CN113378345A - Energy storage type tramcar charging station layout optimization method based on genetic algorithm - Google Patents
Energy storage type tramcar charging station layout optimization method based on genetic algorithm Download PDFInfo
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Abstract
The invention discloses an energy storage type tramcar charging station layout optimization method based on a genetic algorithm. The method comprises the following steps: firstly, establishing a dynamic model of the energy storage type tramcar according to the running characteristics of the energy storage type tramcar, and establishing a calculation model of a charging station; then establishing a train operation energy consumption model and a vehicle-mounted energy storage device energy state model according to the dynamic model of the energy storage type tramcar and the calculation model of the charging station; then, optimizing the layout of the charging station according to the constraint conditions of the layout optimization of the charging station to obtain an optimal layout optimization objective function of the charging station, wherein the optimal layout optimization objective function of the charging station can enable the vehicle to run through all the intervals, the number of the charging stations is the minimum, and the energy lowest point of the super capacitor is the maximum; and finally, carrying out genetic coding on the layout of the charging station, and optimizing the layout of the energy storage type tramcar charging station by adopting a genetic algorithm. The invention reduces the installation quantity of the charging devices, and reduces the project cost while ensuring the normal running of the train.
Description
Technical Field
The invention relates to the technical field of tramcar charging systems, in particular to a layout optimization method of an energy storage type tramcar charging station based on a genetic algorithm.
Background
The energy storage type tramcar is powered by the vehicle-mounted energy storage device and the contact network in a hybrid mode, namely, the tramcar is powered by the vehicle-mounted energy storage device under the condition of no network segment, the tramcar is powered by the contact network under the condition of the network segment, and the vehicle-mounted super capacitor is charged when the platform stops. For energy storage formula tram circuit, scientific design charging station overall arrangement has important meaning, but often does not have reliable data support during actual design, and charging station overall arrangement design scheme is immature yet, leads to having the problem that the charging station overall arrangement is unreasonable, resource utilization is low, project comprehensive cost is high.
Disclosure of Invention
The invention aims to provide an energy storage type tramcar charging station layout optimization method based on a genetic algorithm, which can reduce project cost while ensuring normal operation of a train.
The technical solution for realizing the purpose of the invention is as follows: an energy storage type tramcar charging station layout optimization method based on a genetic algorithm comprises the following steps:
and 4, carrying out genetic coding on the layout of the charging station, and optimizing the layout of the energy storage type tramcar charging station by adopting a genetic algorithm.
Further, according to the operation characteristics of the energy storage type tramcar, a dynamic model of the energy storage type tramcar is established, and a calculation model of the charging station is established in step 1, specifically as follows:
step 1.1, establishing a dynamic model of the energy storage type tramcar according to the running characteristics of the energy storage type tramcar, which comprises the following specific steps:
step 1.1.1, calculating the traction force of the tramcar:
the train traction force is the reaction force of the steel rail to the locomotive, which is obtained after the internal force generated by the traction motor is transmitted to the steel rail through the power transmission device, and the calculation formula is as follows:
Fμ＝Gfμ (1)
in the formula, FμFor adhesive traction, GfThe adhesion weight of the train, mu is the adhesion coefficient, v is the train speed, and the unit is km/h;
step 1.1.2, calculating the braking force of the tramcar:
the train braking force is divided into electric braking and mechanical braking, the electric braking is used as the main braking force of the train, the mechanical braking is used as the auxiliary braking force, and the electric braking feeds back the recovered braking energy to the super capacitor, so that the overall energy consumption is reduced;
step 1.1.3, calculating the resistance of the tramcar:
the resistance suffered by the train during the operation process comprises a basic resistance and an additional resistance:
basic resistance RrThe formula is as follows:
Rr(v)＝A+Bv+Cv2 (3)
wherein A, B, C is a constant; v is the running speed of the train, and the unit is km/h;
the formula for the additional resistance is:
additional resistance w of trainiBy the ramp resistance wiCurve resistance wrAnd tunnel resistance wsThe composition is shown as the following formula:
in the formula, i is a slope thousandth, R is a curve radius, and the tunnel resistance wsIs less influenced, so the tunnel resistance w is neglecteds；
Step 1.2, establishing a calculation model of the energy storage type tramcar charging station, which comprises the following specific steps:
the vehicle-mounted super capacitor is charged by contacting the pantograph with the charging rail, the charging is carried out in a constant-current voltage-limiting mode, and the charging power formula is as follows:
Psc_charge＝UscIscηc_iηsc (5)
wherein, Psc_chargeCharging power for super capacitor, UscIs the voltage of a super capacitor, IscCharging current, η, for the supercapacitorc_iFor the conversion efficiency of the charging device, ηscThe charging conversion efficiency of the super capacitor is improved.
