A multi-virtual power plant sequential game bidding method and device and a storage medium

Abstract

The present application belongs to the virtual power plant operation technical field, and relates to a kind of multi-virtual power plant sequential game bidding method, device and storage medium;The operation model of virtual power plant is constructed, and the operation cost calculation model of virtual power plant;Based on the clearing price of power market trading center, price penalty coefficient, the clearing electric quantity of virtual power plant when the k-1th iteration, the clearing price calculation model of virtual power plant when the k-1th iteration, the clearing price calculation model of power market trading center when the kth iteration are constructed;The bidding income calculation model of virtual power plant is constructed, based on the maximum difference of the sum of the bidding income of virtual power plant in clearing electric quantity bidding period and operation cost as the goal, the clearing electric quantity objective function is constructed;First clearing electric quantity constraint function and second clearing electric quantity constraint function are constructed;The clearing electric quantity objective function, first clearing electric quantity constraint function and second clearing electric quantity constraint function are solved, and the clearing electric quantity of virtual power plant is obtained.

Description

Technical Field

[0001] This invention relates to the field of virtual power plant operation technology, and in particular to a bidding method, apparatus and storage medium for sequential game of multiple virtual power plants. Background Technology

[0002] Due to the large number, small capacity, and irregular geographical distribution of distributed power sources, individual grid connection would face high costs and increase the difficulty of unified dispatch. To address this issue, researchers have proposed virtual power plants, which use modern information and control technologies to achieve unified management of distributed generator sets, wind and solar energy, and other distributed energy sources, further integrating them into the traditional power grid's operation system. The aggregation and coordinated management of different types of energy by virtual power plants significantly improves the grid connection efficiency of distributed energy sources and enhances the stability and profitability of grid-connected operation. Developing effective joint bidding strategies is crucial for the sustainable operation of virtual power plants. However, since virtual power plants belong to different stakeholders, designing joint bidding strategies that can both ensure electricity market stability and improve efficiency remains a significant challenge in the current operation of virtual power plants.

[0003] Existing technologies for optimizing bidding strategies for multiple virtual power plants treat virtual power plants as a single entity, constructing constraint functions and setting macro-level constraints such as upper and lower limits on the total cleared electricity volume. This ignores the differentiated operational constraints of individual virtual power plants, such as wind power units, leading to a final cleared electricity volume exceeding the actual operational capacity of each unit or failing to fully realize the combined operational potential of the units. Furthermore, existing technologies simulate virtual power plant revenue based on the average electricity price of the power market trading center, neglecting the differences in cleared electricity prices among different virtual power plants and the iterative updates of prices during the cleared electricity price confirmation process. This results in the revenue of virtual power plants failing to reflect the cleared electricity volume. The iterative changes in electricity volume and price affect the accuracy of the cleared electricity volume objective function, making it difficult to achieve the core objective of maximizing the difference between revenue and cost. In addition, existing technologies ignore the power deviation between the cleared electricity volume during virtual power plant bidding and the actual cleared electricity volume. When there is a deviation between the actual cleared electricity volume and the bid cleared electricity volume, it is impossible to dynamically adjust the cleared electricity volume of each virtual power plant to adapt to the deviation scenario. This not only reduces the operability of cleared electricity volume, but may also lead to the cleared electricity volume failing to meet the bid value due to the cleared electricity volume of some virtual power plants, resulting in the cleared electricity volume failing to meet the electricity demand required by the power market trading center, thereby affecting the stable operation of the power system.

[0004] In summary, existing bidding methods for multiple virtual power plants neglect the differentiated operational constraints of the internal devices of virtual power plants and the iterative characteristics of the electricity market clearing price. This leads to a discrepancy between the determined clearing volume and the actual operating capacity of the virtual power plants, making it difficult to achieve the economic goal of maximizing the difference between virtual market revenue and cost. Furthermore, the clearing volume cannot be dynamically adjusted, resulting in the actual clearing volume failing to meet market electricity demand and affecting the stable operation of the power system. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the problem that the existing multi-virtual power plant bidding method ignores the differentiated operation constraints of the internal devices of the virtual power plant and the iterative characteristics of the electricity market clearing price, which leads to the determination of the clearing volume not matching the actual operating capacity of the virtual power plant, making it difficult to achieve the economic goal of maximizing the difference between virtual market revenue and cost, and making it impossible to dynamically adjust the clearing volume, which in turn leads to the actual clearing volume failing to meet market electricity demand and affecting the stable operation of the power system.

[0006] To address the aforementioned technical problems, this invention provides a bidding method for a sequential game involving multiple virtual power plants, comprising: Based on the operational constraints of each device within each virtual power plant, an operational model for each virtual power plant is constructed; based on the operational models of each virtual power plant, an operational cost calculation model for each virtual power plant during the cleared electricity bidding period is constructed. Based on the clearing price, price penalty coefficient, and clearing volume of each virtual power plant at the power market trading center during the (k-1)th iteration of time period t, a calculation model for the clearing price of each virtual power plant at the (k-1)th iteration of time period t is constructed; based on the clearing price of each virtual power plant at the (k-1)th iteration of time period t, a calculation model for the clearing price of the power market trading center at the kth iteration of time period t is constructed. Based on the product of the cleared electricity price of the power market trading center at the end of the iteration of time period t and the cleared electricity volume of each virtual power plant, a bidding revenue calculation model for each virtual power plant in time period t is constructed; based on the objective of maximizing the difference between the sum of bidding revenue and operating cost of each virtual power plant in the cleared electricity bidding period, an objective function for the cleared electricity volume of each virtual power plant in the cleared electricity bidding period is constructed. Based on the premise that the cleared power of each virtual power plant in time period t is not less than 0 and not greater than the sum of the maximum output of all devices within each virtual power plant, a first cleared power constraint function is constructed for each virtual power plant; based on the premise that the sum of the cleared power of all virtual power plants in time period t is greater than or equal to the power consumption of the power market trading center, a second cleared power constraint function is constructed for each virtual power plant. Solve the objective function, the first clearing power constraint function, and the second clearing power constraint function for each virtual power plant to obtain the clearing power of each virtual power plant during the bidding period.

[0007] Preferably, based on the operational constraints of each device within each virtual power plant, an operational model for each virtual power plant is constructed, including: Construct output power constraints for wind power generation devices within each virtual power plant; Based on the fact that the sum of the power generation of each segment of the gas turbine in each virtual power plant during the operating period is equal to the output power of the gas turbine, and that the gas turbine is only in the start-up state or the shut-down state at the same time, gas turbine operation constraints are constructed. Construct interruption load constraints for each virtual power plant; Establish storage capacity constraints for energy storage devices within each virtual power plant.

