CN110826801A - Distributed electric energy management method for electric vehicle charging station - Google Patents

Distributed electric energy management method for electric vehicle charging station Download PDF

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CN110826801A
CN110826801A CN201911074045.2A CN201911074045A CN110826801A CN 110826801 A CN110826801 A CN 110826801A CN 201911074045 A CN201911074045 A CN 201911074045A CN 110826801 A CN110826801 A CN 110826801A
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杨宏泽
廖建棠
黄晧玮
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Jiangsu Million Bangde And New Energy Polytron Technologies Inc
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Abstract

An electric vehicle charging station distributed power management method implemented by a processing unit and comprising the steps of: (A) obtaining at least one pane to be planned of each electric vehicle; (B) for each electric vehicle, acquiring the charging electric power or the discharging electric power of the electric vehicle in each pane to be planned according to the electric vehicle information corresponding to the electric vehicle, at least one selling price, at least one buying price, at least one bid price and the degradation cost; (C) obtaining the total consumed electric power of the charging station in each time window; (D) determining whether at least one overload window exists according to the total consumed electric power of each time window; and (E) when the existence of the at least one overload window is judged, adjusting the purchase price of each overload window, and repeating the steps (B) to (D) until the existence of the at least one overload window is judged.

Description

Distributed electric energy management method for electric vehicle charging station
Technical Field
The present invention relates to an electric energy management method for an electric vehicle charging station, and more particularly, to a distributed electric energy management method for an electric vehicle charging station that schedules charging and discharging of each electric vehicle in a distributed manner.
Background
In response to the global warming problem, various national car dealers begin to develop electric cars to replace the conventional fossil fuel engine, however, the charging status of the electric cars cannot be predicted due to different habits of users, and if all the electric cars are charged simultaneously in the peak time period of power consumption, the peak load is high and the backup capacity is low, and in addition, the electric charge required for charging in the peak time period is relatively high, so the electric energy management of the electric car charging station is an urgent problem to be solved.
In the prior art, for example, the method proposed in the "An Optimal Charging/Discharging Strategy for smart electric Car Parks" establishes An electric Car Charging/Discharging Strategy according to the current state of charge, the price of electricity, the expected state of charge, and the power grid requirements of An electric Car, but the method considers the conditions of all electric cars in a Charging station when optimizing the Charging/Discharging Strategy of each electric Car, and optimizes the Charging/Discharging schedule of each electric Car during the solution, so the calculation dimension of the centralized operation is very high, and the operation time required is long.
Disclosure of Invention
The invention aims to provide a charging station distributed electric energy management method which optimizes the charging and discharging schedule of each electric vehicle respectively so as to greatly reduce the calculation dimensionality.
The invention relates to a distributed electric energy management method of an electric vehicle charging station, which is suitable for managing the charge and discharge states of all electric vehicles parked in the charging station, wherein each electric vehicle corresponds to electric vehicle information, and each electric vehicle information comprises the entry time, the exit time, the charge state of an entry battery when the electric vehicle enters the field, the expected charge state of an exit battery, the minimum charge state of the battery, the maximum charge state of the battery, the full charge capacity, the maximum charging electric power and the maximum discharging electric power of the corresponding electric vehicle, and is implemented by a processing unit and comprises the following steps:
(A) for each electric vehicle, mapping the entry time and the exit time in the electric vehicle information corresponding to the electric vehicle to at least one of a plurality of time panes in a scheduling period, and obtaining at least one pane to be planned from the at least one time pane in which the electric vehicle is located, wherein the at least one pane to be planned corresponding to the electric vehicle is the last time pane from the current time pane to the electric vehicle;
(B) for each electric vehicle, according to electric vehicle information corresponding to the electric vehicle, the charging station resales at least one selling price of unit electric power in at least one to-be-planned pane corresponding to the electric vehicle, at least one buying price of the unit electric power in at least one to-be-planned pane corresponding to the electric vehicle, at least one bid price of the charging station participating in demand bidding in at least one to-be-planned pane corresponding to the electric vehicle, and degradation cost consumed by discharging the unit electric power from a battery of the electric vehicle, and charging electric power or discharging electric power of the electric vehicle in each to-be-planned pane corresponding to the electric vehicle is obtained by utilizing nonlinear planning;
(C) for each of the time panes in the schedule cycle, obtaining a total consumed electric power of the charging station in the time pane from the charging electric power or the discharging electric power of each electric vehicle in the time pane;
(D) determining whether at least one overload window exists in the time windows in the scheduling period according to the total consumed electric power of each time window in the scheduling period and the maximum supply electric power related to the charging station, wherein the total consumed electric power of each overload window is larger than the maximum supply electric power; and
(E) when the at least one overload window pane is judged to exist, adjusting the purchase price of each overload window pane, and repeating the steps (B) to (D) until the at least one overload window pane is judged not to exist.
In the electric vehicle charging station electric energy management method of the present invention, in the step (B), the processing unit further obtains the charging electric power or the discharging electric power of the electric energy storage device at the current time point t by using the non-linear programming.
In the step (B), the non-linearly programmed objective function and the plurality of constraints satisfied by the objective function may be expressed as:
Figure BDA0002261830470000021
Figure BDA0002261830470000022
Figure BDA0002261830470000031
Figure BDA0002261830470000032
Figure BDA0002261830470000033
Figure BDA0002261830470000034
constraint 1:
Figure BDA0002261830470000035
constraint 2:
Figure BDA0002261830470000036
constraint 3:
Figure BDA0002261830470000037
constraint 4:
Figure BDA0002261830470000038
constraint 5:
the restriction condition 6:
wherein, KnAt least one pane to be planned corresponding to the nth electric vehicle,charging profit obtained by the charging station when the nth electric vehicle is charged in the t time window,resale the selling price of the unit of electric power for the charging station in the tth time pane,
Figure BDA00022618304700000313
buying the purchase price per unit of electric power for the charging station in the t-th time pane,
Figure BDA00022618304700000314
charging electric power for the nth electric vehicle in the t-th time window,
Figure BDA00022618304700000315
the power saving profit obtained by the charging station when the nth electric vehicle participates in the demand response in the t time window,
Figure BDA00022618304700000316
participating in a bid price of a demand bid for the charging station in a tth time pane,
Figure BDA00022618304700000317
the discharge electric power for the nth electric vehicle in the t-th time window,
Figure BDA00022618304700000318
for the total degradation cost of the nth electric vehicle participating in the demand response at the tth time pane,
Figure BDA00022618304700000319
total cost of battery for nth electric vehicle, mnIs the ratio of the variation of the battery capacity of the battery of the nth electric vehicle to the variation of the number of battery cycles,
Figure BDA00022618304700000320
is the full charge capacity of the battery of the nth electric vehicle,
Figure BDA00022618304700000321
a deterioration cost consumed for discharging the unit electric power for the battery of the nth electric vehicle,the nth electric vehicle participates in the charging fee compensation of demand bidding in the t time window,
Figure BDA00022618304700000323
charging punishment p of the nth electric vehicle in the t time window to participate in the demand responset,nCharging electric power or discharging electric power for the nth electric vehicle in the t-th time window when pt,n≤0，pt,nCharging electric power for the nth electric vehicle in the t-th time window when pt,n>0，pt,nThe discharge electric power for the nth electric vehicle in the t-th time window,
Figure BDA0002261830470000041
the maximum charging electric power for the nth electric vehicle,
Figure BDA0002261830470000042
the maximum discharge for the nth electric vehicleElectric power, N for all electric vehicles, TnAt least one time window of the nth electric vehicle,the minimum battery state of charge for the nth electric vehicle,
Figure BDA0002261830470000044
the maximum battery state of charge, SOC, for the nth electric vehiclet,nThe battery state of charge for the nth electric vehicle in the t-th time window,
Figure BDA0002261830470000045
the charge state of the entrance battery of the nth electric vehicle,
Figure BDA0002261830470000046
min (T) is the off-field battery state of charge of the nth electric vehiclen) The first time window, max (T), in which the nth electric vehicle is locatedn) Is the last time window, delta t, of the nth electric vehiclet,nFor the nth electric vehicle during the time of the tth time window.
In the step (B), the processing unit further obtains the charging electric power or the discharging electric power of the electric vehicle in each corresponding to-be-planned pane by using the nonlinear programming according to a conversion efficiency function related to energy lost during charge-discharge conversion.
