CN116215266A - Multi-charging-mode combined charging guiding method and system for electric automobile - Google Patents

Multi-charging-mode combined charging guiding method and system for electric automobile Download PDF

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Publication number
CN116215266A
CN116215266A CN202310506459.8A CN202310506459A CN116215266A CN 116215266 A CN116215266 A CN 116215266A CN 202310506459 A CN202310506459 A CN 202310506459A CN 116215266 A CN116215266 A CN 116215266A
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charging
constraint
time
slow
period
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CN116215266B (en
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夏方舟
杨洁
陈红坤
徐敬友
唐靖
叶高翔
邵非凡
陈可
周玉洁
陈逸馨
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Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/10—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles characterised by the energy transfer between the charging station and the vehicle
    • B60L53/12—Inductive energy transfer
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors

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  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The method comprises the steps of firstly adopting a charging energy demand distribution model based on a travel-charging chain model to solve and obtain the time-space distribution of the charging energy demand of the electric vehicle in a typical day, inputting the time-space distribution of the charging energy demand into a constructed combined charging guide model, and solving the model to obtain the electric vehicle flow of charging by adopting different charging modes on each travel-charging chain, each period, each path and each road. According to the invention, on one hand, the characteristics of different charging modes are fully considered, the electric vehicle charging guidance under the scenes of multiple charging modes and multiple charging demands is realized, the charging time consumption of the electric vehicle user is reduced while the economic benefit of the charging service is improved, and on the other hand, the finally obtained optimization results of the charging modes, the charging paths and the charging time can be matched with the traffic behaviors of the electric vehicle user, so that the rationality and the effectiveness of the charging guidance result are ensured.

Description

Multi-charging-mode combined charging guiding method and system for electric automobile
Technical Field
The invention belongs to the technical field of electric vehicle charging, and particularly relates to a multi-charging-mode combined charging guiding method and system for an electric vehicle.
Background
The charging guide of the electric automobile is based on the constraint of a traffic network and a power grid, so that an electric automobile user group is helped to make a reasonable charging plan, namely, when and where to adopt what route to charge in what way. The aim is to optimize the distribution of the charging and traffic demands in the electric power-traffic coupling network. In the electric power-traffic coupling network, the charging behavior of the electric vehicle affects both the load distribution of the power grid and the traffic flow distribution of the traffic network.
Because there are different types of electric automobile charging requirements, and different types of charging requirements have respective user behavior characteristics: the low-speed charging power and the long charging time are low, and the electric automobile user often selects to perform the low-speed charging in a longer idle period; the quick charging power is high, the charging time is short, the quick charging is often used as a common en-route energy supplementing mode by an electric automobile user, and the electric automobile user is charged by going to a direct-current quick charging station in the traveling process; the dynamic wireless charging has the characteristic of being capable of charging in the traveling process, and a user can prolong the endurance mileage of the electric automobile without spending extra charging time. The three charging modes are not contradictory in the electric power-traffic coupling network with diversified charging demands, and have stronger complementarity. Therefore, the combined charging guidance considering various charging modes in the electric power-traffic coupling network can effectively improve the economy and convenience of the charging behavior of the electric automobile user. The charging guidance strategy combining multiple charging modes at present only considers the matching optimization of each charging mode, but ignores the influence of traffic flow on charging behavior, and has the problem of insufficient application rationality.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a multi-charging-mode combined charging guiding method and system for an electric automobile.
In order to achieve the above object, the technical scheme of the present invention is as follows:
in a first aspect, the present invention provides a multi-charging-mode joint charging guiding method for an electric vehicle, including:
s1, acquiring the space-time distribution of the charging energy demand of a typical electric automobile in a day;
s2, inputting the space-time distribution of the charging energy requirement into a constructed combined charging guide model, solving the model to obtain the electric vehicle flow which is charged by adopting different charging modes on each travel-charging chain, each period, each path and the road, wherein the combined charging guide model aims at the minimum road passing time and the minimum charging behavior operation time consumption of the electric vehicle, the constraint conditions of the combined charging guide model comprise passing time constraint, traffic flow constraint and electric vehicle flow constraint, and the different charging modes comprise rapid charging, slow charging and dynamic wireless charging.
In the step S2, the objective function of the combined charging guide model is as follows:
Figure SMS_1
；/>
in the above-mentioned method, the step of,
Figure SMS_5
for trip-charging chain->
Figure SMS_8
The route k is adopted in the road in the period t >
Figure SMS_11
Electric vehicle flow rate for quick charging at quick charging station,/-for quick charging>
Figure SMS_2
For trip-charging chain->
Figure SMS_6
In the period t, adopting a path k to charge the electric vehicle flow at a slow speed charging station at a traffic network node ti, and performing ∈10>
Figure SMS_9
、/>
Figure SMS_13
The charging operation of single quick charging and single slow charging is time-consuming respectively, +.>
Figure SMS_3
For trip-charging chain->
Figure SMS_7
Electric vehicle flow using path k in period t, < > in->
Figure SMS_10
、/>
Figure SMS_12
Respectively is the road +.>
Figure SMS_4
A base amount and an uncertainty amount of the transit time of (2);
the transit time constraint includes:
Figure SMS_14
；
Figure SMS_15
；
in the above-mentioned method, the step of,
Figure SMS_16
、/>
Figure SMS_17
the uncertain upper limit coefficients of the total traffic time and the traffic time of each road in each period are respectively;
the traffic flow constraint includes:
Figure SMS_18
；
Figure SMS_19
；
in the above-mentioned method, the step of,
Figure SMS_20
for trip-charging chain->
Figure SMS_21
Electric automobile adopting path k in period t passes through road
Figure SMS_22
Passable parameters of->
Figure SMS_23
For road->
Figure SMS_24
Is a traffic volume of (1);
the electric vehicle flow restriction includes:
Figure SMS_25
；
Figure SMS_26
；
Figure SMS_27
；
Figure SMS_28
；
Figure SMS_29
；
Figure SMS_30
；
Figure SMS_31
；/>
in the above-mentioned method, the step of,
Figure SMS_33
is the unit energy requirement of the electric automobile, +.>
Figure SMS_38
、/>
Figure SMS_41
Respectively is +.>
Figure SMS_35
In the journey with route k during period t +.>
Figure SMS_37
Fast charge energy demand at site, dynamic wireless charge energy demand, +.>
Figure SMS_39
For trip-charging chain->
Figure SMS_42
Slow charging energy demand at traffic network node ti in a journey employing path k during period t, +. >
Figure SMS_32
For trip-charging chain->
Figure SMS_36
The route k is adopted in the road in the period t>
Figure SMS_40
Electric vehicle flow charged by dynamic wireless charging system at the place,/->
Figure SMS_43
、/>
Figure SMS_34
The starting time and the ending time of the idle period of the electric automobile are respectively.
The step S2 of solving the combined charging guide model by adopting a column and constraint generation algorithm comprises the following steps:
s21, reconstructing the original optimization problem into a main problem MP and a sub problem SP, wherein,
Figure SMS_44
；
in the above-mentioned method, the step of,
Figure SMS_45
is a feasible solution of the sub-problem SP;
constraint conditions of the main problem MP comprise a traffic time constraint, a traffic flow constraint, an electric vehicle flow constraint and a feasible solution constraint, wherein the feasible solution constraint is as follows:
Figure SMS_46
；
in the above-mentioned method, the step of,
Figure SMS_47
for the i-th iteration period t road +.>
Figure SMS_48
An uncertainty in the transit time of (2);
the objective function of the subproblem SP is:
Figure SMS_49
；
constraint conditions of the sub-problem SP include a transit time constraint;
s22, initializing an upper bound, a lower bound and iteration times i of a combined charging guide model;
s23, will
Figure SMS_50
I-th iteration value +.>
Figure SMS_51
Carrying out the solution of the main problem MP to obtain the optimal solution of the main problem MP
Figure SMS_52
，/>
Figure SMS_53
，/>
Figure SMS_54
，/>
Figure SMS_55
) And updating the lower bound based on the optimal solution:
Figure SMS_56
；
in the above-mentioned method, the step of,
Figure SMS_57
for the lower bound of the (i+1) th iteration, < ->
Figure SMS_58
、/>
Figure SMS_59
、/>
Figure SMS_60
Respectively->
Figure SMS_61
、/>
Figure SMS_62
、/>
Figure SMS_63
I+1st iteration value of (a);
s24, will
Figure SMS_64
Bringing into the sub-problem SP, solving the sub-problem SP to obtain the optimal solution thereof >
Figure SMS_65
And updates the upper bound based on:
Figure SMS_66
；
in the above-mentioned method, the step of,
Figure SMS_67
is the upper bound of the (i+1) th iteration;
s25, judging whether convergence conditions are met, and if so, outputting
Figure SMS_68
、/>
Figure SMS_69
、/>
Figure SMS_70
If not, it is ++>
Figure SMS_71
After adding the constraint, the process returns to S23 for the next iteration.