[0008] Preferably, the output power constraint of the wind power generation device in each virtual power plant is expressed as follows: , in, This represents the output power of the wind power generation device in the i-th virtual power plant during time period t; This represents the maximum output power of the wind power generation device in the i-th virtual power plant; The operating constraints of a gas turbine are expressed as follows: , , , , , , , in, This represents the output power of the gas turbine in the i-th virtual power plant during time period t; This indicates the number of segments that represent the output power of the gas turbine. This represents the power generation of the gas turbine in the i-th virtual power plant during time period t in the m-th segment; This represents the maximum power generation of the m-th segment of the gas turbine within the i-th virtual power plant; This represents the maximum output power of the gas turbine in the i-th virtual power plant during time period t; This represents the maximum downhill output of the gas turbine in the i-th virtual power plant during adjacent operating cycles; This represents the maximum ramp output of the gas turbine in the i-th virtual power plant within an adjacent operating cycle; This is a binary variable representing the start-up and shutdown status of the gas turbine in the i-th virtual power plant during time period t; , Let $\mathbf{i}$ and $\mathbf{i}$ represent the start-up and shutdown state variables of the gas turbine in the i-th virtual power plant during time period $t$, respectively. , This indicates that the gas turbine in the i-th virtual power plant starts up during time period t; , This indicates that the gas turbine in the i-th virtual power plant is shut down during time period t; The interruption load constraints for each virtual power plant are expressed as follows: , , , in, This represents the interruption load of the i-th virtual power plant during time period t; Indicates the number of interruption load levels; This represents the level r interruption load of the i-th virtual power plant during time period t; This represents the upper limit of the r-level interruption load for the i-th virtual power plant; This indicates the maximum amount of interrupted load that can be called up within a continuous time period of the virtual power plant; The energy storage capacity constraints of the energy storage devices within each virtual power plant are expressed as follows: , , , , in, This represents the stored energy of the energy storage device in the i-th virtual power plant during time period t; This represents the minimum energy storage capacity of the energy storage device within the i-th virtual power plant; This represents the maximum energy storage capacity of the energy storage device within the i-th virtual power plant; This represents the input power of the energy storage device in the i-th virtual power plant during time period t; This represents the maximum acceptable input power of the energy storage device within the i-th virtual power plant during time period t; This represents the output power of the energy storage device in the i-th virtual power plant during time period t; This represents the maximum output power of the energy storage device in the i-th virtual power plant during time period t; Indicates the energy input efficiency of the energy storage device; This indicates the power output efficiency of the energy storage device.

[0009] Preferably, the clearing price calculation model for each virtual power plant in the (k-1)th iteration of time period t is as follows: , , in, This represents the clearing price of the i-th virtual power plant during the (k-1)th iteration in time period t; This represents the clearing price of the electricity market trading center during the (k-1)th iteration in time period t; Indicates the penalty coefficient for price changes; This represents the ratio of the maximum output of the i-th virtual power plant in time period t without considering battery output limitations to the maximum output with battery output limitations. Indicates the number of virtual power plants; The power market trading center's clearing price calculation model for the k-th iteration in time period t is as follows: , in, This represents the clearing price of the electricity market trading center at the k-th iteration in time period t.

[0010] Preferably, the objective function for clearing electricity from each virtual power plant is expressed as: , , , , , in, Let i represent the objective function for clearing electricity from the i-th virtual power plant; Indicates the bidding period for cleared electricity volume; This represents the operating cost of the gas turbine in the i-th virtual power plant during time period t; This represents the demand response penalty cost of the i-th virtual power plant during time period t; This represents the energy storage cost of the i-th virtual power plant during time period t; This represents the cost of wind power generation for the i-th virtual power plant during time period t; This represents the clearing price of the electricity market trading center at the end of the iteration in time period t; This represents the cleared electricity volume of the i-th virtual power plant during time period t; The cost factor representing the output power of the gas turbine in the virtual power plant; This represents the interruption cost coefficient of the virtual power plant during time period t; This represents the cost factor for the input electricity of the energy storage device within the virtual power plant; The cost factor represents the output power of the energy storage device within the virtual power plant; The cost factor representing the output power of the wind power generation unit within the virtual power plant; The first clearing power constraint function for each virtual power plant is expressed as follows: , The second clearing power constraint function for each virtual power plant is expressed as follows: , in, This represents the electricity consumption of the power market trading center during time period t.

[0011] Preferably, the alternating multiplier method is used to solve the cleared power objective function, the first cleared power constraint function, and the second cleared power constraint function for each virtual power plant, specifically including: Step 1: Augment the cleared power objective function of each virtual power plant, initialize the cleared power of all virtual power plants, and set the bidding order of virtual power plants based on sequential game theory. Step 2: Under the bidding order of the virtual power plants, based on the operating parameters, Lagrange multipliers and penalty factors of each device in each virtual power plant at the (k-1)th iteration of time period t within the clearing power bidding period, solve for the operating parameters of each device in each virtual power plant at the kth iteration of time period t. Step 3: Under the bidding order of the virtual power plants, based on the operating parameters of each device in each virtual power plant at the kth iteration of time period t, solve for the Lagrange multipliers of each virtual power plant at the kth iteration of time period t. Step 4: Under the bidding order of virtual power plants, based on the operating parameters and Lagrange multipliers of each device in each virtual power plant at the k-th iteration in time period t, solve for the penalty factor of each virtual power plant at the k-th iteration. Step 5: Based on the operating parameters of each device in each virtual power plant at the k-th iteration of time period t, obtain the cleared power of each virtual power plant at the k-th iteration of time period t; Step 6: Based on the cleared power of each virtual power plant at the k-th iteration of time period t, calculate the value of the objective function of the cleared power of each virtual power plant during the bidding period of cleared power at the end of the k-th iteration, and determine whether the cleared power of each virtual power plant at the k-th iteration of time period t satisfies the first cleared power constraint function and the second cleared power constraint function. Step 7: If the value of the cleared power objective function of each virtual power plant during the cleared power bidding period at the end of the k-th iteration is greater than the value of the cleared power objective function of each virtual power plant during the cleared power bidding period at the end of the (k-1)-th iteration, or if the cleared power of each virtual power plant at the k-th iteration of time period t does not satisfy the first cleared power constraint function and / or the second cleared power constraint function, then update k=k+1 and return to execute step 2 until the value of the cleared power objective function of each virtual power plant during the cleared power bidding period at the end of the k-th iteration converges, and the cleared power of each virtual power plant at the k-th iteration of time period t satisfies the first cleared power constraint function and the second cleared power constraint function, then obtain the cleared power of each virtual power plant at the end of the iteration of time period t.

[0012] Preferably, the objective function for the cleared power of each virtual power plant after augmentation is expressed as: , in, Let represent the clearing power objective function of the i-th virtual power plant after augmentation; Represents the Lagrange multiplier of the i-th virtual power plant in time period t; Let represent the penalty factor for the i-th virtual power plant; This represents the operating parameters of each device within the i-th virtual power plant.

[0013] Preferably, the calculation formula for the operating parameters of each device in each virtual power plant during the k-th iteration in time period t is as follows: , in, This represents the operating parameters of each device within each virtual power plant during the k-th iteration in time period t. This represents the Lagrange multiplier of each virtual power plant during the (k-1)th iteration in time period t; This represents the penalty factor for each virtual power plant during the (k-1)th iteration in time period t; This represents the operating parameters of each device within each virtual power plant during the (k-1)th iteration in time period t. The formula for calculating the Lagrange multipliers of each virtual power plant in the k-th iteration of time period t is: , in, Represents the Lagrange multipliers of each virtual power plant at the k-th iteration in time period t; The formula for calculating the penalty factor for each virtual power plant in the k-th iteration is: , in, This represents the penalty factor for each virtual power plant during the k-th iteration in time period t.