The distributed electric energy management method for electric vehicle charging stations of the present invention, wherein in the step (B), the non-linearly programmed objective function and the plurality of constraints satisfied by the objective function may be expressed as:
Figure BDA0002261830470000047
Figure BDA0002261830470000049
Figure BDA00022618304700000411
Figure BDA00022618304700000412
constraint 1:
Figure BDA00022618304700000413
constraint 2:
Figure BDA00022618304700000414
constraint 3:
Figure BDA00022618304700000415
constraint 4:
Figure BDA0002261830470000051
constraint 5:
Figure BDA0002261830470000052
the restriction condition 6:
Figure BDA0002261830470000053
wherein, KnAt least one pane to be planned, T, corresponding to the nth electric vehiclenAt least one time window of the nth electric vehicle,charging profit obtained by the charging station when the nth electric vehicle is charged in the t time window,
Figure BDA0002261830470000055
resale the selling price of the unit of electric power for the charging station in the tth time pane,
Figure BDA0002261830470000056
buying the purchase price per unit of electric power for the charging station in the t-th time pane,charging electric power for the nth electric vehicle in the t-th time window,the power saving profit obtained by the charging station when the nth electric vehicle participates in the demand response in the t time window,
Figure BDA0002261830470000059
participating in a bid price of a demand bid for the charging station in a tth time pane,
Figure BDA00022618304700000510
the discharge electric power for the nth electric vehicle in the t-th time window,
Figure BDA00022618304700000511
for the total degradation cost of the nth electric vehicle participating in the demand response at the tth time pane,
Figure BDA00022618304700000512
total cost of battery for nth electric vehicle, mnIs the ratio of the variation of the battery capacity of the battery of the nth electric vehicle to the variation of the number of battery cycles,
Figure BDA00022618304700000513
is the full charge capacity of the battery of the nth electric vehicle,
Figure BDA00022618304700000514
a deterioration cost consumed for discharging the unit electric power for the battery of the nth electric vehicle,the nth electric vehicle participates in the charging fee compensation of demand bidding in the t time window,
Figure BDA00022618304700000516
charging punishment p of the nth electric vehicle in the t time window to participate in the demand responset,nCharging electric power or discharging electric power for the nth electric vehicle in the t-th time window when pt,n≤0，pt,nCharging electric power for the nth electric vehicle in the t-th time window when pt,n>0，pt,nThe discharge electric power of the nth electric vehicle in the t-th time window, N is all electric vehicles,
Figure BDA00022618304700000517
the maximum charging electric power for the nth electric vehicle,
Figure BDA00022618304700000518
the maximum discharge electric power of the nth electric vehicle,
Figure BDA00022618304700000519
the minimum battery state of charge for the nth electric vehicle,
Figure BDA00022618304700000520
the maximum battery state of charge, SOC, for the nth electric vehiclet,nThe battery state of charge for the nth electric vehicle in the t-th time window,
Figure BDA00022618304700000521
is the nth electric vehicleThe state of charge of the battery is entered into the field,
Figure BDA00022618304700000522
min (T) is the off-field battery state of charge of the nth electric vehiclen) The first time window, max (T), in which the nth electric vehicle is locatedn) Is the last time window, delta t, of the nth electric vehiclet,nη (x) is the conversion efficiency function related to the energy lost by each electric vehicle during charge-discharge conversion for the time period that the nth electric vehicle waits in the t time window, η (x) 0.0003307x3-0.0209x2+0.3833x+92.3373。
The distributed electric energy management method for the electric vehicle charging station comprises the following steps of (A) setting an electric energy storage device in the charging station, wherein the electric energy storage device corresponds to electric energy information, and the electric energy information comprises the initial charge state, the minimum charge state, the maximum charge state, the full charge capacity and the maximum charging and discharging electric power of the electric energy storage device, and before the step (C):
(F) taking all time panes in the scheduling period as time panes where the electric energy storage device is located, and obtaining at least one pane to be planned from the time panes in the scheduling period, wherein the at least one pane to be planned corresponding to the electric energy storage device is the last time pane in the time panes from the current time pane to the scheduling period; and
(G) according to the electric energy information corresponding to the electric energy storage device, the bid price of the charging station for buying the unit electric power or participating in demand bidding on each of at least one to-be-planned window corresponding to the electric energy storage device and the degradation cost consumed by the electric energy storage device for charging or discharging the unit electric power, the nonlinear programming is utilized to obtain the charging electric power or the discharging electric power of the electric energy storage device in each to-be-planned window corresponding to the electric energy storage device;
in step (C), the total consumed electric power of the charging station in the time pane is obtained not only from the charging electric power or the discharging electric power of each electric vehicle in the time pane but also from the charging electric power or the discharging electric power of the electric energy storage device in the time pane.
The invention discloses a distributed electric energy management method of an electric vehicle charging station, which comprises the following steps:
in step (G), the non-linear schedule is further utilized to obtain the charging electric power or the discharging electric power of the electric energy storage device in each corresponding to-be-scheduled window according to the basic consumption electric power of the charging station in each time window of the scheduling cycle and the charging electric power or the discharging electric power of each electric vehicle in each time window of the electric vehicle; and
in step (C), the total consumed electric power of the charging station is obtained not only from the charging electric power or the discharging electric power of each electric vehicle and the electric energy storage device in the time pane but also from the basic consumed electric power in the time pane.
The invention discloses a distributed electric energy management method of an electric vehicle charging station, which comprises the following steps:
in step (G), the charging electric power or the discharging electric power of the electric energy storage device in each corresponding to-be-planned window is obtained by the nonlinear programming according to the solar electric power generated by the solar module in each time window in the scheduling cycle; and
in step (C), the total consumed electric power of the charging station is obtained not only from the charging electric power or the discharging electric power of each electric vehicle and the electric energy storage device in the time pane and the base consumed electric power in the time pane but also from the solar electric power in the time pane.
The electric energy management method of the electric vehicle charging station comprises the following steps:
in step (C), the processing unit charges or discharges the electric power p according to the charging electric power or the discharging electric power of the nth electric vehicle in the t-th time windowt,nThe charging electric power or the discharging electric power p of the electric energy storage device in the t-th time windowess,tThe base consumption electric power p of the charging station in the t-th time paneload,tAnd said solar electric power p in the t-th time panepv,tObtaining the total consumed electric power P of the charging station in the t-th time window by using the following formulasum,t，
Figure BDA0002261830470000071
Wherein N is all electric vehicles, KessThe window is at least one window to be planned corresponding to the electric energy storage device; and
in step (E), for each overloaded pane, the processing unit adjusts the coefficient f according to the price of electricity corresponding to the overloaded panet(x) To adjust the purchase price of the overloaded pane,
Figure BDA0002261830470000072
wherein,
Figure BDA0002261830470000073
for said maximum supply of electric power, ToverlaodThe at least one overloaded pane.
In the step (B), the non-linearly programmed objective function and the plurality of constraints satisfied by the objective function may be expressed as:
Figure BDA0002261830470000081
Figure BDA0002261830470000082
Figure BDA0002261830470000083
constraint 1:
Figure BDA0002261830470000084
constraint 2:
constraint 3:
Figure BDA0002261830470000086
constraint 4:
Figure BDA0002261830470000087
constraint 5:
the restriction condition 6:
Figure BDA0002261830470000089
constraint 7:
Figure BDA00022618304700000810
wherein, KessAt least one pane to be planned, T, corresponding to the electrical energy storage deviceessThe time pane in which the electrical energy storage device is located,
Figure BDA00022618304700000811
charging costs consumed by the charging station or the obtained power saving profit for the charging or discharging of the electrical energy storage device in the tth time pane,
Figure BDA00022618304700000812
buying the purchase price of the unit of electric power or participating in the purchase price in the t-th time window for the charging stationThe bid price for the demand bid,
Figure BDA00022618304700000813
charging electric power for the electric energy storage device in the t-th time window,
Figure BDA00022618304700000814
discharging electrical power for the electrical energy storage device at a t-th time window,a total degradation cost of charging or discharging the electrical energy storage device in the tth time pane,for the total cost of the electrical energy storage device, messIs the ratio of the variation of the battery capacity of the electrical energy storage device to the variation of the number of battery cycles,
Figure BDA00022618304700000817
for a full charge capacity of the electrical energy storage device,cost of degradation, p, consumed to charge or discharge the unit of electrical power for the battery of the electrical energy storage deviceess,tCharging or discharging electric power for the electric energy storage device in the t-th time window, when pess,t≤0，pess,tCharging electric power for the electric energy storage device in the t-th time window, when pess,t>0，pess,tDischarging electrical power for the electrical energy storage device at a t-th time window,
Figure BDA0002261830470000091
for the maximum charging and discharging electric power of the electric energy storage device,
Figure BDA0002261830470000092
the minimum power for the electrical energy storage deviceThe state of charge of the cell is,
Figure BDA0002261830470000093
for the maximum battery state of charge of the electrical energy storage device,
Figure BDA0002261830470000094
for the battery state of charge of the electrical energy storage device in the tth time pane,
Figure BDA0002261830470000095
is the incoming battery state of charge of the electrical energy storage device,
Figure BDA0002261830470000096
at is the off-field battery state of charge of the electrical energy storage device, Δ t is the time period corresponding to each time pane,
Figure BDA0002261830470000097
is a charge conversion efficiency parameter related to the energy lost by the electrical energy storage device during charge conversion,
Figure BDA0002261830470000098
for the discharge conversion efficiency parameter, p, relating to the energy lost by the electrical energy storage device during discharge conversiont,nThe charging electric power or the discharging electric power for the nth electric vehicle in the t-th time window, ppv,tFor the solar electric power in the t-th time pane, pload,tConsuming electric power for the charging station on the basis of the t-th time pane, N being all electric vehicles.
