Multi-group virtual power plant comprehensive optimization scheduling method, device, equipment and storage medium

Abstract

The invention relates to the technical field of electronics, and discloses a multi-group virtual power plant comprehensive optimization scheduling method, device and equipment and a storage medium, and the method comprises the steps: carrying out the solving of an optimization scheduling model through the interaction electric quantity and electricity price of each group of virtual power plants and a power grid, the selling total electric quantity of a wind power station, and the selling total electric quantity of a photovoltaic power station; and obtaining optimal scheduling information of each group of virtual power plants, and carrying out comprehensive optimal scheduling on the plurality of virtual power plants based on the optimal scheduling information. According to the method provided by the invention, the multiple groups of virtual power plants are regarded as a whole to perform optimal scheduling, the optimal scheduling strategy is obtained by solving the pre-constructed optimal scheduling model, the multiple virtual power plants are subjected to optimal scheduling based on the optimal scheduling strategy, and the mutual influence between the virtual power plants and a power grid system and between the virtual power plants and a load is considered in the scheduling process. The operation efficiency and economic benefits of the whole system are improved, and the energy utilization efficiency is improved by optimizing the charging and discharging strategy of the energy storage system and utilizing renewable energy sources.

Description

Technical Field

[0001] The present invention relates to the field of electronic technology, and in particular to a method, device, equipment and storage medium for comprehensive optimization scheduling of multiple groups of virtual power plants. Background Art

[0002] A virtual power plant is a collection of distributed power sources, energy storage devices, and loads that can operate independently as a system. Virtual power plants can serve as a transition between the power system and consumers, and have the advantages of flexible and controllable power generation resources, market-optimized scheduling, and maintaining the stability of the power system. In order for virtual power plants to maximize their electricity sales revenue, they need to be optimized and scheduled. In related technologies, the initial optimal scheduling strategy for the electricity sales revenue of a single virtual power plant is usually solved, and then the virtual power plant is coordinated and controlled based on the optimal scheduling strategy. However, the implementation of the scheduling strategy requires the cooperation of multiple parties such as the power grid system, other virtual power plants, and loads. The scheduling strategy determined solely based on a single virtual power plant cannot achieve multi-party cooperation, which is difficult to achieve in reality. Summary of the invention

[0003] In view of this, the present invention provides a method, device, equipment and storage medium for comprehensive optimization scheduling of multiple groups of virtual power plants to solve the problem that the scheduling strategy determined based on a single virtual power plant alone cannot achieve multi-party cooperation and is difficult to achieve in reality.

[0004] In a first aspect, the present invention provides a method for comprehensive optimization scheduling of multiple groups of virtual power plants, the method comprising: obtaining first information and second information of multiple groups of virtual power plants, the first information being used to characterize the amount of electricity and electricity price of the corresponding virtual power plant interacting with the power grid, and the second information being used to characterize the total amount of electricity sold by the wind power station and the total amount of electricity sold by the photovoltaic power station of the corresponding virtual power plant; using the first information and the second information of the multiple groups of virtual power plants to solve a pre-constructed optimization scheduling model to obtain optimization scheduling information of each group of virtual power plants, the optimization scheduling information including the amount of electricity and electricity price sold to the load side by the corresponding virtual power plant, the charge and discharge amount of the energy storage system in the corresponding virtual power plant and the charge and discharge electricity price, and the average sold electricity prices corresponding to the wind power station and the photovoltaic power station, respectively, to optimize the scheduling. The model includes a first objective function, a second objective function and constraints. The first objective function is constructed with the goal of maximizing the total net profit from electricity sales of multiple groups of virtual power plants. The total net profit from electricity sales of multiple groups of virtual power plants is calculated based on the first information of each group of virtual power plants, the amount of electricity sold to the load side and the electricity price. The second objective function is constructed with the goal of maximizing the total net profit from electricity sales of each group of virtual power plants. The total net profit from electricity sales of each group of virtual power plants is calculated based on the first information of the corresponding virtual power plant, the second information, the amount of electricity sold to the load side and the electricity price, the charge and discharge amount and charge and discharge electricity price of the energy storage system, and the average sold electricity prices corresponding to the wind power station and the photovoltaic power station respectively; based on the optimized scheduling information, comprehensive optimization scheduling is performed on multiple groups of virtual power plants.

[0005] The comprehensive optimization scheduling method for multiple groups of virtual power plants provided by the present invention solves the optimization scheduling model through the information of the amount of electricity and electricity price interacting between each group of virtual power plants and the power grid, the total amount of electricity sold by the wind power station, and the total amount of electricity sold by the photovoltaic power station, so as to obtain the optimization scheduling information of each group of virtual power plants. The optimization scheduling model includes a first objective function, a second objective function and constraints. The first objective function is constructed with the goal of maximizing the total net income from electricity sales of multiple groups of virtual power plants. The total net income from electricity sales of multiple groups of virtual power plants is calculated based on the first information of each group of virtual power plants, the amount of electricity sold to the load side, and the electricity price. The second objective function is constructed with the goal of maximizing the total net income from electricity sales of each group of virtual power plants. The total net income from electricity sales of each group of virtual power plants is calculated by the first information of the corresponding virtual power plant, the second information, the amount of electricity sold to the load side and the electricity price, the charge and discharge amount and charge and discharge electricity price of the energy storage system, and the average sold electricity prices corresponding to the wind power station and the photovoltaic power station respectively. Based on the optimization scheduling information, the multiple virtual power stations are comprehensively optimized and scheduled. The method provided by the present invention regards multiple groups of virtual power plants as a whole for optimal scheduling, obtains an optimal scheduling strategy by solving a pre-constructed optimal scheduling model, and optimizes the scheduling of multiple virtual power plants based on the optimal scheduling strategy. In the scheduling process, the mutual influence between the virtual power plant and the power grid system and the load is considered, thereby improving the operating efficiency and economic benefits of the entire system. By optimizing the charging and discharging strategy of the energy storage system and the utilization of renewable energy, the optimal allocation of energy resources is achieved and the energy utilization efficiency is improved.

