CN115860433A - Electric vehicle quick charging station and dynamic wireless charging system combined planning method and system - Google Patents

Electric vehicle quick charging station and dynamic wireless charging system combined planning method and system Download PDF

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CN115860433A
CN115860433A CN202310119781.5A CN202310119781A CN115860433A CN 115860433 A CN115860433 A CN 115860433A CN 202310119781 A CN202310119781 A CN 202310119781A CN 115860433 A CN115860433 A CN 115860433A
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dynamic wireless
wireless charging
charging
electric vehicle
charging station
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CN115860433B (en
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夏方舟
杨洁
陈红坤
徐敬友
陈�峰
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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Abstract

The method comprises the steps of firstly constructing a combined planning model of the electric vehicle quick charging station and the dynamic wireless charging system based on charging energy demand distribution, then solving the constructed combined planning model to obtain an optimal combined planning scheme, namely, carrying out site selection and volume fixing on the electric vehicle quick charging station, the electric vehicle dynamic wireless charging system, a photovoltaic cell and an energy storage system, and planning a capacity expansion scheme of a power distribution network line in response. According to the method, on one hand, the solving complexity of the problem is reduced by adopting the energy demand distribution model, and on the other hand, the efficient operation effect of the power-traffic coupling network is improved.

Description

Electric vehicle quick charging station and dynamic wireless charging system combined planning method and system
Technical Field
The invention belongs to the field of power grid system planning, and particularly relates to a combined planning method and system for a quick charging station and a dynamic wireless charging system of an electric vehicle.
Background
With the gradual increase of the permeability of the electric vehicle, the charging demand is gradually diversified, and the distribution range of the charging demand is gradually expanded. In such an electric power-traffic coupling network including different types of areas, a charging service provider needs to provide sufficient and diversified charging facilities in an inaccessible area to meet the charging demand of electric vehicles. The economy and the characteristics of the charging requirements to be met are different according to the types of charging facilities and different location areas. In this case, different types of electric vehicle charging methods have different advantages and disadvantages.
The quick charging station for the electric automobile has the characteristic of quick power compensation, and is considered as one of the most efficient power compensation modes in the long-distance driving process of the electric automobile, so that the quick charging station for the electric automobile is considered as one of solutions for solving the problem of power compensation when the electric automobile drives for a long time in an intercity. However, the electric vehicle rapid charging station occupies a large area, so that the land utilization cost in a central urban area is high.
The dynamic wireless charging system of the electric automobile is characterized in that a wireless charging device is buried under a road surface, and when the electric automobile runs on the road, the electric automobile is supplemented with electricity through dynamic wireless charging of the electric automobile. The charging mode can improve the endurance mileage of the electric automobile on the premise of not increasing the extra time consumption of the electric automobile user. However, the hardware cost of the dynamic wireless charging system is high, and the dynamic wireless charging system is not suitable for large-scale laying.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a method and a system for jointly planning an electric vehicle quick charging station and a dynamic wireless charging system based on energy demand distribution and oriented to a power-traffic coupling network.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
the combined planning method for the electric vehicle quick charging station and the dynamic wireless charging system sequentially comprises the following steps:
step A, constructing a combined planning model of an electric vehicle quick charging station and a dynamic wireless charging system based on charging energy demand distribution, wherein the combined planning model comprises an outer layer model and an inner layer model, and an objective function of the outer layer model is as follows:
Figure SMS_1
in the above formula, the first and second carbon atoms are,
Figure SMS_2
service a charge based on the charge status>
Figure SMS_3
For the operation cost of the electric vehicle quick charging station and the dynamic wireless charging system, the charging station is based on the charge condition>
Figure SMS_4
Based on the cost of purchasing electricity from the power distribution network for the electric vehicle quick charging station and the dynamic wireless charging system>
Figure SMS_5
For the total investment cost, based on the total investment>
Figure SMS_6
For the mark-off rate, is selected>
Figure SMS_7
The investment age is the same;
the decision variables of the outer layer model are the electric automobile quick charging station, the dynamic wireless charging system connection, the photovoltaic cell and the locating and constant volume scheme of the energy storage system;
the objective function of the inner layer model is as follows:
Figure SMS_8
in the above formula, the first and second carbon atoms are,
Figure SMS_9
for the total charging time cost of the electric vehicle, < > >>
Figure SMS_10
Penalty costs incurred for an unsatisfied charge energy gap;
the decision variables of the inner layer model are space-time distribution modes of energy requirements of quick charging and dynamic wireless charging of the electric automobile;
and B, solving the constructed joint planning model to obtain an optimal joint planning scheme.
In the outer layer model, the
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、/>
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、/>
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、/>
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Calculated according to the following formula:
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in the above-mentioned formula, the compound has the following structure,
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、/>
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on the way ^ for each electric vehicle which takes the path k in the O-D pair od assigned to the time period t>
Figure SMS_60
Based on the quick charging energy requirement, the dynamic wireless charging energy requirement>
Figure SMS_27
、/>
Figure SMS_37
Charging prices of quick charging and dynamic wireless charging are respectively set in a time period t, d is the number of typical days in a standard year, and the number of the typical days is greater than or equal to>
Figure SMS_48
、/>
Figure SMS_56
The operation costs of the quick charging station and the dynamic wireless charging system are based on the operating cost of the system>
Figure SMS_47
For the real-time electricity purchase price of the time period t, < >>
Figure SMS_53
Is a t time period road>
Figure SMS_33
Active power output by the distribution network to the quick charging station or the dynamic wireless charging system is judged and judged>
Figure SMS_40
Is a t time period road>
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Is subjected to the absorbed photovoltaic output>
Figure SMS_66
Is expressed as a unit time scale>
Figure SMS_63
、/>
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、/>
Figure SMS_50
The investment costs of the quick charging station, the dynamic wireless charging system, the photovoltaic cell and the energy storage system are respectively->
Figure SMS_57
、/>
Figure SMS_51
The land cost and the expansion investment cost of the power distribution network line are respectively selected as the basis>
Figure SMS_58
、
Figure SMS_26
Respectively, the operating costs of a single typical day quick charging station, a dynamic wireless charging system, based on the charging status of the charging station and the charging status of the charging station, based on the operating costs of the charging station and the charging status of the charging system>
Figure SMS_34
Is a road->
Figure SMS_42
The number of the quick charging piles arranged at the quick charging station is greater than or equal to the number of the quick charging piles arranged at the quick charging station>
Figure SMS_46
、/>
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Are respectively road->
Figure SMS_67
A variable 0-1 of the construction position of the quick charging station and the dynamic wireless charging system is changed into a variable value in the range of>
Figure SMS_31
Is a road->
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Is greater than or equal to>
Figure SMS_54
、/>
Figure SMS_59
Respectively a single quick charging station and dynamic wirelessEarly investment cost of the charging system->
Figure SMS_61
、/>
Figure SMS_64
、/>
Figure SMS_43
The installation costs of the single quick charging pile, the photovoltaic cell and the energy storage system are respectively greater or less>
Figure SMS_49
Is the installation cost of the dynamic wireless charging system per unit length, based on the charging status of the charging station>
Figure SMS_30
、/>
Figure SMS_36
Are respectively road->
Figure SMS_29
The number of the photovoltaic cells and the energy storage system arranged at the position is greater than or equal to>
Figure SMS_38
For expanding 0-1 variable of the power distribution network line w>
Figure SMS_32
Investment cost for expanding capacity of single power distribution network line>
Figure SMS_41
、/>
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、/>
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Are respectively road->
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The investment cost of occupying the land by a single quick charging pile, a photovoltaic cell and an energy storage system is saved;
in the inner layer model, the
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、/>
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Calculated according to the following formula:
Figure SMS_70
Figure SMS_71
in the above formula, the first and second carbon atoms are,
Figure SMS_72
average salary for people>
Figure SMS_73
Rated power for quick charging>
Figure SMS_74
Is a penalty function coefficient>
Figure SMS_75
The total charge energy deficit to od for time t O-D.