The travel-charging chain is a travel and charging energy demand distribution model corresponding to any row Cheng Qiqi point in the traffic network on a typical day.
The S1 comprises the following steps:
s11, constructing an electric vehicle charging energy distribution model, wherein the electric vehicle charging energy distribution model comprises an outer layer model and an inner layer model, the outer layer model aims at the maximum profit of a charging service, real-time charging prices of typical quick charging, slow charging and dynamic wireless charging in a day and the number of quick charging piles and slow charging piles operated in a quick charging station and a slow charging station in each period are taken as decision variables, and constraint conditions of the outer layer model comprise operation constraint, state constraint of a photovoltaic cell and an energy storage system, charging price constraint and distribution network constraint; the inner layer model aims at the minimum total charging cost of the electric automobile user, and takes the time-space distribution of the charging energy demand of the electric automobile in a typical day as a decision variable, and the constraint conditions of the inner layer model comprise charging energy demand constraint and power coupling constraint;
And S12, solving a charging energy distribution model of the electric automobile to obtain the time-space distribution of the charging energy requirement of the electric automobile in a typical day.
The objective function of the outer layer model is as follows:
Figure SMS_72
；
Figure SMS_73
；
Figure SMS_74
；
Figure SMS_75
；
Figure SMS_76
；
Figure SMS_77
；
Figure SMS_78
；
in the above-mentioned method, the step of,
Figure SMS_97
charge service benefit->
Figure SMS_103
For the total operating costs of the fast charging station, the dynamic wireless charging system and the slow charging station +.>
Figure SMS_109
Total cost of purchasing electricity from distribution network for fast charging station, dynamic wireless charging system and slow charging station, +.>
Figure SMS_80
、/>
Figure SMS_84
Respectively is +.>
Figure SMS_90
In the journey with route k during period t +.>
Figure SMS_93
Fast charge energy demand at site, dynamic wireless charge energy demand, +.>
Figure SMS_99
For trip-charging chain->
Figure SMS_101
Slow charging energy demand at traffic network node ti in a journey employing path k during period t, +.>
Figure SMS_106
、/>
Figure SMS_110
、/>
Figure SMS_100
Respectively fast charging, dynamic wireless charging and slow charging in period tCharging price->
Figure SMS_102
、/>
Figure SMS_105
、/>
Figure SMS_108
The operating costs of the fast charging station, the dynamic wireless charging system and the slow charging station are respectively +.>
Figure SMS_96
、/>
Figure SMS_98
Respectively is unit time scale->
Figure SMS_104
Operating costs of a single fast charging peg in an internal fast charging station, of a single fast charging peg in a slow charging station, +.>
Figure SMS_107
For the road +.>
Figure SMS_79
Number of fast charge piles operated in fast charge station +. >
Figure SMS_83
For dynamic wireless charging systems at a power distribution network node>
Figure SMS_88
Access decision variable at->
Figure SMS_92
For road->
Figure SMS_82
Length of->
Figure SMS_86
Is of unit time scale->
Figure SMS_89
Operating cost of dynamic wireless charging system on inner single road, < >>
Figure SMS_95
For the number of slow charging piles operated in the slow charging station at the traffic network node ti in the period t,/->
Figure SMS_85
For the real-time electricity purchase price in period t, < >>
Figure SMS_87
For the distribution network node in period t>
Figure SMS_91
Sum of active power output to fast charging station, dynamic wireless charging system, slow charging station,/->
Figure SMS_94
For the distribution network node in period t>
Figure SMS_81
And the sum of the photovoltaic output consumed in the accessed quick charging station, dynamic wireless charging system and slow charging station.
The objective function of the inner layer model is:
Figure SMS_111
。
the S12 includes: firstly, reconstructing an inner layer model by adopting a KKT condition, converting a double-layer planning problem into a single-layer planning problem, linearizing complementary relaxation conditions generated by reconstruction, linearizing bilinear terms in the outer layer model based on a McCormick relaxation method, and tightening boundaries after McCormick relaxation by adopting an optimization-based constraint tightening method and a sequential constraint tightening method.
In a second aspect, the invention provides a multi-charging-mode combined charging guiding method system of an electric automobile, which comprises a charging energy demand space-time distribution acquisition module, a combined charging guiding model construction module and a combined charging guiding model solving module;
The charging energy demand space-time distribution acquisition module is used for acquiring the charging energy demand space-time distribution of the electric automobile in a typical day;
the combined charging guide model construction module is used for constructing a combined charging guide model, the combined charging guide model aims at minimizing the road passing time and the charging behavior operation time consumption of the electric vehicle, constraint conditions of the combined charging guide model comprise passing time constraint, traffic flow constraint and electric vehicle flow constraint, and different charging modes comprise rapid charging, slow charging and dynamic wireless charging;
the combined charging guide model solving module is used for inputting the space-time distribution of the charging energy requirement into a combined charging guide model constructed, and solving the model to obtain the electric vehicle flow which is charged by adopting different charging modes on each trip-charging chain, each period, each path and the road.
The charging energy demand space-time distribution acquisition module comprises a charging energy distribution model construction unit and a charging energy distribution model solving unit;
the charging energy distribution model building unit is used for building an electric vehicle charging energy distribution model comprising an outer layer model and an inner layer model, wherein the outer layer model aims at the maximum profit of a charging service, takes real-time charging prices of typical quick charging, slow charging and dynamic wireless charging in a day and the number of quick charging piles and slow charging piles operated in quick charging stations and slow charging stations in each period as decision variables, and constraint conditions of the outer layer model comprise operation constraint, photovoltaic cell and energy storage system state constraint, charging price constraint and power distribution network constraint; the inner layer model aims at the minimum total charging cost of the electric automobile user, and takes the time-space distribution of the charging energy demand of the electric automobile in a typical day as a decision variable, and the constraint conditions of the inner layer model comprise charging energy demand constraint and power coupling constraint;
The charging energy distribution model solving unit is used for solving the charging energy distribution model of the electric automobile to obtain the time-space distribution of the charging energy demand of the electric automobile in a typical day.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the multi-charging-mode combined charging guiding method for the electric automobile, firstly, the time-space distribution of the charging energy demands of the electric automobile in a typical day is obtained, then the time-space distribution of the charging energy demands is input into a constructed combined charging guiding model, the model is solved, the electric automobile flow of charging is obtained through each travel-charging chain, each period, each path and on a road in different charging modes, the combined charging guiding model aims at the minimum road passing time and the minimum charging behavior operation time consumption of the electric automobile, and constraint conditions of the combined charging guiding model comprise passing time constraint, traffic flow constraint and electric automobile flow constraint. On the other hand, the travel-charging chain model reflecting the travel and charging behavior characteristics of the electric automobile user is constructed to describe the time and space characteristics of the traffic demand and the charging demand of the user, and the uncertainty of the road traffic time is considered, so that the finally obtained optimization results of the charging mode, the charging path and the charging time can be matched with the traffic behavior of the electric automobile user, and the rationality and the effectiveness of the charging guiding result are ensured.
2. According to the multi-charging-mode combined charging guiding method for the electric automobile, the charging energy demand distribution model based on the travel-charging chain model is adopted, and the charging energy demand space-time distribution of the electric automobile in a typical day is obtained through solving, so that the peak value can be effectively stabilized, the benefits of a charging service provider and electric automobile users are balanced, and the economical efficiency of the charging behaviors of the electric automobile users is improved.
Drawings
Fig. 1 is a schematic diagram of a power-traffic coupling network with multiple charging modes.
Fig. 2 is a diagram of a model structure of the trip-charging chain.
Fig. 3 is a topology structure diagram of the P54-T25 network described in embodiment 1.
Fig. 4 is a graph of the charging energy demand of a typical electric car in the day obtained in example 1.
Fig. 5 is a graph of a typical intra-day electric vehicle charging energy demand using an independent charging guidance strategy.
Fig. 6 is a frame diagram of the system of example 2.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and the accompanying drawings.
The invention aims at considering an electric-traffic coupling network structure of an electric vehicle rapid charging station, an electric vehicle slow charging station, an electric vehicle dynamic wireless charging system, a photovoltaic cell and an energy storage system (as shown in figure 1, the rapid charging station is built along each road in a traffic network, the electric vehicle passing through the road where the rapid charging station is located can enter the rapid charging station to carry out rapid charging, and the rest journey is continuously completed after the charging is finished, the dynamic wireless charging system is built below the road ground, the electric vehicle can be charged in the advancing process without stopping when passing through the road covered by the dynamic wireless charging system, the slow charging station is built at each node in the traffic network, and in the idle period without a traveling plan, the electric vehicle user can select the slow charging station entering the node of a vehicle stop point to carry out slow charging, three charging systems contained in the system are all provided with the photovoltaic cell and the energy storage system. For electric automobile users, charging in different time and place can affect the cost and time cost of charging behavior by adopting different charging modes and travel paths. In order to improve the efficiency of three charging behaviors in an electric power-traffic coupling network on the premise of meeting the interests of a charging service provider and an electric vehicle user group, the invention provides an electric vehicle multi-charging-mode combined charging guiding method. The first stage adopts a charging energy demand distribution model, and solves the dynamic price of three charging services and the distribution of charging demands under the condition of balancing the interests of a charging service provider and an electric automobile user. And in the second stage, the running time cost of the electric automobile user group and the operation time cost of the charging behavior are the minimum targets, and the charging mode, the charging path and the charging time of the electric automobile are optimized. The optimization model of the stage considers the uncertainty of the road traffic time, and solves the robust optimization problem of the stage by adopting a column and constraint generation method.
