CN111967925A

CN111967925A - Multi-virtual power plant P2P transaction method, system, terminal and medium

Info

Publication number: CN111967925A
Application number: CN202010441844.5A
Authority: CN
Inventors: 徐志宇; 吕晓俞; 王宁; 许维胜; 付敏
Original assignee: Tongji University
Current assignee: Tongji University
Priority date: 2020-05-22
Filing date: 2020-05-22
Publication date: 2020-11-20

Abstract

The application provides a multi-virtual power plant P2P trading method, a system, a terminal and a medium, which solves the problems that in the prior art, most of researches on P2P trading only consider a forward path from local scheduling to P2P trading negotiation, namely, the result of the local scheduling is the initial condition of the P2P negotiation, the result of the P2P negotiation does not influence the optimal scheduling of a virtual power plant, and the large-scale calculation caused by the global scheduling of the system and the small power generation potential of the virtual power plant are caused, can more greatly exert the power generation potential of electricity-selling VPPs, can be matched with more P2P trading orders, and obtain greater benefits; each virtual power plant is independently scheduled in parallel by using a P2P trading and multi-round iteration mode, so that the problem of large-scale calculation caused by system global scheduling is avoided; the virtual power plant has the opportunity to dynamically adjust the behavior according to the market supply and demand conditions, so that higher income is obtained.

Description

Multi-virtual power plant P2P transaction method, system, terminal and medium

Technical Field

The application relates to the field of optimal scheduling of power systems, in particular to a multi-virtual power plant P2P trading method, system, terminal and medium.

Background

With the problems of environment deterioration, resource shortage and the like, the attention of countries in the world on distributed power supplies is higher and higher. However, the distributed power supply often has the characteristics of small capacity, large quantity, intermittent output, randomness and the like, so that the traditional scheduling mode is difficult to effectively utilize the distributed power supply. The virtual power plant is used as a method for solving the grid-connected problem of a plurality of distributed power supplies, takes communication and aggregation as the core, and realizes aggregation and coordination optimization of distributed resources such as distributed wind power, distributed photovoltaic, an energy storage system, controllable loads, electric vehicles and the like through an advanced information communication technology and a software system, and is used as a power supply coordination management system for a special power plant to participate in the operation of a power market and a power grid.

The P2P trading mode is started, and a new idea is provided for cooperation and trading of multiple virtual power plants. Compared with the traditional centralized transaction mode, a control center processes information and makes transaction decisions in a centralized mode, the P2P transaction adopts a decentralized and flat transaction mode, a more competitive market can be created, and monopoly behavior of a few electric quantity companies can be prevented. In P2P trading, an automated trading mechanism is an important research direction. However, most of the existing researches on P2P trading only consider a forward path from local scheduling to P2P trading negotiation, that is, the result of the local scheduling is the initial condition negotiated by P2P, and the result negotiated by P2P does not affect the optimized scheduling of the virtual power plant, so that the trading orders are fewer and the profit is low. Under the condition, because each virtual power plant can not be independently scheduled in parallel, the problem of large-scale calculation is caused; moreover, as the result of P2P negotiation does not affect the optimal scheduling of the virtual power plant, the virtual power plant cannot be adjusted in time according to the market supply situation, resulting in many errors in the transaction process.

Content of application

In view of the above drawbacks of the prior art, an object of the present application is to provide a method, a system, a terminal, and a medium for trading multiple virtual power plants P2P, so as to solve the problem that most of the research on P2P trading in the prior art only considers a forward path from local scheduling to P2P trading negotiation, that is, the result of local scheduling is the initial condition negotiated by P2P, and the result negotiated by P2P does not affect the optimized scheduling of virtual power plants, so that orders for trading are fewer and the profit is not high. Under the condition, because each virtual power plant can not be independently scheduled in parallel, the problem of large-scale calculation is caused; moreover, as the result of the P2P negotiation does not affect the optimal scheduling of the virtual power plant, the virtual power plant cannot be adjusted in time according to the market supply condition, which causes many errors in the transaction process.

To achieve the above and other related objects, the present application provides a multi-virtual power plant P2P trading method based on a two-tier interaction mechanism, including: the method comprises a local scheduling layer and a negotiation layer, and comprises the following steps: each virtual power plant locally schedules according to the self information at a local scheduling layer, then releases the self information and the determined tradable electric quantity to a P2P trading market, and selects a trading object; based on a price negotiation algorithm, each virtual power plant negotiates with a corresponding transaction object in a negotiation layer and adjusts the transaction price, and a negotiation result of successful transaction order matching is sent to the local scheduling layer; and the virtual power plant utilizes the negotiation result to adjust orders in the local scheduling layer, and uploads updated self information and the tradable electric quantity to the P2P trading market so as to select a new trading object.

In an embodiment of the application, after each virtual power plant performs local scheduling according to its own information in a local scheduling layer, the own information and the determined tradable electric quantity are issued to a P2P trading market, and a mode of selecting a trading object includes: the multi-virtual power plant realizes local scheduling aiming at maximizing economic benefits according to self information at a local scheduling layer; determining a tradable amount of electricity according to the power generation potential value obtained by local scheduling; publishing self information and the tradable electric quantity to the P2P trading market, and selecting one or more trading objects in the P2P trading market.

In an embodiment of the present application, the method for achieving the goal of maximizing economic profit according to the self information includes: each virtual power plant obtains the income obtained by the transaction between the virtual power plant and the outside and the punishment cost and the operation cost caused by the output deviation according to the information of the virtual power plant; obtaining the maximum value of the probability of combining the profit obtained by the transaction of the virtual power plant and the outside, the punishment cost and the operation cost caused by the output deviation and each output scene; the income obtained by trading the virtual power plant with the outside is related to the real-time electricity selling price of the power grid company and the on-line benchmarking price obtained by the local scheduled self information, and the punishment expense and the operation cost caused by the output deviation are related to the real-time electricity selling price of the power grid company, the on-line benchmarking price, the current plan and the deviation of the actual generated energy obtained by the local scheduled self information.

In an embodiment of the application, the manner of transforming the pose information and determining the tradable electric quantity according to the power generation potential value obtained by the local scheduling includes: and according to the local scheduled self information including the power generation potential value of the maximum output of all the internal equipment, the local scheduled self information is used as the tradable electric quantity.

