Virtual power plant climbing service market optimization scheduling method and device based on flexibility

Abstract

The invention relates to the technical field related to data security, in particular to a flexibility-based virtual power plant climbing service market optimization scheduling method and device. The method comprises the steps of determining composition of internal components of a virtual power plant; establishing a climbing service market clearing model by taking the minimum social cost as an objective function based on the internal component composition of the virtual power plant in combination with a preset rule; adjusting a declaration curve by taking benefit maximization as a target and combining a climbing service market clearing model under a preset constraint condition; a feedback output curve is obtained, and the service market subject determines a market clearing scheme based on each declaration curve and feeds back the output curve; and based on the output curve of the feedback, performing operation optimization on each component in the virtual power plant by taking the minimum social cost as a target, and determining a scheduling strategy.

Description

Technical Field

[0001] The present application relates to the technical field related to data security, and specifically to a flexibility-based virtual power plant ramping service market optimization scheduling method and device. Background Art

[0002] The efficient, safe and clean development of energy has brought about the rapid development of new energy, but the large-scale grid connection of new energy has also brought greater volatility risks to the operation of the power system. Traditional auxiliary services such as frequency regulation and standby cannot meet the net load requirements generated by the large-scale grid connection of new energy. Improving the flexibility and regulation capabilities of the power system has become the focus of current research. Virtual power plants aggregate distributed power sources and flexibility resources so that the power units participating in the aggregation can also participate in the flexibility regulation function. Considering the flexible ramping properties of virtual power plants, if they participate in the ramping service market, the benefits of virtual power plants will be further increased. As for ramping services, there are currently few related optimization scheduling strategy technologies for virtual power plants. Summary of the invention

[0003] In view of this, the embodiments of the present application are committed to providing a flexibility-based virtual power plant ramping service market optimization scheduling method and device to determine the scheduling strategy of the virtual power plant.

[0004] This application provides a flexibility-based virtual power plant ramping service market optimization scheduling method, including:

[0005] Determine the internal components of the virtual power plant;

[0006] Based on the internal components of the virtual power plant and in combination with preset rules, a ramp service market clearing model is established with the minimum social cost as the objective function;

[0007] With the goal of maximizing revenue, under the preset constraints, the declaration curve is adjusted in combination with the ramp service market clearing model;

[0008] Obtaining the output curves fed back, wherein the service market players determine the market clearing plan based on each declared curve and feed back the output curves;

[0009] Based on the feedback output curve, with the goal of minimizing social cost, the operation of each component within the virtual power plant is optimized and the scheduling strategy is determined.

[0010] In some embodiments, the internal components of the virtual power plant include: thermal power units, wind power units, and flexibility components.

[0011] In some embodiments, the output model of the thermal power unit is:

[0012]

[0013] In the formula, and Respectively represent the fuel cost and start-up and shutdown cost of thermal power units. Th ,b Th and c Th is the energy consumption coefficient of the thermal power unit. Th,t is the power generation capacity of the thermal power unit. Th,t It is the power generation state variable of the thermal power unit, a 0-1 variable, 1 means the thermal power unit is in operation, and 0 means the thermal power unit is in shutdown. Th,t It represents the startup cost of a thermal power unit, which is related to the cold start and hot start status of the thermal power unit.

[0014] In some embodiments, the wind turbine output model includes:

[0015]

[0016] In the formula, v represents the natural wind speed, and φ and θ represent the shape and scale parameters of the distribution function. When the wind speed is within the acceptable range of the unit, the unit power increases with the increase of wind speed. However, if it exceeds the acceptable range, that is, the wind speed is too low or too high, the wind turbine will not start to avoid damage to the unit. The functional relationship between wind power output and wind speed is as follows.

[0017]

[0018] In the formula, Q j,w (t) is the available output of wind turbine j at time t; g r is the rated output power of the wind turbine; v i,w 、v o,w Cut is the cut-in and cut-out wind speed respectively; v r,w is the rated wind speed; v(t) is the actual wind speed at time t.

[0019] In some embodiments, the preset rules include:

[0020] The ramping service transaction is combined with the day-ahead transaction declaration model. The independent trading center releases the ramping demand for each time period on T-1 according to market demand. The accessible ramping transaction entities with flexible resources bid, and the bidding content includes the capacity and price that the market entity can provide. The independent trading center summarizes the bidding information of each entity, matches and clears the prices of each time period and releases them to the market entity. At this time, the market entity can modify the bidding information. After modifying the declaration, the independent trading center will finally clear the final price.

[0021] When market players bid, Δt is used as the scheduling time interval, ΔQ c It represents a given capacity declaration unit, and the product of the two is the ramp-up per unit time interval; market players base their decisions on ΔQc Divide the flexibility capacity that can be provided by the company into unified units and declare 0-k times ΔQ c ; The quotation strategy of market member N is μ N ={λ N,1 ,λ N,2 ,...,λ N,k},λ N,k It means that market player N has at most k price strategies, that is, the maximum flexibility capacity that market player N can provide is kΔQ c , and the quotation must meet λ N,k >λ N,k-1 ;

[0022] N market players with flexibility qualifications participate in the day-ahead quotation, and the resulting quotation information set can be expressed as U N ={μ1,μ2,...,μ N The independent trading center sorts the bids of market players from low to high, and sorts the prices of system flexibility capacity requirements from high to low. The intersection of the two is the clearing price. The bid information that is lower than the market clearing price can be the winning bid.

[0023] The independent trading center takes the minimum total mobilization cost as the goal and establishes a clearing function, which can be expressed as:

[0024]

[0025] In the formula, and are the winning bids of market entity N in the mth quotation segment when adjusting the capacity upward or downward, respectively. This means that the market player did not win the bid in period t, that is, its bid in period t is higher than the market clearing price. This means that the market player won the bid in period t, and its bid in period t is less than or equal to the market clearing price, see formulas (6)-(7) for details; and Respectively represent the quotes adjusted upward and downward by market entity N in the mth quotation segment; ΔQ up and ΔQ dn are the upward and downward bid capacities in the system unit interval, respectively;

[0026] The formula used to express the winning bid of market players is:

[0027]

[0028]

[0029] In the formula, represents the market clearing price in period t.

