Multi-virtual power plant optimal scheduling method based on controllable photovoltaic regulation and cooperative game

Abstract

The invention belongs to the technical field of virtual power plant optimal scheduling, and particularly relates to a multi-virtual power plant optimal scheduling method based on controllable photovoltaic regulation and cooperative game, and the method comprises the steps: building a multi-virtual power plant joint operation mechanism; based on a rigid control mode and a flexible control mode of photovoltaic grid connection, establishing a controllable photovoltaic resource model in the VPP; modeling is carried out on a load type VPP, a power supply type VPP and a comprehensive type VPP, and a multi-virtual power plant optimization scheduling model based on Nash negotiation is established; and an augmented Lagrange function is introduced, a multi-virtual power plant joint operation optimization scheduling solving model is established, and an optimal scheduling scheme is solved by using an ADMM distributed algorithm. According to the method, minimization of the new energy consumption rate, the social cost and the payment benefit of the combined operation system are taken as optimization objectives, and maximum consumption of the new energy in the region can be realized while the maximum combined benefit of each VPP is met.

Description

Technical Field

[0001] This invention belongs to the field of virtual power plant optimization scheduling technology, specifically involving a multi-virtual power plant optimization scheduling method based on controllable photovoltaic regulation and cooperative game theory. Background Technology

[0002] Currently, the power system is rapidly transforming into a new type of power system dominated by distributed energy resources (DERs) such as wind power and solar power. This will present a more complex and volatile supply and demand balance situation and higher requirements for regulation and coordination. Virtual power plants (VPPs), as a power structure that integrates information technology, distributed energy resources, and load resources, can connect to flexible resources within the system to achieve functions such as power balance, peak shaving, frequency regulation, and congestion management, becoming an important support for smart grid construction. However, with the increasing integration of distributed energy resources, traditional single-type virtual power plants cannot fully utilize their resource advantages, which will affect the operational efficiency and profitability of virtual power plants and even the entire power system. At this point, multiple virtual power plants operating in concert can improve the overall performance of the power system.

[0003] The joint operation of multiple virtual power plants (VPPs) has become a research hotspot for many scholars. The different DERs (Dynamic Energy Controllers) within these plants can leverage their complementarity to achieve resource sharing during coordinated operation, thereby improving overall efficiency. Furthermore, the joint operation of multiple VPPs can better fill the flexibility gap in the power system, effectively promote the consumption of new energy sources, and achieve centralized and comprehensive management of various distributed energy sources within a region. Current VPP joint operation models primarily analyze the load and resource characteristics within different VPPs within a region, determine their participation in the electricity-peak-shaving ancillary service market trading model, and determine the specific operation and output methods of each VPP to maximize its benefits through various cooperative operation mechanisms. This dispatching model has become the optimal solution for the joint operation of VPPs in a market environment, but it does not consider the internal resource characteristics of the VPPs and the impact of changes in different resources on the final cooperative operation. Therefore, it is necessary to combine the existing VPP joint operation model with current methods for regulating renewable resources and update the original cooperative operation model to maximize the benefits of multi-VPP joint operation. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide a multi-virtual power plant optimization scheduling method based on controllable photovoltaic regulation and cooperative game theory. The optimization objectives are to minimize the renewable energy absorption rate, social costs, and payment benefits of the jointly operated system. This method can maximize the absorption of renewable energy in the region while ensuring that each VPP benefits the most.

[0005] To achieve the above objectives, this invention provides a multi-virtual power plant optimal scheduling method based on controllable photovoltaic regulation and cooperative game theory, comprising the following steps: S1. Establish a joint operation mechanism for multiple virtual power plants: Set up a virtual power plant general operator to uniformly dispatch and manage each virtual power plant VPP, and provide electricity prices and demand to the power generation energy market and peak-shaving auxiliary service market under each VPP. Each VPP is equipped with its own virtual power plant operator, which internally aggregates flexible resources that can participate in regulation, and conducts price negotiation cooperation with other VPPs and adjusts its internal operation strategy in sync based on the issued market signals. S2. Based on rigid and flexible control methods for photovoltaic grid connection, establish a controllable photovoltaic resource model within the VPP; S3. Model load-type VPP, power-type VPP and integrated VPP respectively, and establish a multi-virtual power plant optimal scheduling model based on Nash negotiation; S4. Introduce the augmented Lagrangian function to establish a solution model for the joint operation and optimal scheduling of multiple virtual power plants, and use the ADMM distributed algorithm to obtain the optimal scheduling scheme.

[0006] As a preferred embodiment of the present invention, in S1, the VPP acts as an energy manager internally and meets the power generation output requirements of the upper-level power grid externally, and its previously determined interaction plan cannot be changed after being reported.

[0007] As a preferred embodiment of the present invention, in S2, the rigid control method means that the connected photovoltaic devices can only have two states at the same time period: on and off. That is, the output of the photovoltaic power station during that time period can only be zero or full power, as expressed as: (1); In the formula, The actual output of photovoltaic power after regulation; This is a rigid regulation and control state for photovoltaic systems. To maximize the output of photovoltaic power; Flexible control means that the output of each connected photovoltaic device within the same time period can be adjusted according to the scheduling requirements, provided that its upper and lower photovoltaic processing limits are met. This is expressed as: (2); In the formula, This is the flexible regulation and control state for photovoltaic systems. The controllable photovoltaic resource model within a VPP is based on rigid and flexible control methods, adjusting the photovoltaic power generation resources within the VPP according to the specific operation plan for each time period and region.