In an embodiment of the application, based on the price negotiation algorithm, the method for each virtual power plant to negotiate with a corresponding transaction object at a negotiation layer and adjust the transaction price includes: obtaining basic step length information, market overall supply and demand relation, trading volume matching degree, time pressure, historical trading information and price satisfaction according to local scheduling information and tradeable electric quantity; and carrying out one or more times of transaction price negotiation according to the basic step length information, the market overall supply-demand relationship, the transaction amount matching degree, the time pressure, the historical transaction information and the price satisfaction degree, and adjusting the transaction price according to the negotiation times. The basic step length information and the price satisfaction degree are related to the real-time electricity selling price and the internet access post price of the power grid company obtained through the local scheduling information, the market overall supply and demand relation is related to the determined tradable electric quantity after the local scheduling, the trading quantity matching degree and the historical trading information are related to the trading quantity during each negotiation, and the time pressure is related to the negotiation times.

In an embodiment of the application, a manner that the virtual power plant utilizes the negotiation result to adjust the order in the local scheduling layer includes: each virtual power plant realizes local re-scheduling aiming at maximizing economic benefit according to the negotiation result in the local scheduling layer; determining a new tradable amount according to the power generation potential value obtained by local dispatching again, and adjusting an order; releasing the self information after local scheduling and the new tradable electric quantity to the P2P trading market, and selecting one or more new trading objects in the P2P trading market; and the local dispatching is completed on the basis of adjusting the income obtained by the transaction between the virtual power plant and the outside in the previous round.

To achieve the above and other related objects, the present application provides a multi-virtual plant P2P trading system, comprising: a local scheduling layer comprising: the system comprises an initialization module and a local scheduling adjustment module, wherein the initialization module is used for issuing self information and determined tradable electric quantity to a P2P trading market after each virtual power plant carries out local scheduling according to the self information and selecting a trading object; the negotiation layer is connected with the local scheduling layer and comprises: and the negotiation module is used for negotiating and adjusting the transaction price between each virtual power plant and the corresponding transaction object based on a price negotiation algorithm, sending a negotiation result of successful transaction order matching to the local scheduling adjustment module, so that the virtual power plants perform order adjustment on the local scheduling layer by using the negotiation result, and uploading updated self information and the transactable electric quantity to the P2P transaction market to select a new transaction object.

In an embodiment of the present application, the initialization module includes: the local scheduling unit is used for realizing local scheduling aiming at maximizing economic benefits in each virtual power plant according to self information in a local scheduling layer; the tradable electric quantity unit is used for determining tradable electric quantity according to the power generation potential value obtained by local scheduling; and the trading interest uploading unit is used for issuing self information and the tradeable electric quantity to the P2P trading market and selecting one or more trading objects in the P2P trading market.

To achieve the above and other related objects, the present application provides a multi-virtual plant P2P trading terminal, comprising: a memory for storing a computer program; a processor for running the computer program to perform the multi-virtual plant P2P trading method.

To achieve the above objects and other related objects, the present application provides a computer readable storage medium storing a computer program which, when executed, implements the multi-virtual plant P2P trading method.

To achieve the above and other related objects, the present application provides a robot comprising: one or more cameras and the multi-virtual power plant P2P trading system connected with the cameras.

As mentioned above, the multi-virtual power plant P2P trading method, system, terminal and medium of the application have the following beneficial effects:

1) the double-layer interaction mechanism is provided, the power generation potential of a seller of the virtual power plant can be greatly exerted, more P2P trading orders can be matched, and greater benefits can be obtained.

2) A virtual power plant day-ahead scheduling model is established, each virtual power plant is independently scheduled by using a P2P trading and multi-round iteration mode, and the problem of overlarge calculation complexity caused by multi-virtual power plant joint scheduling is solved.

3) A P2P negotiation algorithm based on multi-dimensional willingness is provided, and the behavior of the virtual power plant can be dynamically adjusted according to the real-time condition of the market so as to obtain more favorable price.

Drawings

Fig. 1 is a schematic structural diagram of an implementation environment in an embodiment of the present application.

Fig. 2 is a flowchart illustrating a transaction method of multiple virtual power plants P2P according to an embodiment of the present application.

Fig. 3a is a schematic diagram of the power of a VPP1 wind power scenario according to an embodiment of the present application.

Fig. 3b shows a power diagram of a photovoltaic scenario of VPP1 in an embodiment of the present application.

FIG. 4 is a diagram illustrating the result of VPP1 local scheduling under a scenario, according to an embodiment of the present invention.

Fig. 5 is a schematic diagram illustrating a P2P price negotiation process for failed order matching in an embodiment of the present application.

Fig. 6 is a schematic diagram illustrating a P2P price negotiation process for successful order matching in an embodiment of the present application.

FIG. 7 is a diagram illustrating the result of a scenario for the re-scheduling of VPP1 in an embodiment of the present application.

Fig. 8 is a schematic diagram illustrating the adjustment results of the power generation amount and the order power amount of the VPP1 according to an embodiment of the present invention.

Fig. 9 is a schematic structural diagram of a multi-virtual power plant P2P trading system in an embodiment of the present application.

Fig. 10 is a schematic structural diagram of a multi-virtual power plant P2P trading terminal in an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application is provided by way of specific examples, and other advantages and effects of the present application will be readily apparent to those skilled in the art from the disclosure herein. The present application is capable of other and different embodiments and its several details are capable of modifications and/or changes in various respects, all without departing from the spirit of the present application. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.

It is noted that in the following description, reference is made to the accompanying drawings which illustrate several embodiments of the present application. It is to be understood that other embodiments may be utilized and that mechanical, structural, electrical, and operational changes may be made without departing from the spirit and scope of the present application. The following detailed description is not to be taken in a limiting sense, and the scope of embodiments of the present application is defined only by the claims of the issued patent. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. Spatially relative terms, such as "upper," "lower," "left," "right," "lower," "below," "lower," "over," "upper," and the like, may be used herein to facilitate describing one element or feature's relationship to another element or feature as illustrated in the figures.

Throughout the specification, when a part is referred to as being "coupled" to another part, this includes not only a case of being "directly connected" but also a case of being "indirectly connected" with another element interposed therebetween. In addition, when a certain part is referred to as "including" a certain component, unless otherwise stated, other components are not excluded, but it means that other components may be included.

The terms first, second, third, etc. are used herein to describe various elements, components, regions, layers and/or sections, but are not limited thereto. These terms are only used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the scope of the present application.

Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," and/or "comprising," when used in this specification, specify the presence of stated features, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, operations, elements, components, items, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C ". An exception to this definition will occur only when a combination of elements, functions or operations are inherently mutually exclusive in some way.

