CN114565153A - Regional wind, light and vehicle combination optimization method based on dispatchable capacity of electric vehicle - Google Patents

Regional wind, light and vehicle combination optimization method based on dispatchable capacity of electric vehicle Download PDF

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CN114565153A
CN114565153A CN202210184219.6A CN202210184219A CN114565153A CN 114565153 A CN114565153 A CN 114565153A CN 202210184219 A CN202210184219 A CN 202210184219A CN 114565153 A CN114565153 A CN 114565153A
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李锋
冯江
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Cisdi Electric Technology Co ltd
CISDI Engineering Co Ltd
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Abstract

The invention relates to a regional wind, light and vehicle combination optimization method based on the schedulable capability of an electric vehicle, which belongs to the technical field of regional energy system optimization scheduling and comprises the following steps: s1: the method comprises the steps of dividing an electric vehicle load into an unordered charging load, a transferable charging load and a schedulable charging load according to the schedulable capacity, and predicting the charging and discharging capacity of the three types of loads according to the charging demand and the running data information of an electric vehicle user; s2: taking the schedulable load of the electric automobile as a variable in the unit combination, and establishing a unit combination model in which the electric automobile participates in cooperation with wind power and photovoltaic power generation; s3: and optimizing the unit combination model by taking the economy of the regional power grid as a target to obtain the optimal wind, light and vehicle combination model. The invention improves the wind power and photovoltaic consumption capability and the enthusiasm of electric vehicle users for participating in scheduling.

Description

Regional wind, light and vehicle combination optimization method based on schedulable capacity of electric vehicle
Technical Field
The invention belongs to the technical field of regional energy system optimization scheduling, and relates to a regional wind, light and vehicle combination optimization method based on the schedulable capacity of an electric vehicle.
Background
Renewable energy sources such as wind power and photovoltaic and electric automobiles are rapidly developed, large-scale wind power and photovoltaic network access is difficult, and the fluctuation and the reverse regulation characteristics of the wind power and the photovoltaic under high permeability cause great challenges to the power balance and the network safety of a comprehensive energy system. Meanwhile, the method also provides a more serious challenge for the traditional unit combination problem, and not only the uncertainty of the output of intermittent energy sources such as wind power, photovoltaic and the like is considered, but also the randomness of loads such as electric vehicles and the like which are gradually increased is required to be considered.
With the development of electric vehicles, large-scale electric vehicles are connected to an integrated energy system, and if the integrated energy system is only used as a conventional load and is superimposed on a traditional load curve, the running cost of a unit in the system is undoubtedly increased. The charging and discharging of the electric automobile are guided and managed in a planned way, so that the electric automobile participates in power balance in idle time, such as peak shaving, frequency modulation, matching with wind power and solar power generation and the like, and the electric automobile charging and discharging management system not only can bring value to a regional power system, but also can create benefits for users and society. In fact most electric vehicles are parked more than 90% of the day, which provides a time possibility for dispatching electric vehicles. The electric automobile can reduce the operation cost by participating in the dispatching of the regional power system, and can assist the regional power system to improve the wind power and photovoltaic receiving capacity and enhance the regional renewable energy consumption capacity. In actual daily life, the number of electric vehicles that can be scheduled and the type of scheduling depend on the user's receptivity. The foundation of the electric automobile participating in the power grid interaction needs the participation of a user, and if the user cannot obtain certain economic benefit, the participation of the electric automobile in the power grid interaction cannot be mentioned.
Disclosure of Invention
In view of this, the present invention provides a regional wind, light and vehicle combination optimization method based on schedulable capability of electric vehicles, which is used for classifying electric vehicles according to schedulable types and combining electric vehicles of different scheduling types to make the electric vehicles meet actual requirements with the goal that the profit cost of electric energy operators in a regional power system is the maximum.
In order to achieve the purpose, the invention provides the following technical scheme:
a regional wind, light and vehicle combination optimization method based on schedulable capacity of electric vehicles comprises the following steps:
s1: the method comprises the following steps of dividing electric vehicle loads into unordered charging loads, transferable charging loads and schedulable charging loads according to schedulable capacity, and predicting charging and discharging capacity of the three types of loads according to charging requirements and driving data information of electric vehicle users;
s2: taking the schedulable load of the electric automobile as a variable in the unit combination, and establishing a unit combination model in which the electric automobile participates in cooperation with wind power and photovoltaic power generation;
s3: and optimizing the unit combination model by taking the economy of the regional power grid as a target to obtain the optimal wind, light and vehicle combination model.