The distributed electric energy management method of the electric vehicle charging station further comprises the following steps before the step (A):
(H) for each electric vehicle, acquiring the charging priority of the electric vehicle according to the entry time, the exit time, the entry battery charge state, the exit battery charge state, the full charge capacity and the maximum charging electric power in electric vehicle information corresponding to the electric vehicle;
(I) for each electric vehicle, acquiring the discharging priority weight of the electric vehicle according to the charging priority weight of the electric vehicle; and
(J) for each electric vehicle, the maximum discharge electric power of the electric vehicle is obtained based on the discharge priority weights of the electric vehicles, the discharge priority weights of all electric vehicles, and the predicted total discharge electric power.
The invention discloses a distributed electric energy management method of an electric vehicle charging station, which comprises the following steps:
in the step (H), the entry time is determined according to the entry time in the electric vehicle information corresponding to the nth electric vehicle
Figure BDA0002261830470000099
The off-field time
Figure BDA00022618304700000910
State of charge of the entry battery
Figure BDA00022618304700000911
The off-field battery state of charge
Figure BDA00022618304700000912
The full charge capacityAnd the maximum charging electric power
Figure BDA00022618304700000914
Obtaining the charging priority weight of the nth electric vehicle by using the following formula
Figure BDA00022618304700000915
Figure BDA0002261830470000101
Wherein, Δ t is the time period corresponding to each time pane; and
in the step (I), charging priority weight of the nth electric vehicle is used
Figure BDA0002261830470000104
Obtaining the discharge priority weight of the nth electric vehicle by using the following formula
Figure BDA0002261830470000105
Figure BDA0002261830470000102
And
in step (J), the processing unit performs discharge priority weighting according to the nth electric vehicleDischarge priority weights of all electric vehicles, and predicted total discharge electric power PV2GObtaining the maximum discharge electric power of the nth electric vehicle using the following formula
Figure BDA0002261830470000107
Figure BDA0002261830470000103
Wherein N is all electric vehicles.
The invention has the beneficial effects that: the processing unit is used for obtaining the charging electric power or the discharging electric power of the corresponding electric vehicle in each window to be planned by utilizing nonlinear programming according to the electric vehicle information, at least one selling price, at least one buying price, at least one bid price and the degradation cost corresponding to each electric vehicle, and then the total consumed electric power consumed by all the electric vehicles in each time window is comprehensively considered to judge whether to perform the optimized scheduling of each electric vehicle again.
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Other features and effects of the present invention will become apparent from the following detailed description of the embodiments with reference to the accompanying drawings, in which:
FIG. 1 is a block diagram illustrating an arithmetic device implementing an embodiment of the distributed power management method for an electric vehicle charging station of the present invention;
FIG. 2 is a flow chart illustrating a discharge allocation procedure of the embodiment of the distributed power management method for an electric vehicle charging station of the present invention;
FIG. 3 is a flowchart illustrating an electric vehicle distributed scheduling procedure of the embodiment of the electric vehicle charging station distributed power management method of the present invention;
FIG. 4 is a schematic diagram illustrating a conversion efficiency map;
FIG. 5 is a flowchart illustrating an electrical energy storage device scheduling process of the embodiment of the distributed electrical energy management method for an electric vehicle charging station of the present invention;
FIG. 6 is a flow chart illustrating a general planning procedure for an embodiment of the distributed power management method for an electric vehicle charging station of the present invention; and
FIG. 7 is a diagram illustrating a linear relationship between a battery capacity and a battery cycle count.
Detailed Description
Referring to fig. 1, the embodiment of the distributed electric energy management method for an electric vehicle charging station according to the present invention is suitable for managing the charging and discharging states of all electric vehicles (not shown) parked in a charging station, and is implemented by a computing device 1. The charging station is provided with an electric energy storage device (not shown) for storing electric energy and electrically connected with the operation device 1, and a solar module (not shown) for generating electric energy and electrically connected with the operation device 1.
The computing device 1 includes an input unit 11, a communication unit 12 connected to a communication network, and a processing unit 13 electrically connecting the input unit 11 and the communication unit 12. In this embodiment, the computing device 1 is, for example, a computer, a server, or a smart phone.
Each electric vehicle corresponds to electric vehicle information, and each electric vehicle information comprises an entry time, an exit time, an entry battery charge state during entry, a desired exit battery charge state, a minimum battery charge state, a maximum battery charge state, a full charge capacity, a maximum charging electric power and a maximum discharging electric power of the corresponding electric vehicle.
It should be noted that the entry time, the exit time, the entry battery state of charge, the exit battery state of charge, the minimum battery state of charge, the maximum battery state of charge, the full charge capacity, the maximum charging power, and the battery degradation cost in the electric vehicle information may be generated by the user of the corresponding electric vehicle performing an input operation using the input unit 11 of the computing device 1, and the entry battery state of charge may also be obtained by measuring the state of charge of the battery of the electric vehicle and transmitting the measured state of charge to the computing device 1 through an electric quantity detector (not shown) installed in the charging station and electrically connected to the computing device 1. In other embodiments, the electric vehicle information may also be generated by a user of the corresponding electric vehicle performing an input operation by using a user terminal (not shown) of the corresponding electric vehicle, and the user terminal transmits the generated electric vehicle information to the computing device 1 via the communication network, but not limited thereto.
The electrical Energy Storage device is, for example, an Energy Storage System (ESS). The electric energy storage device corresponds to electric energy information, and the electric energy information comprises an initial charge state, a minimum charge state, a maximum charge state, a full charge capacity and a maximum charging and discharging electric power of the electric energy storage device. The solar module includes, for example, a solar cell template and is configured to generate a solar electrical power in each time bin of the scheduling cycle.
It is worth mentioning that the initial state of charge, the minimum state of charge, the maximum state of charge, the full charge capacity, and a maximum charging/discharging electric power in the electric energy information are generated by the manager of the charging station performing an input operation using the input unit 11 of the computing device 1.
A first embodiment of the distributed electric energy management method for an electric vehicle charging station according to the present invention will be described with reference to the accompanying drawings, wherein the first embodiment sequentially includes a discharge distribution procedure, an electric vehicle distributed scheduling procedure, an electric energy storage device scheduling procedure, and a comprehensive planning procedure.
Referring to fig. 1 and 2, the discharge allocation procedure of the electric vehicle charging station distributed power management method illustrates how to allocate a maximum discharge electric power corresponding to each electric vehicle, and includes the following steps.
In step 21, for each electric vehicle, the processing unit 13 of the computing device 1 determines the entry time according to the electric vehicle information corresponding to the electric vehicle (i.e. the nth electric vehicle)
Figure BDA0002261830470000122
The time of departure
Figure BDA0002261830470000123
The charge state of the battery
Figure BDA0002261830470000124
The off-field battery state of charge
Figure BDA0002261830470000125
The full charge capacity
Figure BDA0002261830470000126
And the maximum charging electric power
Figure BDA0002261830470000127
Obtaining a charging priority weight of the electric vehicle in a staying time interval between the entering time and the leaving time by using the following formula (1)
Figure BDA0002261830470000128
Figure BDA0002261830470000121
In equation (1), Δ t is the time period corresponding to a time window, and is expressed in hours, in this embodiment, a complete time window is defined as 15 minutes, i.e. 0.25 hours, so the value of Δ t is 0.25.
In step 22, for each electric vehicle, the processing unit 13 of the computing device 1 determines the charging priority of the electric vehicle (i.e., nth electric vehicle) according to the charging priority
Figure BDA0002261830470000129
Obtaining a discharge priority weight of the electric vehicle using the following equation (2)
In step 23, for each electric vehicle, the processing unit 13 of the computing device 1 determines the discharge priority of the electric vehicle (i.e., nth electric vehicle) according to the discharge priority
Figure BDA0002261830470000134
Discharge priority weights of all electric vehicles, and a predicted total discharge electric power PV2GThe maximum discharge electric power of the electric vehicle is obtained using the following formula (3)
Figure BDA0002261830470000135
Wherein N is all electric vehicles.