As shown in fig. 1, an interaction diagram of a two-layer interaction mechanism in the embodiment of the present application is shown.

In this embodiment, the two-layer interaction mechanism includes: a local scheduling layer 11 and a negotiation layer 12;

wherein, each virtual power plant carries out local optimized dispatching in the local dispatching layer 11 to control and manage local resources; receiving information related to local scheduling from the local scheduling layer 11 at the negotiation layer 12 as an initial condition of the negotiation layer 12, negotiating by each virtual power plant in a P2P manner, and determining a final transaction price and electric quantity, wherein the negotiation layer 12 feeds back a negotiation result to the local scheduling layer 11 to reschedule a local resource by the local scheduling layer 11.

The application provides a many virtual power plant P2P transaction method, based on double-deck interactive mechanism, includes: the invention relates to a local scheduling layer and a negotiation layer, which are used for solving the problems that in the prior art, most of researches on P2P transaction only consider a forward path from local scheduling to P2P transaction negotiation, namely the result of the local scheduling is the initial condition of P2P negotiation, the result of the P2P negotiation does not influence the optimal scheduling of a virtual power plant, and the large-scale calculation and the small power generation potential of the virtual power plant are caused by the global scheduling of a system, so that the power generation potential of electricity-selling VPP can be exerted more greatly, the electricity-selling VPP can be matched with more P2P transaction orders, and the greater income is obtained; each virtual power plant is independently scheduled in parallel by using a P2P trading and multi-round iteration mode, so that the problem of large-scale calculation caused by system global scheduling is avoided; the virtual power plant has the opportunity to dynamically adjust the behavior according to the market supply and demand conditions, so that higher income is obtained.

The method comprises the following steps:

each virtual power plant locally schedules according to the self information at a local scheduling layer, then releases the self information and the determined tradable electric quantity to a P2P trading market, and selects a trading object;

based on a price negotiation algorithm, each virtual power plant negotiates with a corresponding transaction object in a negotiation layer and adjusts the transaction price, and a negotiation result of successful transaction order matching is sent to the local scheduling layer;

and the virtual power plants carry out order adjustment by using the negotiation result in the local scheduling layer, and upload updated self information and the tradable electric quantity to the P2P trading market so as to select a new trading object.

As shown in fig. 2, a flow diagram of a multi-virtual power plant P2P trading method in the embodiment of the present application is shown, a local scheduling layer in the method may implement the local scheduling layer 11 in the embodiment of fig. 1, and a negotiation layer may implement the negotiation layer in the embodiment of fig. 1.

The method comprises the following steps:

step S201: and after local scheduling is carried out on each virtual power plant at a local scheduling layer according to the self information, the self information and the determined tradable electric quantity are issued to a P2P trading market, and a trading object is selected.

Optionally, each virtual power plant realizes local scheduling aiming at maximizing economic benefit according to self information at a local scheduling layer;

determining a tradable amount of electricity according to the power generation potential value obtained by local scheduling;

publishing self information and the tradable electric quantity to the P2P trading market, and selecting one or more trading objects in the P2P trading market.

It should be noted that the P2P market has issued trading demands and self information of multiple virtual power plants.

Optionally, each virtual power plant obtains information of the power grid, such as the electricity selling and purchasing price, the state of internal equipment, the predicted output and the like, and performs day-ahead independent optimized scheduling to obtain local scheduling information. It should be noted that, during independent optimal scheduling, each virtual power plant does not know the output conditions of other virtual power plants, and only deals with the power grid. The information of the device itself includes not only the price of electricity purchased by the power grid, the state of the internal devices, and the predicted output, but is not limited in the present application.

And each virtual power plant determines the amount of tradeable electricity according to the difference of the roles. Specifically, the role of the virtual power plant with surplus power generation is S-VPP, and the tradable electric quantity is estimated power generation potential; the role of the virtual power plant with insufficient power generation is B-VPP, and the tradable power is the power in scheduling shortage. The roles played by the virtual power plant may be different for different periods of time. For example, the first round of transaction is performed in a role of S-VPP because the transaction power is surplus; but the second round of transaction causes insufficient power generation due to the reduction of transaction power, and the role of the second round of transaction is B-VPP.

Each virtual power plant distributes the information of the virtual power plant and the trading power quantity to the P2P trading market, and one or more trading objects are selected on the trading market. Preferably, each B-VPP can randomly select up to 3 transaction objects.

Optionally, the method for achieving the goal of maximizing economic benefit according to the self information includes: each virtual power plant obtains the income obtained by the transaction between the virtual power plant and the outside and the punishment cost and the operation cost caused by the output deviation according to the information of the virtual power plant; obtaining the maximum value of the probability of combining the profit obtained by the transaction of the virtual power plant and the outside, the punishment cost and the operation cost caused by the output deviation and each output scene; the income obtained by trading the virtual power plant with the outside is related to the real-time electricity selling price of the power grid company and the on-line benchmarking price obtained by the local scheduled self information, and the punishment expense and the operation cost caused by the output deviation are related to the real-time electricity selling price of the power grid company, the on-line benchmarking price, the current plan and the deviation of the actual generated energy obtained by the local scheduled self information.

Optionally, the specific step of achieving the goal of maximizing economic benefits according to the self information is shown by the following formula:

in the formula, rho represents the probability corresponding to each output scene; the function of s, n, t as superscripts or subscripts is to specify the value of the nth virtual plant at the t-period scene s. W, V and K respectively represent the penalty fee and the operation cost caused by the income and the output deviation of a virtual power plant and the outside world.

Optionally, the penalty cost and the operation cost caused by the income and the output deviation of the virtual power plant and the external trade are expressed by a formula:

W_n,t＝C_FIT,t·Q_sur,n,t-C_RTP,t·Q_short,n,t (2)

g(x)＝max(x,0) (3)

Q_sur,n,t＝g(P_n,t·Δt) (4)

Q_short,n,t＝g(-P_n,t·Δt) (5)

in the formula, C_RTP、C_FITThe price of the real-time power selling and the price of the post power on the internet of the power grid company in the transaction process with the power grid company are respectively. g (x) is an auxiliary function, Q_surAnd Q_shortRespectively representing surplus electric quantity and shortage electric quantity of a virtual power plant; p_n,tRepresenting the projected power generation on day before VPPn.