[0008] As a preferred embodiment of the present invention, the modeling process of the load-type VPP in S3 is as follows: The total revenue is greatest for load-type VPPs. To achieve the objective, define the objective function for the load-type VPP: (3); In the formula, For direct transaction revenue between VPPs; For the revenue of the peak shaving market; For revenue from the electricity market; For load-type VPPs, the network access fee; The cost paid to flexible loads; The calculation method is as follows: (4); In the formula, , These represent the direct transaction electricity volume and direct transaction price between the i-th and j-th load-type VPPs in the time period, respectively. For the collection of VPPs (Vehicle Power Purchasers); For the collection of VPPs of the electricity purchaser; The calculation method is as follows: (5); In the formula, , These are the peak-shaving price and valley-filling price issued by the power trading center for time period t, respectively. , These represent the bidding capacity for VPP peak shaving and valley filling during time period t, respectively. Represents the discharge state, with 1 indicating that it is in the discharge state and 0 indicating that it is not in the discharge state; This represents the charging status; 1 indicates that the device is charging, and 0 indicates that it is not charging. , For Boolean variables, A value of 1 indicates that the load has been transferred in. A value of 1 indicates that the load has been transferred out. The bidding capacity for industrial load to participate in peak shaving and peak regulation within the VPP during time period t; The industrial load during period t represents the increased power consumption actively undertaken to facilitate peak shaving and valley filling. The bidding capacity for temperature-controlled loads within the VPP during time period t to participate in peak shaving and regulation; The bidding capacity for residential load to participate in peak shaving and regulation within the VPP during time period t; , These represent the charging and discharging volumes of electric vehicles in the energy market; , These represent the charging and discharging volumes of electric vehicles in the peak shaving and valley filling markets, respectively. This represents the total discharge of electric vehicles in the energy market and peak shaving market during time period t. The total amount of electric vehicles charged in the energy market and the peak-shaving market during period t; The calculation method is as follows: (6); In the formula, The electricity price for VPP participation in the electricity market during period t; For time period t, the amount of electricity purchased by the VPP from the main grid is t; T is the total amount of electricity purchased during the time period. The calculation method is as follows: (7); In the formula, It is the regularization parameter, i.e., the penalty factor; , These are the line loss compensation conversion factors between the i-th and j-th VPPs, respectively; This is a set of VPPs who are potential participants in the negotiation process. This represents the j-th VPP, excluding the i-th VPP, that may participate in the negotiation. The calculation method is as follows: (8); In the formula, , , , These are the dispatch costs for electric vehicles, temperature-controlled loads, industrial loads, and residential loads, respectively. The electricity price charged by the VPP to the load; , These are the incentive electricity prices paid by the VPP to the load for peak shaving and valley filling. , These are the cost coefficients for dissatisfaction; The bidding capacity for residential load to participate in peak shaving and regulation within the VPP during time period t; This represents the power of electric vehicles participating in valley filling and peak shaving in discharge mode during time period t; The power of the temperature-controlled load during time period t.

[0009] As a preferred embodiment of the present invention, the modeling process of the power supply type VPP in S3 is as follows: Total revenue of power-type VPP With the goal of maximizing, establish the objective function: (9); In the formula, The direct revenue generated by the VPP from selling electricity to the upper-level power grid; This represents the total power generation cost of the VPP; The calculation method is as follows: (10); In the formula, Let t be the cost of electricity sales during period t; The electricity sold during period t; The calculation method is as follows: (11); In the formula, This represents the cost coefficient for thermal power generation. , These represent the electricity generated by thermal power plants participating in the peak-shaving market and the total electricity volume in time period t, respectively. Cost of power generation for photovoltaic systems; The photovoltaic power generation during time period t; , These represent the number of photovoltaic controllers with rigid regulation installed and the unit cost, respectively. , These represent the number of photovoltaic controllers with flexible control installed and the unit cost, respectively.

[0010] As a preferred embodiment of the present invention, the modeling process of the integrated VPP in S3 is as follows: Total revenue of integrated VPP With the goal of maximizing, establish the objective function: (12); In the formula, The calculation method for the energy storage operating cost in a comprehensive VPP is as follows: (13); In the formula, The battery loss cost coefficient per unit charge and discharge time; , These represent the charging and discharging power of the energy storage, respectively. , These refer to the charging and discharging power of energy storage in the energy market; , These represent the charging and discharging power of energy storage in the peak-shaving market.

[0011] As a preferred embodiment of the present invention, in S3, the Nash negotiation breakdown point in the game between each VPP is taken as the individual planning cost of each VPP, that is, there is no power sharing between each VPP. At this time, the planning cost corresponding to the power interaction between each VPP and the external power grid is the Nash negotiation breakdown point. The multi-virtual power plant optimal scheduling model based on Nash negotiation is expressed as follows: (14); In the formula, The cost of planning for the nth Nash negotiating entity is given by the VPP; N is the number of Nash negotiating entities. This represents the final quote for the nth VPP; This represents the thermal power generation in the integrated VPP during time period t; This represents the wind power generation in the integrated VPP during time period t; This represents the photovoltaic power generation in the integrated VPP during time period t; This represents the amount of energy storage discharged in the energy market during time period t in a comprehensive VPP. This represents the amount of energy storage charged in the energy market during time period t in a comprehensive VPP. This represents the electrical energy corresponding to the load in the integrated VPP during time period t; To represent the electricity sales volume in the integrated VPP during time period t; , These represent the electricity sold and purchased by a load-type VPP during time period t, respectively. , These represent the discharge and charging amounts of energy storage in the energy market during time period t; This represents the electrical energy corresponding to the load of the load-type VPP itself during time period t; This indicates the amount of electricity purchased by the power supply type VPP during time period t; This represents the thermal power generation in a power-type VPP during time period t; This represents the photovoltaic power generation in the power-type VPP during time period t; This represents the amount of energy stored in the power-type VPP discharged in the energy market during time period t; This represents the electrical energy transferred from the integrated VPP to the load-type VPP during time period t; This represents the electrical energy transferred from the integrated VPP to the power-type VPP during time period t; This represents the electrical energy transferred from the load-type VPP to the integrated VPP during time period t; This represents the electrical energy transferred from the load-type VPP to the power-type VPP during time period t; This represents the electrical energy transferred from the power-type VPP to the integrated VPP during time period t. This represents the electrical energy transferred from the power source VPP to the load VPP during time period t; Assume that the three Virtual Power Plants (VPPs) are independent, rational, and belong to different interest groups. Each VPP will strategically negotiate the electricity trading and pricing within the cooperative plan to formulate a reasonable and fair profit-sharing scheme, further reducing the overall planning cost. It is also assumed that through cooperative negotiations, each VPP can find a profit-sharing scheme that satisfies all parties, minimizing the planning costs for each VPP. A Nash equilibrium solution is obtained through a multi-virtual power plant optimization scheduling model, and this Nash equilibrium solution is used as the final energy trading and pricing strategy, thereby deriving the optimal solution for the configuration and operation optimization of each VPP. (15); In the formula, The initial quote for the nth VPP; The socially minimized cost is obtained based on equation (15). : (16); In the formula, This represents the planning cost of the nth VPP in time period t; k is also the index of the VPP, used to identify different VPPs, and together with n, it reflects the interaction relationship between VPPs. This represents the correlation coefficient of profit distribution from VPPn to VPPk during time period t; This represents the electricity purchased and sold by the nth VPP to the kth VPP during time period t; This represents the amount of electricity purchased by VPPn during time period t; This represents the photovoltaic power generation of VPPn during time period t; This represents the amount of energy stored in VPPn being charged in the energy market during time period t; This represents the amount of energy stored in VPPn discharged in the energy market during time period t; This represents the electrical energy corresponding to the load in time period t (VPPn). This represents the electricity sold by VPPn during time period t; Will Substituting back into equation (15), the final formula for solving the payoff factor maximization problem is: (17); In the formula, It is the total cost of joint operation and optimized scheduling of multiple virtual power plants.

