CN117808565A - Virtual power plant multi-time bidding method considering green evidence and carbon transaction - Google Patents

Abstract

The invention discloses a virtual power plant multi-time bidding method considering green certificates and carbon transactions, relates to the technical field of power system scheduling operation, and solves the problem that the prior art lacks a VPP bidding strategy considering a joint market mechanism. The method comprises the following steps: in the first stage, the virtual power plant acquires historical data of each time period in the N-1 th trading day and substitutes the historical data into a first stage bidding model to obtain decision variable solving results of each time period in the first stage and then respectively serve as known parameters of the same time period in the N-th trading day; in the second stage, the virtual power plant acquires real-time data of the current time period in the nth trading day in real time, substitutes the known parameters, the real-time data and decision variable solving results of all time periods before the current time period in the nth trading day into a second stage bidding model, solves to obtain the decision variable solving results of the current time period in the nth trading day, and serves as the real-time bidding results of the current time period in the nth trading day.

Description

Virtual power plant multi-time bidding method considering green evidence and carbon transaction

Technical Field

The invention relates to the technical field of power system dispatching operation, in particular to a virtual power plant multi-time-period bidding method considering green certificates and carbon transactions.

Background

With the acceleration of the construction process of a novel power system, the proportion of new energy is greatly improved, and the stable operation and the energy safety supply of the power system face great challenges. Under the background, the virtual power plants (virtual power plant, VPP) integrate the distributed energy sources, the energy storage devices, the adjustable loads and other resources in a concentrated manner, so that a virtual power plant is formed to participate in the unified dispatching and management of the power grid, and meanwhile, various resources are flexibly allocated by participating in marketization transaction, so that the economic and stable operation of an energy supply and demand system is realized. Under the drive of a double-carbon target, a parallel market transaction system of electric power transaction, carbon emission right transaction and green certificate transaction is established in China. Currently, bidding strategies of VPP in the electric power spot market are being studied more abundantly. VPP can also participate in carbon emission right trade and green license trade, so with the deep fusion and reasonable engagement of various markets, research on bidding strategies of VPP participating in the combined electric-carbon market has important and profound significance.

Scholars at home and abroad have developed a great deal of research on bidding strategies of VPP participating in the electric market, the carbon market and the green evidence market. However, since the current market system is still in a state that the electric market, the carbon market and the green market are independently operated respectively under a single system, the combined operation effect of the three markets has not been studied deeply, which makes it difficult to obtain a comprehensive and synergistic management strategy in actual operation. In addition, most of the current research is to sacrifice the economic benefit of the generator set to achieve the goal of carbon emission reduction, and this mode is difficult to be sustained in practice, and is unfavorable for forming a favorable commercial mode to promote VPP active emission reduction, so that the overall carbon emission reduction efficiency is reduced. Therefore, how to establish a VPP bidding strategy considering the joint market mechanism and fully consider the influence of the carbon market on the bidding strategy thereof becomes a current urgent problem to be solved.

Disclosure of Invention

In view of the above analysis, the embodiment of the invention aims to provide a virtual power plant multi-period bidding method considering green certificates and carbon transactions, establishes a VPP bidding strategy considering a joint market mechanism, solves the problem of how to formulate the bidding strategy under the condition of multiple markets by the VPP, and further improves the competitive capacity of the VPP in the electric power market.

The invention provides a virtual power plant multi-time bidding method considering green certificates and carbon transaction, which takes the Nth transaction day as a real-time transaction day and comprises the following steps:

in a first stage, a virtual power plant acquires historical data of each period in an N-1 th trading day, substitutes the historical data into a first stage bidding model, solves to obtain a decision variable solving result of each period in the first stage, and respectively corresponds to the decision variable solving result of each period in the first stage as a known parameter of the same period in the N-th trading day;

in the second stage, the virtual power plant acquires real-time data of the current time period in the nth trading day in real time, substitutes known parameters and real-time data of the current time period and all time periods before the current time period in the nth trading day into a second stage bidding model, substitutes decision variable solving results of all time periods before the current time period in the nth trading day into the second stage bidding model, solves to obtain decision variable solving results of the current time period in the nth trading day, and takes the decision variable solving results of the current time period in the nth trading day as real-time bidding results of the current time period in the nth trading day;

Wherein the historical data and the real-time data both consider a green evidence market and a carbon trade market.

Based on the scheme, the invention also makes the following improvements:

further, the first-stage bidding model includes a first-stage objective function and a first-stage set of constraints; wherein the first stage objective function is as followsThe expression of the period is:

(1)

wherein:is a transaction period; />Is->The period virtual power plant participates in the sum of market operation costs of the day-ahead market and the real-time market; />Is->Time-consuming small gas turbine costs; />Is->The calling cost of the electric automobile in the period; />Is->Time period demand response revenue; />Is->The period virtual power plant participates in the benefits of the carbon market; />Is->The period virtual power plant participates in the benefits of the green evidence market.

Further, the method comprises the steps of,

(2)

wherein:、/>respectively->Market before time period and real-time market electricity price estimation value; />、/>Respectively->The electricity purchase quantity and the electricity sales quantity of the market in the day before the period are all decision variables of the first stage; />、/>Respectively->The electricity purchasing quantity and the electricity selling quantity of the time period real-time market are decision variables of the first stage; />Is the electricity purchasing coefficient;

(3)

wherein:the number of units of the gas turbine; />For gas turbines- >Original cost of->Taking 1 to->The method comprises the steps of carrying out a first treatment on the surface of the Boolean variable->、/>、/>Respectively represent +.>Time period gas turbine->Taking 1 as yes, taking 0 as no, and taking the two as decision variables of the first stage; />、/>Respectively gas turbines->The start cost and the stop cost of the engine; />For gas turbines->In the operation cost function->Power generation cost slope of segment->Representing the total number of segments of the running cost function; />Representation->Time period gas turbine->First->The output of the segment is a decision variable of the first stage;

(4)

wherein:is the total number of the electric automobiles; />Is->Battery acquisition cost of the electric vehicle; />Is->The number of charge and discharge cycles of the electric vehicle; />Is->Period->The battery capacity of the electric vehicle is a decision variable of the first stage; />First->The depth of discharge of the available battery of the electric vehicle; />Is->Period->The discharge power of the electric vehicle is a decision variable in the first stage; />Is->Discharging efficiency of the electric vehicle; />Is->Time period ofVehicle electric vehicle mileage; />Is->The power required to be consumed by the unit driving mileage of the electric vehicle.

Further, the method comprises the steps of,

(5)

wherein:is the overall type of demand response; / >Is->A compensation price for the class demand response; />Is->Period->The variable quantity of the class demand response load is a decision variable of the first stage;

(6)

wherein:、/>respectively represent carbonSelling price and purchasing price of market carbon quota; />Representing the market in the day beforeWind power bidding output of time-period carbon market, +.>Representing +.>Photovoltaic bid out of period carbon market, +.>Representing +.>Total bid output of the time period carbon market;

(7)

wherein:、/>respectively selling price and purchasing price for green certificates; />、/>Virtual power plants in the market before day in the green license trade market respectively +.>Green electricity amount sold in time period and green electricity amount purchased in time period; />Is>Total bid output for a period of time; />Bidding output and +.>Is a ratio of (2); />The power generation quota ratio is the power generation quota ratio of renewable energy sources; />To punish price; />A +.about.f. representing the green syndrome market in the day-ahead market>Time period wind power bidding output; />A +.about.f. representing the green syndrome market in the day-ahead market>Time period photovoltaic bidding force; />A +.about.f. representing the green syndrome market in the day-ahead market>Total bid output for a period of time.

