CN104466999A - Method for determining bidding strategy of virtual power station including electric automobiles and wind turbines - Google Patents
Method for determining bidding strategy of virtual power station including electric automobiles and wind turbines Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention discloses a method for determining a bidding strategy of a virtual power station including electric automobiles and wind turbines. On the basis that an electric automobile battery which can schedule charging and discharging is proposed to be used for energy storage to provide a backup for draught fan output fluctuation and participate in adjustment of market bidding, a mixed integer programming model with the maximum bidding total revenue in the virtual power station market as a target for electric automobile and wind power collaboration bidding is structured; then, the robust optimization theory is introduced, the structured mixed integer programming model is transformed into a robust linear programming model, an electric automobile and wind power collaboration bidding robust optimization model is structured, a solving method is given, the risk level of the bidding strategy of the virtual power station is controlled by adjusting robust control coefficients, and thus a corresponding biding strategy scheme is obtained.
Description
Technical Field
The invention belongs to the technical field of electric power market bidding strategies, and relates to a method for determining a virtual power plant bidding strategy containing an electric automobile and a wind generating set.
Background
For a virtual power plant comprising electric vehicles and wind generating sets, because the number of the electric vehicles capable of being scheduled to be charged and discharged and the wind power output have obvious uncertainties, the uncertainties must be considered when the virtual power plant participates in bidding in the power market. In this context, how to make a bidding strategy for a virtual power plant is an important issue worth studying under the influence of the above uncertainty factor.
Disclosure of Invention
The invention aims to provide a method for determining a virtual power plant bidding strategy containing electric vehicles and wind generating sets, so as to establish the bidding strategy of the virtual power plant on the premise of uncertain factors such as the number of the electric vehicles, the wind power output and the like in the virtual power plant.
The invention discloses a method for determining a virtual power plant bidding strategy containing an electric automobile and a wind generating set, which comprises the following steps of:
1) establishing a mixed integer programming model
The electric automobile battery energy storage capable of scheduling charging and discharging is utilized, and the standby state is provided for the output fluctuation of the fan and the adjustment of market bidding is participated at the same time. Based on the method, a mixed integer programming model of electric automobile and wind power cooperative bidding is established by taking the maximum bidding total income Pmax of the virtual power plant in the power market as an objective function:
an objective function:
constraint conditions are as follows:
udc,t+uc,t＝1 (2)
uec,t+ubl,t＝1 (3)
St,min≤St≤St,max (10)
(PEV,RD,t+PPOP,t+PEV,ctr,t+PEV,W,c,t-PEV,W,dc,t)λcp,t≤PEV,max (11)
(PPOP,t+PEV,W,c,t-PEV,RU,t-PEV,RR,t-PEV,W,dc,t)λcp,t≥-PEV,max (12)
the income sources in the objective function mainly comprise the following electric energy provided by the fans selected in bidding in the power market:wherein λdag,e,tRepresents the price of the energy market at the day-ahead,day-ahead bidding submitted in each time period t when wind power participates in day-ahead energy market biddingCalibration force, Δ T represents the length of a single time period, T is the total time length; providing service for charging batteries of electric vehicles:wherein λ iscPrice of electricity, P, charged for the electric vehicle electricity consumption by the virtual power plantEV,dri,tDriving power consumption for the user in the time period t; the electric automobile provides the system with the service of adjusting the reserve capacity, including the service of up-regulating, down-regulating and rotating the reserve capacity:wherein, PEV,RU,t,PEV,RD,t,PEV,RR,tRespectively representing the competitive bidding values, lambda, of the up-regulation standby service, the down-regulation standby service and the rotation standby service when the electric automobile participates in the regulation of market biddingRU,t,λRD,t,λRR,tCapacity prices for up-regulation reserve, down-regulation reserve and rotation reserve of the regulation market are respectively set; the up-regulation and rotation of the electric vehicle reserve the electric energy actually called:wherein, E (x)RU)、E(xRD)、E(xRR) Calling proportions x respectively representing up-regulation standby, down-regulation standby and rotation standby of the power system in time period tRU，xRDAnd xRRIs expected value ofup,e,tAdjusting the price of the output for the electric automobile in the time t;