It should be noted that, in the embodiment, the total discharging electric power is set by the administrator according to the requirement, because the distributed electric energy management method for the electric vehicle charging station of the present invention adopts the distributed independent operation when optimizing the schedule of each electric vehicle, in order to avoid the situation that the sum of the discharging powers of all electric vehicles is greater than the total discharging electric power due to unlimited discharging of the electric vehicles that are not affected by each other during the schedule, and further the reverse power transmission to the power grid occurs, the processing unit 13 allocates the maximum discharging electric power corresponding to each electric vehicle according to the discharging priority of each electric vehicle, so as to avoid the situation that the reverse power transmission to the power grid occurs. However, in other embodiments, the maximum discharging electric power corresponding to each electric vehicle may also be set by the user of the corresponding electric vehicle according to the requirement.
Referring to fig. 1 and 3, the electric vehicle distributed scheduling procedure of the electric vehicle charging station distributed electric energy management method illustrates how to optimize the charging and discharging schedule corresponding to each electric vehicle, and includes the following steps.
In step 31, for each electric vehicle, the processing unit 13 of the computing device 1 maps the entry time and the exit time in the electric vehicle information corresponding to the electric vehicle (i.e., the nth electric vehicle) to at least one of a plurality of time windows in a scheduling period, and obtains at least one window to be scheduled from at least one time window in which the electric vehicle is located, wherein the at least one window to be scheduled corresponding to the electric vehicle is from a current time window to a last time window in which the electric vehicle is located. In this embodiment, the scheduling period is, for example, one day, each complete time window is 0.25 hours, and one day can be divided into 96 time windows.
In step 32, for each electric vehicle, the processing unit 13 of the computing device 1 determines, according to the electric vehicle information corresponding to the electric vehicle (i.e., the nth electric vehicle), at least one selling price (i.e., selling price of 1 degree of electricity) of a unit of electric power sold by the charging station in at least one to-be-planned window corresponding to the electric vehicle, at least one buying price (i.e., buying price of 1 degree of electricity) of the unit of electric power bought by the charging station in at least one to-be-planned window corresponding to the electric vehicle, at least one bid price (i.e., bid price of 1 degree of electricity) of the charging station in at least one to-be-planned participation demand bid by the electric vehicle, a degradation cost (i.e., degradation cost of consuming 1 degree of electricity) consumed by the battery of the electric vehicle discharging the unit of electric power, and a conversion efficiency function related to energy loss during charge-discharge conversion, and obtaining a charging electric power or a discharging electric power of the electric vehicle in each corresponding window to be planned by utilizing a nonlinear programming. An objective function of the nonlinear programming can be expressed as the following formula (4), and the constraint conditions satisfied by the objective function are as the following constraint conditions 1 to 6.
Wherein,
Figure BDA0002261830470000142
Figure BDA0002261830470000144
Figure BDA0002261830470000145
Figure BDA0002261830470000146
constraint 1:
constraint 2:
Figure BDA0002261830470000148
constraint 3:
constraint 4:
Figure BDA00022618304700001410
constraint 5:
Figure BDA00022618304700001411
the restriction condition 6:
Figure BDA0002261830470000151
wherein, KnAt least one pane to be planned, T, corresponding to the nth electric vehiclenAt least one time window of the nth electric vehicle,
Figure BDA0002261830470000152
the charging profit obtained by the charging station when the nth electric vehicle is charged in the t time window is obtained,
Figure BDA0002261830470000153
resale a selling price of the unit of electric power for the charging station in the t-th time pane,
Figure BDA0002261830470000154
a purchase price per unit of electric power is purchased for the charging station in the t-th time pane,
Figure BDA0002261830470000155
charging electric power for the nth electric vehicle in the t-th time window,the power saving profit is obtained by the charging station when the nth electric vehicle participates in the demand response in the t time window,
Figure BDA0002261830470000157
participating in a bid price for the demand bid for the charging station in the t time pane,
Figure BDA0002261830470000158
discharging electric power of the nth electric vehicle in the t time window，
Figure BDA0002261830470000159
For the total degradation cost of the nth electric vehicle participating in the demand response in the tth time window,
Figure BDA00022618304700001510
is the total cost of the battery of the nth electric vehicle, mnThe ratio of the variation of a battery capacity to the variation of a battery cycle number of the battery of the nth electric vehicle (see the relationship between the battery capacity and the battery cycle number, m in FIG. 7)nI.e., the slope of the linear relationship of the battery capacity of the nth electric vehicle to the number of battery cycles),
Figure BDA00022618304700001511
is a full charge capacity of the battery of the nth electric vehicle,
Figure BDA00022618304700001512
a deterioration cost consumed for discharging the unit electric power for the battery of the nth electric vehicle,
Figure BDA00022618304700001513
for the nth electric vehicle to participate in the charging fee compensation of the demand bidding in the tth time window,
Figure BDA00022618304700001514
a charging penalty, p, for the nth electric vehicle in the t-th time window to participate in the demand responset,nThe charging electric power or the discharging electric power of the nth electric vehicle in the t-th time window is measured in kilowatt (kW), when p ist,n≤0，pt,nCharging electric power for the nth electric vehicle in the t-th time window when pt,n>0，pt,nThe discharge electric power of the nth electric vehicle in the t-th time window, N is all electric vehicles,
Figure BDA00022618304700001515
the maximum charging electric function for the nth electric vehicleThe ratio of the total weight of the particles,
Figure BDA00022618304700001516
the maximum discharge electric power of the nth electric vehicle,
Figure BDA00022618304700001517
the minimum battery state of charge for the nth electric vehicle,
Figure BDA00022618304700001518
the maximum battery state of charge, SOC, for the nth electric vehiclet,nA battery state of charge for the nth electric vehicle in the t-th time window,
Figure BDA00022618304700001519
the charge state of the entrance battery of the nth electric vehicle,
Figure BDA00022618304700001520
min (T) is the off-field battery state of charge of the nth electric vehiclen) The first time window, max (T), in which the nth electric vehicle is locatedn) Is the last time window, delta t, of the nth electric vehiclet,nThe unit of the time period spent by the nth electric vehicle in the t time window is hour, η (x) is the conversion efficiency function related to the energy lost by each electric vehicle during the charge-discharge conversion, η (x) is 0.0003307x3-0.0209x2+0.3833x+92.3373。
It should be noted that, since any electric vehicle has efficiency conversion loss during charging and discharging, in other words, although the dc charging pile converter of the charging station provides 10kW of electricity to charge the electric vehicle, there is a partial loss of the electricity charged by the electric vehicle, so that the electric vehicle actually charges less than 10kW of electric power, similarly, although the electric vehicle discharges 10kW of electricity to the charging station, there is a partial loss of the electricity obtained by the charging station, so that the electric power actually obtained by the charging station is less than 10 kW. In the embodiment, the conversion efficiency function is incorporated into the objective function, so that the charging of the electric vehicle can be realizedThe discharge is operated at the optimum conversion efficiency operating point as much as possible, and the conversion loss is varied according to the charging or discharging power (see fig. 4), and as can be seen from the example of fig. 4, when the charging or discharging power is between 12 to 15kW, the conversion loss is relatively minimal, so that the charging or discharging power of the electric vehicle is operated at about 15kW as much as possible to maximize the operating profit of the charging station in order to minimize the conversion loss. However, in other embodiments of the present invention, the processing unit 13 of the computing device 1 may not include the conversion efficiency function in the objective function when optimizing the charging and discharging schedule corresponding to the electric vehicle, and in this case, the conversion efficiency function in the objective function is not included in the objective function
Figure BDA0002261830470000162
Andis required to be modified to
Figure BDA0002261830470000164
And is
Figure BDA0002261830470000165
The constraint 5 is modified as required
Figure BDA0002261830470000161
Referring to fig. 1 and 5, the energy storage device scheduling procedure of the electric vehicle charging station distributed energy management method illustrates how to optimize the charging and discharging schedule corresponding to the energy storage device, and includes the following steps.
In step 51, the processing unit 13 of the computing device 1 uses all time panes in the scheduling period as the time pane in which the electrical energy storage device is located, and obtains at least one to-be-planned pane from the time pane in the scheduling period, where the at least one to-be-planned pane corresponding to the electrical energy storage device is the last time pane in the time panes from the current time pane to the scheduling period. Since the electric energy storage device is disposed in the charging station, the time window in which the electric energy storage device is located is all the time windows in the scheduling cycle.
