Optimized operation method for virtual power plant with multiple subjects participating in shared energy storage

Abstract

The invention relates to the technical field of energy system optimization scheduling, in particular to a virtual power plant optimization operation method for multiple subjects to participate in shared energy storage, which comprises the following steps: firstly, determining a physical model of shared energy storage and the structure of a virtual power plant so as to form an operation mode when multiple virtual power plants participate in shared energy storage operation; providing an upper-layer model taking the shared energy storage as a main body in combination with a target that the operation cost of the shared energy storage is minimum in system operation; establishing a lower-layer virtual power plant economic operation model taking the minimization of the individual operation cost of the virtual power plant as the target by combining the target function with the minimum overall operation cost of the virtual power plant and linking the cost of purchasing or renting the shared energy storage; and in combination with an ant colony algorithm, the upper-layer model is optimized and solved, and then a solving thought of the lower-layer model is put forward.

Description

Technical Field

[0001] This invention relates to the field of energy system optimization and scheduling technology, specifically to a method for optimizing the operation of a virtual power plant with multi-entity participation in shared energy storage. Background Technology

[0002] With the increasing popularity of distributed renewable energy, virtual power plants, as a core technology for aggregating and coordinating distributed resources, face key challenges in their development. In traditional operating models, each virtual power plant typically requires an independent energy storage system to mitigate the fluctuations in output from renewable energy sources such as wind and solar power. This model not only leads to redundant construction and wasted investment in energy storage facilities, but also results in low utilization rates for the associated energy storage equipment due to the relatively uniform load characteristics of individual virtual power plants. This leads to capacity redundancy and long investment payback periods, hindering the full realization of the time and spatial flexibility of energy storage resources. Consequently, it limits the overall economic benefits of virtual power plants and their ability to absorb renewable energy.

[0003] To improve the utilization efficiency of energy assets, the shared energy storage model has emerged. This model allows a centralized energy storage facility to serve multiple users (e.g., multiple virtual power plants). However, this multi-stakeholder sharing model also introduces new complexities. Existing technologies have proposed methods for optimizing the configuration of multiple virtual power plants and shared energy storage systems based on a two-level decision-making game model. This method typically establishes a two-level model with upper-level planning and lower-level scheduling as objectives. Although such models provide a basic framework for multi-stakeholder collaboration, they fail to adequately address the inherent conflict of interest between the shared energy storage operator (i.e., the resource provider) and the various virtual power plants (i.e., the resource user). The resource provider aims to maximize its own revenue or minimize operating costs, while the resource user hopes to meet its energy needs at the lowest possible cost. Therefore, how to design a systematic decision-making framework that can scientifically and efficiently coordinate these conflicting interests to achieve the globally optimal operation of the entire energy ecosystem is a pressing technical problem that needs to be solved in the current technological field. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method for optimizing the operation of a virtual power plant with multiple participants sharing energy storage, so as to overcome the problems existing in the current technology.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] This application provides a method for optimizing the operation of a virtual power plant with multiple stakeholders participating in shared energy storage, including:

[0007] Determine the components of the virtual power plant;

[0008] A physical model of the virtual power plant is constructed for its constituent components, and the operating costs of the virtual power plant's constituent components are determined.

[0009] Construct a physical model for the operation of shared energy storage and determine the cost of shared energy storage;

[0010] Based on the shared energy storage operation physical model and the shared energy storage cost, an upper-level operation model is established with the goal of minimizing the shared energy storage operation cost.

[0011] Add constraints to the upper-level operating model;

[0012] Based on the components of the virtual power plant, a physical model of the power plant is constructed, and the operating costs of the components of the virtual power plant are considered. With the goal of minimizing the operating costs of the virtual power plant, a lower-level virtual power plant operation optimization model is constructed.

[0013] Add constraints to the lower-level virtual power plant operation optimization model;

[0014] The upper-level operating model is solved using the ant colony algorithm to obtain the capacity configuration and real-time charging and discharging plan of the shared energy storage. Based on the capacity configuration and real-time charging and discharging plan of the shared energy storage, the lower-level virtual power plant operation optimization model is solved to determine the optimal operating strategy of the virtual power plant.

[0015] Furthermore, in the method described above, the components of the virtual power plant include: distributed renewable energy sources, energy storage, and transferable flexible loads.