the economic expenditure terms in the objective function mainly comprise penalties caused by deviation of actual output and competitive output of the fan:wherein, the deviation of the actual wind power output greater than and less than the competitive bidding output is Ppl,ec,tAnd Ppl,bl,tThe corresponding penalty coefficients are respectively omegaimb,ecAnd ωimb,bl，uec,tAnd ubl,tAll are variable from 0 to 1, when Ppl,ec,t>0 hour uec,tIs 1 when Ppl,bl,t>0 hour ubl,tIs 1;cost of charging batteries of electric vehicles, where λα,eRepresenting electricity purchase price, Q, of virtual power plant from bilateral contract marketcRepresents the amount of power purchased from the contract market, λ, by the virtual power plant for charging electric vehicless,e,tRepresenting the electricity purchase price of the virtual power plant from the real-time energy market,expected value, u, representing the actual charging power of the electric vehiclec,tThe variable is 0-1, and the value is 1 when the electric automobile is in a charging state; loss cost of electric vehicle battery discharge:wherein, the discharge loss cost of the unit electric quantity of the battery of the electric automobile is cdis，Expected value, eta, representing the actual discharge power of the electric vehicledcFor cell discharge efficiency, udc,tIs a variable of 0 to 1, and the value is 1 when the electric automobile is in a discharge state;
the electric vehicle state can be divided into two states of charging and discharging, and the case of neither charging nor discharging is used as a special case where the charging and discharging power is 0, so that the constraint represented by the formula (2) needs to be included, and similarly, the deviation P between the actual output of the fan and the competitive output of the fan in the time period tpl,ec,tAnd Ppl,bl,tCannot be positive at the same time, and therefore, the constraint represented by the formula (3) needs to be included;
equations (4) and (5) are constraints related to the fan, and describe the vertical deviation of the actual output and the competitive bidding output of the fan;representing the actual output of the fan, PEV,W,c,tAnd PEV,W,dc,tRespectively shows that the electric automobile takes charging and discharging as wind powerProviding a backup force.
Equation (6) -equation (12) are constraints related to electric vehicles, equation (6) and equation (7) give expected values of electric vehicle charging and discharging power, respectively, and equation (8) represents the amount of electricity purchased by the virtual power plant in the bilateral contract market for charging the electric vehicle; p in formula (7)POP,tRepresents the planned charge and discharge power of the electric vehicle.
With StRepresenting the SOC value of the battery of the electric vehicle in the time period t, and the formula (9) is used for calculating the SOC of the electric vehicle in the time period t, wherein S0Representing the initial state of the battery, the SOC has a minimum value S since the electric vehicle has driving requirements and considering that complete discharge of the battery is to be avoided in order to extend the battery life as much as possiblet,minConstraining; the SOC has a maximum value S because the full charge of the battery of the electric vehicle cannot be achieved due to aging of the battery and the liket,maxConstraint, wherein the equation (10) represents SOC upper and lower limit constraints of the electric vehicle battery at different time intervals t;
in addition, the maximum charge and discharge power P of the electric automobile is also required to be satisfiedEV,maxConstraints, see equations (11) and (12); in the formula (11), PEV,ctr,tA contract charge power representing a decomposition of the contract charge into time periods t; lambda [ alpha ]cp,tAnd the adjustment coefficient of the schedule is shown when one electric vehicle is out of schedule because other electric vehicles cannot be scheduled in the time period t.