Represents the actual output force under each output force scene,

representing actual force

And planned output P_n,tThe deviation of (2). Alpha is alpha⁺、α^-Are pre-set deviation penalty coefficients for positive and negative deviations, respectively. The terms "WT", "PV", "MT", "ES", "DR" and "L" are subscripts, and may respectively indicate any one of output and equipment information of wind power generation, photovoltaic power generation, micro gas turbine, energy storage system, demand response, load, and the like, and are not limited in the present application. a. And b and c respectively represent a quadratic term coefficient, a primary term coefficient and a constant term coefficient of the operation cost of the virtual power plant. Wherein c comprises all fixed expenditures within the virtual power plant.

Optionally, the output upper and lower limit constraint conditions may be obtained according to the maximum economic income in the independent scheduling model, and the output upper and lower limit constraint conditions are expressed by a formula:

Z∈{WT,PV,MT,ES,DR} (11)

the energy storage system constraints may be expressed as:

in the formula (I), the compound is shown in the specification,

representing the charging and discharging power, eta, of the energy storage system, respectively_ch、η_disRespectively representing the efficiency coefficients of charging and discharging,

P_ES,n,trespectively representing the upper and lower output limits of the energy storage system, and respectively corresponding to the upper discharge capacity limit and the upper charge capacity limit of the energy storage system;

representing the amount of electricity, SOC, at the end of the t period of the energy storage system_n,0An initial value of the energy storage system is represented,

and representing the upper limit of the electric quantity of the energy storage system.

The constraints of demand response are expressed as:

in the formula (I), the compound is shown in the specification,

the maximum call proportion of the demand response within a single time period,

is the maximum call proportion of the demand response in the continuous period.

Optionally, the determining the tradable amount of power according to the power generation potential value obtained by the local scheduling includes: and according to the local scheduled self information including the power generation potential value of the maximum output of all the internal equipment, the local scheduled self information is used as the tradable electric quantity.

Optionally, the display is performed according to different roles of the virtual power plant:

and when the role of the virtual power plant is S-VPP, the estimated power generation potential is used as tradable electric quantity by the S-VPP and can be larger than surplus electric quantity of optimized scheduling. The estimated power generation potential is the sum of the maximum output of all internal devices, and the formula is represented as follows:

when the role of the virtual power plant is B-VPP, the tradable electric quantity is equal to the scheduling determined shortage electric quantity, and the formula is as follows:

step S202: based on a price negotiation algorithm, each virtual power plant negotiates with a corresponding transaction object in a negotiation layer, adjusts the transaction price, and sends a negotiation result of successful transaction order matching to the local scheduling layer.

Optionally, price negotiation algorithms based on multi-dimensional willingness are adopted by each virtual power plant in the negotiation layer to independently and parallelly negotiate transaction prices; and when the transaction is successful, obtaining a negotiation result comprising a final transaction price and final transaction electric quantity, updating the transaction electric quantity of both sides, and sending the negotiation result to the local scheduling layer.

Optionally, each virtual power plant independently and concurrently negotiates a trading price by using a price negotiation algorithm based on a multidimensional will, and the maximum negotiation frequency is recorded as r. Each negotiation, the S-VPP will lower its electricity selling price and the B-VPP will raise its electricity purchasing price.

If the price quoted by the buyer is higher than that quoted by the seller, the order matching is successful, the price quoted by the S-VPP is taken as the transaction price of the order, and the tradable electric quantity of the two parties is updated immediately.

Optionally, obtaining basic step length information, a market overall supply-demand relationship, a trading volume matching degree, time pressure, historical trading information and price satisfaction according to local scheduled self information and tradeable electric quantity; and carrying out one or more times of transaction price negotiation according to the basic step length information, the market overall supply-demand relationship, the transaction amount matching degree, the time pressure, the historical transaction information and the price satisfaction degree, and adjusting the transaction price according to the negotiation times. The basic step length information and the price satisfaction degree are related to the real-time electricity selling price and the internet access post price of the power grid company obtained through the local scheduling information, the market overall supply and demand relation is related to the determined tradable electric quantity after the local scheduling, the trading quantity matching degree and the historical trading information are related to the trading quantity during each negotiation, and the time pressure is related to the negotiation times.

Optionally, based on a price negotiation algorithm, each virtual power plant negotiates and adjusts the deal with the corresponding deal object in a negotiation layer

The easy price mode is expressed by the formula:

in the formula (I), the compound is shown in the specification,

respectively representing the purchase electricity price of the B-VPP and the sale electricity price of the S-VPP in the nth price negotiation,

respectively the initial electricity purchasing price of the B-VPP and the initial electricity selling price of the S-VPP. Mu is a small random number, C_TransIndicating the transmission and distribution costs of both. In the present invention, the S-VPP bears the cost of power transmission and distribution, so the B-VPP needs to take this factor into account when setting the purchase price. WN, which represents the willingness of the transaction principal to expect price negotiation to be successful, may be described as a dynamic step size of price updates. WN is influenced by several factors, ST, SDR_n,t、

HTR_n,t、

And the concrete six will are expressed, and respectively comprise basic step length, market overall supply-demand relation, transaction amount matching degree, time pressure, historical deal information and price satisfaction.

Specifically, the basic step length is always represented as:

the overall supply-demand relationship of the market influences the willingness of trading, and the formula is expressed as follows:

in the formula, a_SDRIs a preset constant. Q_ΣS,tAnd Q_ΣB,tRespectively representing the surplus total power and the shortage total power determined according to local scheduling in the market,

representing the power generation potential of all S-VPP,

representing all of the B-VPP in the market,

representing all the S-VPP in the market.

Price satisfaction represents how well the VPP accepts the current price quote, and is expressed as:

in the formula (I), the compound is shown in the specification,

the price tolerance is expressed, and the positive correlation is realized with the price satisfaction willingness.

The transaction amount matching degree represents the supply and demand relationship between one VPP and all transaction objects thereof, and the formula is as follows:

in the formula (I), the compound is shown in the specification,

the trading volume expected by the nth virtual power plant in the nth price negotiation, namely the remaining trading volume of the nth virtual power plant,

representing the sum of the expected transaction amounts of all matching objects of the nth virtual power plant in the nth price negotiation. Wherein, for the S-VPP,

in the case of the B-VPP,

historical deal records are an important factor that affects the willingness to deal. The historical transaction record will formula is expressed as:

HTR_n,t＝a_HTR+b_HTR×(1-HQR_n,t) (35)

the invention describes the influence caused by the number r of negotiations as time pressure, and the formula is expressed as follows:

step S203: and the virtual power plants carry out order adjustment by using the negotiation result in the local scheduling layer, and upload updated self information and the tradable electric quantity to the P2P trading market so as to select a new trading object.

