Virtual power plant optimal scheduling method, device, equipment and medium

Abstract

The embodiment of the invention provides a virtual power plant optimization scheduling method and device, equipment and a medium. The method comprises the following steps: when a virtual power plant is in a day-ahead stage, determining a power demand of a to-be-optimized time period and an operation parameter of the virtual power plant, and determining a power scheduling scheme of the virtual power plant in the to-be-optimized time period based on the power demand and the operation parameter; when the virtual power plant is in the intra-day stage, determining the actual active power of the target power generation equipment in a first reference time period for any one of a plurality of first reference time periods of the intra-day stage; and according to the actual active power and the declaration power corresponding to the target power generation equipment in the first reference time period, performing adjustment processing on the power scheduling scheme, and according to the adjusted power scheduling scheme, controlling the plurality of power generation equipment in the virtual power plant to output power. According to the method, a time scale scheduling method and low-carbon operation are combined, the system deviation of the day-ahead declaration output of the virtual power plant is reduced, and the operation cost of the virtual power plant is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of virtual power plant scheduling, and in particular to a virtual power plant optimization scheduling method, device, equipment and medium. Background Art

[0002] Virtual power plants can aggregate a variety of adjustable resources such as distributed resources, controllable loads, energy storage equipment, etc., and through coordinated control of these resources, optimized operation, and effective interaction with the power system, they can participate in the power market and power grid operation as a special power plant in a power coordination management system.

[0003] In recent years, multi-time scale optimization scheduling methods have been widely used in the field of virtual power plant optimization scheduling to deal with uncertainty factors. Multi-time scales mainly include the day-ahead stage, intraday stage and real-time stage.

[0004] However, there are currently few methods that combine low-carbon operation and multi-time scale optimization scheduling to comprehensively consider the scheduling instructions of virtual power plants. Summary of the invention

[0005] The embodiments of the present application provide a virtual power plant optimization scheduling method, device, equipment and medium. Through a multi-time scale scheduling model, coordinated control of multiple adjustable resources of the virtual power plant is carried out to achieve low-carbon operation, achieve supply and demand balance, reduce the system deviation of the virtual power plant's previously reported output, reduce its operating costs, and improve the power generation and consumption capacity of renewable energy.

[0006] In a first aspect, an embodiment of the present application provides a virtual power plant optimization scheduling method, including:

[0007] When the virtual power plant is in the day-ahead stage, determine the power demand of the time period to be optimized and the operating parameters of the virtual power plant, and determine the power dispatching scheme of the virtual power plant in the time period to be optimized based on the power demand and the operating parameters, wherein the power dispatching scheme is used to indicate the declared power corresponding to the multiple reference time periods in the intraday stage for the multiple power generation equipment in the virtual power plant;

[0008] When the virtual power plant is in the intraday stage, for any one of a plurality of first reference time periods in the intraday stage, determining the actual active power of the target power generation equipment in the first reference time period;

[0009] The power scheduling plan is adjusted according to the actual active power and the declared power corresponding to the target power generation equipment in the first reference time period, and the output of multiple power generation equipment in the virtual power plant is controlled according to the adjusted power scheduling plan. The target power generation equipment is any one of the multiple power generation equipment.

[0010] In a possible implementation, a virtual power plant optimization scheduling method further includes:

[0011] When the virtual power plant is in a real-time stage, for any one of a plurality of second reference time periods in the real-time stage, determining the actual active power of the target power generation equipment in the second reference time period;

[0012] Determine the deviation power of the target power generation equipment in the second reference time period according to the actual active power and the declared power corresponding to the target power generation equipment in the second reference time period;

[0013] Determine power adjustment information according to the deviation power and the complementary costs corresponding to the plurality of power generation devices, wherein the power adjustment information is used to indicate that the deviation power is allocated to at least one other power generation device for output;

[0014] According to the power adjustment information, the at least one other power generation device is controlled to perform a complement process on the deviation power.

[0015] In a possible implementation, determining a power dispatching scheme of the virtual power plant in the period to be optimized based on the power demand and the operating parameters includes:

[0016] Determine, according to the power demand and the operating parameters, the consumption information and revenue information of the virtual power plant in the period to be optimized, wherein the consumption information includes: the natural gas cost, the operation and maintenance costs of multiple power generation equipment, the electric vehicle compensation cost and the output deviation penalty cost in the period to be optimized, and the revenue information includes: the revenue from electricity sales and the revenue from carbon emissions;

[0017] According to the natural gas cost, operation and maintenance cost of multiple power generation equipment, electric vehicle compensation cost, output deviation penalty cost, electricity sales revenue and carbon emission revenue during the period to be optimized, the day-ahead scheduling optimization model is adopted to determine the power scheduling plan of the virtual power plant during the period to be optimized.

[0018] In a possible implementation, according to the natural gas cost, the operation and maintenance cost of multiple power generation equipment, the electric vehicle compensation cost, the output deviation penalty cost, the electricity sales income and the carbon emission income in the period to be optimized, a day-ahead scheduling optimization model is adopted to determine the power scheduling scheme of the virtual power plant in the period to be optimized, including:

[0019] According to the electricity sales revenue and carbon emission revenue, the day-ahead dispatch optimization model is adopted to determine the highest total revenue;

[0020] According to the natural gas cost, the operation and maintenance cost of multiple power generation equipment, the electric vehicle compensation cost, and the output deviation penalty cost, the output decision variables of the multiple power generation equipment are optimized to determine the power dispatch plan of the virtual power plant during the period to be optimized.