[0006] In an optional embodiment, the step of solving a pre-constructed optimization scheduling model using the first information and the second information of multiple groups of virtual power plants to obtain the optimization scheduling information of each group of virtual power plants includes: solving the first objective function using the constraints and the first information of multiple groups of virtual power plants to obtain the corresponding amount of electricity and electricity price sold to the load side of each virtual power plant; solving the second objective function based on the corresponding amount of electricity and electricity price sold to the load side of the virtual power plant, the first information and the second information, and the constraints to obtain the charge and discharge amount and charge and discharge price of the energy storage system in each group of virtual power plants, the average sold electricity price of the wind power station in each group of virtual power plants, and the average sold electricity price of the photovoltaic power station.

[0007] In an optional implementation, the first objective function is:

[0008] F1=max(E)

[0009]

[0010] Among them, F1 is the maximization of the total net revenue from electricity sales of multiple groups of virtual power plants; E is the total net revenue from electricity sales of multiple groups of virtual power plants; is the transaction price of electricity sold by the i-th group of virtual power plants to the load side; is the final amount of electricity sold to the load side by the i-th group of virtual power plants; is the electricity price sold to the grid by the i-th group of virtual power plants; is the amount of electricity sold to the grid by the i-th group of virtual power plants; is the electricity price purchased by the i-th group of virtual power plants from the grid; is the amount of electricity purchased from the power grid by the i-th virtual power plant; N is the number of virtual power plants; They are the buying and selling status of the i-th group of virtual power plants from the power grid, which are variables of 0 or 1.

[0011] In an optional implementation, the second objective function is:

[0012] F2=max(W i )

[0013]

[0014] Among them, F2 is the maximization of the total net revenue of electricity sales of the i-th group of virtual power plants; W i is the net revenue from electricity sales of the i-th group of virtual power plants; The electricity price for discharging the energy storage system of the i-th group of virtual power plants; The amount of electricity discharged by the energy storage system of the i-th group of virtual power plants; The electricity price for charging the energy storage system of the i-th group of virtual power plants; The amount of electricity charged to the energy storage system of the i-th group of virtual power plants; is the average electricity price sold by the wind power plants in the i-th group of virtual power plants; is the total electricity sold by the wind power plants in the i-th group of virtual power plants; is the average electricity price sold by the photovoltaic power station of the i-th group of virtual power plants; is the total electricity sold by the photovoltaic power station of the i-th group of virtual power plants; They are the discharge and charge states of the energy storage system of the i-th group of virtual power plants at time t, which are variables of 0 or 1.

[0015] In an optional embodiment, the constraint conditions include first constraint information, second constraint information, third constraint information and fourth constraint information; the first constraint information is used to characterize the constraint relationship between the function value of the first objective function and the function value of the second objective function; the second constraint information is used to characterize the constraint information of the electricity price and the amount of electricity sold by each virtual power plant to the load side, as well as the constraint relationship between the electricity price and the amount of electricity; the third constraint information is used to characterize the constraint information of the charging and discharging of the energy storage system of each virtual power plant; the fourth constraint information is used to characterize the constraint information of the discharge and charging status of the energy storage system of each group of virtual power plants, as well as the constraint information of the purchase and sale status from the power grid.

[0016] In a second aspect, the present invention provides a comprehensive optimization scheduling device for multiple groups of virtual power plants, the device comprising: an acquisition module, used to acquire first information and second information of multiple groups of virtual power plants, the first information is used to characterize the amount of electricity and electricity price of the corresponding virtual power plant interacting with the power grid, and the second information is used to characterize the total amount of electricity sold by the wind power station and the total amount of electricity sold by the photovoltaic power station of the corresponding virtual power plant; a solution module, used to solve a pre-constructed optimization scheduling model using the first information and the second information of the multiple groups of virtual power plants to obtain optimization scheduling information of each group of virtual power plants, the optimization scheduling information includes the amount of electricity and electricity price sold to the load side by the corresponding virtual power plant, the charge and discharge amount and charge and discharge electricity price of the energy storage system in the corresponding virtual power plant, and the average sold electricity prices corresponding to the wind power station and the photovoltaic power station, respectively. The optimization scheduling model includes a first objective function, a second objective function and constraints. The first objective function is constructed with the goal of maximizing the total net profit from electricity sales of multiple groups of virtual power plants. The total net profit from electricity sales of multiple groups of virtual power plants is calculated based on the first information of each group of virtual power plants, the amount of electricity sold to the load side and the electricity price. The second objective function is constructed with the goal of maximizing the total net profit from electricity sales of each group of virtual power plants. The total net profit from electricity sales of each group of virtual power plants is calculated based on the first information of the corresponding virtual power plant, the second information, the amount of electricity sold to the load side and the electricity price, the charge and discharge amount and charge and discharge electricity price of the energy storage system, and the average sold electricity prices corresponding to the wind power station and the photovoltaic power station respectively; a scheduling module is used to perform comprehensive optimization scheduling of multiple groups of virtual power plants based on the optimization scheduling information.

[0017] In an optional embodiment, the solution module includes: a first solution sub-module, which is used to solve the first objective function using constraints and first information of multiple groups of virtual power plants to obtain the amount of electricity and electricity price sold to the load side corresponding to each virtual power plant; a second solution sub-module, which is used to solve the second objective function based on the amount of electricity and electricity price sold to the load side corresponding to the virtual power plant, the first information and the second information, and constraints to obtain the charge and discharge amount and charge and discharge electricity price of the energy storage system in each group of virtual power plants, the average sold electricity price of the wind power station in each group of virtual power plants, and the average sold electricity price of the photovoltaic power station.