In step 52, the processing unit 13 of the computing device 1 calculates a unit of electric power to be charged by the charging station in each of at least one to-be-planned window corresponding to the electric energy storage device (i.e. a 1-degree-electricity purchase price) or a bid price for participating in bid price bidding of demand (i.e. a 1-degree-electricity bid price), a degradation cost consumed by the electric energy storage device to discharge the unit of electric power (i.e. a 1-degree-electricity degradation cost), a basic consumed electric power of the charging station in each time window of the schedule period (i.e. a basic electric power for maintaining the operation of the charging station, for example, a sum of consumed electric powers of lights, sensors, and other electric appliances installed on the charging station), and a solar electric power generated by the solar module in each time window of the schedule period, and the charging electric power or the discharging electric power of each electric vehicle in each time window is obtained by the nonlinear programming, and the charging electric power or the discharging electric power of the electric energy storage device in each corresponding window to be programmed is obtained by the nonlinear programming. An objective function of the nonlinear programming can be expressed as the following formula (5), and the constraint conditions satisfied by the objective function are as the following constraint conditions 1-7.
Figure BDA0002261830470000171
Wherein,
Figure BDA0002261830470000172
Figure BDA0002261830470000173
constraint 1:
Figure BDA0002261830470000174
constraint 2:
Figure BDA0002261830470000175
constraint 3:
constraint 4:
Figure BDA0002261830470000177
constraint 5:
the restriction condition 6:
Figure BDA0002261830470000179
constraint 7:
Figure BDA00022618304700001710
wherein, KessAt least one pane to be planned, T, corresponding to the electrical energy storage deviceessFor the time pane in which the electrical energy storage device is located,
Figure BDA0002261830470000181
a charging cost consumed by the charging station or an obtained electricity-saving profit when the electric energy storage device is charged or discharged in the t-th time window,
Figure BDA0002261830470000182
a purchase price of the unit of electric power or a bid price for participating in demand bidding is bought for the charging station in the t time pane,
Figure BDA0002261830470000183
charging electric power for the electric energy storage device in the t-th time window,
Figure BDA0002261830470000184
for discharging electrical power of the electrical energy storage device in the t-th time window,
Figure BDA0002261830470000185
a total degradation cost of charging or discharging the electrical energy storage device in the tth time pane,
Figure BDA0002261830470000186
is the total cost of the electrical energy storage device, messIs the ratio of the variation of the battery capacity of the electric energy storage device to the variation of the battery cycle number,for a full charge capacity of the electrical energy storage device,
Figure BDA0002261830470000188
a deterioration cost, p, consumed for charging or discharging the unit electric power to or from the battery of the electric energy storage deviceess,tCharging electric power or discharging electric power of the electric energy storage device in the t-th time window is measured in kilowatt when pess,t≤0，pess,tCharging electric power for the electric energy storage device in the t-th time window, when pess,t>0，pess,tFor discharging electrical power of the electrical energy storage device in the t-th time window,for the maximum charging and discharging electric power of the electric energy storage device,
Figure BDA00022618304700001810
for the minimum battery state of charge of the electrical energy storage device,is the maximum battery state of charge, SOC, of the electrical energy storage deviceess,tA battery charge state for the electric energy storage device in the t-th time windowThe state of the optical disk is changed into a state,
Figure BDA00022618304700001812
is the incoming battery state of charge of the electrical energy storage device,
Figure BDA00022618304700001813
at is the off-field battery state of charge of the electrical energy storage device, Δ t is the time duration corresponding to each time window, which is expressed in hours,
Figure BDA00022618304700001814
for a charge transfer efficiency parameter related to the energy lost by the electrical energy storage device during charge transfer,
Figure BDA00022618304700001815
for a discharge conversion efficiency parameter, p, related to the energy lost by the electrical energy storage device during discharge conversiont,nThe charging electric power or the discharging electric power for the nth electric vehicle in the t-th time window, ppv,tFor the solar electric power in the t-th time window, pload,tFor the charging station, electric power is consumed on this basis in the t-th time window, N being all electric vehicles.
Note that T ∈ T of the constraint condition 7essCan also be set to t ∈ KessSince the previously planned time windows necessarily satisfy the constraint of the constraint 7, it is only necessary that each time window in the at least one to-be-planned window satisfies the constraint 7. In addition, in the present embodiment,
Figure BDA00022618304700001816
and
Figure BDA00022618304700001817
are all a predetermined constant value.
It is noted that the discharge period of the electric energy storage device is not necessarily all the period participating in the Demand Response, so-called Demand Response (DR), which is achieved by the userThe electricity consumption is reduced in an elastic matching manner, the electricity consumption pressure of peak load is reduced, and an energy-saving scheme of a virtual power plant is formed. If the user wants to participate in the demand response, the demand bidding is required, the demand bidding refers to a period of high load of the system, the user is encouraged to save electricity, the user bids for bidding, the power company determines a bid winner in a mode that the lower bid price is obtained first, and if the bid winner really reduces the electricity consumption in the period of suppressing the electricity consumption, the electricity fee deduction can be obtained. Although the electric energy storage device performs the discharging operation during the bid-winning period of the demand bid as much as possible to save the electricity fee, the electric energy storage device still has an opportunity to perform the discharging operation when the bid-winning period is not achieved. At this time, the electricity charge saved by the electric energy storage device is not the bid price for participating in the demand bidding, but the purchase price of the electricity charge at that time, and therefore, the calculation is performed
Figure BDA0002261830470000191
When the electric energy storage device is discharged, the obtained electricity-saving profit of the electric energy storage device needs to be calculated according to whether the electric energy storage device has a bid or not when discharging, and if the electric energy storage device has no bid when discharging, the electricity-saving profit is calculated according to the purchase price of the unit electric power; if the bidding is available during the discharging, the electricity-saving profit is calculated according to the bidding price participating in the demand bidding.
In addition, in the embodiment, when solving the optimized schedule of the electric energy storage device, the basic consumed electric power of the charging station in each time window of the schedule period, the solar electric power generated by the solar module in each time window of the schedule period, and the charging electric power or the discharging electric power of each electric vehicle in each time window of the electric vehicle are taken into particular consideration, so as to avoid the situation that the solar electric power generated by the solar module and the discharging electric power generated by the electric vehicle are excessive and are reversely transmitted to the power grid. However, in other embodiments, the above parameters may not be considered, and in this case, the limitation 7 corresponding to the objective function of the formula (5) may be eliminated.
Referring to fig. 1 and 6, the integrated planning procedure of the distributed power management method for electric vehicle charging stations, which includes the following steps, illustrates how to avoid the lack of overall consideration due to independent scheduling, resulting in violation of a maximum supplied electric power limit under certain specific conditions.
In step 61, for each of the current time window to the last time window in the time windows of the scheduling cycle (i.e., the at least one to-be-scheduled window corresponding to the electrical energy storage device), the processing unit 13 of the computing device 1 obtains a total consumed electrical power P of the charging station in the time window by using the following formula (6) according to the basic consumed electrical power of the charging station in the time window (i.e., the t-th time window), the charged electrical power or the discharged electrical power of each electric vehicle in the time window, the charged electrical power or the discharged electrical power of the electrical energy storage device in the time window, and the solar electrical power generated by the solar module in the time windowsum,t。
Figure BDA0002261830470000201
Wherein p ist,nThe charging electric power or the discharging electric power for the nth electric vehicle in the t-th time window, pess,tThe charging electric power or the discharging electric power, p, for the electric energy storage device in the t-th time windowload,tConsuming electrical power for the charging station on the basis of the t-th time window, ppv,tThe solar electric power generated for the solar module in the t-th time window, N for all electric vehicles, KessAnd the window is at least one window to be planned corresponding to the electric energy storage device.
In step 62, the processing unit 13 of the computing device 1 determines whether there is at least one overload window in the at least one window to be planned according to the total consumed electric power of each time window in the at least one window to be planned and the maximum supplied electric power related to the charging station, wherein the total consumed electric power of each overload window is greater than the maximum supplied electric power. When the processing unit 13 determines that the at least one overloaded pane exists, the process proceeds to step 63; when the processing unit 13 determines that there is no overloaded pane, the process proceeds to step 64.
In step 63, for each overload window, the processing unit 13 of the computing device 1 adjusts the coefficient f according to the electricity price corresponding to the overload windowt(x) The purchase price of the overloaded window is adjusted, and the steps 32, 52, 61-62 are repeated. Wherein each electricity price adjustment coefficient ft(x) Can be expressed as the following equation (7).
Figure BDA0002261830470000202
Wherein p ist,nThe charging electric power or the discharging electric power for the nth electric vehicle in the t-th time window, pess,tThe charging electric power or the discharging electric power, p, for the electric energy storage device in the t-th time windowload,tConsuming electrical power for the charging station on the basis of the t-th time window, ppv,tThe solar electric power generated by the solar module in the t-th time window, N being the number of all electric vehicles,for the maximum supply of electric power, ToverlaodThe at least one overloaded pane.