The constraints of the outer layer model comprise:
Figure SMS_76
Figure SMS_77
Figure SMS_78
Figure SMS_79
Figure SMS_80
Figure SMS_81
in the above formula, the first and second carbon atoms are,
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、/>
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is respectively the maximum construction number of the quick charging station and the dynamic wireless charging system>
Figure SMS_91
Rated power for dynamic wireless charging>
Figure SMS_83
Is a t time period road>
Figure SMS_89
Based on the average passage time of the vehicle, is greater than or equal to>
Figure SMS_90
、/>
Figure SMS_95
Are respectively road->
Figure SMS_82
On the traffic volume and the number of lanes>
Figure SMS_87
For the penetration rate of the electric vehicle in the traffic network, is->
Figure SMS_93
Based on the average energy shortage of each electric vehicle>
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、/>
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、/>
Figure SMS_86
The maximum number of the rapid charging piles, the photovoltaic cells and the energy storage systems which are respectively configured for a single road,
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is a large M constant;
the constraints of the inner layer model comprise:
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Figure SMS_100
Figure SMS_101
Figure SMS_102
Figure SMS_103
in the above formula, the first and second carbon atoms are,
Figure SMS_118
is the minimum percentage of charge energy deficit met by rapid charging and dynamic wireless charging during a typical day, is greater than or equal to->
Figure SMS_108
A route k corresponding to the O-D pair passes through the road->
Figure SMS_119
Is 0-1 variable of (4), based on the status of the signal being asserted, and/or based on the status of the signal being asserted>
Figure SMS_109
Is a t time period road>
Figure SMS_117
Charge and discharge power of the energy storage system is judged>
Figure SMS_111
、/>
Figure SMS_116
The energy transfer efficiency of the quick charging and the dynamic wireless charging are respectively greater or less>
Figure SMS_105
Is a t time period road>
Figure SMS_113
The reactive power which is output to the quick charging station or the dynamic wireless charging system from the distribution network is judged and judged>
Figure SMS_104
、/>
Figure SMS_112
A power factor angle for quick charging and dynamic wireless charging respectively>
Figure SMS_106
For the apparent power of the line w in the period t after the line expansion of the distribution network is completed, the voltage or the power is greater>
Figure SMS_114
Respectively on the road for a period t>
Figure SMS_110
Apparent power of base load of corresponding distribution network bus is greater or less>
Figure SMS_115
、
Figure SMS_107
Respectively on a road in a t time interval>
Figure SMS_120
And the apparent power output by the photovoltaic cell and the energy storage system.
The constraints of the outer layer model further comprise:
and (3) state of charge constraint of the energy storage system:
Figure SMS_121
Figure SMS_122
in the above formula, the first and second carbon atoms are,
Figure SMS_123
for the installation capacity of a single energy storage system, is>
Figure SMS_124
Is a road->
Figure SMS_125
Based on the initial charge of the energy storage system arranged in>
Figure SMS_126
Is the total number of time periods;
charging price constraint:
Figure SMS_127
in the above formula, the first and second carbon atoms are,
Figure SMS_128
、/>
Figure SMS_129
the price upper limits of the quick charging and the dynamic wireless charging are respectively;
and (3) power constraint:
Figure SMS_130
Figure SMS_131
Figure SMS_132
Figure SMS_133
Figure SMS_134
Figure SMS_135
in the above formula, the first and second carbon atoms are,
Figure SMS_138
is the maximum output power of a single photovoltaic cell in the period t, < >>
Figure SMS_142
、/>
Figure SMS_143
Respectively completing the active and reactive power of the line w at t time after the line expansion is completed for the power distribution network, and then judging whether the line w has active and reactive power>
Figure SMS_137
、/>
Figure SMS_139
Respectively on the road for a period t>
Figure SMS_140
Active and reactive power of base load of corresponding distribution network bus>
Figure SMS_141
Apparent power capacity of line w prior to completion of line expansion for a distribution network>
Figure SMS_136
Apparent power capacity increased for expanded distribution network lines;
and (3) power distribution network bus voltage constraint:
Figure SMS_144
Figure SMS_145
Figure SMS_146
in the above formula, the first and second carbon atoms are,
Figure SMS_149
for the voltage drop on line w for a period t>
Figure SMS_151
、/>
Figure SMS_154
Respectively carrying out resistance and reactance of the line w after line expansion for the power distribution network>
Figure SMS_148
For a desired value of the busbar voltage of the distribution network>
Figure SMS_152
、/>
Figure SMS_155
Are respectively a power distribution network node in the t time period>
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、/>
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Based on the bus voltage of>
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、/>
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The lower limit and the upper limit of the bus voltage of the power distribution network are respectively set;
and (3) power distribution network line impedance constraint:
Figure SMS_157
Figure SMS_158
Figure SMS_159
Figure SMS_160
in the above formula, the first and second carbon atoms are,
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、/>
Figure SMS_162
respectively carrying out resistance and reactance of the line w before line expansion for the power distribution network>
Figure SMS_163
、/>
Figure SMS_164
Respectively the combined resistance and reactance of the original cable and the newly-built cable after the capacity expansion of the line w is carried out>
Figure SMS_165
、/>
Figure SMS_166
Respectively, the resistance and reactance of the expansion line at the line w.
The step B comprises the following steps: firstly, carrying out linearization processing on the joint programming model, and then solving the joint programming model, wherein the linearization processing of the joint programming model comprises the following steps:
linearization treatment of the outer layer model: linearizing a bilinear term in the outer layer model by adopting an McCormick relaxation method, and linearizing a nonlinear term in the outer layer model by adopting a large M method;
linearization treatment of the inner layer model: and reconstructing the inner layer model by adopting a KKT condition, and performing linearization treatment on the complementary relaxation condition in the reconstructed inner layer model by a large M method.
The linearization processing of the outer layer model further comprises:
the method comprises the steps of firstly adopting an optimization-based constraint tightening method to tighten the variable boundary of the McCormick relaxation result, and then further tightening the variable boundary through a sequential constraint tightening method according to the optimization-based constraint tightening result.
The method for tightening the variable boundary of the McCormick relaxation result by adopting the optimized constraint tightening method sequentially comprises the following steps of:
s11, constructing a variable upper and lower boundary optimization model, wherein an objective function of the optimization model is as follows:
Figure SMS_167
Figure SMS_168
in the above formula, the first and second carbon atoms are,
Figure SMS_169
、/>
Figure SMS_170
is an upper and a lower boundary optimization function, respectively>
Figure SMS_171
、/>
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Respectively an upper bound set and a lower bound set of the variable;
the constraint conditions of the optimization model comprise an outer layer model and an inner layer model after linearization processing and newly added constraints, wherein the newly added constraints are as follows:
Figure SMS_173
in the above formula, the first and second carbon atoms are,
Figure SMS_174
a local feasible solution for the joint planning model;
s12, inputting the tolerance value of the variable upper bound
Figure SMS_175
And a lower tolerance value>
Figure SMS_176
The initial upper bound set->
Figure SMS_177
And a lower bound set>
Figure SMS_178
；
S13, iteration is carried out according to the following formula:
Figure SMS_179
Figure SMS_180
in the above-mentioned formula, the compound has the following structure,
Figure SMS_181
、/>
Figure SMS_182
respectively an upper bound set and a lower bound set of the variable at the ith iteration time;
s14, comparing the upper and lower bound sets of the ith iteration and the (i-1) th iteration, and determining the final upper and lower bound sets of the iteration according to the following formula:
Figure SMS_183
Figure SMS_184
；
s15, judging whether the following conditions are met simultaneously, if so, finishing the iteration, and if not, returning to the step S13 to carry out the next iteration:
Figure SMS_185
。
the method for tightening the variable boundary further comprises the following steps:
s21, taking the upper and lower bound sets tightened by the constraint tightening method based on optimization as initial upper bound sets
Figure SMS_186
The initial lower bound set->
Figure SMS_187
Is inputted>
Figure SMS_188
、/>
Figure SMS_189
Converging tolerance->
Figure SMS_190
And a decrement sequence->
Figure SMS_191
；
S22, iteration is carried out according to the following formula:
Figure SMS_192
Figure SMS_193
Figure SMS_194
in the above formula, the first and second carbon atoms are,
Figure SMS_195
for the real-time boundary of the jth iteration, <' >>
Figure SMS_196
For the linearized joint programming model, a decision is made as to whether the cell is in the desired cell state>
Figure SMS_197
、
Figure SMS_198
The upper and lower boundaries of the jth iteration are respectively;
s23, judging the relaxation tightness value
Figure SMS_199
Whether or not it is greater than a convergence tolerance>
Figure SMS_200
If yes, the method returns to the step S22 for the next iteration, and if not, the iteration is ended, wherein the judgment is performed on whether the value is greater than or equal to the preset value>
Figure SMS_201
Calculated according to the following formula:
Figure SMS_202
in the above formula, the first and second carbon atoms are,
Figure SMS_203
、/>
Figure SMS_204
respectively is replaced>
Figure SMS_205
、/>
Figure SMS_206
Of the auxiliary variable(s).