Further, the first stage constraint set includes: the method comprises the steps of first-stage gas turbine operation constraint, first-stage electric vehicle operation constraint, first-stage demand response constraint, first-stage green certificate market constraint, first-stage virtual power plant bidding electric quantity constraint and first-stage virtual power plant internal power balance constraint;

And solving the first-stage bidding model by using a solver CPLEX, and solving to obtain a decision variable solving result of each period in the first stage.

Further, the second-stage bidding model includes a second-stage objective function and a second-stage set of constraints; second stage objective function inThe expression of the period is:

(8)

wherein,representing a current time period; />For +.>The period virtual power plant participates in the sum of market operation costs of the day-ahead market and the real-time market; />For +.>Time-consuming small gas turbine costs; />For +.>The calling cost of the electric automobile in the period; />For +.>Time period demand response revenue; />In the second stageThe period virtual power plant participates in the benefits of the carbon market; />For +.>The period virtual power plant participates in the benefits of the green evidence market.

Further, the method comprises the steps of,

(9)

wherein:、/>respectively for the first stage>The electricity purchase quantity and the electricity sales quantity of the market in the day before period are the decision variable solving result of the first stage; />、/>Respectively second stage->The electricity purchase quantity and the electricity sales quantity of the time period real-time market are decision variables of the second stage;

(10)

wherein: boolean variable、/>、/>Respectively representing the second phase +. >Time period gas turbineThe running, starting and stopping states of the system are all decision variables of the second stage; />In the real-time market representing phase two +.>Time period gas turbine->First->The output of the section is a decision variable of the second stage;

(11)

wherein:in the real-time market for phase two +.>Period->Discharging power of electric vehicle, +.>Is the reality of the second stageIn the time market->Period->The battery capacity of the electric automobile is the decision variable of the second stage.

Further, the method comprises the steps of,

(12)

wherein:in the real-time market for phase two +.>Period->The variable quantity of the class demand response load is a decision variable of the second stage;

(13)

wherein:in the real-time market representing phase two +.>Wind power bidding output in time period carbon market, +.>In the real-time market representing phase two +.>Photovoltaic bid out in period carbon market, < ->Representing the secondReal-time market for stages->Total bid output in a time period carbon market;

(14)

wherein:、/>virtual Power plant in the Green market in real-time market of the second stage, respectively +.>Green electricity amount sold in time period and green electricity amount purchased in time period; />In the real-time market for phase two +.>Total bid output for a period of time; />In the green evidence market in the real-time market representing the second phase +. >Time period wind power bidding output; />In the green evidence market in the real-time market representing the second phase +.>Time period photovoltaic bidding force; />In the green evidence market in the real-time market representing the second phase +.>Total bid output for a period of time.

Further, the second stage constraint set includes: the method comprises the steps of a second-stage gas turbine operation constraint, a second-stage electric vehicle operation constraint, a second-stage demand response constraint, a second-stage green license market constraint, a second-stage virtual power plant bidding electric quantity constraint and a second-stage virtual power plant internal power balance constraint;

and solving the bidding model of the second stage by using a solver CPLEX, and solving to obtain a decision variable solving result of each period in the second stage.

Further, the sum of settlement costs of the virtual power plant in the first stage and the second stageThe method comprises the following steps:

(15)

wherein,、/>、/>、/>、/>respectively represent the corresponding ++I when the objective function of the first stage takes the minimum value after the solution of the bidding model of the first stage is completed>、/>、/>、/>、/>；/>、、/>、/>、/>Respectively represent the corresponding ++when the objective function of the second stage takes the minimum value after the solution of the bidding model of the second stage is completed>、/>、/>、/>、/>；

(16)

In the method, in the process of the invention,、/>respectively represent +.>And the time period real-time market electricity purchase quantity and the electricity sales quantity are decision variable solving results in the first stage.

Compared with the prior art, the invention has at least one of the following beneficial effects:

1. the invention realizes the innovative strategy of simultaneously participating in multi-class electric markets, aggregating electric vehicles and exciting user demand response behaviors in the operation of the VPP, and can reduce the cost of the VPP in the peak period, thereby improving the economic benefit of the VPP. This will help to promote sustainable development of the power industry and provide a more competitive power supply solution.

2. The model provided by the invention can realize large-scale EV charge and discharge management, considers the influence of different vehicle parameters, guides a user to cut peaks and fill valleys through DR, and minimizes cost under the condition of ensuring electric power balance. This will help to optimize vehicle charge and discharge management, improving energy efficiency.

3. By comprehensively considering the benefits of the carbon market and the green license market, the invention optimizes the bidding strategy of the VPP so as to better realize the aims of environmental protection and sustainability. This will drive the application and development of clean energy, improve the sustainability of the power system, and promote the development of low carbon economy.

In the invention, the technical schemes can be mutually combined to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.

Drawings

The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, like reference numerals being used to designate like parts throughout the drawings;

FIG. 1 is a flowchart of a virtual power plant multi-time bidding method considering green certificates and carbon transactions according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a coupling framework for a VPP participation green-considered combined electric-carbon market provided by an embodiment of the present invention;

FIG. 3 is a schematic diagram of a bidding process for a VPP participating in an electricity-carbon joint market according to an embodiment of the present invention;

FIG. 4 is a diagram of a demand response bidding result provided by an embodiment of the present invention;

fig. 5 is a diagram of bidding results of a first type of electric vehicle according to an embodiment of the present invention;

fig. 6 is a diagram of bidding results of a second type of electric vehicle according to an embodiment of the present invention;

FIG. 7 is a graph showing the results of a VPP participating in a carbon market and green evidence market syndication unit bid according to an embodiment of the present invention;

FIG. 8 is a graph showing the results of a VPP not participating in a carbon market and green market syndication unit bid according to an embodiment of the present invention;

FIG. 9 is a graph showing the result of the VPP participating in the competitive bidding power of the combined market according to the embodiment of the present invention;

fig. 10 shows the VPP provided by the embodiment of the invention not participating in the combined market bidding power results.

Detailed Description

Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form a part hereof, and together with the description serve to explain the principles of the invention, and are not intended to limit the scope of the invention.

The following describes a preferred embodiment of the present invention with reference to fig. 1 to 10.