It should be noted that, in the embodiment, the processing unit 13 multiplies the original purchase price of the overloaded pane by the electricity price adjustment coefficient corresponding to the overloaded pane to adjust the purchase price of the overloaded pane, so that the electricity price of the overloaded pane is increased. In the step 32, the purchase price of the time window corresponding to the at least one overload window in the at least one purchase price of the unit electric power to be planned by the charging station is the adjusted electricity price. In response to the power rate of the at least one overload window being increased, the charging amount in the at least one overload window can be transferred to other windows to be planned, which are not increased in power rate, in order to optimize the benefit of the charging station. Therefore, the comprehensive planning program can solve the overload problem which can be caused when each electric vehicle is planned independently, so that the limit of the maximum supplied electric power cannot be violated in any time window.
It should be noted that the range of t in the formula (6) can be defined as all time windows of the scheduling period, and since the electric vehicle charging station distributed power management method of the present invention performs the comprehensive planning procedure after performing the electric vehicle distributed scheduling procedure and the electric energy storage device scheduling procedure each time, so that the planned scheduling result does not violate the maximum supply electric power limit in any time window, the previously planned time windows must satisfy the limit not greater than the maximum supply electric power, and therefore, even if the previously planned time windows are taken into consideration of whether any overload window exists, the method is not limited.
In step 64, the processing unit 13 of the computing device 1 controls the charging station to charge or discharge each electric vehicle and the electric energy storage device in the current time window according to the charging electric power or the discharging electric power of each electric vehicle in each time window where the electric vehicle is scheduled to be present, and the charging electric power or the discharging electric power of each planned window where the electric energy storage device is scheduled to be present, wherein the electric vehicle is not present in any overload window.
In step 65, the processing unit 13 of the computing device 1 determines whether the current time pane is the last time pane in the schedule cycle. When the processing unit 13 determines that the current time window is the last time window in the scheduling cycle, the process ends; when the processing unit 13 determines that the current time pane is not the last time pane in the scheduled cycle, the process proceeds to step 66.
In step 66, when the time shifts to the next time window of the current time window (i.e., the next time window becomes the new current time window), the processing unit 13 of the computing device 1 determines whether a new electric vehicle is parked in the charging station. When the processing unit 13 of the computing device 1 determines that a new electric vehicle stops at the charging station, the flow returns to step 21; when the processing unit 13 of the computing device 1 determines that no new electric vehicle is parked at the charging station, the flow returns to step 64. It should be noted that if a new electric vehicle stops at the charging station, the charging and discharging schedule of each electric vehicle and the charging and discharging schedule of the electric energy storage device need to be re-planned, wherein, when step 31 is performed again, it is only necessary to map the entry time and the exit time of the electric vehicle newly joining the charging station to at least one of the time windows in the scheduling period, and the electric vehicle mapped previously does not need to be mapped again. In addition, if no new electric vehicle is parked into the charging station, the processing unit 13 of the computing device 1 can control the charging station to charge or discharge each electric vehicle and the electric energy storage device according to the charging electric power or the discharging electric power corresponding to each electric vehicle and the electric energy storage device planned in step 64 in the next time window.
The following illustrates an operation manner of the distributed electric energy management method for an electric vehicle charging station according to the present invention, if a scheduling period is one day, the one day includes 0 to 95 time windows, and it is assumed that there are 3 electric vehicles stopped at the charging station in the 0 th time window, wherein the first electric vehicle is mapped to the 0 th to 3 time windows of the 0 to 95 time windows, the second electric vehicle is mapped to the 0 th to 5 time windows of the 0 to 95 time windows, the third electric vehicle is mapped to the 0 th to 8 time windows of the 0 to 95 time windows, and the current time window is the 1 st time window, at least one to-be-planned window of the first electric vehicle is the 1 st to 3 time windows, which are represented by [1,2,3], at least one to-be-planned window of the second electric vehicle is [1,2,3,4,5], and at least one to-be-planned window of the third electric vehicle is [1,2,3,4,5,6,7,8], the processing unit 13 of the computing device 1 first performs the discharging distribution procedure to find the maximum discharging electric power corresponding to each electric vehicle, and then the processing unit 13 of the computing device 1 performs the electric vehicle distributed scheduling procedure to find the charging electric power or the discharging electric power of the 1 st electric vehicle in each window to be scheduled (i.e., each of the 1 st to 3 rd time windows), the charging electric power or the discharging electric power of the 2 nd electric vehicle in each window to be scheduled (i.e., each of the 1 st to 5 th time windows), and the charging electric power or the discharging electric power of the 3 rd electric vehicle in each window to be scheduled (i.e., each of the 1 st to 8 th time windows). Then, the processing unit 13 of the computing device 1 performs the electric energy storage device scheduling procedure to solve the charging electric power or the discharging electric power of the electric energy storage device in each to-be-planned window (i.e., each of the 1 st to 95 th time windows). Finally, the processing unit 13 of the computing device 1 first performs the comprehensive planning procedure to determine whether at least one overload window exists in the 1 st to 95 th time windows, and if the processing unit 13 determines that the 2 nd to 3 rd time windows in the 1 st to 95 th time windows are overload windows, the processing unit 13 will adjust the purchase price of the overload windows (that is, the 2 nd to 3 rd time windows are represented by [2,3 ]), and perform the charge and discharge planning of each electric vehicle and the electric energy storage device again until no overload window exists in the 1 st to 95 th time windows. Then, the processing unit 13 calculates the charging electric power or the discharging electric power for each of the 1 st electric vehicle in the 1 st to 3 rd time windows, the charging electric power or the discharging electric power for each of the 1 st to 5 th electric vehicles in the 2 nd electric vehicle in the 1 st to 5 th time windows, and the charging electric power or the discharging electric power for each of the 1 st to 8 th electric vehicles in the 3 rd time windows according to the planned charging electric power or the discharging electric power, and the charging electric power or the discharging electric power of the electric energy storage device in each of the 1 st to 95 th time windows controls the charging station to charge or discharge each electric vehicle and the electric energy storage device in the current time window (namely, the 1 st time window) according to the charging electric power or the discharging electric power corresponding to each electric vehicle and the electric energy storage device in the 1 st time window. Assuming that no new electric vehicle (i.e. 4 th electric vehicle) is parked in the charging station until the 3 rd time window, when the time shifts to the 2 nd time window (i.e. the 2 nd time window becomes the new current time window), the processing unit 13 can still control the charging station to operate in the current time window according to the charging electric power or the discharging electric power of the 1 st electric vehicle in each of the 1 st to 3 th time windows, the charging electric power or the discharging electric power of the 2 nd electric vehicle in each of the 1 st to 5 th time windows, the charging electric power or the discharging electric power of the 3 rd electric vehicle in each of the 1 st to 8 th time windows, and the charging electric power or the discharging electric power of the electric energy storage device in each of the 1 st to 95 th time windows (i.e., the 2 nd time window) charges or discharges each electric vehicle and the electric energy storage device according to the charging electric power or the discharging electric power corresponding to each electric vehicle and the electric energy storage device in the 2 nd time window. When the time moves to the 3 rd time window (i.e., the 3 rd time window becomes the new current time window), the processing unit 13 needs to re-plan the charging and discharging schedule of each electric vehicle (i.e., each of the 1 st to 4 th electric vehicles) and the charging and discharging schedule of the electric energy storage device.
In summary, the distributed electric energy management method for the electric vehicle charging station of the present invention has the following effects, first: the processing unit 13 can greatly reduce the calculation dimension by distributively planning the charging electric power or the discharging electric power of each electric vehicle in each corresponding to-be-planned window, and secondly: the processing unit 13 can avoid the condition of reverse power transmission to the power grid by distributing the maximum discharging electric power corresponding to each electric vehicle according to the discharging priority of each electric vehicle, and thirdly: the processing unit 13 incorporates the conversion efficiency function into an objective function, so that the charging and discharging of the electric vehicle can be operated at an optimal conversion efficiency operating point as much as possible, and fourth: the processing unit 13 avoids the situation that the solar electric power generated by the solar module and the discharging electric power generated by the electric vehicle are excessive and are transmitted back to the power grid by adding the limiting condition 7 to the objective function of formula (5), and fifth: the processing unit 13 does not violate the maximum electric power supply limit in any time window by performing the comprehensive planning procedure, so the objective of the present invention can be achieved.
The above description is only an example of the present invention, and the scope of the present invention should not be limited thereby, and the invention is still within the scope of the present invention by simple equivalent changes and modifications made according to the claims and the contents of the specification.