Further, in step S1, a charging load prediction is performed for the disordered charging load; evaluating the schedulable charging capacity range of the schedulable charging load; predicting the charging load and the discharging capacity of the schedulable charging and discharging load;
further, in step S2, the unit combination model in which the electric vehicle participates in cooperation with wind power and photovoltaic power generation includes:
the electric automobile can transfer the charging load model: obtaining the actual load P of the transferable charging type electric automobile load at the moment t through the state matrix of the transferable charging type electric automobile load at the moment tevtAnd predicting the maximum load P which can be reached by the transferable charging type electric automobile at the next momentt+1 ev,maxAnd minimum load Pt+1 ev,min：
Figure BDA0003518509220000021
Figure BDA0003518509220000022
Figure BDA0003518509220000023
Figure BDA00035185092200000211
In the formula:
Figure BDA0003518509220000024
indicating the electric vehicle load that must be increased at time t +1, the load that must be switched in at the next time, and the load that must be cut off
Figure BDA0003518509220000025
Two parts are formed;
Figure BDA0003518509220000026
the access condition T is satisfied for the moment T +1sT +1, and a chargeable duration Ts.factThe number of electric vehicles is greater than the actual charging time T;
Figure BDA0003518509220000027
the access condition T is satisfied for the moment T +1sT +1, and a chargeable duration Ts.factThe number of electric vehicles which are smaller than the actual charging time T and need to start charging;
Figure BDA0003518509220000028
satisfaction of the excision Condition T for time T +1sT +1 or Sn＝SeThe number of electric vehicles that must be cut;
the schedulable charging load model of the electric automobile is as follows:
electric automobile discharge electrode time limit t3Comprises the following steps:
Figure BDA0003518509220000029
the adjustable discharge capacity of the electric automobile is as follows:
Figure BDA00035185092200000210
the charging capacity of the electric automobile is as follows:
Pev,c＝Cs(Q2+Q4-Q1-Q3)
wind power plant output model:
the wind power characteristic function is:
Figure BDA0003518509220000031
in the formula: pWTFor the wind turbine to output power v, vC、vNRespectively the actual wind speed, the cut-out wind speed and the rated wind speed; pNRated output power;
photovoltaic output model: the output power of the photovoltaic is determined by the output power Y under the standard rated conditionPVIllumination amplitude GSTCAmbient temperature TSTCJointly determining:
Figure BDA0003518509220000032
wherein, fPVA photovoltaic derating factor, which is set according to external factors of the photovoltaic panel; gTIrradiance of the current environment; alpha is alphaPIs the power temperature coefficient; t isCIs the temperature in the current environment.
Further, in step S3, regarding the unit combination model in which the electric vehicle and the wind power and the photovoltaic power generation cooperatively participate in step S2, the load balance constraint condition, the generator set constraint condition, and the electric vehicle constraint condition under which the electric vehicle participates in the scheduling of the integrated energy system are considered, an optimization target of the unit combination model is constructed, and the CPLEX is used to solve and calculate the nonlinear mixed integer programming problem in the model, so as to obtain an optimal combination model.
