CN113364016A - Electric vehicle charging optimization scheduling method considering transformer capacity elasticity - Google Patents

Electric vehicle charging optimization scheduling method considering transformer capacity elasticity Download PDF

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CN113364016A
CN113364016A CN202110557327.9A CN202110557327A CN113364016A CN 113364016 A CN113364016 A CN 113364016A CN 202110557327 A CN202110557327 A CN 202110557327A CN 113364016 A CN113364016 A CN 113364016A
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张金江
蔡天宇
周志强
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Zhejiang Lover Health Science and Technology Development Co Ltd
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Abstract

The invention belongs to the technical field of electric vehicle dispatching optimization, and particularly relates to an electric vehicle charging optimization dispatching method considering transformer capacity elasticity, which comprises the following steps of: (1) establishing an electric vehicle charging scheduling model of energy peak clipping and valley filling income and power distribution network cost, and determining a corresponding objective function and transformer capacity elastic constraint conditions to form an original optimization problem; (2) optimizing through a genetic algorithm to solve the electric vehicle charging scheduling model, so as to realize the electric vehicle charging scheduling optimization; the invention can realize the minimized peak value demand cost, power loss cost and transformer aging cost, and can realize the stabilization of the peak-valley load of the power grid and effectively slow down the aging of the transformer while reducing the charging cost of the electric automobile.

Description

Electric vehicle charging optimization scheduling method considering transformer capacity elasticity
Technical Field
The invention belongs to the technical field of electric vehicle dispatching optimization, and particularly relates to an electric vehicle charging optimization dispatching method considering transformer capacity elasticity.
Background
More and more electric vehicles are connected to a power grid charging and power distribution system, so that a lot of adverse effects are brought, especially, the rapid aging of a transformer is caused, and the safe and stable operation of power equipment is not facilitated. Therefore, it is necessary to improve it to overcome the disadvantages in practical applications.
Disclosure of Invention
Based on the above-mentioned shortcomings and drawbacks of the prior art, an object of the present invention is to solve at least one or more of the above-mentioned problems of the prior art, in other words, to provide an optimal scheduling method for charging of an electric vehicle, which takes into account the transformer capacity flexibility, and meets one or more of the above-mentioned requirements.
In order to achieve the purpose, the invention adopts the following technical scheme:
an electric vehicle charging optimization scheduling method considering transformer capacity elasticity comprises the following steps:
(1) establishing an electric vehicle charging scheduling model of energy peak clipping and valley filling income and power distribution network cost, and determining a corresponding objective function and related constraint conditions to form an original optimization problem;
(2) and (4) considering the elastic capacity constraint condition of the transformer, and optimizing through a genetic algorithm to solve the electric vehicle charging scheduling model so as to realize the electric vehicle charging scheduling optimization.
Preferably, the energy peak clipping and valley filling gains are as follows:
Figure BDA0003077658120000011
wherein, CAB,iEarnings for the daily energy arbitrage of the ith electric vehicle,
Figure BDA0003077658120000012
and
Figure BDA0003077658120000013
represents the discharge and charge power of the ith electric automobile at time t, and the unit is kW; Δ t is the time interval at time t, ctIs the electricity rate for time T, which is the total number of times of day.
Preferably, the cost of the power distribution network includes a peak demand cost, a power loss cost, and a transformer aging cost.
Preferably, the peak demand cost is:
Cpeak＝Ppeakct
Figure BDA0003077658120000021
wherein, CpeakFor peak load costs, PpeakFor daily peak load, ctFor electricity prices at time t, PtIs the load at time t.
Preferably, the power loss cost is:
Figure BDA0003077658120000022
wherein, ClossIs the daily loss cost of the network,
Figure BDA0003077658120000023
is the active power loss of branch k at time t.
As a preferred scheme, the aging cost of the transformer is as follows:
Figure BDA0003077658120000024
wherein, CTxlossFor the daily aging cost of the transformer, STxAnd CvalueRated power of the transformer and unit transformer installation cost, L0Is the hot spot temperature.