[0021] In a possible implementation, adjusting the power dispatching scheme according to the actual active power and the declared power corresponding to the target power generation equipment in the first reference time period includes:

[0022] Determine the cost deviation between the day-ahead phase and the intraday phase according to the actual active power of multiple historical first reference periods in the intraday phase;

[0023] The power dispatching plan is adjusted according to the cost and the declared power corresponding to the target power generation equipment in the first reference time period.

[0024] In a possible implementation manner, determining the deviation power of the target power generation equipment in the second reference time period according to the actual active power and the declared power corresponding to the target power generation equipment in the second reference time period further includes:

[0025] Determining the deviation power of the target power generation equipment in the second reference time period according to the actual active power and the declared power corresponding to the target power generation equipment in the second reference time period;

[0026] According to the deviation power in the second reference period and the equipment parameters of the target power generation equipment, a deviation penalty function is constructed to optimize the virtual power plant scheduling cost.

[0027] In a possible implementation, constructing a deviation penalty function according to the deviation power in the second reference period and the equipment parameters of the target power generation equipment to optimize the virtual power plant scheduling cost includes:

[0028] Determining a time decoupling load regulation cost according to the deviation power in the second reference period and the equipment parameters of the target power generation equipment;

[0029] According to the deviation power and time-decoupled load regulation cost within the second benchmark period, a deviation penalty function is constructed to optimize the virtual power plant scheduling cost.

[0030] In a second aspect, an embodiment of the present application provides a virtual power plant optimization scheduling device, including:

[0031] a determination module, configured to determine the power demand of the time period to be optimized and the operating parameters of the virtual power plant when the virtual power plant is in the day-ahead stage, and determine the power dispatching scheme of the virtual power plant in the time period to be optimized based on the power demand and the operating parameters, wherein the power dispatching scheme is used to indicate the declared powers corresponding to the multiple reference time periods in the intraday stage for the multiple power generation equipment in the virtual power plant;

[0032] an adjustment module, configured to determine, when the virtual power plant is in an intraday phase, for any first reference period of a plurality of first reference periods in the intraday phase, an actual active power of a target power generation device in the first reference period;

[0033] The adjustment module is also used to adjust the power scheduling plan according to the actual active power and the declared power corresponding to the target power generation equipment in the first reference time period, and control the output of multiple power generation equipment in the virtual power plant according to the adjusted power scheduling plan, and the target power generation equipment is any one of the multiple power generation equipment.

[0034] In one possible implementation, the adjustment module is also used to determine, when the virtual power plant is in a real-time stage, the actual active power of the target power generation equipment in any one of the multiple second reference time periods in the real-time stage; determine the deviation power of the target power generation equipment in the second reference time period based on the actual active power and the declared power corresponding to the target power generation equipment in the second reference time period; determine power adjustment information based on the deviation power and the completion costs corresponding to multiple power generation equipment, the power adjustment information being used to indicate that the deviation power is allocated to at least one other power generation equipment for output; and control the at least one other power generation equipment to complete the deviation power according to the power adjustment information.

[0035] In a possible implementation, the determination module is also used to determine the consumption information and revenue information of the virtual power plant during the period to be optimized based on the power demand and the operating parameters, the consumption information including: the natural gas cost, the operation and maintenance costs of multiple power generation equipment, the electric vehicle compensation cost, and the output deviation penalty cost during the period to be optimized, and the revenue information includes: electricity sales revenue and carbon emission revenue; based on the natural gas cost, the operation and maintenance costs of multiple power generation equipment, the electric vehicle compensation cost, the output deviation penalty cost, electricity sales revenue, and carbon emission revenue during the period to be optimized, a day-ahead stage scheduling optimization model is used to determine the power scheduling plan of the virtual power plant during the period to be optimized.

[0036] In a possible implementation, the determination module is also used to determine the highest total revenue based on the electricity sales revenue and carbon emission revenue by adopting a day-ahead stage scheduling optimization model; optimize the output decision variables of the multiple power generation equipment based on the natural gas cost, the operation and maintenance costs of multiple power generation equipment, the electric vehicle compensation cost, and the output deviation penalty cost, and determine the power scheduling plan of the virtual power plant during the period to be optimized.

[0037] In a possible implementation, the adjustment module is also used to determine the cost deviation between the day-ahead stage and the intraday stage based on the actual active power of multiple historical first reference time periods in the intraday stage; and adjust the power scheduling plan based on the cost and the declared power corresponding to the target power generation equipment in the first reference time period.

[0038] In one possible implementation, the adjustment module is also used to determine the deviation power of the target power generation equipment within the second reference time period based on the actual active power and the declared power corresponding to the target power generation equipment in the second reference time period; and construct a deviation penalty function based on the deviation power within the second reference time period and the equipment parameters of the target power generation equipment to optimize the virtual power plant scheduling cost.

[0039] In one possible implementation, the adjustment module is also used to determine the time-decoupled load regulation cost based on the deviation power within the second reference period and the equipment parameters of the target power generation equipment; and to construct a deviation penalty function based on the deviation power and the time-decoupled load regulation cost within the second reference period to optimize the virtual power plant scheduling cost.

[0040] In a third aspect, an embodiment of the present application provides a virtual power plant optimization scheduling device, including: a memory, a processor;

[0041] Memory stores computer-executable instructions;

[0042] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and various possible implementations of the first aspect.

[0043] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the above first aspect and various possible implementation methods of the first aspect.

[0044] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and various possible implementation methods of the first aspect.