The combined planning system for the electric vehicle quick charging station and the dynamic wireless charging system comprises a combined planning model construction module and a combined planning model solving module;
the combined planning model building module is used for building a combined planning model of the electric vehicle quick charging station and the dynamic wireless charging system based on charging energy demand distribution;
and the joint planning model solving module is used for solving the constructed joint planning model to obtain an optimal joint planning scheme.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention relates to a combined planning method of an electric vehicle quick charging station and a dynamic wireless charging system, which comprises the steps of firstly constructing a combined planning model of the electric vehicle quick charging station and the dynamic wireless charging system based on charging energy demand distribution, and then solving the constructed combined planning model to obtain an optimal combined planning scheme, namely, carrying out site selection and volume fixing on an electric vehicle quick charging station, an electric vehicle dynamic wireless charging system, a photovoltaic cell and an energy storage system, and planning a capacity expansion scheme of a responsive power distribution network circuit; on the other hand, the method makes full use of the complementary characteristics of the electric vehicle rapid charging station and the electric vehicle dynamic wireless charging system, has a remarkable effect of improving the efficient operation of the electric power-traffic coupling network, can improve the charging lubrication of a charging service provider, and saves the charging cost of an electric vehicle user group.
2. Aiming at the defect of inaccurate approximation of the original problem of the McCormick relaxation method, the electric vehicle quick charging station and dynamic wireless charging system combined planning method firstly adopts an optimized constraint tightening method to tighten the variable boundary of the McCormick relaxation result, and then further tightens the variable boundary through a sequential constraint tightening method according to the optimized constraint tightening result, thereby ensuring the accuracy of the planning result.
Drawings
FIG. 1 is a flow diagram of an energy demand allocation model.
Fig. 2 is a schematic diagram of the electric power-traffic coupling network system structure adopted in example 1.
Fig. 3 is a topology structure diagram of the electric power-traffic coupling network employed in embodiment 1.
Fig. 4 is a schematic diagram of site selection and power distribution network line expansion scheme in scene 1.
Fig. 5 is a schematic diagram of site selection and capacity expansion of a power distribution network line in scenario 2.
Fig. 6 is a schematic diagram of site selection and capacity expansion of a power distribution network line in scene 3.
Fig. 7 is a block diagram of the system described in example 2.
In the figure, a joint planning model building module 1 and a joint planning model solving module 2 are shown.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings.
The invention provides an electric vehicle rapid charging station and electric vehicle dynamic wireless charging system combined planning method based on energy demand distribution and oriented to a power-traffic coupling network. Because the hardware cost of the electric vehicle dynamic wireless charging system is higher than that of the electric vehicle rapid charging station, the electric vehicle dynamic wireless charging system occupies a smaller installation area than the electric vehicle rapid charging station. So that complementary effects can be achieved by joint planning using their respective characteristics.
Energy demand distribution model:
the flow of the energy demand distribution model provided by the invention is shown in fig. 1, and the energy demand distribution model is represented by the following formula:
Figure SMS_207
(1)
Figure SMS_208
(2)
Figure SMS_209
(3)
in the above formula, the first and second carbon atoms are,
Figure SMS_210
for the total charging cost, equation 1 is an objective function, the objective is that the total charging cost is minimal, and the total charging cost is affected by the fast charging requirement and the dynamic wireless charging requirement; equation 2 constrains the charging energy demand allocated in any time space to not exceed the charging energy demand when the road reaches the upper limit of the traffic capacity; equation 3 constrains the total charging energy requirement of the electric vehicle in the origin-destination pair (O-D pair) od during the t period to not exceed the corresponding total energy deficit. As can be seen from the formulas 1 to 3, the energy demand distribution model is linear, and the problem solving difficulty can be effectively reduced.
Example 1:
the present embodiment is directed to a 21-node distribution network-12-node traffic network system (topology shown in fig. 3) shown in fig. 2. The electric automobile rapid charging station and the dynamic wireless charging system couple the power distribution network with the traffic network, and the distribution of charging loads of the electric automobiles can influence the power flow distribution in the power distribution network while the electric automobiles are used as a part of traffic flow. In the system, the photovoltaic cell and the energy storage system are used as distributed power supplies matched with the electric vehicle rapid charging station and the electric vehicle dynamic wireless charging system, and the function of reducing the electricity purchasing cost of a charging service provider is achieved.
The investment year is 20 years, a year has typical days of 365 days, and the time scale is 1 hour. To convert the total investment cost to an adult investment cost, a discount rate is introduced, and the discount rate is set to 0.05. In the aspect of hardware parameters, the rated power of a single rapid charging pile is 44kW; the rated power of the single electric automobile for carrying out wireless dynamic charging through the electric automobile dynamic wireless charging system is 40kW; the power factor angles of the rapid charging and the dynamic wireless charging are both
Figure SMS_211
(ii) a The energy transfer efficiency of the fast charging and the dynamic wireless charging is 0.92 and 0.9 respectively; in a typical day, the minimum proportion of charging energy deficit met by fast charging and dynamic wireless charging is 0.6; in each electric vehicle rapid charging station, the maximum number of charging piles is 400, and the maximum capacities of the photovoltaic cells and the energy storage system are 4MW and 4MWh; the single installation capacity of the photovoltaic cells and the energy storage system is 100kW and 100kWh. In the electric vehicle sector, the electric vehicle battery capacity is 75kWh, and the average energy demand per electric vehicle is 30% of the battery capacity. In the aspect of the electric power-traffic coupling network, the penetration rate of the electric vehicles is 0.8, the number of O-D pairs is 20 at any time, the number of candidate paths is 3, and the number of the electric vehicles with charging energy requirements in each group of O-D pairs is 5000; the rated voltage of a power distribution network bus is 10kV, and the upper limit and the lower limit of the bus voltage are 9.5kV and 10.5kV respectively. The economic parameters are listed in table 1:
TABLE 1 System economics parameters
Figure SMS_212
A combined planning method for an electric vehicle quick charging station and a dynamic wireless charging system sequentially comprises the following steps:
1. the method comprises the following steps of constructing a combined planning model of the electric automobile quick charging station and the dynamic wireless charging system based on charging energy demand distribution, wherein the combined planning model comprises an outer layer model and an inner layer model, the outer layer model takes the site selection and volume fixing schemes of the electric automobile quick charging station, the dynamic wireless charging system connection, the photovoltaic cell and the energy storage system as decision variables, and the maximum charging profit of a charging service provider is an objective function:
Figure SMS_213
(4)
Figure SMS_214
（5）
Figure SMS_215
（6）
Figure SMS_216
（7）
Figure SMS_217
（8）
Figure SMS_218
（9）
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（10）
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（11）/>
Figure SMS_221
（12）
Figure SMS_222
（13）
Figure SMS_223
（14）
Figure SMS_224
（15）
Figure SMS_225
（16）
Figure SMS_226
（17）
Figure SMS_227
（18）
Figure SMS_228
（19）
Figure SMS_229
（20）
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（21）
Figure SMS_231
（22）
Figure SMS_232
（23）
Figure SMS_233
（24）
Figure SMS_234
（25）
Figure SMS_235
（26）
Figure SMS_236
（27）
Figure SMS_237
（28）
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（29）
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（30）
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（31）/>
Figure SMS_241
（32）
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（33）
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（34）
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（35）
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（36）
Figure SMS_246
（37）
in the above formula, the first and second carbon atoms are,
Figure SMS_249
service a charge based on the charge status>
Figure SMS_272
For the operation cost of the electric vehicle quick charging station and the dynamic wireless charging system, the condition is determined>
Figure SMS_282
Based on the cost of purchasing electricity from the power distribution network for the electric vehicle quick charging station and the dynamic wireless charging system>
Figure SMS_291