As a preferred scheme, the objective function is:
f＝CABpen+Cpeak+Closs+CTxloss
wherein the content of the first and second substances,
Figure BDA0003077658120000025
CABpenpenalty cost for arbitrage profit loss of electric vehicle owners; cAb,iThe peak clipping and valley filling income for the energy of the ith electric vehicle;
Figure BDA0003077658120000026
and (5) optimizing the charging power for the ith electric automobile.
As a preferred scheme, the constraint conditions comprise electric vehicle charging constraint, transformer capacity elastic constraint and power grid flow constraint;
wherein the electric vehicle constraints are:
SOCi,int＝SOCi,depart-(100*εidi)/ci,bat
Figure BDA0003077658120000031
therein, SOCi,departIs the battery charge, ε, of the ith vehicle EV when it leaves homeiIs the power consumption of the ith electric vehicle; diFor daily driving distance, ci,batThe battery capacity of the electric automobile; SOCi,tAnd SOCi,t-1Battery charge of the ith EV at time t and a previous time (t-1), respectively;
when the EV battery is in the charging modeIn operation, ηEVIs equal to etacIf operating in discharge mode, ηEVIs equal to-1/etad，ηcAnd ηdRespectively representing charge and discharge efficiency;
the elastic constraint conditions of the transformer capacity are as follows:
Figure BDA0003077658120000032
St≤Sflex≤Smax
wherein, thetah,maxThe maximum limit of the hottest point temperature of the winding; sflexSelecting different capacity constraints for the elastic constraint capacity of the transformer, namely according to the current working conditions such as the environmental temperature of the transformermaxFor maximum transformer capacity constraints, e.g. SmaxThe maximum rating of the transformer nameplate can be 1.4 times;
the power grid constraint is as follows:
Figure BDA0003077658120000033
Figure BDA0003077658120000034
wherein, Vj,tIs the voltage at node j at time t; vminAnd VmaxLower and upper limits of node voltage offset, respectively; i isk,tThe current for branch k at time t; i isk,maxThe maximum current limit at branch k.
Preferably, the genetic algorithm specifically comprises:
and inputting the journey data and the regional information data of the electric automobiles into an electric automobile aggregation system, and solving through an electric automobile charging scheduling model of energy peak clipping and valley filling income and power distribution network cost to obtain the optimal charging power of each electric automobile during charging at any time interval.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides an electric vehicle charging optimization scheduling method considering transformer capacity elasticity, which can realize minimized peak value demand cost, power loss cost and transformer aging cost through a genetic algorithm by establishing an electric vehicle charging scheduling model of energy peak clipping and valley filling income and power distribution network cost, and is beneficial to effectively reducing the aging of a transformer.
Drawings
FIG. 1 is a flow chart of a first embodiment of the present invention;
FIG. 2 is a graph of the load of the summer transformer according to the first embodiment of the present invention;
FIG. 3 is a graph of a winter transformer load curve according to a first embodiment of the present invention;
FIG. 4 is a diagram of the relationship between the accelerated aging factor of the transformer in summer and the charging load according to the first embodiment of the present invention;
FIG. 5 is a graph of the accelerated aging factor of the winter transformer with respect to the charging load according to the first embodiment of the present invention;
FIG. 6 is a diagram of the relationship between the transformer loss in summer and the hot spot temperature in accordance with the first embodiment of the present invention;
fig. 7 is a diagram of the relationship between the transformer loss in winter and the temperature of the hot spot according to the first embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention, the following description will explain the embodiments of the present invention with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
The first embodiment is as follows:
as shown in fig. 1, the present embodiment provides an electric vehicle charging optimization scheduling method considering transformer capacity elasticity, including the following steps:
(1) establishing an electric vehicle charging scheduling model of energy peak clipping and valley filling income and power distribution network cost, and determining a corresponding objective function and related constraint conditions to form an original optimization problem;
the peak clipping and valley filling gains of the energy are as follows:
Figure BDA0003077658120000041
wherein, CAB,iEarnings for the daily energy arbitrage of the ith electric vehicle,
Figure BDA0003077658120000042
and
Figure BDA0003077658120000043
represents the discharge and charge power of the ith electric automobile at time t, and the unit is kW; deltatFor a time interval at time t, ctIs the electricity rate for time T, which is the total number of times of day.