[0045] The virtual power plant optimization scheduling method, device, equipment and medium provided in the embodiments of the present application, through a multi-time-scale scheduling model, coordinately control various adjustable resources of the virtual power plant, realize low-carbon operation, achieve supply and demand balance, reduce the system deviation of the virtual power plant's previously reported output, reduce its operating costs, and improve the power generation and consumption capacity of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0047] Figure 1 A flow chart of a virtual power plant optimization scheduling method provided in an embodiment of the present application Figure 1 ;

[0048] Figure 2 A flow chart of a virtual power plant optimization scheduling method provided in an embodiment of the present application Figure 2 ;

[0049] Figure 3 A flow chart of a virtual power plant optimization scheduling method provided in an embodiment of the present application Figure 3 ;

[0050] Figure 4 A schematic diagram of the structure of a virtual power plant optimization scheduling device provided in this application;

[0051] Figure 5 A structural schematic diagram of a virtual power plant optimization scheduling device provided for this application.

[0052] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] "Multiple" means two or more, and other quantifiers are similar. "And / or" describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0055] In the embodiments of the present application, the words "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0056] Virtual power plant is an Internet of Things technology that aggregates adjustable loads, energy storage and power supply resources in different spaces to achieve autonomous coordinated optimization control, and aggregate participation in power system operation and power market transactions.

[0057] There is a lot of uncertainty in the adjustable resources of virtual power plants, and the dispatch instructions of virtual power plants are usually formulated according to the power forecast of the day before to formulate full-time dispatch plans. The prediction error will increase accordingly with the increase of the prediction period. When there is a large deviation between the power forecast result and the actual power, it is difficult to achieve accurate dispatch, which causes many problems in the actual application of virtual power plants. It is urgent to optimize the operation of virtual power plants.

[0058] Multi-time scale optimization scheduling methods are widely used in the field of virtual power plant optimization scheduling to deal with uncertainty factors, mainly including the day-ahead stage, intraday stage and real-time stage. However, there are currently few comprehensive considerations of the low-carbon operation of virtual power plants and multi-time scale optimization scheduling methods.

[0059] In response to the above problems, the present application provides a virtual power plant optimization scheduling method, which comprehensively considers the low-carbon operation and multi-time scale optimization scheduling of the virtual power plant, guides the virtual power plant to form a coordinated optimization plan for various distributed energy sources according to energy prices and low-carbon benefits, and coordinates the coordinated optimization plans for various distributed energy sources step by step through the day-ahead stage, intraday stage and real-time stage, gradually reducing the impact of multiple uncertain factors.

[0060] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0061] Figure 1A flow chart of a virtual power plant optimization scheduling method provided in an embodiment of the present application Figure 1 .like Figure 1 As shown, the method includes:

[0062] S101. When the virtual power plant is in the day-ahead stage, determine the power demand for the time period to be optimized and the operating parameters of the virtual power plant;

[0063] The day-ahead phase is a long-term coordination and optimization phase, which refers to the scheduling and planning arrangements made before the start of the day. For example, a scheduling plan is made 24 hours in advance.

[0064] The time period to be optimized is a time period in which there is a need to optimize the power scheduling scheme;

[0065] The power demand is the power demand response value of the time period to be optimized; for example, if the power demand response value tomorrow is 2.1 million Kw, the power demand is 2.1 million Kw.

[0066] Operation parameters may be, for example, power generation factor, distributed resources, load response speed, etc.

[0067] 24 hours in advance, determine the power demand corresponding to the time period when the power dispatch plan needs to be optimized and the operating parameters of the virtual power plant, such as the power generation factor, distributed resources, and load response speed.

[0068] Based on the power demand and the operating parameters, a power dispatching plan of the virtual power plant during the period to be optimized is determined, and the power dispatching plan is used to indicate the declared power corresponding to multiple reference time periods in the daily stage for multiple power generation equipment in the virtual power plant.

[0069] Among them, the intraday stage is the short-time-scale rolling optimization stage; the benchmark period refers to the specified time scale, for example: one benchmark period is one hour, and one benchmark period is two hours.

[0070] According to the power demand and operating parameters of the time period to be optimized, with the goal of minimizing the overall operating cost of the virtual power plant, the power dispatch plan corresponding to multiple power generation equipment of the virtual power plant is determined 24 hours in advance, where the power dispatch plan is used to indicate the declared power of multiple power generation equipment corresponding to multiple benchmark time periods in the intraday stage, and the sum of the multiple declared powers is the power demand of the time period to be optimized.

[0071] For example, 24 hours before the beginning of tomorrow, based on tomorrow's power demand and the operating parameters of the virtual power plant, with the goal of minimizing the overall operating cost of the virtual power plant, determine the power scheduling plan corresponding to each power generation equipment in multiple benchmark time periods when the virtual power plant meets the power demand.

[0072] S102. When the virtual power plant is in an intraday phase, for any first reference time period among a plurality of first reference time periods in the intraday phase, determine the actual active power of the target power generation equipment in the first reference time period.

[0073] The first reference period is one hour, and the start time of the first reference period may be, for example, the hour or half hour; the target power generation equipment is any one of the multiple power generation equipment; and the actual active power is the actual output power of the power generation equipment.

[0074] When the virtual power plant is in the intraday stage, for any first reference time period among a plurality of first reference time periods in the intraday stage, the actual active power corresponding to the power generation equipment in the first reference time period is determined.

[0075] S103. Adjust the power dispatching plan according to the actual active power and the declared power corresponding to the target power generation equipment in the first reference time period, and control the output of multiple power generation equipment in the virtual power plant according to the adjusted power dispatching plan.

[0076] According to the actual active power and the declared power corresponding to the target power generation equipment in the first reference time period, the deviation value between the actual value and the optimized scheduling plan formulated in the day-ahead stage is determined; according to the deviation value, the power scheduling plan of other first reference time periods in the intraday stage is adjusted to obtain the adjusted power scheduling plan; according to the adjusted power scheduling plan, the output of multiple power generation equipment in the virtual power plant is controlled.