Further, the load balance constraint conditions for the electric vehicle to participate in the scheduling of the comprehensive energy system are as follows:
Figure BDA0003518509220000033
in the formula:
Figure BDA0003518509220000034
wind power output power in t time period;
Figure BDA0003518509220000035
photovoltaic output power for a period t;
Figure BDA0003518509220000036
the total discharge power of the electric automobile to the power grid can be scheduled for the t period;
Figure BDA0003518509220000037
load power for a period t;
Figure BDA0003518509220000038
the total charging power of the electric automobile can be scheduled for the t time period;
Figure BDA0003518509220000039
the total charging load power of the electric automobile in the t time period under the conditions of random charging and transferable charging;
the generator set constraints include:
wind power output constraint conditions:
Figure BDA00035185092200000310
wherein
Figure BDA00035185092200000311
The predicted value of the wind power in the t period is obtained;
photovoltaic output constraint conditions:
Figure BDA00035185092200000312
wherein
Figure BDA00035185092200000313
The predicted value of the photovoltaic power in the t period is obtained;
the spinning standby constraint conditions are as follows:
Figure BDA0003518509220000041
in the formula: rtRepresenting the rotating standby requirement of the system, and being the maximum conventional unit capacity in the installed capacity of the gas turbine of the system; delta PWT,tRepresenting reserve capacity demand, Δ P, due to wind power output randomnessPV,tRepresenting reserve capacity requirements due to photovoltaic output randomness;
the constraint conditions of the gas turbine unit comprise unit output upper and lower limit constraint conditions, start and stop constraint conditions and climbing constraint conditions, and specifically comprise the following steps:
output of machine set
Figure BDA0003518509220000042
And (4) upper and lower limit constraint conditions:
Figure BDA0003518509220000043
wherein
Figure BDA0003518509220000044
The maximum output and the minimum output of the unit i are respectively;
start-stop constraint conditions:
Figure BDA0003518509220000045
Figure BDA0003518509220000046
wherein
Figure BDA00035185092200000414
Respectively the continuous on-off time and the minimum on-off time of the unit i;
climbing constraint conditions:
Figure BDA0003518509220000048
wherein
Figure BDA0003518509220000049
Respectively limiting the power of climbing up and down slopes of the unit i;
the electric automobile constraint conditions are as follows:
Figure BDA00035185092200000410
wherein
Figure BDA00035185092200000411
Respectively an actual scheduling load value and a predicted load value of an operator in a t period;
for the transferable charging load, the transferable load quantity of the electric automobile needs to meet the upper and lower limits of the dispatching charging capacity as follows:
Figure BDA00035185092200000412
in the schedulable charging load, the schedulable discharging capacity of the electric automobile meets the upper and lower limits of the scheduling discharging capacity as follows:
Figure BDA00035185092200000413
further, the optimization objective function of the unit combination model is as follows:
Fc＝max(F2+F3-F1)
in the formula: fCThe maximum value of the cost of the regional power system; f1The operating cost of the gas turbine unit comprises the unit fuel cost and the start-stop cost; f2Operating profits for the electric vehicle charging pile; f3In order to reduce the wind and light abandoning benefits;
the gas turbine unit operating cost is calculated as:
Figure BDA0003518509220000051
in the formula:
Figure BDA0003518509220000052
representing the fuel cost function of the unit i in the t period by using a quadratic function;
Figure BDA0003518509220000053
taking a constant for simplifying calculation for a start-stop cost function of the unit i in a time period t;
Figure BDA0003518509220000054
starting and stopping a unit i at a time t;
Figure BDA0003518509220000055
generating power of the unit i in a time period t; n is a radical ofgThe total number of the fuel generator sets; t is the total optimization time period;
the unit fuel cost is as follows:
Figure BDA0003518509220000056
in the formula ai、bi、ciThe fuel cost coefficient of the ith generating set;
the operation income of the electric automobile charging pile is calculated as follows:
Figure BDA0003518509220000057
in the formula: n is a radical of1The total number of the electric vehicles;
Figure BDA0003518509220000058
and
Figure BDA0003518509220000059
respectively charging quantity and discharging quantity of the first electric automobile in a t period;
Figure BDA00035185092200000510
and
Figure BDA00035185092200000511
charging electricity price and discharging electricity price of the electric automobile in a time period t are respectively obtained;
the yield of reducing the abandoned wind and the abandoned light is as follows:
F3＝Pwd-qρwd+Ppv-qρpv
in the formula: pwd-qAnd Ppv-qRespectively reducing the total air abandon quantity and the light abandon quantity in one day; rhowdAnd ρpvWind power and photovoltaic grid-connected electricity prices are respectively.
Further, in step S3, the green license generated by the scheduled electric quantity of the schedulable electric vehicle is distributed to the electric vehicle user, and the calculation formula of the electric vehicle user profit is as follows:
Figure BDA00035185092200000512
in the formula: psitThe price of one green certificate for the time period t.