For the total investment cost, based on the total investment>
Figure SMS_295
For the mark-off rate, is selected>
Figure SMS_300
For the duration of investment>
Figure SMS_306
、/>
Figure SMS_311
On the way ^ for each electric vehicle which takes the path k in the O-D pair od assigned to the time period t>
Figure SMS_319
Based on the rapid charging energy requirement, the dynamic wireless charging energy requirement>
Figure SMS_326
、/>
Figure SMS_333
The charging prices of the quick charging and the dynamic wireless charging are respectively t time period, and d is typical in a standard yearNumber of days->
Figure SMS_338
、
Figure SMS_343
The operation cost of the quick charging station and the operation cost of the dynamic wireless charging system are respectively->
Figure SMS_348
For the real-time electricity purchase price of the time period t, < >>
Figure SMS_349
Is a t time period road>
Figure SMS_248
Active power output by the distribution network to the quick charging station or the dynamic wireless charging system is judged and judged>
Figure SMS_257
Is a t time period road>
Figure SMS_261
Is subjected to the absorbed photovoltaic output>
Figure SMS_268
Is expressed as a unit time scale>
Figure SMS_271
、/>
Figure SMS_277
、/>
Figure SMS_281
The investment costs of the quick charging station, the dynamic wireless charging system, the photovoltaic cell and the energy storage system are respectively->
Figure SMS_289
、/>
Figure SMS_253
The land cost and the expansion investment cost of the power distribution network line are respectively selected as the basis>
Figure SMS_273
、/>
Figure SMS_278
Respectively, the operating costs of a single typical day quick charging station, a dynamic wireless charging system, based on the charging status of the charging station and the charging status of the charging station, based on the operating costs of the charging station and the charging status of the charging system>
Figure SMS_284
Is a road->
Figure SMS_287
The number of the quick charging piles arranged at the quick charging station is greater than or equal to the number of the quick charging piles arranged at the quick charging station>
Figure SMS_292
、/>
Figure SMS_307
Are respectively road->
Figure SMS_316
When the variable is equal to 1, the variable represents on a road ^ 1>
Figure SMS_294
An electric vehicle rapid charging station or an electric vehicle dynamic wireless charging system is built in the place, and the position is matched with the position of the electric vehicle rapid charging station or the position of the electric vehicle dynamic wireless charging system>
Figure SMS_298
Is a road->
Figure SMS_303
Is greater than or equal to>
Figure SMS_310
、/>
Figure SMS_317
The early investment costs of a single quick charging station and a dynamic wireless charging system are respectively->
Figure SMS_324
、/>
Figure SMS_331
、/>
Figure SMS_336
The installation costs of the single quick charging pile, the photovoltaic cell and the energy storage system are respectively greater or less>
Figure SMS_251
Is the installation cost of the dynamic wireless charging system per unit length, based on the charging status of the charging station>
Figure SMS_259
、/>
Figure SMS_263
Are respectively road->
Figure SMS_269
The number of the photovoltaic cells and the energy storage system arranged at the position is greater than or equal to>
Figure SMS_274
The capacity expansion of the power distribution network line w is a variable 0-1, and when the variable is equal to 1, the line w is subjected to capacity expansion and is subjected to bright and dark>
Figure SMS_279
Investment cost for expanding capacity of single power distribution network line>
Figure SMS_285
、/>
Figure SMS_290
、/>
Figure SMS_252
Are respectively road->
Figure SMS_256
The investment cost of occupying the land for a single quick charging pile, a photovoltaic cell and an energy storage system is saved, and the area is changed according to the investment cost>
Figure SMS_265
、/>
Figure SMS_267
Is respectively the maximum construction number of the quick charging station and the dynamic wireless charging system>
Figure SMS_276
Rated power for dynamic wireless charging>
Figure SMS_280
For the t time period
Figure SMS_288
Based on the average passage time of the vehicle, is greater than or equal to>
Figure SMS_296
、/>
Figure SMS_254
Are respectively road->
Figure SMS_255
On the traffic volume and the number of lanes>
Figure SMS_262
For the penetration rate of the electric vehicle in the traffic network, is->
Figure SMS_314
In order to average the energy deficit per electric vehicle, based on the vehicle speed>
Figure SMS_320
、/>
Figure SMS_327
、/>
Figure SMS_334
The maximum number of the quick charging pile, the photovoltaic cell and the energy storage system which are respectively configured for a single road is greater or less than the maximum number of the quick charging pile, the photovoltaic cell and the energy storage system which are respectively configured for a single road>
Figure SMS_341
Is large M constant, represents a very large positive number, is based on>
Figure SMS_297
Is a period of tRoad->
Figure SMS_302
Charge and discharge power of the energy storage system is judged>
Figure SMS_312
>When 0, the energy storage system works in a charging mode and is/are>
Figure SMS_321
<The energy storage system operates in a discharge mode at 0->
Figure SMS_328
For the installation capacity of a single energy storage system, is>
Figure SMS_337
Is a road->
Figure SMS_345
In conjunction with the initial charge of the energy storage system arranged>
Figure SMS_347
Is the total number of time periods>
Figure SMS_299
、/>
Figure SMS_304
The price upper limit of the quick charging and the dynamic wireless charging are respectively,
Figure SMS_313
is the maximum output power of a single photovoltaic cell in the period t, < >>
Figure SMS_318
、/>
Figure SMS_325
Respectively completing the active and reactive power of the line w at t time after the line expansion is completed for the power distribution network, and then judging whether the line w has active and reactive power>
Figure SMS_332
、/>
Figure SMS_339
Respectively on the road for a period t>
Figure SMS_344
Active and reactive power of base load of corresponding distribution network bus>
Figure SMS_247
Apparent power capacity of line w prior to completion of line expansion for a distribution network>
Figure SMS_258
The apparent power capacity is increased for the expanded distribution network line>
Figure SMS_264
For a voltage drop on line w for a period t, < >>
Figure SMS_308
、/>
Figure SMS_315
Resistance and reactance of the line w after line expansion is carried out on the distribution network respectively>
Figure SMS_322
For a desired value of the busbar voltage of the distribution network>
Figure SMS_330
、/>
Figure SMS_342
Are respectively a power distribution network node in the t time period>
Figure SMS_301
、/>
Figure SMS_305
In the bus voltage of (c), in the on-line voltage of (c)>
Figure SMS_309
、/>
Figure SMS_323
Is respectively the lower limit and the upper limit of the bus voltage of the power distribution network>
Figure SMS_329
、/>
Figure SMS_335
Resistance and reactance of the line w before line expansion are carried out on the distribution network respectively>
Figure SMS_340
、/>
Figure SMS_346
Respectively the combined resistance and reactance of the original cable and the newly-built cable after the line w is expanded, and then the combined resistance and reactance are combined>
Figure SMS_250
、/>
Figure SMS_260
Respectively is the resistance and reactance of the capacity expansion line at the line w, if the capacity expansion is carried out on the line w, then the device is turned on or off>
Figure SMS_266
，/>
Figure SMS_270
，/>
Figure SMS_275
(ii) a If the line w is not expanded, then->
Figure SMS_283
，/>
Figure SMS_286
，
Figure SMS_293
。
Equation 4 represents that the charging profit of the charging service provider is the maximum, which takes into account the charging service profit, the electricity purchase cost, the total investment cost, and the operation cost; equation 16 limits the lower limit of the number of extended distribution network lines; formula 17 limits the upper and lower limits of the number of electric vehicle rapid charging stations and electric vehicle dynamic wireless charging systems; formula 18 limits the installed capacity of the electric vehicle rapid charging station and the electric vehicle dynamic wireless charging system so that they do not exceed the maximum charging energy requirement on each road; formula 19 limits the upper and lower limits of the configuration number of the rapid charging pile, the photovoltaic cell and the energy storage system; formula 20 limits the charging pile to be installed only in the electric vehicle rapid charging station; formula 21 limits that the photovoltaic cell and the energy storage system can only be configured with a charging facility in a matching manner, namely, can only be configured on a road where an electric vehicle rapid charging station and an electric vehicle dynamic wireless charging system are established; equation 22 limits the state of charge of the energy storage system after charging and discharging, so that the energy storage system does not generate overcharge or overdischarge; equation 23 limits the upper limit of the charge and discharge power of the energy storage system; formula 24 limits the charging price of the two charging modes to be higher than the real-time electricity purchasing price and lower than the upper limit; formulas 25-26 limit the active and reactive power output from the distribution grid to the electric vehicle rapid charging station and the electric vehicle dynamic wireless charging system; equation 27 limits the range of the absorbed output power of the photovoltaic cell; equations 28-30 are active, reactive, and apparent power balance constraints; equations 31-33 are voltage constraints for the distribution network bus; equations 34-37 are distribution network line impedance constraints.