[0016] Furthermore, in the methods described above, the distributed renewable energy sources include wind power and photovoltaic power generation;

[0017] The physical model for the power plant is as follows:

[0018]

[0019] in, and V represents the power output of wind power and photovoltaic power at time t, respectively. t V represents the wind speed at time t. in and v out These are the cut-in and cut-out wind speeds for the wind turbine, v ra The rated wind speed of the fan. S represents the rated power of the fan. PV For the area of ​​the photovoltaic panel, Let η be the light intensity at time t. PV The average efficiency of the photovoltaic module is μ. inv and μ sor Here, represents the conversion rate and absorption rate of solar energy, and loss represents the photovoltaic power generation loss.

[0020] The operating cost of the components of the virtual power plant is:

[0021]

[0022] in, and The operating costs of wind and solar power units during time period t. and For the depreciation costs of wind power and photovoltaic units, and These represent the real-time power generation of wind power and photovoltaic units, respectively, p WPP and p PV The recycling price for wind and solar power generation.

[0023] Furthermore, in the above-described method, the shared energy storage operation physical model is as follows:

[0024]

[0025] Among them, SES t δ represents the capacity of the shared energy storage at time t. chr and δ disc The charging and discharging efficiencies of shared energy storage are P and P, respectively. chr and P disc These represent the charging and discharging power of the shared energy storage, and P... chr ·P disc =0.

[0026] Furthermore, in the above-described method, the shared energy storage cost includes: the initial investment cost of energy storage, the operation and maintenance cost, and the cost of purchasing electricity from a virtual power plant or grid.

[0027] Furthermore, in the method described above, the upper-level operating model is as follows:

[0028]

[0029] in, To reduce the operation and maintenance costs of shared energy storage, The annual investment amount for shared energy storage, Y represents the battery replacement cost for shared energy storage, and Y represents the lifespan of the shared energy storage. Net purchase and sale cost of shared energy storage at time t Let be the cost of electricity purchase at time t. Let be the cost of electricity sales at time t. and These represent the electricity sold by shared energy storage to virtual power plants and the power grid, respectively.

[0030] Furthermore, in the method described above, the constraints of the upper-level operating model include:

[0031] The runtime constraints are:

[0032]

[0033] in, and These are the minimum and maximum values ​​for shared energy storage, respectively. and For the minimum and maximum values ​​of the shared energy storage discharge power, SES min and SES max These are the lower and upper limits of the shared energy storage capacity, respectively.

[0034] The charge constraint for energy storage operation is:

[0035]

[0036] in, α represents the state of charge of shared energy storage at time t on a typical day i. char and α disc This indicates the charging and discharging efficiency of the shared energy storage power station. and These represent the charging and discharging efficiencies of the shared energy storage power station at time t on a typical day i, respectively.

[0037] Furthermore, in the method described above, the lower-level virtual power plant operation optimization model is as follows:

[0038]

[0039] Among them, C grid,t This represents the inter-grid electricity purchase cost of virtual power plants. This indicates the amount of electricity purchased by the virtual power plant from shared energy storage. C represents the amount of electricity sold by a virtual power plant to shared energy storage. V-S C represents the capacity leasing fee paid by the virtual power plant to the shared energy storage. curt The penalty cost for curtailment of wind and solar power in virtual power plants.

[0040] Furthermore, in the method described above, the constraints of the lower-level virtual power plant operation optimization model include:

[0041] The system power balance constraint is:

[0042] P WPP，t +P PV,t +P grid,t +P SES-VPP,t =P VPP-SES,t +L t

[0043] Among them, P WPP,t P represents the power of the wind turbine at time t. PV,t P represents the power output of the photovoltaic generator at time t. grid,t P represents the power that the virtual power plant buys from the grid at time t. SES-VPP,t P represents the power purchased by the virtual power plant from the shared energy storage at time t. VPP-SES,t L represents the power sold by the virtual power plant to the shared energy storage at time t. t Let be the load of the virtual power plant at time t;

[0044] The power purchase constraint for the virtual power plant is:

[0045]

[0046] 0≤P SES-VPP,t ≤L max

[0047] in, L represents the maximum power that the virtual power plant can purchase from the grid. max This represents the maximum load power in the virtual power plant.

[0048] Furthermore, the method described above, wherein solving the upper-level operating model using the ant colony algorithm to obtain the shared energy storage capacity configuration and real-time charging / discharging plan, and solving the lower-level virtual power plant operation optimization model based on the shared energy storage capacity configuration and real-time charging / discharging plan to determine the optimal operating strategy of the virtual power plant, includes:

[0049] The wind power output and photovoltaic power output in the virtual power plant, as well as the electricity price and real-time load status of each power source and load in the virtual power plant, are selected as the initial input data.