[0014] The present invention also provides a bidding device for sequential game of multiple virtual power plants, comprising: The model building module is used to build the operation model of each virtual power plant based on the operation constraints of each device in each virtual power plant; and based on the operation model of each virtual power plant, to build the operation cost calculation model of each virtual power plant during the cleared electricity bidding period. The clearing price calculation module is used to construct a calculation model for the clearing price of each virtual power plant in the (k-1)th iteration of the power market trading center during the clearing electricity bidding period, based on the clearing price of the power market trading center, the price penalty coefficient, and the clearing electricity of each virtual power plant in the (k-1)th iteration of the clearing electricity bidding period; and to construct a calculation model for the clearing price of the power market trading center in the (k)th iteration of the power market trading center based on the clearing price of each virtual power plant in the (k-1)th iteration of the power market trading center during the (k)th iteration of the power market trading center. The objective function construction module is used to construct a bidding revenue calculation model for each virtual power plant in time period t based on the product of the cleared electricity price of the power market trading center at the end of the iteration in time period t and the cleared electricity volume of each virtual power plant; and to construct the cleared electricity volume objective function of each virtual power plant in the cleared electricity bidding period based on the objective of maximizing the difference between the sum of bidding revenue and operating cost of each virtual power plant in the cleared electricity bidding period. The constraint function construction module is used to construct the first clearing power constraint function for each virtual power plant based on the fact that the clearing power of each virtual power plant in time period t is not less than 0 and not greater than the sum of the maximum output of all devices within each virtual power plant; and to construct the second clearing power constraint function for each virtual power plant based on the fact that the sum of the clearing power of all virtual power plants in time period t is greater than or equal to the power consumption of the power market trading center. The cleared power acquisition module is used to solve the cleared power objective function, the first cleared power constraint function and the second cleared power constraint function for each virtual power plant, so as to obtain the cleared power of each virtual power plant during the cleared power bidding period.

[0015] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the bidding method for the multi-virtual power plant sequential game described above.

[0016] The bidding method for multi-virtual sequential games provided in this application has the following beneficial effects: This application constructs a virtual power plant operation model based on the operational constraints of each device within the virtual power plant, and then builds an operation cost calculation model that considers the differences in device operation. Simultaneously, it achieves iterative updates of the electricity market clearing price through a tiered pricing model, restoring the actual characteristics of the electricity market clearing price being repeatedly iterated and dynamically confirmed based on the clearing volume of each virtual power plant. An objective function is constructed with the goal of maximizing the difference between the sum of bidding revenue and operating costs across all time periods. This deeply couples the iteratively accurate clearing price, the device-level calculated operating costs, and the clearing volume, enabling the objective function to reflect the joint changes of these three factors in real time. Finally, the clearing volume of the virtual power plant is determined through iterative solving and constraint verification. During the solution process, each iteration is based on the clearing data of the previous iteration. The parameters for updating electricity prices and cleared power output results are dynamically fine-tuned multiple times to effectively address potential discrepancies between the bid-based cleared power output and the actual cleared power output. Furthermore, a first cleared power output constraint function constrains the cleared power output of each device within a single virtual power plant, while a second cleared power output constraint function ensures that the cleared power output of all virtual power plants always meets electricity demand. This ensures that the final determined cleared power output not only adapts to the design and operating status of each device within the virtual power plant but also avoids the problem of actual power output falling short of standards through dynamic adjustments. Consequently, the cleared power output of each virtual power plant maximizes revenue while adapting to the operating status of its internal devices and meeting the electricity demand of the consumer side, ensuring the stable operation of the power system. Attached Figure Description

[0017] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein: Figure 1 A flowchart of the bidding method for the multi-virtual power plant sequential game provided in this application; Figure 2 A schematic diagram of energy and information flow in multiple virtual power plants under sequential game theory provided for this application; Figure 3 This application provides a 24-hour load diagram of a virtual power plant. Figure 4 Schematic diagrams of the operating parameters of the wind power output devices of various virtual power plants provided for this application; wherein, Figure 4 (a) in the diagram is a schematic diagram of the predicted wind power output of each virtual power plant over 24 hours. Figure 4 (b) in the figure is a schematic diagram of the predicted maximum output of wind turbines for each virtual power plant over 24 hours. Figure 4 (c) in the figure is a schematic diagram of the reference electricity price of the 24-hour electricity market trading center; Figure 5 The schematic diagram of the operating parameters of each virtual power plant under the sequential game provided in this application; wherein, Figure 5(a) in the diagram is a schematic diagram of the gas turbine output of each virtual power plant. Figure 5 (b) in the diagram is a schematic diagram of the wind turbine output of each virtual power plant. Figure 5 (c) in the diagram is a schematic diagram of the charging and discharging of batteries in each virtual power plant. Figure 5 (d) in the diagram is a schematic diagram of the changes in battery energy storage in each virtual power plant. Figure 5 (e) in the diagram represents the 24-hour clearing power volume of each virtual power plant. Figure 5 (f) in the figure is a schematic diagram of the 24-hour clearing volume of the electricity market trading center; Figure 6 A schematic diagram illustrating the iterative changes in revenue of each virtual power plant as electricity prices occur, as provided in this application. Figure 7 A schematic diagram of the operating parameters of each virtual power plant under another game theory approach provided in this application; Figure 7 (a) in the diagram is a schematic diagram of the gas turbine output of each virtual power plant. Figure 7 (b) in the diagram is a schematic diagram of the wind power output of each virtual power plant. Figure 7 (c) in the diagram is a schematic diagram of the energy storage charging and discharging of each virtual power plant. Figure 7 (d) in the diagram is a schematic diagram of the energy storage changes of each virtual power plant. Figure 7 (e) in the diagram represents the output of each virtual power plant. Figure 7 (f) in the diagram is a schematic diagram of the clearing price of electricity in the electricity market trading center. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0019] Please see Figure 1 , Figure 1 The diagram shows the bidding method for the multi-virtual power plant sequential game provided in this application, which includes steps S10 to S50: S10: Based on the operational constraints of each device within each virtual power plant, construct the operational model of each virtual power plant; based on the operational models of each virtual power plant, construct the operational cost calculation model of each virtual power plant during the cleared electricity bidding period.

[0020] S20: Based on the clearing price, price penalty coefficient, and clearing volume of each virtual power plant at the power market trading center during the (k-1)th iteration of the clearing volume bidding period, construct a calculation model for the clearing price of each virtual power plant at the (k-1)th iteration of the t-th period; based on the clearing price of each virtual power plant at the (k-1)th iteration of the t-th period, construct a calculation model for the clearing price of the power market trading center at the k-th iteration of the t-th period.

[0021] S30: Based on the product of the cleared electricity price of the power market trading center at the end of the iteration of time period t and the cleared electricity volume of each virtual power plant, construct a bidding revenue calculation model for each virtual power plant in time period t; based on the objective of maximizing the difference between the sum of bidding revenue and operating cost of each virtual power plant in the cleared electricity bidding period, construct an objective function for the cleared electricity volume of each virtual power plant in the cleared electricity bidding period.