Trip-charging chain: in order to describe a traffic demand and charging demand distribution mode of an electric automobile user in a typical day, the invention provides a travel-charging chain model for describing a plurality of travel behaviors and charging behaviors of the electric automobile user by considering quick charging, dynamic wireless charging and slow charging, wherein the model corresponds to a set of travel and charging demands for each travel origin-destination od, namely travel and charging demands corresponding to a travel from an o point to a d point, and the structure of the model is shown in fig. 2.
On a temporal level, the model divides the time of an electric car user in a typical day into a trip period and an idle period. In the idle period, the electric automobile is in a state that the electric automobile does not start to start the next stroke or has reached the end of the previous stroke. Since the electric vehicle in this state is not used, if a slow charging station is built at the parking node of the vehicle, the electric vehicle user may choose to park the vehicle into the slow charging station for charging until the end of the idle period. In the travel time period, the electric automobile is in a driving process and cannot be charged at a low speed, and because the quick charging and the dynamic wireless charging are both in the middle of supplying electricity, in the travel time period, users of the electric automobile can charge on the quick charging station along the road and the road covered by the dynamic wireless charging system. Because in the urban traffic system, more than one path exists between any two traffic nodes, the influence of different paths on the distribution of charging demands when the travel starting and ending points are the same is considered in the model. Formulas (1) - (5) represent the spatial-temporal distribution relationship of three charging energy demands in the travel-charging chain:
Figure SMS_112
（1）/>
Figure SMS_113
（2）
Figure SMS_114
（3）
Figure SMS_115
（4）
Figure SMS_116
（5）
In the above-mentioned method, the step of,
Figure SMS_118
for the total charging energy demand of an electric vehicle in a typical day, < > for>
Figure SMS_121
、/>
Figure SMS_124
Charging energy requirements of electric vehicles in travel time and idle time respectively, < >>
Figure SMS_119
、/>
Figure SMS_123
、/>
Figure SMS_126
The fast charging energy requirement, the dynamic wireless charging energy requirement and the slow charging energy requirement of the electric automobile in the period t are respectively +.>
Figure SMS_128
、/>
Figure SMS_117
Respectively, a travel period, an idle period, < >>
Figure SMS_122
、/>
Figure SMS_125
Respectively is the road +.>
Figure SMS_127
Fast charge energy requirement and dynamic wireless charge energy requirement of electric vehicle at the location, +.>
Figure SMS_120
Is the road through which the journey is made.
Example 1:
the embodiment applies the method to a P54-T25 network (the topology structure of which is shown in figure 3) which comprises 54 power distribution network nodes (S1-S4, P1-P50), 25 traffic network nodes (T1-T25) and 50 roads. The coupling nodes of the power distribution network and the traffic network are as follows: p4 (T7), P9 (T4), P12 (T14), P28 (T16), P30 (T8), P35 (T11), P46 (T19). In the network, 8 quick charging stations and 8 dynamic wireless charging systems are planned, 7 slow charging stations are planned, wherein 200 quick charging piles and 200 slow charging piles are respectively configured in each quick charging station and each slow charging station, and 200 ESS and 200 PVs are respectively configured in each quick charging station, each dynamic wireless charging system and each slow charging station. 8 rapid charging stations in the network are respectively built at roads T2-T4, T4-T5, T4-T8, T5-T7, T7-T8, T8-T10, T8-T11 and T9-T10; 8 dynamic wireless charging systems are respectively built at the positions of roads T3-T4, T6-T7, T7-T11, T8-T13, T10-T14, T11-T12 and T11-T13; and 7 slow charging stations are respectively built at traffic nodes T4, T7, T8, T11, T12, T16 and T19.
In terms of time scale, the scale of each period in a typical day is set to 1 hour. In terms of hardware parameters, rated output power of a single fast charging pile and a single slow charging pile is 5 kW and 50 kW respectively; the rated power of the single electric automobile for wireless dynamic charging through the dynamic wireless charging system is 40 kW; the power factor angles of the quick charge, the quick charge and the dynamic wireless charge are all
Figure SMS_129
The energy transfer efficiency is 0.9; the unit operation cost of a single quick charging pile and a single slow charging pile is 19.5 yuan/hour and 3.25 yuan/hour respectively, and the operation cost of a dynamic wireless charging system in unit time is 975 yuan/hour. On a typical day, the minimum proportion of charge energy shortage met by fast charge, slow charge and dynamic wireless charge is 0.6. In the aspect of electric vehicles, the battery capacity of the electric vehicles is 75 kWh, and the average energy requirement of each electric vehicle in any travel-charging chain is 30% of the battery capacity. The number of travel-charging chains is 200, and the number of electric vehicles with charging requirements corresponding to each travel-charging chain is 100; for any journey, the number of candidate paths is 3. In the aspect of the electric power-traffic coupling network, the rated voltage of a bus of the power distribution network is 10 kV, and the upper limit and the lower limit of the bus voltage are 9.5 kV and 10.5 kV respectively. / >
The method sequentially comprises the following steps:
1. the method comprises the steps of constructing an electric vehicle charging energy distribution model, wherein the electric vehicle charging energy distribution model comprises an outer layer model and an inner layer model, the outer layer model takes real-time charging prices of typical quick charging, slow charging and dynamic wireless charging in the day and the quantity of quick charging piles and slow charging piles running in the quick charging station and the slow charging station in each period as decision variables, and the aim of charging service profit maximization is achieved:
Figure SMS_130
（6）
Figure SMS_131
（7）
Figure SMS_132
（8）
Figure SMS_133
（9）
Figure SMS_134
（10）
Figure SMS_135
（11）
Figure SMS_136
（12）
in the above-mentioned method, the step of,
Figure SMS_157
charge service benefit->
Figure SMS_160
For the total operating costs of the fast charging station, the dynamic wireless charging system and the slow charging station +.>
Figure SMS_166
Total cost of purchasing electricity from distribution network for fast charging station, dynamic wireless charging system and slow charging station, +.>
Figure SMS_139
、/>
Figure SMS_143
Respectively is +.>
Figure SMS_146
In the journey with route k during period t +.>
Figure SMS_149
Fast charge energy demand at site, dynamic wireless charge energy demand, +.>
Figure SMS_150
For trip-charging chain->
Figure SMS_154
Slow charging energy demand at traffic network node ti in a journey employing path k during period t, +.>
Figure SMS_158
、/>
Figure SMS_161
、/>
Figure SMS_163
Charging prices of fast charging, dynamic wireless charging and slow charging in period t respectively, +.>
Figure SMS_165
、/>
Figure SMS_167
、/>
Figure SMS_169
The operating costs of the fast charging station, the dynamic wireless charging system and the slow charging station are respectively +. >
Figure SMS_155
、/>
Figure SMS_162
Respectively isUnit time scale->
Figure SMS_164
Operating costs of a single fast charging peg in an internal fast charging station, of a single fast charging peg in a slow charging station, +.>
Figure SMS_168
For the road +.>
Figure SMS_137
Number of fast charge piles operated in fast charge station +.>
Figure SMS_142
For dynamic wireless charging systems at a power distribution network node>
Figure SMS_147
Access judgment variable at the node of the distribution network>
Figure SMS_152
When the dynamic wireless charging system is accessed, the dynamic wireless charging system is 1, otherwise, the dynamic wireless charging system is 0,
Figure SMS_140
for road->
Figure SMS_141
Length of->
Figure SMS_145
Is of unit time scale->
Figure SMS_148
Operating cost of dynamic wireless charging system on inner single road, < >>
Figure SMS_151
For the number of slow charging piles operated in the slow charging station at the traffic network node ti in the period t,/->
Figure SMS_153
For the real-time electricity purchase price in period t, < >>
Figure SMS_156
For the distribution network node in period t>
Figure SMS_159
Sum of active power output to fast charging station, dynamic wireless charging system, slow charging station,/->
Figure SMS_138
For the distribution network node in period t>
Figure SMS_144
And the sum of the photovoltaic output consumed in the accessed quick charging station, dynamic wireless charging system and slow charging station.
Constraints of the outer layer model include:
operational constraints
Figure SMS_170
（13）
Figure SMS_171
（14）
In the above-mentioned method, the step of,
Figure SMS_172
for road->
Figure SMS_173
The number of installation of quick charge piles in a quick charge station at a site is +.>
Figure SMS_174
Is the installation number of the slow charging piles in the slow charging station at the node ti of the traffic network.