For government, through VPP multi-period bidding strategy research, the method is beneficial to promoting VPP to actively reduce emission, mobilizing the enthusiasm of VPP to participate in market competition, providing effective reference basis for constructing business modes taking VPP bidding transaction as a core, and the VPP multi-period bidding strategy makes positive contribution to the realization of the positive development of energy conservation, emission reduction and sustainable industry. For a power grid enterprise, discharge management of a large-scale electric automobile can be realized through researching a VPP multi-period bidding strategy, peak clipping and valley filling of a user can be guided through demand response, and safe and reliable operation of a power grid is not affected. For the electric power market, through VPP multi-time bidding strategy research, the economic benefit of the VPP can be improved under the condition of meeting the environmental protection and sustainability targets, and the problem that the VPP formulates bidding strategies under multiple market conditions is solved. For power users, through VPP multi-period bidding strategy research, the power supply can be optimized according to the power consumption mode adjustment according to the demand response, the energy efficiency is improved, and the power cost is reduced. For the carbon market, through VPP multi-period bidding strategy research, the bidding output level of a high-carbon unit can be reduced, and the energy output structure can be optimized, so that the development of the carbon market and the reduction of carbon emission are promoted. Through VPP multi-time bidding strategy research, market price is used as guide, VPP and user demand reaction behavior is stimulated, and VPP can be enabled to have maximum benefit and minimum cost in the process of bidding in the electricity-carbon combined market, and the aims of environmental protection and sustainability are met. Under the background, the embodiment of the invention provides a virtual power plant multi-period bidding method considering green certificates and carbon transactions, solves the problem that the VPP formulates bidding strategies under multiple market conditions, and further improves the competitiveness of the VPP in the power market. As shown in FIG. 1, the invention establishes a two-stage bidding model of VPP in the integrated operation day-ahead market, real-time market, carbon market and green license trading market. In the first stage, the VPP acquires historical data through the information platform, formulates bidding strategies and submits the bidding strategies to a market operating mechanism so as to actively participate in market competition. In the second stage, the VPP will readjust the real-time electricity-carbon combined bidding strategy according to the price of electricity discharged and the bidding power of the market in the day-ahead, and then conduct the trade according to the submitted bidding power.

The virtual power plant multi-period bidding method considering green certificates and carbon transaction provided by the embodiment of the invention is shown in fig. 1, and the nth transaction day is taken as a real-time transaction day, and the specific implementation steps are as follows:

in a first stage, a virtual power plant acquires historical data of each period in an N-1 th trading day, substitutes the historical data into a first stage bidding model, solves to obtain a decision variable solving result of each period in the first stage, and respectively corresponds to the decision variable solving result of each period in the first stage as a known parameter of the same period in the N-th trading day;

in the second stage, the virtual power plant acquires real-time data of the current time period in the nth trading day in real time, substitutes known parameters and real-time data of the current time period and all time periods before the current time period in the nth trading day into a second stage bidding model, substitutes decision variable solving results of all time periods before the current time period in the nth trading day into the second stage bidding model, solves to obtain decision variable solving results of the current time period in the nth trading day, and takes the decision variable solving results of the current time period in the nth trading day as real-time bidding results of the current time period in the nth trading day;

Wherein the historical data and the real-time data both consider a green evidence market and a carbon trade market.

Next, the specific implementation procedure of the method in this embodiment will be described below.

1. Virtual power plant participation electricity-carbon combined market operation mechanism

(1) Electric-carbon combined market operation mechanism

The carbon market is fundamentally different from the electricity market, but thermal power generation occupies a significant proportion of carbon emissions for the electricity industry. Due to this factor, there is a tight coupling between the electricity market and the carbon market. For the user side, participation in the electricity-carbon joint market can enable the electricity-carbon joint market to trade with other participants so as to achieve the aims of carbon emission reduction and renewable energy development. In the electricity-carbon combined market, if the quantity of basic carbon quota distributed by government in the primary carbon market is insufficient or surplus, the user side can participate in carbon transaction in the secondary carbon market by purchasing and selling carbon quota, so that the management and compensation of carbon emission are realized. After the green license market is considered, the user side can purchase the green license through the green license transaction platform so as to prove that the user side can actually use renewable energy sources to generate power. If the purchased green license is rich, the purchased green license can be sold in the green license market to obtain renewable energy subsidies.

A schematic of the coupling framework of VPP participation in the green-considered electro-carbon joint market is shown in fig. 2. VPP acts as a load aggregator and can participate in three types of markets simultaneously. In the electric market, VPP can participate in day-ahead market (DAM) and real-time market (RTM) of the electric market, and trade its own energy capacity as trade product with participants of other electric market. Because the VPP contains both a small gas unit and a wind turbine unit and a photovoltaic unit, in the carbon market, the VPP can purchase quotas in the carbon market and sell redundant carbon quotas. If the actual carbon emissions inside the VPP are greater than its carbon allowance, then the missing carbon allowance needs to be purchased in the carbon market; otherwise, the seller of the carbon market can participate in the transaction to sell the redundant carbon quota. In addition, the VPP can apply for the verification of green evidence according to the actual online green electric quantity, and further participate in the green evidence selling market to obtain subsidies; the small-sized gas unit part can also be used as a buyer, and the purchasing green certificate meets the carbon emission target requirement of the buyer.

(2) Virtual power plant bidding process under combined market

In a two-carbon context, VPP may participate in power spot transactions, carbon emissions trading, and green certificate trading. The trading period of the electric power spot market is thatEach generator set needs to provide a bidding curve, and the embodiment of the invention sets the trading period of the electric power spot market to be 1h. The trade period of the carbon market and the green market is +.>. The fossil energy power generation main body and the renewable energy power generation main body can participate in the carbon market in three trade modes of a carbon emission right trade station through a listing protocol trade, and trade carbon emission quota. Meanwhile, the green license can be transacted by adopting modes such as protocol transfer and the like through the green license transaction platform. Both the carbon quota allowance and the renewable energy consumption completion can affect bidding of fossil energy power generation bodies in the power spot market. The embodiment of the invention aims to study the influence of carbon market and green license market transaction on the bidding decision of a power generation main body in the power market, and the transaction weeks of green license and carbon quotaPhase of timeSet to 1 day.

At each trade period, the generator set and the load are simultaneously provided in the VPP. Thus, the VPP may choose to act as a generator or consumer in bidding. The transaction of the electric power market is settled according to the unified price. VPP does not directly affect market price as an identity of price recipients for the electricity and carbon markets under the influence of capacity limitations. But as a price recipient, a lower selling price and a higher purchasing price can be submitted to ensure that the bidding amount is all bid. Therefore, the VPP only needs to optimize its bid amount according to the predicted electricity price of the electricity market. The markets in which the VPP participates include DAM, RTM, carbon market, and green market, and the bidding flow chart is shown in fig. 3.

2. Two-stage bidding model of virtual power plant

(1) First-stage bidding model of virtual power plant

In the first stage, the VPP acquires historical data through the virtual power plant trading platform, and formulates bidding strategies based on the historical data and submits the bidding strategies to a market operating mechanism to actively participate in market competition. The core task of the VPP when participating in DAM bidding is to submit the DAM bidding scheme to the dispatch center. Because the electric quantity of the DAM and the RTM needs to be kept stable, the VPP needs to plan the bidding schemes of the DAM and the RTM at the same time, but only needs to report the DAM bidding scheme. Thus, the first-stage bidding model includes a first-stage objective function and a first-stage set of constraints; first stage objective function atThe expression of the period is:

(1)

wherein:is a transaction period; />Is->The period VPP participates in the sum of market operating costs of DAM and RTM;is->Time-consuming small gas turbine costs; />Is->The calling cost of the electric automobile in the period; />Is->Time period demand response revenue; />Is->Period VPP participates in the benefits of the carbon market; />Is->The period VPP participates in the benefits of the green market.