[0050] Initialize the ant colony size and location information using the objective function of the upper-level running model, initialize the number of iterations, and begin iteration;

[0051] After each iteration, the pheromone concentration and behavioral changes of the ant colony are calculated, the ant colony location information is updated, the global optimal solution is found, and the shared energy storage configuration and charging and discharging strategy are obtained.

[0052] The shared energy storage configuration and charging / discharging strategy are input into the lower-level virtual power plant operation optimization model to solve for the optimal operation strategy of the virtual power plant.

[0053] The beneficial effects of this invention are as follows:

[0054] First, the components of the virtual power plant are determined, and a physical model of the virtual power plant is built for each component. The operating costs of the virtual power plant components are then determined. A shared energy storage operation physical model is constructed, and the shared energy storage cost is determined. Based on the shared energy storage operation physical model and the shared energy storage cost, an upper-level operation model is established with the goal of minimizing the shared energy storage operation cost. Constraints are added to the upper-level operation model. Then, based on the virtual power plant's components and their operating costs, a lower-level virtual power plant operation optimization model is constructed with the goal of minimizing the virtual power plant's operation cost. Constraints are added to the lower-level virtual power plant operation optimization model. Finally, the upper-level operation model is solved using the ant colony algorithm to obtain the shared energy storage capacity configuration and real-time charging and discharging plan. Based on the shared energy storage capacity configuration and real-time charging and discharging plan, the lower-level virtual power plant operation optimization model is solved to determine the optimal operation strategy of the virtual power plant. This application provides an operational approach for shared energy storage in a virtual power plant, combining the virtual power plant with shared energy storage to reduce the sunk costs of building energy storage separately and improve the investment-return ratio of the virtual power plant. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a flowchart provided by one embodiment of the virtual power plant optimization operation method for multi-entity shared energy storage according to the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0058] Figure 1 This is a flowchart illustrating one embodiment of a virtual power plant operation optimization method for multi-entity shared energy storage according to the present invention. Please refer to... Figure 1 This embodiment may include the following steps:

[0059] S1. Determine the components of the virtual power plant;

[0060] S2. Construct a physical model of the virtual power plant for its constituent components and determine the operating costs of the virtual power plant's constituent components.

[0061] S3. Construct a physical model for the operation of shared energy storage and determine the cost of shared energy storage;

[0062] S4. Based on the physical model of shared energy storage operation and the cost of shared energy storage, establish an upper-level operation model with the goal of minimizing the operating cost of shared energy storage;

[0063] S5. Add constraints to the upper-level running model;

[0064] S6. Based on the components of the virtual power plant, construct a physical model of the power plant and the operating costs of the components of the virtual power plant. With the goal of minimizing the operating costs of the virtual power plant, construct a lower-level virtual power plant operation optimization model.

[0065] S7. Add constraints to the lower-level virtual power plant operation optimization model;

[0066] S8. Solve the upper-level operation model using the ant colony algorithm to obtain the capacity configuration and real-time charging and discharging plan of the shared energy storage. Then, solve the lower-level virtual power plant operation optimization model based on the capacity configuration and real-time charging and discharging plan of the shared energy storage to determine the optimal operation strategy of the virtual power plant.

[0067] Understandably, this application first determines the components of the virtual power plant, constructs a physical model for these components, and determines the operating costs of each component. It then constructs a shared energy storage operation physical model and determines the shared energy storage cost. Based on the shared energy storage operation physical model and the shared energy storage cost, an upper-level operation model is established with the goal of minimizing the shared energy storage operating cost. Constraints are added to the upper-level operation model. Next, based on the virtual power plant's components and their operating costs, a lower-level virtual power plant operation optimization model is constructed with the goal of minimizing the virtual power plant's operating cost. Constraints are added to the lower-level virtual power plant operation optimization model. Finally, the upper-level operation model is solved using the ant colony algorithm to obtain the shared energy storage capacity configuration and real-time charging / discharging plan. The lower-level virtual power plant operation optimization model is then solved based on the shared energy storage capacity configuration and real-time charging / discharging plan to determine the optimal operation strategy for the virtual power plant. This application provides an operational approach for shared energy storage in a virtual power plant, combining the virtual power plant with shared energy storage to reduce the sunk costs of building energy storage separately and improve the investment-return ratio of the virtual power plant.

[0068] Preferably, the components of the virtual power plant include: distributed renewable energy sources, energy storage, and transferable flexible loads.