[0012] As a preferred embodiment of the present invention, the construction process of the multi-virtual power plant joint operation optimization scheduling solution model in S4 is as follows: S4.1 To solve the subproblem of minimizing social cost, we first construct its augmented Lagrangian function, introducing Lagrange multipliers and a penalty factor, thus obtaining the augmented Lagrangian function of the social cost minimization objective function model: (18); In the formula, L is the augmented Lagrangian function of the social cost minimization objective function model; The auxiliary variable introduced is the amount of electricity purchased and sold from the nth VPP to the kth VPP during time period t. For Lagrange multipliers; As a penalty factor; It is an L2 norm; All participating VPP entities contribute to forming a electricity price scheme that satisfies all parties. The constraints at this point are: (19); S4.2 Solve the subproblem of maximizing payment benefits and construct its augmented Lagrangian function: (20); In the formula, The auxiliary variable introduced is the purchase and sale price of electricity between the nth VPP and the kth VPP in time period t; This represents the electricity price when the nth VPP transmits electrical energy to the kth VPP during time period t; when At that time, all participating VPP entities will form a satisfactory electricity price scheme for all parties; S4.3. The solution model for the joint operation and optimal scheduling of multiple virtual power plants is obtained by synthesis: (twenty one).

[0013] As a preferred embodiment of the present invention, the solution process in S4 is as follows: Step 1: Initialize the number of iterations x=1, the maximum number of iterations is X, and the initial transaction price for each VPP is... Lagrange multipliers Set the convergence precision to Punishment factor The interactive electrical energy obtained from each VPP is then introduced; Step 2: For this combined power output system, receive the expected purchase and sale of electricity from VPPs in other regions. Thus, by solving equations (19) and (21), the planned electricity sales or purchases of the combined power system can be obtained. , These represent the planned electrical energy sold or purchased from the k-th VPP to the n-th VPP at the (x+1)-th and x-th iterations in time period t, respectively. Step 3: Update the Lagrange factor according to the following formula: (twenty two); In the formula, , Let these represent the Lagrange multipliers at the (x+1)th and xth iterations in time interval t, respectively; , These represent the electricity purchased and sold by the nth VPP to the kth VPP at the (x+1)th and xth iterations in time period t, respectively. Step 4: Update the iteration count x = x + 1; Step 5: Determine the convergence status of the ADMM algorithm using the following formula: (twenty three); If the above formula is satisfied, the iteration terminates; otherwise, return to step two and continue the loop until the convergence condition is met or the maximum number of iterations is exceeded. When the iteration terminates, output the interactive electrical energy of each VPP and the optimization target value during this optimization process.

[0014] The multi-virtual power plant optimization scheduling device based on controllable photovoltaic regulation and cooperative game theory includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The above-mentioned method is implemented by executing the computer program through the processor.

[0015] The beneficial effects of this invention are: This invention introduces controllable photovoltaic resources into the joint operation and scheduling method of multiple virtual power plants, which can effectively reduce the curtailment rate of the joint system during operation. Furthermore, the proposed model considers risk utility, making risk quantification more comprehensive. This enables VPPs to adapt to operation in various scenarios, promotes the implementation of direct VPP transactions, balances the benefits of regional power grids and the main grid, ensures that the system operates economically while improving the power curve tracking capability, and ultimately plays a positive role in promoting the construction of a new type of power system. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the principle of this invention; Figure 2 This is a schematic diagram of the multi-virtual power plant joint operation mechanism of the present invention; Figure 3 This is a complete flowchart of the operation of the present invention. Detailed Implementation

[0017] The embodiments of the present invention will be further described below with reference to the accompanying drawings: Example 1: As Figure 1 and Figure 2 As shown, the multi-virtual power plant optimal scheduling method based on controllable photovoltaic regulation and cooperative game theory includes the following steps: S1. Establish a joint operation mechanism for multiple virtual power plants: Set up a virtual power plant general operator to uniformly dispatch and manage each virtual power plant VPP, and provide electricity prices and demand to the power generation energy market and peak-shaving auxiliary service market under each VPP. Each VPP is equipped with its own virtual power plant operator, which internally aggregates flexible resources that can participate in regulation, and conducts price negotiation cooperation with other VPPs and adjusts its internal operation strategy in sync based on the issued market signals. S2. Based on rigid and flexible control methods for photovoltaic grid connection, establish a controllable photovoltaic resource model within the VPP; S3. Model load-type VPP, power-type VPP and integrated VPP respectively, and establish a multi-virtual power plant optimal scheduling model based on Nash negotiation; S4. Introduce the augmented Lagrangian function to establish a solution model for the joint operation and optimal scheduling of multiple virtual power plants, and use the ADMM distributed algorithm to obtain the optimal scheduling scheme.

[0018] The future electricity market will inevitably feature multiple different types of Virtual Power Plants (VPPs). As the electricity market deepens and diversifies, the division of labor and collaboration among various markets, such as the energy market, peak-shaving market, and ancillary services market, will become increasingly important. Considering the significant differences in the nature, dispatch characteristics, and response times of the resources aggregated by various VPPs, Virtual Power Plant Operators (VPPs) can not only increase their market share during the bidding stage by integrating multiple types of resources and cooperating with other VPP operators, but also complement each other's resource shortcomings and optimize their market participation strategies.

[0019] VPP operators internally aggregate flexible resources that can be regulated, and externally cooperate with other VPPs to participate in the electricity market and peak-shaving ancillary service market, meeting the internal power balance and profit optimization needs of VPPs. Each VPP receives electricity market and peak-shaving ancillary service market prices and demands from its superior operator, negotiates prices and cooperates with other VPPs, and adjusts its internal strategies to maximize overall and individual interests. The MVPP operator centrally manages and dispatches all VPPs.