Further, the step 2 of establishing a train operation energy consumption model and a vehicle-mounted energy storage device energy state model according to the dynamic model of the energy storage type tramcar and the calculation model of the charging station specifically includes the following steps:
step 2.1, establishing a train operation energy consumption model according to the kinetic model of the energy storage type tramcar, which comprises the following specific steps:
respectively establishing train operation energy consumption models under a traction working condition, a cruise working condition, an idle working condition and a braking working condition according to the operation working condition of the train:
when a train is in a traction working condition and a cruising working condition, the train operation energy consumption consists of traction energy consumption and auxiliary energy consumption, the traction energy consumption is the energy consumption of a train traction motor, the auxiliary energy consumption is the energy consumption of equipment such as vehicle-mounted lighting equipment, an air conditioner and the like, and the operation energy consumption calculation formula is as follows:
wherein J is the running energy consumption, and the unit is kWh; fkIs traction force with kN; v is the running speed, and the unit is km/h; pKFThe unit is kW for auxiliary traction power; eta is the efficiency of the traction motor; delta T is the simulation step length;
when the train is in the idle working condition, the train operation energy consumption is auxiliary energy consumption, and the operation energy consumption calculation formula is as follows:
in the formula, PKFThe unit is kW for auxiliary traction power;
when the tramcar is in the braking operating mode, the tramcar adopts regenerative braking and mechanical braking, and when adopting electric braking, the formula for calculating the regenerative braking recovered energy is as follows:
in the formula, JZRepresenting the regeneration energy in kWh; fZBRepresents the electric braking force in kN;
2.2, establishing an energy state model of the vehicle-mounted energy storage device according to the calculation model of the energy storage type tramcar charging station:
vehicle-mounted energy storage electric quantity of train starting from starting station
The formula of (1) is:
in the formula EessRepresenting the rated electric quantity of the vehicle-mounted energy storage, and the unit is kWh; etastartRepresenting the percentage of the initial electric quantity of the vehicle-mounted energy storage device;
in the interval from any (i-1) th station to the (i-1) th station, i is more than or equal to 2 and less than or equal to nstationThe formula of the residual electric quantity of the vehicle-mounted energy storage device when the train arrives at the station is as follows:
in the formula (I), the compound is shown in the specification,
representing the vehicle-mounted energy storage residual capacity when the train arrives at the station i, wherein the unit is kWh;
the unit of the vehicle-mounted energy storage electric quantity is kWh when the train leaves the station from i-1; qiThe unit of the electric quantity for the i-interval running of the train is kWh; giThe power supply quantity of the contact network in the i interval to the vehicle-mounted energy storage is represented, and the unit is kWh;
the formula of the minimum electric quantity of the interval vehicle-mounted energy storage is as follows;
in the formula, Ei_lowThe lowest vehicle-mounted energy storage electric quantity in the train i interval is represented, and the unit is kWh; qi_maxThe maximum value of the electric quantity of the i-interval running of the train provided by the vehicle-mounted energy storage is represented in the unit of kWh; gi_lowRepresents i section Qi_maxThe contact network at the moment supplies electric quantity to the vehicle-mounted energy storage, and the unit is kWh;
the formula of the residual electric quantity of the vehicle-mounted energy storage device when the train is started after stopping is as follows:
in the formula (I), the compound is shown in the specification,
to representThe unit of the vehicle-mounted energy storage electric quantity when the train leaves the station i is kWh; b isiThe charging station represents that the charging station of the station i supplements the vehicle-mounted energy storage with electric quantity, and the unit is kWh; eFiAnd the electric quantity which represents the auxiliary energy consumption provided by the vehicle-mounted energy storage at the ith station is the kWh unit.
Further, the charging station layout is optimized according to the constraint conditions of the charging station layout optimization in step 3, so as to obtain an optimal layout optimization objective function of the charging station, where the vehicle can travel through all the intervals, the number of the charging stations is the minimum, and the energy lowest point of the super capacitor is the maximum, specifically as follows:
step 3.1, establishing a charging station layout optimization constraint condition, including the constraint of charging and discharging current of the super capacitor, the constraint of charging time of the super capacitor and the constraint of the number and installation positions of the ground charging stations, which is specifically as follows:
step 3.1.1, constraint of charging and discharging currents of the super capacitor: the maximum power when the train is pulled must be less than or equal to the maximum power that the super capacitor can provide, so the discharge current I of the super capacitor must be limitedsc_outMaximum discharge current I less than or equal to super capacitorsc_out_maxAnd the current I of the super capacitor during chargingsc_inShort-time maximum charging current I which must be less than or equal to the supercapacitorsc_in_max：
Isc_out≤Isc_out_max (13)
Isc_in≤Isc_in_max (14)
Step 3.1.2, constraint of charging time of the super capacitor: station charging time T of super capacitorinScheduled time-to-charge time TmaxAnd (3) limiting:
Tin≤Tmax (15)
step 3.1.3, the number of the ground charging stations and the constraint of the installation positions: the number N of the ground charging stations is influenced by the total number N of the platformstotalAnd (3) constraint:
N≤Ntotal (16)
the optimization goal of the system is to install the minimum ground charging stations and simultaneously improve the minimum voltage of the super capacitor in the operation process as much as possible under the condition of meeting the constraint conditions;
step 3.2, analyzing the train interval running capacity, the charging station layout and the train endurance redundancy capacity, and specifically comprising the following steps:
step 3.2.1, analyzing the train interval operation capacity:
dividing the whole line of the operation line into intervals by taking the platform as a boundary, setting that the whole line is provided with N stations, setting the number of the intervals to be N-1, setting that the current train runs from the i-1 th station to the i-th station, and setting that i is more than or equal to 2 and less than or equal to NstationI.e., traveling in the interval i-1,
the energy storage capacity of the train starting from the i-1 station,
the energy storage electric quantity is the energy storage electric quantity when the train arrives at the station i;
setting PiFor the vehicle endurance condition of the interval i, if the current segment is the contact network segment, PiIs 1; if the current section is a non-contact network segment, if the train runs in the section i, the energy storage capacity at any time t
If not less than 0, the train can normally run through the interval, PiIs 1, otherwise PiIs 0;
step 3.2.2, charging station layout setting analysis:
the current line is set to have n stations, and because each station can only be provided with or not provided with a charging device, the current line has 2 stationsnPossibly, a binary coding mode is adopted, n bits are counted, 1 is set, 0 is not set, and the total number of charging station settings is recorded as x;
step 3.2.3, train endurance redundancy capability analysis:
the current train is set to run from the station i-1 to the station i, namely to run in the section i-1,
the energy storage capacity of the train starting from the i-1 station,
for the stored energy and electric quantity when the train arrives at station i, order
The percentage value of the lowest point of the energy of the vehicle-mounted energy storage during the full-line operation is the percentage value, and when the number of the charging stations cannot be reduced, the percentage value E is increased as much as possibleextraA value of (d);
step 3.3, establishing a charging station optimal layout optimization objective function with the vehicle capable of driving through all intervals, the minimum number of charging stations and the maximum lowest energy point of the super capacitor, wherein the optimal layout optimization objective function specifically comprises the following steps:
the maximum value point of the default optimization objective function is an optimal solution, and the optimization objective function is formed by three parts as follows because the optimization objective is that a train can travel through all intervals, the number of charging stations is minimum, and the energy minimum point of the super capacitor is maximum:
②-x
③Eextra
the first part is used for ensuring that the train can run through all sections of the upper and lower rows, wherein nsectionThe number of sections is 2n since the train is continuously running in the up-down directionsection；nstationFor the number of charging stations to be set, PiThe vehicle endurance condition of the interval i is set;
the second part is used for ensuring that as few charging stations as possible are arranged, wherein x is the number of the arranged charging stations;
the third part is used to ensure that the train has the highest possible redundancy for endurance, wherein EextraIs the percentage of the lowest energy point of the super capacitor;
in summary, the optimization objective function is shown as follows:
when the function f (x) obtains the maximum value, namely the vehicle can run through all the intervals, the number of the charging stations is minimum, and the energy lowest point of the super capacitor is maximum, the optimal layout of the charging stations is obtained.
Further, the layout of the charging station in step 4 is subjected to genetic coding, and the layout of the energy storage type tramcar charging station is optimized by adopting a genetic algorithm, which specifically comprises the following steps:
step 4.1, determining the coding rule, the fitness function, the selection factor, the crossover operator and the mutation probability of the genetic algorithm, wherein t is the iteration number:
and (3) encoding rules: setting n stations in total on the current line, and counting n bits in a binary coding mode, wherein 1 is set and 0 is not set;
fitness function: selecting the optimized objective function in the step 3.3 as a fitness function, as follows:
selecting a factor: determining a selection factor by adopting a betting rotation method;
and (3) a crossover operator: adopting single-point crossing, wherein the crossing rate is 0.5-0.95;
the mutation probability: single-point variation is adopted, and the variation rate is 0.01-0.2;
4.2, optimizing the layout of the energy storage type tramcar charging station by adopting a genetic algorithm:
step 4.2.1, initialize father group Pt，t＝0；
Step 4.2.2, calculating the father group PtAn individual fitness value of (a);
4.2.3, judging whether t reaches the maximum iteration times, outputting an optimization result if t reaches the maximum iteration times, and entering the next step if t does not reach the maximum iteration times;
step 4.2.4, genetic manipulation to generate a sub-population Qt；
And 4. step 4.2.5, calculating the sub-population QtAn individual fitness value of (a);
step 4.2.6, generating P using business strategyt+1；
And step 4.2.7, when t is t +1, accumulating the iteration times for 1 time, and returning to step 4.2.3.
Compared with the prior art, the invention has the remarkable advantages that: (1) the installation quantity of the charging devices is reduced, and the project cost is reduced while the normal running of the train is ensured; (2) has good convergence and reliability and has certain engineering significance.
Drawings
Fig. 1 is a flow diagram of the energy storage type tramcar charging station layout optimization method based on the genetic algorithm.
Figure 2 is a graph of the train tractive effort characteristics of an embodiment of the present invention.
Fig. 3 is a characteristic diagram of train braking force in the embodiment of the invention.
FIG. 4 is a graph of the voltage and current characteristics of a super capacitor according to an embodiment of the present invention.
FIG. 5 is a flowchart illustrating an iterative process of a genetic algorithm according to an embodiment of the present invention.
FIG. 6 is a graphical representation of the results of the genetic optimization algorithm in an embodiment of the present invention.
FIG. 7 is a comparative plot of the SOC of the super capacitor before and after the downlink optimization in the embodiment of the present invention.
FIG. 8 is a comparative plot of the SOC of the super capacitor before and after the uplink optimization in the embodiment of the present invention.
FIG. 9 is a schematic diagram comparing the layout of the charging stations before and after optimization according to the embodiment of the invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments.