[0069] Understandably, current virtual power plants primarily aggregate distributed resources, including distributed renewable energy sources, energy storage, and transferable flexible loads (capable of demand response). Distributed generators within a virtual power plant can supply power to loads within its jurisdiction. When there is a surplus of electricity, it can be sold to neighboring areas or the power grid through the virtual power plant control center. When there is a shortage of electricity, it can purchase electricity from neighboring virtual power plants, shared energy storage, or the power grid.

[0070] Preferably, distributed renewable energy sources include wind power and photovoltaic power generation;

[0071] The physical model of the power plant is as follows:

[0072]

[0073] in, and V represents the power output of wind power and photovoltaic power at time t, respectively. t V represents the wind speed at time t. in and v out These are the cut-in and cut-out wind speeds for the wind turbine, v ra The rated wind speed of the fan. S represents the rated power of the fan. PV For the area of ​​the photovoltaic panel, Let η be the light intensity at time t. PV The average efficiency of the photovoltaic module is μ. inv and μ sor Here, represents the conversion rate and absorption rate of solar energy, and loss represents the photovoltaic power generation loss.

[0074] The operating cost of the components of the virtual power plant is:

[0075]

[0076] in, and The operating costs of wind and solar power units during time period t. and For the depreciation costs of wind power and photovoltaic units, and These represent the real-time power generation of wind power and photovoltaic units, respectively, p WPP and p PV The recycling price for wind and solar power generation.

[0077] The preferred physical model for shared energy storage operation is as follows:

[0078]

[0079] Among them, SES t δ represents the capacity of the shared energy storage at time t.chr and δ disc The charging and discharging efficiencies of shared energy storage are P and P, respectively. chr and P disc These represent the charging and discharging power of the shared energy storage, and P... chr ·P disc =0.

[0080] Preferably, the shared energy storage cost includes: the initial investment cost of energy storage, the operation and maintenance cost, and the cost of purchasing electricity from a virtual power plant or grid.

[0081] It is understandable that, since shared energy storage cannot perform charging and discharging simultaneously, there is P chr ·P disc =0, meaning that when one term is a non-zero positive number, the other term must be 0.

[0082] Preferably, the upper-level operating model is:

[0083]

[0084] in,, The operation and maintenance cost of shared energy storage is usually calculated on an annual basis and is taken as 2% of the original value of the fixed assets of shared energy storage. The annual investment in shared energy storage is allocated using the annual average method, which includes both investment and battery replacement costs. The cost of battery replacement for shared energy storage is typically 10 years, and Y represents the lifespan of the shared energy storage. Net purchase and sale cost of shared energy storage at time t Let be the cost of electricity purchase at time t. Let t be the cost of electricity sold at time t. This is because shared energy storage cannot pursue the lowest possible cost and needs a certain utilization rate; therefore, the net cost of electricity purchase is expressed using the cost of buying and selling electricity. These represent the electricity sold by shared energy storage to virtual power plants and the power grid, respectively.

[0085] Preferably, the constraints of the upper-level operating model include:

[0086] The runtime constraints are:

[0087]

[0088] in, and These are the minimum and maximum values ​​for shared energy storage, respectively. and For the minimum and maximum values ​​of the shared energy storage discharge power, SES min and SES max These are the lower and upper limits of the shared energy storage capacity, respectively.

[0089] The charge constraint for energy storage operation is:

[0090]

[0091] in, α represents the state of charge of shared energy storage at time t on a typical day i. char and α disc This indicates the charging and discharging efficiency of the shared energy storage power station. and These represent the charging and discharging efficiencies of the shared energy storage power station at time t on a typical day i, respectively.

[0092] The preferred optimization model for the operation of the lower-level virtual power plant is as follows:

[0093]

[0094] Among them, C grid,t This represents the inter-grid electricity purchase cost of virtual power plants. This represents the amount of electricity a virtual power plant purchases from shared energy storage. Over a complete time period, the amount of electricity a virtual power plant purchases from shared energy storage equals the total electricity sold by shared energy storage minus the electricity sold to the grid. C represents the amount of electricity sold by a virtual power plant to shared energy storage. V-S C represents the capacity leasing fee paid by the virtual power plant to the shared energy storage. curt The penalty cost for curtailment of wind and solar power in virtual power plants.