The formula (13) defines that the values of the model decision variables are all non-negative values;
2) introducing a robust optimization theory, and converting the mixed integer programming model constructed in the step 1) into a robust linear programming model:
an objective function:
max w (14)
constraint conditions are as follows:
-y≤P≤y (23)
y＝(yW,B,t,yRU,t,yRD,t,yRR,t,ypl,ec,t,ypl,bl,t,yEV,c,t) (25)
p1,qW,B,t,qRU,t,qRD,t,qRR,t,qpl,ec,t,qpl,bl,t,qEV,c,t≥0 (26)
in the formula: w is an objective function, and P is a vector formed by decision variables; y is a vector consisting of auxiliary decision variables introduced by the dual transformation; thetaup,eIs an adjustment factor; pbatRepresenting equivalent capacity of all electric vehicle batteries in the virtual power plant; mu.sa,W,tAnd σa,W,tAre respectively asIs normally distributed with an expected value and a standard deviation Plow,W,t(ii) a Fan competitive bidding outputUpper and lower limits P ofup,W,tAnd Plow,W,tAre all random variables, muup,W,t，σup,W,tAnd mulow,W,t，σlow,W,tAre respectively Pup,W,tExpected value and standard deviation of the sum.up,W、low,W、pl,ec、pl,blRepresenting the probability that the opportunistic constraint holds. z is a radical of The upper quantile of the standard normal distribution isRespectively represent the upper side of a standard normal distributionup,W、1-low,W、1-pl,ec、pl,blAre divided into dots. Day-ahead energy market price λdag,e,tUpper adjustment reserve capacity price lambdaRU,tLowering reserve capacity price lambdaRD,tPrice lambda of reserve capacity in rotationRR,tFrom the electricity purchase price lambda of the real-time energy market of the virtual power plants,e,tPower price lambda for power generation and network accessg,e,tPredictions can be made from historical data and are assumed to be all in the intervalAn internal variation. Wherein,a predicted value representing the electricity price is displayed,and represents the power price fluctuation interval radius.
In the robust collaborative bidding model, the charging and discharging power constraint of the electric vehicle is an equation (27) and an equation (28), the SOC constraint is an equation (29), equations (30) and (31) are respectively an upper limit constraint and a lower limit constraint of fan bidding output, equations (32) and (33) are deviation constraints of the fan bidding output and actual output, and the other constraints, namely the forms of equations (10) to (13), are unchanged;
the decision variables include: competitive bidding output of wind power in day-ahead energy marketRegulated reserve and spinning reserve bid capacities, i.e., P, for electric vehicles in the regulated marketEV,RU,t，PEV,RD,tAnd PEV,RR,t(ii) a Standby P provided by electric automobile for stabilizing output fluctuation of fanEV,W,c,tAnd PEV,W,dc,t(ii) a Planned charging power P of electric vehiclePOP,t(ii) a Contract electric quantity charging power P of electric automobileEV,ctr,t(ii) a Decision variable P added by model linearization transformationpl,ec,t，Ppl,bl,t，Andauxiliary decision variable p introduced by dual transformation1Q and y, wherein q ═ q (q)W,B,t,qRU,t,qRD,t,qRR,t,qpl,ec,t,qpl,bl,t,qEV,c,t)，y＝(yW,B,t,yRU,t,yRD,t,yRR,t,ypl,ec,t,ypl,bl,t,yEV,c,t) (ii) a Robust control coefficient of1；
3) And controlling the risk level of the virtual power plant bidding strategy by adjusting the robust control coefficient to obtain a corresponding bidding strategy scheme.
The method has the beneficial effects that the example results show that a series of virtual power plant bidding strategy schemes with different risk levels can be obtained by adopting the constructed model and the proposed solving method, and a new solution is provided for the problem of output fluctuation when wind power output participates in the electric power market by utilizing the battery energy storage of the electric automobile.