With reference to fig. 1, the invention relates to a layout optimization method for an energy storage type tramcar charging station based on a genetic algorithm, which comprises the following steps:
step 1.1, establishing a dynamic model of the energy storage type tramcar according to the running characteristics of the energy storage type tramcar, which comprises the following specific steps:
step 1.1.1, calculating the traction force of the tramcar:
the train traction force is the reaction force of the steel rail to the locomotive, which is obtained after the internal force generated by the traction motor is transmitted to the steel rail through the power transmission device, and the calculation formula is as follows:
Fμ＝Gfμ (1)
in the formula, FμFor adhesive traction, GfThe adhesion weight of the train, mu is the adhesion coefficient, v is the train speed, and the unit is km/h; the magnitude of the sticking coefficient μ is related to many factors, such as weather, train configuration, etc., and it is difficult to obtain an accurate value thereof, and a vehicle traction characteristic curve is shown in fig. 2 below;
step 1.1.2, calculating the braking force of the tramcar:
the train braking force is divided into electric braking and mechanical braking, the electric braking is used as the main braking force of the train, the mechanical braking is used as the auxiliary braking force, and the electric braking feeds back the recovered braking energy to the super capacitor, so that the overall energy consumption is reduced; FIG. 3 is a brake characteristic curve for a train when the train speed is below V0Mechanical braking will be employed;
step 1.1.3, calculating the resistance of the tramcar:
the resistance suffered by the train during the operation process comprises a basic resistance and an additional resistance:
the basic formula of resistance is:
Rr(v)＝A+Bv+Cv2 (3)
wherein A, B, C is a constant (depending on the train type); v is the running speed of the train, and the unit is km/h;
the formula for the additional resistance is:
the additional resistance of the train consists of ramp resistance, curve resistance and tunnel resistance, and is as follows:
in the formula, i is a slope thousandth, R is a curve radius, and tunnel resistance is ignored because the influence of the tunnel resistance is small;
step 1.2, establishing a calculation model of the energy storage type tramcar charging station, which comprises the following specific steps:
the vehicle-mounted super capacitor is charged by contacting the pantograph with the charging rail, the charging is carried out in a constant-current voltage-limiting mode, and a charging power formula is shown as follows.
Psc_charge＝UscIscηc_iηsc (5)
Wherein, Psc_chargeCharging power for super capacitor, UscIs the voltage of a super capacitor, IscCharging current, η, for the supercapacitorc_iFor the conversion efficiency of the charging device, ηscThe charging conversion efficiency of the super capacitor is improved; the characteristic curves of the voltage and the current of the super capacitor in the constant-current voltage-limiting charging mode are shown in fig. 4.
step 2.1, establishing a train operation energy consumption model according to the kinetic model of the energy storage type tramcar, which comprises the following specific steps:
respectively establishing train operation energy consumption models under a traction working condition, a cruise working condition, an idle working condition and a braking working condition according to the operation working condition of the train:
when a train is in a traction working condition and a cruising working condition, the train operation energy consumption consists of traction energy consumption and auxiliary energy consumption, the traction energy consumption is the energy consumption of a train traction motor, the auxiliary energy consumption is the energy consumption of equipment such as vehicle-mounted lighting equipment, an air conditioner and the like, and the operation energy consumption calculation formula is as follows:
wherein J is the running energy consumption, and the unit is kWh; fkIs traction force with kN; v is the running speed, and the unit is km/h; pKFThe unit is kW for auxiliary traction power; eta is the efficiency of the traction motor; delta T is the simulation step length;
when the train is in the idle working condition, the train operation energy consumption is auxiliary energy consumption, and the operation energy consumption calculation formula is as follows:
in the formula, PKFThe unit is kW for auxiliary traction power;
when the tramcar is in the braking operating mode, the tramcar adopts regenerative braking and mechanical braking, and when adopting electric braking, the formula for calculating the regenerative braking recovered energy is as follows:
in the formula, JZRepresenting the regeneration energy in kWh; fZBRepresents the electric braking force in kN;
2.2, establishing an energy state model of the vehicle-mounted energy storage device according to the calculation model of the energy storage type tramcar charging station:
for guaranteeing tram safe and reliable's operation, must satisfy contact net, on-vehicle energy memory and provide the electric quantity and be greater than the required electric quantity of train operation, on-vehicle energy memory electric quantity state divide into following several kinds of condition:
when the train runs in a network segment, the vehicle-mounted energy storage electric quantity is increased, and if the electric quantity reaches the allowable charging upper limit, the charging is stopped;
when the train runs to a non-contact network segment, the electric quantity of the vehicle-mounted energy storage device is consumed under the working conditions of train traction, cruising and coasting, and energy is fed back to the vehicle-mounted energy storage device under the working condition of electric braking;
when the train stops at a platform provided with a charging station, the vehicle-mounted energy storage device is charged, and the charging is stopped when the train is fully charged;
when the train stops at a platform without a charging station, the vehicle-mounted energy storage device provides auxiliary energy consumption for the train, and the energy storage electric quantity is reduced;
the formula of the vehicle-mounted energy storage electric quantity when the train starts from the starting station is as follows:
in the formula EessRepresenting the rated electric quantity of the vehicle-mounted energy storage, and the unit is kWh; etastartRepresenting the percentage of the initial electric quantity of the vehicle-mounted energy storage device;
in the interval from any (i-1) th station to the (i-1) th station, i is more than or equal to 2 and less than or equal to nstationThe formula of the residual electric quantity of the vehicle-mounted energy storage device when the train arrives at the station is as follows:
in the formula (I), the compound is shown in the specification,
representing the vehicle-mounted energy storage residual capacity when the train arrives at the station i, wherein the unit is kWh;
the unit of the vehicle-mounted energy storage electric quantity is kWh when the train leaves the station from i-1; qiThe unit of the electric quantity for the i-interval running of the train is kWh; giThe power supply quantity of the contact network in the i interval to the vehicle-mounted energy storage is represented, and the unit is kWh;
the formula of the minimum electric quantity of the interval vehicle-mounted energy storage is as follows;
in the formula, Ei_lowRepresenting trainsThe lowest vehicle-mounted energy storage electric quantity in the interval i is in a unit of kWh; qi_maxThe maximum value of the electric quantity of the i-interval running of the train provided by the vehicle-mounted energy storage is represented in the unit of kWh; gi_lowRepresents i section Qi_maxThe contact network at the moment supplies electric quantity to the vehicle-mounted energy storage, and the unit is kWh;
the formula of the residual electric quantity of the vehicle-mounted energy storage device when the train is started after stopping is as follows:
in the formula, BiThe charging station represents that the charging station of the station i supplements the vehicle-mounted energy storage with electric quantity, and the unit is kWh; eFiAnd the electric quantity which represents the auxiliary energy consumption provided by the vehicle-mounted energy storage at the ith station is the kWh unit.