The inner layer model takes a space-time distribution mode of energy requirements of rapid charging and dynamic wireless charging of the electric automobile as a decision variable and takes the minimum total charging cost of an electric automobile user as an objective function:
Figure SMS_350
（38）
Figure SMS_351
（39）
Figure SMS_352
（40）
Figure SMS_353
（41）
Figure SMS_354
（42）
Figure SMS_355
（43）
Figure SMS_356
（44）
Figure SMS_357
（45）
Figure SMS_358
（46）
Figure SMS_359
（47）
Figure SMS_360
（48）
in the above formula, the first and second carbon atoms are,
Figure SMS_380
for the total charging time cost of the electric vehicle, < > >>
Figure SMS_364
Penalty costs for an unsatisfied charge energy gap>
Figure SMS_371
Average salary for people>
Figure SMS_377
Rated power for quick charging>
Figure SMS_383
Is a penalty function coefficient>
Figure SMS_378
For a total charge energy deficit of O-D to od for a period t, < >>
Figure SMS_382
Is the minimum percentage of charge energy deficit met by rapid charging and dynamic wireless charging during a typical day, is greater than or equal to->
Figure SMS_365
The path k corresponding to the od pair passes through the road ^ for the t period O-D>
Figure SMS_372
If it passes the road, a 0-1 variable of>
Figure SMS_361
Then->
Figure SMS_379
=1，/>
Figure SMS_362
、/>
Figure SMS_370
The energy transfer efficiency of the quick charging and the dynamic wireless charging are respectively greater or less>
Figure SMS_367
Is a t-interval road>
Figure SMS_373
The reactive power which is output to the quick charging station or the dynamic wireless charging system from the distribution network is judged and judged>
Figure SMS_366
、/>
Figure SMS_374
Power factor angle for quick charging and dynamic wireless charging respectively>
Figure SMS_368
For the apparent power of the line w in the period t after the line expansion of the distribution network is completed, the voltage or the power is greater>
Figure SMS_375
Respectively on the road for a period t>
Figure SMS_363
Apparent power of base load of corresponding distribution network bus is greater or less>
Figure SMS_369
、/>
Figure SMS_376
Respectively on the road for a period t>
Figure SMS_381
And the apparent power output by the photovoltaic cell and the energy storage system is measured.
Equation 38 considers the charge service cost, the charge time cost, and the penalty cost; equation 40 indicates that the penalty cost is an additional cost due to an energy gap that is not met, thereby encouraging more charging demand; equation 41 limits the charging energy demand on od by O-D during t to not exceed the maximum charging energy demand; equations 42-43 limit the charging energy requirements should not exceed the installed capacity limits of the charging facility. In the energy demand distribution model, equations 18, 42, and 43 are more stringent constraints than equation 2. Equations 46-48 are the coupling constraints of active power, reactive power, and apparent power, i.e., the output power of the distribution grid, photovoltaic cells, and energy storage system is balanced with the charging load of the two charging modes.
2. And reconstructing the inner layer model by adopting a KKT condition so as to reconstruct the double-layer planning model into a single-layer model. The original feasible conditions in the reconstructed inner layer model are charging energy requirement constraint formulas 41-45 and coupling constraint formulas 46-47, dual feasible conditions are shown in formulas 49-50, and complementary relaxation conditions are shown in formulas 51-64:
Figure SMS_384
(49)
Figure SMS_385
(50)
Figure SMS_386
(51)
Figure SMS_387
(52)
Figure SMS_388
（53）
Figure SMS_389
（54）
Figure SMS_390
(55)
Figure SMS_391
(56)
Figure SMS_392
(57)/>
Figure SMS_393
(58)
Figure SMS_394
(59)
Figure SMS_395
(60)
Figure SMS_396
(61)
Figure SMS_397
(62)
Figure SMS_398
(63)
Figure SMS_399
(64)
in the above formula, the first and second carbon atoms are,
Figure SMS_401
-/>
Figure SMS_403
、/>
Figure SMS_405
-/>
Figure SMS_402
lagrangian multiplier that assigns a constraint to charge energy, based on the charge energy value in the charge accumulator>
Figure SMS_404
-/>
Figure SMS_406
、/>
Figure SMS_407
-/>
Figure SMS_400
Lagrange multipliers, which are power balance constraints.
3. The complementary relaxation conditional expressions 51-64 were linearized using the large M method. Taking formula 51 as an example, the linearized form is shown in formulas 65-66:
Figure SMS_408
(65)
Figure SMS_409
(66)
in the above formula, the first and second carbon atoms are,
Figure SMS_410
is an auxiliary binary variable, if>
Figure SMS_411
=1, then have->
Figure SMS_412
And->
Figure SMS_413
(ii) a If/or>
Figure SMS_414
=0, then>
Figure SMS_415
And->
Figure SMS_416
。
4. For bilinear terms present in the outer model
Figure SMS_417
And &>
Figure SMS_418
And linearizing the bilinear terms in the outer layer model by adopting an McCormick relaxation method.
Introducing auxiliary variables
Figure SMS_419
、/>
Figure SMS_420
Respectively replace>
Figure SMS_421
And &>
Figure SMS_422
As shown in formulas 67 and 68, while formula 5 is restated as formula 69Introducing the additional constraint of formulas 70-77:
Figure SMS_423
(67)
Figure SMS_424
(68)/>
Figure SMS_425
(69)
Figure SMS_426
（70）
Figure SMS_427
(71)
Figure SMS_428
(72)
Figure SMS_429
(73)
Figure SMS_430
(74)
Figure SMS_431
(75)
Figure SMS_432
(76)
Figure SMS_433
(77)。
5. and (3) linearizing the nonlinear terms in the bus voltage constraint by adopting a large M method.
When the bus voltage constraint 31 is combined with the line impedance constraints 34, 35, the equation 31 can be expressed as equation 78, obviously including the non-linear term
Figure SMS_434
And &>
Figure SMS_435
Therefore, equation 78 is linearized using the large M method and the new constraints are expressed as equations 79-85:
Figure SMS_436
(78)
Figure SMS_437
(79)
Figure SMS_438
(80)
Figure SMS_439
(81)
Figure SMS_440
(82)
Figure SMS_441
(83)
Figure SMS_442
(84)/>
Figure SMS_443
(85)
in the above formula, the first and second carbon atoms are,
Figure SMS_444
、/>
Figure SMS_445
are auxiliary variables.
6. Tightening a variable boundary of the McCormick relaxation result by adopting an optimization-based constraint tightening method, which specifically comprises the following steps:
s11, constructing a variable upper and lower bound optimization model, wherein an objective function of the optimization model is as follows:
Figure SMS_446
Figure SMS_447
in the above formula, the first and second carbon atoms are,
Figure SMS_448
、/>
Figure SMS_449
is an upper and a lower boundary optimization function, respectively>
Figure SMS_450
、/>
Figure SMS_451
Respectively an upper bound set and a lower bound set of the variable;
the constraint conditions of the optimization model comprise formulas 5-85, and the following constraint formulas are added:
Figure SMS_452
in the above formula, the first and second carbon atoms are,
Figure SMS_453
is a locally feasible solution of the joint planning model.