[0022] S40: Based on the premise that the cleared power of each virtual power plant in time period t is not less than 0 and not greater than the sum of the maximum output of all devices within each virtual power plant, construct the first cleared power constraint function for each virtual power plant; based on the premise that the sum of the cleared power of all virtual power plants in time period t is greater than or equal to the power consumption of the power market trading center, construct the second cleared power constraint function for each virtual power plant.

[0023] S50: Solve the cleared power objective function, the first cleared power constraint function and the second cleared power constraint function for each virtual power plant to obtain the cleared power of each virtual power plant during the cleared power bidding period.

[0024] Furthermore, based on the operational constraints of each device within each virtual power plant, an operational model for each virtual power plant is constructed, including S100~S103: S100: Construct output power constraints for wind power generation devices within each virtual power plant.

[0025] Specifically, the output power constraints of the wind power generation devices within each virtual power plant are expressed as follows: , in, This represents the output power of the wind power generation device in the i-th virtual power plant during time period t; This represents the maximum output power of the wind power generation device in the i-th virtual power plant.

[0026] S101: Based on the fact that the sum of the power generation of each segment of the gas turbine in each virtual power plant during the operating period is equal to the output power of the gas turbine, and that the gas turbine is only in the start-up state or the shut-down state during the same period, construct the gas turbine operation constraints.

[0027] Specifically, the operating constraints of the gas turbine are expressed as follows: , , , , , , , in, This represents the output power of the gas turbine in the i-th virtual power plant during time period t; This indicates the number of segments that represent the output power of the gas turbine. This represents the power generation of the gas turbine in the i-th virtual power plant during time period t in the m-th segment; This represents the maximum power generation of the m-th segment of the gas turbine within the i-th virtual power plant; This represents the maximum output power of the gas turbine in the i-th virtual power plant during time period t; This represents the maximum downhill output of the gas turbine in the i-th virtual power plant during adjacent operating cycles; This represents the maximum ramp output of the gas turbine in the i-th virtual power plant within an adjacent operating cycle; This is a binary variable representing the start-up and shutdown status of the gas turbine in the i-th virtual power plant during time period t; , Let $\mathbf{i}$ and $\mathbf{i}$ represent the start-up and shutdown state variables of the gas turbine in the i-th virtual power plant during time period $t$, respectively. , This indicates that the gas turbine in the i-th virtual power plant starts up during time period t; , This indicates that the gas turbine in the i-th virtual power plant is shut down during time period t.

[0028] S102: Construct interruption load constraints for each virtual power plant.

[0029] Specifically, the interruption load constraints for each virtual power plant are expressed as follows: , , , in, This represents the interruption load of the i-th virtual power plant during time period t; Indicates the number of interruption load levels; This represents the level r interruption load of the i-th virtual power plant during time period t; This represents the upper limit of the r-level interruption load for the i-th virtual power plant; This indicates the maximum amount of interrupted load that can be called up within a continuous time period of the virtual power plant.

[0030] S103: Construct the energy storage capacity constraints for energy storage devices within each virtual power plant.

[0031] Specifically, the energy storage capacity constraint of the energy storage devices within each virtual power plant is expressed as follows: , , , , in, This represents the stored energy of the energy storage device in the i-th virtual power plant during time period t; This represents the minimum energy storage capacity of the energy storage device within the i-th virtual power plant; This represents the maximum energy storage capacity of the energy storage device within the i-th virtual power plant; This represents the input power of the energy storage device in the i-th virtual power plant during time period t; This represents the maximum acceptable input power of the energy storage device within the i-th virtual power plant during time period t; This represents the output power of the energy storage device in the i-th virtual power plant during time period t; This represents the maximum output power of the energy storage device in the i-th virtual power plant during time period t; Indicates the energy input efficiency of the energy storage device; This indicates the power output efficiency of the energy storage device.

[0032] Furthermore, the clearing price calculation model for each virtual power plant in the (k-1)th iteration of time period t is as follows: , , in, This represents the clearing price of the i-th virtual power plant during the (k-1)th iteration in time period t; This represents the clearing price of the electricity market trading center during the (k-1)th iteration in time period t; Indicates the penalty coefficient for price changes; This represents the ratio of the maximum output of the i-th virtual power plant in time period t without considering battery output limitations to the maximum output with battery output limitations. This indicates the number of virtual power plants.

[0033] The power market trading center's clearing price calculation model for the k-th iteration in time period t is as follows: , in, This represents the clearing price of the electricity market trading center at the k-th iteration in time period t.

[0034] Furthermore, the objective function for clearing electricity from each virtual power plant is expressed as: , , , , , in, Let i represent the objective function for clearing electricity from the i-th virtual power plant; Indicates the bidding period for cleared electricity volume; This represents the operating cost of the gas turbine in the i-th virtual power plant during time period t; This represents the demand response penalty cost of the i-th virtual power plant during time period t; This represents the energy storage cost of the i-th virtual power plant during time period t; This represents the cost of wind power generation for the i-th virtual power plant during time period t; This represents the clearing price of the electricity market trading center at the end of the iteration in time period t; This represents the cleared electricity volume of the i-th virtual power plant during time period t; The cost factor representing the output power of the gas turbine in the virtual power plant; This represents the interruption cost coefficient of the virtual power plant during time period t; This represents the cost factor for the input electricity of the energy storage device within the virtual power plant; The cost factor represents the output power of the energy storage device within the virtual power plant; The cost factor representing the output power of the wind power generation unit within the virtual power plant.

[0035] The first clearing power constraint function for each virtual power plant is expressed as follows: , The second clearing power constraint function for each virtual power plant is expressed as follows: , in, This represents the electricity consumption of the power market trading center during time period t.

[0036] Furthermore, the alternating multiplier method is used to solve the cleared power objective function, the first cleared power constraint function, and the second cleared power constraint function for each virtual power plant, specifically including steps 1 to 7: Step 1: Augment the clearing power objective function of each virtual power plant, initialize the clearing power of all virtual power plants, and set the bidding order of virtual power plants based on sequential game theory.

[0037] Step 2: Under the bidding order of the virtual power plants, based on the operating parameters, Lagrange multipliers and penalty factors of each device in each virtual power plant at the (k-1)th iteration of time period t within the clearing power bidding period, solve for the operating parameters of each device in each virtual power plant at the kth iteration of time period t.

[0038] Step 3: Under the bidding order of virtual power plants, based on the operating parameters of each device in each virtual power plant at the kth iteration of time period t, solve for the Lagrange multipliers of each virtual power plant at the kth iteration of time period t.

[0039] Step 4: Under the bidding order of virtual power plants, based on the operating parameters and Lagrange multipliers of each device in each virtual power plant at the k-th iteration in time period t, solve for the penalty factor of each virtual power plant at the k-th iteration.

[0040] Step 5: Based on the operating parameters of each device in each virtual power plant at the k-th iteration of time period t, obtain the cleared power of each virtual power plant at the k-th iteration of time period t.