And 3, optimizing the layout of the charging station according to the constraint conditions of the layout optimization of the charging station to obtain an optimal layout optimization objective function of the charging station, wherein the optimal layout optimization objective function of the charging station has the minimum number of the charging stations and the maximum energy lowest point of the super capacitor, and the optimal layout optimization objective function of the charging station is as follows:
step 3.1, establishing a charging station layout optimization constraint condition, including the constraint of charging and discharging current of the super capacitor, the constraint of charging time of the super capacitor and the constraint of the number and installation positions of the ground charging stations, which is specifically as follows:
step 3.1.1, constraint of charging and discharging currents of the super capacitor: the maximum power when the train is pulled must be less than or equal to the maximum power that the super capacitor can provide, so the discharge current I of the super capacitor must be limitedsc_outMaximum discharge current I less than or equal to super capacitorsc_out_maxAnd current I of super capacitor in chargingsc_inShort-time maximum charging current I which must be less than or equal to the supercapacitorsc_in_max：
Isc_out≤Isc_out_max (13)
Isc_in≤Isc_in_max (14)
Step 3.1.2, constraint of charging time of the super capacitor: station charging time T of super capacitorinScheduled time-to-charge time TmaxAnd (3) limiting:
Tin≤Tmax (15)
step 3.1.3, the number of the ground charging stations and the constraint of the installation positions: the number N of the ground charging stations is influenced by the total number N of the platformstotalAnd (3) constraint:
N≤Ntotal (16)
the optimization goal of the system is to install the minimum ground charging stations under the condition of meeting the constraint conditions, and simultaneously improve the minimum voltage of the super capacitor in the operation process as much as possible so as to ensure higher endurance redundancy;
step 3.2, analyzing the train interval running capacity, the charging station layout and the train endurance redundancy capacity, and specifically comprising the following steps:
step 3.2.1, analyzing the train interval operation capacity:
dividing the whole line of the operation line into intervals by taking the platform as a boundary, setting that the whole line is provided with N stations, setting the number of the intervals to be N-1, setting that the current train runs from the i-1 th station to the i-th station, and setting that i is more than or equal to 2 and less than or equal to NstationI.e., traveling in the interval i-1,
the energy storage capacity of the train starting from the i-1 station,
the energy storage electric quantity is the energy storage electric quantity when the train arrives at the station i;
setting PiFor the vehicle endurance condition of the interval i, if the current segment is the contact network segment, PiIs 1; if the current section is a non-contact network segment, if the train runs in the section i, the energy storage capacity at any time t
If not less than 0, the train can normally run through the interval, PiIs 1, otherwise PiIs 0;
step 3.2.2, charging station layout setting analysis:
the current line is set to have n stations, and because each station can only be provided with or not provided with a charging device, the current line has 2 stationsnPossibly, a binary coding mode is adopted, n bits are counted, 1 is set, 0 is not set, and the total number of charging station settings is recorded as x;
step 3.2.3, train endurance redundancy capability analysis:
the current train is set to run from the station i-1 to the station i, namely to run in the section i-1,
the energy storage capacity of the train starting from the i-1 station,
for the stored energy and electric quantity when the train arrives at station i, order
The percentage value of the lowest point of the energy of the vehicle-mounted energy storage during the full-line operation is the percentage value, and when the number of the charging stations cannot be reduced, the percentage value E is increased as much as possibleextraA value of (d);
step 3.3, establishing a charging station optimal layout optimization objective function with the vehicle capable of driving through all intervals, the minimum number of charging stations and the maximum lowest energy point of the super capacitor, wherein the optimal layout optimization objective function specifically comprises the following steps:
the maximum value point of the default optimization objective function is an optimal solution, and the optimization objective function is formed by three parts as follows because the optimization objective is that a train can travel through all intervals, the number of charging stations is minimum, and the energy minimum point of the super capacitor is maximum:
②-x
③Eextra
the first part is used for ensuring that the train can run through all sections of the upper and lower rows, wherein nsectionThe number of sections is 2n since the train is continuously running in the up-down directionsection；nstationFor the number of charging stations to be set, PiThe vehicle endurance condition of the interval i is set;
the second part is used for ensuring that fewer charging stations are arranged, wherein x is the number of the arranged charging stations;
the third part is used for ensuring that the train has high endurance redundancy, wherein EextraIs the percentage of the lowest point of energy of the supercapacitor, if the lowest point is 20%, then EextraIs 0.2;
in summary, the optimization objective function is shown as follows:
when the function f (x) obtains the maximum value, namely the vehicle can run through all the intervals, the number of the charging stations is minimum, and the energy lowest point of the super capacitor is maximum, the optimal layout of the charging stations is obtained.