Power distribution network costs include peak demand costs, power loss costs, and transformer aging costs; the implementation steps of the cost of the power distribution network are as follows:
setting a power flow balance equation of the power grid:
Figure BDA0003077658120000051
Figure BDA0003077658120000052
Figure BDA0003077658120000053
Figure BDA0003077658120000054
Figure BDA0003077658120000055
wherein, PtAnd QtRespectively the total active power and the total reactive power at time t;
Figure BDA0003077658120000056
charging power for the ith electric vehicle at time t;
Figure BDA0003077658120000057
and
Figure BDA0003077658120000058
respectively, the basic load of the j node at time t;
Figure BDA0003077658120000059
and
Figure BDA00030776581200000510
respectively the active power loss and the reactive power of the branch k at time t;
Stis the total apparent power at time t;
Figure BDA00030776581200000511
and
Figure BDA00030776581200000512
respectively, the power at the time t to the receiving end of branch k;
Figure BDA00030776581200000513
and
Figure BDA00030776581200000514
respectively the power transmitted from the transmitting end of branch k at time t; n is a radical ofevIs the total number of electric vehicles, NbusAnd NbraThe total number of nodes and branches, respectively.
The cost is required by the peak value so as to reduce the peak load when optimizing; the peak demand cost is calculated by multiplying the daily peak load by the electricity price at that time, and is:
Cpeak＝Ppeakct (7)
Figure BDA00030776581200000515
wherein, CpeakFor peak load costs, PpeakFor daily peak load, ctIs the electricity price at time t;
the formula for the power loss cost is expressed as:
Figure BDA00030776581200000516
wherein, ClossIs the network daily loss cost;
the formula for the aging cost of the transformer is:
Figure BDA0003077658120000061
wherein, CTxlossRepresents the daily aging cost of the transformer, STxAnd CvalueRated power of the transformer and unit transformer installation cost, L0Is the hotspot temperature, L0The service life of the transformer is taken as the factory standard service life of the transformer, which is generally 150000h, when the transformer is continuously operated at 110 ℃.
The optimization target is to minimize the energy arbitrage revenue loss of the electric vehicle owner by optimizing charging and reducing the operation cost of the power distribution network, and the objective function of the embodiment is as follows:
f＝CABpen+Cpeak+Closs+CTxloss (11)
Figure BDA0003077658120000062
wherein, CABpenPunishment for arbitrage and yield loss of electric vehicle ownersCost; cAb,iEarnings for energy arbitrage for the ith electric vehicle;
Figure BDA0003077658120000063
and (5) optimizing the charging power for the ith electric automobile. The optimum charge power value may be a positive value (G2V) during charging or a negative value (V2G) during discharging.
The constraints of the present embodiment are as follows:
1) electric Vehicle (EV) charging constraints: after the electric automobile goes home and is switched in charging, the charging optimization is carried out on the electric automobile according to the state when the electric automobile is switched in charging, and the initial SOC value of the ith electric automobile is at the beginning of chargingi,intThe battery energy change at the time of charge and discharge can be calculated using equation (13), and the battery energy change at the time of charge and discharge can be calculated using equation (14).
SOCi,int＝SOCi,depart-(100*εidi)/ci,bat (13)
Figure BDA0003077658120000064
Therein, SOCi,departIs the battery charge, ε, of the ith vehicle EV when it leaves homeiIs the power consumption of the ith electric vehicle; diFor daily driving distance, ci,batThe battery capacity of the electric automobile; SOCi,tAnd SOCi,t-1Battery charge of the ith EV at time t and a previous time (t-1), respectively; when the EV battery is operated in the charging mode, ηEVIs equal to etacIf operating in discharge mode, ηEVIs equal to-1/etadWherein etacAnd ηdRepresenting charge and discharge efficiency, respectively.
The charging power rating of a home electric vehicle is limited by the nominal power rating of its on-board charger, as shown in the following equation,
Figure BDA0003077658120000071
wherein the content of the first and second substances,
Figure BDA0003077658120000072
is the on-board charger rated power of the ith EV.
