Multi-virtual power plant operation optimization method, system and equipment and storage medium

Abstract

The invention discloses a multi-virtual power plant operation optimization method, system and device and a storage medium, and the method comprises the steps: building an upper model which is composed of three virtual power plants and shared energy storage power stations thereof as a whole, and introducing a cooperative game mechanism to optimize the charging and discharging strategies of the shared energy storage power stations and the operation of the three shared energy storage power stations; establishing each virtual power plant unit consisting of a photovoltaic power generation unit, a wind power generation unit and a controllable load unit as a lower layer model, and distributing output data and benefits of the photovoltaic power generation unit and the wind power generation unit in each virtual power plant at the lower layer through a Shapley value method; and searching a global optimal solution by combining a mixed integer programming method with an optimization algorithm of a whale algorithm, and respectively obtaining an optimal charging and discharging strategy of the upper-layer shared energy storage power station and an optimal operation scheduling strategy of the three virtual power plants. According to the method, the new energy consumption capability, the overall robustness, the accuracy and the flexibility of the multi-virtual power plant system under the participation of the shared energy storage power station can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant operation, and in particular to a method, system, device and storage medium for optimizing the operation of multiple virtual power plants. Background Art

[0002] At present, distributed power generation technology is developing rapidly. However, since photovoltaic and wind power generation are greatly affected by environmental factors and their output is intermittent and volatile, their large-scale access to the power grid will have a certain impact on the stability of the power system, power quality and operation planning. Virtual power plants "gather sources", "adjust flows" and "improve efficiency" of the power system, playing a role in optimizing the coordination mechanism of the energy network, shaving peaks and filling valleys, smoothing the power curve, improving the utilization rate of new energy and improving the stability of the power system. In addition, energy storage technology can also effectively cope with the challenges brought by the intermittent and unstable nature of renewable energy to the safe and stable operation of the power system.

[0003] However, in the existing technology, the research on virtual power plant operation optimization still has the following problems:

[0004] (1) Only the optimization scheduling of a single virtual power plant is carried out, without considering the energy synergy and complementarity among multiple virtual power plants;

[0005] (2) In the overall structure of a virtual power plant, each virtual power plant includes its own energy storage system built on the user side, without considering economic factors. The energy storage systems of several virtual power plants are concentrated and used together to improve energy distribution and utilization efficiency;

[0006] In response to the above problems, there is an urgent need for an operation optimization solution for multiple virtual power plants that considers the participation of shared energy storage power stations. Summary of the invention

[0007] Purpose of the invention: The first purpose of the present invention is to provide a method for optimizing the operation of multiple virtual power plants, and to consider the addition of energy storage systems in the form of shared energy storage power stations, so as to effectively and reasonably optimize the operation of multiple virtual power plants, improve the new energy absorption capacity of each shared energy storage power station, improve the overall energy utilization efficiency of the system, and significantly improve the stability, efficiency and safety of the operation of the multi-virtual power plant system.

[0008] A second object of the present invention is to provide a multi-virtual power plant operation optimization system.

[0009] A third object of the present invention is to provide an electronic device.

[0010] A fourth object of the present invention is to provide a computer storage medium.

[0011] Technical solution: To achieve the above objectives, the present invention discloses a multi-virtual power plant operation optimization method, comprising the following steps:

[0012] (1) First, the economic operation model of the shared energy storage power station was established. Combined with the constraints of the shared energy storage power station, an upper-level model consisting of three virtual power plants and their shared energy storage power stations was established;

[0013] (2) Establish each virtual power plant unit consisting of photovoltaic power generation units, wind power generation units, and controllable load units as the lower-level model, and use the Shapley value method to allocate the output data and benefits of the photovoltaic power generation units and wind power generation units in each virtual power plant;

[0014] (3) Based on the shared energy storage economic operation model established in step (1) and the upper-level model composed of three virtual power plants and their shared energy storage power stations as a whole, and the lower-level model of each virtual power plant unit constructed in step (2), the operation of multiple virtual power plants with the participation of shared energy storage power stations is optimized by using a mixed integer programming method combined with a whale algorithm to determine the optimal charging and discharging strategy of the shared energy storage power station and the optimal operation and scheduling strategy of the three virtual power plants.

[0015] Among them, the economic operation model of the shared energy storage power station in step (1) is:

[0016] The comprehensive benefits of a shared energy storage power station are expressed as:

[0017]

[0018] Where: F g is the comprehensive income of the shared energy storage power station; F bfg,i F is the cost of purchasing electricity from the shared energy storage power station by the i-th virtual power plant; btg,i P is the revenue from the i-th virtual power plant selling electricity to the shared energy storage power station; grid (t) is the power purchased by the shared energy storage power station from the large power grid at time t; M grid (t) is the electricity price that the shared energy storage power station purchases from the large power grid at time t; P i VPP (t) is the power sold by the shared energy storage power station to the i-th virtual power plant at time t; P is the electricity price sold by the shared energy storage power station to the i-th virtual power plant at time t; i g (t) is the power purchased by the shared energy storage power station from the i-th virtual power plant at time t; is the electricity price purchased by the shared energy storage power station from the i-th virtual power plant at time t; T is the total operation time of the system; C g is the electricity price per kWh of the shared energy storage power station. Specifically, the electricity price per kWh of the shared energy storage power station is C gIt is derived from the annualized investment cost and annualized operation and maintenance cost of the shared energy storage power station. The specific relationship is as follows:

[0019]

[0020] Where: C inv is the annual investment cost of the shared energy storage power station; C OM is the annual operation and maintenance cost of the shared energy storage power station; C P P is the unit investment of energy storage power of shared energy storage power station; g is the energy storage power of the shared energy storage power station; C E E is the unit investment of energy storage capacity of shared energy storage power station; g is the energy storage capacity of the shared energy storage power station; r is the benchmark discount rate; n is the operating life of the shared energy storage power station; K o K is the unit energy storage power operation and maintenance cost coefficient of the shared energy storage power station; m is the unit energy storage capacity operation and maintenance cost coefficient of the shared energy storage power station; Q g is the annual power generation of the shared energy storage power station; η is the energy conversion efficiency of the shared energy storage power station; H g It is the annual utilization hours of electricity stored in the shared energy storage power station.