Drawings
FIG. 1 is a wind power competitive bidding output variation curve
FIG. 2 is a graph of the output of an electric vehicle providing backup for a fan
FIG. 3 shows the competitive bidding capacity for electric vehicles on the adjustment market
FIG. 4 shows the reserve bidding capacity of an electric vehicle under regulation market
FIG. 5 shows the spinning reserve bid capacity of an electric vehicle
FIG. 6 is a graph showing the influence of the number of electric vehicles and the output of the fan on the bidding result
Detailed Description
Aiming at the bidding strategy problem of a virtual power plant comprising an electric automobile and a wind power generation unit, the invention constructs a mixed integer programming model of electric automobile and wind power cooperative bidding aiming at the maximum bidding total yield of the virtual power plant in the power market on the basis of providing reserve for the output fluctuation of a fan and simultaneously participating in the adjustment of market bidding by using the battery energy storage of the electric automobile capable of scheduling charge and discharge. Then, a robust optimization theory is introduced, the constructed mixed integer programming model is converted into a robust linear programming model, an electric automobile and wind power cooperative bidding robust optimization model is constructed, a solving method is provided, the risk level of a virtual power plant bidding strategy is controlled by adjusting a robust control coefficient, and a corresponding bidding strategy scheme is obtained, wherein the specific process comprises the following steps:
1) establishing a mixed integer programming model
The electric automobile battery energy storage capable of scheduling charging and discharging is utilized, and the standby state is provided for the output fluctuation of the fan and the adjustment of market bidding is participated at the same time. Based on the method, a mixed integer programming model of electric automobile and wind power cooperative bidding is established by taking the maximum bidding total income Pmax of the virtual power plant in the power market as an objective function:
s.t.
udc,t+uc,t＝1 (2)
uec,t+ubl,t＝1 (3)
St,min≤St≤St,max (10)
(PEV,RD,t+PPOP,t+PEV,ctr,t+PEV,W,c,t-PEV,W,dc,t)λcp,t≤PEV,max (11)
(PPOP,t+PEV,W,c,t-PEV,RU,t-PEV,RR,t-PEV,W,dc,t)λcp,t≥-PEV,max (12)
the income sources in the objective function mainly comprise the following electric energy provided by the fans selected in bidding in the power market:wherein λdag,e,tRepresents the price of the energy market at the day-ahead,the method comprises the steps of providing day-ahead bidding output submitted in each time period T when wind power participates in day-ahead energy market bidding, wherein delta T represents the length of a single time period, and T is the total time length; is an electric steamVehicle battery charging provides service:wherein λ iscPrice of electricity, P, charged for the electric vehicle electricity consumption by the virtual power plantEV,dri,tDriving power consumption for the user in the time period t; the electric automobile provides the system with the service of adjusting the reserve capacity, including the service of up-regulating, down-regulating and rotating the reserve capacity:wherein, PEV,RU,t,PEV,RD,t,PEV,RR,tRespectively representing the competitive bidding values, lambda, of the up-regulation standby service, the down-regulation standby service and the rotation standby service when the electric automobile participates in the regulation of market biddingRU,t,λRD,t,λRR,tCapacity prices for up-regulation reserve, down-regulation reserve and rotation reserve of the regulation market are respectively set; the up-regulation and rotation of the electric vehicle reserve the electric energy actually called:wherein, E (x)RU)、E(xRD)、E(xRR) Calling proportions x respectively representing up-regulation standby, down-regulation standby and rotation standby of the power system in time period tRU，xRDAnd xRRIs expected value ofup,e,tAdjusting the price of the output for the electric automobile in the time t;
the economic expenditure terms in the objective function mainly comprise penalties caused by deviation of actual output and competitive output of the fan:wherein, the deviation of the actual wind power output greater than and less than the competitive bidding output is Ppl,ec,tAnd Ppl,bl,tThe corresponding penalty coefficients are respectively omegaimb,ecAnd ωimb,bl，uec,tAnd ubl,tAll are variable from 0 to 1, when Ppl,ec,t>0 hour uec,tIs 1 when Ppl,bl,t>0 hour ubl,tIs 1; cost of charging batteries for electric vehiclesWherein λ isα,eRepresenting electricity purchase price, Q, of virtual power plant from bilateral contract marketcRepresents the amount of power purchased from the contract market, λ, by the virtual power plant for charging electric vehicless,e,tRepresenting the electricity purchase price of the virtual power plant from the real-time energy market,expected value, u, representing the actual charging power of the electric vehiclec,tThe variable is 0-1, and the value is 1 when the electric automobile is in a charging state; loss cost of electric vehicle battery discharge:wherein, the discharge loss cost of the unit electric quantity of the battery of the electric automobile is cdis，Expected value, eta, representing the actual discharge power of the electric vehicledcFor cell discharge efficiency, udc,tIs a variable of 0 to 1, and the value is 1 when the electric automobile is in a discharge state;
in the formula, PbatAnd the equivalent capacity of all the batteries of the electric automobiles in the virtual power plant is represented.