[0077] For example, in the case of the intraday stage, in the first reference period, the actual active power of multiple power generation equipment may be the same as or different from the scheduling plan formulated in the day-ahead stage. Therefore, it is necessary to adjust the power scheduling plan corresponding to the subsequent first reference period of the intraday stage according to the actual active power and the declared power in the day-ahead stage, obtain the adjusted power scheduling plan, and control the output of multiple power generation equipment in the virtual power plant according to the adjusted power scheduling plan.

[0078] In a possible implementation manner, the specific implementation manner of adjusting the power dispatching scheme according to the actual active power and the declared power corresponding to the target power generation equipment in the first reference time period is:

[0079] Determine the cost deviation between the day-ahead phase and the intraday phase according to the actual active power of multiple historical first reference periods in the intraday phase;

[0080] The power dispatching plan is adjusted according to the cost and the declared power corresponding to the target power generation equipment in the first reference time period.

[0081] Among them, the cost deviation is the cost deviation caused by the difference between the scheduling plan in the day-ahead stage and the actual active power.

[0082] The optimization is carried out once every hour during the day, with the minimum deviation between the power sold outside the virtual power plant and the day-ahead declaration as the optimization goal, and the power dispatch plan is adjusted using the following formula:

[0083]

[0084] Where: is the incremental cost of purchasing natural gas; is the cost deviation function for the intraday stage, and the deviation itself is the calculated output increment; is the reduction in carbon market trading revenue; ΔP gas_t is the active power increment of the gas turbine output at time t; ΔP gas_t,i is the active power increment output by the i-th gas turbine at time t; and are penalty coefficients, where ΔP t i+ The difference between the active output of the virtual power plant during the intraday stage and the active output of the day-ahead stage, ΔP t i- It is the difference between the active output of the virtual power plant in the intraday stage and the active output in the day-ahead stage; are the active power sold and purchased between the virtual power plant and the power grid during the day; P t el,s , P t el,b are the active power sold and purchased between the virtual power plant and the power grid in the day-ahead phase; Δ'E t It is the difference between the carbon quota and carbon emissions of the virtual power plant in period t within the day.

[0085] The carbon emissions of a virtual power plant are calculated using the following formula:

[0086]

[0087] Where: E t is the carbon emissions of the virtual power plant at time t; P i,t is the active output of the i-th gas turbine in the virtual power plant at time t; e a_i 、e b_i 、e c_i are the carbon emission coefficient of the i-th steam turbine in the virtual power plant; i,t is the shutdown status of the i-th steam turbine in the virtual power plant at time t. i,t=1, it means that the i-th steam turbine in the current virtual power plant is in operation at time t. i,t When =0, it means that the i-th steam turbine in the current virtual power plant is in shutdown state at time t.

[0088] The embodiment of the present application provides a virtual power plant optimization scheduling method, which comprehensively considers the low-carbon operation of the virtual power plant and multi-time scale optimization scheduling. In the day-ahead stage, optimization is performed 24 hours in advance, and a scheduling plan is formulated with the goal of minimizing the overall operating cost of the virtual power plant; in the intra-day stage, the difference between the real-time active power and the declared power of each power generation equipment is used, and the minimum deviation between the external power sales of the virtual power plant and the day-ahead declaration is taken as the optimization goal. The scheduling plan formulated in the day-ahead stage is adjusted, and the coordinated optimization plans of various distributed energy sources are coordinated step by step to gradually reduce the impact of multiple uncertain factors. Through the multi-time scale scheduling model, the various adjustable resources of the virtual power plant are coordinated to achieve low-carbon operation, achieve supply and demand balance, reduce the system deviation of the day-ahead declared output of the virtual power plant, and reduce its operating cost.

[0089] Figure 2 A flow chart of a virtual power plant optimization scheduling method provided in an embodiment of the present application Figure 2 .like Figure 2 As shown, in this embodiment Figure 1 Based on the embodiment, a virtual power plant optimization scheduling method is supplemented, and the method includes:

[0090] S201. When the virtual power plant is in a real-time stage, for any second reference time period among a plurality of second reference time periods in the real-time stage, determine the actual active power of the target power generation equipment in the second reference time period.

[0091] Among them, the real-time stage uses half an hour as a step to further reduce the deviation between the virtual power plant's declared output and actual output; the second reference period is half an hour, and the starting time of the second reference period can be the entire hour or half an hour.

[0092] When optimizing the scheduling plan of the virtual power plant in real time, the actual active power of the target power generation equipment in any second reference period among multiple second reference periods is determined, so as to prepare for further reducing the deviation between the declared output and actual output of the virtual power plant.

[0093] S202: Determine the deviation power of the target power generation equipment in the second reference time period according to the actual active power and the declared power corresponding to the target power generation equipment in the second reference time period.

[0094] Based on the actual active power of multiple power generation equipment in the virtual power plant in the second reference time period and the corresponding declared power, the declared power can be obtained based on the power scheduling plan formulated in the day-ahead stage, or can be obtained based on the power scheduling plan adjusted in the intraday stage. The specific actual situation shall prevail. According to the difference between the actual active power and the corresponding declared power, the deviation power of the power generation equipment in the second reference time period is determined.

[0095] S203: Determine power adjustment information according to the deviation power and the corresponding completion costs of the plurality of power generation equipment.

[0096] The complementary cost is the power generation cost corresponding to the power generation equipment used as the scheduling resource, for example, the complementary cost of electric vehicles includes the battery unit price, the efficiency of electric vehicles, etc. The power adjustment information is used to indicate that the deviation power is allocated to at least one other power generation equipment for output; the power adjustment information is the scheduling tasks corresponding to multiple power generation equipment used as scheduling resources.