[0021] Preferably, the constraints on the shared energy storage power station in step (1) are:

[0022] Shared energy storage power station power balance:

[0023]

[0024] Power and capacity constraints of shared energy storage power stations:

[0025]

[0026] Where: The lower limit of energy storage power of the shared energy storage power station; The upper limit of energy storage power of the shared energy storage power station; The lower limit of energy storage capacity for shared energy storage power stations; The upper limit of the energy storage capacity of the shared energy storage power station;

[0027] Operating charge constraints of shared energy storage power stations:

[0028]

[0029] Where: E g (t) is the energy storage capacity of the shared energy storage power station at time t; E g (t-1) is the energy storage capacity of the shared energy storage power station at time t-1; α ais the charging efficiency of the shared energy storage power station; α r It is the discharge efficiency of the shared energy storage power station.

[0030] Furthermore, the upper-level model established in step (1) consisting of three virtual power plants and their shared energy storage power stations is:

[0031] The mathematical expression of the multi-agent alliance cooperation game in the upper model is:

[0032]

[0033] Where: S is a multi-agent cooperative game alliance; k n are three virtual power plant game participants; g n For the participants of the shared energy storage power station; θ x1 ,θ x2 ,θ x3 are the operation strategies of virtual power plant A, virtual power plant B, and virtual power plant C respectively; θ g Charging and discharging strategy for shared energy storage power station; F x1 、F x2 、F x3 are the revenues of virtual power plant A, virtual power plant B, and virtual power plant C respectively; F g To share the benefits of energy storage power stations; is the control function of the alliance on the state of subject x1; are the influence of subject x2 and subject x3 on the state of subject x1; i (s) is the utility vector in the utility transferable alliance.

[0034] Furthermore, in step (2), each virtual power plant unit consisting of a photovoltaic power generation unit, a wind power generation unit, and a controllable load unit is established as a lower-level model, specifically as follows:

[0035] The minimum cost function of each virtual power plant is taken as the objective function, and its expression is as follows:

[0036]

[0037] Where: F i is the total operating cost of the i-th virtual power plant unit, F wind,i is the cost of wind power generation unit of the i-th virtual power plant; F ph,i is the cost of the photovoltaic power generation unit of the i-th virtual power plant; F cl,i is the electricity cost of the controllable load of the i-th virtual power plant; F grid,i F is the cost of purchasing electricity from the large power grid for the i-th virtual power plant; pu,i F is the penalty fee for abandoning wind and solar power in the i-th virtual power plant; bfg,i F is the cost of purchasing electricity from the shared energy storage power station by the i-th virtual power plant;btg,i is the revenue from electricity sales from the i-th virtual power plant to the shared energy storage power station; T is the total operation time of the system;

[0038] Among them, the costs of wind power generation units and photovoltaic power generation units included in the virtual power plant are expressed as:

[0039]

[0040]

[0041] Where: F wind is the total cost of three wind power generation units; F ph is the total cost of three photovoltaic power generation units; F pu Penalties for wind and solar curtailment at three virtual power plants, is the output power of the wind power generation unit of the i-th virtual power plant at time t; is the output power of the photovoltaic power generation unit of the i-th virtual power plant at time t; is the unit cost price of wind power generation at time t; is the unit grid-connected price of wind power at time t; is the government subsidy price for wind power generation units at time t; is the unit cost price of photovoltaic power generation at time t; is the unit grid-connected price of photovoltaic power generation at time t; is the government subsidy price for photovoltaic power generation units at time t; is the discounted investment cost of the wind power generation unit of the i-th virtual power plant (yuan / W); is the discounted operation and maintenance cost of the wind power generation unit of the i-th virtual power plant (yuan / W·year); is the discounted investment cost of the photovoltaic power generation unit of the i-th virtual power plant (yuan / W); is the discounted operation and maintenance cost of the photovoltaic power generation unit of the i-th virtual power plant (yuan / W·year); pu is the penalty cost coefficient for wind and solar power abandonment of the i-th virtual power plant; is the actual power consumption of the wind power generation unit of the i-th virtual power plant at time t; is the actual power consumed by the user of the photovoltaic power generation unit of the i-th virtual power plant at time t.

[0042] Furthermore, in step (2), the output data and benefits of the photovoltaic power generation units and wind power generation units in each virtual power plant at the lower level are distributed by the Shapley value method, as follows:

[0043] S21. Obtaining the overall output data, power consumption and revenue of each virtual power plant;

[0044] S22, determining a profit distribution plan for the photovoltaic power generation unit and the wind power generation unit;

[0045] S23, determining the output distribution values ​​of the photovoltaic power generation unit and the wind power generation unit, specifically including:

[0046]

[0047]

[0048]

[0049] Where: is the set of power generation of all power generation members in the i-th virtual power plant; is the number of subsets included in the i-th virtual power plant; is the wind power generation unit in the i-th virtual power plant; is the photovoltaic power generation unit in the i-th virtual power plant; Assign a value to the benefit of the wind power unit in the i-th virtual power plant; Assign a value to the benefit of the photovoltaic power generation unit in the i-th virtual power plant; is the revenue of the ith virtual power plant; n is the number of participants in the benefit distribution.

[0050] Furthermore, in step (3), the operation of multiple virtual power plants with the participation of shared energy storage power stations is optimized by using mixed integer programming combined with the whale algorithm, as follows:

[0051] S31, setting initial values ​​of initial decision variables and initializing the algorithm;

[0052] S32. In the calculation of the upper model, the important variables used to calculate the annual investment cost and annual operation and maintenance costs of the shared energy storage power station - the amount of electricity purchased by the shared energy storage power station from the large power grid and the amount of electricity purchased and sold to the three virtual power plants are used as parameters and transmitted to the lower model to calculate the results of the virtual power plant's optimized operation strategy.

[0053] S33. The lower-level model obtains the optimal decision variable value each time, calculates the optimal solution for the optimized operation strategy of the three virtual power plants after allocating the photovoltaic power generation units and wind power generation units through the Shapley value method, and updates and corrects the amount of electricity purchased and sold by each virtual power plant to the shared energy storage power station, and transmits this parameter to the upper-level iterative calculation of the cooperative game results between the shared energy storage power station and the three virtual power plants.

[0054] S34. The algorithm calculates until the results converge and determines the optimal charging and discharging strategy of the shared energy storage power station and the optimal operation and scheduling strategy of the three virtual power plants.

[0055] Based on the same inventive concept, the present invention discloses a multi-virtual power plant operation optimization system, comprising:

[0056] The upper model building module is used to first establish the economic operation model of the shared energy storage power station. Combined with the constraints on the shared energy storage power station, an upper model consisting of three virtual power plants and their shared energy storage power stations is established as a whole.