The invention has the beneficial effects that: in a regional power system, the schedulable characteristic of the electric automobile is utilized, so that the operation cost of a gas turbine set can be reduced, the wind power and photovoltaic receiving capacity can be improved, and an electric energy operator of a comprehensive energy system can benefit, so that the enthusiasm of the operator is improved. The green certificate that the scheduling electric quantity that will schedule electric automobile produced distributes electric automobile user in order to improve the enthusiasm that electric automobile user participated in the dispatch for more and more electric automobile users accept that operator's dispatch electric automobile charges and discharges and reduce partial charge cost, can also promote regional green power consumption simultaneously, accomplish green power consumption index.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a diagram of a scheduling model of an electric vehicle participating in a comprehensive energy system;
FIG. 2 is a schematic diagram of a transferable charging model of an electric vehicle;
FIG. 3 is a schematic diagram of a schedulable charging model of an electric vehicle;
FIG. 4 is a flowchart of an integrated energy system combined optimization strategy based on electric vehicle scheduling.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
Regional electric vehicles are classified into three categories: the first category is random charging load, which has high requirements on charging speed and charging time and cannot participate in power grid interaction, such as taxis, electric buses, logistics cars and a part of private cars; the second type is a transferable charging load, which means that the electric automobile only receives the transfer scheduling of charging time by an operator; the third type of load is a schedulable charging load, and means that the electric automobile receives charge and discharge bidirectional scheduling performed by an operator.
Electric automobile is mutual with regional electric wire netting through filling electric pile platform, as shown in fig. 1 and fig. 4, electric automobile centralized management ware collects all electric automobile user's the demand of charging, the data information of traveling to carry out charge-discharge capacity prediction to 3 types of loads: predicting the charging load of the disordered charging load; the schedulable charging load evaluates the schedulable charging capacity range; the charge and discharge load can be scheduled to predict the charge load and discharge capacity.
The regional power grid dispatching center collects traditional load forecasting information, wind power output forecasting information, electric automobile forecasting load and electric automobile schedulable charge and discharge capacity, and takes the charge and discharge load of the electric automobile as a variable to be added into unit combination optimization to carry out optimized dispatching on the traditional unit output, the wind power output and the electric automobile charge and discharge load. And the electric vehicle centralized manager takes the charge-discharge optimization curve received from the power grid scheduling as a control instruction to carry out charge-discharge control on the electric vehicles in the jurisdiction of the electric vehicle centralized manager.
1. Transferable charging load model of electric automobile
For the two types of electric vehicles, namely transferable charging and schedulable charging, the charging time period is usually longer than the actual charging time period, so that the possibility of scheduling the charging time of the electric vehicle is provided for an operator.
As shown in fig. 2, it is only necessary to have a chargeable duration Ts.factSelecting proper actual charging time length T0The charging requirement of the user can be met.
Knowing the state matrix of the transferable charging type electric automobile load at the moment t, the actual load P of the transferable charging type electric automobile load at the moment t can be obtainedevtAnd thus the maximum load P that such an electric vehicle can reach at the next moment in time can be predictedt+1 ev,maxAnd minimum load Pt+1 ev,minAs shown in formulas (1) - (4).
Figure BDA0003518509220000071
Figure BDA0003518509220000072
Figure BDA0003518509220000073
Figure BDA0003518509220000074
In the formula:
Figure BDA0003518509220000075
indicating the electric vehicle load that must be increased at time t +1, the load that must be switched in at the next time, and the load that must be cut off
Figure BDA0003518509220000076
Two parts are formed;
Figure BDA0003518509220000077
the access condition T is satisfied for the moment T +1sT +1, and a chargeable duration Ts.factThe number of electric vehicles is greater than the actual charging time T;
Figure BDA0003518509220000078
the access condition T is satisfied for the moment T +1sT +1, and a chargeable duration Ts.factThe number of electric vehicles which are smaller than the actual charging time T and need to start charging;
Figure BDA0003518509220000079
satisfaction of the excision Condition T for time T +1sT +1 or Sn＝SeThe number of electric vehicles that must be cut. The method can obtain the capacity range of all transferable charging loads 24h a day.
2. Adjustable charging load model of electric automobile
For a schedulable charging load type electric automobile, when the electric automobile is connected to a power grid after driving, an automobile battery can be used as distributed energy storage to discharge to the power grid when the power generation is insufficient or the power grid needs. Because frequent discharging of the battery can cause certain loss on the service life of the battery of the electric automobile, in order to slow down the attenuation of the service life of the battery, the charging and discharging switching times of the battery are reduced as much as possible, and therefore, in the model, only one discharging scheduling is carried out on each electric automobile within one day.