(2)

Wherein:、/>respectively->A time period DAM, RTM electricity price estimation value; />、/>Respectively->The electricity purchase quantity and the electricity sales quantity of the DAM in the time period are all decision variables in the first stage; / >、/>Respectively->The electricity purchasing quantity and the electricity selling quantity of the RTM in the period are decision variables in the first stage; />For electricity purchase factor, electricity purchase number generally refers to the proportion of electricity users purchase from the market in an electricity market in one country or region.

The cost of a small gas turbine consists of the running cost and the start-stop cost. The running cost of the small gas turbine covers the running cost of fuel and the like, and the starting and stopping cost of the small gas turbine considers the cost caused by starting and stopping the gas turbine.

(3)

Wherein:the number of units of the gas turbine; />For gas turbines->Original cost of->Taking 1 to->The method comprises the steps of carrying out a first treatment on the surface of the Boolean variable->、/>、/>Respectively express +.>Time period gas turbine->Taking 1 as yes, taking 0 as no, and taking the two as decision variables of the first stage; wherein (1)>Representation->Time period gas turbine->In the operating state of the device, the device is in a running state,representation->Time period gas turbine->Is not in an operating state; />Representation->Time period gas turbine->In the start-up state->Representation->Time period gas turbine->Is not in a starting state; />Representation->Time period gas turbine->In a stopped state->Representation->Time period gas turbine->Is not in a stopped state. The same gas turbine is usually only in one of operation, start and stop, i.e. in the case of- >、/>、/>One variable is 1 and the remaining two variables are 0. This is because different states of the gas turbine involve different operating parameters, control strategies and safety considerations. />、/>Respectively gas turbines->Start-up cost, stop cost of (c). Piecewise linearizing a common gas turbine secondary operation cost function, wherein +.>Total number of segments representing the running cost function, +.>For gas turbines->In the operation cost function->A power generation cost slope of the segment; />Representation->Time period gas turbine->First->The output of the segment. In the gas turbine power generation cost model, the first>Cost of power generation of a segmentThe slope is generally referred to as +.f. of the power generation curve of the gas turbine>The rate of change of the cost of power generation versus the amount of power generation over a segment (typically a different interval of power output) is a decision variable for the first phase.

EV cost mainly considers battery loss cost, and when the number of discharges exceeds a certain limit, the battery must be replaced.

(4)

Wherein:is the total number of the electric automobiles; />Is->Battery acquisition cost of the electric vehicle; />Is->The number of charge and discharge cycles of the electric vehicle; />Is->Period->The battery capacity of the electric vehicle is a decision variable of the first stage; / >First->The depth of discharge of the available battery of the electric vehicle; />Is->Period->The discharge power of the electric vehicle is a decision variable; />Is->Discharging efficiency of the electric vehicle; />Is->Period->Vehicle electric vehicle mileage; />Is->The power required to be consumed by the unit driving mileage of the electric vehicle.

Demand response revenue is the subsidy fee that the grid company gives to the VPP for users of different response types. According to the demand response subsidy policy of China, the embodiment of the invention divides the participating demand response users into three categories according to different participating demand response degrees, the subsidy cost is related to the demand response types, and the higher the participating demand response degree is, the higher the subsidy cost is.

(5)

Wherein:as the total type of demand response, in this embodiment, +.>Taking 3, the types of the demand response are respectively as follows: translatable load (first class), transferable load (second class), load shedding (third class-highest cost); />Is->A compensation price for the class demand response; />Is->Period->The variable quantity of class demand response load is the decision variable of the first stage.

The carbon trade mechanism introduces additional costs that must be paid to purchase emissions for carbon emission enterprises, while low carbon enterprises can harvest additional emissions to realize profitability. VPP is divided into two cases in the carbon trade market: 1. the new energy yield is high, and carbon quota profit can be sold; 2. the new energy is insufficient, carbon quota needs to be purchased, and if the new energy does not reach the standard, the new energy is penalized. The benefits of the carbon trade market are piecewise functions, which are as follows:

(6)/>

Wherein:can be positive or negative; />、/>The selling price and the purchasing price of the carbon market and the carbon quota are respectively expressed. />Representing +.>Wind power bidding output of time-period carbon market, +.>Representing +.>The photovoltaic bid competence of the time-period carbon market,representing +.>Total bid output of the time-slot carbon market.

When researching green market, VPP is divided into two cases, considering continuity of quotation of electronic commerce and uncertainty of wind and light power generation: firstly, the new energy is abundant and is profitable through green selling. Secondly, the new energy is insufficient, green purchasing is needed, and if the new energy does not reach the standard, the new energy is penalized. If the green market is not entered, then this benefit is not relevant. Thus, the revenue of participating in the green license market can be regarded as a piecewise function, which is as follows:

(7)

wherein:can be positive or negative; />、/>Respectively selling price and purchasing price for green certificates; />、/>VPP in DAM in green license trade market +.>Green electricity amount sold in time period and green electricity amount purchased in time period; />For +.>Total bid output of time period (including gas turbine output, wind turbine output and photovoltaic output); />Bidding output and +.>Is a ratio of (2); />The power generation quota ratio is the power generation quota ratio of renewable energy sources; />To punish the price. />Representing the green market in DAM +. >Time period wind power bidding output; />Representing the green market in DAM +.>Time period photovoltaic bidding force;representing the green market in DAM +.>Total bid output for a period of time.

The first stage constraint set that the VPP also needs to satisfy includes: the method comprises the steps of first-stage gas turbine operation constraint, first-stage electric vehicle operation constraint, first-stage demand response constraint, first-stage green market constraint, first-stage virtual power plant bidding electric quantity constraint and first-stage virtual power plant internal power balance constraint. The specific description is as follows.

1) First stage gas turbine operation constraints

(8)

(9)

(10)

(11)

(12)

(13)/>

(14)

(15)

(16)

(17)

Wherein:is->Time period gas turbine->Is a force of the (a); />、/>Respectively gas turbines->Maximum and minimum output power of (a); />For gas turbines->First->An upper output limit of the segment; />、/>Respectively gas turbines->An upward ramp rate and a downward ramp rate of (a); />、/>Respectively gas turbines->Minimum on-time and off-time of (a); />、Respectively gas turbines->Initial on-time, off-time of (a). In the present embodiment, formulas (13), (14) and (15), (16) are gas turbines respectively>Minimum on-off time and initial on-off time constraint.