[0057] The lower-level model building module is used to establish each virtual power plant unit composed of photovoltaic power generation units, wind power generation units, and controllable load units as the lower-level model, and to distribute the output data and benefits of the photovoltaic power generation units and wind power generation units in each virtual power plant of the lower level through the Shapley value method;

[0058] The virtual power plant operation optimization module uses the shared energy storage economic operation model established in the upper-level model construction module and the upper-level model composed of three virtual power plants and their shared energy storage power stations as a whole, and the individual virtual power plant units constructed by the lower-level model construction module as the lower-level model. It uses the mixed integer programming method combined with the whale algorithm to optimize the operation of multiple virtual power plants with the participation of shared energy storage power stations, and determines the optimal charging and discharging strategy of the shared energy storage power station and the optimal operation scheduling strategy of the three virtual power plants.

[0059] The present invention provides an electronic device, comprising a processor and a memory.

[0060] The memory is used to store a computer program, and the computer program is executed by the processor to perform the above-mentioned multi-virtual power plant operation optimization method.

[0061] The present invention provides a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is run by a processor to execute the above-mentioned multi-virtual power plant operation optimization method.

[0062] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0063] The present invention establishes an upper-level model composed of three virtual power plants and their shared energy storage power stations as a whole, and uses the three virtual power plants and the shared energy storage power stations as multiple subjects to participate in the cooperative game. During operation, the three virtual power plants are interconnected to realize mutual transmission of electric power, and are independent of each other, which can promote market competition and improve the overall wind and solar power absorption capacity of the three virtual power plants. The electric power between the shared energy storage power station and the virtual power plant is transmitted to each other, which improves the energy utilization efficiency, reasonably distributes electric energy, maximizes the interests of multiple subjects, reduces the electricity cost on the user side, and improves the robustness of the overall system. By establishing virtual power plants composed of photovoltaic power generation units, wind power generation units, and controllable load units The unit is the lower-level model. The output data and benefits of the photovoltaic power generation units and wind power generation units in each virtual power plant in the lower level are allocated through the Shapley value method, which can optimize the power generation plan, make each power generation unit fully utilized, reduce the risks brought by the uncertainty of wind and light, ensure the stable operation of the system, and improve the economic benefits of the overall system; by adopting the mixed integer programming method combined with the whale algorithm, the operation of the virtual power plant with the participation of shared energy storage power stations is optimized. The algorithm has fast convergence speed, strong global search ability, and simple implementation process. It can find the global optimal solution in a shorter time, which improves the speed and efficiency of solving the optimization operation problem of the virtual power plant group, and improves the accuracy and flexibility of the operation and scheduling of the virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 Schematic diagram of the process of the method of Example 1 of the present invention;

[0065] Figure 2 This is a schematic diagram of a network topology of multiple virtual power plants including a shared energy storage power station in Embodiment 1 of the present invention;

[0066] Figure 3 This is an algorithm flow chart of the solution obtained by the mixed integer programming method combined with the whale algorithm in Example 1 of the present invention;

[0067] Figure 4 This is a schematic diagram of the optimized operation results of multiple virtual power plants and the charging and discharging strategy of a shared energy storage power station in Example 1 of the present invention. DETAILED DESCRIPTION

[0068] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0069] Example 1

[0070] like Figure 1 As shown, a method, system, device and storage medium for optimizing the operation of multiple virtual power plants include the following steps:

[0071] (1) First, an economic operation model of a shared energy storage power station is established. Combined with the constraints on the shared energy storage power station, an upper-level model consisting of three virtual power plants and their shared energy storage power station is established.

[0072] like Figure 2 As shown, in this embodiment, its structure includes a power trading center, a power grid dispatching center, a shared energy storage power station, a VPP system coordination and control center A, a VPP system coordination and control center B, and a VPP system coordination and control center C, wherein each VPP is interconnected and has the ability to communicate with each other, and is both interconnected to realize mutual transmission of electric power and independent of each other as a separate VPP system unit. The shared energy storage power station can realize bidirectional transmission of electric energy with the VPP system coordination and control center, that is, the VPP system can buy electricity from the shared energy storage power station and sell excess electricity to the shared energy storage power station. As a unit for regulating electricity prices, the power trading center transmits electricity price information to the VPP system coordination center, so that it can dispatch electricity according to its own interests and feed back the dispatching information to the power grid dispatching center.

[0073] Specifically, the economic operation model of the shared energy storage power station is as follows:

[0074] The comprehensive benefits of the shared energy storage power station in the upper model are expressed as:

[0075]

[0076] Where: F g is the comprehensive income of the shared energy storage power station; F bfg,i F is the cost of purchasing electricity from the shared energy storage power station by the i-th virtual power plant; btg,i P is the revenue from the i-th virtual power plant selling electricity to the shared energy storage power station; grid (t) is the power purchased by the shared energy storage power station from the large power grid at time t; M grid (t) is the electricity price that the shared energy storage power station purchases from the large power grid at time t; P i VPP (t) is the power sold by the shared energy storage power station to the i-th virtual power plant at time t; P is the electricity price sold by the shared energy storage power station to the i-th virtual power plant at time t; i g (t) is the power purchased by the shared energy storage power station from the i-th virtual power plant at time t; is the electricity price purchased by the shared energy storage power station from the i-th virtual power plant at time t; T is the total operation time of the system; C g is the electricity price per kilowatt-hour of the shared energy storage power station. Specifically, it is derived from the annualized investment cost and annualized operation and maintenance cost of the shared energy storage power station. The specific relationship is as follows:

[0077]

[0078] Where: C inv is the annual investment cost of the shared energy storage power station; C OM is the annual operation and maintenance cost of the shared energy storage power station; C P P is the unit investment of energy storage power of shared energy storage power station; g is the energy storage power of the shared energy storage power station; C E E is the unit investment of energy storage capacity of shared energy storage power station; g is the energy storage capacity of the shared energy storage power station; r is the benchmark discount rate; n is the operating life of the shared energy storage power station; K o K is the unit energy storage power operation and maintenance cost coefficient of the shared energy storage power station; m is the unit energy storage capacity operation and maintenance cost coefficient of the shared energy storage power station; Q g is the annual power generation of the shared energy storage power station; η is the energy conversion efficiency of the shared energy storage power station; H g It is the annual utilization hours of electricity stored in the shared energy storage power station.

[0079] In this embodiment, in the cooperative game optimization operation of the shared energy storage power station and the three virtual power plants, in addition to considering the relationship between the economic efficiency and power generation of the shared energy storage power station, it is also necessary to consider the internal operation characteristics of the shared energy storage power station. Specifically, the constraints on the shared energy storage power station include:

[0080] Shared energy storage power station power balance:

[0081]

[0082] Power and capacity constraints of shared energy storage power stations:

[0083]

[0084] Where: The lower limit of energy storage power of the shared energy storage power station; The upper limit of energy storage power of the shared energy storage power station; The lower limit of energy storage capacity for shared energy storage power stations; It is the upper limit of energy storage capacity of shared energy storage power station.