The electric vehicle state can be divided into two states of charging and discharging, and the case of neither charging nor discharging is used as a special case where the charging and discharging power is 0, so that the constraint represented by the formula (2) needs to be included, and similarly, the deviation P between the actual output of the fan and the competitive output of the fan in the time period tpl,ec,tAnd Ppl,bl,tCannot be positive at the same time, and therefore, the constraint represented by the formula (3) needs to be included;
equations (4) and (5) are constraints related to the fan, and describe the vertical deviation of the actual output and the competitive bidding output of the fan;
equation (6) -equation (12) are constraints related to electric vehicles, equation (6) and equation (7) give expected values of electric vehicle charging and discharging power, respectively, and equation (8) represents the amount of electricity purchased by the virtual power plant in the bilateral contract market for charging the electric vehicle;
with StRepresenting the SOC value of the battery of the electric vehicle in the time period t, and the formula (9) is used for calculating the SOC of the electric vehicle in the time period t, wherein S0Representing the initial state of the battery, the SOC has a minimum value S since the electric vehicle has driving requirements and considering that complete discharge of the battery is to be avoided in order to extend the battery life as much as possiblet,minConstraining; the SOC has a maximum value S because the full charge of the battery of the electric vehicle cannot be achieved due to aging of the battery and the liket,maxConstraint, wherein the equation (10) represents SOC upper and lower limit constraints of the electric vehicle battery at different time intervals t;
in addition, the maximum charge and discharge power P of the electric automobile is also required to be satisfiedEV,maxConstraints, see equations (11) and (12); the formula (13) defines that the values of the model decision variables are all non-negative values;
2) introducing a robust optimization theory, and converting the mixed integer programming model constructed in the step 1) into a robust linear programming model:
max w (14)
s.t.
-y≤P≤y (23)
y＝(yW,B,t,yRU,t,yRD,t,yRR,t,ypl,ec,t,ypl,bl,t,yEV,c,t) (25)
p1,qW,B,t,qRU,t,qRD,t,qRR,t,qpl,ec,t,qpl,bl,t,qEV,c,t≥0 (26)
in the formula: p is a vector formed by decision variables; y is a vector consisting of auxiliary decision variables introduced by the dual transformation; z is a radical of Is an upper quantile of standard normal distribution; mu.sa,W,tAnd σa,W,tAre respectively asThe expected value and standard deviation of the normal distribution of (1);
in the robust collaborative bidding model, the charging and discharging power constraint of the electric vehicle is an equation (27) and an equation (28), the SOC constraint is an equation (29), equations (30) and (31) are respectively an upper limit constraint and a lower limit constraint of fan bidding output, equations (32) and (33) are deviation constraints of the fan bidding output and actual output, and the other constraints, namely the forms of equations (10) to (13), are unchanged;
the decision variables include: competitive bidding output of wind power in day-ahead energy marketRegulated reserve and spinning reserve bid capacities, i.e., P, for electric vehicles in the regulated marketEV,RU,t，PEV,RD,tAnd PEV,RR,t(ii) a Electric automobileStandby P provided for stabilizing output fluctuation of fanEV,W,c,tAnd PEV,W,dc,t(ii) a Planned charging power P of electric vehiclePOP,t(ii) a Contract electric quantity charging power P of electric automobileEV,ctr,t(ii) a Decision variable P added by model linearization transformationpl,ec,t，Ppl,bl,t，Andauxiliary decision variables p, q and y introduced by dual transformation; robust control coefficient of1；
3) And controlling the risk level of the virtual power plant bidding strategy by adjusting the robust control coefficient to obtain a corresponding bidding strategy scheme.