Claims (11)

1. A distributed electric energy management method of an electric vehicle charging station is suitable for managing the charging and discharging states of all electric vehicles parked in the charging station, each electric vehicle corresponds to electric vehicle information, and each electric vehicle information comprises the entry time, the exit time, the entry battery charge state when entering the corresponding electric vehicle, the expected exit battery charge state, the minimum battery charge state, the maximum battery charge state, the full charge capacity, the maximum charging electric power and the maximum discharging electric power of the corresponding electric vehicle, and is characterized in that: the electric vehicle charging station distributed power management method is implemented by a processing unit and comprises the following steps:
(A) for each electric vehicle, mapping the entry time and the exit time in the electric vehicle information corresponding to the electric vehicle to at least one of a plurality of time panes in a scheduling period, and obtaining at least one pane to be planned from the at least one time pane in which the electric vehicle is located, wherein the at least one pane to be planned corresponding to the electric vehicle is the last time pane from the current time pane to the electric vehicle;
(B) for each electric vehicle, according to electric vehicle information corresponding to the electric vehicle, the charging station resales at least one selling price of unit electric power in at least one to-be-planned pane corresponding to the electric vehicle, at least one buying price of the unit electric power in at least one to-be-planned pane corresponding to the electric vehicle, at least one bid price of the charging station participating in demand bidding in at least one to-be-planned pane corresponding to the electric vehicle, and degradation cost consumed by discharging the unit electric power from a battery of the electric vehicle, and charging electric power or discharging electric power of the electric vehicle in each to-be-planned pane corresponding to the electric vehicle is obtained by utilizing nonlinear planning;
(C) for each of the current time pane to a last one of the time panes of the scheduled cycle, obtaining a total consumed electric power of the charging station in the time pane from the charging electric power or the discharging electric power of each electric vehicle in the time pane;
(D) determining whether there is at least one overload window in the last time window from the current time window to the time window of the scheduled cycle according to the total consumed electric power of each time window obtained in the step (C) and the maximum supplied electric power related to the charging station, wherein the total consumed electric power of each overload window is greater than the maximum supplied electric power; and
(E) when the at least one overload window pane is judged to exist, adjusting the purchase price of each overload window pane, and repeating the steps (B) to (D) until the at least one overload window pane is judged not to exist.
2. The electric vehicle charging station distributed power management method of claim 1, wherein: in the step (B), the non-linearly planned objective function and the plurality of constraints satisfied by the objective function may be expressed as:
Figure FDA0002261830460000021
Figure FDA0002261830460000022
Figure FDA0002261830460000023
Figure FDA0002261830460000024
Figure FDA0002261830460000025
Figure FDA0002261830460000026
constraint 1:
Figure FDA0002261830460000027
constraint 2:
Figure FDA0002261830460000028
constraint 3:
constraint 4:
Figure FDA00022618304600000210
constraint 5:
Figure FDA00022618304600000211
the restriction condition 6:
Figure FDA00022618304600000212
wherein, KnAt least one pane to be planned corresponding to the nth electric vehicle,
Figure FDA00022618304600000213
charging acquired by the charging station when charging the nth electric vehicle in the tth time windowThe profit of the user is obtained,resale the selling price of the unit of electric power for the charging station in the tth time pane,
Figure FDA00022618304600000215
buying the purchase price per unit of electric power for the charging station in the t-th time pane,
Figure FDA00022618304600000216
charging electric power for the nth electric vehicle in the t-th time window,
Figure FDA00022618304600000217
the power saving profit obtained by the charging station when the nth electric vehicle participates in the demand response in the t time window,participating in a bid price of a demand bid for the charging station in a tth time pane,
Figure FDA00022618304600000219
the discharge electric power for the nth electric vehicle in the t-th time window,
Figure FDA00022618304600000220
for the total degradation cost of the nth electric vehicle participating in the demand response at the tth time pane,
Figure FDA0002261830460000031
total cost of battery for nth electric vehicle, mnIs the ratio of the variation of the battery capacity of the battery of the nth electric vehicle to the variation of the number of battery cycles,
Figure FDA0002261830460000032
for batteries of nth electric vehicleThe capacity of the full charge is set to be,
Figure FDA0002261830460000033
a deterioration cost consumed for discharging the unit electric power for the battery of the nth electric vehicle,the nth electric vehicle participates in the charging fee compensation of demand bidding in the t time window,
Figure FDA0002261830460000035
charging punishment p of the nth electric vehicle in the t time window to participate in the demand responset,nCharging electric power or discharging electric power for the nth electric vehicle in the t-th time window when pt,n≤0，pt,nCharging electric power for the nth electric vehicle in the t-th time window when pt,n>0，pt,nThe discharge electric power for the nth electric vehicle in the t-th time window,
Figure FDA0002261830460000036
the maximum charging electric power for the nth electric vehicle,
Figure FDA0002261830460000037
the maximum discharge electric power of the nth electric vehicle, N being all electric vehicles, TnAt least one time window of the nth electric vehicle,
Figure FDA0002261830460000038
the minimum battery state of charge for the nth electric vehicle,
Figure FDA0002261830460000039
the maximum battery state of charge, SOC, for the nth electric vehiclet,nThe battery state of charge for the nth electric vehicle in the t-th time window,
Figure FDA00022618304600000310
the charge state of the entrance battery of the nth electric vehicle,
Figure FDA00022618304600000311
min (T) is the off-field battery state of charge of the nth electric vehiclen) The first time window, max (T), in which the nth electric vehicle is locatedn) Is the last time window, delta t, of the nth electric vehiclet,nFor the nth electric vehicle during the time of the tth time window.
3. The electric vehicle charging station distributed power management method of claim 1, wherein: in the step (B), the processing unit further obtains the charging electric power or the discharging electric power of the electric vehicle in each corresponding pane to be planned by using the nonlinear programming according to a conversion efficiency function related to energy lost in charge-discharge conversion.
4. The electric vehicle charging station distributed power management method of claim 3, wherein: in the step (B), the non-linearly planned objective function and the plurality of constraints satisfied by the objective function may be expressed as:
Figure FDA00022618304600000312
Figure FDA00022618304600000314
Figure FDA0002261830460000041
Figure FDA0002261830460000042
constraint 1:
constraint 2:
Figure FDA0002261830460000045
constraint 3:
constraint 4:
constraint 5:
the restriction condition 6:
wherein, KnAt least one pane to be planned, T, corresponding to the nth electric vehiclenAt least one time window of the nth electric vehicle,
Figure FDA00022618304600000410
charging profit obtained by the charging station when the nth electric vehicle is charged in the t time window,
Figure FDA00022618304600000411
resale the selling price of the unit of electric power for the charging station in the tth time pane,buying the purchase price per unit of electric power for the charging station in the t-th time pane,
Figure FDA00022618304600000413
charging electric power for the nth electric vehicle in the t-th time window,
Figure FDA00022618304600000414
the power saving profit obtained by the charging station when the nth electric vehicle participates in the demand response in the t time window,participating in a bid price of a demand bid for the charging station in a tth time pane,
Figure FDA00022618304600000416
the discharge electric power for the nth electric vehicle in the t-th time window,
Figure FDA00022618304600000417
for the total degradation cost of the nth electric vehicle participating in the demand response at the tth time pane,
Figure FDA00022618304600000418
total cost of battery for nth electric vehicle, mnIs the ratio of the variation of the battery capacity of the battery of the nth electric vehicle to the variation of the number of battery cycles,is the full charge capacity of the battery of the nth electric vehicle,
Figure FDA00022618304600000420
a deterioration cost consumed for discharging the unit electric power for the battery of the nth electric vehicle,
Figure FDA00022618304600000421
the nth electric vehicle participates in the charging fee compensation of demand bidding in the t time window,
Figure FDA00022618304600000422
charging punishment p of the nth electric vehicle in the t time window to participate in the demand responset,nCharging electric power or discharging electric power for the nth electric vehicle in the t-th time window when pt,n≤0，pt,nCharging electric power for the nth electric vehicle in the t-th time window when pt,n>0，pt,nThe discharge electric power of the nth electric vehicle in the t-th time window, N is all electric vehicles,
Figure FDA0002261830460000051
the maximum charging electric power for the nth electric vehicle,
Figure FDA0002261830460000052
the maximum discharge electric power of the nth electric vehicle,the minimum battery state of charge for the nth electric vehicle,
Figure FDA0002261830460000054
the maximum battery state of charge, SOC, for the nth electric vehiclet,nThe battery state of charge for the nth electric vehicle in the t-th time window,the charge state of the entrance battery of the nth electric vehicle,
Figure FDA0002261830460000056
min (T) is the off-field battery state of charge of the nth electric vehiclen) The first time window, max (T), in which the nth electric vehicle is locatedn) Is the last time window, delta t, of the nth electric vehiclet,nη (x) is the conversion efficiency function related to the energy lost by each electric vehicle during charge-discharge conversion for the time period that the nth electric vehicle waits in the t time window, η (x) 0.0003307x3-0.0209x2+0.3833x+92.3373。
5. The electric vehicle charging station distributed power management method of claim 1, the charging station being provided with an electric energy storage device corresponding to power information comprising an initial state of charge, a minimum state of charge, a maximum state of charge, a full charge capacity, and a maximum charging and discharging electric power of the electric energy storage device, the electric vehicle charging station distributed power management method comprising: before step (C), further comprising the following steps:
(F) taking all time panes in the scheduling period as time panes where the electric energy storage device is located, and obtaining at least one pane to be planned from the time panes in the scheduling period, wherein the at least one pane to be planned corresponding to the electric energy storage device is the last time pane in the time panes from the current time pane to the scheduling period; and
(G) according to the electric energy information corresponding to the electric energy storage device, a bid price of the charging station for buying the unit electric power or participating in demand bidding on each of at least one to-be-planned window corresponding to the electric energy storage device, and the degradation cost consumed by the electric energy storage device for charging or discharging the unit electric power, the nonlinear programming is utilized to obtain the charging electric power or the discharging electric power of the electric energy storage device in each to-be-planned window corresponding to the electric energy storage device;
wherein, in step (C), the total consumed electric power of the charging station in the time pane is obtained not only from the charging electric power or the discharging electric power of each electric vehicle in the time pane but also from the charging electric power or the discharging electric power of the electric energy storage device in the time pane.