[0041] Step 6: Based on the cleared power of each virtual power plant at the k-th iteration of time period t, calculate the value of the objective function of the cleared power of each virtual power plant during the bidding period of cleared power at the end of the k-th iteration, and determine whether the cleared power of each virtual power plant at the k-th iteration of time period t satisfies the first cleared power constraint function and the second cleared power constraint function.

[0042] Step 7: If the value of the cleared power objective function of each virtual power plant during the cleared power bidding period at the end of the k-th iteration is greater than the value of the cleared power objective function of each virtual power plant during the cleared power bidding period at the end of the (k-1)-th iteration, or if the cleared power of each virtual power plant at the k-th iteration of time period t does not satisfy the first cleared power constraint function and / or the second cleared power constraint function, then update k=k+1 and return to execute step 2 until the value of the cleared power objective function of each virtual power plant during the cleared power bidding period at the end of the k-th iteration converges, and the cleared power of each virtual power plant at the k-th iteration of time period t satisfies the first cleared power constraint function and the second cleared power constraint function, then obtain the cleared power of each virtual power plant at the end of the iteration of time period t.

[0043] Specifically, the objective function for the cleared power volume of each virtual power plant after augmentation is expressed as follows: , in, Let represent the clearing power objective function of the i-th virtual power plant after augmentation; Represents the Lagrange multiplier of the i-th virtual power plant in time period t; Let represent the penalty factor for the i-th virtual power plant; This represents the operating parameters of each device within the i-th virtual power plant.

[0044] Specifically, the formula for calculating the operating parameters of each device in each virtual power plant during the k-th iteration in time period t is as follows: , in, This represents the operating parameters of each device within each virtual power plant during the k-th iteration in time period t. This represents the Lagrange multiplier of each virtual power plant during the (k-1)th iteration in time period t; This represents the penalty factor for each virtual power plant during the (k-1)th iteration in time period t; This represents the operating parameters of each device within each virtual power plant during the (k-1)th iteration in time period t.

[0045] The formula for calculating the Lagrange multipliers of each virtual power plant in the k-th iteration of time period t is: , in, Let represent the Lagrange multipliers of each virtual power plant at the k-th iteration in time period t.

[0046] The formula for calculating the penalty factor for each virtual power plant in the k-th iteration is: , in, This represents the penalty factor for each virtual power plant during the k-th iteration in time period t.

[0047] Furthermore, the formula for verifying the convergence of the objective function value of the cleared electricity for each virtual power plant during the cleared electricity bidding period at the end of the k-th iteration is expressed as follows: , This indicates the preset value.

[0048] Based on the bidding method for sequential multi-virtual power plant games provided in the above embodiments, this application also provides a bidding apparatus for sequential multi-virtual power plant games, comprising: The model building module is used to build the operation model of each virtual power plant based on the operation constraints of each device in each virtual power plant; and based on the operation model of each virtual power plant, to build the operation cost calculation model of each virtual power plant during the cleared electricity bidding period. The clearing price calculation module is used to construct a calculation model for the clearing price of each virtual power plant in the (k-1)th iteration of the power market trading center during the clearing electricity bidding period, based on the clearing price of the power market trading center, the price penalty coefficient, and the clearing electricity of each virtual power plant in the (k-1)th iteration of the clearing electricity bidding period; and to construct a calculation model for the clearing price of the power market trading center in the (k)th iteration of the power market trading center based on the clearing price of each virtual power plant in the (k-1)th iteration of the power market trading center during the (k)th iteration of the power market trading center. The objective function construction module is used to construct a bidding revenue calculation model for each virtual power plant in time period t based on the product of the cleared electricity price of the power market trading center at the end of the iteration in time period t and the cleared electricity volume of each virtual power plant; and to construct the cleared electricity volume objective function of each virtual power plant in the cleared electricity bidding period based on the objective of maximizing the difference between the sum of bidding revenue and operating cost of each virtual power plant in the cleared electricity bidding period. The constraint function construction module is used to construct the first clearing power constraint function for each virtual power plant based on the fact that the clearing power of each virtual power plant in time period t is not less than 0 and not greater than the sum of the maximum output of all devices within each virtual power plant; and to construct the second clearing power constraint function for each virtual power plant based on the fact that the sum of the clearing power of all virtual power plants in time period t is greater than or equal to the power consumption of the power market trading center. The cleared power acquisition module is used to solve the cleared power objective function, the first cleared power constraint function and the second cleared power constraint function for each virtual power plant, so as to obtain the cleared power of each virtual power plant during the cleared power bidding period.

[0049] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the bidding method for the multi-virtual power plant sequential game described above.

[0050] The bidding method for the sequential game of multiple virtual power plants described above will be further explained and illustrated through a specific example below.

[0051] like Figure 2 The diagram illustrates the energy and information flow of multiple virtual power plants under a sequential game theory approach, as provided in this embodiment. When considering the sequential game bidding problem involving multiple virtual power plants, this embodiment uses a single virtual power plant as the bidding entity for mathematical modeling. The main consideration among multiple virtual power plants is the mutual flow and sharing of bidding information. The interaction and strategy selection during the bidding process constitute the basic framework of the game. After reaching a transaction agreement, each virtual power plant transmits electricity to the power load or grid according to the agreement. Specifically, each virtual power plant proposes a corresponding bid price and power supply capacity based on its own costs, demand, and market environment. This process achieves effective resource allocation and trading through certain bidding rules. Under this framework, the optimal bidding problem is not merely an optimization problem for a single virtual power plant, but a collaborative optimization problem involving multiple virtual power plants. Each virtual power plant adjusts its behavior based on market feedback and the strategies of other virtual power plants during the bidding process, forming a complex game theory process. Therefore, solving the problem requires not only considering the optimal bidding strategy of a single virtual power plant, but also fully considering the interaction and interdependence between multiple virtual power plants, and ultimately achieving the optimal operation of the overall system while ensuring the stability of the electricity market.

[0052] like Figure 3 The diagram shown is a 24-hour load diagram of the virtual power plant provided in this application. It can be seen that the load is at its peak from 7:00 to 20:00, reaches its peak around 12:00 and then gradually decreases. The low-peak period is from 23:00 to 5:00 the next day. The overall load changes significantly over time.

[0053] like Figure 4 The diagram shows the operating parameters of the wind power output devices in each virtual power plant. Figure 4 (a) in the diagram is a schematic diagram of the predicted wind power output of each virtual power plant (VPP1, VPP2, VPP3) over 24 hours. Figure 4 (b) in the figure is a schematic diagram of the predicted maximum output of wind turbines for each virtual power plant over 24 hours. Figure 4 (c) in the figure is a schematic diagram of the reference electricity price of the 24-hour electricity market trading center.

[0054] Table 1 shows the relevant parameters of the virtual power plant VPP1: Table 1

[0055] Table 2 shows the relevant parameters inside the virtual power plant VPP2: Table 2

[0056] Table 3 shows the relevant internal parameters of the virtual power plant VPP3: Table 3

[0057] As shown in the table above, VPP2 has a larger gas turbine capacity than the other two power plants and a lower unit cost of power generation, but its fixed costs are higher. In the example, the three virtual power plants have the same level of wind power grid connection technology, and their unit wind curtailment penalty costs are the same. Overall, VPP2 is larger than the other two virtual power plants. This embodiment simulates the situation where virtual power plants of different capacities participate in a bidding game, making it more realistic.