Photovoltaic cell and energy storage system state constraints
Figure SMS_175
（15）
Figure SMS_176
（16）
Figure SMS_177
（17）
In the above-mentioned method, the step of,
Figure SMS_179
、/>
Figure SMS_183
respectively are distribution network nodes->
Figure SMS_187
Fast charging station, dynamic wireless charging system, sum of photovoltaic cell number and sum of energy storage system number configured by slow charging station>
Figure SMS_178
Maximum output power for a single photovoltaic cell during period t, < >>
Figure SMS_185
、/>
Figure SMS_189
The lower and upper limits of the state of charge of the energy storage system are respectively +.>
Figure SMS_191
For the installation capacity of a single energy storage system, +.>
Figure SMS_180
For distribution network node->
Figure SMS_184
A fast charging station, a dynamic wireless charging system, a sum of initial electric quantity of energy storage systems installed in slow charging stations, and +.>
Figure SMS_188
For the distribution network node in period t>
Figure SMS_190
The sum of charging and discharging power of the energy storage system installed in the quick charging station, the dynamic wireless charging system and the slow charging station which are connected in the process when ∈>
Figure SMS_181
When the energy storage system works in a charging mode, when +.>
Figure SMS_182
When the energy storage system works in a discharging mode, +.>
Figure SMS_186
Is the number of time periods.
Charging price constraint
Figure SMS_192
（18）
Figure SMS_193
（19）
Figure SMS_194
（20）
In the above-mentioned method, the step of,
Figure SMS_195
、/>
Figure SMS_196
、/>
Figure SMS_197
upper limits of charge service prices for fast charge, dynamic wireless charge and slow charge, respectively. />
Power distribution network constraints
Figure SMS_198
（21）
Figure SMS_199
（22）
Figure SMS_200
（23）
Figure SMS_201
（24）
Figure SMS_202
（25）
Figure SMS_203
（26）
Figure SMS_204
（27）
Figure SMS_205
（28）
In the above-mentioned method, the step of,
Figure SMS_220
、/>
Figure SMS_223
the power distribution network nodes are respectively a fast charging station and a slow charging station>
Figure SMS_227
Access decision variable at->
Figure SMS_207
Is a large M constant, +. >
Figure SMS_211
For the distribution network node in period t>
Figure SMS_215
Sum of active power output to fast charging station, dynamic wireless charging system, slow charging station,/->
Figure SMS_217
、/>
Figure SMS_208
、/>
Figure SMS_210
After the capacity expansion of the distribution network is finished, the active power, the reactive power and the apparent power of the distribution network line w in the period t are respectively, wherein w is equal to the road +.>
Figure SMS_214
All distribution network lines connected by the coupled distribution network nodes, < >>
Figure SMS_219
、/>
Figure SMS_209
Respectively is a power distribution network node in a period t>
Figure SMS_212
Active, reactive power of the base load connected in>
Figure SMS_216
For the apparent power capacity of the distribution network line w, < >>
Figure SMS_218
For the time period t for the voltage drop over the distribution network line w,/, for>
Figure SMS_222
Rated value of bus voltage of distribution network, < >>
Figure SMS_226
、/>
Figure SMS_230
Resistance and reactance of the distribution network line w, respectively, < >>
Figure SMS_233
、/>
Figure SMS_206
、/>
Figure SMS_213
Respectively is a power distribution network node in a period t>
Figure SMS_225
、/>
Figure SMS_229
、/>
Figure SMS_221
Bus voltage of>
Figure SMS_224
、/>
Figure SMS_228
For any two distribution network nodes connected to a distribution network line w,/->
Figure SMS_232
、/>
Figure SMS_231
The upper limit and the lower limit of the bus voltage of the power distribution network are respectively set;
the constraint considers the influence of space-time distribution of charging demands corresponding to three charging modes on the power flow and bus voltage of the power distribution network, and ensures that the power flow of each power distribution network line is not out of limit and the bus voltage deviation of each power distribution network node is in an allowable range after a charging load is accessed.
The inner layer model takes the space-time distribution of the charging energy demands of the electric automobile in a typical day as a decision variable and takes the minimum total charging cost of the electric automobile user as a target:
Figure SMS_234
（29）。
The constraint conditions of the inner layer model include:
charging energy demand constraints
Figure SMS_235
（30）
Figure SMS_236
（31）
Figure SMS_237
（32）
Figure SMS_238
（33）
Figure SMS_239
（34）
Figure SMS_240
（35）
Figure SMS_241
（36）
Figure SMS_242
（37）
Figure SMS_243
（38）
In the above-mentioned method, the step of,
Figure SMS_244
for trip-charging chain->
Figure SMS_249
The total electric vehicle charging energy demand in a typical day,
Figure SMS_253
for a lower limit of charge energy demand satisfied by fast charge, dynamic wireless charge and slow charge in a typical day, +.>
Figure SMS_247
、/>
Figure SMS_248
Output power of a single fast charging pile and output power of a single slow charging device are respectively +.>
Figure SMS_252
Charging output power for single electric automobile when running on road covered by dynamic wireless charging system and carrying out dynamic wireless charging>
Figure SMS_255
For passing the road in period t->
Figure SMS_245
The time required,/->
Figure SMS_251
For road->
Figure SMS_254
Traffic volume of->
Figure SMS_256
、/>
Figure SMS_246
、/>
Figure SMS_250
Feasibility matrixes for fast charging, dynamic wireless charging and slow charging energy demand distribution are respectively adopted.
The formula (30) is constraint on total charging energy requirement of the electric automobile for any travel-charging chain; equation (31) constrains the lower limit of the charge energy deficit satisfied by the three charging modes in a typical day; formulas (32), (33) represent that the fast charge energy demand on any road at any time does not exceed the upper limit of the fast charge and dynamic wireless charge service capabilities in the corresponding fast charging station and dynamic wireless charging system; equation (34) indicates that the slow charge energy demand allocated at any time and at any traffic node cannot exceed the slow charge service capability of the corresponding slow charge station. Equations (35) - (38) are value constraints for the three charge energy demands allocated on each trip-charging chain, time period, path and road, which limits that the three charge energy demands can only be allocated at roads and traffic nodes where the respective charging facilities are built.
Power coupling constraints
Figure SMS_257
（39）
Figure SMS_258
（40）
Figure SMS_259
（41）
In the above-mentioned method, the step of,
Figure SMS_261
、/>
Figure SMS_263
、/>
Figure SMS_266
the energy transfer efficiency of the fast charge, the dynamic wireless charge and the slow charge are respectively,
Figure SMS_262
、/>
Figure SMS_265
、/>
Figure SMS_268
respectively, quick charge and dynamic noPower factor angle of line charging, slow charging, +.>
Figure SMS_269
、/>
Figure SMS_260
、/>
Figure SMS_264
Respectively is a power distribution network node in a period t>
Figure SMS_267
The visual power of the accessed base load, photovoltaic cell and energy storage system.
Formulas (39) - (41) are coupling constraints of active power, reactive power and apparent power, i.e. the output power of the distribution network, photovoltaic cells and energy storage system is balanced with the charging loads of the three charging modes.
2. Solving an electric vehicle charging energy distribution model to obtain the charging energy demand space-time distribution of the electric vehicle, wherein the method specifically comprises the following steps of:
and 2.1, reconstructing an inner layer model by adopting a KKT condition, and converting the double-layer planning problem into a single-layer planning problem. The reconstructed model comprises an original feasible condition, a dual feasible condition and a complementary relaxation condition, wherein the original feasible condition is inner-layer constraint type (30) - (38), the dual feasible condition is shown as formulas (42) - (44), and the complementary relaxation condition of the rapid charging energy requirement is shown as formulas (45) - (51):
Figure SMS_270
（42）
Figure SMS_271
（43）
Figure SMS_272
（44）
Figure SMS_273
（45）/>
Figure SMS_274
（46）
Figure SMS_275
（47）
Figure SMS_276
（48）
Figure SMS_277
（49）
Figure SMS_278
（50）
Figure SMS_279
（51）
in the above-mentioned method, the step of,
Figure SMS_280
-/>
Figure SMS_281
is a lagrange multiplier.
2.2 linearizing complementary relaxation conditions generated by reconstruction based on Big-M method. Taking the complementary relaxation condition 45 as an example, the linearization is followed by the following equations (52) - (53):
Figure SMS_282
（52）
Figure SMS_283
（53）
in the above-mentioned method, the step of,
Figure SMS_284
is an auxiliary variable.