S12, inputting the tolerance value of the variable upper bound
Figure SMS_454
And a lower tolerance value>
Figure SMS_455
The initial upper bound set->
Figure SMS_456
And a lower bound set>
Figure SMS_457
The number of initialization iterations i =1.
S13, iteration is carried out according to the following formula:
Figure SMS_458
Figure SMS_459
in the above formula, the first and second carbon atoms are,
Figure SMS_460
、/>
Figure SMS_461
respectively an upper bound set and a lower bound set of variables during the ith iteration.
S14, comparing the upper and lower bound sets of the ith iteration and the (i-1) th iteration, and determining the final upper and lower bound sets of the iteration according to the following formula:
Figure SMS_462
Figure SMS_463
。
s15, judging whether infinite norms of an upper bound difference and a lower bound difference of two adjacent iterations are both smaller than a tolerance value, if so, ending the iteration, otherwise, returning to the step S13 to carry out the next iteration:
Figure SMS_464
。
7. according to the optimized constraint tightening result, further tightening the variable boundary by a sequential constraint tightening method, which specifically comprises the following steps:
s21, taking the upper and lower bound sets tightened by the constraint tightening method based on optimization as initial upper bound sets
Figure SMS_465
Initial lower bound set +>
Figure SMS_466
Is inputted>
Figure SMS_467
、/>
Figure SMS_468
Converging tolerance->
Figure SMS_469
And a decrementing sequence>
Figure SMS_470
。/>
S22, iteration is carried out according to the following formula:
Figure SMS_471
Figure SMS_472
Figure SMS_473
in the above formula, the first and second carbon atoms are,
Figure SMS_474
for the real-time boundary of the jth iteration, <' >>
Figure SMS_475
For the linearized joint programming model, a decision is made as to whether the cell is in the desired cell state>
Figure SMS_476
、
Figure SMS_477
The upper and lower bounds of the jth iteration, respectively.
S23, judging the relaxation tightness value
Figure SMS_478
Whether or not it is greater than a convergence tolerance>
Figure SMS_479
If yes, the method returns to the step S22 for the next iteration, and if not, the iteration is ended, wherein the judgment is performed on whether the value is greater than or equal to the preset value>
Figure SMS_480
Calculated according to the following formula:
Figure SMS_481
。
8. and solving the joint planning model after the linearization treatment to obtain an optimal joint planning scheme.
This example gives the following 3 scenarios:
scene 1, an electric vehicle rapid charging station and an electric vehicle dynamic wireless charging system are built in a network simultaneously. The maximum number of the two is 5;
and 2, establishing the electric vehicle rapid charging station only in the network. The maximum number of the electric vehicle quick charging stations is 5;
and 3, only constructing the dynamic wireless charging system of the electric automobile in the network. The maximum number of the dynamic wireless charging systems of the electric automobile is 5.
Planning is performed respectively for the 3 scenes to obtain the address selection and capacity expansion schemes shown in fig. 4-6. The detailed planning results of the above 3 scenarios are shown in table 2:
table 2 detailed planning results for the scenarios
Figure SMS_482
。
In fig. 4, an electric vehicle fast charging station and an electric vehicle dynamic wireless charging system are simultaneously built in the network. In the planning scheme, two dynamic wireless charging systems of the electric automobile are built and are respectively positioned on roads T4-T8 and roads T9-T12. As can be seen from fig. 6, in scenario 3, the electric vehicle dynamic wireless charging system is also built at the roads T4 to T8, which means that the urban road has a higher degree of engagement with the electric vehicle dynamic wireless charging system: the urban area roads have large traffic flow, can provide a large amount of dynamic wireless charging demands for the dynamic wireless charging system of the electric automobile, and simultaneously, because the dynamic wireless charging system of the electric automobile has the characteristic of saving the land investment cost, the influence of the high land price of the urban area on the investment cost is small. As can be seen from fig. 4 and 5, the construction sites of the electric vehicle quick charging stations are far away from urban areas, because the land investment cost of the electric vehicle quick charging stations constructed in urban areas is high due to the large occupied area, and the economical efficiency of the electric vehicle quick charging stations is inferior to that of the electric vehicle dynamic wireless charging system.
The economic results for the charging service provider and electric vehicle users of scenarios 1-3 are shown in table 3:
TABLE 3 economic results for each scenario
Figure SMS_483
。
As can be seen from table 3, the planning scheme of scenario 1 is most economical for the charging service provider, and the planning scheme of scenario 3 is most user-friendly for the electric vehicle. In scenario 2, the charging service provider has the lowest charging revenue and the electric vehicle user has the highest charging cost. Compared with scenario 3, scenario 1 has much higher charging service profit and total charging cost for electric vehicle users than the latter. However, the penalty cost for scenario 3 is much higher than scenario 1 in terms of penalty cost, which means that the advantage of scenario 3 in terms of charging cost for the electric vehicle user is based on meeting a lower proportion of the charging energy requirement.
In charging the service provider, either scenario 1 or scenario 2, the land investment cost is a significant portion of the total investment cost. Comparing scenario 1 and scenario 2, it can be seen that the penalty cost for scenario 2 is much higher than scenario 1, while the charging service profit for the charging service provider for scenario 2 is significantly lower than scenario 1. This means that scenario 1 can meet more charging energy requirements, with better economy. The rapid charging station for the electric vehicles is constructed in the suburbs, so that the large amount of land investment cost is avoided, and meanwhile, the large amount of charging energy requirements of urban roads are not covered by the rapid charging station for the electric vehicles, so that the income of rapid charging service is remarkably reduced. A similar conclusion can be drawn comparing scene 1 with scene 3: scenario 1 can still meet more charging energy requirements and also have better economic benefits. This is mainly because the investment cost of the dynamic wireless charging system for electric vehicles partially constructed in suburbs is higher than that of the rapid charging station for electric vehicles when the same amount of charging energy needs are satisfied, so that the higher investment cost results in a reduction in profit. In the aspect of electric vehicle users, the charging cost of the electric vehicle users is the lowest in the scene 3, and the charging cost of the electric vehicle users is the highest in the scene 2. The reason is that the time is consumed when the electric vehicle is used for quick charging, and the dynamic wireless charging system for the electric vehicle is adopted for dynamic wireless charging, so that the extra time of an electric vehicle user is not occupied.
The above results show that the electric vehicle rapid charging station and the electric vehicle dynamic wireless charging system have significant complementary characteristics in terms of economy. The multi-charging-mode combined planning method based on charging energy demand distribution can make full use of the characteristic, improve the charging service profit of a charging service provider, and keep the total charging cost of an electric vehicle user group at a lower level.
Example 2:
referring to fig. 7, the electric vehicle rapid charging station and dynamic wireless charging system joint planning system includes a joint planning model building module 1 and a joint planning model solving module 2;
the combined planning model building module 1 is used for building a combined planning model of an electric vehicle quick charging station and a dynamic wireless charging system based on charging energy demand distribution;
and the joint planning model solving module 2 is used for solving the constructed joint planning model to obtain an optimal joint planning scheme.

Claims (9)

1. The joint planning method for the electric vehicle quick charging station and the dynamic wireless charging system is characterized by comprising the following steps:
the planning method sequentially comprises the following steps:
step A, constructing a combined planning model of an electric vehicle quick charging station and a dynamic wireless charging system based on charging energy demand distribution, wherein the combined planning model comprises an outer layer model and an inner layer model, and an objective function of the outer layer model is as follows:
Figure QLYQS_1
；
in the above formula, the first and second carbon atoms are,
Figure QLYQS_2
service a charge based on the charge status>
Figure QLYQS_3
For the operation cost of the electric vehicle quick charging station and the dynamic wireless charging system, the charging station is based on the charge condition>
Figure QLYQS_4
Based on the cost of purchasing electricity from the power distribution network for the electric vehicle quick charging station and the dynamic wireless charging system>
Figure QLYQS_5
For the total investment cost, based on the total investment>
Figure QLYQS_6
For the mark-off rate, is selected>
Figure QLYQS_7
The investment age is the same;
the decision variables of the outer layer model are the electric automobile quick charging station, the dynamic wireless charging system connection, the photovoltaic cell and the locating and constant volume scheme of the energy storage system;
the objective function of the inner layer model is as follows:
Figure QLYQS_8
；
in the above formula, the first and second carbon atoms are,
Figure QLYQS_9
for the total charging time cost of an electric vehicle>
Figure QLYQS_10
Penalty costs incurred for an unsatisfied charge energy gap;
the decision variables of the inner layer model are space-time distribution modes of energy requirements of quick charging and dynamic wireless charging of the electric automobile;
and B, solving the constructed joint planning model to obtain an optimal joint planning scheme.