[0020] Furthermore, while pursuing the maximization of its own interests, the VPP should also act as an energy manager internally and as a power plant externally, namely, meeting the power generation output requirements assigned by the upper-level power grid. Therefore, the interaction plan determined by the VPP recently cannot be changed after being submitted.

[0021] Currently, photovoltaic (PV) power generation systems mainly consist of three parts: solar cell modules, inverters, and controllers. Regarding the controller, by replacing the inverter-side equipment with new PV protocol converters and smart PV inverters, remote synchronous regulation of the PV equipment can be achieved through intelligent control. Smart PV inverters have protection functions such as overload, short circuit, over / under voltage, and anti-islanding protection, protecting lines and power equipment from damage. Through their equipped data acquisition system, they can exchange information on the grid-connected voltage, current, and power of distributed PV customers with the intelligent integrated terminal of the upper-level power grid via power line broadband carrier communication, and upload the data to the distribution energy internet cloud platform. This enables minute-level information monitoring and dispatching, allowing for tripping and closing control of PV circuit breakers and remote dynamic adjustment of the inverter's active and reactive power, achieving flexible and controllable management of PV users.

[0022] In S1, the VPP acts as the energy manager internally and meets the power generation output requirements of the upper-level power grid externally. Moreover, the interaction plan determined by the VPP cannot be changed after it is reported.

[0023] In S2, rigid control means that the connected photovoltaic devices can only operate in two states at any given time: on or off. This means the photovoltaic power station's output during that time period is either zero or full capacity. This control method is beneficial for handling sudden photovoltaic events and for protecting the photovoltaic inverter side when photovoltaic power is not generating electricity at night. However, this rigid on / off method may impact the power grid, potentially damage various components on the inverter side, and is more prone to curtailment. The photovoltaic output in this mode can be expressed by the following formula: (1); In the formula, The actual output of photovoltaic power after regulation; This is a rigid regulation and control state for photovoltaic systems. To maximize the output of photovoltaic power; Flexible control refers to a method where the output of connected photovoltaic (PV) devices within a given time period can be adjusted according to dispatch requirements, provided that the upper and lower limits of their PV processing capacity are met. This method is widely used in various optimized dispatching systems involving PV systems, and it facilitates the scheduling of PV power plants during operation planning and better addresses PV consumption issues. However, this method may lead to insufficient PV output when demand is high, as PV systems may need to meet other requirements, resulting in increased grid power purchases and higher electricity costs. The PV output in this mode can be expressed by the following formula: (2); In the formula, This is the flexible regulation and control state for photovoltaic systems. The controllable photovoltaic resource model within a VPP is based on rigid and flexible control methods. According to the specific operation plan of each time period and region, the photovoltaic power generation resources within the VPP are adjusted, thereby making the adjustment capabilities of each virtual power plant more flexible while meeting the requirements for renewable energy consumption capacity.

[0024] Because there are various types of Virtual Power Plants (VPPs) within a combined system, including load-type VPPs that only contain electricity consumption loads, power-generating VPPs that can only generate electricity, and hybrid VPPs that combine both generation and consumption functions, and considering the current early stage of electricity market development, this study will primarily focus on exploring the possibility of direct electricity trading between VPPs, fully tapping their potential, and achieving optimal profitability for themselves while cooperating with the grid to complete system regulation. The following sections will model the operational objectives of each of the three types of virtual power plants based on their internal resources, and establish an optimal dispatch model based on Nash negotiation.

[0025] In S3, the modeling process for load-type VPPs is as follows: The total revenue is greatest for load-type VPPs. To achieve the objective, define the objective function for the load-type VPP: (3); In the formula, This refers to the direct transaction revenue between VPPs (i.e., the revenue generated by the coordinated scheduling of resources within VPPs). For the revenue of the peak shaving market; For revenue from the electricity market; For load-type VPPs, the network access fee; The cost paid to flexible loads; The calculation method is as follows: (4); In the formula, , These represent the direct transaction electricity volume and direct transaction price between the i-th and j-th load-type VPPs in the time period, respectively. For the collection of VPPs (Vehicle Power Purchasers); For the collection of VPPs of the electricity purchaser; The calculation method is as follows: (5); In the formula, , These are the peak-shaving price and valley-filling price issued by the power trading center for time period t, respectively. , These represent the bidding capacity for VPP peak shaving and valley filling during time period t, respectively. Represents the discharge state (Boolean variable, the same applies to the rest), 1 indicates that it is in the discharge state, and 0 indicates that it is not in the discharge state; This represents the charging status; 1 indicates that the device is charging, and 0 indicates that it is not charging. , For Boolean variables, A value of 1 indicates that the load has been transferred in. A value of 1 indicates that the load has been transferred out. The bidding capacity for industrial load to participate in peak shaving and peak regulation within the VPP during time period t; The industrial load during period t represents the increased power consumption actively undertaken to facilitate peak shaving and valley filling. The bidding capacity for temperature-controlled loads within the VPP during time period t to participate in peak shaving and regulation; The bidding capacity for residential load to participate in peak shaving and regulation within the VPP during time period t; , These represent the charging and discharging amounts of electric vehicles in the energy market (c corresponds to charging, d corresponds to discharging, the same below). , These represent the charging and discharging volumes of electric vehicles in the peak shaving and valley filling markets, respectively. This represents the total discharge of electric vehicles in the energy market and peak shaving market during time period t. The total amount of electric vehicles charged in the energy market and the peak-shaving market during period t; The calculation method is as follows: (6); In the formula, The electricity price for VPP participation in the electricity market during period t; For time period t, the amount of electricity purchased by the VPP from the main grid is t; T is the total amount of electricity purchased during the time period. The calculation method is as follows: (7); In the formula, It is the regularization parameter, i.e., the penalty factor; , These are the line loss compensation conversion factors between the i-th and j-th VPPs, respectively; This is a set of VPPs who are potential participants in the negotiation process. This represents the j-th VPP, excluding the i-th VPP, that may participate in the negotiation. The calculation method is as follows: (8); In the formula, , , , These are the dispatch costs for electric vehicles, temperature-controlled loads, industrial loads, and residential loads, respectively. The electricity price charged by the VPP to the load; , These are the incentive electricity prices paid by the VPP to the load for peak shaving and valley filling. , These are the cost coefficients for dissatisfaction; The bidding capacity for residential load to participate in peak shaving and regulation within the VPP during time period t; This represents the power of electric vehicles participating in valley filling and peak shaving in discharge mode during time period t; The power of the temperature-controlled load during time period t.