Specific combination parameters: the total number of the electric automobiles in the virtual power plant is 2 thousands; the battery capacity of a single electric automobile is 24 kW.h; the purchase cost per unit battery capacity is $ 400/(kW · h); the maximum charge and discharge power of the battery is 3 kW; the average charging and discharging efficiency of the battery is 0.95; the charging price of the electric automobile is 0.14 dollars/(kW.h); the punishment coefficient of the fan when the actual output of the fan is higher than or lower than the competitive bidding output is 0.95/1.05; the price of power transmission and distribution is 0.07 dollar/(kW.h); the electricity price of the power generation side of the virtual power plant for purchasing electricity through the bilateral contract is 0.06 dollar/(kW & h), and the contract electricity quantity is assumed to be 288MW & h in the optimization period (24 h); taking hours as time periods, 24 time periods are provided each day, and delta t is 1; the capacity is adjusted up and down, and the rotational reserve capacity is scaled to the desired value for the actual call rate at each transaction interval. Setting the day-ahead energy market electricity price, the real-time energy market electricity price (both electricity prices on the power generation side), the capacity electricity prices of the up-regulation capacity, the down-regulation capacity and the rotation reserve capacity, and setting the prediction deviation of the 4 prices to be +/-15%; thetaup,eThe value is 1.0.
The parameter values and the optimal value of the objective function (i.e. the total income of virtual power plant bidding) at different constraint violation probability levels are given in table 1, different constraint violation probabilities correspond to different decision-making economic risks, and the smaller the constraint violation probability, the smaller the economic risk born by a decision-maker is. As can be seen from the calculation results in Table 1, as the constraint violation probability is reduced, the risk of the virtual power plant bidding result violating the constraint is reduced, and the bidding income of the virtual power plant is reduced.
TABLE 1
Note:up,W，low,W，pl,ec，pl,blthe values are the same and all.
Fig. 1 to 5 show the calculation results of bidding strategies of the fans and the electric vehicles in the virtual power plant participating in market bidding in each scene when the parameters are valued according to table 1.
Fig. 6 is a comparison graph of the influence of the number of electric vehicles and the fan output on the bidding result, and it can be seen from fig. 6 that when the relative variation amounts of the number of electric vehicles and the fan output are the same, the bidding result variation caused by the variation in the number of electric vehicles is larger, that is, the sensitivity of the virtual power plant bidding strategy to the number of electric vehicles is larger than the sensitivity to the fan output. This is because when the number of electric vehicles in the system increases, the electric vehicles can provide a reserve for the fan, and can also participate in adjusting market bids with abundant capacity to obtain a profit as an energy storage device to provide a reserve for the system. And when the number of the fans in the system is increased, whether the abundant output of the fans can participate in the energy market bidding depends on whether the battery energy storage of the electric automobile in the system can provide a reserve for the output fluctuation of the fans, so that the influence of the output of the fans on the bidding result is restricted. Thus, the bidding strategy is relatively insensitive to changes in fan output.