[0097] According to the deviation power of the power generation equipment in the second reference time period and the corresponding complement cost of the power generation equipment used as the scheduling resource, the scheduling tasks corresponding to the plurality of power generation equipment used as the scheduling resource are determined.

[0098] S204: Control the at least one other power generation device to complement the deviation power according to the power adjustment information.

[0099] According to the scheduling tasks corresponding to multiple power generation equipment used as scheduling resources, the power generation equipment used as scheduling resources is controlled to complete the deviation value between the actual power and the declared power, so as to further reduce the deviation between the declared output and the actual output of the virtual power plant.

[0100] In a possible implementation manner, the above-mentioned method of determining the deviation power of the target power generating equipment within the second reference time period based on the actual active power and the declared power corresponding to the target power generating equipment in the second reference time period is specifically implemented as follows: determining the deviation power of the target power generating equipment within the second reference time period based on the actual active power and the declared power corresponding to the target power generating equipment in the second reference time period; constructing a deviation penalty function based on the deviation power within the second reference time period and the equipment parameters of the target power generating equipment to optimize the scheduling cost of the virtual power plant.

[0101] Among them, the equipment parameters may be, for example, power carrying capacity, regulation cost, etc.; the error penalty is the error cost caused by the deviation between the power scheduling scheme and the actual active power, and the error penalty function is a function corresponding to the error cost.

[0102] According to the actual active power of the target power generation equipment and the declared power corresponding to the target power generation equipment in the second reference time period, the declared power can be obtained according to the power scheduling plan formulated in the day-ahead stage, or can be obtained according to the power scheduling plan adjusted in the intraday stage. The power deviation corresponding to the target power generation equipment in the second reference time period is determined, and a deviation penalty function is constructed according to the power deviation and the equipment parameters of the target power generation equipment to further reduce the scheduling cost of the virtual power plant.

[0103] In one possible implementation, a deviation penalty function is constructed based on the deviation power within the second reference time period and the equipment parameters of the target power generation equipment. The specific implementation method for optimizing the virtual power plant scheduling cost is as follows: based on the deviation power within the second reference time period and the equipment parameters of the target power generation equipment, the time-decoupled load regulation cost is determined; based on the deviation power within the second reference time period and the time-decoupled load regulation cost, a deviation penalty function is constructed to optimize the virtual power plant scheduling cost.

[0104] The objective function of the real-time phase is as follows:

[0105]

[0106] Where: is the deviation penalty function in the real-time stage; Decouple the load regulation cost for the time used to compensate for the real-time load deviation; and are penalty coefficients, where (P t el,s , P t el,b are the active power sold and purchased from the grid by the virtual power plant at time t) ΔP t i+ It is the difference between the active output of the virtual power plant in the real-time stage and the active output in the intraday stage, ΔP t i- It is the difference between the active output of the virtual power plant in the real-time stage and the active output in the intraday stage; They are the active power purchased and sold between the virtual power plant and the power grid in the real-time stage; is the unit regulation cost of the time-decoupled load; P t ac It is the virtual energy storage power output of the time-decoupled load used to compensate for the real-time load deviation at time t.

[0107] A virtual power plant optimization scheduling method provided in an embodiment of the present application adjusts the optimization scheduling of the virtual power plant in real time, comprehensively considers the actual situation and the scheduling plan of the virtual power plant for each power generation equipment, and compensates for the real-time active power deviation by scheduling resources as a time-decoupled load. According to the actual active power and the optimization scheduling plan, a deviation penalty function is constructed, and the equipment parameters of the resources are comprehensively scheduled to coordinate control of various adjustable resources of the virtual power plant to achieve low-carbon operation, achieve supply and demand balance, promote the consumption of clean energy, reduce the impact of the randomness of renewable energy output on the output reported by the virtual power plant in the day-ahead stage, reduce the operating cost of the virtual power plant, and further reduce the scheduling cost of the virtual power plant.

[0108] Figure 3 A flow chart of a virtual power plant optimization scheduling method provided in an embodiment of the present application Figure 3 .like Figure 3 In this embodiment, Figure 1 Example or Figure 2 Based on the embodiment, determining the power dispatching scheme of the virtual power plant in the period to be optimized based on the power demand and the method is described in detail. The method includes:

[0109] S301. Determine the consumption information and revenue information of the virtual power plant during the period to be optimized based on power demand and operating parameters. The consumption information includes: natural gas cost, operation and maintenance cost of multiple power generation equipment, electric vehicle compensation cost and output deviation penalty cost during the period to be optimized. The revenue information includes: electricity sales revenue and carbon emission revenue.

[0110] According to the power demand in the period to be optimized and the operating parameters of the virtual power plant, the consumption information of the virtual power plant in the period to be optimized is determined, including: natural gas cost, operation and maintenance cost of multiple power generation equipment, electric vehicle compensation cost, output deviation penalty cost, etc., as well as revenue information, including: electricity sales revenue and carbon emission revenue.

[0111] The cost of natural gas is determined using the following formula:

[0112]

[0113] In the formula, price gas,t is the unit price of natural gas purchased by the virtual power plant at time t; P gas_t is the sum of the gas turbine output power of the virtual power plant at time t; η i,t is the operating efficiency of the i-th gas turbine in the virtual power plant at time t;

[0114] in:

[0115]

[0116] Pgas_t,i is the active power output of the i-th gas turbine of the virtual power plant at time t

[0117] The following formula is used to determine the operation and maintenance costs of multiple power generation equipment:

[0118]

[0119] Where: n gas 、n wind 、n pv and n st are the total number of gas turbines, wind power, photovoltaic units and energy storage equipment in the virtual power plant; h gas,i 、h wind,i 、h pv,i and h st,i are the maintenance cost coefficients of the i-th gas turbine, wind power and photovoltaic units, and energy storage equipment in the virtual power plant; P gas_t,i , P wind_t,i , P pv_t,i and P st_t,i are the active power outputs of the i-th gas turbine, wind power, photovoltaic unit and energy storage respectively.