[0030] When independent trading centers conduct unified clearing of the ramp-up service trading market, they need to meet the system flexibility balance constraints.

[0031]

[0032]

[0033] In the formula, and are the winning bids of the market entity N in the positive and negative climbing periods in period t; Q D,up and Q D,dn They are the upward and downward capacity demands published by the independent trading center on T-1 day, respectively.

[0034] When market players settle profits, they calculate based on the marginal electricity price and the ramp demand published by the independent trading center.

[0035] As a market entity, virtual power plants participate in ramp-up transactions, and their internal units must also meet unit output constraints.

[0036]

[0037] In the formula, They are the minimum and maximum values ​​of the virtual power plant’s upward adjustment capacity, respectively; They are the minimum and maximum values ​​of the virtual power plant’s downward adjustment capacity, respectively; and They are respectively the declared capacity of the virtual power plant adjusted upward / downward in period t.

[0038] In some embodiments, based on the market clearing scheme, the virtual power plant performs internal operation optimization and determines a dispatch strategy, including:

[0039] Reference market players’ initial quotation strategy μ N ={λ N,1 ,λ N,2 ,...,λ N,k},have

[0040] During operation of the virtual power plant, a certain amount of capacity must be reserved for wind power for regulation

[0041]

[0042] Wherein, δ is the capacity regulation parameter required for wind power output, and δ' is the regulation parameter converted into unit market regulation capacity. In actual calculation, δ' needs to be rounded, that is, δ'=[δ'].

[0043] The virtual power plant quotation aims to maximize revenue. Its quotation needs to be greater than or equal to the operating cost of the virtual power plant itself, and cannot be greater than the quotation ceiling issued by the ISO.

[0044]

[0045] In the formula, is the quotation for the mth quotation period of the virtual power plant, and must meet is the operating cost of the virtual power plant in the mth quotation period; ω is the flexibility quotation interval, The upper limit of the quotation limit for ISO.

[0046]

[0047] in,

[0048]

[0049] In the formula, is the operating cost of the wind turbine in period t; is the depreciation cost of the wind turbine; is the power generation of the wind turbine.

[0050] The objective function of the virtual power plant participating in the ramp service transaction is as follows

[0051]

[0052] In the formula, and They are respectively the regulation capacities that won the bid of the virtual power plant in period t. and Can be quoted by interval Weighted average.

[0053] In some embodiments, the preset constraints include: internal supply and demand balance of the virtual power plant, power flow, and processing constraints of each unit.

[0054] The present application also provides a flexibility-based virtual power plant ramping service market optimization scheduling device, which is characterized by comprising:

[0055] A determination module is used to determine the internal component composition of the virtual power plant;

[0056] Establishing a module for establishing a ramp service market clearing model based on the internal components of the virtual power plant and in combination with preset rules, with the minimum social cost as the objective function;

[0057] The adjustment module is used to adjust the declaration curve with the goal of maximizing revenue under preset constraints and in combination with the ramp service market clearing model;

[0058] The dispatching module is used to obtain the feedback output curve, in which the service market players determine the market clearing plan based on each declared curve and feedback the output curve; based on the feedback output curve, with the goal of minimizing social costs, the operation of each component within the virtual power plant is optimized and the dispatching strategy is determined.

[0059] In some embodiments, the internal components of the virtual power plant include: thermal power units, wind power units, and flexibility components.

[0060] In some embodiments, the output model of the thermal power unit is:

[0061]

[0062]

[0063] In the formula, and Respectively represent the fuel cost and start-up and shutdown cost of thermal power units; a Th ,b Th and c Th is the energy consumption coefficient of thermal power unit; Q Th,t is the power generation capacity of the thermal power unit; u Th,t is the power generation state variable of the thermal power unit, a 0-1 variable, 1 means the thermal power unit is in operation, 0 means the thermal power unit is in shutdown; D Th,t It represents the startup cost of a thermal power unit, which is related to the cold start and hot start status of the thermal power unit.

[0064] In some embodiments, the wind turbine output model includes:

[0065]

[0066] In the formula, v represents the natural wind speed, φ and θ represent the shape and scale parameters of the distribution function; when the wind speed is within the tolerable range of the unit, the unit power increases with the increase of wind speed, but if it exceeds the tolerable range, that is, the wind speed is too low or too high, the wind turbine will not be turned on to avoid damage to the machine body; the functional relationship between wind power output and wind speed is as follows;

[0067]

[0068] In the formula, Q j,w (t) is the available output of wind turbine j at time t; g r is the rated output power of the wind turbine; v i,w 、v o,wCut is the cut-in and cut-out wind speed respectively; v r,w is the rated wind speed; v(t) is the actual wind speed at time t.

[0069] In some embodiments, the preset rules include: in combination with the day-ahead transaction declaration mode, the independent trading center publishes the ramp demand for day T according to market demand on day T-1; the accessible trading entities with flexible resources bid, and the bidding content includes the output curve and price that the market entity can provide; the independent trading center summarizes the bidding information of each entity, and matches and clears the prices of each time period and publishes it to the market entity. At this time, the market entity can modify the bidding information. After modifying the declaration, the independent trading center finally performs the final clearing and forms the clearing price;

[0070] When market players bid, Δt is used to represent the scheduling time interval, ΔQ c It represents a given capacity declaration unit. The product of the two in a period of time is the total ramp-up amount in this period of time. Market players determine the total ramp-up amount based on ΔQ. c Divide the flexibility capacity that can be provided by the company into unified units and declare 0-k times ΔQ c ; The quotation strategy of market member N is μ N ={λ N,1 ,λ N,2 ,...,λ N,k},λ N,k It means that market player N has at most k price strategies, that is, the maximum flexibility capacity that market player N can provide is kΔQ c , and the quotation must meet λ N,k >λ N,k-1 ;

[0071] N market players with flexibility qualifications participate in the day-ahead quotation, and the resulting quotation information set can be expressed as U N ={μ1,μ2,...,μ N The independent trading center sorts the bids of market players from low to high, and sorts the prices of system flexibility capacity requirements from high to low. The intersection of the two is the clearing price. The bid information that is lower than the market clearing price can be the winning bid.