Optionally, the step S203 includes:

each virtual power plant realizes local re-scheduling aiming at maximizing economic benefit according to the negotiation result in the local scheduling layer; and according to the negotiation, each virtual power plant carries out local optimization scheduling again.

Determining a new tradable amount according to the power generation potential value obtained by local dispatching again, and adjusting an order; as part of the tradable electric quantity, the estimated power generation potential of the virtual power plant may be higher. Thus, the rescheduled result may not fully satisfy the order of the matched trading object. To obtain greater revenue, each virtual power plant will preferentially fill higher price orders. For example, assuming that there is an order with a trading object, the trading power amount of the order is the second trading power amount, but the remaining power amount of the virtual power plant is smaller than the second trading power amount after the first trading power amount of the order of the previous round, so the order needs to be readjusted to meet the supply of the remaining power amount. And the virtual power plant still having the tradable electric quantity reselects a trading object, and returns to the step S202 to perform a new round of order negotiation.

Releasing the self information after local scheduling and the new tradable electric quantity to the P2P trading market, and selecting one or more new trading objects in the P2P trading market;

and the local dispatching is completed on the basis of adjusting the income obtained by the transaction between the virtual power plant and the outside in the previous round.

Optionally, the target of the new round of independent scheduling model of the virtual power plant is the same as that in step S201, and only the income W obtained by trading with the outside is modified:

in the formula (I), the compound is shown in the specification,

is the aggregate value of the proceeds of the first R-1 round of P2P transactions,

is the predicted P2P trade new revenue of the R-th round.

And

respectively representing the transaction price and the transaction amount between the B-VPPi and the S-VPPj in the matching order,

indicating the amount of transaction power between B-VPPi and S-VPPj after the order is adjusted. [ R ]]Is a superscript indicating the order negotiation and order reconciliation for round R.

Is the accumulated value of the P2P trading electric quantity of the previous R-1 round,

indicating that the nth virtual plant is the S-VPP,

indicating that the nth virtual plant is a B-VPP.

Representing the price of the transmission and distribution between the nth virtual power plant and the ith virtual power plant, it is assumed herein that the transmission and distribution costs are borne by the S-VPP. In the R-th wheel, the first wheel,

is a decision variable.

Is already in the R-1 roundAnd calculating a finished constant value.

The rescheduling constraints are the same as step S201, except for some constraints related to the P2P transaction. The total transaction electric quantity of the front R-1 round of matching orders and the R-th round of newly-increased matching orders cannot exceed the upper limit of the electric quantity:

since S-VPPs may overestimate power generation potential, the total amount of energy sold to other VPPs at this stage may be less than the total amount in matching orders:

0≤q_j→i,t≤Q_j→i,t (41)

optionally, after the order adjustment is completed, the new order in the R-th round may be confirmed and remain unchanged in the subsequent transaction. For a B-VPPi, the R-th new order can be expressed as:

similarly, for S-VPPj, the R < th > new order can be expressed as:

in the formula (I), the compound is shown in the specification,

is the seller transaction object set of the B-VPPi,

is a set of buyer transaction objects for S-VPPj.

In addition to this, the present invention is,

can be confirmed and used for order negotiation and order adjustment in round R + 1.

Confirming the newly added P2P transaction revenue may be expressed as:

the accumulated P2P revenue is updated as:

the accumulated P2P transaction amount is updated as:

the tradable electric quantity is updated as follows:

the multi-virtual power plant P2P trading method is described with reference to specific embodiments.

Example (b): P2P trading method among 14 virtual power plants.

The method is based on a two-layer interaction mechanism, and the two-layer interaction mechanism comprises the following steps: a local scheduling layer and a negotiation layer.

The method comprises the following steps:

the first stage is as follows: according to virtual power plants by local scheduling layerThe information of the power selling and purchasing price, the state of internal equipment, the predicted output and the like of the power grid; the components of each virtual power plant and the corresponding maximum output parameters are shown in table 1 (the parameters of wind power, photovoltaic and load are maximum values in 24 time periods). Through scene generation and pruning techniques. Each virtual power plant generates 20 representative classical wind-solar output scenes, and fig. 3 shows a VPP1 wind scene, wherein (a) is a wind power scene and (b) is a photovoltaic scene. In the day-ahead market, the penalty factor for the output bias is set to α⁺0.4 and α^-When the power transmission and distribution price is ignored, the time-of-use electricity price is adopted for both the real-time electricity selling price and the internet post price, as shown in table 2.

TABLE 1 VPP component parts and corresponding maximum output parameters (kW)

TABLE 2 RTP and FIT in the day ahead market

Time of day	FIT(CNY/kWh)	RTP(CNY/kWh)
			7:00–22:00	0.40	0.75
22:00–7:00(D+1)	0.28	0.63

After self information is obtained, each virtual power plant carries out local independent scheduling, and the aim is to maximize economic benefits:

W, V and K are calculated through the formulas (2) to (9) to respectively represent the income obtained by the transaction of a virtual power plant and the outside and the penalty cost and the operation cost caused by the output deviation, and the output upper and lower limit constraint conditions and the energy storage system constraint conditions are obtained through the formulas (10) to (20); FIG. 4 illustrates the results of a local scheduling of VPP1 in a scenario. Most of the time, the VPP1 has surplus power generation and has more controllable resources in the VPP 1. The purchase price of the external power grid is higher at 7:00-22:00, and the output of the VPP1 gas turbine unit is higher. At a temperature of 0: 00-8: 00, the purchase price of the external power grid is lower, the VPP1 gas turbine set keeps lower output, and the potential of generating electricity is larger.

After the local scheduling is finished, the estimated power generation potential is used as tradable electric quantity by the S-VPP, and the estimated power generation potential can be larger than surplus electric quantity of the optimized scheduling. And the estimated power generation potential is the sum of the maximum output of all internal equipment, and then the B-VPP randomly selects at most 3S-VPPs for trading to complete the order initialization stage.