[0120] The following formula is used to determine the electric vehicle compensation cost:

[0121]

[0122] Where: C ev_i is the battery unit price of the i-th electric car; Loop i Cap is the number of charge and discharge cycles of the ith electric vehicle in its entire life cycle; i is the battery capacity of the i-th electric car; D i is the battery discharge depth of the i-th electric vehicle; η ev_t,i is the efficiency of the i-th electric vehicle in the virtual power plant at time t.

[0123] The output deviation penalty cost is determined using the following formula:

[0124]

[0125] Where:

[0126] τ gas , τ wind , τ pv , τ net They are the gas turbine deviation penalty coefficient, wind turbine output deviation penalty coefficient, photovoltaic array output deviation penalty coefficient, and virtual power plant power purchase and sales transaction deviation penalty coefficient;

[0127] is the value of the i-th gas turbine at time t within the day, P gas_t,i (t) The optimization result of the i-th gas turbine at time t the day before; is the value of the i-th wind turbine at time t within the day, P wind_t,i (t) The optimization result of the i-th wind turbine at time t the day before; is the value of the ith photovoltaic array at time t within the day, P pv_t,i (t) The optimization result of the ith PV array at time t the day before; is the value of the i-th grid transaction power at time t within the day, P net_t,i (t) The optimization result of the i-th grid trading power at time t within the day.

[0128] Among them, the following formula is used to constrain the optimization of the power dispatch scheme of the virtual power plant:

[0129]

[0130] Where: is the demand response value at time t within the day.

[0131] The following formula is used to determine the revenue from electricity sales:

[0132]

[0133] Where:

[0134] are the active power sold and purchased from the grid by the virtual power plant at time t; They are the unit prices of electricity sold and purchased from the power grid by the virtual power plant at time t.

[0135] The carbon emission benefits are determined using the following formula:

[0136]

[0137] Where:

[0138] price carbon is the transaction price of carbon quota per unit of virtual power plant in period t; ΔE t is the difference between the carbon quota and carbon emissions of the virtual power plant in period t, ΔE t >0 means that the current virtual power plant has remaining carbon emission rights available for sale, ΔE t <0 means that the virtual power plant has no remaining carbon emission rights and needs to purchase carbon emission rights from the carbon trading market. is the carbon quota of the virtual power plant, E t Carbon emissions from virtual power plants.

[0139] S302. According to the natural gas cost, operation and maintenance cost of multiple power generation equipment, electric vehicle compensation cost, output deviation penalty cost, electricity sales revenue and carbon emission revenue during the period to be optimized, a day-ahead scheduling optimization model is adopted to determine the power scheduling plan of the virtual power plant during the period to be optimized.

[0140] According to the natural gas cost, operation and maintenance cost of multiple power generation equipment, electric vehicle compensation cost, output deviation penalty cost, electricity sales income and carbon emission income in the period to be optimized, with the lowest cost of the virtual power plant in the day-ahead stage as the goal, the optimization of each component of the cost is carried out, that is, the optimization of power generation and electricity consumption behavior of thermal power units, new energy units, electric vehicles and other equipment, which is manifested as the optimization of the scheduling plan of the virtual power plant. The day-ahead scheduling optimization model is used to determine the power scheduling plan of the virtual power plant in the period to be optimized.

[0141] In one possible implementation manner, based on the natural gas cost, the operation and maintenance costs of multiple power generation equipment, the electric vehicle compensation cost, the output deviation penalty cost, the electricity sales revenue and the carbon emission revenue during the period to be optimized, a day-ahead stage scheduling optimization model is adopted to determine the power scheduling scheme of the virtual power plant during the period to be optimized. The specific implementation method is as follows: based on the electricity sales revenue and the carbon emission revenue, a day-ahead stage scheduling optimization model is adopted to determine the highest total revenue; based on the natural gas cost, the operation and maintenance costs of multiple power generation equipment, the electric vehicle compensation cost and the output deviation penalty cost, the output decision variables of the multiple power generation equipment are optimized to determine the power scheduling scheme of the virtual power plant during the period to be optimized.

[0142] The following formula is used to perform a double-layer optimization of the operating cost of the virtual power plant in the day-ahead phase:

[0143]

[0144] In the formula, is the output decision variable of each device in the virtual power plant at time t; wind_t,i and P pv_t,i are the actual active outputs of wind power and photovoltaic units at time t; is the natural gas cost, is the operation and maintenance cost of wind power, photovoltaic power and energy storage, is the electric vehicle compensation cost, is the output deviation penalty cost, is the revenue from electricity sales, It is the carbon emission benefit; It is a fine if the actual output of renewable energy is lower than the predicted power value a few days ago. is the penalty coefficient, It is a fine if the actual output of renewable energy is higher than the predicted power value a few days ago. is the penalty coefficient,

[0145] in:

[0146]

[0147] Where:

[0148] are the predicted active power output of wind power and photovoltaic units at time t in the day-ahead period; ΔP t + is the difference between the predicted and actual active output of wind power and photovoltaic units at time t; ΔP t - It is the difference between the actual value and the predicted value of the active output of wind power and photovoltaic units at time t;

[0149]

[0150]

[0151] Where:

[0152] p wind_t It is the deviation of wind power output forecast, generally ±15%;

[0153] p pv_t It is the deviation of the photovoltaic output forecast, generally ±10%;

[0154]

[0155] Where:

[0156] Γ wind , Γ pv are the robustness coefficients of wind power and photovoltaic units respectively.