2. The electric vehicle rapid charging station and dynamic wireless charging system joint planning method according to claim 1, characterized in that:
in the outer layer model, the
Figure QLYQS_11
、/>
Figure QLYQS_12
、/>
Figure QLYQS_13
、/>
Figure QLYQS_14
Calculated according to the following formula:
Figure QLYQS_15
；
Figure QLYQS_16
；
Figure QLYQS_17
；
Figure QLYQS_18
；
Figure QLYQS_19
；
Figure QLYQS_20
；
Figure QLYQS_21
；/>
Figure QLYQS_22
；
Figure QLYQS_23
；
Figure QLYQS_24
；
Figure QLYQS_25
；
in the above-mentioned formula, the compound has the following structure,
Figure QLYQS_41
、/>
Figure QLYQS_43
on the way ^ for each electric vehicle which takes the path k in the O-D pair od assigned to the time period t>
Figure QLYQS_48
Based on the quick charging energy requirement, the dynamic wireless charging energy requirement>
Figure QLYQS_29
、/>
Figure QLYQS_30
Charging prices of quick charging and dynamic wireless charging are respectively set in a time period t, d is the number of typical days in a standard year, and the number of the typical days is greater than or equal to>
Figure QLYQS_34
、/>
Figure QLYQS_37
The operation cost of the quick charging station and the operation cost of the dynamic wireless charging system are respectively->
Figure QLYQS_51
For the real-time electricity purchase price of the time period t, < >>
Figure QLYQS_56
Is a t time period road>
Figure QLYQS_58
Active power output by the distribution network to the quick charging station or the dynamic wireless charging system is judged and judged>
Figure QLYQS_64
Is a t time period road>
Figure QLYQS_54
Is subjected to the absorbed photovoltaic output>
Figure QLYQS_60
Is expressed as a unit time scale>
Figure QLYQS_66
、/>
Figure QLYQS_67
、/>
Figure QLYQS_53
The investment costs of the quick charging station, the dynamic wireless charging system, the photovoltaic cell and the energy storage system are respectively combined>
Figure QLYQS_55
、/>
Figure QLYQS_59
The land cost and the expansion investment cost of the power distribution network line are respectively selected as the basis>
Figure QLYQS_62
、
Figure QLYQS_26
Respectively, the operating costs of a single typical day quick charging station, a dynamic wireless charging system, based on the charging status of the charging station and the charging status of the charging station, based on the operating costs of the charging station and the charging status of the charging system>
Figure QLYQS_31
Is a road->
Figure QLYQS_39
The number of the quick charging piles arranged at the quick charging station is greater than or equal to the number of the quick charging piles arranged at the quick charging station>
Figure QLYQS_45
、/>
Figure QLYQS_28
Are respectively road->
Figure QLYQS_32
A 0-1 variable of the construction position of the quick charging station and the dynamic wireless charging system is selected, and the parameters are changed according to the requirements>
Figure QLYQS_35
Is a road->
Figure QLYQS_38
Is greater than or equal to>
Figure QLYQS_40
、/>
Figure QLYQS_44
The early investment costs of a single quick charging station and a dynamic wireless charging system are respectively->
Figure QLYQS_47
、/>
Figure QLYQS_50
、/>
Figure QLYQS_42
The installation costs of the single quick charging pile, the photovoltaic cell and the energy storage system are respectively greater or less>
Figure QLYQS_46
Is the installation cost of the dynamic wireless charging system per unit length, based on the charging status of the charging station>
Figure QLYQS_49
、/>
Figure QLYQS_52
Are respectively road->
Figure QLYQS_57
The number of the photovoltaic cells and the energy storage system arranged at the position is greater than or equal to>
Figure QLYQS_61
For expanding 0-1 variable of the power distribution network line w>
Figure QLYQS_63
Investment cost for expanding capacity of single power distribution network line>
Figure QLYQS_65
、/>
Figure QLYQS_27
、/>
Figure QLYQS_33
Are respectively road->
Figure QLYQS_36
The investment cost of occupying the land by a single quick charging pile, a photovoltaic cell and an energy storage system is saved;
in the inner layer model, the
Figure QLYQS_68
、/>
Figure QLYQS_69
Calculated according to the following formula:
Figure QLYQS_70
；
Figure QLYQS_71
；
in the above formula, the first and second carbon atoms are,
Figure QLYQS_72
is always well-paid for people>
Figure QLYQS_73
Rated power for quick charging>
Figure QLYQS_74
For penalty function coefficients>
Figure QLYQS_75
The total charge energy deficit to od for time t O-D.
3. The electric vehicle rapid charging station and dynamic wireless charging system joint planning method according to claim 2, characterized in that:
the constraints of the outer layer model comprise:
Figure QLYQS_76
；
Figure QLYQS_77
；
Figure QLYQS_78
；
Figure QLYQS_79
；
Figure QLYQS_80
；
Figure QLYQS_81
；
in the above formula, the first and second carbon atoms are,
Figure QLYQS_85
、/>
Figure QLYQS_86
is respectively the maximum construction number of the quick charging station and the dynamic wireless charging system>
Figure QLYQS_89
Rated power for dynamic wireless charging>
Figure QLYQS_82
Is a t time period road>
Figure QLYQS_87
Based on the average passage time of the vehicle, is greater than or equal to>
Figure QLYQS_92
、/>
Figure QLYQS_94
Are respectively road->
Figure QLYQS_84
On the traffic volume and the number of lanes>
Figure QLYQS_91
For the penetration rate of the electric vehicle in the traffic network, is->
Figure QLYQS_93
To average the energy deficit per electric vehicle,
Figure QLYQS_95
、/>
Figure QLYQS_83
、/>
Figure QLYQS_88
the maximum quantity of the rapid charging piles, the photovoltaic cells and the energy storage systems which are respectively configured for a single road,
Figure QLYQS_90
is a large M constant;
the constraints of the inner layer model comprise:
Figure QLYQS_96
；
Figure QLYQS_97
；
Figure QLYQS_98
；
Figure QLYQS_99
；
Figure QLYQS_100
；
Figure QLYQS_101
；
Figure QLYQS_102
；/>
Figure QLYQS_103
；
in the above formula, the first and second carbon atoms are,
Figure QLYQS_111
is the minimum percentage of charge energy deficit met by rapid charging and dynamic wireless charging during a typical day, is greater than or equal to->
Figure QLYQS_105
The path k corresponding to the od pair passes through the road ^ for the t period O-D>
Figure QLYQS_108
Is 0-1 variable of (4), based on the status of the signal being asserted, and/or based on the status of the signal being asserted>
Figure QLYQS_107
Is a t time period road>
Figure QLYQS_109
Charge and discharge power of the energy storage system is judged>
Figure QLYQS_112
、/>
Figure QLYQS_116
The energy transfer efficiency of the quick charging and the dynamic wireless charging are respectively greater or less>
Figure QLYQS_114
Is a t time period road>
Figure QLYQS_117
The reactive power which is output to the quick charging station or the dynamic wireless charging system from the distribution network is judged and judged>
Figure QLYQS_104
、/>
Figure QLYQS_118
A power factor angle for quick charging and dynamic wireless charging respectively>
Figure QLYQS_113
For the apparent power of the line w in the period t after the line expansion of the distribution network is completed, the voltage or the power is greater>
Figure QLYQS_115
Respectively on the road for a period t>
Figure QLYQS_119
Apparent power of base load of corresponding distribution network bus is greater or less>
Figure QLYQS_120
、/>
Figure QLYQS_106
Respectively on the road for a period t>
Figure QLYQS_110
And the apparent power output by the photovoltaic cell and the energy storage system.