2.3 for bilinear terms in the skin model
Figure SMS_285
、/>
Figure SMS_286
And->
Figure SMS_287
Linearizing based on McCormick relaxation method, introducing auxiliary variable +.>
Figure SMS_288
、
Figure SMS_289
And->
Figure SMS_290
As shown in equations (54) - (56), equation (7) may be re-expressed as equation (57). Additional constraints of the mccomick relaxation process are shown in equations (58) - (69):
Figure SMS_291
（54）
Figure SMS_292
（55）/>
Figure SMS_293
（56）
Figure SMS_294
（57）
Figure SMS_295
（58）
Figure SMS_296
（59）
Figure SMS_297
（60）
Figure SMS_298
（61）
Figure SMS_299
（62）
Figure SMS_300
（63）
Figure SMS_301
（64）
Figure SMS_302
（65）
Figure SMS_303
（66）
Figure SMS_304
（67）
Figure SMS_305
（68）
Figure SMS_306
（69）
in the above-mentioned method, the step of,
Figure SMS_307
、/>
Figure SMS_308
the maximum number of fast and slow charging piles that can be operated in a single fast and slow charging station, respectively.
And 2.4, tightening the boundary after the McCormick is relaxed by adopting a constraint tightening method based on optimization and a sequential constraint tightening method. Relaxation values in sequential constraint tightening
Figure SMS_309
And->
Figure SMS_310
As shown in formulas (70) - (71): />
Figure SMS_311
（70）
Figure SMS_312
（71）。
The charging energy demand distribution result of the typical daily electric automobile obtained in this embodiment is shown in fig. 4.
3. The method comprises the steps of constructing a combined charging guide model, taking the traffic of electric vehicles charged by different travel-charging chains in different time periods, paths and roads in different charging modes as decision variables, taking the minimum road traffic time and charging behavior operation time of the electric vehicles as targets, and regarding the time consumption of travel behaviors, traffic accidents possibly existing on the roads can influence the traffic time of each road, so that uncertainty of the road traffic time is considered in an optimization model of the stage, and the optimization problem of the stage is constructed as a robust optimization problem, wherein the objective function is as follows:
Figure SMS_313
（72）
In the above-mentioned method, the step of,
Figure SMS_316
for trip-charging chain->
Figure SMS_318
The route k is adopted in the road in the period t>
Figure SMS_322
Electric vehicle flow rate for quick charging at quick charging station,/-for quick charging>
Figure SMS_315
For trip-charging chain->
Figure SMS_319
In the period t, adopting a path k to charge the electric vehicle flow at a slow speed charging station at a traffic network node ti, and performing ∈10>
Figure SMS_321
、/>
Figure SMS_325
The charging operation of single quick charging and single slow charging is time-consuming respectively, +.>
Figure SMS_317
For trip-charging chain->
Figure SMS_320
Electric vehicle flow using path k in period t, < > in->
Figure SMS_323
、/>
Figure SMS_324
Respectively is the road +.>
Figure SMS_314
A base amount and an uncertainty amount of the transit time of (c).
The constraint conditions of the combined charging guide model include:
time of flight constraints
Figure SMS_326
（73）
Figure SMS_327
（74）
In the above-mentioned method, the step of,
Figure SMS_328
、/>
Figure SMS_329
the uncertain upper limit coefficients of the total traffic time and the traffic time of each road in each period are respectively defined.
Equation (73) constrains an uncertainty upper limit for the total transit time in the traffic network for each time period; equation (74) constrains the range of values for the amount of uncertainty in the passage time on each road for each time period.
Traffic flow constraints
Figure SMS_330
（75）/>
Figure SMS_331
（76）
In the above-mentioned method, the step of,
Figure SMS_332
for trip-charging chain->
Figure SMS_333
Electric automobile adopting path k in period t passes through road
Figure SMS_334
Is to pass through the road +.>
Figure SMS_335
Then 1, otherwise 0, +. >
Figure SMS_336
For road->
Figure SMS_337
Is a traffic volume of (a).
Formula (75) is a non-negative constraint of traffic flow; equation (76) constrains the traffic flow on each link so that it does not exceed the upper limit of the traffic capacity of the link itself.
Flow restriction of electric automobile
Figure SMS_338
（77）
Figure SMS_339
（78）
Figure SMS_340
（79）
Figure SMS_341
（80）
Figure SMS_342
（81）
Figure SMS_343
（82）
Figure SMS_344
（83）
In the above-mentioned method, the step of,
Figure SMS_345
is the unit energy requirement of the electric automobile, +.>
Figure SMS_349
、/>
Figure SMS_352
Respectively is +.>
Figure SMS_347
In the journey with route k during period t +.>
Figure SMS_350
Fast charge energy demand at site, dynamic wireless charge energy demand, +.>
Figure SMS_353
For trip-charging chain->
Figure SMS_356
Slow charging energy demand at traffic network node ti in a journey employing path k during period t, +.>
Figure SMS_348
For trip-charging chain->
Figure SMS_351
The route k is adopted in the road in the period t>
Figure SMS_354
Electric vehicle flow charged by dynamic wireless charging system at the place,/->
Figure SMS_355
、/>
Figure SMS_346
The starting time and the ending time of the idle period of the electric automobile are respectively.
Equation (77) represents that for any trip-charging chain, any period of time, any path, the total charging energy demand of the three charging modes needs to be no greater than the maximum charging energy deficiency of the corresponding traffic flow; formulas (78) - (80) respectively restrict the energy shortage of the electric vehicle flow adopting the fast charging, the dynamic wireless charging and the slow charging from being not less than the corresponding charging energy requirement; the formula (81) restricts the flow of the electric vehicle which adopts quick charge for any travel-charging chain, any period of time and any path; the electric vehicle traffic using dynamic wireless charging and slow charging at any travel-charging chain, time period, path, road and traffic network node is respectively constrained by the formula (82) and the formula (83) not to exceed the corresponding traffic flow.
4. The method comprises the steps of inputting the space-time distribution of the charging energy requirement into a constructed combined charging guide model, solving the model by adopting a column and constraint generation algorithm to obtain the electric vehicle flow of charging by adopting different charging modes on each travel-charging chain, each period, each path and the road, and specifically comprises the following steps:
4.1, reconstructing the original optimization problem into a main problem MP and a sub problem SP, wherein the formula (84) is an objective function of the main problem MP, the formulas (85) and (73) - (83) are constraint conditions of the main problem MP, the formula (86) is an objective function of the sub problem SP, and the formulas (73) - (75) are constraint conditions of the sub problem SP.
Figure SMS_357
（84）
Figure SMS_358
（85）
Figure SMS_359
（86）
In the above-mentioned method, the step of,
Figure SMS_360
for a feasible solution of the sub-problem SP, +.>
Figure SMS_361
For the i-th iteration period t road +.>
Figure SMS_362
Is used for determining the uncertainty of the transit time of the vehicle.
4.2, input Convergence judgment constant
Figure SMS_363
Initializing the upper bound of the combined charge guidance model +.>
Figure SMS_364
Lower boundary of
Figure SMS_365
And the number of iterations i=0.
4.3, will
Figure SMS_366
I-th iteration value +.>
Figure SMS_367
Carrying out the solution of the main problem MP to obtain the optimal solution of the main problem MP
Figure SMS_368
，/>
Figure SMS_369
，/>
Figure SMS_370
，/>
Figure SMS_371
) And updating the lower bound based on the optimal solution:
Figure SMS_372
（87）；
in the above-mentioned method, the step of,
Figure SMS_373
for the lower bound of the (i+1) th iteration, < ->
Figure SMS_374
、/>
Figure SMS_375
、/>
Figure SMS_376
Respectively->
Figure SMS_377
、/>
Figure SMS_378
、/>
Figure SMS_379
I+1st iteration value of (c).
4.4, will
Figure SMS_380
Bringing into the sub-problem SP, solving the sub-problem SP to obtain the optimal solution thereof >
Figure SMS_381
And updates the upper bound based on:
Figure SMS_382
in the above-mentioned method, the step of,
Figure SMS_383
is the upper bound for the i+1st iteration.
4.5, judging whether the convergence condition is satisfied, if so, outputting
Figure SMS_384
、/>
Figure SMS_385
、/>
Figure SMS_386
If not, it is ++>
Figure SMS_387
Returning to 4.3 for the next iteration after adding the following constraints: />
Figure SMS_388
（88）
Figure SMS_389
（89）
Figure SMS_390
（90）。
In order to examine the effectiveness of the method of the present invention, for the P54-T25 network adopted in embodiment 1, an independent charging guidance strategy (the independent charging guidance strategy does not consider the information intercommunication among charging service providers of different charging types and the coordination and complementation of different types of charging modes, that is, when three charging modes are adopted, the charging service providers corresponding to the three charging modes respectively conduct independent charging guidance) is adopted to determine the charging energy demand distribution of the electric automobile in a typical day, and the result is shown in fig. 5. By comparing fig. 4 and fig. 5, it is found that the peak period of the charging energy demand in fig. 4 occurs once in the morning and evening, respectively, and the distribution of the total charging energy demand has a significant similarity to the distribution of the traffic demand. In fig. 5, the charging energy demand is at a peak from midday to evening, and its peak is significantly higher than that of fig. 4. This also demonstrates the effectiveness of the combined charge guidance strategy employed by the present invention in stabilizing peaks.