[0058] The charge / discharge efficiencies of the batteries in the three virtual power plants are 0.98 / 0.98, 0.95 / 0.95, and 0.98 / 0.98, respectively. Considering the time required for the conversion between chemical energy and electrical energy in the energy storage battery, its charge / discharge power per unit time has a certain upper limit. The specific parameters of the batteries in the virtual power plants are shown in Table 4. Table 4

[0059] Demand response interruption penalties are divided into three levels based on interruption severity, with each level corresponding to a certain upper limit for interruption capacity. When calculating interruption capacity, the penalties are accumulated sequentially from highest to lowest level, and the cost of the interruption penalty increases with the interruption level. This penalty mechanism creates a constraint relationship with the virtual power plant's electricity sales, increasing the load party's weight in the game system and ensuring the stability of the virtual power plant's power supply to the load. The interruption capacity and interruption penalty cost for each level are shown in Table 5. Table 5

[0060] The payoffs of each virtual power plant under the sequential game are shown in Table 6: Table 6

[0061] like Figure 5 The diagram shows the operating parameters of each virtual power plant under a sequential game; where, Figure 5 (a) in the diagram is a schematic diagram of the gas turbine output of each virtual power plant. Figure 5 (b) in the diagram is a schematic diagram of the wind turbine output of each virtual power plant. Figure 5 (c) in the diagram is a schematic diagram of the charging and discharging of batteries in each virtual power plant. Figure 5 (d) in the diagram is a schematic diagram of the changes in battery energy storage in each virtual power plant. Figure 5 (e) in the diagram represents the 24-hour clearing power volume of each virtual power plant. Figure 5 (f) in the figure is a schematic diagram of the 24-hour clearing of electricity in the power market trading center.

[0062] Depend on Figure 5 As can be seen, under this example, the total revenue of the three virtual power plants is 1507.448 yuan, with VPP2 accounting for the largest share at 42.697%, followed by VPP3. Overall, the revenue is positively correlated with the overall scale of each power plant, which basically meets the requirement of reasonably allocating the clearing volume according to the output capacity of each virtual power plant under the sequential game.

[0063] from Figure 5 (c) Figure 5As shown in (d) of the diagram, during the initial calculation periods, since this period falls during off-peak electricity demand and the batteries still have surplus power, and given the inherent startup costs of gas turbines, it is less economical to put them into operation when power demand is low. Therefore, the batteries begin to discharge in the first few periods to supply power to the load. During this stage, wind resources are limited and electricity prices are low. To fully utilize wind energy, part of the wind power output is used to supply power to the load, while the surplus electricity is used to charge the batteries to store relatively cheap electricity. This stored electricity is then utilized when electricity prices and load demand are higher, indirectly increasing the value of wind power and thus improving the sales revenue of the virtual power plant. It also achieves peak shaving and valley filling to a certain extent, improving the application efficiency of the virtual power plant. At the same time, analysis of the wind power output diagram shows that the wind power output of each virtual power plant reaches its maximum output value as much as possible throughout the process. The surplus electricity is used to charge the batteries to avoid wind curtailment and waste, maximizing the utilization of wind energy and achieving reasonable wind energy consumption.

[0064] Secondly, during peak electricity consumption periods, when wind power and battery power cannot meet load demands, starting gas turbines to provide additional power is a better option than incurring high penalties for load interruption. Figure 5 As shown in (a), only the gas turbine unit of VPP2 starts during peak load. This is because the gas turbine unit of VPP2 has sufficient capacity and its unit cost of power generation is low at this time. Compared with starting multiple gas turbine units at the same time, its fixed operating costs will also cause additional expenses, which is not conducive to maximizing the overall benefits.

[0065] It can be observed that the output ratios of the larger VPP1 and VPP3 are relatively stable. VPP1 experiences greater output fluctuations during peak and off-peak electricity consumption periods, primarily serving a peak-shaving and valley-filling function to maintain overall output balance and stability, thus enhancing the system's overall ability to withstand load changes. Overall, the sequential game-based clearing process among the three virtual power plants maximizes the utilization of their resources. By comprehensively analyzing and comparing power supply and load demands across different time periods, and allocating generation plans among different power plants, the characteristics and advantages of each virtual power plant are leveraged, ensuring both power supply reliability and overall plant profitability.

[0066] Table 7 shows the revenue changes of each virtual power plant as electricity prices iterate: Table 7

[0067] Analyzing the table above, the electricity sales profits of the three virtual power plants do not change significantly with the number of electricity price iterations. VPP2, due to its larger capacity, received bids exceeding its available output, triggering a price penalty in the bidding mechanism. This led to a gradual decrease in its clearing price with each iteration. The other two virtual power plants were less affected, and VPP3's profits slightly rebounded upon clearing. The overall trend is as follows: Figure 6 As shown, this game theory model effectively suppresses the unbalanced power output relationship during the clearing of virtual power plants, coordinates the cooperation among various virtual power plants, and reduces the impact of oligopolies on the overall electricity price during the bidding process.

[0068] This embodiment also uses maximizing the revenue of a specific virtual power plant as the clearing objective, and re-analyzes the clearing situation of various virtual power plants. To explore the differences in virtual power plants of different sizes under this new clearing objective more deeply, two representative virtual power plants were selected for comparative analysis: VPP2 with a larger overall capacity and VPP1 with a smaller overall capacity. It is important to emphasize that in this analysis, the example data used remains unchanged, and only the solution objective in the solution process is adjusted. The previous pursuit of maximizing overall revenue is changed to maximizing the revenue of a single virtual power plant. Based on this, the changing patterns of the clearing state of different virtual power plants under the guidance of the new solution objective and the resulting impact are observed and systematically studied. The clearing results are shown in Table 8: Table 8

[0069] Simple analysis reveals that when clearing is targeted at maximizing the profit of VPP1 or VPP2, the corresponding virtual power plant profits increase to varying degrees compared to clearing with the overall profit maximization target. However, overall electricity sales revenue declines significantly. When clearing is targeted at maximizing VPP2 profit, VPP3 incurs losses, and VPP1's electricity sales revenue is also low. If clearing is targeted at maximizing VPP1 profit, the clearing result is as follows: Figure 7 As shown, where, Figure 7 (a) in the diagram is a schematic diagram of the gas turbine output of each virtual power plant. Figure 7 (b) in the diagram is a schematic diagram of the wind power output of each virtual power plant. Figure 7 (c) in the diagram is a schematic diagram of the energy storage charging and discharging of each virtual power plant. Figure 7 (d) in the diagram is a schematic diagram of the energy storage changes of each virtual power plant. Figure 7 (e) in the diagram represents the output of each virtual power plant. Figure 7 (f) in the diagram is a schematic diagram of the clearing price of electricity in the electricity market trading center.