In order to prolong the life of the batteries of the electric vehicles, the charging state of each electric vehicle at the time t should be limited to a minimum (SOC)min) And maximum (SOC)max) Between state of charge limits; in addition, the final state of charge (SOC) of each EV batteryi,final) Must not fall below its minimum desired level (SOC)i,des) Nor above its maximum level (SOC)max) These two constraints are expressed in equations (16) and (17), respectively, and the minimum required charging time of the ith EV
Figure BDA0003077658120000073
The calculation can be performed using equation (18).
Figure BDA0003077658120000074
Figure BDA0003077658120000075
Figure BDA0003077658120000076
Wherein eta iscThe setting was 95%. The charging state of each electric vehicle battery at the time t is in SOCminAnd SOCmaxTo (c) to (d); in order to prolong the service life of the battery of the electric automobile, SOCminSet to 20%, and SOCmaxSet to 95% to prevent overcharging of the cell (not more than 100%); in addition, when the parking time is longer than
Figure BDA0003077658120000077
SOC of each EVi,desIs set to SOCmaxOtherwise, it must be continuously charged at rated power, the final SOCDepending on the charging time available at home.
2) Elastic restraint of transformer capacity: the loss of life of the transformer is related to the hottest point temperature of the winding, which is related to the load of the transformer and the ambient temperature, so in order to protect the aging of the transformer, the hot point temperature of the transformer winding can be controlled to ensure that the temperature does not exceed the limit, which condition can be achieved by optimizing the charging load of the EV, and therefore the constraint is as follows:
Figure BDA0003077658120000078
St≤Smax (20)
wherein, thetah,maxIs the maximum limit of the temperature of the hottest point of the winding, thetah,maxSet at 140 ℃ and, furthermore, SmaxThe maximum limit of the transformer load is limited to prevent the switch from being damaged due to high temperature; to reduce the cost of upgrading distribution transformers, transformer overload may be allowed to reach some suitable limit. S in the present examplemaxThe setting is 1.4 times of the rating of the transformer nameplate.
3) And (3) power grid constraint: the grid constraints are made by the following constraint conditions,
Figure BDA0003077658120000081
Figure BDA0003077658120000082
wherein, Vj,tThe voltage at node j at time t, and VminAnd VmaxLower and upper limits of node voltage offset, respectively, set to 0.9-1.1p.u. of rated voltage; in addition, Ik,tIs the current of branch k at time t, Ik,maxIs the maximum current limit at branch k.
(2) Optimizing through a genetic algorithm to solve the electric vehicle charging scheduling model, so as to realize the electric vehicle charging scheduling optimization;
1) first, a random initial population of each individual that is feasible in the area is created, considering the constraints in equations (14) to (17), based on information such as the driving range, electric vehicle model, arrival time, departure time, and minimum required energy electric vehicle driving data set.
2) Secondly, information about the distribution network topology, the base load, the electricity prices and the ambient temperature distribution is taken into account in the process; the power grid flow is calculated using the feasibility of each individual to evaluate the optimization constraint functions in equations (19) to (22).
3) Calculating cost functions of a profit-in-profit formula (1), a peak load cost formula (7), a power loss formula (9) and a transformer life loss formula (10) of an electric vehicle user; these associated costs are used to evaluate the fitness function in equation (11).
4) The GA optimization process continues to generate new populations by crossing, mutating and selecting until one of the stopping criteria is met, by using both stopping criteria; if the convergence tolerance is 10-6When so, the process will stop; otherwise the generation process reaches a maximum number.
5) Finally, the optimal solution of the genetic algorithm gives the optimal charging power of each electric automobile during charging at any time interval; on the basis, planning optimization is carried out by using the electric vehicle charging and discharging plan of the embodiment.
Simulation design of the present embodiment:
based on a network topological structure and an EV model, a Newton-Raphson method is used for power flow analysis, MATLAB simulation is carried out to demonstrate the performance of the proposed optimization method, in order to run simulation, existing EV data sets in summer and winter are used, and finally simulation is carried out on three conditions, as follows:
(1) uncontrolled charging, with each EV arriving at home, starts charging immediately at rated charging power, without V2G operation.
(2) The charging of the TOU is delayed because of the lower electric charge of the TOU, and the discharging mode is used in the time period of high TOU rate.