[0072] The independent trading center takes the minimum total mobilization cost as the goal and establishes a clearing function, which can be expressed as:

[0073]

[0074] In the formula, and are the winning bids of market entity N in the mth quotation segment when adjusting the capacity upward or downward, respectively. This means that the market player did not win the bid in period t, that is, its bid in period t is higher than the market clearing price. This means that the market player won the bid in period t, and its bid in period t is less than or equal to the market clearing price, see formulas (6)-(7) for details; and Respectively represent the quotes adjusted upward and downward by market entity N in the mth quotation segment; ΔQ up and ΔQ dn are the upward and downward bid capacities in the system unit interval, respectively;

[0075] The formula used to express the winning bid of market players is:

[0076]

[0077]

[0078] In the formula, represents the market clearing price in period t;

[0079] When independent trading centers perform unified clearing of ramp service transactions, they need to meet the system flexibility balance constraint;

[0080]

[0081]

[0082] In the formula, and are the winning bids of the upward and downward climbing services of market entity N in period t, respectively; Q D,up and Q D,dn are the upward and downward flexibility capacity requirements published by the independent trading center on T-1, respectively;

[0083] When market players settle their earnings, they calculate based on the marginal electricity price and the real-time demand for ramping published by the independent trading center;

[0084] Virtual power plants participate in ramp-up transactions as market entities, and their internal units must also meet unit output constraints;

[0085]

[0086] In the formula, They are the minimum and maximum values ​​of the virtual power plant’s upward adjustment capacity, respectively; They are the minimum and maximum values ​​of the virtual power plant’s downward adjustment capacity, respectively; and They are respectively the declared capacity of the virtual power plant adjusted upward / downward in period t.

[0087] In some embodiments, based on the market clearing scheme, the virtual power plant performs internal operation optimization and determines a dispatch strategy, including:

[0088] Reference market players’ initial quotation strategy μ N ={λ N,1 ,λ N,2 ,...,λ N,k},have

[0089] During operation of the virtual power plant, a certain amount of capacity must be reserved for wind power for regulation

[0090]

[0091] In the formula, δ is the capacity regulation parameter required for wind power output, and δ' is the regulation parameter converted into unit market regulation capacity. In actual calculation, δ' needs to be rounded, that is, δ' = [δ'];

[0092] The virtual power plant quotation aims to maximize revenue. Its quotation needs to be greater than or equal to the operating cost of the virtual power plant itself, and cannot be greater than the quotation ceiling issued by the ISO.

[0093]

[0094] In the formula, is the quotation for the mth quotation period of the virtual power plant, and must meet is the operating cost of the virtual power plant in the mth quotation period; ω is the flexibility quotation interval, The quotation ceiling of ISO limit;

[0095]

[0096] in,

[0097]

[0098] In the formula, is the operating cost of the wind turbine in period t; is the depreciation cost of the wind turbine; is the power generation of the wind turbine;

[0099] The objective function of the virtual power plant participating in ramp trading is as follows

[0100]

[0101] In the formula, and are the regulation capacities of the virtual power plant that won the bid in period t; and Can be quoted by interval Weighted average.

[0102] In some embodiments, the preset constraints include: internal supply and demand balance of the virtual power plant, power flow, and processing constraints of each unit.

[0103] The present application provides an electronic device, including:

[0104] A processor, and a memory for storing a program executable by the processor;

[0105] The processor is used to implement the flexibility-based virtual power plant auxiliary service market optimization scheduling method as described above by running the program in the memory.

[0106] The present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor executes the flexibility-based virtual power plant ramping service market optimization scheduling method as described above.

[0107] The present application provides a method for optimizing the scheduling of the virtual power plant ramping service market based on flexibility, which determines the internal component composition of the virtual power plant; based on the internal component composition of the virtual power plant, combined with preset rules, a ramping service market clearing model is established with the minimum social cost as the objective function; with the goal of maximizing revenue, under preset constraints, combined with the ramping service market clearing model, the declaration curve is adjusted; the output curve of the feedback is obtained, wherein the service market subject determines the market clearing plan based on each declaration curve and feeds back the output curve; based on the feedback output curve, with the minimum social cost as the goal, the operation of each component in the virtual power plant is optimized and the scheduling strategy is determined. . In this way, the optimization scheduling of the virtual power plant ramping service market based on flexibility can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0109] Figure 1 It is a flow chart of a flexibility-based virtual power plant ramping service market optimization scheduling method provided in an embodiment of the present application.

[0110] Figure 2It is a structural diagram of a flexibility-based virtual power plant ramping service market optimization scheduling method provided in an embodiment of the present application.

[0111] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0112] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0113] Application Overview

[0114] The efficient, safe and clean development of energy has brought about the rapid development of new energy, but the large-scale grid connection of new energy has also brought greater volatility risks to the operation of the power system. Traditional frequency regulation, standby and other ramping services cannot meet the net load requirements generated by the large-scale grid connection of new energy. Improving the flexibility and regulation capabilities of the power system has become the focus of current research. Virtual power plants aggregate distributed power sources and flexibility resources, so that the power units participating in the aggregation can also participate in the flexibility regulation function.

[0115] During the aggregation process, the virtual power plant will not significantly change the output characteristics of each component, but by fitting the output characteristics of different types of power sources and combining flexibility resources, the overall output of the virtual power plant can be flexibly adjusted. Based on this, this technology considers the virtual power plant to participate in the ramp service market based on its flexibility. Combined with the distributed component model within the virtual power plant, the flexibility of the virtual power plant is summarized and reported to the market. The market clearing is carried out with the goal of minimizing the total social cost, and the virtual power plant makes a profit based on the clearing price.

[0116] After introducing the basic principles of the present application, various non-limiting embodiments of the present application will be described in detail with reference to the accompanying drawings.

[0117] Exemplary Methods

[0118] Figure 1 1 is a flow chart of a method for optimizing the scheduling of a virtual power plant ramping service market based on flexibility provided by an embodiment of the present application. Figure 1 As shown, the method includes the following contents.