As shown in fig. 3, the discharge limit time point of the electric vehicle is beyond which the battery capacity cannot meet the user requirement when the electric vehicle leaves. Q3The charge amount of the electric vehicle at the discharge limit time point is represented.
Electric automobile discharge electrode time limit t3The calculation is as shown in formula (5)
Figure BDA0003518509220000081
Calculation of the schedulable discharge capacity of the electric automobile is shown as a formula (6)
Figure BDA0003518509220000082
Calculation of the charging capacity of the electric vehicle is shown in the formula (7)
Pev,c＝Cs(Q2+Q4-Q1-Q3) (7)
3. Wind power plant output model
The wind power characteristic function is shown as the formula (8)
Figure BDA0003518509220000083
In the formula: pWTFor wind turbine generator output power, vC、vNRespectively the actual wind speed, the cut-out wind speed and the rated wind speed; pNIs the rated output power.
4. Photovoltaic output model
The output power of the photovoltaic is determined by the output power Y under the standard rated conditionPVIllumination amplitude GSTCAmbient temperature TSTCJointly determining:
Figure BDA0003518509220000084
wherein f isPVThe photovoltaic derating factor is set according to factors such as dirt, wiring loss, shading, snow accumulation and aging of the photovoltaic panel; gTIrradiance of the current environment; alpha is alphaPIs the power temperature coefficient; t isCIs the temperature of the current environment。
5. System constraints
(1) Load balance constraint for electric vehicle to participate in comprehensive energy system scheduling
Figure BDA0003518509220000091
In the formula:
Figure BDA0003518509220000092
wind power output power in t time period;
Figure BDA0003518509220000093
photovoltaic output power for a period t;
Figure BDA0003518509220000094
the total discharge power of the electric automobile to the power grid can be scheduled for the t period;
Figure BDA0003518509220000095
load power for a period t;
Figure BDA0003518509220000096
scheduling the total charging power of the electric automobile for the t time period;
Figure BDA0003518509220000097
the total charging load power of the electric automobile in the time period t under the conditions of random charging and transferable charging.
(2) Constraint conditions of generator set
Wind power and photovoltaic output are also one of the control objects of the model, and the constraint thereof is shown as formulas (11) and (12)
Figure BDA0003518509220000098
Figure BDA0003518509220000099
In the formula:
Figure BDA00035185092200000910
and the predicted value of the wind power in the t period is obtained.
Figure BDA00035185092200000911
The predicted value of the photovoltaic power in the t period is obtained. Rotational standby constraint is as shown in equation (13)
Figure BDA00035185092200000912
In the formula: rtRepresenting the system rotation standby requirement, which is usually the maximum conventional unit capacity in the installed capacity of the gas turbine of the system; delta PWT,tRepresenting reserve capacity demand, Δ P, due to wind power output randomnessPV,tIndicating reserve capacity requirements due to photovoltaic output randomness.
The gas turbine unit constraint comprises unit output upper and lower limit constraint, start and stop constraint and climbing constraint, and is represented by formula (14) - (17):
Figure BDA00035185092200000913
Figure BDA00035185092200000914
Figure BDA00035185092200000915
Figure BDA00035185092200000916
in the formula:
Figure BDA00035185092200000917
the maximum output and the minimum output of the unit i are respectively;
Figure BDA00035185092200000918
respectively limiting the power of climbing up and down slopes of the unit i;
Figure BDA00035185092200000919
the continuous on-off time and the minimum on-off time of the unit i are respectively set.
(3) Constraint conditions of electric vehicle
In order to meet the charging requirements of electric vehicle users, the actual charging load total amount and the predicted load total amount of the electric vehicle scheduled by the regional power grid are ensured to be equal in the whole scheduling period. Constraint is as shown in equation (18)
Figure BDA0003518509220000101
Figure BDA0003518509220000102
Respectively the actual scheduling load value and the predicted load value of the operator in the t period.
For the transferable charging load, the transferable load quantity of the electric automobile needs to meet the upper and lower limits of the dispatching charging capacity
Figure BDA0003518509220000103
In the schedulable charging load, the schedulable discharging amount of the electric automobile needs to meet the upper and lower limits of the scheduling discharging capacity
Figure BDA0003518509220000104
5. Objective function
The photovoltaic and wind power output cost is not considered in the set combination model established by the invention. The optimization target of the unit combination model is the maximum benefit of a regional power grid system.