[0026] In S3, the modeling process for a power supply-type VPP is as follows: Total revenue of power-type VPP With the goal of maximizing, establish the objective function: (9); In the formula, The direct revenue generated by the VPP from selling electricity to the upper-level power grid; This represents the total power generation cost of the VPP; The calculation method is as follows: (10); In the formula, Let t be the cost of electricity sales during period t; The electricity sold during period t; The calculation method is as follows: (11); In the formula, This represents the cost coefficient for thermal power generation. , These represent the electricity generated by thermal power plants participating in the peak-shaving market and the total electricity volume in time period t, respectively. Cost of power generation for photovoltaic systems; The photovoltaic power generation during time period t; , These represent the number of photovoltaic controllers with rigid regulation installed and the unit cost, respectively. , These represent the number of photovoltaic controllers with flexible control installed and the unit cost, respectively.

[0027] The modeling process for a comprehensive VPP is as follows: Total revenue of integrated VPP With the goal of maximizing, establish the objective function: (12); In the formula, The operating cost of energy storage in a comprehensive VPP mainly consists of the cost incurred due to charging and discharging losses. The calculation method is as follows: (13); In the formula, The battery loss cost coefficient per unit charge and discharge time; , These represent the charging and discharging power of the energy storage, respectively. , These refer to the charging and discharging power of energy storage in the energy market; , These represent the charging and discharging power of energy storage in the peak-shaving market.

[0028] The breakdown point of the Nash negotiation in the game between each VPP is taken as the individual planning cost of each VPP. That is, there is no power sharing among the VPPs. At this point, the planning cost corresponding to the power interaction between each VPP and the external power grid is the breakdown point of the Nash negotiation. The multi-virtual power plant optimal scheduling model based on Nash negotiation is expressed as follows: (14); In the formula, The cost of planning for the nth Nash negotiating entity is given by the VPP; N is the number of Nash negotiating entities. This represents the final quote for the nth VPP; This represents the thermal power generation in the integrated VPP during time period t; This represents the wind power generation in the integrated VPP during time period t; This represents the photovoltaic power generation in the integrated VPP during time period t; This represents the amount of energy storage discharged in the energy market during time period t in a comprehensive VPP. This represents the amount of energy storage charged in the energy market during time period t in a comprehensive VPP. This represents the electrical energy corresponding to the load in the integrated VPP during time period t; To represent the electricity sales volume in the integrated VPP during time period t; , These represent the electricity sold and purchased by a load-type VPP during time period t, respectively. , These represent the discharge and charging amounts of energy storage in the energy market during time period t; This represents the electrical energy corresponding to the load of the load-type VPP itself during time period t; This indicates the amount of electricity purchased by the power supply type VPP during time period t; This represents the thermal power generation in a power-type VPP during time period t; This represents the photovoltaic power generation in the power-type VPP during time period t; This represents the amount of energy stored in the power-type VPP discharged in the energy market during time period t; This represents the electrical energy transferred from the integrated VPP to the load-type VPP during time period t; This represents the electrical energy transferred from the integrated VPP to the power-type VPP during time period t; This represents the electrical energy transferred from the load-type VPP to the integrated VPP during time period t; This represents the electrical energy transferred from the load-type VPP to the power-type VPP during time period t; This represents the electrical energy transferred from the power-type VPP to the integrated VPP during time period t. This represents the electrical energy transferred from the power source VPP to the load VPP during time period t; Assume that the three Virtual Power Plants (VPPs) are independent, rational, and belong to different interest groups. Each VPP will strategically negotiate the electricity trading and pricing within the cooperative plan to formulate a reasonable and fair profit-sharing scheme, further reducing the overall planning cost. It is also assumed that through cooperative negotiations, each VPP can find a profit-sharing scheme that satisfies all parties, minimizing the planning costs for each VPP. A Nash equilibrium solution is obtained through a multi-virtual power plant optimization scheduling model, and this Nash equilibrium solution is used as the final energy trading and pricing strategy, thereby deriving the optimal solution for the configuration and operation optimization of each VPP. (15); In the formula, The initial quote for the nth VPP; The socially minimized cost is obtained based on equation (15). : (16); In the formula, This represents the planning cost of the nth VPP in time period t; k is also the index of the VPP, used to identify different VPPs, and together with n, it reflects the interaction relationship between VPPs. This represents the correlation coefficient of profit distribution from VPPn to VPPk during time period t; This represents the electricity purchased and sold by the nth VPP to the kth VPP during time period t; This represents the amount of electricity purchased by VPPn during time period t; This represents the photovoltaic power generation of VPPn during time period t; This represents the amount of energy stored in VPPn being charged in the energy market during time period t; This represents the amount of energy stored in VPPn discharged in the energy market during time period t; This represents the electrical energy corresponding to the load in time period t (VPPn). This represents the electricity sold by VPPn during time period t; Will Substituting back into equation (15), the final formula for solving the payoff factor maximization problem is: (17); In the formula, It is the total cost of joint operation and optimized scheduling of multiple virtual power plants.

[0029] Therefore, solving equation (15) is equivalent to solving the subproblem of maximizing payment benefits. At the same time, the electricity price of interactive electricity can be determined by solving the subproblem of maximizing payment benefits. Thus, equation (14) is equivalently transformed into two strictly convex subproblems: the social cost minimization equation (16) and the payment benefit maximization equation (17), which are solved.

[0030] In S4, the construction process of the multi-virtual power plant joint operation optimization scheduling solution model is as follows: S4.1 To solve the subproblem of minimizing social cost, we first construct its augmented Lagrangian function, introducing Lagrange multipliers and a penalty factor, thus obtaining the augmented Lagrangian function of the social cost minimization objective function model: (18); In the formula, L is the augmented Lagrangian function of the social cost minimization objective function model; The auxiliary variable introduced is the amount of electricity purchased and sold from the nth VPP to the kth VPP during time period t. For Lagrange multipliers; As a penalty factor; It is an L2 norm; All participating VPP entities contribute to forming a electricity price scheme that satisfies all parties. The constraints at this point are: (19); S4.2 Solve the subproblem of maximizing payment benefits and construct its augmented Lagrangian function: (20); In the formula, The auxiliary variable introduced is the purchase and sale price of electricity between the nth VPP and the kth VPP in time period t; This represents the electricity price when the nth VPP transmits electrical energy to the kth VPP during time period t; when At that time, all participating VPP entities will form a satisfactory electricity price scheme for all parties; S4.3. The solution model for the joint operation and optimal scheduling of multiple virtual power plants is obtained by synthesis: (twenty one).