[0119] Step S110, determining the internal component composition of the virtual power plant;

[0120] Step S120, based on the internal components of the virtual power plant and in combination with preset rules, a ramp service market clearing model is established with the minimum social cost as the objective function;

[0121] Step S130, taking maximizing revenue as the goal, under preset constraints, and combining the ramp service market clearing model, adjust the declaration curve;

[0122] Step S140, obtaining the feedback output curve, wherein the service market entity determines the market clearing plan based on each declared curve and feeds back the output curve; based on the feedback output curve, with the goal of minimizing social costs, the operation of each component within the virtual power plant is optimized and the scheduling strategy is determined.

[0123] Determine the internal components of the virtual power plant;

[0124] In this way, the solution provided by this application can be optimized by combining the characteristics of the virtual power plant and the preset rules of the ramping service market. By iterative optimization with the goal of minimizing social costs and maximizing internal profits, a scheduling strategy is obtained to achieve flexibility-based optimized scheduling of the virtual power plant ramping service market.

[0125] In some embodiments, the internal components of the virtual power plant include: thermal power units, wind power units, and flexibility components.

[0126] Determine the internal structure of the virtual power plant, which includes the following steps:

[0127] Identify the components of a virtual power plant.

[0128] Specifically, virtual power plants include power sources, such as thermal power units, wind power units, and flexibility components, with energy storage as the main consideration. Among power sources, photovoltaic power generation cannot provide sufficient adjustment capabilities in flexibility regulation because its output is mainly concentrated during the day and is greatly affected by the weather. It even needs to consider purchasing electricity from the main grid at extreme times. Therefore, it is not considered in this technology.

[0129] Typical unit output characteristics

[0130] Thermal power units

[0131] The output model of thermal power unit is:

[0132]

[0133]

[0134] In the formula, and Respectively represent the fuel cost and start-up and shutdown cost of thermal power units. Th ,b Th and cTh is the energy consumption coefficient of the thermal power unit. Th,t is the power generation capacity of the thermal power unit. Th,t It is the power generation state variable of the thermal power unit, a 0-1 variable, 1 means the thermal power unit is in operation, and 0 means the thermal power unit is in shutdown. Th,t It represents the startup cost of a thermal power unit, which is related to the cold start and hot start status of the thermal power unit.

[0135] Wind turbines

[0136] Natural wind has a strong randomness, and the output power of wind turbines fluctuates with wind speed. Combining Weibull distribution to simulate natural wind speed, the probability density function is as follows:

[0137]

[0138] In the formula, v represents the natural wind speed, and φ and θ represent the shape and scale parameters of the distribution function. When the wind speed is within the acceptable range of the unit, the unit power increases with the increase of wind speed. However, if it exceeds the acceptable range, that is, the wind speed is too low or too high, the wind turbine will not start to avoid damage to the unit. The functional relationship between wind power output and wind speed is as follows.

[0139]

[0140] In the formula, Q j,w (t) is the available output of wind turbine j at time t; g r is the rated output power of the wind turbine; v i,w 、v o,w Cut is the cut-in and cut-out wind speed respectively; v r,w is the rated wind speed; v(t) is the actual wind speed at time t.

[0141] In some embodiments, the ramp service market transaction process is clarified and a market clearing model ramp service market transaction mechanism is established.

[0142] Similar to the electric energy market, ramping service transactions also adopt the day-ahead transaction declaration model, with the independent trading center (ISO) publishing the real-time ramping demand for day T based on market demand on day T-1. Climbing service transaction entities with flexible resources that can be admitted bid, and the bidding content includes the time-slot capacity and price that the market entity can provide. ISO summarizes the bidding information of each entity, matches and clears the prices of each time period, and publishes it to the market entity. At this time, the market entity can modify the bidding information, and submit the modified declaration to ISO for final price clearing.

[0143] When market players bid, they use ΔQ c represents a given capacity reporting unit, and Δt is the dispatching time interval. Market players can cDivide the flexibility capacity that can be provided by the company into unified units and declare 0-k times ΔQ c .

[0144] The quotation strategy of market member N is μ N ={λ N,1 ,λ N,2 ,...,λ N,k},λ N,k It means that market player N has at most k price strategies, that is, the maximum flexibility capacity that market player N can provide is kΔQ c , and the quotation must meet λ N,k >λ N,k-1 , this is to ensure that prices increase as unit capacity increases, thereby ensuring the stability of market transactions.

[0145] N market players with flexibility qualifications participate in the day-ahead quotation, and the resulting quotation information set can be expressed as U N ={μ1,μ2,...,μ N ISO sorts the bids of market players from low to high, and sorts the prices of system flexibility capacity requirements from high to low. The intersection of the two is the clearing price, and the bid information below the market clearing price can be awarded.

[0146] ISO combines the minimum total cost of mobilization as the goal and establishes a clearing function, which can be expressed as:

[0147]

[0148] In the formula, and are the winning bids of market entity N in the mth quotation segment when adjusting the capacity upward or downward, respectively. This means that the market player did not win the bid in period t, that is, its bid in period t is higher than the market clearing price. This means that the market player won the bid in period t, and its bid in period t is less than or equal to the market clearing price, see formulas (6)-(7) for details; and Respectively represent the quotes adjusted upward and downward by market entity N in the mth quotation segment; ΔQ up and ΔQ dn are the upward and downward winning capacities in the system unit interval respectively.

[0149] The formula used to express the winning bid of market players is:

[0150]

[0151]

[0152] In the formula, represents the market clearing price in period t.

[0153] In addition, when ISO conducts unified clearing of the ramp service trading market, it needs to meet the system flexibility balance constraints.

[0154]

[0155]

[0156] In the formula, and are the winning bids of the upward and downward adjustment ramp services of market entity N in period t, respectively; Q D,up and Q D,dn They are the upward and downward flexibility capacity requirements released by ISO on T-1, respectively.

[0157] When market players settle profits, they calculate based on the marginal electricity price and the demand for ramping services published by the ISO.

[0158] As a market entity, virtual power plants participate in ramping service transactions, and their internal units must also meet unit output constraints.

[0159]

[0160] In the formula, They are the minimum and maximum values ​​of the virtual power plant’s upward adjustment capacity, respectively; They are the minimum and maximum values ​​of the virtual power plant’s downward adjustment capacity, respectively; and They are respectively the declared capacity of the virtual power plant adjusted upward / downward in period t.