[0085] Operating charge constraints of shared energy storage power stations:

[0086]

[0087] Where: E g (t) is the energy storage capacity of the shared energy storage power station at time t; E g (t-1) is the energy storage capacity of the shared energy storage power station at time t-1; α a is the charging efficiency of the shared energy storage power station; αr The discharge efficiency of the shared energy storage power station;

[0088] Specifically, the mathematical expression of the multi-agent alliance cooperation game in the upper model is:

[0089]

[0090] Where: S is a multi-agent cooperative game alliance; k n are three virtual power plant game participants; g n For the participants of the shared energy storage power station; θ x1 ,θ x2 ,θ x3 are the operation strategies of virtual power plant A, virtual power plant B, and virtual power plant C respectively; θ g Charging and discharging strategy for shared energy storage power station; F x1 、F x2 、F x3 are the revenues of virtual power plant A, virtual power plant B, and virtual power plant C respectively; F g To share the comprehensive benefits of energy storage power stations; is the control function of the alliance on the state of subject x1; are the influence of subject x2 and subject x3 on the state of subject x1; i (s) is the utility vector in the utility transferable alliance.

[0091] In this embodiment, three virtual power plants and a shared energy storage power station are used as multiple subjects to participate in the cooperative game. During operation, the three virtual power plants are interconnected to realize mutual transmission of electric power, and are independent of each other, which can promote market competition and improve the overall wind and solar power absorption capacity of the three virtual power plants. In addition, the mutual transmission of electricity between the shared energy storage power station and the virtual power plant improves energy utilization efficiency and reasonably distributes electric energy, thereby maximizing the interests of multiple subjects, reducing the electricity cost on the user side, and improving the robustness of the overall system.

[0092] (2) Establish each virtual power plant unit consisting of photovoltaic power generation units, wind power generation units, and controllable load units as the lower-level model, and use the Shapley value method to allocate the output data and benefits of the photovoltaic power generation units and wind power generation units in each virtual power plant at the lower level;

[0093] Specifically, the minimum cost function of each virtual power plant is taken as the objective function, and its expression is as follows:

[0094]

[0095] Where: F i is the total operating cost of the i-th virtual power plant unit, F wind,i is the cost of wind power generation unit of the i-th virtual power plant; F ph,iis the cost of the photovoltaic power generation unit of the i-th virtual power plant; F cl,i is the electricity cost of the controllable load of the i-th virtual power plant; F grid,i F is the cost of purchasing electricity from the large power grid for the i-th virtual power plant; pu,i F is the penalty fee for abandoning wind and solar power in the i-th virtual power plant; bfg,i F is the cost of purchasing electricity from the shared energy storage power station by the i-th virtual power plant; btg,i is the revenue from the i-th virtual power plant selling electricity to the shared energy storage power station; T is the total operation time of the system.

[0096] Among them, the cost of wind power generation units and photovoltaic power generation units included in the virtual power plant can be expressed as:

[0097]

[0098] Where: F wind is the total cost of three wind power generation units; F ph is the total cost of three photovoltaic power generation units; F pu Penalties for wind and solar curtailment at three virtual power plants, is the output power of the wind power generation unit of the i-th virtual power plant at time t; is the output power of the photovoltaic power generation unit of the i-th virtual power plant at time t; is the unit cost price of wind power generation at time t; is the unit grid-connected price of wind power at time t; is the government subsidy price for wind power generation units at time t; is the unit cost price of photovoltaic power generation at time t; is the unit grid-connected price of photovoltaic power generation at time t; is the government subsidy price for photovoltaic power generation units at time t; is the discounted investment cost of the wind power generation unit of the i-th virtual power plant (yuan / W); is the discounted operation and maintenance cost of the wind power generation unit of the i-th virtual power plant (yuan / W·year); is the discounted investment cost of the photovoltaic power generation unit of the i-th virtual power plant (yuan / W); is the discounted operation and maintenance cost of the photovoltaic power generation unit of the i-th virtual power plant (yuan / W·year); pu is the penalty cost coefficient for wind and solar power abandonment of the i-th virtual power plant; is the actual power consumption of the wind power generation unit of the i-th virtual power plant at time t; is the actual power consumed by the user of the photovoltaic power generation unit of the i-th virtual power plant at time t.

[0099] In this embodiment, each virtual power plant needs to consider its power constraints during the operation optimization process, and its constraints specifically include:

[0100] Power constraints for virtual power plants to purchase electricity from the large power grid:

[0101] 0≤P grid,i ≤P grid,i,max

[0102] Where: P grid,i P is the power purchased by the ith virtual power plant from the large power grid; grid,i,max is the maximum power purchased by the i-th virtual power plant from the large power grid;

[0103] Power constraints for virtual power plants to purchase electricity from shared energy storage power stations:

[0104]

[0105] Where: E g (t) is the storage capacity of the shared energy storage power station at time t;

[0106] Power consumption constraints of virtual power plant equipment:

[0107]

[0108] Where: is the power consumption of the power-consuming equipment in the i-th virtual power plant, is the minimum power of the power-consuming equipment in the i-th virtual power plant; is the maximum power of the power-consuming equipment in the i-th virtual power plant;

[0109] Specifically, in step (2), the output data and benefits of the photovoltaic power generation units and wind power generation units in each virtual power plant at the lower level are distributed by the Shapley value method. The specific steps include:

[0110] 1. Obtain the overall output data, power consumption and revenue of each virtual power plant;

[0111] 2. Determine the profit distribution plan for photovoltaic power generation units and wind power generation units;

[0112] 3. Determine the output distribution values ​​of the photovoltaic power generation unit and the wind power generation unit, including:

[0113]

[0114] Where: is the set of power generation of all power generation members in the i-th virtual power plant; is the number of subsets included in the i-th virtual power plant; is the wind power generation unit in the i-th virtual power plant; is the photovoltaic power generation unit in the i-th virtual power plant; Assign a value to the benefit of the wind power unit in the i-th virtual power plant; Assign a value to the benefit of the photovoltaic power generation unit in the i-th virtual power plant; is the revenue of the ith virtual power plant; n is the number of participants in the benefit distribution.

[0115] In this embodiment, by establishing virtual power plant units composed of photovoltaic power generation units, wind power generation units, and controllable load units as the lower-level model, and allocating the output data and benefits of the photovoltaic power generation units and wind power generation units within each lower-level virtual power plant through the Shapley value method, the power generation plan can be optimized, so that each power generation unit can be used to its full potential, reducing the risks brought by the uncertainty of wind and solar power, ensuring the stable operation of the system, and improving the economic benefits of the overall system.