Fc＝max(F2+F3-F1) (21)
In the formula: fCRegional power system cost (maximum); f1The running cost of the gas turbine unit mainly comprises the unit fuel cost and the start-stop cost; f2Operating profits for the electric vehicle charging pile; f3In order to reduce the yield of the abandoned wind and the abandoned light.
The operating cost of the gas turbine set is calculated as:
Figure BDA0003518509220000105
in the formula:
Figure BDA0003518509220000106
representing the fuel cost function of the unit i in the time period t by using a quadratic function;
Figure BDA0003518509220000107
taking a constant for simplifying calculation for a start-stop cost function of the unit i in a time period t;
Figure BDA0003518509220000108
starting and stopping a unit i at a time t;
Figure BDA0003518509220000109
generating power of the unit i in a time period t; n is a radical of hydrogengThe total number of the fuel generator sets; t-24, the total optimization period.
The unit fuel cost is as shown in formula (12)
Figure BDA00035185092200001010
In the formula ai、bi、ciAnd the fuel cost coefficient of the ith generating set is shown.
The operation income of the electric automobile charging pile is calculated as follows:
Figure BDA00035185092200001011
in the formula: n is a radical of1The total number of the electric vehicles is;
Figure BDA00035185092200001012
and
Figure BDA00035185092200001013
respectively charging quantity and discharging quantity of the first electric automobile in a t period;
Figure BDA00035185092200001014
and
Figure BDA00035185092200001015
the charging electricity price and the discharging electricity price of the electric automobile in the t period are respectively.
The yield of reducing the abandoned wind and the abandoned light is as follows:
F3＝Pwd-qρwd+Ppv-qρpv (25)
in the formula: pwd-qAnd Ppv-qRespectively reducing the total air abandon quantity and the light abandon quantity in one day; rhowdAnd ρpvWind power and photovoltaic grid-connected electricity prices are respectively.
The income of the user participating in dispatching the electric automobile is as follows:
Figure BDA0003518509220000111
in the formula: psitThe price of one green certificate for the period t. A green certificate containing 1000kWh of green electricity is traded through a green certificate center.
And (3) solving the unit combination problem based on the mixed integer linear programming by adopting CPLEX to obtain the optimal combination model strategy of the wind, light and vehicle in the comprehensive energy system by the unit combination model obtained in the formula (10) -26.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (7)

1. A regional wind, light and vehicle combination optimization method based on schedulable capacity of electric vehicles is characterized by comprising the following steps: the method comprises the following steps:
s1: the method comprises the following steps of dividing electric vehicle loads into unordered charging loads, transferable charging loads and schedulable charging loads according to schedulable capacity, and predicting charging and discharging capacity of the three types of loads according to charging requirements and driving data information of electric vehicle users;
s2: taking the schedulable load of the electric automobile as a variable in the unit combination, and establishing a unit combination model in which the electric automobile participates in cooperation with wind power and photovoltaic power generation;
s3: and optimizing the unit combination model by taking the economy of the regional power grid as a target to obtain an optimal wind, light and vehicle combination model.
2. The regional wind, light and vehicle combination optimization method based on the dispatchable capacity of the electric vehicle as set forth in claim 1, wherein: in step S1, a charging load prediction is performed for the disordered charging load; evaluating the schedulable charging capacity range of the schedulable charging load; and predicting the charging load and the discharging capacity of the schedulable charging and discharging load.