[0161] The virtual power plant optimizes its internal operations by combining unit output and market clearing feedback.

[0162] In order to maximize the benefits of ramping service transactions, virtual power plants can refer to the initial bidding strategy μ of market players. N ={λ N,1 ,λ N,2 ,...,λ N,k},have ISO conducts unified clearing based on the bid curves of market entities that have submitted reports. After the first clearing of all submitted curves, market entities can adjust their quotation strategies. Finally, ISO summarizes the final submission results and conducts unified clearing and release.

[0163] There is wind power in the virtual power plant, and the wind power output is uncertain, so a certain capacity needs to be reserved for wind power during operation for adjustment.

[0164]

[0165] Wherein, δ is the capacity regulation parameter required for wind power output, and δ' is the regulation parameter converted into unit market regulation capacity. In actual calculation, δ' needs to be rounded, that is, δ'=[δ'].

[0166] In addition, when participating in the market bidding process, the virtual power plant's quotation aims to maximize revenue, so its quotation needs to be greater than or equal to the operating cost of the virtual power plant itself, and cannot be greater than the quotation ceiling issued by the ISO.

[0167]

[0168] In the formula, is the quotation for the mth quotation period of the virtual power plant, and must meet is the operating cost of the virtual power plant in the mth quotation period; ω is the flexibility quotation interval, The upper limit of the quotation limit for ISO.

[0169]

[0170] in,

[0171]

[0172] In the formula, is the operating cost of the wind turbine in period t; is the depreciation cost of the wind turbine; P t WPP is the power generation of the wind turbine.

[0173] The virtual power plant participates in the ramp service transaction with the objective function of maximizing the revenue in the ramp service market.

[0174]

[0175] In the formula, and They are respectively the regulation capacities that won the bid of the virtual power plant in period t. and Can be quoted by interval Weighted average.

[0176] In addition to the market rules constraints mentioned in this section, the constraints for virtual power plants to participate in ramping service transactions must also meet internal operating constraints, such as the internal supply and demand balance of the virtual power plant, power flow, processing constraints of each unit, and other general operating constraints, which will not be repeated here.

[0177] Exemplary Devices

[0178] The device embodiments of the present application can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0179] Figure 2 FIG. 1 is a block diagram of a virtual power plant ramping service market optimization scheduling device based on flexibility provided by an embodiment of the present application. Figure 2 As shown, the device comprises:

[0180] A determination module 21 is used to determine the internal component composition of the virtual power plant;

[0181] Establishing module 22, for establishing a ramp service market clearing model based on the internal components of the virtual power plant and in combination with preset rules, with the minimum social cost as the objective function;

[0182] The adjustment module 23 is used to adjust the declaration curve with the goal of maximizing revenue under preset constraints and in combination with the ramp service market clearing model;

[0183] The dispatch module 24 is used to obtain the feedback output curve, wherein the service market entity determines the market clearing plan based on each declared curve and feeds back the output curve; based on the feedback output curve, with the goal of minimizing social costs, the operation of each component within the virtual power plant is optimized and the dispatch strategy is determined.

[0184] In some embodiments, the internal components of the virtual power plant include: thermal power units, wind power units, and flexibility components.

[0185] In some embodiments, the output model of the thermal power unit is:

[0186]

[0187]

[0188] In the formula, and Respectively represent the fuel cost and start-up and shutdown cost of thermal power units; a Th ,b Th and c Th is the energy consumption coefficient of thermal power unit; Q Th,t is the power generation capacity of the thermal power unit; u Th,t is the power generation state variable of the thermal power unit, a 0-1 variable, 1 means the thermal power unit is in operation, 0 means the thermal power unit is in shutdown; D Th,t It represents the startup cost of a thermal power unit, which is related to the cold start and hot start status of the thermal power unit.

[0189] In some embodiments, the wind turbine output model includes:

[0190]

[0191] In the formula, v represents the natural wind speed, φ and θ represent the shape and scale parameters of the distribution function; when the wind speed is within the tolerable range of the unit, the unit power increases with the increase of wind speed, but if it exceeds the tolerable range, that is, the wind speed is too low or too high, the wind turbine will not be turned on to avoid damage to the machine body; the functional relationship between wind power output and wind speed is as follows;

[0192]

[0193] In the formula, Q j,w (t) is the available output of wind turbine j at time t; g r is the rated output power of the wind turbine; v i,w 、v o,w Cut is the cut-in and cut-out wind speed respectively; v r,w is the rated wind speed; v(t) is the actual wind speed at time t.

[0194] In some embodiments, the preset rules include: in combination with the day-ahead transaction declaration mode, the independent trading center publishes the ramp demand for day T according to market demand on day T-1; the accessible trading entities with flexible resources bid, and the bidding content includes the output curve and price that the market entity can provide; the independent trading center summarizes the bidding information of each entity, and matches and clears the prices of each time period and publishes it to the market entity. At this time, the market entity can modify the bidding information. After modifying the declaration, the independent trading center finally performs the final clearing and forms the clearing price;

[0195] When market players bid, Δt is used to represent the scheduling time interval, ΔQ c It represents a given capacity declaration unit. The product of the two in a period of time is the total ramp-up amount in this period of time. Market players determine the total ramp-up amount based on ΔQ. c Divide the flexibility capacity that can be provided by the company into unified units and declare 0-k times ΔQ c ; The quotation strategy of market member N is μ N ={λ N,1 ,λ N,2 ,...,λ N,k},λ N,k It means that market player N has at most k price strategies, that is, the maximum flexibility capacity that market player N can provide is kΔQ c , and the quotation must meet λ N,k >λ N,k-1 ;

[0196] N market players with flexibility qualifications participate in the day-ahead quotation, and the resulting quotation information set can be expressed as U N={μ1,μ2,...,μ N The independent trading center sorts the bids of market players from low to high, and sorts the prices of system flexibility capacity requirements from high to low. The intersection of the two is the clearing price. The bid information that is lower than the market clearing price can be the winning bid.