4. The electric vehicle rapid charging station and dynamic wireless charging system joint planning method according to claim 3, characterized in that:
the constraints of the outer layer model further comprise:
and (3) state of charge constraint of the energy storage system:
Figure QLYQS_121
；
Figure QLYQS_122
；
in the above formula, the first and second carbon atoms are,
Figure QLYQS_123
for the installation capacity of a single energy storage system, is>
Figure QLYQS_124
Is a road->
Figure QLYQS_125
Based on the initial charge of the energy storage system arranged in>
Figure QLYQS_126
Is the total number of time periods;
charging price constraint:
Figure QLYQS_127
；
in the above formula, the first and second carbon atoms are,
Figure QLYQS_128
、/>
Figure QLYQS_129
the price upper limits of the quick charging and the dynamic wireless charging are respectively;
and (3) power constraint:
Figure QLYQS_130
；
Figure QLYQS_131
；
Figure QLYQS_132
；
Figure QLYQS_133
；/>
Figure QLYQS_134
；
Figure QLYQS_135
；
in the above formula, the first and second carbon atoms are,
Figure QLYQS_136
for the maximum output power of a single photovoltaic cell in the t period>
Figure QLYQS_140
、/>
Figure QLYQS_142
Respectively completing the active and reactive power of the line w at t time after the line expansion is completed for the power distribution network, and then judging whether the line w has active and reactive power>
Figure QLYQS_138
、/>
Figure QLYQS_139
Respectively on the road for a period t>
Figure QLYQS_141
Active and reactive power of base load of corresponding distribution network bus>
Figure QLYQS_143
The apparent power capacity of the line w before the line capacity expansion is completed for the distribution network,
Figure QLYQS_137
apparent power capacity increased for expanded distribution network lines;
and (3) power distribution network bus voltage constraint:
Figure QLYQS_144
；
Figure QLYQS_145
；
Figure QLYQS_146
；
in the above formula, the first and second carbon atoms are,
Figure QLYQS_148
for a voltage drop on line w for a period t, < >>
Figure QLYQS_152
、/>
Figure QLYQS_155
Respectively carrying out resistance and reactance of the line w after line expansion for the power distribution network,/>
Figure QLYQS_149
for a desired value of the busbar voltage of the distribution network>
Figure QLYQS_151
、/>
Figure QLYQS_154
Are respectively a power distribution network node in the t time period>
Figure QLYQS_156
、/>
Figure QLYQS_147
In the bus voltage of (c), in the on-line voltage of (c)>
Figure QLYQS_150
、/>
Figure QLYQS_153
The lower limit and the upper limit of the bus voltage of the power distribution network are respectively set;
and (3) power distribution network line impedance constraint:
Figure QLYQS_157
；
Figure QLYQS_158
；
Figure QLYQS_159
；
Figure QLYQS_160
；
in the above formula, the first and second carbon atoms are,
Figure QLYQS_161
、/>
Figure QLYQS_162
resistance and reactance of the line w before line expansion are carried out on the distribution network respectively>
Figure QLYQS_163
、/>
Figure QLYQS_164
Respectively the combined resistance and reactance of the original cable and the newly-built cable after the capacity expansion of the line w is carried out>
Figure QLYQS_165
、/>
Figure QLYQS_166
Respectively, the resistance and reactance of the expansion line at the line w.
5. The electric vehicle rapid charging station and dynamic wireless charging system joint planning method according to claim 2, characterized in that:
the step B comprises the following steps: firstly, carrying out linearization processing on the joint planning model, and then solving the joint planning model, wherein the linearization processing of the joint planning model comprises the following steps:
linearization treatment of the outer layer model: linearizing a bilinear term in the outer layer model by adopting an McCormick relaxation method, and linearizing a nonlinear term in the outer layer model by adopting a large M method;
linearization treatment of the inner layer model: and reconstructing the inner layer model by adopting a KKT condition, and performing linearization treatment on the complementary relaxation condition in the reconstructed inner layer model by a large M method.
6. The electric vehicle rapid charging station and dynamic wireless charging system joint planning method according to claim 5, characterized in that:
the linearization processing of the outer layer model further comprises:
the method comprises the steps of firstly adopting an optimization-based constraint tightening method to tighten the variable boundary of the McCormick relaxation result, and then further tightening the variable boundary through a sequential constraint tightening method according to the optimization-based constraint tightening result.
7. The electric vehicle rapid charging station and dynamic wireless charging system joint planning method according to claim 6, characterized in that:
the method for tightening the variable boundary of the McCormick relaxation result by adopting the constraint tightening method based on optimization sequentially comprises the following steps of:
s11, constructing a variable upper and lower boundary optimization model, wherein an objective function of the optimization model is as follows:
Figure QLYQS_167
；
Figure QLYQS_168
；
in the above formula, the first and second carbon atoms are,
Figure QLYQS_169
、/>
Figure QLYQS_170
is an upper and a lower boundary optimization function, respectively>
Figure QLYQS_171
、/>
Figure QLYQS_172
Respectively an upper bound set and a lower bound set of the variable;
the constraint conditions of the optimization model comprise an outer layer model and an inner layer model after linearization processing and newly added constraints, wherein the newly added constraints are as follows:
Figure QLYQS_173
；
in the above formula, the first and second carbon atoms are,
Figure QLYQS_174
a local feasible solution of the joint planning model;
s12, inputting the tolerance value of the variable upper bound
Figure QLYQS_175
And a lower tolerance value>
Figure QLYQS_176
The initial upper bound set->
Figure QLYQS_177
And a lower bound set +>
Figure QLYQS_178
；
S13, iteration is carried out according to the following formula:
Figure QLYQS_179
；
Figure QLYQS_180
；
in the above-mentioned formula, the compound has the following structure,
Figure QLYQS_181
、/>
Figure QLYQS_182
respectively an upper bound set and a lower bound set of the variable at the ith iteration time;
s14, comparing the upper and lower bound sets of the ith iteration and the (i-1) th iteration, and determining the final upper and lower bound sets of the iteration according to the following formula:
Figure QLYQS_183
；
Figure QLYQS_184
；
s15, judging whether the following conditions are met simultaneously, if so, finishing the iteration, and if not, returning to the step S13 to carry out the next iteration:
Figure QLYQS_185
。/>
8. the electric vehicle rapid charging station and dynamic wireless charging system joint planning method according to claim 6, characterized in that:
the method for tightening the variable boundary further comprises the following steps:
s21, taking the upper and lower bound sets tightened by the constraint tightening method based on optimization as initial upper bound sets
Figure QLYQS_186
Initial lower bound set
Figure QLYQS_187
In or on>
Figure QLYQS_188
、/>
Figure QLYQS_189
Converging tolerance->
Figure QLYQS_190
And a decrementing sequence>
Figure QLYQS_191
；
S22, iteration is carried out according to the following formula:
Figure QLYQS_192
；
Figure QLYQS_193
；
Figure QLYQS_194
；
in the above formula, the first and second carbon atoms are,
Figure QLYQS_195
for the real-time boundary of the jth iteration, <' >>
Figure QLYQS_196
For the linearized joint programming model, a decision is made as to whether the cell is in the desired cell state>
Figure QLYQS_197
、/>
Figure QLYQS_198
The upper and lower boundaries of the jth iteration are respectively;
s23, judging the relaxation tightness value
Figure QLYQS_199
Whether or not it is greater than a convergence tolerance>
Figure QLYQS_200
If yes, the method returns to the step S22 for the next iteration, and if not, the iteration is ended, wherein the judgment is performed on whether the value is greater than or equal to the preset value>
Figure QLYQS_201
Calculated according to the following formula:
Figure QLYQS_202
；
in the above formula, the first and second carbon atoms are,
Figure QLYQS_203
、/>
Figure QLYQS_204
respectively is replaced>
Figure QLYQS_205
、/>
Figure QLYQS_206
Of the auxiliary variable(s).
9. Electric automobile fills station and wireless charging system of developments jointly planning system, its characterized in that:
the system comprises a joint planning model construction module (1) and a joint planning model solving module (2);
the combined planning model building module (1) is used for building a combined planning model of the electric vehicle quick charging station and the dynamic wireless charging system based on charging energy demand distribution;
and the joint planning model solving module (2) is used for solving the constructed joint planning model to obtain an optimal joint planning scheme.
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