And 4, carrying out genetic coding on the layout of the charging station, and optimizing the layout of the energy storage type tramcar charging station by adopting a genetic algorithm, wherein the genetic coding specifically comprises the following steps:
step 4.1, determining the coding rule, the fitness function, the selection factor, the crossover operator and the mutation probability of the genetic algorithm, wherein t is the iteration number:
and (3) encoding rules: setting n stations in total on the current line, and counting n bits in a binary coding mode, wherein 1 is set and 0 is not set;
fitness function: selecting the optimized objective function in the step 3.3 as a fitness function, as follows:
selecting a factor: determining a selection factor by adopting a betting rotation method;
and (3) a crossover operator: adopting single-point crossing, wherein the crossing rate is 0.5-0.95;
the mutation probability: single-point variation is adopted, and the variation rate is 0.01-0.2;
step 4.2, optimizing the layout of the energy storage type tramcar charging station by adopting a genetic algorithm, and combining with the graph 5, specifically, the method comprises the following steps:
step 4.2.1, initialize father group Pt(t＝0)；
Step 4.2.2, calculating the father group PtAn individual fitness value of (a);
4.2.3, judging whether t reaches the maximum iteration times, outputting an optimization result if t reaches the maximum iteration times, and entering the next step if t does not reach the maximum iteration times;
step 4.2.4, genetic manipulation to generate a sub-population Qt；
Step 4.2.5, calculate sub-population QtAn individual fitness value of (a);
step 4.2.6, generating P using business strategyt+1；
And step 4.2.7, when t is t +1, accumulating the iteration times for 1 time, and returning to step 4.2.3.
Example 1
In this embodiment, the optimization simulation is performed on the charging station layout of a certain tramcar operation line by using the energy storage type tramcar charging station layout optimization method based on the genetic algorithm. The whole line of the line is provided with 10 platforms, the characteristic parameters of the running train and the characteristic parameters of the super capacitor are shown in the following tables 1 and 2, and the layout of the original charging station is shown in the following table 3. All the platforms are provided with charging stations during engineering design, and the vehicle-mounted energy storage device is charged when the train stops.
TABLE 1 train characteristic parameters
TABLE 2 supercapacitors characteristic parameters
In the optimization simulation, a genetic algebra is set to be 50 generations, each generation of population has 50 individuals, the crossing rate is 0.7, the variation rate is 0.1, the residual effective energy of the super capacitor is specified to be not lower than 35% in consideration of the requirement of safe running of the train, the simulation result is shown in a graph 6 below, the abscissa is the genetic algebra, and the ordinate is the fitness value.
From the optimization simulation results, the binary code of the optimal arrangement is 1101001001, and the charging station layouts before and after optimization are compared, as shown in table 3 below.
TABLE 3 comparison of charging station layouts before and after optimization
6.2 simulation result verification
In order to verify the optimal arrangement obtained by the optimization simulation and keep the characteristic data, the line parameters and other data of the vehicle unchanged, the uplink simulation and the downlink simulation are respectively carried out according to the optimal charging station arrangement in the table 3, and the running state pair of the vehicle-mounted super capacitor is shown in the tables 4 and 5.
TABLE 4 comparison of operating states of supercapacitors before and after downlink optimization
TABLE 5 comparison of operating states of super capacitors before and after uplink optimization
The platform is used as an abscissa, and the SOC of the super capacitor is used as an ordinate, so as to obtain SOC comparison graphs of the super capacitor before and after uplink and downlink optimization, as shown in fig. 7 and 8. Similarly, the platform is used as the abscissa, the charging station setting is used as the ordinate, and a comparison graph of the charging station layout before and after optimization is obtained from table 3, as shown in fig. 9, when the value in the graph is 1, the charging station is set, and when the value is 0, the charging station is not set. As can be seen from tables 4 and 5, and fig. 7 and 8, after optimization, the lowest voltage points of the descending super capacitor and the ascending super capacitor are 467.082v and 465.005v, respectively, the energy at the moment is 38.19% and 36.71%, and the requirement that the lowest effective energy point is not lower than 35% is met, 10 charging devices need to be arranged before optimization, only 5 charging devices need to be arranged after optimization, the project cost is greatly reduced while the normal operation of the train is ensured, and the layout optimization method of the energy storage type tramcar charging station based on the genetic algorithm has certain engineering guidance significance.