[0116] (3) Based on the shared energy storage economic operation model established in step (1) and the upper-level model composed of three virtual power plants and their shared energy storage power stations as a whole, and the lower-level model of each virtual power plant unit constructed in step (2), the operation of multiple virtual power plants with the participation of shared energy storage power stations is optimized by using a mixed integer programming method combined with a whale algorithm to determine the optimal charging and discharging strategy of the shared energy storage power station and the optimal operation and scheduling strategy of the three virtual power plants.

[0117] like Figure 3 As shown, Figure 3 This is the algorithm flow chart for solving the mixed integer programming method combined with the whale algorithm. The specific steps include:

[0118] 1. Set the initial values ​​of the initial decision variables and initialize the algorithm;

[0119] 2. In the calculation of the upper-level model, the important variables used to calculate the annual investment cost and annual operation and maintenance costs of the shared energy storage power station - the amount of electricity purchased by the shared energy storage power station from the large power grid and the amount of electricity purchased and sold to the three virtual power plants - are used as parameters and transmitted to the lower-level model to calculate the results of the virtual power plant's optimized operation strategy.

[0120] 3. The lower-level model obtains the optimal decision variable value each time, calculates the optimal solution for the optimized operation strategy of the three virtual power plants after allocating the photovoltaic power generation units and wind power generation units through the Shapley value method, and updates and corrects the amount of electricity purchased and sold by each virtual power plant to the shared energy storage power station, and transmits this parameter to the upper-level iterative calculation of the cooperative game results between the shared energy storage power station and the three virtual power plants.

[0121] 4. The algorithm calculates until the results converge and determines the optimal charging and discharging strategy for the shared energy storage power station and the optimal operation and scheduling strategy for the three virtual power plants.

[0122] like Figure 4As shown, for a multi-virtual power plant system with the participation of a shared energy storage power station, the optimal charging and discharging strategy of the shared energy storage power station and the optimal operation and scheduling strategy of the three virtual power plants are calculated by using a mixed integer programming method combined with the whale algorithm. The capacity configuration of the shared energy storage power station can be carried out based on this result.

[0123] In this embodiment, the operation of the virtual power plant with the participation of a shared energy storage power station is optimized by adopting a mixed integer programming method combined with the whale algorithm. The algorithm has a fast convergence speed, a strong global search capability, and a simple implementation process. The global optimal solution can be found in a shorter time, thereby improving the speed and efficiency of solving the optimization operation problem of the virtual power plant group and improving the accuracy and flexibility of the operation scheduling of the virtual power plant.

[0124] Example 2

[0125] The present invention discloses a multi-virtual power plant operation optimization system, comprising:

[0126] The upper model building module is used to first establish the economic operation model of the shared energy storage power station. Combined with the constraints on the shared energy storage power station, an upper model consisting of three virtual power plants and their shared energy storage power stations is established as a whole.

[0127] like Figure 2 As shown, in this embodiment, its structure includes a power trading center, a power grid dispatching center, a shared energy storage power station, a VPP system coordination and control center A, a VPP system coordination and control center B, and a VPP system coordination and control center C, wherein each VPP is interconnected and has the ability to communicate with each other, and is both interconnected to realize mutual transmission of electric power and independent of each other as a separate VPP system unit. The shared energy storage power station can realize bidirectional transmission of electric energy with the VPP system coordination and control center, that is, the VPP system can buy electricity from the shared energy storage power station and sell excess electricity to the shared energy storage power station. As a unit for regulating electricity prices, the power trading center transmits electricity price information to the VPP system coordination center, so that it can dispatch electricity according to its own interests and feed back the dispatching information to the power grid dispatching center.

[0128] Specifically, the economic operation model of the shared energy storage power station is as follows:

[0129] The comprehensive benefits of the shared energy storage power station in the upper model are expressed as:

[0130]

[0131] Where: F g is the comprehensive income of the shared energy storage power station; F bfg,i F is the cost of purchasing electricity from the shared energy storage power station by the i-th virtual power plant; btg,i P is the revenue from the i-th virtual power plant selling electricity to the shared energy storage power station;grid (t) is the power purchased by the shared energy storage power station from the large power grid at time t; M grid (t) is the electricity price that the shared energy storage power station purchases from the large power grid at time t; P i VPP (t) is the power sold by the shared energy storage power station to the i-th virtual power plant at time t; P is the electricity price sold by the shared energy storage power station to the i-th virtual power plant at time t; i g (t) is the power purchased by the shared energy storage power station from the i-th virtual power plant at time t; is the electricity price purchased by the shared energy storage power station from the i-th virtual power plant at time t; T is the total operation time of the system; C g is the electricity price per kilowatt-hour of the shared energy storage power station. Specifically, it is derived from the annualized investment cost and annualized operation and maintenance cost of the shared energy storage power station. The specific relationship is as follows:

[0132]

[0133] Where: C inv is the annual investment cost of the shared energy storage power station; C OM is the annual operation and maintenance cost of the shared energy storage power station; C P P is the unit investment of energy storage power of shared energy storage power station; g is the energy storage power of the shared energy storage power station; C E E is the unit investment of energy storage capacity of shared energy storage power station; g is the energy storage capacity of the shared energy storage power station; r is the benchmark discount rate; n is the operating life of the shared energy storage power station; K o K is the unit energy storage power operation and maintenance cost coefficient of the shared energy storage power station; m is the unit energy storage capacity operation and maintenance cost coefficient of the shared energy storage power station; Q g is the annual power generation of the shared energy storage power station; η is the energy conversion efficiency of the shared energy storage power station; H g It is the annual utilization hours of electricity stored in the shared energy storage power station.

[0134] In this embodiment, in the cooperative game optimization operation of the shared energy storage power station and the three virtual power plants, in addition to considering the relationship between the economic efficiency and power generation of the shared energy storage power station, it is also necessary to consider the internal operation characteristics of the shared energy storage power station. Specifically, the constraints on the shared energy storage power station include:

[0135] Shared energy storage power station power balance:

[0136]

[0137] Power and capacity constraints of shared energy storage power stations:

[0138]

[0139] Where: The lower limit of energy storage power of the shared energy storage power station; The upper limit of energy storage power of the shared energy storage power station; The lower limit of energy storage capacity for shared energy storage power stations; It is the upper limit of energy storage capacity of shared energy storage power station.