In addition, table 1 shows the economics and time consuming results of employing both of the above-described charge guidance strategies in a P54-T25 network. As can be seen from the results, the charging service income using the independent charging guidance strategy is higher than that of example 1, and the operation cost is lower than that of example 1, so that the independent charging guidance strategy is 6.01% higher than that of example 1 in terms of the profit of the charging service, which proves the advantage of the combined charging guidance strategy used by the present invention in terms of economy. Meanwhile, in terms of average time consumption of a single electric vehicle, the average driving time consumption, the average operation time consumption and the total average time consumption of the combined charging guide are all lower than those of the independent charging guide, wherein the total average time consumption is reduced by 4.34%. Therefore, the effect of improving the profit of the charging service and the effect of reducing the average time consumption of the charging are more obvious by adopting the combined charging guide, which also shows that the complementary characteristics among three different charging modes can be better utilized by adopting the combined charging guide in a large network with wider coverage of charging facilities and higher flexibility of charging space.
Table 1 economic and time consuming results when two charge guidance strategies were employed
Figure SMS_391
。
Example 2:
referring to fig. 6, the system of the multi-charging-mode combined charging guiding method for the electric automobile comprises a charging energy demand space-time distribution acquisition module, a combined charging guiding model construction module and a combined charging guiding model solving module, wherein the charging energy demand space-time distribution acquisition module comprises a charging energy distribution model construction unit and a charging energy distribution model solving unit;
The charging energy distribution model construction unit is configured to construct an electric vehicle charging energy distribution model including an outer layer model and an inner layer model according to embodiment 1, where the outer layer model targets at maximum profit of a charging service, and uses real-time charging prices of typical fast charging, slow charging and dynamic wireless charging in a day, and the number of fast charging piles and slow charging piles operating in the fast charging station and the slow charging station in each period as decision variables, and constraint conditions of the outer layer model include operation constraint, photovoltaic cell and energy storage system state constraint, charging price constraint, and power distribution network constraint; the inner layer model aims at the minimum total charging cost of the electric automobile user, and takes the time-space distribution of the charging energy demand of the electric automobile in a typical day as a decision variable, and the constraint conditions of the inner layer model comprise charging energy demand constraint and power coupling constraint;
the charging energy distribution model solving unit is used for solving the charging energy distribution model of the electric automobile according to the method described in the embodiment 1 to obtain the time-space distribution of the charging energy demand of the electric automobile in a typical day;
the combined charging guide model construction module is used for constructing the combined charging guide model according to the embodiment 1, the combined charging guide model aims at minimizing the road passing time and the charging behavior operation time consumption of the electric vehicle, the constraint conditions of the combined charging guide model comprise passing time constraint, traffic flow constraint and electric vehicle flow constraint, and the different charging modes comprise rapid charging, slow charging and dynamic wireless charging;
The combined charging guide model solving module inputs the space-time distribution of the charging energy requirement into a constructed combined charging guide model, and solves the model based on the list and constraint generating algorithm described in the embodiment 1 to obtain the electric vehicle flow of charging by adopting different charging modes on each travel-charging chain, each period, each path and the road.

Claims (10)

1. The utility model provides an electric automobile multi-charging mode joint charging guiding method which is characterized in that:
the method sequentially comprises the following steps:
s1, acquiring the space-time distribution of the charging energy demand of a typical electric automobile in a day;
s2, inputting the space-time distribution of the charging energy requirement into a constructed combined charging guide model, solving the model to obtain the electric vehicle flow which is charged by adopting different charging modes on each travel-charging chain, each period, each path and the road, wherein the combined charging guide model aims at the minimum road passing time and the minimum charging behavior operation time consumption of the electric vehicle, the constraint conditions of the combined charging guide model comprise passing time constraint, traffic flow constraint and electric vehicle flow constraint, and the different charging modes comprise rapid charging, slow charging and dynamic wireless charging.
2. The multi-charging-mode combined charging guiding method for the electric automobile according to claim 1, wherein the method comprises the following steps:
in the step S2, the objective function of the combined charging guide model is as follows:
Figure QLYQS_1
；
in the above-mentioned method, the step of,
Figure QLYQS_3
for trip-charging chain->
Figure QLYQS_9
The route k is adopted in the road in the period t>
Figure QLYQS_13
Electric vehicle flow rate for quick charging at quick charging station,/-for quick charging>
Figure QLYQS_4
For trip-charging chain->
Figure QLYQS_7
In the period t, adopting a path k to charge the electric vehicle flow at a slow speed charging station at a traffic network node ti, and performing ∈10>
Figure QLYQS_11
、/>
Figure QLYQS_12
The charging operation of single quick charging and single slow charging is time-consuming respectively, +.>
Figure QLYQS_2
For trip-charging chain->
Figure QLYQS_6
Electric vehicle flow using path k in period t, < > in->
Figure QLYQS_8
、/>
Figure QLYQS_10
Respectively is the road +.>
Figure QLYQS_5
Is based on the transit time of (a)Amount and uncertainty;
the transit time constraint includes:
Figure QLYQS_14
；
Figure QLYQS_15
；
in the above-mentioned method, the step of,
Figure QLYQS_16
、/>
Figure QLYQS_17
the uncertain upper limit coefficients of the total traffic time and the traffic time of each road in each period are respectively;
the traffic flow constraint includes:
Figure QLYQS_18
；
Figure QLYQS_19
；
in the above-mentioned method, the step of,
Figure QLYQS_20
for trip-charging chain->
Figure QLYQS_21
Electric automobile adopting path k in period t passes through road +.>
Figure QLYQS_22
Passable parameters of->
Figure QLYQS_23
For road->
Figure QLYQS_24
Is a traffic volume of (1);
the electric vehicle flow restriction includes:
Figure QLYQS_25
；
Figure QLYQS_26
；
Figure QLYQS_27
；
Figure QLYQS_28
；
Figure QLYQS_29
；
Figure QLYQS_30
；
Figure QLYQS_31
；
In the above-mentioned method, the step of,
Figure QLYQS_33
is the unit energy requirement of the electric automobile, +.>
Figure QLYQS_36
、/>
Figure QLYQS_39
Respectively for travel-charging chains
Figure QLYQS_34
In the journey with route k during period t +.>
Figure QLYQS_37
Fast charge energy requirements and dynamic wireless charge energy requirements,
Figure QLYQS_41
for trip-charging chain->
Figure QLYQS_43
Slow charging energy demand at traffic network node ti in a journey employing path k during period t, +.>
Figure QLYQS_32
For trip-charging chain->
Figure QLYQS_38
The route k is adopted in the road in the period t>
Figure QLYQS_40
Electric vehicle flow charged by dynamic wireless charging system at the place,/->
Figure QLYQS_42
、/>
Figure QLYQS_35
The starting time and the ending time of the idle period of the electric automobile are respectively.
3. The multi-charging-mode combined charging guiding method for the electric automobile according to claim 2, wherein the method comprises the following steps:
the step S2 of solving the combined charging guide model by adopting a column and constraint generation algorithm comprises the following steps:
s21, reconstructing the original optimization problem into a main problem MP and a sub problem SP, wherein,
the objective function of the master question MP is:
Figure QLYQS_44
；
in the above-mentioned method, the step of,
Figure QLYQS_45
is a feasible solution of the sub-problem SP;
constraint conditions of the main problem MP comprise a traffic time constraint, a traffic flow constraint, an electric vehicle flow constraint and a feasible solution constraint, wherein the feasible solution constraint is as follows:
Figure QLYQS_46
；
in the above-mentioned method, the step of,
Figure QLYQS_47
for the i-th iteration period t road +. >
Figure QLYQS_48
An uncertainty in the transit time of (2);
the objective function of the subproblem SP is:
Figure QLYQS_49
；
constraint conditions of the sub-problem SP include a transit time constraint;
s22, initializing an upper bound, a lower bound and iteration times i of a combined charging guide model;
s23, will
Figure QLYQS_50
I-th iteration value +.>
Figure QLYQS_51
Carrying out the solution of the main problem MP to obtain the optimal solution of the main problem MP
Figure QLYQS_52
，/>
Figure QLYQS_53
，/>
Figure QLYQS_54
，/>
Figure QLYQS_55
) And updating the lower bound based on the optimal solution:
Figure QLYQS_56
；
in the above-mentioned method, the step of,
Figure QLYQS_57
for the lower bound of the (i+1) th iteration, < ->
Figure QLYQS_58
、/>
Figure QLYQS_59
、/>
Figure QLYQS_60
Respectively->
Figure QLYQS_61
、
Figure QLYQS_62
、/>
Figure QLYQS_63
I+1st iteration value of (a);
s24, will
Figure QLYQS_64
Bringing into the sub-problem SP, solving the sub-problem SP to obtain the optimal solution thereof>
Figure QLYQS_65
And updates the upper bound based on:
Figure QLYQS_66
；
in the above-mentioned method, the step of,
Figure QLYQS_67
is the upper bound of the (i+1) th iteration;
s25, judging whether convergence conditions are met, and if so, outputting
Figure QLYQS_68
、/>
Figure QLYQS_69
、/>
Figure QLYQS_70
If not, it is ++>
Figure QLYQS_71
After adding the constraint, the process returns to S23 for the next iteration.