[0031] In S4, the solution process is as follows: Step 1: Initialize the number of iterations x=1, the maximum number of iterations is X, and the initial transaction price for each VPP is... Lagrange multipliers Set the convergence precision to Punishment factor The interactive electrical energy obtained from each VPP is then introduced; Step 2: For this combined power output system, receive the expected purchase and sale of electricity from VPPs in other regions. Thus, by solving equations (19) and (21), the planned electricity sales or purchases of the combined power system can be obtained. , These represent the planned electrical energy sold or purchased from the k-th VPP to the n-th VPP at the (x+1)-th and x-th iterations in time period t, respectively. Step 3: Update the Lagrange factor according to the following formula: (twenty two); In the formula, , Let these represent the Lagrange multipliers at the (x+1)th and xth iterations in time interval t, respectively; , These represent the electricity purchased and sold by the nth VPP to the kth VPP at the (x+1)th and xth iterations in time period t, respectively. Step 4: Update the iteration count x = x + 1; Step 5: Determine the convergence status of the ADMM algorithm using the following formula: (twenty three); If the above formula is satisfied, the iteration terminates; otherwise, return to step two and continue the loop until the convergence condition is met or the maximum number of iterations is exceeded. When the iteration terminates, output the interactive electrical energy of each VPP and the optimization target value during this optimization process.

[0032] Based on the above, the complete operation flowchart of this embodiment is as follows: Figure 3 As shown.

[0033] In addition, corresponding constraints are set for each type of VPP, as follows: The constraints for a load-type VPP include: Market transaction constraints: (twenty four); In the formula, , These represent the amount of electricity purchased by the VPP in the electricity market and the maximum restricted amount of electricity purchased during time period t, respectively. Electric vehicle constraints: Assuming the electric vehicle has V2G functionality, including three states: charging, discharging, and off-grid driving, its operation model considers upper and lower power limits and state of charge constraints. (25); In the formula, , These are the state variables for charging and discharging electric vehicles, respectively. A value of 1 indicates that the electric vehicle is in a charging state. A value of 1 indicates that the electric vehicle is in a discharging state; , These represent the maximum charging and discharging power of the electric vehicle, respectively. Let be the total capacity of the battery of the z-th electric vehicle; Let be the battery charge of the z-th electric vehicle during time period t; , These are the upper and lower limits of the state of charge of the battery of the z-th electric vehicle, respectively. , These are the charging and discharging status indicators of the z-th electric vehicle at time periods t-1 and t, respectively. They are binary switch variables, where 1 represents the charging state and 0 represents the discharging state or idle state. Let Z be the expected state of charge of the battery of the z-th electric vehicle; The battery charge of the electric vehicle during time period T; The initial state of charge of the battery of the z-th electric vehicle; , These represent the remaining battery charge of the electric vehicle at time periods t and t-1, respectively. The charging and discharging coefficient for electric vehicles; The net charging and discharging power of the electric vehicle during time period t; This represents the charging status identifier of the electric vehicle cluster during time period t. The power output of the electric vehicle while it is in motion; For time intervals; Temperature control load constraints, mainly based on air conditioning load, and its operating model primarily considers power upper and lower limit constraints, regulation rate constraints, and indoor temperature constraints. (26); In the formula, , These are the upper and lower limits for adjusting the power of the temperature-controlled load during time period t; , Indoor temperatures during time period t and time period t-1, respectively; , These are the upper and lower limits of the ideal indoor temperature, respectively. For the thermal resistance of the house; The specific heat of air; The ambient temperature during time period t; This is the initial power of the temperature-controlled load; Industrial load constraints: The operating model considers upper and lower power limits, regulation rate constraints, and total energy constraints. (27); In the formula, The industrial load power after the response time period t; This represents the initial power of the industrial load. , These represent the industrial load transfer-in and transfer-out power during time period t; , These represent the upper limits for industrial load transfer-in and transfer-out during period t, respectively. This represents the total number of time periods within the industrial load scheduling cycle; it is necessary to ensure that the total transfer-in volume equals the total transfer-out volume within a day. Residential load constraints: (28); In the formula, This represents the upper limit for adjusting the residential load power during time period t; Initial power of residential load; The power balance constraint is: (29).

[0034] The constraints of a power supply-type VPP include: Market transaction constraints, taking into account the upper and lower limits of power purchases from the upper-level power grid: (30); In the formula, This represents the maximum electricity sales volume in the electricity market. This represents the maximum amount of electricity purchased from the electricity market. Constraints on thermal power generation mainly consider the constraints on output power: (31); In the formula, , These represent the minimum and maximum output power of the micro gas turbine, respectively. Peak shaving and regulation periods are set for the power trading center; Power balance constraints: (32); In the formula, Let t be the planned power generation amount allocated to the i-th power source VPP during time period t.

[0035] A comprehensive VPP is a virtual power plant that includes both power generation and load-side resources. It must simultaneously meet the constraints of both load-type and power generation-type VPPs, and also includes: Energy storage related constraints: (33); (34); In the formula, This represents the maximum discharge power of the stored energy. Maximum charging power of energy storage; , These are the charge and discharge coefficients when energy storage participates in the electricity market; , These are the charge and discharge coefficients when energy storage participates in the peak-shaving market; , These represent the state of charge at time points t and t-1, respectively. , These represent the charging and discharging efficiencies of energy storage, respectively. , These are the upper and lower limits of the state of charge of energy storage, respectively; This is a collection of peak-shaving periods; Power balance constraints: (35).

[0036] Example 2: A multi-virtual power plant optimization scheduling device based on controllable photovoltaic regulation and cooperative game theory, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The method in Example 1 is implemented by executing the computer program through the processor.

Claims

1. A multi-virtual power plant optimal scheduling method based on controllable photovoltaic regulation and cooperative game theory, characterized in that... Includes the following steps: S1. Establish a joint operation mechanism for multiple virtual power plants: Set up a virtual power plant general operator to uniformly dispatch and manage each virtual power plant VPP, and provide electricity prices and demand to the power generation energy market and peak-shaving auxiliary service market under each VPP. Each VPP is equipped with its own virtual power plant operator, which internally aggregates flexible resources that can participate in regulation, and conducts price negotiation cooperation with other VPPs and adjusts its internal operation strategy in sync based on the issued market signals. S2. Based on rigid and flexible control methods for photovoltaic grid connection, establish a controllable photovoltaic resource model within the VPP; S3. Model load-type VPP, power-type VPP and integrated VPP respectively, and establish a multi-virtual power plant optimal scheduling model based on Nash negotiation; S4. Introduce the augmented Lagrangian function to establish a solution model for the joint operation and optimal scheduling of multiple virtual power plants, and use the ADMM distributed algorithm to obtain the optimal scheduling scheme.