[0018] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method for comprehensive optimization and scheduling of multiple groups of virtual power plants according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0019] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for comprehensive optimization and scheduling of multiple groups of virtual power plants according to the first aspect or any corresponding embodiment thereof.

[0020] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, which are used to enable a computer to execute the method for comprehensive optimization and scheduling of multiple groups of virtual power plants according to the above-mentioned first aspect or any corresponding embodiment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 is a flow chart of a method for comprehensive optimization and dispatching of multiple virtual power plants according to an embodiment of the present invention;

[0023] Figure 2 is a flow chart of another method for comprehensive optimization and scheduling of multiple virtual power plants according to an embodiment of the present invention;

[0024] Figure 3 is a structural block diagram of a device for comprehensive optimization and dispatching of multiple virtual power plants according to an embodiment of the present invention;

[0025] Figure 4 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0027] In related technologies, the optimal dispatching strategy is usually initially solved for the electricity sales revenue of a single virtual power plant, and then the virtual power plant is coordinated and controlled based on the optimal dispatching strategy. However, the implementation of the dispatching strategy requires the cooperation of multiple parties such as the power grid system, other virtual power plants, and loads. The dispatching strategy determined based solely on a single virtual power plant cannot achieve multi-party cooperation and is difficult to achieve in reality.

[0028] In view of this, a method for comprehensive optimization and scheduling of multiple groups of virtual power plants provided in an embodiment of the present application can be applied to a server to achieve comprehensive optimization and scheduling of multiple groups of virtual power plants. The method provided by the present invention regards multiple groups of virtual power plants as a whole for optimization and scheduling, obtains an optimization scheduling strategy by solving a pre-built optimization scheduling model, optimizes and schedules multiple virtual power plants based on the optimization scheduling strategy, considers the mutual influence between virtual power plants and power grid systems and loads during the scheduling process, improves the operating efficiency and economic benefits of the entire system, and also optimizes the charging and discharging strategy of the energy storage system and the use of renewable energy to achieve optimal allocation of energy resources and improve energy utilization efficiency.

[0029] According to an embodiment of the present invention, an embodiment of a method for comprehensive optimization scheduling of multiple groups of virtual power plants is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] In this embodiment, a multi-group virtual power plant comprehensive optimization scheduling method is provided, which can be used for the above-mentioned server. Figure 1 is a flow chart of a method for comprehensive optimization and scheduling of multiple virtual power plants according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0031] Step S101, obtaining first information and second information of multiple groups of virtual power plants.

[0032] Exemplarily, the first information is used to characterize the amount of electricity and the electricity price of the interaction between the corresponding virtual power plant and the power grid, and the second information is used to characterize the total amount of electricity sold by the wind power station and the total amount of electricity sold by the photovoltaic power station of the corresponding virtual power plant. In the embodiment of the present application, the amount of electricity and the electricity price of each virtual power plant interacting with the power grid include the amount of electricity sold by the virtual power plant to the power grid and the electricity price of the electricity, as well as the amount of electricity purchased by the virtual power plant from the power grid and the electricity price of the electricity. The total amount of electricity sold by the wind power station is the total amount of electricity sold by the wind power station in the virtual power plant, and the total amount of electricity sold by the photovoltaic power station is the total amount of electricity sold by the photovoltaic power station in the virtual power plant.

[0033] Step S102, using the first information and the second information of multiple groups of virtual power plants to solve the pre-constructed optimization scheduling model, to obtain the optimization scheduling information of each group of virtual power plants, the optimization scheduling information includes the amount of electricity and electricity price sold to the load side by the corresponding virtual power plant, the charge and discharge amount and charge and discharge electricity price of the energy storage system in the corresponding virtual power plant, and the average selling prices of the wind power station and the photovoltaic power station respectively.

[0034] Exemplarily, the optimization scheduling model includes a first objective function, a second objective function and constraints. The first objective function is constructed with the goal of maximizing the total net revenue from electricity sales of multiple groups of virtual power plants. The total net revenue from electricity sales of multiple groups of virtual power plants is calculated based on the first information of each group of virtual power plants, the amount of electricity sold to the load side and the electricity price. The second objective function is constructed with the goal of maximizing the total net revenue from electricity sales of each group of virtual power plants. The total net revenue from electricity sales of each group of virtual power plants is calculated based on the first information of the corresponding virtual power plant, the second information, the amount of electricity sold to the load side and the electricity price, the charge and discharge amount of the energy storage system and the charge and discharge electricity price, and the average sold electricity price corresponding to the wind power station and the photovoltaic power station. In the embodiment of the present application, the optimal solution of the model is obtained by solving the optimization scheduling model, and the optimal scheduling strategy of each group of virtual power plants can be obtained based on the optimal solution.

[0035] Step S103, performing comprehensive optimization scheduling on multiple groups of virtual power plants based on the optimization scheduling information.

[0036] Illustratively, in an embodiment of the present application, the scheduling strategy obtained by solving the optimal scheduling model considers multiple groups of virtual power plants as a whole for optimal scheduling, takes into account the mutual influence between the virtual power plants and the power grid system and loads, and improves the operating efficiency and economic benefits of the entire system.

[0037] The comprehensive optimization and scheduling method for multiple groups of virtual power plants provided in this embodiment regards the multiple groups of virtual power plants as a whole for optimization and scheduling, obtains the optimization scheduling strategy by solving the pre-constructed optimization scheduling model, and optimizes and schedules the multiple virtual power plants based on the optimization scheduling strategy. In the scheduling process, the mutual influence between the virtual power plants and the power grid system and the load is considered, thereby improving the operating efficiency and economic benefits of the entire system. By optimizing the charging and discharging strategy of the energy storage system and the utilization of renewable energy, the optimal allocation of energy resources is achieved and the energy utilization efficiency is improved.