And a second stage: based on a price negotiation algorithm, a negotiation layer negotiates with the transaction object and adjusts the transaction price according to the negotiation times:

maximum number of negotiations is noted as

Each negotiation, the S-VPP will lower its electricity selling price and the B-VPP will raise its electricity purchasing price. For different transaction objects, each virtual electricityThe price quotes for the plant may be different. And if the price quoted by the buyer is higher than that quoted by the seller, the order is successfully matched, and the price quoted by the S-VPP is taken as the transaction price of the order. The formula for the price update process is:

in the formula (I), the compound is shown in the specification,

HTR_n,t、

And calculating the basic step length, the overall supply-demand relationship of the market, the matching degree of the transaction amount, the time pressure, the historical transaction information and the price satisfaction degree according to the formulas (26) to (36) to obtain the negotiated transaction price.

Fig. 5 and 6 show 2 representative price negotiation processes in the P2P trading process, respectively.

In FIG. 5, the S-VPP10 and the B-VPP2 are relatively equivalent to each other and do not have strong transaction willingness. Although time pressure strengthens the willingness to trade in the latter half of the curve, there is no order match when the deadline is reached, resulting in a failure in price negotiation. This will update the historical deal record so that the VPP adjusts the policy in the next price negotiation round, enhancing the transaction willingness appropriately.

In FIG. 6, the transaction intent of S-VPP5 has changed abruptly. This is because the process of price negotiation is parallel and there are other transaction objects. Completion of order matching by other transaction objects results in a reduction in tradable volume in the S-VPP5 perspective. Influenced by the willingness of the transaction amount matching degree, the S-VPP5 dynamically adjusts the quotation strategy, so that the willingness of the transaction is greatly increased, and the transaction is promoted to be successful.

And (3) stage: and the local scheduling layer re-determines the tradeable electric quantity according to the negotiation result and selects a new trading object on the P2P trading market.

Since the S-VPP predicts the power generation potential, its tradable amount of power may be higher than its power generation capacity, requiring rescheduling to determine the order for P2P trading. Order reconciliation may change some orders that are matched in this round of negotiation.

The goal of the new local scheduling model of the virtual power plant is the same as that of the stage 1, and only the income W obtained by trading with the outside world is modified:

in the formula (I), the compound is shown in the specification,

is the predicted P2P trade new revenue of the R-th round.

And

indicating that VPPn is S-VPP,

VPPn is shown as B-VPP.

Representing the price of the transmission and distribution between VPPn and VPPi, it is assumed herein that the transmission and distribution costs are borne by the S-VPP. In the R-th wheel, the first wheel,

is a decision variable.

Is a fixed value that has been calculated in the R-1 round.

The rescheduling constraints are the same as for phase 1, except for some constraints related to the P2P transaction. The total transaction electric quantity of the front R-1 round of matching orders and the R-th round of newly-increased matching orders cannot exceed the upper limit of the electric quantity:

0≤q_j→i,t≤Q_j→i,t

the results of the VPP1 rescheduling and the adjustment to the matching order are shown from FIGS. 7 and 8, respectively. As can be seen from fig. 7, to meet the requirements of the P2P transaction, the output provided by the gas turbine set is changed more, and the main purpose of the energy storage system and the demand response is to alleviate the uncertainty of the output of the distributed power supply. As can be seen from fig. 8, the S-VPP develops the potential of power generation, increases the power generation amount, and has a small ratio of the adjustment power to the matching power.

After order adjustment is complete, the new order of the R-th round may be confirmed and remain unchanged for subsequent transactions. For a B-VPPi, the R-th new order can be expressed as:

similarly, for S-VPPj, the R < th > new order can be expressed as:

in the formula (I), the compound is shown in the specification,

is the seller transaction object set of the B-VPPi,

is a set of buyer transaction objects for S-VPPj.

In addition to this, the present invention is,

The confirmation of the new P2P trading gain, the updated cumulative P2P gain, the updated cumulative P2P trading power, and the updated tradable power may be:

through three rounds of order negotiation and order adjustment (i.e., setup)

) The VPPs ultimately determine the outcome of each transaction at time period P2P. The cumulative revenue and transaction amount for each VPP over a 24 hour period may then be calculated. For comparison, Table 3 also lists the results of 14 VPPs all Participating in the P2P transaction (Participating in P2P tracing, P-P2P) and all Not Participating in the P2P transaction (Not Participating in P2P tracing, N-P2P).

TABLE 3 cumulative revenue and transaction amount for each VPP over 24 hours

By engaging in the P2P transaction, all VPPs can receive higher revenue. The total profit for 14 VPPs increased 22,964CNY, with an average growth of 1640CNY per VPP. From the perspective of the tradable electric quantity, the electric generation potential is considered, so that the electric generation capacity (surplus electric quantity) of the S-VPP is increased by 14,386kWh, increased by 18.1 percent and satisfied with 92.9 percent of shortage electric quantity.

In principle similarity with the above embodiment, the present application provides a multi-virtual power plant P2P trading system, the system comprising:

a local scheduling layer comprising: the system comprises an initialization module and a local scheduling adjustment module, wherein the initialization module is used for issuing self information and determined tradable electric quantity to a P2P trading market after each virtual power plant carries out local scheduling according to the self information and selecting a trading object;

the negotiation layer is connected with the local scheduling layer and comprises: and the negotiation module is used for negotiating and adjusting the transaction price between each virtual power plant and the corresponding transaction object based on a price negotiation algorithm, sending a negotiation result of successful transaction order matching to the local scheduling adjustment module, so that the virtual power plants perform order adjustment on the local scheduling layer by using the negotiation result, and uploading updated self information and the transactable electric quantity to the P2P transaction market to select a new transaction object.

Specific embodiments are provided below in conjunction with the attached figures:

fig. 9 is a schematic structural diagram showing a multi-virtual power plant P2P trading system in an embodiment of the present application.

The system comprises:

the local scheduling layer 91 includes: the system comprises an initialization module 911 and a local scheduling adjustment module 912, wherein the initialization module is used for issuing self information and determined tradable electric quantity to a P2P trading market after each virtual power plant performs local scheduling according to the self information, and selecting a trading object;

a negotiation layer 92, connected to the local scheduling layer 91, including: the negotiation module 921 is configured to negotiate and adjust a transaction price between each virtual power plant and a corresponding transaction object based on a price negotiation algorithm, send a negotiation result of successful matching of a transaction order to the local scheduling adjustment module, so that the virtual power plants perform order adjustment on the local scheduling layer by using the negotiation result, and upload updated self information and tradable electric quantity to the P2P transaction market to select a new transaction object.

Optionally, the initialization module 911 includes: the local scheduling unit is used for realizing local scheduling aiming at maximizing economic benefits of the multiple virtual power plants in a local scheduling layer according to self information; the tradable electric quantity unit is used for determining tradable electric quantity according to the power generation potential value obtained by local scheduling; and the trading interest uploading unit is used for issuing self information and the tradeable electric quantity to the P2P trading market and selecting one or more trading objects in the P2P trading market.