[0197] The independent trading center takes the minimum total mobilization cost as the goal and establishes a clearing function, which can be expressed as:

[0198]

[0199] In the formula, and are the winning bids of market entity N in the mth quotation segment when adjusting the capacity upward or downward, respectively. This means that the market player did not win the bid in period t, that is, its bid in period t is higher than the market clearing price. This means that the market player won the bid in period t, and its bid in period t is less than or equal to the market clearing price, see formulas (6)-(7) for details; and Respectively represent the quotes adjusted upward and downward by market entity N in the mth quotation segment; ΔQ up and ΔQ dn are the upward and downward bid capacities in the system unit interval, respectively;

[0200] The formula used to express the winning bid of market players is:

[0201]

[0202]

[0203] In the formula, represents the market clearing price in period t;

[0204] When independent trading centers perform unified clearing of ramp service transactions, they need to meet the system flexibility balance constraint;

[0205]

[0206]

[0207] In the formula, and are the winning bids of the upward and downward climbing services of market entity N in period t, respectively; Q D,up and Q D,dn are the upward and downward flexibility capacity requirements published by the independent trading center on T-1, respectively;

[0208] When market players settle their earnings, they calculate based on the marginal electricity price and the real-time demand for ramping published by the independent trading center;

[0209] Virtual power plants participate in ramp-up transactions as market entities, and their internal units must also meet unit output constraints;

[0210]

[0211] In the formula, They are the minimum and maximum values ​​of the virtual power plant’s upward adjustment capacity, respectively; They are the minimum and maximum values ​​of the virtual power plant’s downward adjustment capacity, respectively; and They are respectively the declared capacity of the virtual power plant adjusted upward / downward in period t.

[0212] In some embodiments, based on the market clearing scheme, the virtual power plant performs internal operation optimization and determines a dispatch strategy, including:

[0213] Reference market players’ initial quotation strategy μ N ={λ N,1 ,λ N,2 ,...,λ N,k},have

[0214] During operation of the virtual power plant, a certain amount of capacity must be reserved for wind power for regulation

[0215]

[0216] In the formula, δ is the capacity regulation parameter required for wind power output, and δ' is the regulation parameter converted into unit market regulation capacity. In actual calculation, δ' needs to be rounded, that is, δ' = [δ'];

[0217] The virtual power plant quotation aims to maximize revenue. Its quotation needs to be greater than or equal to the operating cost of the virtual power plant itself, and cannot be greater than the quotation ceiling issued by the ISO.

[0218]

[0219] In the formula, is the quotation for the mth quotation period of the virtual power plant, and must meet is the operating cost of the virtual power plant in the mth quotation period; ω is the flexibility quotation interval, The quotation ceiling of ISO limit;

[0220]

[0221] in,

[0222]

[0223] In the formula, is the operating cost of the wind turbine in period t; is the depreciation cost of the wind turbine; P t WPP is the power generation of the wind turbine;

[0224] The objective function of the virtual power plant participating in ramp trading is as follows

[0225]

[0226] In the formula, and are the regulation capacities of the virtual power plant that won the bid in period t; and Can be quoted by interval Weighted average.

[0227] In some embodiments, the preset constraints include: internal supply and demand balance of the virtual power plant, power flow, and processing constraints of each unit.

[0228] Exemplary Electronic Devices

[0229] Below, an electronic device according to an embodiment of the present application is described with reference to the drawings. Figure 3 A block diagram of an electronic device according to an embodiment of the present application is illustrated.

[0230] like Figure 3 As shown, electronic device 300 includes one or more processors 310 and memory 320 .

[0231] The processor 310 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 300 to perform desired functions.

[0232] The memory 320 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 310 may run the program instructions to implement the flexibility-based virtual power plant ramping service market optimization scheduling method and / or other desired functions of the various embodiments of the present application described above. Various contents such as category correspondences may also be stored in the computer-readable storage medium.

[0233] In one example, the electronic device 300 may further include: an input device 330 and an output device 340 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0234] In addition, the input device 330 may also include, for example, a keyboard, a mouse, an interface, etc. The output device 340 may output various information to the outside, including analysis results, etc. The output device 340 may include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0235] Of course, to simplify, Figure 3 Only some of the components in the electronic device related to the present application are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0236] Exemplary computer program products and computer-readable storage media

[0237] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the flexibility-based virtual power plant ramping service market optimization scheduling method according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0238] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0239] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, causes the processor to execute the steps of the flexibility-based virtual power plant ramping service market optimization scheduling method according to various embodiments of the present application described in the above “Exemplary Method” section of this specification.

[0240] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0241] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A flexibility-based virtual power plant ramping service market optimization scheduling method, characterized in that: include: Determine the internal components of the virtual power plant; Based on the internal components of the virtual power plant and in combination with preset rules, a ramp service market clearing model is established with the minimum social cost as the objective function; With the goal of maximizing revenue, under the preset constraints, the declaration curve is adjusted in combination with the ramp service market clearing model; Obtaining the output curves fed back, wherein the service market players determine the market clearing plan based on each declared curve and feed back the output curves; Based on the feedback output curve, with the goal of minimizing social cost, the operation of each component within the virtual power plant is optimized and the scheduling strategy is determined.

2. The flexibility-based virtual power plant ramping service market optimization scheduling method according to claim 1 is characterized in that: The internal components of the virtual power plant include: thermal power units, wind power units, and flexibility components.

3. The flexibility-based virtual power plant ramping service market optimization scheduling method according to claim 2 is characterized in that: The output model of the thermal power unit is: In the formula, and Respectively represent the fuel cost and start-up and shutdown cost of thermal power units; a Th ,b Th and c Th is the energy consumption coefficient of thermal power unit; Q Th,t is the power generation capacity of the thermal power unit; u Th,t is the power generation state variable of the thermal power unit, a 0-1 variable, 1 means the thermal power unit is in operation, 0 means the thermal power unit is in shutdown; D Th,t It represents the startup cost of a thermal power unit, which is related to the cold start and hot start status of the thermal power unit.