[0038] In this embodiment, a multi-group virtual power plant comprehensive optimization scheduling method is provided, which can be used for the above-mentioned server. Figure 2 is a flow chart of a method for comprehensive optimization and scheduling of multiple virtual power plants according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0039] Step S201, obtain first information and second information of multiple groups of virtual power plants, the first information is used to characterize the amount of electricity and electricity price interacting between the corresponding virtual power plant and the power grid, and the second information is used to characterize the total amount of electricity sold by the wind power station and the total amount of electricity sold by the photovoltaic power station of the corresponding virtual power plant. For details, please refer to Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0040] Step S202, using the first information and the second information of multiple groups of virtual power plants to solve the pre-constructed optimization scheduling model, to obtain the optimization scheduling information of each group of virtual power plants, the optimization scheduling information includes the amount of electricity and electricity price sold to the load side by the corresponding virtual power plant, the charging and discharging amount of the energy storage system and the charging and discharging electricity price in the corresponding virtual power plant, and the average selling electricity prices corresponding to the wind power station and the photovoltaic power station respectively. The optimization scheduling model includes a first objective function, a second objective function and constraints. The first objective function is constructed with the goal of maximizing the total net profit of electricity sales of multiple groups of virtual power plants. The total net profit of electricity sales of multiple groups of virtual power plants is calculated based on the first information of each group of virtual power plants, the amount of electricity sold to the load side and the electricity price. The second objective function is constructed with the goal of maximizing the total net profit of electricity sales of each group of virtual power plants. The total net profit of electricity sales of each group of virtual power plants is calculated by the first information of the corresponding virtual power plant, the second information, the amount of electricity and electricity price sold to the load side, the charging and discharging amount of the energy storage system and the charging and discharging electricity price, and the average selling electricity prices corresponding to the wind power station and the photovoltaic power station respectively.

[0041] Specifically, the above step S202 includes:

[0042] Step S2021, using the constraint conditions and the first information of multiple groups of virtual power plants to solve the first objective function, and obtain the corresponding amount of electricity and electricity price sold to the load side of each virtual power plant.

[0043] Exemplarily, the first objective function is solved by means of constraints and first information of multiple groups of virtual power plants to obtain an optimal solution to the first objective function, which maximizes the total net revenue from electricity sales of the multiple groups of virtual power plants.

[0044] Step S2022, based on the amount of electricity and electricity price sold to the load side corresponding to the virtual power plant, the first information, the second information and the constraints, the second objective function is solved to obtain the charge and discharge amount and charge and discharge electricity price of the energy storage system in each group of virtual power plants, the average sold electricity price of the wind power station in each group of virtual power plants, and the average sold electricity price of the photovoltaic power station.

[0045] Exemplarily, the second objective function is solved based on the first information, the second information of each group of virtual power plants and the optimal solution of the first objective function to obtain the optimal solution of the second objective function, which maximizes the total net profit of electricity sales of the corresponding virtual power plant.

[0046] In some optional implementations, the first objective function is shown in the following formula (1):

[0047]

[0048] Among them, F1 is the maximization of the total net revenue from electricity sales of multiple groups of virtual power plants; E is the total net revenue from electricity sales of multiple groups of virtual power plants; is the transaction price of electricity sold by the i-th group of virtual power plants to the load side; is the final amount of electricity sold to the load side by the i-th group of virtual power plants; is the electricity price sold to the grid by the i-th group of virtual power plants; is the amount of electricity sold to the grid by the i-th group of virtual power plants; is the electricity price purchased by the i-th group of virtual power plants from the grid; is the amount of electricity purchased from the power grid by the i-th virtual power plant; N is the number of virtual power plants; They are the buying and selling status of the i-th group of virtual power plants from the power grid, which are variables of 0 or 1. Indicates that the i-th group of virtual power plants is in the sold state from the power grid, It indicates that the i-th group of virtual power plants is in buying state from the power grid.

[0049] In some optional implementations, the second objective function is shown in the following formula (2):

[0050]

[0051] Among them, F2 is the maximization of the total net revenue of electricity sales of the i-th group of virtual power plants; W i is the net revenue from electricity sales of the i-th group of virtual power plants; The electricity price for discharging the energy storage system of the i-th group of virtual power plants; The amount of electricity discharged by the energy storage system of the i-th group of virtual power plants; The electricity price for charging the energy storage system of the i-th group of virtual power plants; The amount of electricity charged to the energy storage system of the i-th group of virtual power plants; is the average electricity price sold by the wind power plants in the i-th group of virtual power plants; is the total electricity sold by the wind power plants in the i-th group of virtual power plants; is the average electricity price sold by the photovoltaic power station of the i-th group of virtual power plants; is the total electricity sold by the photovoltaic power station of the i-th group of virtual power plants; They are the discharge and charge states of the energy storage system of the i-th group of virtual power plants at time t, which are variables of 0 or 1. It means that the energy storage system of the i-th virtual power plant is in the discharging state at time t. It indicates that the energy storage system of the i-th group of virtual power plants is in a charging state at time t.

[0052] In some optional implementations, the constraint conditions include first constraint information, second constraint information, third constraint information, and fourth constraint information.