2) First-stage electric automobile operation constraint

(18)

(19)

(20)

(21)

(22)

(23)/>

(24)

Wherein: 、/>Each is +.>Upper and lower limits of battery capacity of a vehicle electric vehicle; />Is->Period->The charging power of the electric vehicle is a decision variable in the first stage; />、/>Respectively +.>Upper limit of charging and discharging power of the electric vehicle; boolean variable->Representation->Period->Whether the electric automobile is in a charging state or not is 1, and whether the electric automobile is in a charging state or not is 0; />Representation->Period->Whether the electric automobile is in a discharging state or not is 1, if not, 0, and both are decision variables; />Representation->Period->Whether the electric automobile is in a state of being connected with a power grid or not is 1, and whether the electric automobile is in a state of being connected with the power grid or not is 0; />、/>Respectively the (th) in DAM>The battery capacity of the electric automobile in the beginning and ending time periods, wherein the beginning and ending time periods respectively represent 0 time period and 24 time period; />Is->Charging efficiency of an electric vehicle.

3) First stage demand response constraints

(25)

(26)

(27)

(28)

(29)

Wherein:is->Period->Class demand response upper limit; />Is->Time period demand response is a decision variable; />A maximum adjustable amount for demand response over a continuous time period; />Is->The transfer load of the time period is a decision variable of the first stage; />Is->Period transferable load; />Is->The period shifts the upper load limit.

4) Market constraints for first stage green evidence

The green certification output constraint of the green certification market is described as follows:

(30)

(31)

5) First stage VPP bidding power constraint

Due to the limitation of transmission power between the VPP and the power grid, the constraint of bidding power of the VPP in the joint market is as follows:

(32)

(33)

(34)

in the method, in the process of the invention,the maximum competitive bidding power is used for the electric power market.

6) First stage VPP internal power balance constraint

(35)

(36)

Wherein:、/>the number of wind power units and the number of photovoltaic units are respectively; />、/>Respectively +.>Market wind power generation predicted value (known quantity) and photovoltaic power generation predicted value (known quantity) before the period; />、/>Respectively->Total load in time period, fixed load.

All decision variables solved in the first stage are used as known quantities in the second stage, with superscripts added to the bar to show the distinction.

In the first stage, after the virtual power plant acquires all the historical data of 24 time periods in the N-1 transaction day, substituting the historical data into the formula, and solving a first stage bidding model by using a solver CPLEX to obtain a decision variable solving result of each time period in the first stage.

(2) Second-stage bidding model of virtual power plant

In the second stage, the VPP needs to formulate an RTM bidding strategy every time period. The second-stage bidding model comprises a second-stage objective function and a second-stage constraint condition set; second stage objective function in The expression of the period is:

(37)

wherein,representing a current time period; />For +.>The period VPP participates in the sum of market operating costs of DAM and RTM; />For +.>Time-consuming small gas turbine costs; />For +.>The calling cost of the electric automobile in the period; />For +.>Time period demand response revenue; />For +.>Period VPP participates in the benefits of the carbon market; />Is of the second orderIn section->The period VPP participates in the benefits of the green market.

The detailed expression of each part of the objective function in the second stage and the constraint condition expression to be satisfied in the second stage are as follows:

(38)

wherein:、/>respectively for the first stage>The purchase quantity and the sales quantity of the DAM in the time period are the decision variable solving result in the first stage; />、/>Respectively second stage->The electricity purchase quantity and the electricity sales quantity of the RTM in the period are decision variables in the second stage.

(39)

Wherein: boolean variable、/>、/>Respectively are provided withIn RTM representing the second phase +.>Time period gas turbine->And (3) the running, starting and stopping states of the system are the decision variables of the second stage, wherein 1 is yes, 0 is no. Wherein (1)>Representation->Time period gas turbine->In the operating state->Representation->Time period gas turbine->Is not in an operating state; / >Representation->Time period gas turbine->In the start-up state->Representation->Time period gas turbine->Is not in a starting state;representation->Time period gas turbine->In a stopped state->Representation->Time period gas turbine->Is not in a stopped state. />In RTM representing the second phase +.>Time period gas turbine->First->The output of the segment is the decision variable of the second stage.

The cost expression of the electric automobile in the second stage is as follows:

(40)

wherein:is +.>Period->Discharging power of electric vehicle, +.>Is +.>Period->The battery capacity of the electric automobile is the decision variable of the second stage.

The second stage demand response revenue expression is:

(41)/>

wherein:is +.>Period->The variable quantity of the class demand response load is the decision variable of the second stage.

Because wind power and photovoltaic prediction data in the real-time market are continuously updated, the benefits of the carbon trade market in the second stage are also piecewise functions, and the piecewise functions are as follows:

(42)

wherein:can be positive or negative; />In RTM representing the second phase +.>Wind power bidding output in the period carbon market,in RTM representing the second phase +.>Photovoltaic bid out in period carbon market, < - >In RTM representing the second phase +.>Total bid output in the time-slot carbon market.

The green evidence market in the second stage is similar to the carbon market income and is influenced by continuous updating of the wind power and photovoltaic prediction data of the real-time market, and the green evidence market income in the second stage is a piecewise function, wherein the piecewise function is as follows:

(43)

wherein:、/>VPP in RTM of the second phase respectively +.>Green electricity amount sold in time period and green electricity amount purchased in time period; />Is +.>Total bid output of time period (including gas turbine output, wind turbine output and photovoltaic output); />In the green evidence market in RTM representing the second phase +.>Time period wind power bidding output; />In the green evidence market in RTM representing the second phase +.>Time period photovoltaic bidding force; />In the green evidence market in RTM representing the second phase +.>Total bid output for a period of time.

Unlike the first stage, when the RTM bidding strategy is formulated for each period in the second stage RTM to determine the next period bidding strategy, the RTM is used for the periodThe decision variables before the period are known, so the constraints need to be modified accordingly, and the second stage constraint set that the VPP needs to meet includes: the method comprises the steps of second-stage gas turbine operation constraint, second-stage electric vehicle operation constraint, second-stage demand response constraint, second-stage green license market constraint, second-stage virtual power plant bidding electric quantity constraint and second-stage virtual power plant internal power balance constraint. The specific description is as follows.

1) Second stage gas turbine operation constraints

(44)

(45)/>

(46)

(47)

(48)

(49)

(50)

(51)

(52)

(53)

In the formula, since the RTM bidding strategy is formulated for each period in the second stage RTM, the RTM is used forThe decision variables before the time period are known. At the same time (I)>、/>And respectively solving the decision variable of the first stage.

2) Second-stage electric automobile operation constraint

(54)

(55)

(56)

(57)/>

(58)

(59)

(60)

Wherein:is +.>Period->The charging power of the electric vehicle is a decision variable of the second stage; boolean variable->In RTM representing the second phase +.>Period->Whether the electric automobile is in a charging state or not is 1, and whether the electric automobile is in a charging state or not is 0; />Representation ofIn RTM of the second stage->Period->Whether the electric automobile is in a discharging state is 1, and whether the electric automobile is in a discharging state is 0; both are decision variables; />Representing +.>Period->Whether the electric automobile is in a state of being connected with a power grid or not is 1, and whether the electric automobile is in a state of being connected with the power grid or not is 0; />For +.>Period->The storage capacity of the electric vehicle is a known quantity; />、/>In RTM of the second phase respectively +.>Battery capacity of the electric vehicle at the beginning and end time periods.