4. The multi-charging-mode joint charging guiding method for an electric automobile according to any one of claims 1 to 3, wherein:
the travel-charging chain is a travel and charging energy demand distribution model corresponding to any row Cheng Qiqi point in the traffic network on a typical day.
5. The multi-charging-mode combined charging guiding method for the electric automobile according to claim 1, wherein the method comprises the following steps:
The S1 comprises the following steps:
s11, constructing an electric vehicle charging energy distribution model, wherein the electric vehicle charging energy distribution model comprises an outer layer model and an inner layer model, the outer layer model aims at the maximum profit of a charging service, real-time charging prices of typical quick charging, slow charging and dynamic wireless charging in a day and the number of quick charging piles and slow charging piles operated in a quick charging station and a slow charging station in each period are taken as decision variables, and constraint conditions of the outer layer model comprise operation constraint, state constraint of a photovoltaic cell and an energy storage system, charging price constraint and distribution network constraint; the inner layer model aims at the minimum total charging cost of the electric automobile user, and takes the time-space distribution of the charging energy demand of the electric automobile in a typical day as a decision variable, and the constraint conditions of the inner layer model comprise charging energy demand constraint and power coupling constraint;
and S12, solving a charging energy distribution model of the electric automobile to obtain the time-space distribution of the charging energy requirement of the electric automobile in a typical day.
6. The method for guiding multi-charging-mode combined charging of an electric automobile according to claim 5, wherein the method comprises the following steps:
the objective function of the outer layer model is as follows:
Figure QLYQS_72
；
Figure QLYQS_73
；
Figure QLYQS_74
；
Figure QLYQS_75
；
Figure QLYQS_76
；
Figure QLYQS_77
；
Figure QLYQS_78
；
In the above-mentioned method, the step of,
Figure QLYQS_86
charge service benefit->
Figure QLYQS_89
For the total operating costs of the fast charging station, the dynamic wireless charging system and the slow charging station +.>
Figure QLYQS_92
For the total cost of fast charging stations, dynamic wireless charging systems and slow charging stations purchasing electricity from the distribution grid,
Figure QLYQS_79
、/>
Figure QLYQS_83
respectively is +.>
Figure QLYQS_87
In the journey with route k during period t +.>
Figure QLYQS_90
Fast charge energy demand at site, dynamic wireless charge energy demand, +.>
Figure QLYQS_81
For trip-charging chain->
Figure QLYQS_84
Slow charging energy demand at traffic network node ti in a journey employing path k during period t, +.>
Figure QLYQS_88
、/>
Figure QLYQS_91
、/>
Figure QLYQS_94
Charging prices of fast charging, dynamic wireless charging and slow charging in period t respectively, +.>
Figure QLYQS_97
、/>
Figure QLYQS_100
、/>
Figure QLYQS_108
The operating costs of the fast charging station, the dynamic wireless charging system and the slow charging station are respectively +.>
Figure QLYQS_105
、/>
Figure QLYQS_107
Respectively is unit time scale->
Figure QLYQS_109
Operating costs of a single fast charging peg in an internal fast charging station, of a single fast charging peg in a slow charging station, +.>
Figure QLYQS_110
For the road +.>
Figure QLYQS_82
Number of fast charge piles operated in fast charge station +.>
Figure QLYQS_85
For dynamic wireless charging systems at a power distribution network node>
Figure QLYQS_103
Access decision variable at->
Figure QLYQS_106
For road->
Figure QLYQS_93
Length of->
Figure QLYQS_96
Is of unit time scale->
Figure QLYQS_99
Operating cost of dynamic wireless charging system on inner single road, < > >
Figure QLYQS_102
For the number of slow charging piles operated in the slow charging station at the traffic network node ti in the period t,/->
Figure QLYQS_95
For the real-time electricity purchase price in period t, < >>
Figure QLYQS_98
For the distribution network node in period t>
Figure QLYQS_101
Sum of active power output to fast charging station, dynamic wireless charging system, slow charging station,/->
Figure QLYQS_104
For the distribution network node in period t>
Figure QLYQS_80
The sum of the consumed photovoltaic output in the accessed quick charging station, dynamic wireless charging system and slow charging station; />
The operating constraints include:
Figure QLYQS_111
；
Figure QLYQS_112
；
in the above-mentioned method, the step of,
Figure QLYQS_113
for road->
Figure QLYQS_114
The number of installation of quick charge piles in a quick charge station at a site is +.>
Figure QLYQS_115
The installation quantity of the slow charging piles in the slow charging station at the node ti of the traffic network is set;
the photovoltaic cell and energy storage system state constraints include:
Figure QLYQS_116
；
Figure QLYQS_117
；
Figure QLYQS_118
；
in the above-mentioned method, the step of,
Figure QLYQS_120
、/>
Figure QLYQS_123
respectively are distribution network nodes->
Figure QLYQS_129
Fast charging station with access, dynamic wireless charging system, sum of photovoltaic cell numbers configured by slow charging station and energy storage system numberSum of quantity,/->
Figure QLYQS_122
Maximum output power for a single photovoltaic cell during period t, < >>
Figure QLYQS_124
、/>
Figure QLYQS_126
The lower and upper limits of the state of charge of the energy storage system are respectively +.>
Figure QLYQS_128
For the installation capacity of a single energy storage system, +.>
Figure QLYQS_119
For distribution network node->
Figure QLYQS_125
A fast charging station, a dynamic wireless charging system, a sum of initial electric quantity of energy storage systems installed in slow charging stations, and +. >
Figure QLYQS_127
For the distribution network node in period t>
Figure QLYQS_130
The sum of charge and discharge power of an energy storage system installed in a quick charging station, a dynamic wireless charging system and a slow charging station which are connected in the process>
Figure QLYQS_121
Is the number of time periods;
the charging price constraint includes:
Figure QLYQS_131
；
Figure QLYQS_132
；
Figure QLYQS_133
；
in the above-mentioned method, the step of,
Figure QLYQS_134
、/>
Figure QLYQS_135
、/>
Figure QLYQS_136
upper limits of charging service prices for fast charging, dynamic wireless charging, and slow charging, respectively;
the power distribution network constraints include:
Figure QLYQS_137
；
Figure QLYQS_138
；
Figure QLYQS_139
；/>
Figure QLYQS_140
；
Figure QLYQS_141
；
Figure QLYQS_142
；
Figure QLYQS_143
；
Figure QLYQS_144
；
in the above-mentioned method, the step of,
Figure QLYQS_160
、/>
Figure QLYQS_163
the power distribution network nodes are respectively a fast charging station and a slow charging station>
Figure QLYQS_164
Access decision variable at->
Figure QLYQS_146
Is a large M constant, +.>
Figure QLYQS_150
For the distribution network node in period t>
Figure QLYQS_153
Sum of active power output to fast charging station, dynamic wireless charging system, slow charging station,/->
Figure QLYQS_157
、/>
Figure QLYQS_147
、/>
Figure QLYQS_151
After the capacity expansion of the distribution network is finished, the active power, the reactive power and the apparent power of the distribution network line w in the period t are respectively, wherein w is equal to the road +.>
Figure QLYQS_154
Coupled distribution network node connectionsAll of the distribution network lines,
Figure QLYQS_158
、/>
Figure QLYQS_148
respectively is a power distribution network node in a period t>
Figure QLYQS_152
Active, reactive power of the base load connected in>
Figure QLYQS_156
For the apparent power capacity of the distribution network line w, < >>
Figure QLYQS_159
For the time period t for the voltage drop over the distribution network line w,/, for>
Figure QLYQS_166
Rated value of bus voltage of distribution network, < >>
Figure QLYQS_169
、/>
Figure QLYQS_171
Resistance and reactance of the distribution network line w, respectively, < > >
Figure QLYQS_172
、/>
Figure QLYQS_145
、/>
Figure QLYQS_149
Respectively is a power distribution network node in a period t>
Figure QLYQS_155
、/>
Figure QLYQS_162
、/>
Figure QLYQS_161
Bus voltage of>
Figure QLYQS_165
、/>
Figure QLYQS_168
For any two distribution network nodes connected to a distribution network line w,
Figure QLYQS_170
、/>
Figure QLYQS_167
the upper limit and the lower limit of the bus voltage of the distribution network are respectively.