Optionally, the initialization module 911 implements local scheduling aiming at maximizing economic benefit according to self information at a local scheduling layer; determining tradable electricity quantities from power generation potential values obtained by local scheduling

Optionally, the initialization module 911 obtains information of each virtual power plant, including the electricity price for sale and purchase, the state of the internal device, the predicted output, and the like of the power grid, and performs independent optimization scheduling in the future to obtain local scheduling information. It should be noted that, during independent optimal scheduling, each virtual power plant does not know the output conditions of other virtual power plants, and only deals with the power grid. The information of the device itself includes not only the price of electricity purchased by the power grid, the state of the internal devices, and the predicted output, but is not limited in the present application.

Optionally, the method for the initialization module 911 to achieve the goal of maximizing economic benefit according to the information thereof includes: each virtual power plant obtains the income obtained by the transaction between the virtual power plant and the outside and the punishment cost and the operation cost caused by the output deviation according to the information of the virtual power plant; obtaining the maximum value of the probability of combining the profit obtained by the transaction of the virtual power plant and the outside, the punishment cost and the operation cost caused by the output deviation and each output scene; the income obtained by trading the virtual power plant with the outside is related to the real-time electricity selling price of the power grid company and the on-line benchmarking price obtained by the local scheduled self information, and the punishment expense and the operation cost caused by the output deviation are related to the real-time electricity selling price of the power grid company, the on-line benchmarking price, the current plan and the deviation of the actual generated energy obtained by the local scheduled self information.

Optionally, the specific step of the initialization module 911 according to its own information to achieve the goal of maximizing economic benefits is shown by the following formula:

Optionally, the profit obtained by trading one virtual power plant with the outside, the penalty cost caused by the output deviation, and the operation cost in the initialization module 911 are expressed by the following formula: formulas (2) to (9) will not be described in detail herein.

Optionally, the initialization module 911 may obtain the upper and lower output limit constraint conditions according to the maximum economic income in the independent scheduling model, and may obtain the upper and lower output limit constraint conditions by using formula formulas (10) to (11). The energy storage system constraints can be obtained by using the equations (12) to (15); the constraints of the demand response are obtained by using (16) to (17), and will not be described in detail herein.

Optionally, the manner of determining the tradable electric quantity by the initialization module 911 according to the power generation potential value obtained by the local scheduling includes: and according to the local scheduled self information including the power generation potential value of the maximum output of all the internal equipment, the local scheduled self information is used as the tradable electric quantity.

Optionally, the manner for the initialization module 911 to obtain the estimated power generation potential includes:

and when the role of the virtual power plant is S-VPP, the estimated power generation potential is used as tradable electric quantity by the S-VPP and can be larger than surplus electric quantity of optimized scheduling. The estimated power generation potential is the sum of the maximum output of all internal devices, and the formula is represented as follows: formulae (18) to (20).

Optionally, the negotiation module 921 independently negotiates the transaction price in parallel by each virtual power plant in the negotiation layer by using a price negotiation algorithm based on a multidimensional will; and when the transaction is successful, obtaining a negotiation result comprising a final transaction price and final transaction electric quantity, updating the transaction electric quantity of both sides, and sending the negotiation result to the local scheduling layer.

Optionally, the negotiation module 921 adopts a price negotiation algorithm based on multidimensional will to negotiate transaction prices independently and concurrently, and the maximum negotiation times is recorded as

Each negotiation, the S-VPP will lower its electricity selling price and the B-VPP will raise its electricity purchasing price.

Optionally, the negotiation module 921 obtains basic step length information, market overall supply-demand relationship, transaction amount matching degree, time pressure, historical transaction information, and price satisfaction according to locally scheduled self information and tradable electric quantity; and carrying out one or more times of transaction price negotiation according to the basic step length information, the market overall supply-demand relationship, the transaction amount matching degree, the time pressure, the historical transaction information and the price satisfaction degree, and adjusting the transaction price according to the negotiation times. The basic step length information and the price satisfaction degree are related to the real-time electricity selling price and the internet access post price of the power grid company obtained through the local scheduling information, the market overall supply and demand relation is related to the determined tradable electric quantity after the local scheduling, the trading quantity matching degree and the historical trading information are related to the trading quantity during each negotiation, and the time pressure is related to the negotiation times.

Optionally, the negotiation module 921 negotiates with the transaction object based on a price negotiation algorithm, and the negotiation step is expressed by a formula as follows:

in the formula (I), the compound is shown in the specification,

HTR_n,t、

The basic step length, the market overall supply-demand relation, the transaction amount matching degree, the time pressure, the historical deal information and the price satisfaction degree can be obtained by calculating the formulas (18) to (20).

Optionally, the local scheduling adjustment module 912 receives a new local scheduling from each virtual power plant at the local scheduling layer according to the negotiation result, wherein the goal of the new local scheduling is to maximize economic benefit; and according to the negotiation, each virtual power plant carries out local optimization scheduling again.

Optionally, the manner for the local scheduling adjustment module 912 to re-determine the tradable electric quantity according to the negotiation result includes:

based on a new round of independent scheduling model, local scheduling is carried out according to the obtained self information to obtain local scheduling information for realizing the maximum economic benefit;

and obtaining estimated power generation potential according to the local scheduling information to serve as tradable electric quantity.

Specifically, the goal of the new round of independent scheduling model of the virtual power plant is the same as step S201, and only the earnings W traded with the outside world are modified by equations (37) and (38).

similarly, for S-VPPj, the R < th > new order can be expressed as:

in the formula (I), the compound is shown in the specification,

is the seller transaction object set of the B-VPPi,

is a set of buyer transaction objects for S-VPPj.

In addition to this, the present invention is,

Confirming the newly added P2P transaction revenue may be expressed as:

the accumulated P2P revenue is updated as:

the accumulated P2P transaction amount is updated as:

the tradable electric quantity is updated as follows:

as shown in fig. 10, a schematic structural diagram of a multi-virtual power plant P2P trading terminal 100 in the embodiment of the present application is shown.

The multi-virtual power plant P2P trading terminal 100 comprises: memory 101 and processor 102 the memory 101 is used to store computer programs; the processor 102 runs a computer program to implement the multi-virtual plant P2P trading method as described in FIG. 2.