3) Second stage demand response constraints

(61)

(62)

(63)

(64)

(65)

Wherein:is +.>The time period demand response is a decision variable of the second stage; />Is +. >The transfer load of the time period is a decision variable of the second stage; />For the second stage->Period transferable load; />Solving for the first phase +.>Period->The variable quantity of the class demand response load is a decision variable solving result of the first stage; />Solving for the first phase +.>The transfer load of the time period is the decision variable solving result of the first stage.

4) Market constraints for green evidence in second stage

(66)

(67)

5) Second stage VPP bidding electric quantity constraint

(68)

(69)

6) Second stage VPP internal power balance constraint

(70)

(71)

Wherein:、/>the number of wind power units and the number of photovoltaic units are respectively; />、/>Respectively +.>Market wind power generation predicted value (known quantity) and photovoltaic power generation predicted value (known quantity) before the period. After the transaction is completed, the market operation mechanism carries out transaction settlement of the first two stages according to the DAM and RTM output clear electricity price and VPP, and the settlement cost of the first two stages of VPP is +.>The method comprises the following steps:

(72)

wherein,、/>、/>、/>、/>respectively represent the corresponding ++I when the objective function of the first stage takes the minimum value after the solution of the bidding model of the first stage is completed>、/>、/>、/>、/>；/>、、/>、/>、/>Respectively represent the corresponding ++when the objective function of the second stage takes the minimum value after the solution of the bidding model of the second stage is completed>、/>、/>、/>、/>。

(73)

In the method, in the process of the invention,、/>respectively represent +. >The electric quantity is purchased and sold in the market in real time in time period,and solving the result for the decision variable of the first stage.

And in the second stage, solving the bidding model of the second stage by using a solver CPLEX, and solving to obtain a decision variable solving result of each period in the second stage. To facilitate further understanding of the real-time process of the present embodiment, the second stage solution process will now be described in detail as follows:

in the 1 st period of the second stage, since no decision variables of all periods before the current period in the N transaction day exist, at this time, based on the known parameters of the 1 st period and real-time data, the known parameters are substituted into the bidding model of the second stage, and the solver CPLEX is utilized to solve to obtain the decision variable solving result of the 1 st period in the second stage. Then, after the virtual power plant acquires real-time data of the 2 nd time period in the N transaction day in real time, obtaining a decision variable solving result of the 1 st time period in the second stage based on the known parameters of the 1 st time period, the real-time data and the decision variable solving result of the 1 st time period in the formula (37)、/>、/>、/>、/>、/>. In the formula (37) and the related constraint conditions, the unknown quantity only remains the decision variable of the 2 nd time period in the second stage, then the known parameter of the 2 nd time period and the real-time data are substituted into the formula (37), the minimum value is solved for the objective function of the first two time periods, and finally the decision variable solving result of the 2 nd time period in the second stage is determined. The solution process of each period is similar and will not be repeated.

(3) Solving process

The change of the bidding strategy of the VPP and other new energy units in the market in the day-ahead influences the bidding strategy of the real-time combined market through decision interaction, and further influences the size of the objective function value so as to change the bidding strategy. In order to verify that the VPP participates in the two-stage combined market bidding model, each EV parameter is counted according to an EV travel rule, meanwhile, the influence of the demand response subsidy price is considered, a virtual power plant participation multi-type combined market bidding model considering the electric automobile and the demand response is established, and a commercial solver CPLEX is used for solving the mixed integer linear programming model.

In another embodiment of the present invention, an example of a virtual power plant multi-time bidding method considering green certificates and carbon transactions is also provided to verify the effectiveness of the above method, as described in detail below.

(1) Model parameters

To check the rationality of the bidding model being raised, embodiments of the present invention construct VPP from the power station of a gas turbine, a offshore wind power plant, a photovoltaic plant, and about 4000 households in the above-sea puradon harbor area. The secondary operating cost function of the gas turbine is piecewise linearized. And distinguishing the electricity purchase price from the electricity selling price, and taking the DAM and RTM electricity purchase coefficients to be 1.05. Assume 4000 families have 1000 EVs of one and two types each, and the upper and lower EV battery capacities define 95% and 15% of the battery capacity, respectively. The EV charge and discharge power is 20% of the battery capacity, and the efficiency is 90%. The demand response categories are set to three, and the price of each type of demand response patch is set to be 5 yuan/kW.h, 8 yuan/kW.h and 10 yuan/kW.h. In this example, the bidding scheme of VPP participating in the combined electricity-carbon market and green market is scheme 1, and for comparison, a comparison scheme of 2 nd is given: in scheme 2 VPP does not participate in carbon market and green market transactions.

(2) Bidding results

1) VPP revenue and cost analysis

The VPP aggregate multi-type units in this embodiment participate in the combined market of electricity and carbon and green certificate market bidding, wherein the partial incomes of the VPP participating in the combined market and the cost of each unit are shown in tables 1 and 2.

TABLE 1VPP total revenue comparison

Table 2 comparison of specific costs for each unit

As can be seen by comparing the two schemes in table 1, the participation of VPP in the carbon market and the green market helps to increase its overall revenue in the joint market. This is because VPP obtains a certain profit in the carbon market and green market, thereby increasing its total revenue. Combining tables 1 and 2 shows that the participation of VPP results in a cost reduction for the gas turbine, as the gas turbine participation in the carbon market and the green market requires purchase of quotas, resulting in an increase in cost. However, the cost of the loss of demand response and EV does not change, as the two polymeric units do not participate in the carbon market and green market, and therefore the cost remains unchanged.

2) Responsive bidding outcome analysis

The required response volume and the different response types reflect the response patterns of the different user types, as shown in fig. 4. It is observed that different demand response users may have their own features and flexibility, and thus, at each stage, various response types are actively involved.

In the decision process, the trend of the market price curve reflects the market signal, and when the market price rises, the VPP sees a higher revenue potential, thereby stimulating a more aggressive demand response to achieve higher revenue. Conversely, as electricity prices decrease, demand response may decrease due to potential revenue reduction. In addition, the balance between cost and benefit is also an important factor in decision making. The rising market price will result in lower cost responses and therefore demand responses are typically more invoked at higher prices. Thus, the demand response profile over the day shows a similar trend as the market price profile because market price has a significant incentive to VPP and the user's demand response behavior.

3) Electric vehicle bidding result analysis

The bidding results of the electric vehicles of each type are shown in fig. 5 and fig. 6, wherein fig. 5 is a diagram of bidding results of the electric vehicles of the first type provided by the embodiment of the invention, and fig. 6 is a diagram of bidding results of the electric vehicles of the second type provided by the embodiment of the invention. Different types of electric vehicles may be observed to exhibit similar trends in bidding schemes. First, the charging activity is mainly focused between 3 and 6 points. At the moment, the electricity price of the combined market is relatively low, carbon emission can be reduced by using low-carbon electric power for charging, and charging management is performed in the period of time, so that the charging cost can be reduced, and wind power and photovoltaic electric power can be utilized to the greatest extent. Second, discharge activity is primarily focused between 11 and 14 and 24 pm to 1 day. During these periods, the electric vehicle has the opportunity to sell the stored electricity to the power dispatching mechanism, thereby obtaining electricity revenue.