[0157] An optimization scheduling method for a virtual power plant provided in an embodiment of the present application adopts a day-ahead scheduling optimization model based on natural gas costs, operation and maintenance costs of multiple power generation equipment, electric vehicle compensation costs, output deviation penalty costs, electricity sales revenue, and carbon emission revenue. The optimization is performed 24 hours in advance, and a scheduling plan is formulated with the goal of minimizing the overall operating cost of the virtual power plant. The power scheduling plan for each power generation equipment of the virtual power plant during the period to be optimized is determined, thereby reducing the operating cost of the virtual power plant and improving the power generation and consumption capacity of renewable energy.

[0158] Figure 4 A schematic diagram of the structure of a virtual power plant optimization scheduling device provided in this application, such as Figure 4 As shown, a virtual power plant optimization scheduling device 40 provided in this embodiment includes:

[0159] The determination module 401 is used to determine the power demand of the time period to be optimized and the operating parameters of the virtual power plant when the virtual power plant is in the day-ahead stage, and determine the power dispatching scheme of the virtual power plant in the time period to be optimized based on the power demand and the operating parameters. The power dispatching scheme is used to indicate the declared power corresponding to the multiple reference time periods in the intraday stage for multiple power generation equipment in the virtual power plant;

[0160] The adjustment module 402 is used to determine the actual active power of the target power generation equipment in the first reference period for any one of the multiple first reference periods in the intraday stage when the virtual power plant is in the intraday stage;

[0161] The adjustment module 402 is also used to adjust the power scheduling plan according to the actual active power and the declared power corresponding to the target power generation equipment in the first reference period, and control the output of multiple power generation equipment in the virtual power plant according to the adjusted power scheduling plan, and the target power generation equipment is any one of the multiple power generation equipment.

[0162] In one possible implementation, the adjustment module 402 is also used to determine, when the virtual power plant is in a real-time stage, the actual active power of the target power generation equipment in the second reference time period for any one of the multiple second reference time periods in the real-time stage; determine the deviation power of the target power generation equipment in the second reference time period based on the actual active power and the declared power corresponding to the target power generation equipment in the second reference time period; determine power adjustment information based on the deviation power and the completion costs corresponding to the multiple power generation equipment, the power adjustment information being used to indicate that the deviation power is allocated to at least one other power generation equipment for output; and control at least one other power generation equipment to complete the deviation power according to the power adjustment information.

[0163] In a possible implementation, the determination module 401 is also used to determine the consumption information and revenue information of the virtual power plant during the period to be optimized based on power demand and operating parameters, the consumption information including: the natural gas cost, the operation and maintenance cost of multiple power generation equipment, the electric vehicle compensation cost, and the output deviation penalty cost during the period to be optimized, and the revenue information includes: the electricity sales revenue and the carbon emission revenue; based on the natural gas cost, the operation and maintenance cost of multiple power generation equipment, the electric vehicle compensation cost, the output deviation penalty cost, the electricity sales revenue and the carbon emission revenue during the period to be optimized, the day-ahead stage scheduling optimization model is used to determine the power scheduling plan of the virtual power plant during the period to be optimized.

[0164] In a possible implementation, the determination module 401 is also used to determine the highest total revenue based on the electricity sales revenue and carbon emission revenue by adopting the day-ahead stage scheduling optimization model; based on the natural gas cost, the operation and maintenance cost of multiple power generation equipment, the electric vehicle compensation cost, and the output deviation penalty cost, the output decision variables of multiple power generation equipment are optimized to determine the power scheduling plan of the virtual power plant during the period to be optimized.

[0165] In a possible implementation, the adjustment module 402 is also used to determine the cost deviation between the day-ahead stage and the intraday stage based on the actual active power of multiple historical first reference time periods in the intraday stage; and adjust the power scheduling plan based on the cost and the declared power corresponding to the target power generation equipment in the first reference time period.

[0166] In a possible implementation, the adjustment module 402 is also used to determine the deviation power of the target power generation equipment within the second reference period based on the actual active power and the declared power corresponding to the target power generation equipment in the second reference period; construct a deviation penalty function based on the deviation power within the second reference period and the equipment parameters of the target power generation equipment to optimize the virtual power plant scheduling cost.

[0167] In a possible implementation, the adjustment module 402 is also used to determine the time-decoupled load regulation cost based on the deviation power within the second reference period and the equipment parameters of the target power generation equipment; construct a deviation penalty function based on the deviation power within the second reference period and the time-decoupled load regulation cost to optimize the virtual power plant scheduling cost.

[0168] The present embodiment provides a virtual power plant optimization scheduling device, which can execute the method provided by the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail in this embodiment.

[0169] Figure 5 A schematic diagram of the structure of a virtual power plant optimization scheduling device provided in this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 also includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0170] During the specific implementation process, at least one processor 501 executes the computer execution instructions stored in the memory 502, so that at least one processor 501 executes the above-mentioned virtual power plant optimization scheduling method.

[0171] The specific implementation process of the processor 501 can be found in the above-mentioned virtual power plant optimization scheduling method embodiment, and its implementation principle and technical effects are similar, so this embodiment will not be repeated here.

[0172] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0173] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0174] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0175] The present application also provides a computer program product, including a computer program, which implements the above-mentioned virtual power plant optimization scheduling method when executed by a processor.

[0176] The present application also provides a computer-readable storage medium, in which computer execution instructions are stored. When a processor executes the computer execution instructions, the above-mentioned virtual power plant optimization scheduling method is implemented.