Claims (1)
1. A method for determining a virtual power plant bidding strategy containing an electric automobile and a wind generating set is characterized by comprising the following steps:
1) establishing a mixed integer programming model
Establishing a mixed integer programming model of electric automobile and wind power cooperative bidding according to the maximum bidding total income P of the virtual power plant in the power market as an objective function:
the objective function is:
the constraint conditions are as follows:
udc,t+uc,t＝1 (2)
uec,t+ubl,t＝1 (3)
St,min≤St≤St,max (10)
(PEV,RD,t+PPOP,t+PEV,ctr,t+PEV,W,c,t-PEV,W,dc,t)λcp,t≤PEV,max (11)
(PPOP,t+PEV,W,c,t-PEV,RU,t-PEV,RR,t-PEV,W,dc,t)λcp,t≥-PEV,max (12)
the income sources in the objective function comprise the electric energy income provided by the fans selected in bidding in the electric power market:wherein λdag,e,tRepresents the price of the energy market at the day-ahead,the method comprises the steps of providing day-ahead bidding output submitted in each time period T when wind power participates in day-ahead energy market bidding, wherein delta T represents the length of a single time period, and T is the total time length; providing service revenue for charging batteries of electric vehicles:wherein λ iscPrice of electricity, P, charged for the electric vehicle electricity consumption by the virtual power plantEV,dri,tDriving power consumption for the user in the time period t; electric vehicles provide regulated reserve capacity service revenue for systems including up-regulation, down-regulation, and rotational reserve capacity servicesWherein, PEV,RU,t,PEV,RD,t,PEV,RR,tRespectively representing the competitive bidding values, lambda, of the up-regulation standby service, the down-regulation standby service and the rotation standby service when the electric automobile participates in the regulation of market biddingRU,t,λRD,t,λRR,tCapacity prices for up-regulation reserve, down-regulation reserve and rotation reserve of the regulation market are respectively set; the up-regulation and rotation of the electric vehicle reserve the electric energy actually called:wherein, E (x)RU)、E(xRR) Calling proportions x respectively representing up-regulation standby and rotation standby of power system in time period tRU、xRRIs expected value ofup,e,tAdjusting the price of the output for the electric automobile in the time t;
the economic expenditure terms in the objective function comprise penalties caused by deviation of actual output and competitive output of the fan:wherein, the deviation of the actual wind power output greater than and less than the competitive bidding output is Ppl,ec,tAnd Ppl,bl,tThe corresponding penalty coefficients are respectively omegaimb,ecAnd ωimb,bl，uec,tAnd ubl,tAll are variable from 0 to 1, when Ppl,ec,t>0 hour uec,tIs 1 when Ppl,bl,t>0 hour ubl,tIs 1;cost of charging batteries of electric vehicles, where λα,eRepresenting electricity purchase price, Q, of virtual power plant from bilateral contract marketcRepresenting virtual power plant charging electric vehicles from a contract marketPurchase of electricity, lambdas,e,tRepresenting the electricity purchase price of the virtual power plant from the real-time energy market,expected value, u, representing the actual charging power of the electric vehiclec,tThe variable is 0-1, and the value is 1 when the electric automobile is in a charging state; loss cost of electric vehicle battery discharge:wherein, the discharge loss cost of the unit electric quantity of the battery of the electric automobile is cdis，Expected value, eta, representing the actual discharge power of the electric vehicledcFor cell discharge efficiency, udc，tIs a variable of 0 to 1, and the value is 1 when the electric automobile is in a discharge state;
the electric vehicle state can be divided into two states of charging and discharging, and the case of neither charging nor discharging is used as a special case where the charging and discharging power is 0, so that the constraint represented by the formula (2) needs to be included, and similarly, the deviation P between the actual output of the fan and the competitive output of the fan in the time period tpl,ec,tAnd Ppl,bl,tCannot be positive at the same time, and therefore, the constraint represented by the formula (3) needs to be included;
equations (4) and (5) are constraints related to the fan, and describe the vertical deviation of the actual output and the competitive bidding output of the fan;representing the actual output of the fan, PEV,W,c,tAnd PEV,W,dc,tRespectively representing the backup output of the electric automobile for providing the wind power by charging and discharging;
equation (6) -equation (12) are constraints related to electric vehicles, equation (6) and equation (7) give expected values of electric vehicle charging and discharging power, respectively, and equation (8) represents the amount of electricity purchased by the virtual power plant in the bilateral contract market for charging the electric vehicle; p in formula (7)POP,tRepresents a planned charge/discharge power of the electric vehicle;