7. The method for guiding multi-charging-mode combined charging of the electric automobile according to claim 6, wherein the method comprises the following steps:
the objective function of the inner layer model is:
Figure QLYQS_173
；
the charging energy demand constraint includes:
Figure QLYQS_174
；
Figure QLYQS_175
；/>
Figure QLYQS_176
；
Figure QLYQS_177
；
Figure QLYQS_178
；
Figure QLYQS_179
；
Figure QLYQS_180
；
Figure QLYQS_181
；
Figure QLYQS_182
；
in the above-mentioned method, the step of,
Figure QLYQS_184
for trip-charging chain->
Figure QLYQS_194
Total electric vehicle charging energy demand in a typical day, < >>
Figure QLYQS_195
For a lower limit of charge energy demand satisfied by fast charge, dynamic wireless charge and slow charge in a typical day, +.>
Figure QLYQS_185
、
Figure QLYQS_188
Output power of a single fast charging pile and output power of a single slow charging device are respectively +.>
Figure QLYQS_190
Charging output power for single electric automobile when running on road covered by dynamic wireless charging system and carrying out dynamic wireless charging>
Figure QLYQS_193
For passing the road in period t->
Figure QLYQS_183
The time required,/->
Figure QLYQS_189
For road->
Figure QLYQS_191
Traffic volume of->
Figure QLYQS_192
、/>
Figure QLYQS_186
、/>
Figure QLYQS_187
Feasibility matrixes for quick charge, dynamic wireless charge and slow charge energy demand distribution respectively;
the power coupling constraint includes:
Figure QLYQS_196
；
Figure QLYQS_197
；
Figure QLYQS_198
；
in the above-mentioned method, the step of,
Figure QLYQS_200
、/>
Figure QLYQS_204
、/>
Figure QLYQS_206
energy transfer efficiency of fast charge, dynamic wireless charge, slow charge, respectively, +. >
Figure QLYQS_201
、/>
Figure QLYQS_203
、/>
Figure QLYQS_207
Power factor angle for fast charging, dynamic wireless charging, slow charging respectively +.>
Figure QLYQS_208
、/>
Figure QLYQS_199
、/>
Figure QLYQS_202
Respectively is a power distribution network node in a period t>
Figure QLYQS_205
The visual power of the accessed base load, photovoltaic cell and energy storage system.
8. The electric vehicle multi-charging-mode joint charging guiding method according to claim 3, wherein:
the S12 includes: firstly, reconstructing an inner layer model by adopting a KKT condition, converting a double-layer planning problem into a single-layer planning problem, linearizing complementary relaxation conditions generated by reconstruction, linearizing bilinear terms in the outer layer model based on a McCormick relaxation method, and tightening boundaries after McCormick relaxation by adopting an optimization-based constraint tightening method and a sequential constraint tightening method.
9. The utility model provides an electric automobile multi-charging mode joint charge guiding method system which characterized in that:
the system comprises a charging energy demand space-time distribution acquisition module, a combined charging guide model construction module and a combined charging guide model solving module;
the charging energy demand space-time distribution acquisition module is used for acquiring the charging energy demand space-time distribution of the electric automobile in a typical day;
the combined charging guide model construction module is used for constructing a combined charging guide model, the combined charging guide model aims at minimizing the road passing time and the charging behavior operation time consumption of the electric vehicle, constraint conditions of the combined charging guide model comprise passing time constraint, traffic flow constraint and electric vehicle flow constraint, and different charging modes comprise rapid charging, slow charging and dynamic wireless charging;
The combined charging guide model solving module is used for inputting the space-time distribution of the charging energy requirement into a combined charging guide model constructed, and solving the model to obtain the electric vehicle flow which is charged by adopting different charging modes on each trip-charging chain, each period, each path and the road.
10. The multi-charging-mode combined charging guiding method system of the electric automobile according to claim 9, wherein the system is characterized in that:
the charging energy demand space-time distribution acquisition module comprises a charging energy distribution model construction unit and a charging energy distribution model solving unit;
the charging energy distribution model building unit is used for building an electric vehicle charging energy distribution model comprising an outer layer model and an inner layer model, wherein the outer layer model aims at the maximum profit of a charging service, takes real-time charging prices of typical quick charging, slow charging and dynamic wireless charging in a day and the number of quick charging piles and slow charging piles operated in quick charging stations and slow charging stations in each period as decision variables, and constraint conditions of the outer layer model comprise operation constraint, photovoltaic cell and energy storage system state constraint, charging price constraint and power distribution network constraint; the inner layer model aims at the minimum total charging cost of the electric automobile user, and takes the time-space distribution of the charging energy demand of the electric automobile in a typical day as a decision variable, and the constraint conditions of the inner layer model comprise charging energy demand constraint and power coupling constraint;
The charging energy distribution model solving unit is used for solving the charging energy distribution model of the electric automobile to obtain the time-space distribution of the charging energy demand of the electric automobile in a typical day.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116611673A (en) * 2023-07-20 2023-08-18 国网湖北省电力有限公司经济技术研究院 Electric traffic coupling network-oriented optical storage charging station planning method and system
CN117313292A (en) * 2023-11-22 2023-12-29 国网湖北省电力有限公司经济技术研究院 Method, system and equipment for configuring dynamic wireless charging system of electric bus

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014127849A1 (en) * 2013-02-25 2014-08-28 Nec Europe Ltd. Method and system for determining routes of vehicles
US20170310140A1 (en) * 2016-04-26 2017-10-26 Nec Laboratories America, Inc. System and method for reducing time-averaged peak charges
CN110570050A (en) * 2019-09-25 2019-12-13 国网浙江省电力有限公司经济技术研究院 Road-network-vehicle-related electric vehicle charging guiding method
CN110880755A (en) * 2019-11-06 2020-03-13 国网浙江省电力有限公司经济技术研究院 Collaborative planning method for charging network and power distribution network considering guide type charging control
WO2021098352A1 (en) * 2019-11-22 2021-05-27 国网福建省电力有限公司 Active power distribution network planning model establishment method taking into consideration site selection and capacity determination of electric vehicle charging stations
US20210155111A1 (en) * 2019-11-22 2021-05-27 State Grid Fujian Electric Power Co., Ltd. Method for establishing active distribution network planning model considering location and capacity determination of electric vehicle charging station
WO2022142392A1 (en) * 2020-12-28 2022-07-07 国网天津市电力公司电力科学研究院 Method for formulating spatio-temporal combined optimization scheduling policy for mobile energy storage
CN115860433A (en) * 2023-02-16 2023-03-28 国网湖北省电力有限公司经济技术研究院 Electric vehicle quick charging station and dynamic wireless charging system combined planning method and system

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014127849A1 (en) * 2013-02-25 2014-08-28 Nec Europe Ltd. Method and system for determining routes of vehicles
US20170310140A1 (en) * 2016-04-26 2017-10-26 Nec Laboratories America, Inc. System and method for reducing time-averaged peak charges
CN110570050A (en) * 2019-09-25 2019-12-13 国网浙江省电力有限公司经济技术研究院 Road-network-vehicle-related electric vehicle charging guiding method
CN110880755A (en) * 2019-11-06 2020-03-13 国网浙江省电力有限公司经济技术研究院 Collaborative planning method for charging network and power distribution network considering guide type charging control
WO2021098352A1 (en) * 2019-11-22 2021-05-27 国网福建省电力有限公司 Active power distribution network planning model establishment method taking into consideration site selection and capacity determination of electric vehicle charging stations
US20210155111A1 (en) * 2019-11-22 2021-05-27 State Grid Fujian Electric Power Co., Ltd. Method for establishing active distribution network planning model considering location and capacity determination of electric vehicle charging station
WO2022142392A1 (en) * 2020-12-28 2022-07-07 国网天津市电力公司电力科学研究院 Method for formulating spatio-temporal combined optimization scheduling policy for mobile energy storage
CN115860433A (en) * 2023-02-16 2023-03-28 国网湖北省电力有限公司经济技术研究院 Electric vehicle quick charging station and dynamic wireless charging system combined planning method and system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
唐可;邱高;邱晓燕;闫天泽;刘延博;万成江;: "考虑电动汽车空间分配的多目标配电网重构优化", 电测与仪表, no. 12, pages 24 - 30 *
范磊等: "考虑城市不同功能区的电动汽车负荷时空分布建模方法", 电力建设, no. 6, pages 67 - 74 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116611673A (en) * 2023-07-20 2023-08-18 国网湖北省电力有限公司经济技术研究院 Electric traffic coupling network-oriented optical storage charging station planning method and system
CN116611673B (en) * 2023-07-20 2023-10-03 国网湖北省电力有限公司经济技术研究院 Electric traffic coupling network-oriented optical storage charging station planning method and system
CN117313292A (en) * 2023-11-22 2023-12-29 国网湖北省电力有限公司经济技术研究院 Method, system and equipment for configuring dynamic wireless charging system of electric bus
CN117313292B (en) * 2023-11-22 2024-02-27 国网湖北省电力有限公司经济技术研究院 Method, system and equipment for configuring dynamic wireless charging system of electric bus

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