(3) Optimal charging, all electric vehicles control charging and discharging by the proposed method.
Influence of electric vehicle charging scheduling on transformer aging based on energy peak clipping and valley filling:
the load curve of the calculation formula (4) of the transformer power is shown in fig. 2 and fig. 3, in addition, the curve of the transformer aging curve and the EV charging load is shown in fig. 4 and fig. 5, and the relative life loss and winding hot spot temperature of the transformer are shown in fig. 6 and fig. 7.
In charging of electric vehicles using an uncontrolled charging method, the results show that the peak voltage of the transformer increases to 95.56kVA (1.91pu) in winter and 90.87kVA (1.82pu) in summer when the charging vehicle is connected to the grid in an uncontrolled charging mode. Therefore, the winding hottest point temperature exceeds 110 ℃, the relative aging acceleration factor in summer and winter is greater than 1, in which case the transformer will age more rapidly and its service life will be shortened.
When the electric vehicle is charged in the TOU charging mode, without optimization, an EV operating in V2G mode can release its stored energy in the peak electricity price phase; therefore, a large amount of electric vehicle discharge may cause a reverse current flow between 7 pm and 10 pm, thereby causing an overvoltage problem in rural areas. When the electricity price is low at the valley time, the states of all the electric vehicles are changed to a charged state, which causes the peak load of the transformer to be 112.78kVA (2.26pu) in summer and 104.62kVA (2.09pu) in winter, the winding hot spot temperature to be rapidly increased to more than 250 ℃ in summer and more than 190 ℃ in winter, and thus, a great increase in the accelerated aging factor is caused, the life of the transformer is finally shortened, and the transformer may fail or even explode.
In the case of no control, since the energy arbitrage opportunity brought by the V2G operation is not considered, compared with other charging modes, the penalty cost and the grid loss cost are the highest, the TOU charging shows no penalty cost, which means that the owner of the electric automobile can profit from the energy arbitrage using the V2G mode, but the peak demand cost, the transformer loss cost and the total cost are the highest. The results show that for optimal charging of electric vehicles a penalty cost is incurred, but this value is lower than for uncontrolled charging, since in the proposed optimization method, by adding objective functions and constraints that minimize peak load, power loss and transformer aging, it follows that peak demand cost, power loss and transformer loss are lowest for the proposed optimized electric vehicle approach compared to other charging approaches in summer and winter, and that the optimal charging approach has the lowest total cost in two seasons.
The foregoing has outlined rather broadly the preferred embodiments and principles of the present invention and it will be appreciated that those skilled in the art may devise variations of the present invention that are within the spirit and scope of the appended claims.

Claims (9)

1. An electric vehicle charging optimization scheduling method considering transformer capacity elasticity is characterized by comprising the following steps:
(1) establishing an electric vehicle charging scheduling model of energy peak clipping and valley filling income and power distribution network cost, and determining a corresponding objective function and related constraint conditions to form an original optimization problem;
(2) and optimizing through a genetic algorithm to solve the electric vehicle charging scheduling model, so as to realize the electric vehicle charging scheduling optimization.
2. The electric vehicle charging optimal scheduling method considering transformer capacity elasticity of claim 1, wherein the energy peak clipping and valley filling gains are as follows:
Figure FDA0003077658110000011
wherein, CAB,iEarnings for the daily energy arbitrage of the ith electric vehicle,
Figure FDA0003077658110000012
and
Figure FDA0003077658110000013
represents the discharge and charge power of the ith electric automobile at time t, and the unit is kW; Δ t is the time interval at time t, ctIs the electricity rate for time T, which is the total number of times of day.
3. The electric vehicle charging optimal scheduling method considering transformer capacity elasticity of claim 1, wherein the distribution network cost comprises a peak demand cost, a power loss cost and a transformer aging cost.
4. The optimal scheduling method for electric vehicle charging considering transformer capacity elasticity of claim 3, wherein the peak demand cost is as follows:
Cpeak＝Ppeakct
Figure FDA0003077658110000014
wherein, CpeakFor peak load costs, PpeakFor daily peak load, ctFor electricity prices at time t, PtIs the load at time t.