The main reason for the diversity of bidding results of electric vehicles is that the difference between the electric power storage parameters and the driving modes is remarkable. Particularly, for an electric vehicle (such as type two) with a low initial charge capacity, in order to meet the driving requirement and realize high-power charging, the electric vehicle needs to be charged in a period from 3 to 6 am with a low market price of electricity, so as to charge the charge capacity to an upper limit. In contrast, an electric vehicle (such as type one) with higher initial electric quantity can choose to discharge in a period from 1 point to 2 points with relatively higher market electricity price, and then charge for a short time in a period from 3 points to 6 points in the early morning with lower market electricity price, so that the upper limit of the electric quantity can be reached. In addition, the electric automobile with lower final electric quantity can discharge in the period from 11 points to 13 points with higher market electricity price under the premise of meeting the required final electric quantity, and electric energy is injected into the electric network to obtain higher benefits.

Comprehensively considering, the bidding of the electric automobile is influenced by the electric storage parameters and the market price, and the income obtained by participating in the carbon market and the green evidence market. The bidding mode can generate linkage effect, thereby influencing the carbon market and the green market, and providing new possibility for the sustainability of an energy system and the reduction of carbon emission.

4) Basic bid outcome analysis for aggregated units

Fig. 7 is a schematic diagram of a result of a bid of a aggregated unit in a carbon market and a green license market, which is provided by an embodiment of the present invention, and fig. 8 is a result of a bid of an aggregated unit in a carbon market and a green license market, which is not provided by an embodiment of the present invention. In the case of VPP participation in the carbon market and green market, a significant decrease in power of the gas turbine was observed over a period of 20 to 24 points, as compared to the participation in the electricity market alone, and remained unchanged after the decrease. Mainly caused by the following factors:

carbon emission limits: the gas turbine may be limited in carbon dioxide emissions and the operating time of the gas turbine may be reduced to meet carbon emission credits or emissions reduction objectives.

The green evidence market participates in: participation in the green market may require VPP to increase the proportion of renewable energy sources, and thus VPP may limit the power output of the gas turbine.

Price fluctuation in the electric market: at a 20 point to 24 point time period, the electricity price is lower and the VPP may choose to reduce the power output of the gas turbine during this time period in order to obtain greater revenue in other markets.

5) DAM and RAM transaction case analysis

Fig. 9 is a VPP participation in a result of a bid amount in a combined market according to an embodiment of the present invention, and fig. 10 is a result of a VPP not participating in a bid amount in a combined market according to an embodiment of the present invention. Two bidding electric quantity optimization results show that: in the 11-13 period, the electricity price of the electric power market is highest, the VPP electricity purchasing meets the peak demand, and the arbitrage opportunity exists. 3-6 time periods, the electricity price is low, the charging requirement of the electric automobile is high, and the VPP electric power sales are increased. In other time periods, the VPP adjusts the power purchase and sales according to the market price difference, and the arbitrage is continued.

The bidding strategy of VPP varies in both DAM and RTM, taking into account carbon market and green market revenue:

1) At the DAM, the sales power of the VPP is reduced. Because VPP is more focused on environmental protection and sustainability of energy after considering carbon market and green market income, sales power at RTM is reduced to reduce carbon emissions or increase use of renewable energy.

2) At RTM, the sales of VPP increases. After considering the carbon market and green license market revenues, the enthusiasm of VPP to participate in DAM bidding is increased to obtain more carbon emissions or green license, thereby increasing revenues.

3) During periods 1 and 5-9, the bid amount of the DAM decreases. VPP is more interested in the carbon market and green market opportunities during these periods, reducing the bid amount at the DAM.

4) During the 2-point period, the bid amounts for both DAM and RTM decrease. VPP adjusts its bidding strategy after accounting for revenue from both the carbon market and the green market during this period, resulting in reduced bidding in both markets.

In summary, the embodiment of the invention provides a two-stage bidding model and strategy for simultaneously participating in the day-ahead market, the real-time market, the carbon market and the green certificate market by the VPP, considers the influence of large-scale charging and discharging electric vehicles and demand response on the VPP participating in the combined market bidding, carries out simulation verification on practical calculation examples, and provides basis for the VPP bidding transaction under the electric-carbon combined market by the research result, wherein the main conclusion is as follows:

1) When the VPP participates in multiple types of electric markets, electric vehicles are aggregated and users are prompted to participate in demand response behaviors, the cost of the VPP is reduced in the peak period, and the economic benefit of the VPP is improved.

2) The model provided by the embodiment can realize large-scale EV charge and discharge management, and the difference of EV parameters has influence on an optimization result, and in addition, the peak clipping and valley filling of a user can be guided through DR, so that the cost is minimized under the condition of ensuring electric power balance.

3) The VPP optimizes bidding strategies of the VPP to better meet environmental protection and sustainability targets after considering benefits of the carbon market and the green license market, and influences the sales power, bidding quantity, bidding period and the like.

Those skilled in the art will appreciate that all or part of the flow of the methods of the embodiments described above may be accomplished by way of a computer program to instruct associated hardware, where the program may be stored on a computer readable storage medium. Wherein the computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory, etc.

The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.

Claims (10)

1. A virtual power plant multi-time bidding method considering green certificates and carbon trade, characterized in that the method takes the nth trade day as a real-time trade day, and comprises the following steps:

in a first stage, a virtual power plant acquires historical data of each period in an N-1 th trading day, substitutes the historical data into a first stage bidding model, solves to obtain a decision variable solving result of each period in the first stage, and respectively corresponds to the decision variable solving result of each period in the first stage as a known parameter of the same period in the N-th trading day;

in the second stage, the virtual power plant acquires real-time data of the current time period in the nth trading day in real time, substitutes known parameters and real-time data of the current time period and all time periods before the current time period in the nth trading day into a second stage bidding model, substitutes decision variable solving results of all time periods before the current time period in the nth trading day into the second stage bidding model, solves to obtain decision variable solving results of the current time period in the nth trading day, and takes the decision variable solving results of the current time period in the nth trading day as real-time bidding results of the current time period in the nth trading day;

wherein the historical data and the real-time data both consider a green evidence market and a carbon trade market.

2. The virtual power plant multi-time bidding method of claim 1, wherein the first-stage bidding model comprises a first-stage objective function and a first-stage set of constraints; wherein the first stage objective function is as followsThe expression of the period is:

(1)

wherein:is a transaction period; />Is->The period virtual power plant participates in the sum of market operation costs of the day-ahead market and the real-time market; />Is->Time-consuming small gas turbine costs; />Is->The calling cost of the electric automobile in the period; />Is->Time period demand response revenue; />Is->The period virtual power plant participates in the benefits of the carbon market; />Is->The period virtual power plant participates in the benefits of the green evidence market.