2. The multi-virtual power plant optimal scheduling method based on controllable photovoltaic regulation and cooperative game theory as described in claim 1, characterized in that: In S1, the VPP acts as an energy manager internally and meets the power generation output requirements of the upper-level power grid externally. Moreover, the interaction plan determined by the VPP in the previous period cannot be changed after it is reported.

3. The multi-virtual power plant optimal scheduling method based on controllable photovoltaic regulation and cooperative game theory as described in claim 1, characterized in that: In S2, rigid control means that the connected photovoltaic devices can only have two states at the same time period: on and off. That is, the output of the photovoltaic power station during that time period can only be zero or full power, as shown below: （1）； In the formula, The actual output of photovoltaic power after regulation; This is a rigid regulation and control state for photovoltaic systems. To maximize the output of photovoltaic power; Flexible control means that the output of each connected photovoltaic device within the same time period can be adjusted according to the scheduling requirements, provided that its upper and lower photovoltaic processing limits are met. This is expressed as: （2）； In the formula, This is the flexible regulation and control state for photovoltaic systems. The controllable photovoltaic resource model within a VPP is based on rigid and flexible control methods, adjusting the photovoltaic power generation resources within the VPP according to the specific operation plan for each time period and region.

4. The multi-virtual power plant optimal scheduling method based on controllable photovoltaic regulation and cooperative game theory as described in claim 1, characterized in that: In S3, the modeling process for the load-type VPP is as follows: The total revenue is greatest for load-type VPPs. To achieve the objective, define the objective function for the load-type VPP: （3）； In the formula, For direct transaction revenue between VPPs; For the revenue of the peak shaving market; For revenue from the electricity market; For load-type VPPs, the network access fee; The cost paid to flexible loads; The calculation method is as follows: （4）； In the formula, , These represent the direct transaction electricity volume and direct transaction price between the i-th and j-th load-type VPPs in the time period, respectively. For the collection of VPPs (Vehicle Power Purchasers); For the collection of VPPs of the electricity purchaser; The calculation method is as follows: （5）； In the formula, , These are the peak-shaving price and valley-filling price issued by the power trading center for time period t, respectively. , These represent the bidding capacity for VPP peak shaving and valley filling during time period t, respectively. Represents the discharge state, with 1 indicating that it is in the discharge state and 0 indicating that it is not in the discharge state; This represents the charging status; 1 indicates that the device is charging, and 0 indicates that it is not charging. , For Boolean variables, A value of 1 indicates that the load has been transferred in. A value of 1 indicates that the load has been transferred out. The bidding capacity for industrial load to participate in peak shaving and peak regulation within the VPP during time period t; The industrial load during period t represents the increased power consumption actively undertaken to facilitate peak shaving and valley filling. The bidding capacity for temperature-controlled loads within the VPP during time period t to participate in peak shaving and regulation; The bidding capacity for residential load to participate in peak shaving and regulation within the VPP during time period t; , These represent the charging and discharging volumes of electric vehicles in the energy market; , These represent the charging and discharging volumes of electric vehicles in the peak shaving and valley filling markets, respectively. This represents the total discharge of electric vehicles in the energy market and peak shaving market during time period t. The total amount of electric vehicles charged in the energy market and the peak-shaving market during period t; The calculation method is as follows: （6）； In the formula, The electricity price for VPP participation in the electricity market during period t; For time period t, the amount of electricity purchased by the VPP from the main grid is t; T is the total amount of electricity purchased during the time period. The calculation method is as follows: （7）； In the formula, It is the regularization parameter, i.e., the penalty factor; , These are the line loss compensation conversion factors between the i-th and j-th VPPs, respectively; This is a set of VPPs who are potential participants in the negotiation process. This represents the j-th VPP, excluding the i-th VPP, that may participate in the negotiation. The calculation method is as follows: （8）； In the formula, , , , These are the dispatch costs for electric vehicles, temperature-controlled loads, industrial loads, and residential loads, respectively. The electricity price charged by the VPP to the load; , These are the incentive electricity prices paid by the VPP to the load for peak shaving and valley filling. , These are the cost coefficients for dissatisfaction; The bidding capacity for residential load to participate in peak shaving and regulation within the VPP during time period t; This represents the power of electric vehicles participating in valley filling and peak shaving in discharge mode during time period t; The power of the temperature-controlled load during time period t.

5. The multi-virtual power plant optimal scheduling method based on controllable photovoltaic regulation and cooperative game theory according to claim 4, characterized in that: In S3, the modeling process for the power supply type VPP is as follows: Total revenue of power-type VPP With the goal of maximizing, establish the objective function: （9）； In the formula, The direct revenue generated by the VPP from selling electricity to the upper-level power grid; This represents the total power generation cost of the VPP; The calculation method is as follows: （10）； In the formula, Let t be the cost of electricity sales during period t; The electricity sold during period t; The calculation method is as follows: （11）； In the formula, This represents the cost coefficient for thermal power generation. , These represent the electricity generated by thermal power plants participating in the peak-shaving market and the total electricity volume in time period t, respectively. Cost of power generation for photovoltaic systems; The photovoltaic power generation during time period t; , These represent the number of photovoltaic controllers with rigid regulation installed and the unit cost, respectively. , These represent the number of photovoltaic controllers with flexible control installed and the unit cost, respectively.

6. The multi-virtual power plant optimal scheduling method based on controllable photovoltaic regulation and cooperative game theory as described in claim 5, characterized in that: In S3, the modeling process for the integrated VPP is as follows: Total revenue of integrated VPP With the goal of maximizing, establish the objective function: （12）； In the formula, The calculation method for the energy storage operating cost in a comprehensive VPP is as follows: （13）； In the formula, The battery loss cost coefficient per unit charge and discharge time; , These represent the charging and discharging power of the energy storage, respectively. , These refer to the charging and discharging power of energy storage in the energy market; , These represent the charging and discharging power of energy storage in the peak-shaving market.

