula, For electric vehicles The second charging load at that moment, For the charging load of electric vehicles before demand response is implemented, Price elasticity of demand is a response to price-driven demand. Electric vehicles after demand response implementation The electricity price for charging at any given moment. Electric vehicles before demand response implementation The electricity price for charging at any given moment; , For scheduling periods, ; Electric vehicles after demand response implementation The electricity price for charging at any given moment. Electric vehicles before demand response implementation The electricity price for charging at any given moment; The step of obtaining the next day's output plan of the virtual power plant based on the available output curve, the discharge power or the first charging load, and the second charging load includes: The next day's power output plan of the virtual power plant is obtained based on the available output curve, the discharge power or the first charging load, and the second charging load, as shown in the following formula. In the formula, For virtual power plants Constant effort , , and These respectively represent thermal power units, wind power units, photovoltaic units, and energy storage systems in Constant effort For energy storage systems in The charging load at any given moment is the first charging load. For electric vehicles The charging load at a given moment is the second charging load; The daily power generation plan for the next day is adjusted according to the optimized operation model to obtain the optimized operation results of the virtual power plant.

2. The virtual power plant optimization operation method based on aggregating multiple flexible resources according to claim 1, characterized in that, Before constructing the optimized operation model with the objective of minimizing the total operating cost of the virtual power plant, the following steps are also included: The thermal power unit is calculated according to the following formula. The cost of generating electricity at any given moment; ； ； ； in, Indicates that thermal power units are in The cost of generating electricity at any given time , These respectively represent the thermal power units at Fuel costs and start-stop costs at all times; , , This is the power generation energy consumption coefficient of the thermal power unit; For thermal power units in Power generation at any given moment; For thermal power units in The power generation state variable at any given time is a 0-1 variable, where 1 indicates that the thermal power unit is in operation and 0 indicates that the thermal power unit is in shutdown. Indicates that thermal power units are in The startup cost at any given moment.

3. The virtual power plant optimization operation method based on aggregating multiple flexible resources according to claim 1, characterized in that, The operational constraints include at least the following: Constraints on power supply and demand balance, constraints on the operation of thermal power units, constraints on the operation of energy storage systems, and constraints on the operation of electric vehicles.

4. A virtual power plant optimization operation device that integrates multiple flexible resources, characterized in that: include: The model building module is used to build an output model for the power supply side flexibility resources in the virtual power plant, a first charge-discharge model for the energy storage side flexibility resources, and a second charge-discharge model for the demand side flexibility resources; the power supply side flexibility resources include at least thermal power units, wind power units, and photovoltaic units, the energy storage side flexibility resources include at least energy storage systems, and the demand side flexibility resources include at least electric vehicles. The output or load calculation module is used to generate the available output curve of the power supply-side flexibility resources according to the output model, determine the discharge power or first charging load of the energy storage-side flexibility resources according to the first charge-discharge model, and determine the second charging load of the demand-side flexibility resources according to the second charge-discharge model. The second charging load is determined based on the real-time charging price and the power corresponding to the user demand response. The module also includes a power output or load calculation module for electric vehicles. Real-time charging prices and The second charging load calculation of the electric vehicle at time is performed. The operating cost at any given moment; The electric vehicle is determined according to the following formula. The second charging load at that moment: ； In the formula, For electric vehicles The second charging load at that moment, For the charging load of electric vehicles before demand response is implemented, Price elasticity of demand is a response to price-driven demand. Electric vehicles after demand response implementation The electricity price for charging at any given moment. Electric vehicles before demand response implementation The electricity price for charging at any given moment; , For scheduling periods, ; Electric vehicles after demand response implementation The electricity price for charging at any given moment. Electric vehicles before demand response implementation The electricity price for charging at any given moment; The power output planning module is used to obtain the next day's power output plan of the virtual power plant based on the available power output curve, the discharge power or the first charging load, and the second charging load, and report it to the main power grid, including: In the formula, For virtual power plants Constant effort , , and These respectively represent thermal power units, wind power units, photovoltaic units, and energy storage systems in Constant effort For energy storage systems in The charging load at any given moment is the first charging load. For electric vehicles The charging load at a given moment is the second charging load; The optimization model construction module is used to construct an optimization operation model with the objective of minimizing the total operating cost of the virtual power plant, based on the next day's total output plan returned by the main power grid and the operating constraints of the virtual power plant. This includes constructing an optimization operation model with the following objective function: ； in, This represents the total operating cost of the virtual power plant. , These represent virtual power plants in Real-time external electricity purchase volume and price , , These respectively represent thermal power units, wind power units, and photovoltaic units in The cost of generating electricity at any given time Indicates that the energy storage system is in Operating costs at any time Indicates electric vehicles The operating cost at any given moment; An optimized operation module is used to adjust the daily power generation plan for the next day according to the optimized operation model, so as to obtain the optimized operation results of the virtual power plant.

5. An electronic device, characterized in that, include: Processor and memory; The memory stores a computer program, and the processor executes the computer program to implement the steps of the method as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

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