3. The regional wind, light and vehicle combination optimization method based on the dispatchable capacity of the electric vehicle as set forth in claim 1, wherein: the unit combination model in which the electric vehicle and wind power and photovoltaic power generation cooperatively participate in step S2 includes:
the electric automobile can transfer the charging load model: obtaining a state matrix of the transferable charging type electric automobile load at the time tActual load P of transferable charging type electric automobile load at t momentevtAnd predicting the maximum load P which can be reached by the transferable charging type electric automobile at the next momentt+1 ev,maxAnd minimum load Pt+1 ev,min：
Figure FDA0003518509210000011
Figure FDA0003518509210000012
Figure FDA0003518509210000013
Figure FDA0003518509210000014
In the formula:
Figure FDA0003518509210000015
indicating the electric vehicle load that must be increased at time t +1, the load that must be switched in at the next time, and the load that must be cut off
Figure FDA0003518509210000016
Two parts are formed;
Figure FDA0003518509210000017
the access condition T is satisfied for the moment T +1sT +1, and a chargeable duration Ts.factThe number of electric vehicles is greater than the actual charging time T;
Figure FDA0003518509210000018
the access condition T is satisfied for the moment T +1sT +1, and a chargeable duration Ts.factThe number of electric vehicles which are smaller than the actual charging time T and need to start charging;
Figure FDA0003518509210000019
satisfaction of the excision Condition T for time T +1sT +1 or Sn＝SeThe number of electric vehicles that must be cut;
the schedulable charging load model of the electric automobile is as follows:
electric automobile discharge electrode time limit t3Comprises the following steps:
Figure FDA00035185092100000110
the adjustable discharge capacity of the electric automobile is as follows:
Figure FDA0003518509210000021
the charging capacity of the electric automobile is as follows:
Pev,c＝Cs(Q2+Q4-Q1-Q3)
wind power plant output model:
the wind power characteristic function is:
Figure FDA0003518509210000022
in the formula: pWTFor wind turbine generator output power, vC、vNActual wind speed, cut-out wind speed and rated wind speed are respectively set; pNIs the rated output power;
photovoltaic output model: the output power of the photovoltaic is determined by the output power Y under the standard rated conditionPVIllumination amplitude GSTCAmbient temperature TSTCJointly determining:
Figure FDA0003518509210000023
wherein f isPVA photovoltaic derating factor, which is set according to external factors of the photovoltaic panel; gTIrradiance of the current environment; alpha is alphaPIs the power temperature coefficient; t isCIs the temperature in the current environment.
4. The regional wind, light and vehicle combination optimization method based on the dispatchable capacity of the electric vehicle as set forth in claim 3, wherein: in step S3, for the unit combination model in which the electric vehicle cooperatively participates in wind power generation and photovoltaic power generation in step S2, an optimization target of the unit combination model is constructed in consideration of a load balance constraint condition, a generator set constraint condition, and an electric vehicle constraint condition under which the electric vehicle participates in the scheduling of the integrated energy system, and the CPLEX is used to solve and calculate the nonlinear mixed integer programming problem in the model, so that an optimal combination model is obtained.
5. The regional wind, light and vehicle combination optimization method based on the dispatchable capacity of the electric vehicle as set forth in claim 4, wherein: the load balance constraint conditions for the electric automobile to participate in the dispatching of the comprehensive energy system are as follows:
Figure FDA0003518509210000024
in the formula:
Figure FDA0003518509210000025
wind power output power in t time period;
Figure FDA0003518509210000026
photovoltaic output power for a period t;
Figure FDA0003518509210000027
the total discharge power of the electric automobile to the power grid can be scheduled for the t period;
Figure FDA0003518509210000028
load power for a period t;
Figure FDA0003518509210000029
the total charging power of the electric automobile can be scheduled for the t time period;
Figure FDA00035185092100000210
the total charging load power of the electric automobile in the t time period under the conditions of random charging and transferable charging;
the generator set constraints include:
wind power output constraint conditions:
Figure FDA0003518509210000031
wherein
Figure FDA0003518509210000032
The predicted value of the wind power in the t period is obtained;
photovoltaic output constraint conditions:
Figure FDA0003518509210000033
wherein
Figure FDA0003518509210000034
The predicted value of the photovoltaic power in the t period is obtained;
the spinning standby constraints are as follows:
Figure FDA0003518509210000035
in the formula: rtIndicating the reserve demand for system rotation for the installed capacity of the system gas turbineThe capacity of the maximum conventional unit; delta PWT,tRepresenting reserve capacity demand, Δ P, due to wind power output randomnessPV,tRepresenting reserve capacity requirements due to photovoltaic output randomness;