[0053] The first constraint information is used to characterize the constraint relationship between the function value of the first objective function and the function value of the second objective function. Exemplarily, the constraint relationship between the first objective function and the second objective function is

[0054] The second constraint information is used to characterize the constraint information of the electricity price and quantity sold by each virtual power plant to the load side, as well as the constraint relationship between the electricity price and quantity. For example, in the actual operation of the power market, the electricity demand on the load side often has a certain elasticity. For example, when the electricity selling price increases, part of the transferable load will be transferred to consumption when the electricity selling price is lower. Therefore, changes in the electricity selling price may encourage the load to change its own electricity consumption habits, thereby affecting the final transaction volume. The constraints on the electricity price and quantity sold by the virtual power plant to the load side are shown in the following equations (3) to (11):

[0055]

[0056] in, is the transaction price of electricity sold by the i-th group of virtual power plants to the load side; is the electricity price sold to the grid by the i-th group of virtual power plants; is the electricity price purchased by the i-th group of virtual power plants from the grid; is the predicted electricity price sold by the i-th group of virtual power plants to the load side;

[0057]

[0058] Among them, e i ≥0, is the change in the amount of electricity sold to the load side by the i-th group of virtual power plants, e i is the load elasticity coefficient of the i-th group of virtual power plants, is the predicted amount of electricity sold to the load side by the i-th group of virtual power plants,

[0059] is the change in electricity price sold by the i-th group of virtual power plants to the load side;

[0060]

[0061] in, is the change in the amount of electricity sold to the load side by the i-th group of virtual power plants, is the amount of electricity sold to the load side by the i-th group of virtual power plants;

[0062]

[0063] in, is the change in electricity price sold by the i-th group of virtual power plants to the load side, is the transaction price of electricity sold by the i-th group of virtual power plants to the load side; is the predicted electricity price sold by the i-th group of virtual power plants to the load side;

[0064]

[0065] in, is the predicted amount of electricity sold to the load side by the i-th group of virtual power plants, is the transaction price of electricity sold by the i-th group of virtual power plants to the load side; is the electricity price sold to the grid by the i-th group of virtual power plants; is the predicted electricity price sold by the i-th group of virtual power plants to the load side, is the amount of electricity sold to the load side by the i-th group of virtual power plants;

[0066]

[0067] Among them, t represents the time, is the change in the amount of electricity sold to the load side by the i-th group of virtual power plants at time t; T represents the T-th moment;

[0068]

[0069] in, and They represent the lower and upper limits of the change in the amount of electricity sold to the load side by the i-th group of virtual power plants, is the change in the amount of electricity sold to the load side by the i-th group of virtual power plants at time t;

[0070]

[0071] in, and They represent the lower and upper limits of the price change of electricity sold by the i-th group of virtual power plants to the load side;

[0072]

[0073] in, is the amount of electricity sold to the load side by the i-th group of virtual power plants at time T, and the meanings of other variables are not repeated here. In the embodiment of the present application, by considering the elastic changes of the load to the electricity price in the constraint conditions, the dispatching strategy can adapt to the dynamic changes of the market and load, thereby enhancing the flexibility and adaptability of the system.

[0074] The third constraint information is used to characterize the constraint information of charging and discharging of the energy storage system of each virtual power plant. For example, when the energy generated by the virtual power plant is greater than the load demand, the energy storage system should store the energy. Conversely, when the energy generated by the virtual power plant is less than the load demand, the energy storage system should release the stored energy. The model of the energy storage system and the constraint conditions of the charge / discharge amount are as follows (12) to (18):

[0075]

[0076] Among them, SOC i,t The charge of the energy storage system of the i-th virtual power plant at time t; SOC i,t+1 The charge of the energy storage system of the i-th virtual power plant at time t+1; is the charging efficiency of the energy storage system of the i-th group of virtual power plants at time t; is the discharge efficiency of the energy storage system of the i-th group of virtual power plants at time t; are the discharging and charging states of the energy storage system of the i-th group of virtual power plants at time t; is the amount of electricity discharged by the energy storage system of the i-th group of virtual power plants at time t; The amount of electricity charged to the energy storage system of the i-th group of virtual power plants at time t;

[0077]

[0078] in, are the lower and upper limits of the discharge of the energy storage system of the i-th group of virtual power plants, respectively;

[0079]

[0080] in, are the lower and upper limits of the charging of the energy storage system of the i-th group of virtual power plants, respectively;

[0081]

[0082] in, are the upper and lower limits of the charge of the energy storage system of the i-th group of virtual power plants respectively;

[0083] SOC i,0 =SOC i,T (16)

[0084] Among them, SOC i,t The charge of the energy storage system of the i-th virtual power plant at time 0, SOC i,T The charge of the energy storage system of the i-th virtual power plant at time T;

[0085]

[0086] in, are the buying and selling states of the i-th group of virtual power plants from the power grid, which are 0 or 1 variables, is the total electricity sold by the wind power plants in the i-th group of virtual power plants; is the total electricity sold by the photovoltaic power station of the i-th group of virtual power plants; are the discharging and charging states of the energy storage system of the i-th group of virtual power plants respectively;

[0087]

[0088] in, No. i The rated capacity of the virtual power plant, is the total electricity sold by the wind power plants in the i-th group of virtual power plants; is the total electricity sold by the photovoltaic power station of the i-th group of virtual power plants; are the discharging and charging states of the energy storage system of the i-th group of virtual power plants respectively; The amount of electricity discharged by the energy storage system of the i-th group of virtual power plants.

[0089] The fourth constraint information is used to characterize the constraint information of the energy storage system discharge and charge status of each group of virtual power plants, as well as the constraint information of the purchase and sale status from the power grid. For example, under normal operation, the energy storage system cannot be charged and discharged at the same time. Similarly, under normal operation, the virtual system should not sell and purchase electricity from the power grid at the same time. The fourth constraint information is as follows (19) to (22):

[0090]

[0091]

[0092]

[0093]

[0094] The meaning of each parameter is described in the above related content and will not be repeated here.