[0095] Preferably, the constraints of the lower-level virtual power plant operation optimization model include:

[0096] The system power balance constraint is:

[0097] P WPP，t +P PV，t +P grid，t +P SES-VPP，t =P VPP-SES，t +L t

[0098] Among them, P WPP,t P represents the power of the wind turbine at time t. PV,t P represents the power output of the photovoltaic generator at time t. grid,t P represents the power that the virtual power plant buys from the grid at time t. SES-VPP,t P represents the power purchased by the virtual power plant from the shared energy storage at time t. VPP-SES,t L represents the power sold by the virtual power plant to the shared energy storage at time t. t Let be the load of the virtual power plant at time t;

[0099] The power purchase constraint for the virtual power plant is:

[0100]

[0101] in, L represents the maximum power that the virtual power plant can purchase from the grid. max This represents the maximum load power in the virtual power plant.

[0102] Preferably, step S8 includes:

[0103] The initial input data are the wind power output and photovoltaic power output in the virtual power plant, as well as the electricity price and real-time load of each power source and load in the virtual power plant.

[0104] Initialize the ant colony size and location information, initialize the number of iterations, and start the iteration using the objective function of the upper-level running model;

[0105] After each iteration, the pheromone concentration and behavioral changes of the ant colony are calculated, the ant colony location information is updated, the global optimal solution is found, and the shared energy storage configuration and charging and discharging strategy are obtained.

[0106] The shared energy storage configuration and charging / discharging strategy are input into the lower-level virtual power plant operation optimization model to solve for the optimal operation strategy of the virtual power plant.

[0107] It's understandable that the Ant Colony Optimization (ACO) algorithm is a biomimetic algorithm that incorporates the foraging behavior of ants in nature. In ACO, the paths traversed by ants represent feasible solutions to the problem, and all existing paths constitute the feasible solution space of the optimization problem.

[0108] During foraging in an ant colony, ant behavior is influenced by pheromone concentration. The probability of ant a moving from node m to node n can be expressed as:

[0109]

[0110] In the formula, τ mn (t) represents the pheromone along the path of ant a from node m to node n at time t; η mn (t) represents the expected degree of choosing the path from node m to node n. The longer the path, the cheaper the optimal solution, i.e., the smaller the expectation. Let α be the set of distances that ant a can reach from node m to node n; α and λ are heuristic factors that control the influence of pheromone and distance on the ant's transfer probability.

[0111] Each ant leaves pheromones along its path when it reaches a food source, meaning the pheromone concentration along that path increases. The change in pheromone concentration along that path can be expressed as:

[0112]

[0113] In the formula, τ'(a) is the pheromone concentration at the latest position of ant a; ρ is the evaporation coefficient of the original pheromone along the path; Δτ n (a) represents the pheromone left by the ant representing the optimal path in this iteration; τ(a) represents the pheromone of the ant belonging to the optimal path after the previous iteration.

[0114] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0115] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.

[0116] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0117] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0118] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0119] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0120] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0121] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0122] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for optimizing the operation of a virtual power plant with multi-participant shared energy storage, characterized in that, include: Determine the components of the virtual power plant; A physical model of the virtual power plant is constructed for its constituent components, and the operating costs of the virtual power plant's constituent components are determined. Construct a physical model for the operation of shared energy storage and determine the cost of shared energy storage; Based on the shared energy storage operation physical model and the shared energy storage cost, an upper-level operation model is established with the goal of minimizing the shared energy storage operation cost. Add constraints to the upper-level operating model; Based on the components of the virtual power plant, a physical model of the power plant is constructed, and the operating costs of the components of the virtual power plant are considered. With the goal of minimizing the operating costs of the virtual power plant, a lower-level virtual power plant operation optimization model is constructed. Add constraints to the lower-level virtual power plant operation optimization model; The upper-level operating model is solved using the ant colony algorithm to obtain the capacity configuration and real-time charging and discharging plan of the shared energy storage. Based on the capacity configuration and real-time charging and discharging plan of the shared energy storage, the lower-level virtual power plant operation optimization model is solved to determine the optimal operating strategy of the virtual power plant.

2. The method according to claim 1, characterized in that, The components of the virtual power plant include: distributed renewable energy sources, energy storage, and transferable flexible loads.

3. The method according to claim 2, characterized in that, The distributed renewable energy sources include: wind power and photovoltaic power generation; The physical model for the power plant is as follows: Among them, P t WPP and P t PV V represents the power output of wind power and photovoltaic power at time t, respectively. t V represents the wind speed at time t. in and v out These are the cut-in and cut-out wind speeds for the wind turbine, v ra The rated wind speed of the fan. S represents the rated power of the fan. PV For the area of ​​the photovoltaic panel, Let η be the light intensity at time t. PV The average efficiency of the photovoltaic module is μ. inv and μ sor Here, represents the conversion rate and absorption rate of solar energy, and loss represents the photovoltaic power generation loss. The operating cost of the components of the virtual power plant is: in, and The operating costs of wind and solar power units during time period t. and P represents the depreciation cost of wind and solar power units. t WPP With P t PV These represent the real-time power generation of wind power and photovoltaic units, respectively, p WPP and p PV The recycling price for wind and solar power generation.