the constraint conditions of the gas turbine unit comprise unit output upper and lower limit constraint conditions, start and stop constraint conditions and climbing constraint conditions, and specifically comprise the following steps:
output of machine set
Figure FDA0003518509210000036
And (4) upper and lower limit constraint conditions:
Figure FDA0003518509210000037
wherein
Figure FDA0003518509210000038
The maximum output and the minimum output of the unit i are respectively;
start-stop constraint conditions:
Figure FDA0003518509210000039
Figure FDA00035185092100000310
wherein
Figure FDA00035185092100000311
Respectively the continuous on-off time and the minimum on-off time of the unit i;
climbing constraint conditions:
Figure FDA00035185092100000312
wherein
Figure FDA00035185092100000313
Respectively limiting the power of climbing up and down slopes of the unit i;
the electric automobile constraint conditions are as follows:
Figure FDA00035185092100000314
wherein
Figure FDA0003518509210000041
Respectively an actual scheduling load value and a predicted load value of an operator in a t period;
for the transferable charging load, the transferable load quantity of the electric automobile needs to meet the upper and lower limits of the dispatching charging capacity as follows:
Figure FDA0003518509210000042
in the schedulable charging load, the schedulable discharging capacity of the electric automobile meets the upper and lower limits of the scheduling discharging capacity as follows:
Figure FDA0003518509210000043
6. the regional wind, light and vehicle combination optimization method based on the dispatchable capacity of the electric vehicle as set forth in claim 5, wherein: the optimization objective function of the unit combination model is as follows:
Fc＝max(F2+F3-F1)
in the formula: fCThe maximum value of the cost of the regional power system; f1The operating cost of the gas turbine unit comprises the unit fuel cost and the start-stop cost; f2Operating profits for the electric vehicle charging pile; f3In order to reduce the wind and light abandoning benefits;
the operating cost of the gas turbine set is calculated as:
Figure FDA0003518509210000044
in the formula:
Figure FDA0003518509210000045
representing the fuel cost function of the unit i in the t period by using a quadratic function;
Figure FDA0003518509210000046
taking a constant as a start-stop cost function of the unit i in the time period t for simplifying calculation;
Figure FDA0003518509210000047
starting and stopping a unit i at a time t;
Figure FDA0003518509210000048
generating power of the unit i in a time period t; n is a radical ofgThe total number of the fuel generator sets; t is the total optimization time period;
the unit fuel cost is:
Figure FDA0003518509210000049
in the formula ai、bi、ciThe fuel cost coefficient of the ith generating set;
the operation income of the electric automobile charging pile is calculated as follows:
Figure FDA00035185092100000410
in the formula: n is a radical of1The total number of the electric vehicles is;
Figure FDA00035185092100000411
and
Figure FDA00035185092100000412
respectively charging quantity and discharging quantity of the first electric automobile in a t period;
Figure FDA00035185092100000413
and
Figure FDA00035185092100000414
charging electricity price and discharging electricity price of the electric automobile in a time period t are respectively obtained;
the yield of reducing the abandoned wind and the abandoned light is as follows:
F3＝Pwd-qρwd+Ppv-qρpv
in the formula: pwd-qAnd Ppv-qRespectively reducing the total air abandon quantity and the light abandon quantity in one day; rhowdAnd ρpvRespectively wind power and photovoltaic grid-connected electricity prices.
7. The regional wind, light and vehicle combination optimization method based on the dispatchable capacity of the electric vehicle as set forth in claim 6, wherein: in step S3, the green license generated by the scheduled electric quantity of the schedulable electric vehicle is distributed to the electric vehicle user, and the calculation formula of the electric vehicle user profit is:
Figure FDA0003518509210000051
in the formula: psitThe price of one green certificate for the time period t.
CN202210184219.6A 2022-02-24 2022-02-24 Regional wind, light and vehicle combination optimization method based on dispatchable capacity of electric vehicle Pending CN114565153A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115360740A (en) * 2022-10-19 2022-11-18 中社科(北京)城乡规划设计研究院 Community electric bicycle energy control system
CN115800385A (en) * 2022-08-15 2023-03-14 国网安徽省电力有限公司经济技术研究院 Electric energy quality regulation and control method based on adjustable and controllable capacity of photovoltaic inverter and charging pile

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115800385A (en) * 2022-08-15 2023-03-14 国网安徽省电力有限公司经济技术研究院 Electric energy quality regulation and control method based on adjustable and controllable capacity of photovoltaic inverter and charging pile
CN115800385B (en) * 2022-08-15 2024-04-19 国网安徽省电力有限公司经济技术研究院 Power quality regulation method based on capacity regulation of photovoltaic inverter and charging pile
CN115360740A (en) * 2022-10-19 2022-11-18 中社科(北京)城乡规划设计研究院 Community electric bicycle energy control system
CN115360740B (en) * 2022-10-19 2023-03-07 中社科(北京)城乡规划设计研究院 Community electric bicycle energy control system

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