[0095] In the embodiment of the present application, a relaxation factor can be introduced to solve the optimization scheduling. The relaxation factor is used to control the change of each iteration of the variable, which mainly affects the convergence speed and convergence of the iteration. The relaxation factor is between 0-1, and the smaller the change between two iterations, the smaller the change. In this way, the calculation is more stable, but the calculation speed is slow. In the embodiment of the present application, the relaxation factor can be determined based on actual needs, and the embodiment of the present application does not limit the specific content of the preset relaxation factor. The solution process includes the following steps:

[0096] (1) Use relaxation factors to transform each constraint into a corresponding equality constraint;

[0097] (2) Solve the objective function using the transformed equation constraints, the first information and the second information of each virtual power plant, and a preset solution algorithm to obtain a solution result.

[0098] The above-mentioned preset solution algorithm may include but is not limited to a genetic algorithm, which optimizes the solution to the problem by simulating the genetic mechanism of nature (such as selection, crossover, mutation, etc.). It starts with a set of randomly generated initial solutions (populations) and continuously optimizes the individuals in the population in an iterative manner in the hope of finding the optimal solution or approximate optimal solution to the problem. The genetic algorithm mainly includes the following steps:

[0099] (1) Randomly generate a population.

[0100] (2) Determine whether the fitness of the individual meets the optimization criteria based on the strategy. If so, output the best individual and its optimal solution and end. Otherwise, proceed to the next step.

[0101] (3) Parents are selected based on fitness. Individuals with high fitness are more likely to be selected, while individuals with low fitness are eliminated.

[0102] (4) Use the chromosomes of the parents to cross over according to a certain method to produce offspring.

[0103] (5) Mutate the chromosomes of offspring.

[0104] (6) Generate a new generation of population through crossover and mutation, and return to step (2) until the optimal solution is produced.

[0105] Step S203: Comprehensively optimize and dispatch multiple groups of virtual power plants based on the optimization dispatch information. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0106] The comprehensive optimization scheduling method provided by the present invention has the following advantages:

[0107] (1) Systematic optimization: Multiple groups of virtual power plants are optimized and dispatched as a whole, taking into account the mutual impact between virtual power plants and the power grid system and loads, thereby improving the operating efficiency and economic benefits of the entire system.

[0108] (2) Flexibility and adaptability: By considering the elastic changes of load to electricity sales prices, the dispatch strategy can adapt to the dynamic changes of the market and load, thereby enhancing the flexibility and adaptability of the system.

[0109] (3) Maximization of economic benefits: Through a two-layer nested optimization model, it aims to maximize the total net revenue from electricity sales of the virtual power plant, thereby improving economic benefits.

[0110] (4) Optimal resource allocation: By optimizing the charging and discharging strategies of the energy storage system and the use of renewable energy, optimal allocation of energy resources is achieved and energy utilization efficiency is improved.

[0111] (5) Power system stability: The integration and optimized scheduling of virtual power plants help maintain the stability of the power system and reduce fluctuations caused by supply and demand imbalances.

[0112] (6) Improved market competitiveness: Optimizing dispatch strategies can help virtual power plants gain a better competitive position in the electricity market and attract more consumers by providing more cost-effective electricity services.

[0113] (7) Risk Management: Through the constraints and parameter estimation of the model, potential market and technical risks can be evaluated and managed to reduce operational risks.

[0114] (8) Market adaptability: The solution design takes market dynamics into consideration, enabling the virtual power plant to quickly adapt to changes in the external environment.

[0115] (9) Data-driven decision-making: The solution relies on accurate data and parameter estimation, making the decision-making process more scientific and data-driven.

[0116] (10) Scalability: The optimization scheduling method has good scalability and can be applied to virtual power plants of different sizes and types, as well as different market and policy environments.

[0117] In this embodiment, a multi-group virtual power plant comprehensive optimization scheduling device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated here. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0118] This embodiment provides a multi-group virtual power plant comprehensive optimization scheduling device, such as Figure 3 As shown, including:

[0119] An acquisition module 301 is used to acquire first information and second information of multiple groups of virtual power plants, wherein the first information is used to characterize the amount of electricity and electricity price interacting between the corresponding virtual power plant and the power grid, and the second information is used to characterize the total amount of electricity sold by the wind power station and the total amount of electricity sold by the photovoltaic power station of the corresponding virtual power plant;

[0120] The solution module 302 is used to solve the pre-constructed optimization scheduling model using the first information and the second information of the multiple groups of virtual power plants to obtain the optimization scheduling information of each group of virtual power plants, the optimization scheduling information includes the amount of electricity and the electricity price sold to the load side by the corresponding virtual power plant, the charging and discharging amount of the energy storage system and the charging and discharging electricity price in the corresponding virtual power plant, and the average selling electricity prices corresponding to the wind power station and the photovoltaic power station respectively. The optimization scheduling model includes a first objective function, a second objective function and constraints. The first objective function is constructed with the goal of maximizing the total net income from electricity sales of the multiple groups of virtual power plants. The total net income from electricity sales of the multiple groups of virtual power plants is calculated based on the first information of each group of virtual power plants, the amount of electricity sold to the load side and the electricity price. The second objective function is constructed with the goal of maximizing the total net income from electricity sales of each group of virtual power plants. The total net income from electricity sales of each group of virtual power plants is calculated based on the first information of the corresponding virtual power plant, the second information, the amount of electricity sold to the load side and the electricity price, the charging and discharging amount of the energy storage system and the charging and discharging electricity price, and the average selling electricity prices corresponding to the wind power station and the photovoltaic power station respectively.

[0121] The scheduling module 303 is used to perform comprehensive optimization scheduling on multiple groups of virtual power plants based on the optimization scheduling information.