4. The flexibility-based virtual power plant ramping service market optimization scheduling method according to claim 2 is characterized in that: The wind turbine output model includes: In the formula, v represents the natural wind speed, φ and θ represent the shape and scale parameters of the distribution function; when the wind speed is within the tolerable range of the unit, the unit power increases with the increase of wind speed, but if it exceeds the tolerable range, that is, the wind speed is too low or too high, the wind turbine will not be turned on to avoid damage to the machine body; the functional relationship between wind power output and wind speed is as follows; In the formula, Q j,w (t) is the available output of wind turbine j at time t; g r is the rated output power of the wind turbine; v i,w 、v o,w Cut is the cut-in and cut-out wind speed respectively; v r,w is the rated wind speed; v(t) is the actual wind speed at time t.

5. The flexibility-based virtual power plant ramping service market optimization scheduling method according to claim 1 is characterized in that: The preset rules include: combined with the day-ahead transaction declaration model, the independent trading center publishes the ramp-up demand for day T based on market demand on day T-1; the trading entities with flexible resources that can be admitted bid, and the bidding content includes the output curve and price that the market entity can provide; the independent trading center summarizes the bidding information of each entity, and matches and clears the prices of each time period and publishes them to the market entity. At this time, the market entity can modify the bidding information. After modifying the declaration, the independent trading center will finally perform the final clearing and form the clearing price; When market players bid, Δt is used to represent the scheduling time interval, ΔQ c It represents a given capacity declaration unit. The product of the two in a period of time is the total ramp-up amount in this period of time. Market players determine the total ramp-up amount based on ΔQ. c Divide the flexibility capacity that can be provided by the company into unified units and declare 0-k times ΔQ c ; The quotation strategy of market member N is μ N ={λ N,1 ,λ N,2 ,...,λ N,k },λ N,k It means that market player N has at most k price strategies, that is, the maximum flexibility capacity that market player N can provide is kΔQ c , and the quotation must meet λ N,k >λ N,k-1 ; N market players with flexibility qualifications participate in the day-ahead quotation, and the resulting quotation information set can be expressed as U N ={μ1,μ2,...,μ N }; The independent trading center sorts the bids of market players from low to high, and sorts the prices of system flexibility capacity requirements from high to low. The intersection of the two is the clearing price. The bid information that is lower than the market clearing price can be the winning bid; The independent trading center takes the minimum total mobilization cost as the goal and establishes a clearing function, which can be expressed as: In the formula, and are the winning bids of market entity N in the mth quotation segment when adjusting the capacity upward or downward, respectively. This means that the market player did not win the bid in period t, that is, its bid in period t is higher than the market clearing price. This means that the market player won the bid in period t, and its bid in period t is less than or equal to the market clearing price, see formulas (6)-(7) for details; and Respectively represent the quotes adjusted upward and downward by market entity N in the mth quotation segment; ΔQ up and ΔQ dn are the upward and downward bid capacities in the system unit interval, respectively; The formula used to express the winning bid of market players is: In the formula, represents the market clearing price in period t; When independent trading centers perform unified clearing of ramp service transactions, they need to meet the system flexibility balance constraint; In the formula, and are the winning bids of the upward and downward climbing services of market entity N in period t, respectively; Q D,up and Q D,dn are the upward and downward flexibility capacity requirements published by the independent trading center on T-1, respectively; When market players settle their earnings, they calculate based on the marginal electricity price and the real-time demand for ramping published by the independent trading center; Virtual power plants participate in ramp-up transactions as market entities, and their internal units must also meet unit output constraints; In the formula, They are the minimum and maximum values ​​of the virtual power plant’s upward adjustment capacity, respectively; They are the minimum and maximum values ​​of the virtual power plant’s downward adjustment capacity, respectively; and They are respectively the declared capacity of the virtual power plant adjusted upward / downward in period t.

6. The flexibility-based virtual power plant ramping service market optimization scheduling method according to claim 5 is characterized in that: Based on the market clearing scheme, the virtual power plant optimizes internal operations and determines a dispatching strategy, including: Reference market players’ initial quotation strategy μ N ={λ N,1 ,λ N,2 ,...,λ N,k },have During operation of the virtual power plant, a certain amount of capacity must be reserved for wind power for regulation In the formula, δ is the capacity regulation parameter required for wind power output, and δ' is the regulation parameter converted into unit market regulation capacity. In actual calculation, δ' needs to be rounded, that is, δ' = [δ']; The virtual power plant quotation aims to maximize revenue. Its quotation needs to be greater than or equal to the operating cost of the virtual power plant itself, and cannot be greater than the quotation ceiling issued by the ISO. In the formula, λ m vpp is the quotation for the mth quotation period of the virtual power plant, and must meet is the operating cost of the virtual power plant in the mth quotation period; ω is the flexibility quotation interval, The quotation ceiling of ISO limit; in, In the formula, is the operating cost of the wind turbine in period t; is the depreciation cost of the wind turbine; P t WPP is the power generation of the wind turbine; The objective function of the virtual power plant participating in ramp trading is as follows In the formula, and are the regulation capacities of the virtual power plant that won the bid in period t; and Can be quoted by interval Weighted average.

7. The flexibility-based virtual power plant ramping service market optimization scheduling method according to claim 5 is characterized in that: The preset constraints include: internal supply and demand balance of the virtual power plant, power flow, and processing constraints of each unit.

8. A flexibility-based virtual power plant ramping service market optimization scheduling device, characterized in that: include: A determination module is used to determine the internal component composition of the virtual power plant; Establishing a module for establishing a ramp service market clearing model based on the internal components of the virtual power plant and in combination with preset rules, with the minimum social cost as the objective function; The adjustment module is used to adjust the declaration curve with the goal of maximizing revenue under preset constraints and in combination with the ramp service market clearing model; The dispatching module is used to obtain the feedback output curve, in which the service market players determine the market clearing plan based on each declared curve and feedback the output curve; based on the feedback output curve, with the goal of minimizing social costs, the operation of each component within the virtual power plant is optimized and the dispatching strategy is determined.

9. The flexibility-based virtual power plant ramping service market optimization and dispatching device according to claim 8 is characterized in that: The internal components of the virtual power plant include: thermal power units, wind power units, and flexibility components.