[0140] Operating charge constraints of shared energy storage power stations:

[0141]

[0142] Where: E g (t) is the energy storage capacity of the shared energy storage power station at time t; E g (t-1) is the energy storage capacity of the shared energy storage power station at time t-1; α a is the charging efficiency of the shared energy storage power station; α r The discharge efficiency of the shared energy storage power station;

[0143] Specifically, the mathematical expression of the multi-agent alliance cooperation game in the upper model is:

[0144]

[0145] Where: S is a multi-agent cooperative game alliance; k n are three virtual power plant game participants; g n For the participants of the shared energy storage power station; θ x1 ,θ x2 ,θ x3 are the operation strategies of virtual power plant A, virtual power plant B, and virtual power plant C respectively; θ g Charging and discharging strategy for shared energy storage power station; F x1 、F x2 、F x3 are the revenues of virtual power plant A, virtual power plant B, and virtual power plant C respectively; F g To share the comprehensive benefits of energy storage power stations; is the control function of the alliance on the state of subject x1; are the influence of subject x2 and subject x3 on the state of subject x1; i (s) is the utility vector in the utility transferable alliance.

[0146] In this embodiment, three virtual power plants and a shared energy storage power station are used as multiple subjects to participate in the cooperative game. During operation, the three virtual power plants are interconnected to realize mutual transmission of electric power, and are independent of each other, which can promote market competition and improve the overall wind and solar power absorption capacity of the three virtual power plants. In addition, the mutual transmission of electricity between the shared energy storage power station and the virtual power plant improves energy utilization efficiency and reasonably distributes electric energy, thereby maximizing the interests of multiple subjects, reducing the electricity cost on the user side, and improving the robustness of the overall system.

[0147] The lower-level model building module is used to establish each virtual power plant unit composed of photovoltaic power generation units, wind power generation units, and controllable load units as the lower-level model, and to distribute the output data and benefits of the photovoltaic power generation units and wind power generation units in each virtual power plant of the lower level through the Shapley value method;

[0148] Specifically, the minimum cost function of each virtual power plant is taken as the objective function, and its expression is as follows:

[0149]

[0150] Where: F i is the total operating cost of the i-th virtual power plant unit, F wind,i is the cost of wind power generation unit of the i-th virtual power plant; F ph,i is the cost of the photovoltaic power generation unit of the i-th virtual power plant; F cl,i is the electricity cost of the controllable load of the i-th virtual power plant; F grid,i F is the cost of purchasing electricity from the large power grid for the i-th virtual power plant; pu,i F is the penalty fee for abandoning wind and solar power in the i-th virtual power plant; bfg,i F is the cost of purchasing electricity from the shared energy storage power station by the i-th virtual power plant; btg,i is the revenue from the i-th virtual power plant selling electricity to the shared energy storage power station; T is the total operation time of the system.

[0151] Among them, the cost of wind power generation units and photovoltaic power generation units included in the virtual power plant can be expressed as:

[0152]

[0153] Where: F wind is the total cost of three wind power generation units; F ph is the total cost of three photovoltaic power generation units; F pu Penalties for wind and solar curtailment at three virtual power plants, is the output power of the wind power generation unit of the i-th virtual power plant at time t; is the output power of the photovoltaic power generation unit of the i-th virtual power plant at time t; is the unit cost price of wind power generation at time t; is the unit grid-connected price of wind power at time t; is the government subsidy price for wind power generation units at time t; is the unit cost price of photovoltaic power generation at time t; is the unit grid-connected price of photovoltaic power generation at time t; is the government subsidy price for photovoltaic power generation units at time t; is the discounted investment cost of the wind power generation unit of the i-th virtual power plant (yuan / W); is the discounted operation and maintenance cost of the wind power generation unit of the i-th virtual power plant (yuan / W·year); is the discounted investment cost of the photovoltaic power generation unit of the i-th virtual power plant (yuan / W); is the discounted operation and maintenance cost of the photovoltaic power generation unit of the i-th virtual power plant (yuan / W·year); pu is the penalty cost coefficient for wind and solar power abandonment of the i-th virtual power plant; is the actual power consumption of the wind power generation unit of the i-th virtual power plant at time t; is the actual power consumed by the user of the photovoltaic power generation unit of the i-th virtual power plant at time t.

[0154] In this embodiment, each virtual power plant needs to consider its power constraints during the operation optimization process, and its constraints specifically include:

[0155] Power constraints for virtual power plants to purchase electricity from the large power grid:

[0156] 0≤P grid,i ≤P grid,i,max

[0157] Where: P grid,i P is the power purchased by the ith virtual power plant from the large power grid; grid,i,max is the maximum power purchased by the i-th virtual power plant from the large power grid;

[0158] Power constraints for virtual power plants to purchase electricity from shared energy storage power stations:

[0159]

[0160] Where: E g (t) is the storage capacity of the shared energy storage power station at time t;

[0161] Power consumption constraints of virtual power plant equipment:

[0162]

[0163] Where: is the power consumption of the power-consuming equipment in the i-th virtual power plant, is the minimum power of the power-consuming equipment in the i-th virtual power plant; is the maximum power of the power-consuming equipment in the i-th virtual power plant;

[0164] Specifically, the output data and benefits of the photovoltaic power generation units and wind power generation units in each virtual power plant at the lower level are distributed through the Shapley value method. The specific steps include:

[0165] 1. Obtain the overall output data, power consumption and revenue of each virtual power plant;

[0166] 2. Determine the profit distribution plan for photovoltaic power generation units and wind power generation units;

[0167] 3. Determine the output distribution values ​​of the photovoltaic power generation unit and the wind power generation unit, including:

[0168]

[0169]

[0170] Where: is the set of power generation of all power generation members in the i-th virtual power plant; is the number of subsets included in the i-th virtual power plant; is the wind power generation unit in the i-th virtual power plant; is the photovoltaic power generation unit in the i-th virtual power plant; Assign a value to the benefit of the wind power unit in the i-th virtual power plant; Assign a value to the benefit of the photovoltaic power generation unit in the i-th virtual power plant; is the revenue of the ith virtual power plant; n is the number of participants in the benefit distribution.

[0171] In this embodiment, by establishing virtual power plant units composed of photovoltaic power generation units, wind power generation units, and controllable load units as the lower-level model, and allocating the output data and benefits of the photovoltaic power generation units and wind power generation units within each lower-level virtual power plant through the Shapley value method, the power generation plan can be optimized, so that each power generation unit can be used to its full potential, reducing the risks brought by the uncertainty of wind and solar power, ensuring the stable operation of the system, and improving the economic benefits of the overall system.

[0172] The virtual power plant operation optimization module uses the shared energy storage economic operation model established in the upper-level model construction module and the upper-level model composed of three virtual power plants and their shared energy storage power stations as a whole, and the individual virtual power plant units constructed by the lower-level model construction module as the lower-level model. It uses the mixed integer programming method combined with the whale algorithm to optimize the operation of multiple virtual power plants with the participation of shared energy storage power stations, and determines the optimal charging and discharging strategy of the shared energy storage power station and the optimal operation scheduling strategy of the three virtual power plants.