[0070] analyze Figure 7It is known that during the early morning off-peak period, VPP2 primarily discharges to meet load demand, while VPP1 and VPP3 charge at this time. Around midday, during the peak load period, all three virtual power plant energy storage facilities begin discharging, reducing the power supply pressure on the power plant during peak load times. From approximately 3 PM to 5 PM, VPP2 and VPP3 continue charging to prepare for discharging during the evening peak, ensuring load supply and avoiding economic losses due to load interruptions. From around 9 AM until the evening peak load period, due to the higher electricity price, the revenue from selling electricity is higher. The gas turbine unit of VPP1 is put into operation and reaches its maximum output after a two-hour ramp-up. Afterward, the gas turbine maintains maximum power operation to fully output electricity. After charging in the early morning, the batteries discharge during the midday peak, when electricity prices are highest, converting the low-priced wind energy of the early morning into high-priced electricity during the midday peak. After deducting energy losses and costs during charging and discharging, the value of the wind energy in the early morning is increased by approximately three times. Therefore, it can be seen that if there is sufficient wind energy during the off-peak hours of the bidding process, storing it in batteries and selling it when electricity prices are higher can significantly increase the revenue of virtual power plants.

[0071] Overall, under this game theory model, VPP1 clears more electricity than the three virtual power plants when the overall profit is maximized, and its output is more stable during peak electricity consumption periods. Compared to the parallel bidding situation of the three virtual power plants, the revenue of VPP2, which has a larger capacity, is significantly affected.

[0072] A sound joint bidding strategy is the foundation for the sustainable operation of virtual power plants. This application analyzes the necessary components in the bidding process of virtual power plants, including gas turbines, new energy sources, energy storage facilities, and load response. Taking into account practical applications, it establishes a corresponding mathematical model and proposes a game theory mode that can coordinate the output of various bidding virtual power plants, thereby reducing the risk of output instability caused by virtual power plants.

[0073] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0075] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0076] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0077] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A bidding method for a sequential game involving multiple virtual power plants, characterized in that, include: Based on the operational constraints of each device within each virtual power plant, an operational model for each virtual power plant is constructed. Based on the operation models of each virtual power plant, a calculation model for the operating costs of each virtual power plant during the bidding period for cleared electricity is constructed. Based on the clearing price, price penalty coefficient, and clearing volume of each virtual power plant at the power market trading center during the (k-1)th iteration of time period t, a calculation model for the clearing price of each virtual power plant at the (k-1)th iteration of time period t is constructed; based on the clearing price of each virtual power plant at the (k-1)th iteration of time period t, a calculation model for the clearing price of the power market trading center at the kth iteration of time period t is constructed. Based on the product of the cleared electricity price of the power market trading center at the end of the t-period iteration and the cleared electricity volume of each virtual power plant, a bidding revenue calculation model for each virtual power plant in the t-period is constructed. Based on the objective of maximizing the difference between the sum of bidding revenue and operating cost of each virtual power plant during the clearing power bidding period, an objective function for the clearing power of each virtual power plant during the clearing power bidding period is constructed. Based on the premise that the cleared power of each virtual power plant in time period t is not less than 0 and not greater than the sum of the maximum output of all devices within each virtual power plant, a first cleared power constraint function is constructed for each virtual power plant; based on the premise that the sum of the cleared power of all virtual power plants in time period t is greater than or equal to the power consumption of the power market trading center, a second cleared power constraint function is constructed for each virtual power plant. Solve the objective function, the first clearing power constraint function, and the second clearing power constraint function for each virtual power plant to obtain the clearing power of each virtual power plant during the bidding period.

2. The bidding method for sequential game theory involving multiple virtual power plants according to claim 1, characterized in that, Based on the operational constraints of each device within each virtual power plant, an operational model for each virtual power plant is constructed, including: Construct output power constraints for wind power generation devices within each virtual power plant; Based on the fact that the sum of the power generation of each segment of the gas turbine in each virtual power plant during the operating period is equal to the output power of the gas turbine, and that the gas turbine is only in the start-up state or the shut-down state at the same time, gas turbine operation constraints are constructed. Construct interruption load constraints for each virtual power plant; Establish storage capacity constraints for energy storage devices within each virtual power plant.

3. The bidding method for sequential game theory involving multiple virtual power plants according to claim 2, characterized in that, The output power constraints of the wind power generation devices in each virtual power plant are expressed as follows: ， in, This represents the output power of the wind power generation device in the i-th virtual power plant during time period t; This represents the maximum output power of the wind power generation device in the i-th virtual power plant; The operating constraints of a gas turbine are expressed as follows: ， ， ， ， ， ， ， in, This represents the output power of the gas turbine in the i-th virtual power plant during time period t; This indicates the number of segments that represent the output power of the gas turbine. This represents the power generation of the gas turbine in the i-th virtual power plant during time period t in the m-th segment; This represents the maximum power generation of the m-th segment of the gas turbine within the i-th virtual power plant; This represents the maximum output power of the gas turbine in the i-th virtual power plant during time period t; This represents the maximum downhill output of the gas turbine in the i-th virtual power plant during adjacent operating cycles; This represents the maximum ramp output of the gas turbine in the i-th virtual power plant within an adjacent operating cycle; This is a binary variable representing the start-up and shutdown status of the gas turbine in the i-th virtual power plant during time period t; , Let $\mathbf{i}$ and $\mathbf{i}$ represent the start-up and shutdown state variables of the gas turbine in the i-th virtual power plant during time period $t$, respectively. , This indicates that the gas turbine in the i-th virtual power plant starts up during time period t; , This indicates that the gas turbine in the i-th virtual power plant is shut down during time period t; The interruption load constraints for each virtual power plant are expressed as follows: ， ， ， in, This represents the interruption load of the i-th virtual power plant during time period t; Indicates the number of interruption load levels; This represents the level r interruption load of the i-th virtual power plant during time period t; This represents the upper limit of the r-level interruption load for the i-th virtual power plant; This indicates the maximum amount of interrupted load that can be called up within a continuous time period of the virtual power plant; The energy storage capacity constraints of the energy storage devices within each virtual power plant are expressed as follows: ， ， ， ， in, This represents the stored energy of the energy storage device in the i-th virtual power plant during time period t; This represents the minimum energy storage capacity of the energy storage device within the i-th virtual power plant; This represents the maximum energy storage capacity of the energy storage device within the i-th virtual power plant; This represents the input power of the energy storage device in the i-th virtual power plant during time period t; This represents the maximum acceptable input power of the energy storage device in the i-th virtual power plant during time period t; This represents the output power of the energy storage device in the i-th virtual power plant during time period t; This represents the maximum output power of the energy storage device in the i-th virtual power plant during time period t; Indicates the energy input efficiency of the energy storage device; This indicates the energy output efficiency of the energy storage device.

4. The bidding method for sequential game theory involving multiple virtual power plants according to claim 3, characterized in that, The clearing price calculation model for each virtual power plant in the (k-1)th iteration of time period t is as follows: ， ， in, This represents the clearing price of the i-th virtual power plant during the (k-1)th iteration in time period t; This represents the clearing price of the electricity market trading center during the (k-1)th iteration in time period t; Indicates the penalty coefficient for price changes; This represents the ratio of the maximum output of the i-th virtual power plant in time period t without considering battery output limitations to the maximum output with battery output limitations. Indicates the number of virtual power plants; The power market trading center's clearing price calculation model for the k-th iteration in time period t is as follows: ， in, This represents the clearing price of the electricity market trading center at the k-th iteration in time period t.