Optionally, the number of the memories 101 may be one or more, the number of the processors 102 may be one or more, and fig. 10 is taken as an example.

Optionally, the processor 102 in the multi-virtual plant P2P trading terminal 100 may load one or more instructions corresponding to the processes of the application program into the memory 101 according to the steps described in fig. 2, and the processor 102 runs the application program stored in the first memory 101, so as to implement various functions in the multi-virtual plant P2P trading method described in fig. 2.

Optionally, the memory 101 may include, but is not limited to, a high speed random access memory, a non-volatile memory. Such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state storage devices; the Processor 102 may include, but is not limited to, a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.

Optionally, the Processor 102 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.

The present application further provides a computer readable storage medium storing a computer program which when executed implements the multi-virtual plant P2P trading method as shown in FIG. 2. The computer-readable storage medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs (compact disc-read only memories), magneto-optical disks, ROMs (read-only memories), RAMs (random access memories), EPROMs (erasable programmable read only memories), EEPROMs (electrically erasable programmable read only memories), magnetic or optical cards, flash memory, or other type of media/machine-readable medium suitable for storing machine-executable instructions. The computer readable storage medium may be a product that is not accessed by the computer device or may be a component that is used by an accessed computer device.

In summary, the multi-virtual-power-plant P2P trading method, system, terminal and medium solve the problem that in most P2P trading researches in the prior art, only one forward path from local scheduling to P2P trading negotiation is considered, namely the result of the local scheduling is the initial condition of P2P negotiation, and the result of the P2P negotiation does not influence the optimal scheduling of a virtual power plant, so that trading orders are few, and the profit is low. Under the condition, because each virtual power plant can not be independently scheduled in parallel, the problem of large-scale calculation is caused; moreover, the optimized scheduling of the virtual power plant is not influenced by the P2P negotiation result, so that the virtual power plant cannot be adjusted in time according to the market supply condition, and a plurality of errors occur in the transaction process. A virtual power plant day-ahead scheduling model is established, each virtual power plant is independently scheduled by using a P2P trading and multi-round iteration mode, and the problem of overlarge calculation complexity caused by multi-virtual power plant joint scheduling is solved. A P2P negotiation algorithm based on multi-dimensional willingness is provided, and the behavior of the virtual power plant can be dynamically adjusted according to the real-time condition of the market so as to obtain more favorable price. Therefore, the application effectively overcomes various defects in the prior art and has high industrial utilization value.

The above embodiments are merely illustrative of the principles and utilities of the present application and are not intended to limit the application. Any person skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical concepts disclosed in the present application shall be covered by the claims of the present application.

Claims

1. A multi-virtual power plant P2P trading method is characterized in that based on a double-layer interaction mechanism, the trading method comprises the following steps: the method comprises a local scheduling layer and a negotiation layer, and comprises the following steps:

2. The multi-virtual power plant P2P trading method of claim 1, wherein each virtual power plant issues its own information and the determined tradable electric quantity to a P2P trading market after performing local scheduling according to its own information at a local scheduling layer, and a manner of selecting a trading object comprises:

local scheduling of each virtual power plant aiming at maximizing economic benefits is realized at a local scheduling layer according to self information;

3. The multi-virtual plant P2P trading method of claim 2, wherein the manner of achieving the goal to maximize economic benefits based on self information comprises:

each virtual power plant obtains the income obtained by the transaction between the virtual power plant and the outside and the punishment cost and the operation cost caused by the output deviation according to the information of the virtual power plant;

obtaining the maximum value of the probability of combining the profit obtained by the transaction of the virtual power plant and the outside, the punishment cost and the operation cost caused by the output deviation and each output scene;

the income obtained by trading the virtual power plant with the outside is related to the real-time electricity selling price of the power grid company and the on-line benchmarking price obtained by the local scheduled self information, and the punishment expense and the operation cost caused by the output deviation are related to the real-time electricity selling price of the power grid company, the on-line benchmarking price, the current plan and the deviation of the actual generated energy obtained by the local scheduled self information.

4. The multi-virtual power plant P2P trading method of claim 1, wherein the manner of determining the tradable amount of electricity based on the power generation potential value obtained by local scheduling comprises:

and according to the local scheduled self information including the power generation potential value of the maximum output of all the internal equipment, the local scheduled self information is used as the tradable electric quantity.

5. The multi-virtual power plant P2P trading method of claim 1, wherein based on a price negotiation algorithm, the manner of each virtual power plant negotiating and adjusting a trading price with a corresponding trading object at a negotiation layer comprises:

obtaining basic step length information, market overall supply and demand relation, trading volume matching degree, time pressure, historical trading information and price satisfaction according to local scheduling information and tradeable electric quantity;

and carrying out one or more times of transaction price negotiation according to the basic step length information, the market overall supply-demand relationship, the transaction amount matching degree, the time pressure, the historical transaction information and the price satisfaction degree, and adjusting the transaction price according to the negotiation times.

The basic step length information and the price satisfaction degree are related to the real-time electricity selling price and the internet access post price of the power grid company obtained through the local scheduling information, the market overall supply and demand relation is related to the determined tradable electric quantity after the local scheduling, the trading quantity matching degree and the historical trading information are related to the trading quantity during each negotiation, and the time pressure is related to the negotiation times.

6. The multi-virtual power plant P2P trading method of claim 1, wherein the manner in which each virtual power plant utilizes the negotiation result at the local scheduling layer to make order adjustments includes:

each virtual power plant realizes local re-scheduling aiming at maximizing economic benefit according to the negotiation result in the local scheduling layer;

determining a new tradable amount according to the power generation potential value obtained by local dispatching again, and adjusting an order;

7. A multi-virtual plant P2P transaction control system, comprising:

8. The multi-virtual plant P2P trading system of claim 7, wherein the initialization module comprises:

the local scheduling unit is used for realizing local scheduling aiming at maximizing economic benefits of the multiple virtual power plants in a local scheduling layer according to self information;

the tradable electric quantity unit is used for determining tradable electric quantity according to the power generation potential value obtained by local scheduling;

and the trading interest uploading unit is used for issuing self information and the tradeable electric quantity to the P2P trading market and selecting one or more trading objects in the P2P trading market.

9. A multi-virtual power plant P2P trading terminal, comprising:

a memory for storing a computer program;

a processor for running the computer program to perform the multi-virtual plant P2P trading method of any of claims 1-6.

10. A computer storage medium, characterized in that a computer program is stored, which when run implements the multi-virtual plant P2P trading method of any of claims 1-6.