[0173] Figure 3 This is the algorithm flow chart for solving the mixed integer programming method combined with the whale algorithm. The specific steps include:

[0174] 1. Set the initial values ​​of the initial decision variables and initialize the algorithm;

[0175] 2. In the calculation of the upper-level model, the important variables used to calculate the annual investment cost and annual operation and maintenance costs of the shared energy storage power station - the amount of electricity purchased by the shared energy storage power station from the large power grid and the amount of electricity purchased and sold to the three virtual power plants - are used as parameters and transmitted to the lower-level model to calculate the results of the virtual power plant's optimized operation strategy.

[0176] 3. The lower-level model obtains the optimal decision variable value each time, calculates the optimal solution for the optimized operation strategy of the three virtual power plants after allocating the photovoltaic power generation units and wind power generation units through the Shapley value method, and updates and corrects the amount of electricity purchased and sold by each virtual power plant to the shared energy storage power station, and transmits this parameter to the upper-level iterative calculation of the cooperative game results between the shared energy storage power station and the three virtual power plants.

[0177] 4. The algorithm calculates until the results converge and determines the optimal charging and discharging strategy for the shared energy storage power station and the optimal operation and scheduling strategy for the three virtual power plants.

[0178] like Figure 4 As shown, for a multi-virtual power plant system with the participation of a shared energy storage power station, the optimal charging and discharging strategy of the shared energy storage power station and the optimal operation and scheduling strategy of the three virtual power plants are calculated by using a mixed integer programming method combined with the whale algorithm. The capacity configuration of the shared energy storage power station can be carried out based on this result.

[0179] Example 3

[0180] Corresponding to the method of embodiment 1 of the present invention, embodiment 3 of the present invention further provides an electronic device. In this embodiment, the electronic device includes: at least one processor and at least one memory, and the memory contains programs and data. The memory may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor calls the program and data stored in the memory to execute the multi-virtual power plant operation optimization method provided in the embodiment of the present invention.

[0181] Example 4

[0182] Corresponding to the method of embodiment 1 of the present invention, embodiment 4 of the present invention further provides a computer storage medium, which stores a computer program, and the computer program is executed by a processor to execute a multi-virtual power plant operation optimization method provided by an embodiment of the present invention. The functional units in various embodiments of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware, or in the form of a software functional unit, or in the form of a combination of software and hardware. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in various embodiments of the present invention. The aforementioned storage media include: mobile hard disk, USB flash drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for optimizing the operation of multiple virtual power plants, characterized in that: The steps include: (1) First, the economic operation model of the shared energy storage power station was established. Combined with the constraints of the shared energy storage power station, an upper-level model consisting of three virtual power plants and their shared energy storage power stations was established; (2) Establish each virtual power plant unit consisting of photovoltaic power generation units, wind power generation units, and controllable load units as the lower-level model, and use the Shapley value method to allocate the output data and benefits of the photovoltaic power generation units and wind power generation units in each virtual power plant; (3) Based on the shared energy storage economic operation model established in step (1) and the upper-level model composed of three virtual power plants and their shared energy storage power stations as a whole, and the lower-level model of each virtual power plant unit constructed in step (2), the operation of multiple virtual power plants with the participation of shared energy storage power stations is optimized by using a mixed integer programming method combined with a whale algorithm to determine the optimal charging and discharging strategy of the shared energy storage power station and the optimal operation and scheduling strategy of the three virtual power plants.

2. A multi-virtual power plant operation optimization method according to claim 1, characterized in that: The economic operation model of the shared energy storage power station in step (1) is: The comprehensive benefits of a shared energy storage power station are expressed as: Where: F g is the comprehensive income of the shared energy storage power station; F bfg,i F is the cost of purchasing electricity from the shared energy storage power station by the i-th virtual power plant; btg,i P is the revenue from the i-th virtual power plant selling electricity to the shared energy storage power station; grid (t) is the power purchased by the shared energy storage power station from the large power grid at time t; M grid (t) is the electricity price that the shared energy storage power station purchases from the large power grid at time t; P i VPP (t) is the power sold by the shared energy storage power station to the i-th virtual power plant at time t; P is the electricity price sold by the shared energy storage power station to the i-th virtual power plant at time t; i g (t) is the power purchased by the shared energy storage power station from the i-th virtual power plant at time t; is the electricity price purchased by the shared energy storage power station from the i-th virtual power plant at time t; T is the total operation time of the system; C g is the electricity price per kWh of the shared energy storage power station. Specifically, the electricity price per kWh of the shared energy storage power station is C g It is derived from the annualized investment cost and annualized operation and maintenance cost of the shared energy storage power station. The specific relationship is as follows: Where: C inv is the annual investment cost of the shared energy storage power station; C OM is the annual operation and maintenance cost of the shared energy storage power station; C P P is the unit investment of energy storage power of shared energy storage power station; g is the energy storage power of the shared energy storage power station; C E E is the unit investment of energy storage capacity of shared energy storage power station; g is the energy storage capacity of the shared energy storage power station; r is the benchmark discount rate; n is the operating life of the shared energy storage power station; K o K is the unit energy storage power operation and maintenance cost coefficient of the shared energy storage power station; m Q is the unit energy storage capacity operation and maintenance cost coefficient of the shared energy storage power station; g is the annual power generation of the shared energy storage power station; η is the energy conversion efficiency of the shared energy storage power station; H g It is the annual utilization hours of electricity stored in the shared energy storage power station.

3. A multi-virtual power plant operation optimization method according to claim 2, characterized in that: The constraints on the shared energy storage power station in step (1) are: Shared energy storage power station power balance: Power and capacity constraints of shared energy storage power stations: Where: The lower limit of energy storage power of the shared energy storage power station; The upper limit of energy storage power of the shared energy storage power station; The lower limit of energy storage capacity for shared energy storage power stations; The upper limit of the energy storage capacity of the shared energy storage power station; Operating charge constraints of shared energy storage power stations: Where: E g (t) is the energy storage capacity of the shared energy storage power station at time t; E g (t-1) is the energy storage capacity of the shared energy storage power station at time t-1; α a is the charging efficiency of the shared energy storage power station; α r It is the discharge efficiency of the shared energy storage power station.