4. The method according to claim 3, characterized in that, The physical model for the operation of the shared energy storage is as follows: Among them, SES t δ represents the capacity of the shared energy storage at time t. chr and δ disc The charging and discharging efficiencies of shared energy storage are P and P, respectively. chr and P disc These represent the charging and discharging power of the shared energy storage, and P... chr ·P disc =0.

5. The method according to claim 4, characterized in that, The shared energy storage cost includes: the initial investment cost of energy storage, the operation and maintenance cost, and the cost of purchasing electricity from virtual power plants or the power grid.

6. The method according to claim 5, characterized in that, The upper-level operating model is as follows: in, To reduce the operation and maintenance costs of shared energy storage, The annual investment amount for shared energy storage, Y represents the battery replacement cost for shared energy storage, and Y represents the lifespan of the shared energy storage. Net purchase and sale cost of shared energy storage at time t Let be the cost of electricity purchase at time t. Let be the cost of electricity sales at time t. and These represent the electricity sold by shared energy storage to virtual power plants and the power grid, respectively.

7. The method according to claim 6, characterized in that, The constraints of the upper-level operating model include: The runtime constraints are: HIS min ≤SES t ≤SES max in, and These are the minimum and maximum values ​​for shared energy storage, respectively. and For the minimum and maximum values ​​of the shared energy storage discharge power, SES min and SES max These are the lower and upper limits of the shared energy storage capacity, respectively. The charge constraint for energy storage operation is: in, α represents the state of charge of shared energy storage at time t on a typical day i. char and α disc This indicates the charging and discharging efficiency of the shared energy storage power station. and These represent the charging and discharging efficiencies of the shared energy storage power station at time t on a typical day i, respectively.

8. The method according to claim 7, characterized in that, The lower-level virtual power plant operation optimization model is as follows: Among them, C grid,t This represents the inter-grid electricity purchase cost of virtual power plants. This indicates the amount of electricity purchased by the virtual power plant from shared energy storage. C represents the amount of electricity sold by a virtual power plant to shared energy storage. V-S C represents the capacity leasing fee paid by the virtual power plant to the shared energy storage. curt The penalty cost for curtailment of wind and solar power in virtual power plants.

9. The method according to claim 8, characterized in that, The constraints of the lower-level virtual power plant operation optimization model include: The system power balance constraint is: P WPP，t +P PV,t +P grid,t +P SES-VPP，t =P VPP-SES，t +L t Among them, P WPP,t P represents the power of the wind turbine at time t. PV,t P represents the power output of the photovoltaic generator at time t. grid,t P represents the power that the virtual power plant buys from the grid at time t. SES-VPP,t P represents the power purchased by the virtual power plant from the shared energy storage at time t. VPP-SES,t L represents the power sold by the virtual power plant to the shared energy storage at time t. t Let be the load of the virtual power plant at time t; The power purchase constraint for the virtual power plant is: 0≤P SES-VPP,t ≤L max in, L represents the maximum power that the virtual power plant can purchase from the grid. max This represents the maximum load power in the virtual power plant.

10. The method according to claim 9, characterized in that, The process involves solving the upper-level operating model using the ant colony algorithm to obtain the shared energy storage capacity configuration and real-time charging / discharging plan, and then solving the lower-level virtual power plant operation optimization model based on the shared energy storage capacity configuration and real-time charging / discharging plan to determine the optimal operating strategy of the virtual power plant, including: The wind power output and photovoltaic power output in the virtual power plant, as well as the electricity price and real-time load status of each power source and load in the virtual power plant, are selected as the initial input data. Initialize the ant colony size and location information using the objective function of the upper-level running model, initialize the number of iterations, and begin iteration; After each iteration, the pheromone concentration and behavioral changes of the ant colony are calculated, the ant colony location information is updated, the global optimal solution is found, and the shared energy storage configuration and charging and discharging strategy are obtained. The shared energy storage configuration and charging / discharging strategy are input into the lower-level virtual power plant operation optimization model to solve for the optimal operation strategy of the virtual power plant.

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