Claims (5)
1. An energy storage type tramcar charging station layout optimization method based on a genetic algorithm is characterized by comprising the following steps:
step 1, establishing a dynamic model of the energy storage type tramcar according to the running characteristics of the energy storage type tramcar, and establishing a calculation model of a charging station;
step 2, establishing a train operation energy consumption model and a vehicle-mounted energy storage device energy state model according to the dynamic model of the energy storage type tramcar and the calculation model of the charging station;
step 3, optimizing the layout of the charging station according to the constraint conditions of the layout optimization of the charging station to obtain an optimal layout optimization objective function of the charging station, wherein the optimal layout optimization objective function of the charging station has the minimum number of the charging stations and the maximum lowest energy point of the super capacitor, and the vehicle can run through all the intervals;
and 4, carrying out genetic coding on the layout of the charging station, and optimizing the layout of the energy storage type tramcar charging station by adopting a genetic algorithm.
2. The method for optimizing the layout of the charging station of the energy storage type tramcar based on the genetic algorithm as claimed in claim 1, wherein the step 1 is to establish a dynamic model of the energy storage type tramcar and a calculation model of the charging station according to the operation characteristics of the energy storage type tramcar, and specifically comprises the following steps:
step 1.1, establishing a dynamic model of the energy storage type tramcar according to the running characteristics of the energy storage type tramcar, which comprises the following specific steps:
step 1.1.1, calculating the traction force of the tramcar:
the train traction force is the reaction force of the steel rail to the locomotive, which is obtained after the internal force generated by the traction motor is transmitted to the steel rail through the power transmission device, and the calculation formula is as follows:
Fμ＝Gfμ (1)
in the formula, FμFor adhesive traction, GfThe adhesion weight of the train, mu is the adhesion coefficient, v is the train speed, and the unit is km/h;
step 1.1.2, calculating the braking force of the tramcar:
the train braking force is divided into electric braking and mechanical braking, the electric braking is used as the main braking force of the train, the mechanical braking is used as the auxiliary braking force, and the electric braking feeds back the recovered braking energy to the super capacitor, so that the overall energy consumption is reduced;
step 1.1.3, calculating the resistance of the tramcar:
the resistance suffered by the train during the operation process comprises a basic resistance and an additional resistance:
the formula of the basic resistance Rr is as follows:
Rr(v)＝A+BV+Cv2 (3)
wherein A, B, C is a constant; v is the running speed of the train, and the unit is km/h;
the formula for the additional resistance is:
additional resistance w of trainjBy the ramp resistance wiCurve resistance wrAnd tunnel resistance wsThe composition is shown as the following formula:
in the formula, i is a slope thousandth, R is a curve radius, and the tunnel resistance wsIs less influenced, so the tunnel resistance w is neglecteds；
Step 1.2, establishing a calculation model of the energy storage type tramcar charging station, which comprises the following specific steps:
the vehicle-mounted super capacitor is charged by contacting the pantograph with the charging rail, the charging is carried out in a constant-current voltage-limiting mode, and the charging power formula is as follows:
Psc_charge＝UscIscηc_iηsc (5)
wherein, Psc_chargeCharging power for super capacitor, UscIs the voltage of a super capacitor, IscCharging current, η, for the supercapacitorc_iFor the conversion efficiency of the charging device, ηscThe charging conversion efficiency of the super capacitor is improved.
3. The energy storage type tramcar charging station layout optimization method based on genetic algorithm as claimed in claim 1, wherein the step 2 is to establish a train operation energy consumption model and a vehicle-mounted energy storage device energy state model according to the energy storage type tramcar dynamic model and the charging station calculation model, and specifically comprises the following steps:
step 2.1, establishing a train operation energy consumption model according to the kinetic model of the energy storage type tramcar, which comprises the following specific steps:
respectively establishing train operation energy consumption models under a traction working condition, a cruise working condition, an idle working condition and a braking working condition according to the operation working condition of the train:
when a train is in a traction working condition and a cruising working condition, the train operation energy consumption consists of traction energy consumption and auxiliary energy consumption, the traction energy consumption is the energy consumption of a train traction motor, the auxiliary energy consumption is the energy consumption of equipment such as vehicle-mounted lighting equipment, an air conditioner and the like, and the operation energy consumption calculation formula is as follows:
wherein J is the running energy consumption, and the unit is kWh; fkIs traction force with kN; v is the running speed, and the unit is km/h; pKFThe unit is kW for auxiliary traction power; eta is the efficiency of the traction motor; delta T is the simulation step length;
when the train is in the idle working condition, the train operation energy consumption is auxiliary energy consumption, and the operation energy consumption calculation formula is as follows:
in the formula, PKFThe unit is kW for auxiliary traction power;
when the tramcar is in the braking operating mode, the tramcar adopts regenerative braking and mechanical braking, and when adopting electric braking, the formula for calculating the regenerative braking recovered energy is as follows:
in the formula, JZRepresenting the regeneration energy in kWh; fZBRepresents the electric braking force in kN;
2.2, establishing an energy state model of the vehicle-mounted energy storage device according to the calculation model of the energy storage type tramcar charging station:
vehicle-mounted energy storage electric quantity of train starting from starting station