[0122] In some optional implementations, the solution module 302 includes:

[0123] A first solving submodule is used to solve the first objective function by using the constraint conditions and the first information of the plurality of groups of virtual power plants, so as to obtain the amount of electricity and the electricity price corresponding to each virtual power plant sold to the load side;

[0124] The second solving submodule is used to solve the second objective function based on the amount of electricity and electricity price sold to the load side corresponding to the virtual power plant, the first information, the second information and the constraints, and obtain the charging and discharging amount of electricity and charging and discharging electricity price of the energy storage system in each group of virtual power plants, the average selling price of wind power stations in each group of virtual power plants, and the average selling price of photovoltaic power stations.

[0125] In some optional implementations, the first objective function is:

[0126] F1=max(E)

[0127]

[0128] Among them, F1 is the maximization of the total net revenue from electricity sales of multiple groups of virtual power plants; E is the total net revenue from electricity sales of multiple groups of virtual power plants; is the transaction price of electricity sold by the i-th group of virtual power plants to the load side; is the final amount of electricity sold to the load side by the i-th group of virtual power plants; is the electricity price sold to the grid by the i-th group of virtual power plants; is the amount of electricity sold to the grid by the i-th group of virtual power plants; is the electricity price purchased by the i-th group of virtual power plants from the grid; is the amount of electricity purchased from the power grid by the i-th virtual power plant; N is the number of virtual power plants; They are the buying and selling status of the i-th group of virtual power plants from the power grid, which are variables of 0 or 1.

[0129] In some optional implementations, the second objective function is:

[0130] F2=max(W i )

[0131]

[0132] Among them, F2 is the maximization of the total net revenue of electricity sales of the i-th group of virtual power plants; W i is the net revenue from electricity sales of the i-th group of virtual power plants; The electricity price for discharging the energy storage system of the i-th group of virtual power plants; The amount of electricity discharged by the energy storage system of the i-th group of virtual power plants; The electricity price for charging the energy storage system of the i-th group of virtual power plants; The amount of electricity charged to the energy storage system of the i-th group of virtual power plants; is the average electricity price sold by the wind power plants in the i-th group of virtual power plants; is the total electricity sold by the wind power plants in the i-th group of virtual power plants; is the average electricity price sold by the photovoltaic power station of the i-th group of virtual power plants; is the total electricity sold by the photovoltaic power station of the i-th group of virtual power plants; They are the discharge and charge states of the energy storage system of the i-th group of virtual power plants at time t, which are variables of 0 or 1.

[0133] In some optional implementations, the constraint condition includes first constraint information, second constraint information, third constraint information, and fourth constraint information;

[0134] The first constraint information is used to characterize the constraint relationship between the function value of the first objective function and the function value of the second objective function;

[0135] The second constraint information is used to characterize the constraint information of the electricity price and quantity sold by each virtual power plant to the load side, as well as the constraint relationship between the electricity price and quantity;

[0136] The third constraint information is used to characterize the constraint information of charging and discharging of the energy storage system of each virtual power plant;

[0137] The fourth constraint information is used to characterize the constraint information of the discharge and charging status of the energy storage system of each group of virtual power plants and the constraint information of the purchase and sale status from the power grid.

[0138] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0139] In this embodiment, multiple groups of virtual power plant comprehensive optimization scheduling devices are presented in the form of functional units, where the units refer to ASIC (Application Specific Integrated Circuit) circuits, processors and memories that execute one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0140] The embodiment of the present invention also provides a computer device having the above Figure 3 Multiple groups of virtual power plant comprehensive optimization scheduling devices are shown.

[0141] See also Figure 4 , Figure 4 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 4 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 4 A processor 10 is taken as an example.

[0142] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0143] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0144] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0145] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0146] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0147] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0148] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0149] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A comprehensive optimization scheduling method for multiple virtual power plants, characterized in that: The method comprises: Acquire first information and second information of multiple groups of virtual power plants, wherein the first information is used to characterize the amount of electricity and electricity price of the corresponding virtual power plant interacting with the power grid, and the second information is used to characterize the total amount of electricity sold by the wind power station and the total amount of electricity sold by the photovoltaic power station of the corresponding virtual power plant; The pre-constructed optimization scheduling model is solved by using the first information and the second information of the multiple groups of virtual power plants to obtain the optimization scheduling information of each group of virtual power plants, wherein the optimization scheduling information includes the amount of electricity and the electricity price sold to the load side by the corresponding virtual power plant, the charge and discharge amount of the energy storage system and the charge and discharge electricity price in the corresponding virtual power plant, and the average selling electricity price corresponding to the wind power station and the photovoltaic power station respectively. The optimization scheduling model includes a first objective function, a second objective function and constraints. The first objective function is constructed with the goal of maximizing the total net income from electricity sales of the multiple groups of virtual power plants. The total net income from electricity sales of the multiple groups of virtual power plants is calculated based on the first information of each group of virtual power plants, the amount of electricity sold to the load side and the electricity price. The second objective function is constructed with the goal of maximizing the total net income from electricity sales of each group of virtual power plants. The total net income from electricity sales of each group of virtual power plants is calculated based on the first information of the corresponding virtual power plant, the second information, the amount of electricity and the electricity price sold to the load side, the charge and discharge amount of the energy storage system and the charge and discharge electricity price, and the average selling electricity price corresponding to the wind power station and the photovoltaic power station respectively. Based on the optimized scheduling information, the multiple groups of virtual power plants are comprehensively optimized and scheduled.