7. The multi-virtual power plant optimal scheduling method based on controllable photovoltaic regulation and cooperative game theory as described in claim 6, characterized in that: In S3, the breakdown point of the Nash negotiation in the game between each VPP is the individual planning cost of each VPP. That is, there is no power sharing between the VPPs. At this time, the planning cost corresponding to the power interaction between each VPP and the external power grid is the breakdown point of the Nash negotiation. The multi-virtual power plant optimal scheduling model based on Nash negotiation is expressed as follows: （14）； In the formula, The cost of planning for the nth Nash negotiating entity is given by the VPP; N is the number of Nash negotiating entities. This represents the final quote for the nth VPP; This represents the thermal power generation in the integrated VPP during time period t; This represents the wind power generation in the integrated VPP during time period t; This represents the photovoltaic power generation in the integrated VPP during time period t; This represents the amount of energy storage discharged in the energy market during time period t in a comprehensive VPP. This represents the amount of energy storage charged in the energy market during time period t in a comprehensive VPP. This represents the electrical energy corresponding to the load in the integrated VPP during time period t; To represent the electricity sales volume in the integrated VPP during time period t; , These represent the electricity sold and purchased by a load-type VPP during time period t, respectively. , These represent the discharge and charging amounts of energy storage in the energy market during time period t; This represents the electrical energy corresponding to the load of the load-type VPP itself during time period t; This indicates the amount of electricity purchased by the power supply type VPP during time period t; This represents the thermal power generation in a power-type VPP during time period t; This represents the photovoltaic power generation in the power-type VPP during time period t; This represents the amount of energy stored in the power-type VPP discharged in the energy market during time period t; This represents the electrical energy transferred from the integrated VPP to the load-type VPP during time period t; This represents the electrical energy transferred from the integrated VPP to the power-type VPP during time period t; This represents the electrical energy transferred from the load-type VPP to the integrated VPP during time period t; This represents the electrical energy transferred from the load-type VPP to the power-type VPP during time period t; This represents the electrical energy transferred from the power-type VPP to the integrated VPP during time period t. This represents the electrical energy transferred from the power source VPP to the load VPP during time period t; Assume that the three Virtual Power Plants (VPPs) are independent, rational, and belong to different interest groups. Each VPP will strategically negotiate the electricity trading and pricing within the cooperative plan to formulate a reasonable and fair profit-sharing scheme, further reducing the overall planning cost. It is also assumed that through cooperative negotiations, each VPP can find a profit-sharing scheme that satisfies all parties, minimizing the planning costs for each VPP. A Nash equilibrium solution is obtained through a multi-virtual power plant optimization scheduling model, and this Nash equilibrium solution is used as the final energy trading and pricing strategy, thereby deriving the optimal solution for the configuration and operation optimization of each VPP. （15）； In the formula, The initial quote for the nth VPP; The socially minimized cost is obtained based on equation (15). : （16）； In the formula, This represents the planning cost of the nth VPP in time period t; k is also the index of the VPP, used to identify different VPPs, and together with n, it reflects the interaction relationship between VPPs. This represents the correlation coefficient of profit distribution from VPPn to VPPk during time period t; This represents the electricity purchased and sold by the nth VPP to the kth VPP during time period t; This represents the amount of electricity purchased by VPPn during time period t; This represents the photovoltaic power generation of VPPn during time period t; This represents the amount of energy stored in VPPn being charged in the energy market during time period t; This represents the amount of energy stored in VPPn discharged in the energy market during time period t; This represents the electrical energy corresponding to the load in time period t (VPPn). This represents the electricity sold by VPPn during time period t; Will Substituting back into equation (15), the final formula for solving the payoff factor maximization problem is: （17）； In the formula, It is the total cost of joint operation and optimized scheduling of multiple virtual power plants.

8. The multi-virtual power plant optimal scheduling method based on controllable photovoltaic regulation and cooperative game theory as described in claim 7, characterized in that: In S4, the construction process of the multi-virtual power plant joint operation optimization scheduling solution model is as follows: S4.1 To solve the subproblem of minimizing social cost, we first construct its augmented Lagrangian function, introducing Lagrange multipliers and a penalty factor, thus obtaining the augmented Lagrangian function of the social cost minimization objective function model: （18）； In the formula, L is the augmented Lagrangian function of the social cost minimization objective function model; The auxiliary variable introduced is the amount of electricity purchased and sold from the nth VPP to the kth VPP during time period t. For Lagrange multipliers; As a penalty factor; It is an L2 norm; All participating VPP entities contribute to forming a electricity price scheme that satisfies all parties. The constraints at this point are: （19）； S4.2 Solve the subproblem of maximizing payment benefits and construct its augmented Lagrangian function: （20）； In the formula, The auxiliary variable introduced is the purchase and sale price of electricity between the nth VPP and the kth VPP in time period t; This represents the electricity price when the nth VPP transmits electrical energy to the kth VPP during time period t; when At that time, all participating VPP entities will form a satisfactory electricity price scheme for all parties; S4.

3. The solution model for the joint operation and optimal scheduling of multiple virtual power plants is obtained by synthesis: （21）。 9. The multi-virtual power plant optimal scheduling method based on controllable photovoltaic regulation and cooperative game theory as described in claim 8, characterized in that: In S4, the solution process is as follows: Step 1: Initialize the number of iterations x=1, the maximum number of iterations is X, and the initial transaction price for each VPP is... Lagrange multipliers Set the convergence precision to Punishment factor The interactive electrical energy obtained from each VPP is then introduced; Step 2: For this combined power output system, receive the expected purchase and sale of electricity from VPPs in other regions. Thus, by solving equations (19) and (21), the planned electricity sales or purchases of the combined power system can be obtained. , These represent the planned electrical energy sold or purchased from the k-th VPP to the n-th VPP at the (x+1)-th and x-th iterations in time period t, respectively. Step 3: Update the Lagrange factor according to the following formula: （22）； In the formula, , Let these represent the Lagrange multipliers at the (x+1)th and xth iterations in time interval t, respectively; , These represent the electricity purchased and sold by the nth VPP to the kth VPP at the (x+1)th and xth iterations in time period t, respectively. Step 4: Update the iteration count x = x + 1; Step 5: Determine the convergence status of the ADMM algorithm using the following formula: （23）； If the above formula is satisfied, the iteration terminates; otherwise, return to step two and continue the loop until the convergence condition is met or the maximum number of iterations is exceeded. When the iteration terminates, output the interactive electrical energy of each VPP and the optimization target value during this optimization process.

10. A multi-virtual power plant optimization scheduling device based on controllable photovoltaic regulation and cooperative game theory, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the method described in any one of claims 1-9.

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