3. The virtual power plant multi-time bidding method of claim 2, wherein the green certificates and carbon transactions are considered,

(2)

wherein:、/>respectively->Market before time period and real-time market electricity price estimation value; />、/>Respectively->The electricity purchase quantity and the electricity sales quantity of the market in the day before the period are all decision variables of the first stage; />、/>Respectively->The electricity purchasing quantity and the electricity selling quantity of the time period real-time market are decision variables of the first stage; />Is the electricity purchasing coefficient;

(3)

wherein:the number of units of the gas turbine; / >For gas turbines->Original cost of->Taking 1 to->The method comprises the steps of carrying out a first treatment on the surface of the Boolean variable、/>、/>Respectively represent +.>Time period gas turbine->Taking 1 as yes, taking 0 as no, and taking the two as decision variables of the first stage; />、/>Respectively gas turbines->The start cost and the stop cost of the engine; />For gas turbines->In the operation cost function->Power generation cost slope of segment->Representing the total number of segments of the running cost function; />Representation->Time period gas turbine->First->The output of the segment is a decision variable of the first stage;

(4)

wherein:is the total number of the electric automobiles; />Is->Battery acquisition cost of the electric vehicle; />Is->The number of charge and discharge cycles of the electric vehicle; />Is->Period->The battery capacity of the electric vehicle is a decision variable of the first stage; />First->The depth of discharge of the available battery of the electric vehicle; />Is->Period->The discharge power of the electric vehicle is a decision variable in the first stage; />Is->Discharging efficiency of the electric vehicle; />Is->Period->Vehicle electric vehicle mileage; />Is->The power required to be consumed by the unit driving mileage of the electric vehicle.

4. The virtual power plant multi-time bidding method of claim 3, wherein the green license and carbon trade are considered,

(5)

Wherein:is the overall type of demand response; />Is->A compensation price for the class demand response; />Is->Time period ofThe variable quantity of the class demand response load is a decision variable of the first stage;

(6)

wherein:、/>the selling price and the purchasing price of the carbon market and the carbon quota are respectively represented; />Representing +.>Wind power bidding output of time-period carbon market, +.>Representing +.>Photovoltaic bid out of period carbon market, +.>Representing +.>Time period ofTotal bid output of the carbon market;

(7)

wherein:、/>respectively selling price and purchasing price for green certificates; />、/>Virtual power plants in the market before day in the green license trade market respectively +.>Green electricity amount sold in time period and green electricity amount purchased in time period; />Is>Total bid output for a period of time; />Bidding output and +.>Is a ratio of (2); />The power generation quota ratio is the power generation quota ratio of renewable energy sources;to punish priceA grid; />A +.about.f. representing the green syndrome market in the day-ahead market>Time period wind power bidding output; />A +.about.f. representing the green syndrome market in the day-ahead market>Time period photovoltaic bidding force; />A +.about.f. representing the green syndrome market in the day-ahead market>Total bid output for a period of time.

5. The virtual power plant multi-time bidding method with green certificates and carbon trade consideration of claim 4, wherein the first set of stage constraints comprises: the method comprises the steps of first-stage gas turbine operation constraint, first-stage electric vehicle operation constraint, first-stage demand response constraint, first-stage green certificate market constraint, first-stage virtual power plant bidding electric quantity constraint and first-stage virtual power plant internal power balance constraint;

And solving the first-stage bidding model by using a solver CPLEX, and solving to obtain a decision variable solving result of each period in the first stage.

6. The virtual power plant multi-time bidding method with green certificates and carbon trade consideration of claim 5, wherein the second-stage bidding model comprises a second-stage objective function and a second-stage constraint set; second stage objective function inThe expression of the period is:

(8)

wherein,representing a current time period; />For +.>The period virtual power plant participates in the sum of market operation costs of the day-ahead market and the real-time market; />For +.>Time-consuming small gas turbine costs; />For +.>The calling cost of the electric automobile in the period; />For +.>Time period demand response revenue; />For +.>The period virtual power plant participates in the benefits of the carbon market; />For +.>The period virtual power plant participates in the benefits of the green evidence market.

7. The virtual power plant multi-time bidding method of claim 6, wherein the green certificates and carbon transactions are considered,

(9)

wherein:、/>respectively for the first stage>The electricity purchase quantity and the electricity sales quantity of the market in the day before period are the decision variable solving result of the first stage; / >、/>Respectively second stage->The electricity purchase quantity and the electricity sales quantity of the time period real-time market are decision variables of the second stage;

(10)

wherein: boolean variable、/>、/>Respectively representing the second phase +.>Time period gas turbine->The running, starting and stopping states of the system are all decision variables of the second stage; />In the real-time market representing phase two +.>Time period gas turbine->First->The output of the section is a decision variable of the second stage;

(11)

wherein:in the real-time market for phase two +.>Period->Discharging power of electric vehicle, +.>In the real-time market for phase two +.>Period->The battery capacity of the electric automobile is the decision variable of the second stage.

8. The virtual power plant multi-time bidding method of claim 7, wherein the green certificates and carbon transactions are considered,

(12)

wherein:in the real-time market for phase two +.>Period->The variable quantity of the class demand response load is a decision variable of the second stage;

(13)

wherein:in the real-time market representing phase two +.>Wind power bidding output in time period carbon market, +.>In the real-time market representing phase two +.>Photovoltaic bid out in period carbon market, < ->In the real-time market representing phase two +. >Total bid output in a time period carbon market;

(14)

wherein:、/>virtual Power plant in the Green market in real-time market of the second stage, respectively +.>Green electricity amount sold in time period and green electricity amount purchased in time period; />In the real-time market for phase two +.>Total race for a period of timePrice force;in the green evidence market in the real-time market representing the second phase +.>Time period wind power bidding output; />In the green evidence market in the real-time market representing the second phase +.>Time period photovoltaic bidding force; />In the green evidence market in the real-time market representing the second phase +.>Total bid output for a period of time.

9. The virtual power plant multi-time bidding method with green certificates and carbon trade consideration of claim 8, wherein the second set of stage constraints comprises: the method comprises the steps of a second-stage gas turbine operation constraint, a second-stage electric vehicle operation constraint, a second-stage demand response constraint, a second-stage green license market constraint, a second-stage virtual power plant bidding electric quantity constraint and a second-stage virtual power plant internal power balance constraint;

and solving the bidding model of the second stage by using a solver CPLEX, and solving to obtain a decision variable solving result of each period in the second stage.

10. The virtual power plant multi-time bidding method considering green certificates and carbon trade as recited in claim 9, wherein the sum of settlement costs of the virtual power plant in the first and second stages The method comprises the following steps:

(15)

wherein,、/>、/>、/>、/>respectively represent the corresponding ++I when the objective function of the first stage takes the minimum value after the solution of the bidding model of the first stage is completed>、/>、/>、/>、/>；/>、/>、/>、/>、/>Respectively represent the corresponding ++when the objective function of the second stage takes the minimum value after the solution of the bidding model of the second stage is completed>、/>、/>、/>、/>；

(16)

In the method, in the process of the invention,、/>respectively represent +.>And the time period real-time market electricity purchase quantity and the electricity sales quantity are decision variable solving results in the first stage.