[0177] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0178] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0179] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0180] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0181] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0182] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0183] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0184] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A virtual power plant optimization scheduling method, characterized in that: include: When the virtual power plant is in the day-ahead stage, determine the power demand of the time period to be optimized and the operating parameters of the virtual power plant, and determine the power dispatching scheme of the virtual power plant in the time period to be optimized based on the power demand and the operating parameters, wherein the power dispatching scheme is used to indicate the declared power corresponding to the multiple reference time periods in the intraday stage for the multiple power generation equipment in the virtual power plant; When the virtual power plant is in the intraday stage, for any one of a plurality of first reference time periods in the intraday stage, determining the actual active power of the target power generation equipment in the first reference time period; The power scheduling plan is adjusted according to the actual active power and the declared power corresponding to the target power generation equipment in the first reference time period, and the output of multiple power generation equipment in the virtual power plant is controlled according to the adjusted power scheduling plan. The target power generation equipment is any one of the multiple power generation equipment.

2. The method according to claim 1, characterized in that The method further comprises: When the virtual power plant is in a real-time stage, for any one of a plurality of second reference time periods in the real-time stage, determining the actual active power of the target power generation equipment in the second reference time period; Determine the deviation power of the target power generation equipment in the second reference time period according to the actual active power and the declared power corresponding to the target power generation equipment in the second reference time period; Determine power adjustment information according to the deviation power and the complementary costs corresponding to the plurality of power generation devices, wherein the power adjustment information is used to indicate that the deviation power is allocated to at least one other power generation device for output; According to the power adjustment information, the at least one other power generation device is controlled to perform a complement process on the deviation power.

3. The method according to claim 1, characterized in that The determining, based on the power demand and the operating parameters, a power dispatching scheme of the virtual power plant in the period to be optimized includes: Determine, according to the power demand and the operating parameters, the consumption information and revenue information of the virtual power plant in the period to be optimized, wherein the consumption information includes: the natural gas cost, the operation and maintenance costs of multiple power generation equipment, the electric vehicle compensation cost and the output deviation penalty cost in the period to be optimized, and the revenue information includes: the revenue from electricity sales and the revenue from carbon emissions; According to the natural gas cost, operation and maintenance cost of multiple power generation equipment, electric vehicle compensation cost, output deviation penalty cost, electricity sales revenue and carbon emission revenue during the period to be optimized, the day-ahead scheduling optimization model is adopted to determine the power scheduling plan of the virtual power plant during the period to be optimized.

4. The method according to claim 3, characterized in that The power dispatching scheme of the virtual power plant in the period to be optimized is determined by using the day-ahead stage dispatching optimization model according to the natural gas cost, the operation and maintenance cost of multiple power generation equipment, the electric vehicle compensation cost, the output deviation penalty cost, the electricity sales income and the carbon emission income in the period to be optimized, including: According to the electricity sales revenue and carbon emission revenue, the day-ahead dispatch optimization model is adopted to determine the highest total revenue; According to the natural gas cost, the operation and maintenance cost of multiple power generation equipment, the electric vehicle compensation cost, and the output deviation penalty cost, the output decision variables of the multiple power generation equipment are optimized to determine the power dispatch plan of the virtual power plant during the period to be optimized.

5. The method according to claim 1, characterized in that: The adjusting the power dispatching scheme according to the actual active power and the declared power corresponding to the target power generation equipment in the first reference time period includes: Determine the cost deviation between the day-ahead phase and the intraday phase according to the actual active power of multiple historical first reference periods in the intraday phase; The power dispatching plan is adjusted according to the cost and the declared power corresponding to the target power generation equipment in the first reference time period.

6. The method according to claim 2, characterized in that The method further comprises: determining the deviation power of the target power generation equipment in the second reference time period according to the actual active power and the declared power corresponding to the target power generation equipment in the second reference time period; Determining the deviation power of the target power generation equipment in the second reference time period according to the actual active power and the declared power corresponding to the target power generation equipment in the second reference time period; According to the deviation power in the second reference period and the equipment parameters of the target power generation equipment, a deviation penalty function is constructed to optimize the virtual power plant scheduling cost.

7. The method according to claim 6, characterized in that The step of constructing a deviation penalty function according to the deviation power in the second reference period and the equipment parameters of the target power generation equipment to optimize the virtual power plant scheduling cost includes: Determining a time decoupling load regulation cost according to the deviation power in the second reference period and the equipment parameters of the target power generation equipment; According to the deviation power and time-decoupled load regulation cost within the second benchmark period, a deviation penalty function is constructed to optimize the virtual power plant scheduling cost.

8. A virtual power plant optimization scheduling device, characterized in that: include: a determination module, configured to determine the power demand of the time period to be optimized and the operating parameters of the virtual power plant when the virtual power plant is in the day-ahead stage, and determine the power dispatching scheme of the virtual power plant in the time period to be optimized based on the power demand and the operating parameters, wherein the power dispatching scheme is used to indicate the declared powers corresponding to the multiple reference time periods in the intraday stage for the multiple power generation equipment in the virtual power plant; an adjustment module, configured to determine, when the virtual power plant is in an intraday phase, for any first reference period of a plurality of first reference periods in the intraday phase, an actual active power of a target power generation device in the first reference period; The adjustment module is also used to adjust the power scheduling plan according to the actual active power and the declared power corresponding to the target power generation equipment in the first reference time period, and control the output of multiple power generation equipment in the virtual power plant according to the adjusted power scheduling plan, and the target power generation equipment is any one of the multiple power generation equipment.

9. A virtual power plant optimization dispatching device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer execution instructions stored in the memory, so that the processor executes the virtual power plant optimization scheduling method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the virtual power plant optimization scheduling method as described in any one of claims 1 to 7.

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