2. The method according to claim 1, characterized in that: The step of solving a pre-built optimization scheduling model by using the first information and the second information of the plurality of groups of virtual power plants to obtain the optimization scheduling information of each group of virtual power plants includes: The first objective function is solved by using the constraint conditions and the first information of the plurality of groups of virtual power plants to obtain the amount of electricity and the electricity price sold to the load side corresponding to each virtual power plant; The second objective function is solved based on the amount of electricity and the electricity price sold to the load side corresponding to the virtual power plant, the first information, the second information and the constraint conditions to obtain the charging and discharging amount of electricity and the charging and discharging electricity price of the energy storage system in each group of virtual power plants, the average selling price of electricity of the wind power station in each group of virtual power plants, and the average selling price of electricity of the photovoltaic power station.

3. The method according to claim 1, characterized in that The first objective function is: Among them, F1 is the maximization of the total net revenue from electricity sales of multiple groups of virtual power plants; E is the total net revenue from electricity sales of multiple groups of virtual power plants; is the transaction price of electricity sold by the i-th group of virtual power plants to the load side; P i load ' is the final amount of electricity sold by the i-th group of virtual power plants to the load side; i sell is the electricity price sold by the i-th group of virtual power plants to the grid; P i sell is the amount of electricity sold to the grid by the i-th group of virtual power plants; i buy is the electricity price purchased by the i-th group of virtual power plants from the grid; P i buy is the amount of electricity purchased from the power grid by the i-th virtual power plant; N is the number of virtual power plants; They are the buying and selling status of the i-th group of virtual power plants from the power grid, which are variables of 0 or 1.

4. The method according to claim 3, characterized in that The second objective function is: F2=max(W i ) Among them, F2 is the maximization of the total net revenue of electricity sales of the i-th group of virtual power plants; W i is the net revenue from electricity sales of the i-th group of virtual power plants; is the electricity price of the energy storage system of the i-th group of virtual power plants; P i d The amount of electricity discharged by the energy storage system of the i-th group of virtual power plants; The electricity price for charging the energy storage system of the i-th group of virtual power plants; P i c The amount of electricity charged to the energy storage system of the i-th virtual power plant; i wind is the average electricity price of the wind power plant in the i-th group of virtual power plants; P i wind is the total electricity sold by the wind power plants of the i-th group of virtual power plants; i pho is the average electricity price of the photovoltaic power station of the i-th group of virtual power plants; P i pho is the total electricity sold by the photovoltaic power station of the i-th group of virtual power plants; They are the discharge and charge states of the energy storage system of the i-th group of virtual power plants at time t, which are variables of 0 or 1.

5. The method according to any one of claims 1 to 4, characterized in that: The constraint conditions include first constraint information, second constraint information, third constraint information and fourth constraint information; The first constraint information is used to characterize a constraint relationship between a function value of the first objective function and a function value of the second objective function; The second constraint information is used to represent the constraint information of the electricity price and quantity sold by each virtual power plant to the load side, as well as the constraint relationship between the electricity price and the quantity; The third constraint information is used to characterize constraint information for charging and discharging of energy storage systems of each virtual power plant; The fourth constraint information is used to characterize the constraint information of the discharge and charge status of the energy storage system of each group of virtual power plants and the constraint information of the purchase and sale status from the power grid.

6. A multi-group virtual power plant comprehensive optimization dispatching device, characterized in that: The device comprises: An acquisition module, used to acquire first information and second information of multiple groups of virtual power plants, wherein the first information is used to characterize the amount of electricity and electricity price interacting between the corresponding virtual power plant and the power grid, and the second information is used to characterize the total amount of electricity sold by the wind power station and the total amount of electricity sold by the photovoltaic power station of the corresponding virtual power plant; A solution module, used to solve a pre-constructed optimization scheduling model using the first information and the second information of the multiple groups of virtual power plants to obtain the optimization scheduling information of each group of virtual power plants, wherein the optimization scheduling information includes the amount of electricity and the electricity price sold to the load side by the corresponding virtual power plant, the charge and discharge amount of the energy storage system and the charge and discharge electricity price in the corresponding virtual power plant, and the average selling electricity price corresponding to the wind power station and the photovoltaic power station respectively. The optimization scheduling model includes a first objective function, a second objective function and constraints. The first objective function is constructed with the goal of maximizing the total net income from electricity sales of the multiple groups of virtual power plants. The total net income from electricity sales of the multiple groups of virtual power plants is calculated based on the first information of each group of virtual power plants, the amount of electricity sold to the load side and the electricity price. The second objective function is constructed with the goal of maximizing the total net income from electricity sales of each group of virtual power plants. The total net income from electricity sales of each group of virtual power plants is calculated based on the first information of the corresponding virtual power plant, the second information, the amount of electricity and the electricity price sold to the load side, the charge and discharge amount of the energy storage system and the charge and discharge electricity price, and the average selling electricity price corresponding to the wind power station and the photovoltaic power station respectively. A scheduling module is used to perform comprehensive optimization scheduling on the multiple groups of virtual power plants based on the optimization scheduling information.

7. The device according to claim 6, characterized in that The solution module includes: A first solving submodule is used to solve the first objective function by using the constraint conditions and the first information of the plurality of groups of virtual power plants, so as to obtain the amount of electricity and the electricity price sold to the load side corresponding to each virtual power plant; The second solving submodule is used to solve the second objective function based on the electricity amount and electricity price sold to the load side corresponding to the virtual power plant, the first information and the second information, and the constraint conditions, to obtain the charging and discharging electricity amount and charging and discharging electricity price of the energy storage system in each group of virtual power plants, the average selling electricity price of the wind power station in each group of virtual power plants, and the average selling electricity price of the photovoltaic power station.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for comprehensive optimization and scheduling of multiple groups of virtual power plants according to any one of claims 1 to 5 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the comprehensive optimization scheduling method for multiple groups of virtual power plants according to any one of claims 1 to 5.

10. A computer program product, characterized in that It includes computer instructions, which are used to enable a computer to execute the comprehensive optimization scheduling method for multiple groups of virtual power plants as described in any one of claims 1 to 5.

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