5. The bidding method for sequential game theory involving multiple virtual power plants according to claim 4, characterized in that, The objective function for clearing electricity from each virtual power plant is expressed as follows: ， ， ， ， ， in, Let i represent the objective function for clearing electricity from the i-th virtual power plant; Indicates the period for bidding on cleared electricity volume; This represents the operating cost of the gas turbine in the i-th virtual power plant during time period t; This represents the demand response penalty cost of the i-th virtual power plant during time period t; This represents the energy storage cost of the i-th virtual power plant during time period t; This represents the cost of wind power generation for the i-th virtual power plant during time period t; This represents the clearing price of the electricity market trading center at the end of the iteration in time period t; This represents the cleared electricity volume of the i-th virtual power plant during time period t; The cost factor representing the output power of the gas turbine in the virtual power plant; This represents the interruption cost coefficient of the virtual power plant during time period t; This represents the cost factor for the input electricity of the energy storage device within the virtual power plant; The cost factor represents the output power of the energy storage device within the virtual power plant; The cost factor representing the output power of the wind power generation unit within the virtual power plant; The first clearing power constraint function for each virtual power plant is expressed as follows: ， The second clearing power constraint function for each virtual power plant is expressed as follows: ， in, This represents the electricity consumption of the power market trading center during time period t.

6. The bidding method for sequential game theory involving multiple virtual power plants according to claim 5, characterized in that, The method of alternating multipliers is used to solve the cleared power objective function, the first cleared power constraint function, and the second cleared power constraint function for each virtual power plant. Specifically, this includes: Step 1: Augment the cleared power objective function of each virtual power plant, initialize the cleared power of all virtual power plants, and set the bidding order of virtual power plants based on sequential game theory. Step 2: Under the bidding order of the virtual power plants, based on the operating parameters, Lagrange multipliers and penalty factors of each device in each virtual power plant at the (k-1)th iteration of time period t within the clearing power bidding period, solve for the operating parameters of each device in each virtual power plant at the kth iteration of time period t. Step 3: Under the bidding order of the virtual power plants, based on the operating parameters of each device in each virtual power plant at the kth iteration of time period t, solve for the Lagrange multipliers of each virtual power plant at the kth iteration of time period t. Step 4: Under the bidding order of virtual power plants, based on the operating parameters and Lagrange multipliers of each device in each virtual power plant at the k-th iteration in time period t, solve for the penalty factor of each virtual power plant at the k-th iteration. Step 5: Based on the operating parameters of each device in each virtual power plant at the k-th iteration of time period t, obtain the cleared power of each virtual power plant at the k-th iteration of time period t; Step 6: Based on the cleared power of each virtual power plant at the k-th iteration of time period t, calculate the value of the objective function of the cleared power of each virtual power plant during the bidding period of cleared power at the end of the k-th iteration, and determine whether the cleared power of each virtual power plant at the k-th iteration of time period t satisfies the first cleared power constraint function and the second cleared power constraint function. Step 7: If the value of the cleared power objective function of each virtual power plant during the cleared power bidding period at the end of the k-th iteration is greater than the value of the cleared power objective function of each virtual power plant during the cleared power bidding period at the end of the (k-1)-th iteration, or if the cleared power of each virtual power plant at the k-th iteration of time period t does not satisfy the first cleared power constraint function and / or the second cleared power constraint function, then update k=k+1 and return to execute step 2 until the value of the cleared power objective function of each virtual power plant during the cleared power bidding period at the end of the k-th iteration converges, and the cleared power of each virtual power plant at the k-th iteration of time period t satisfies the first cleared power constraint function and the second cleared power constraint function, then obtain the cleared power of each virtual power plant at the end of the iteration of time period t.

7. The bidding method for sequential game theory involving multiple virtual power plants according to claim 6, characterized in that, The objective function for clearing electricity from each virtual power plant after augmentation is expressed as follows: ， in, Let represent the clearing power objective function of the i-th virtual power plant after augmentation; Represents the Lagrange multiplier of the i-th virtual power plant in time period t; Let represent the penalty factor for the i-th virtual power plant; This represents the operating parameters of each device within the i-th virtual power plant.

8. The bidding method for sequential game theory involving multiple virtual power plants according to claim 7, characterized in that, The formula for calculating the operating parameters of each device in each virtual power plant during the k-th iteration in time period t is as follows: ， in, This represents the operating parameters of each device within each virtual power plant during the k-th iteration in time period t. This represents the Lagrange multiplier of each virtual power plant during the (k-1)th iteration in time period t; This represents the penalty factor for each virtual power plant during the (k-1)th iteration in time period t; This represents the operating parameters of each device within each virtual power plant during the (k-1)th iteration in time period t. The formula for calculating the Lagrange multipliers of each virtual power plant in the k-th iteration of time period t is: ， in, Represents the Lagrange multipliers of each virtual power plant at the k-th iteration in time period t; The formula for calculating the penalty factor for each virtual power plant in the k-th iteration is: ， in, This represents the penalty factor for each virtual power plant during the k-th iteration in time period t.

9. A bidding device for sequential game of multiple virtual power plants, characterized in that, include: The model building module is used to build the operation model of each virtual power plant based on the operation constraints of each device in each virtual power plant; and based on the operation model of each virtual power plant, to build the operation cost calculation model of each virtual power plant during the cleared electricity bidding period. The clearing price calculation module is used to construct a calculation model for the clearing price of each virtual power plant in the (k-1)th iteration of the power market trading center during the clearing electricity bidding period, based on the clearing price of the power market trading center, the price penalty coefficient, and the clearing electricity of each virtual power plant in the (k-1)th iteration of the clearing electricity bidding period; and to construct a calculation model for the clearing price of the power market trading center in the (k)th iteration of the power market trading center based on the clearing price of each virtual power plant in the (k-1)th iteration of the power market trading center during the (k)th iteration of the power market trading center. The objective function construction module is used to construct a bidding revenue calculation model for each virtual power plant in time period t based on the product of the cleared electricity price of the power market trading center at the end of the iteration in time period t and the cleared electricity volume of each virtual power plant. Based on the objective of maximizing the difference between the sum of bidding revenue and operating cost of each virtual power plant during the clearing power bidding period, an objective function for the clearing power of each virtual power plant during the clearing power bidding period is constructed. The constraint function construction module is used to construct the first clearing power constraint function for each virtual power plant based on the fact that the clearing power of each virtual power plant in time period t is not less than 0 and not greater than the sum of the maximum output of all devices within each virtual power plant; and to construct the second clearing power constraint function for each virtual power plant based on the fact that the sum of the clearing power of all virtual power plants in time period t is greater than or equal to the power consumption of the power market trading center. The cleared power acquisition module is used to solve the cleared power objective function, the first cleared power constraint function and the second cleared power constraint function for each virtual power plant, so as to obtain the cleared power of each virtual power plant during the cleared power bidding period.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the bidding method for the multi-virtual power plant sequential game as described in any one of claims 1 to 8.

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