5. The electric vehicle charging optimal scheduling method considering transformer capacity elasticity of claim 3, wherein the power loss cost is as follows:
Figure FDA0003077658110000015
wherein, ClossIs the daily loss cost of the network,
Figure FDA0003077658110000021
is the active power loss of branch k at time t.
6. The electric vehicle charging optimal scheduling method considering transformer capacity elasticity of claim 3, wherein the transformer aging cost is as follows:
Figure FDA0003077658110000022
wherein, CTxlossFor the daily aging cost of the transformer, STxAnd CvalueRated power of the transformer and unit transformer installation cost, L0And obtaining the service life duration of the transformer by continuously operating at 110 ℃ for the hot spot temperature to obtain the standard service life of the transformer.
7. The electric vehicle charging optimal scheduling method considering transformer capacity elasticity of claim 1, wherein the objective function is as follows:
f＝CABpen+Cpeak+Closs+CTxloss
wherein the content of the first and second substances,
Figure FDA0003077658110000023
CABpenpenalty cost for arbitrage profit loss of electric vehicle owners; cAb,iThe peak clipping and valley filling income for the energy of the ith electric vehicle;
Figure FDA0003077658110000024
and (5) optimizing the charging power for the ith electric automobile.
8. The electric vehicle charging optimal scheduling method considering transformer capacity elasticity of claim 1, wherein the constraint conditions comprise electric vehicle charging constraint, transformer constraint and grid constraint;
wherein the electric vehicle constraints are:
SOCi,int＝SOCi,depart-(100*εidi)/ci,bat
Figure FDA0003077658110000025
therein, SOCi,departIs the battery charge, ε, of the ith vehicle EV when it leaves homeiIs the power consumption of the ith electric vehicle; diFor daily driving distance, ci,batThe battery capacity of the electric automobile; SOCi,tAnd SOCi,t-1Battery charge of the ith EV at time t and a previous time (t-1), respectively;
when the EV battery is operated in the charging mode, ηEVIs equal to etacIf operating in discharge mode, ηEVIs equal to-1/etad，ηcAnd ηdRespectively representing charge and discharge efficiency;
the transformer constraints are:
Figure FDA0003077658110000031
St≤Sflex≤Smax
wherein, thetah,maxThe maximum limit of the hottest point temperature of the winding; sflexSelecting different capacity constraints for the elastic constraint capacity of the transformer, namely according to the current working conditions such as the environmental temperature of the transformermaxFor maximum transformer capacity constraint, SmaxThe maximum rated value of the transformer nameplate is 1.4 times;
the power grid constraint is as follows:
Figure FDA0003077658110000032
Figure FDA0003077658110000033
wherein, Vj,tIs the voltage at node j at time t; vminAnd VmaxLower and upper limits of node voltage offset, respectively; i isk,tThe current for branch k at time t; i isk,maxThe maximum current limit at branch k.
9. The electric vehicle charging optimal scheduling method considering transformer capacity elasticity of claim 1, wherein the genetic algorithm specifically comprises:
and inputting the journey data and the regional information data of the electric automobiles into an electric automobile aggregation system, and solving through an electric automobile charging scheduling model of energy peak clipping and valley filling income and power distribution network cost to obtain the optimal charging power of each electric automobile during charging at any time interval.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110443415A (en) * 2019-07-24 2019-11-12 三峡大学 It is a kind of meter and dynamic electricity price strategy electric automobile charging station Multiobjective Optimal Operation method
CN111674276A (en) * 2020-06-11 2020-09-18 国网重庆市电力公司营销服务中心 Electric automobile ordered charging control method and system

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110443415A (en) * 2019-07-24 2019-11-12 三峡大学 It is a kind of meter and dynamic electricity price strategy electric automobile charging station Multiobjective Optimal Operation method
CN111674276A (en) * 2020-06-11 2020-09-18 国网重庆市电力公司营销服务中心 Electric automobile ordered charging control method and system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
蔡天宇: "电动汽车规模接入对变压器的影响及调控方法研究", 《中国优秀博硕士学位论文全文数据库(硕士) 工程科技II辑》 *

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