4. A multi-virtual power plant operation optimization method according to claim 3, characterized in that: The upper-level model established in step (1) consisting of three virtual power plants and their shared energy storage power stations as a whole is: The mathematical expression of the multi-agent alliance cooperation game in the upper model is: Where: S is a multi-agent cooperative game alliance; k n are three virtual power plant game participants; g n For the participants of the shared energy storage power station; θ x1 ,θ x2 ,θ x3 are the operation strategies of virtual power plant A, virtual power plant B, and virtual power plant C respectively; θ g Charging and discharging strategy for shared energy storage power station; F x1 、F x2 、F x3 are the revenues of virtual power plant A, virtual power plant B, and virtual power plant C respectively; F g To share the benefits of energy storage power stations; is the control function of the alliance on the state of subject x1; are the influence of subject x2 and subject x3 on the state of subject x1; i (s) is the utility vector in the utility transferable alliance.

5. The method for optimizing the operation of multiple virtual power plants according to claim 4, characterized in that: In the step (2), each virtual power plant unit consisting of a photovoltaic power generation unit, a wind power generation unit, and a controllable load unit is established as a lower layer model, which is specifically as follows: The minimum cost function of each virtual power plant is taken as the objective function, and its expression is as follows: Where: F i is the total operating cost of the i-th virtual power plant unit, F wind,i is the cost of wind power generation unit of the i-th virtual power plant; F ph,i is the cost of the photovoltaic power generation unit of the i-th virtual power plant; F cl,i is the electricity cost of the controllable load of the i-th virtual power plant; F grid,i F is the cost of purchasing electricity from the large power grid for the i-th virtual power plant; pu,i F is the penalty fee for abandoning wind and solar power in the i-th virtual power plant; bfg,i F is the cost of purchasing electricity from the shared energy storage power station by the i-th virtual power plant; btg,i is the revenue from electricity sales from the i-th virtual power plant to the shared energy storage power station; T is the total operation time of the system; Among them, the costs of wind power generation units and photovoltaic power generation units included in the virtual power plant are expressed as: Where: F wind is the total cost of three wind power generation units; F ph is the total cost of three photovoltaic power generation units; F pu Penalties for wind and solar curtailment at three virtual power plants, is the output power of the wind power generation unit of the i-th virtual power plant at time t; is the output power of the photovoltaic power generation unit of the i-th virtual power plant at time t; is the unit cost price of wind power generation at time t; is the unit grid-connected price of wind power at time t; is the government subsidy price for wind power generation units at time t; is the unit cost price of photovoltaic power generation at time t; is the unit grid-connected price of photovoltaic power generation at time t; is the government subsidy price for photovoltaic power generation units at time t; is the discounted investment cost of the wind power generation unit of the i-th virtual power plant (yuan / W); is the discounted operation and maintenance cost of the wind power generation unit of the i-th virtual power plant (yuan / W·year); is the discounted investment cost of the photovoltaic power generation unit of the i-th virtual power plant (yuan / W); is the discounted operation and maintenance cost of the photovoltaic power generation unit of the i-th virtual power plant (yuan / W·year); pu is the penalty cost coefficient for wind and solar power abandonment of the i-th virtual power plant; is the actual power consumption of the wind power generation unit of the i-th virtual power plant at time t; is the actual power consumed by the user of the photovoltaic power generation unit of the i-th virtual power plant at time t.

6. A method for optimizing operation of multiple virtual power plants according to claim 5, characterized in that: In step (2), the output data and benefits of the photovoltaic power generation units and wind power generation units in each virtual power plant at the lower level are distributed by the Shapley value method, as follows: S21. Obtaining the overall output data, power consumption and revenue of each virtual power plant; S22, determining a profit distribution plan for the photovoltaic power generation unit and the wind power generation unit; S23, determining the output distribution values ​​of the photovoltaic power generation unit and the wind power generation unit, specifically including: Where: is the set of power generation of all power generation members in the i-th virtual power plant; is the number of subsets included in the i-th virtual power plant; is the wind power generation unit in the i-th virtual power plant; is the photovoltaic power generation unit in the i-th virtual power plant; Assign a value to the benefit of the wind power unit in the i-th virtual power plant; Assign a value to the benefit of the photovoltaic power generation unit in the i-th virtual power plant; is the revenue of the ith virtual power plant; n is the number of participants in the benefit distribution.

7. A method for optimizing operation of multiple virtual power plants according to claim 6, characterized in that: In step (3), the operation of multiple virtual power plants with the participation of shared energy storage power stations is optimized by using a mixed integer programming method combined with a whale algorithm, as follows: S31, setting initial values ​​of initial decision variables and initializing the algorithm; S32. In the calculation of the upper model, the important variables used to calculate the annual investment cost and annual operation and maintenance costs of the shared energy storage power station - the amount of electricity purchased by the shared energy storage power station from the large power grid and the amount of electricity purchased and sold to the three virtual power plants are used as parameters and transmitted to the lower model to calculate the results of the virtual power plant's optimized operation strategy. S33. The lower-level model obtains the optimal decision variable value each time, calculates the optimal solution for the optimized operation strategy of the three virtual power plants after allocating the photovoltaic power generation units and wind power generation units through the Shapley value method, and updates and corrects the amount of electricity purchased and sold by each virtual power plant to the shared energy storage power station, and transmits this parameter to the upper-level iterative calculation of the cooperative game results between the shared energy storage power station and the three virtual power plants. S34. The algorithm calculates until the results converge and determines the optimal charging and discharging strategy of the shared energy storage power station and the optimal operation and scheduling strategy of the three virtual power plants.

8. A multi-virtual power plant operation optimization system, characterized in that: include: The upper model building module is used to first establish the economic operation model of the shared energy storage power station. Combined with the constraints on the shared energy storage power station, an upper model consisting of three virtual power plants and their shared energy storage power stations is established as a whole. The lower-level model building module is used to establish each virtual power plant unit composed of photovoltaic power generation units, wind power generation units, and controllable load units as the lower-level model, and to distribute the output data and benefits of the photovoltaic power generation units and wind power generation units in each virtual power plant of the lower level through the Shapley value method; The virtual power plant operation optimization module uses the shared energy storage economic operation model established in the upper-level model construction module and the upper-level model composed of three virtual power plants and their shared energy storage power stations as a whole, and the individual virtual power plant units constructed by the lower-level model construction module as the lower-level model. It uses the mixed integer programming method combined with the whale algorithm to optimize the operation of multiple virtual power plants with the participation of shared energy storage power stations, and determines the optimal charging and discharging strategy of the shared energy storage power station and the optimal operation scheduling strategy of the three virtual power plants.

9. An electronic device, characterized in that: including a processor and memory, A memory for storing a computer program, wherein the computer program is executed by a processor to perform the method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer program, which is executed by a processor to perform the method according to any one of claims 1 to 7.

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