10. The flexibility-based virtual power plant ramping service market optimization scheduling device according to claim 9 is characterized in that: The output model of the thermal power unit is: In the formula, and Respectively represent the fuel cost and start-up and shutdown cost of thermal power units; a Th ,b Th and c Th is the energy consumption coefficient of thermal power unit; Q Th,t is the power generation capacity of the thermal power unit; u Th,t is the power generation state variable of the thermal power unit, a 0-1 variable, 1 means the thermal power unit is in operation, 0 means the thermal power unit is in shutdown; D Th,t It represents the startup cost of a thermal power unit, which is related to the cold start and hot start status of the thermal power unit.

11. The flexibility-based virtual power plant ramping service market optimization scheduling device according to claim 8 is characterized in that: The wind turbine output model includes: In the formula, v represents the natural wind speed, φ and θ represent the shape and scale parameters of the distribution function; when the wind speed is within the tolerable range of the unit, the unit power increases with the increase of wind speed, but if it exceeds the tolerable range, that is, the wind speed is too low or too high, the wind turbine will not be turned on to avoid damage to the machine body; the functional relationship between wind power output and wind speed is as follows; In the formula, Q j,w (t) is the available output of wind turbine j at time t; g r is the rated output power of the wind turbine; v i,w 、v o,w Cut is the cut-in and cut-out wind speed respectively; v r,w is the rated wind speed; v(t) is the actual wind speed at time t.

12. The flexibility-based virtual power plant ramping service market optimization scheduling device according to claim 8 is characterized in that: The preset rules include: combined with the day-ahead transaction declaration model, the independent trading center publishes the ramp-up demand for day T based on market demand on day T-1; the trading entities with flexible resources that can be admitted bid, and the bidding content includes the output curve and price that the market entity can provide; the independent trading center summarizes the bidding information of each entity, and matches and clears the prices of each time period and publishes them to the market entity. At this time, the market entity can modify the bidding information. After modifying the declaration, the independent trading center will finally perform the final clearing and form the clearing price; When market players bid, Δt is used to represent the scheduling time interval, ΔQ c It represents a given capacity declaration unit. The product of the two in a period of time is the total ramp-up amount in this period of time. Market players determine the total ramp-up amount based on ΔQ. c Divide the flexibility capacity that can be provided by the company into unified units and declare 0-k times ΔQ c ; The quotation strategy of market member N is μ N ={λ N,1 ,λ N,2 ,...,λ N,k },λ N,k It means that market player N has at most k price strategies, that is, the maximum flexibility capacity that market player N can provide is kΔQ c , and the quotation must meet λ N,k >λ N,k-1 ; N market players with flexibility qualifications participate in the day-ahead quotation, and the resulting quotation information set can be expressed as U N ={μ1,μ2,...,μ N }; The independent trading center sorts the bids of market players from low to high, and sorts the prices of system flexibility capacity requirements from high to low. The intersection of the two is the clearing price. The bid information that is lower than the market clearing price can be the winning bid; The independent trading center takes the minimum total mobilization cost as the goal and establishes a clearing function, which can be expressed as: In the formula, and are the winning bids of market entity N in the mth quotation segment when adjusting the capacity upward or downward, respectively. This means that the market player did not win the bid in period t, that is, its bid in period t is higher than the market clearing price. This means that the market player won the bid in period t, and its bid in period t is less than or equal to the market clearing price, see formulas (6)-(7) for details; and Respectively represent the quotes adjusted upward and downward by market entity N in the mth quotation segment; ΔQ up and ΔQ dn are the upward and downward bid capacities in the system unit interval, respectively; The formula used to express the winning bid of market players is: In the formula, represents the market clearing price in period t; When independent trading centers perform unified clearing of ramp service transactions, they need to meet the system flexibility balance constraint; In the formula, and are the winning bids of the upward and downward climbing services of market entity N in period t, respectively; Q D,up and Q D,dn are the upward and downward flexibility capacity requirements published by the independent trading center on T-1, respectively; When market players settle their earnings, they calculate based on the marginal electricity price and the real-time demand for ramping published by the independent trading center; Virtual power plants participate in ramp-up transactions as market entities, and their internal units must also meet unit output constraints; In the formula, They are the minimum and maximum values ​​of the virtual power plant’s upward adjustment capacity, respectively; They are the minimum and maximum values ​​of the virtual power plant’s downward adjustment capacity, respectively; and They are respectively the declared capacity of the virtual power plant adjusted upward / downward in period t.

13. The flexibility-based virtual power plant ramping service market optimization scheduling device according to claim 12 is characterized in that: Based on the market clearing scheme, the virtual power plant optimizes internal operations and determines a dispatching strategy, including: Reference market players’ initial quotation strategy μ N ={λ N,1 ,λ N,2 ,...,λ N,k },have During operation of the virtual power plant, a certain amount of capacity must be reserved for wind power for regulation In the formula, δ is the capacity regulation parameter required for wind power output, and δ' is the regulation parameter converted into unit market regulation capacity. In actual calculation, δ' needs to be rounded, that is, δ' = [δ']; The virtual power plant quotation aims to maximize revenue. Its quotation needs to be greater than or equal to the operating cost of the virtual power plant itself, and cannot be greater than the quotation ceiling issued by the ISO. In the formula, is the quotation for the mth quotation period of the virtual power plant, and must meet is the operating cost of the virtual power plant in the mth quotation period; ω is the flexibility quotation interval, The quotation ceiling of ISO limit; in, In the formula, is the operating cost of the wind turbine in period t; is the depreciation cost of the wind turbine; P t WPP is the power generation of the wind turbine; The objective function of the virtual power plant participating in ramp trading is as follows In the formula, and are the regulation capacities of the virtual power plant that won the bid in period t; and Can be quoted by interval Weighted average.

14. The flexibility-based virtual power plant ramping service market optimization scheduling device according to claim 12 is characterized in that: The preset constraints include: internal supply and demand balance of the virtual power plant, power flow, and processing constraints of each unit.

15. An electronic device, characterized in that: include: A processor, and a memory for storing a program executable by the processor; The processor is configured to implement any one of claims 1 to 7 by running the program in the memory.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to execute the flexibility-based virtual power plant ancillary service market optimization scheduling method as described in any one of claims 1 to 7.

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