with StRepresenting the SOC value of the battery of the electric vehicle in the time period t, and the formula (9) is used for calculating the SOC of the electric vehicle in the time period t, wherein S0Representing the initial state of the battery, the SOC has a minimum value S since the electric vehicle has driving requirements and considering that complete discharge of the battery is to be avoided in order to extend the battery life as much as possiblet,minConstraining; the SOC has a maximum value S because the full charge of the battery of the electric vehicle cannot be achieved due to aging of the battery and the liket,maxConstraint, wherein the equation (10) represents SOC upper and lower limit constraints of the electric vehicle battery at different time intervals t;
in addition, the maximum charge and discharge power P of the electric automobile is also required to be satisfiedEV,maxConstraints, see equations (11) and (12); in the formula (11), PEV,ctr,tA contract charge power representing a decomposition of the contract charge into time periods t; lambda [ alpha ]cp,tThe adjustment coefficient represents that when a certain electric automobile is scheduled, other electric automobiles cannot be scheduled in the time period t, and the electric automobiles are subjected to scheduling;
the formula (13) defines that the values of the model decision variables are all non-negative values;
2) introducing a robust optimization theory, and converting the mixed integer programming model constructed in the step 1) into a robust linear programming model:
an objective function:
max w (14)
constraint conditions are as follows:
-y≤P≤y (23)
y＝(yW,B,t,yRU,t,yRD,t,yRR,t,ypl,ec,t,ypl,bl,t,yEV,c,t) (25)
p1,qW,B,t,qRU,t,qRD,t,qRR,t,qpl,ec,t,qpl,bl,t,qEV,c,t≥0 (26)
in the formula: w is an objective function, and P is a vector formed by decision variables; y is a vector consisting of auxiliary decision variables introduced by the dual transformation; thetaup,eIs an adjustment factor; pbatRepresenting virtual premises in a power plantEquivalent capacity of the battery of the electric automobile; mu.sa,W,tAnd σa,W,tAre respectively asIs normally distributed with an expected value and a standard deviation Plow,W,t(ii) a Fan competitive bidding outputUpper and lower limits P ofup,W,tAnd Plow,W,tAre all random variables, muup,W,t，σup,W,tAnd mulow,W,t，σlow,W,tAre respectively Pup,W,tExpected value and standard deviation of the sum.up,W、low,W、pl,ec、pl,blRepresenting the probability that the opportunity constraint holds; z is a radical of The upper quantile of the standard normal distribution isRespectively represent the upper side of a standard normal distributionup,W、1-low,W、1-pl,ec、pl,blDividing into points; e (x)RD) Calling proportion x representing stand-by of power system at down regulation of time period tRDExpected value of (A), day-ahead energy market price lambdadag,e,tUpper adjustment reserve capacity price lambdaRU,tLowering reserve capacity price lambdaRD,tPrice lambda of reserve capacity in rotationRR,tFrom the electricity purchase price lambda of the real-time energy market of the virtual power plants,e,tPower price lambda for power generation and network accessg,e,tPredictions can be made from historical data and are assumed to be all in the intervalAn internal variation; wherein,a predicted value representing the electricity price is displayed,indicating electricityA valence fluctuation interval radius;
in the robust collaborative bidding model, the charging and discharging power constraint of the electric vehicle is an equation (27) and an equation (28), the SOC constraint is an equation (29), equations (30) and (31) are respectively an upper limit constraint and a lower limit constraint of fan bidding output, equations (32) and (33) are deviation constraints of the fan bidding output and actual output, and the other constraints, namely the forms of equations (10) to (13), are unchanged;
the decision variables include: competitive bidding output of wind power in day-ahead energy marketRegulated reserve and spinning reserve bid capacities, i.e., P, for electric vehicles in the regulated marketEV,RU,t，PEV,RD,tAnd PEV,RR,t(ii) a Standby P provided by electric automobile for stabilizing output fluctuation of fanEV,W,c,tAnd PEV,W,dc,t(ii) a Planned charging power P of electric vehiclePOP,t(ii) a Contract electric quantity charging power P of electric automobileEV,ctr,t(ii) a Decision variable P added by model linearization transformationpl,ec,t，Andauxiliary decision variable p introduced by dual transformation1Q and y, wherein q ═ q (q)W,B,t,qRU,t,qRD,t,qRR,t,qpl,ec,t,qpl,bl,t,qEV,c,t)，y＝(yW,B,t,yRU,t,yRD,t,yRR,t,ypl,ec,t,ypl,bl,t,yEV,c,t) (ii) a Robust control coefficient of1；
3) And controlling the risk level of the virtual power plant bidding strategy by adjusting the robust control coefficient to obtain a corresponding bidding strategy scheme.
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