6. The electric vehicle charging station distributed power management method of claim 5, wherein:
in step (G), the non-linear schedule is further utilized to obtain the charging electric power or the discharging electric power of the electric energy storage device in each corresponding to-be-scheduled window according to a basic consumption electric power of the charging station in each time window of the scheduling cycle and the charging electric power or the discharging electric power of each electric vehicle in each time window of the electric vehicle; and
in step (C), the total consumed electric power of the charging station is obtained not only from the charging electric power or the discharging electric power of each electric vehicle and the electric energy storage device in the time pane but also from the basic consumed electric power in the time pane.
7. The electric vehicle charging station distributed power management method of claim 6, the charging station further provided with a solar module, characterized in that:
in step (G), the charging electric power or the discharging electric power of the electric energy storage device in each corresponding to-be-planned window is obtained by the nonlinear programming according to the solar electric power generated by the solar module in each time window in the scheduling cycle; and
in step (C), the total consumed electric power of the charging station is obtained not only from the charging electric power or the discharging electric power of each electric vehicle and the electric energy storage device in the time pane and the base consumed electric power in the time pane but also from the solar electric power in the time pane.
8. The electric vehicle charging station distributed power management method of claim 7, wherein:
in step (C), the processing unit charges or discharges the electric power p according to the charging electric power or the discharging electric power of the nth electric vehicle in the t-th time windowt,nThe charging electric power or the discharging electric power p of the electric energy storage device in the t-th time windowess,tThe base consumption electric power p of the charging station in the t-th time paneload,tAnd said solar electric power p in the t-th time panepv,tObtaining the total consumed electric power P of the charging station in the t-th time window by using the following formulasum,t，
Figure FDA0002261830460000071
Wherein N is all electric vehicles, KessThe window is at least one window to be planned corresponding to the electric energy storage device; and
in step (E), for each overloaded pane, the processing unit adjusts the coefficient f according to the price of electricity corresponding to the overloaded panet(x) To adjust the purchase price of the overloaded pane,
Figure FDA0002261830460000072
wherein,
Figure FDA0002261830460000073
for said maximum supply of electric power, ToverlaodThe at least one overloaded pane.
9. The electric vehicle charging station distributed power management method of claim 7, wherein: in the step (B), the non-linearly planned objective function and the plurality of constraints satisfied by the objective function may be expressed as:
Figure FDA0002261830460000074
Figure FDA0002261830460000075
Figure FDA0002261830460000076
constraint 1:
Figure FDA0002261830460000077
constraint 2:
Figure FDA0002261830460000078
constraint 3:
Figure FDA0002261830460000079
constraint 4:
Figure FDA00022618304600000710
constraint 5:
Figure FDA00022618304600000711
the restriction condition 6:
Figure FDA00022618304600000712
constraint 7:
wherein, KessAt least one pane to be planned, T, corresponding to the electrical energy storage deviceessIs said electricityThe time pane in which the device is located can be stored,
Figure FDA0002261830460000081
charging costs consumed by the charging station or the obtained power saving profit for the charging or discharging of the electrical energy storage device in the tth time pane,
Figure FDA0002261830460000082
buying the purchase price of the unit of electric power or the bid price for participating in demand bidding for the charging station at the t-th time pane,
Figure FDA0002261830460000083
charging electric power for the electric energy storage device in the t-th time window,
Figure FDA0002261830460000084
discharging electrical power for the electrical energy storage device at a t-th time window,
Figure FDA0002261830460000085
a total degradation cost of charging or discharging the electrical energy storage device in the tth time pane,
Figure FDA0002261830460000086
for the total cost of the electrical energy storage device, messIs the ratio of the variation of the battery capacity of the electrical energy storage device to the variation of the number of battery cycles,
Figure FDA0002261830460000087
for a full charge capacity of the electrical energy storage device,
Figure FDA0002261830460000088
cost of degradation, p, consumed to charge or discharge the unit of electrical power for the battery of the electrical energy storage deviceess,tCharging the electric energy storage device in the tth time windowElectric power or discharge power, when pess,t≤0，pess,tCharging electric power for the electric energy storage device in the t-th time window, when pess,t>0，pess,tDischarging electrical power for the electrical energy storage device at a t-th time window,for the maximum charging and discharging electric power of the electric energy storage device,
Figure FDA00022618304600000810
for the minimum battery state of charge of the electrical energy storage device,is the maximum battery state of charge, SOC, of the electrical energy storage deviceess,tFor the battery state of charge of the electrical energy storage device in the tth time pane,is the incoming battery state of charge of the electrical energy storage device,
Figure FDA00022618304600000813
at is the off-field battery state of charge of the electrical energy storage device, Δ t is the time period corresponding to each time pane,
Figure FDA00022618304600000814
is a charge conversion efficiency parameter related to the energy lost by the electrical energy storage device during charge conversion,for the discharge conversion efficiency parameter, p, relating to the energy lost by the electrical energy storage device during discharge conversiont,nThe charging electric power or the discharging electric power for the nth electric vehicle in the t-th time window, ppv,tFor the solar electric power in the t-th time pane, pload,tConsuming electric power for the charging station on the basis of the t-th time pane, N being all electric vehicles.
10. The electric vehicle charging station distributed power management method of claim 1, wherein: before step (A), the method also comprises the following steps:
(H) for each electric vehicle, acquiring the charging priority of the electric vehicle according to the entry time, the exit time, the entry battery charge state, the exit battery charge state, the full charge capacity and the maximum charging electric power in electric vehicle information corresponding to the electric vehicle;
(I) for each electric vehicle, acquiring the discharging priority weight of the electric vehicle according to the charging priority weight of the electric vehicle; and
(J) for each electric vehicle, the maximum discharge electric power of the electric vehicle is obtained based on the discharge priority weights of the electric vehicles, the discharge priority weights of all electric vehicles, and the predicted total discharge electric power.
11. The electric vehicle charging station distributed power management method of claim 10, wherein:
in the step (H), the entry time is determined according to the entry time in the electric vehicle information corresponding to the nth electric vehicleThe off-field time
Figure FDA0002261830460000092
State of charge of the entry battery
Figure FDA0002261830460000093
The off-field battery state of charge
Figure FDA0002261830460000094
The full charge capacity
Figure FDA0002261830460000095
And the maximum charging electric powerObtaining the charging priority weight of the nth electric vehicle by using the following formula
Figure FDA0002261830460000097
Figure FDA0002261830460000098
Wherein, Δ t is the time period corresponding to each time pane; and
in the step (I), charging priority weight of the nth electric vehicle is usedObtaining the discharge priority weight of the nth electric vehicle by using the following formula
Figure FDA00022618304600000910
Figure FDA00022618304600000911
And
in step (J), the processing unit performs discharge priority weighting according to the nth electric vehicle
Figure FDA00022618304600000912
Discharge priority weights of all electric vehicles, and predicted total discharge electric power PV2GObtaining the maximum discharge electric power of the nth electric vehicle using the following formula
Figure FDA00022618304600000913
Figure FDA00022618304600000914
Wherein N is all electric vehicles.
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