The formula of (1) is:
in the formula EessRepresenting the rated electric quantity of the vehicle-mounted energy storage, and the unit is kWh; etastartRepresenting the percentage of the initial electric quantity of the vehicle-mounted energy storage device;
in the interval from any (i-1) th station to the (i-1) th station, i is more than or equal to 2 and less than or equal to nstationThe formula of the residual electric quantity of the vehicle-mounted energy storage device when the train arrives at the station is as follows:
in the formula (I), the compound is shown in the specification,
representing the vehicle-mounted energy storage residual capacity when the train arrives at the station i, wherein the unit is kWh;
the unit of the vehicle-mounted energy storage electric quantity is kWh when the train leaves the station from i-1; qiThe unit of the electric quantity for the i-interval running of the train is kWh; giThe power supply quantity of the contact network in the i interval to the vehicle-mounted energy storage is represented, and the unit is kWh;
the formula of the minimum electric quantity of the interval vehicle-mounted energy storage is as follows;
in the formula, Ei_lowThe lowest vehicle-mounted energy storage electric quantity in the train i interval is represented, and the unit is kWh; qi_maxThe maximum value of the electric quantity of the i-interval running of the train provided by the vehicle-mounted energy storage is represented in the unit of kWh; gi_lowRepresents i section Qi_maxThe contact network at the moment supplies electric quantity to the vehicle-mounted energy storage, and the unit is kWh;
the formula of the residual electric quantity of the vehicle-mounted energy storage device when the train is started after stopping is as follows:
in the formula (I), the compound is shown in the specification,
the unit of the vehicle-mounted energy storage electric quantity is kWh when the train leaves the station i; b isiThe charging station represents that the charging station of the station i supplements the vehicle-mounted energy storage with electric quantity, and the unit is kWh; eFiAnd the electric quantity which represents the auxiliary energy consumption provided by the vehicle-mounted energy storage at the ith station is the kWh unit.
4. The energy storage type tram charging station layout optimization method based on genetic algorithm as claimed in claim 1, 2 or 3, wherein the charging station layout is optimized according to the constraint condition of charging station layout optimization in step 3, so as to obtain the optimal layout optimization objective function of the charging station, where the vehicle can travel through all the intervals, the number of charging stations is the minimum, and the lowest energy point of the super capacitor is the maximum, specifically as follows:
step 3.1, establishing a charging station layout optimization constraint condition, including the constraint of charging and discharging current of the super capacitor, the constraint of charging time of the super capacitor and the constraint of the number and installation positions of the ground charging stations, which is specifically as follows:
step 3.1.1, constraint of charging and discharging currents of the super capacitor: the maximum power when the train is pulled must be less than or equal to the maximum power that the super capacitor can provide, so the discharge current I of the super capacitor must be limitedsc_outMaximum discharge current I less than or equal to super capacitorsc_out_maxAnd the current I of the super capacitor during chargingsc_inShort-time maximum charging current I which must be less than or equal to the supercapacitorsc_in_max：
Isc_out≤Isc_out_max (13)
Isc_in≤Isc_in_max (14)
Step 3.1.2, constraint of charging time of the super capacitor: station charging time T of super capacitorinScheduled time-to-charge time TmaxAnd (3) limiting:
Tin≤Tmax (15)
step 3.1.3, the number of the ground charging stations and the constraint of the installation positions: the number N of the ground charging stations is influenced by the total number N of the platformstotalAnd (3) constraint:
N≤Ntotal (16)
the optimization goal of the system is to install the minimum ground charging stations and simultaneously improve the minimum voltage of the super capacitor in the operation process as much as possible under the condition of meeting the constraint conditions;
step 3.2, analyzing the train interval running capacity, the charging station layout and the train endurance redundancy capacity, and specifically comprising the following steps:
step 3.2.1, analyzing the train interval operation capacity:
dividing the whole line of the operation line into intervals by taking the platform as a boundary, setting that the whole line is provided with N stations, setting the number of the intervals to be N-1, setting that the current train runs from the i-1 th station to the i-th station, and setting that i is more than or equal to 2 and less than or equal to NstationI.e., traveling in the interval i-1,
the energy storage capacity of the train starting from the i-1 station,
the energy storage electric quantity is the energy storage electric quantity when the train arrives at the station i;
setting PiFor the vehicle endurance condition of the interval i, if the current segment is the contact network segment, PiIs 1; if the current section is a non-contact network segment, if the train runs in the section i, the energy storage capacity at any time t
If not less than 0, the train can normally run through the interval, PiIs 1, otherwise PiIs 0;
step 3.2.2, charging station layout setting analysis:
the current line is set to have n stations, and because each station can only be provided with or not provided with a charging device, the current line has 2 stationsnPossibly, a binary coding mode is adopted, n bits are counted, 1 is set, 0 is not set, and the total number of charging station settings is recorded as x;
step 3.2.3, train endurance redundancy capability analysis:
the current train is set to run from the station i-1 to the station i, namely to run in the section i-1,
the energy storage capacity of the train starting from the i-1 station,
for the stored energy and electric quantity when the train arrives at station i, order
The percentage value of the lowest point of the energy of the vehicle-mounted energy storage during the full-line operation is the percentage value, and when the number of the charging stations cannot be reduced, the percentage value E is increased as much as possibleextraA value of (d);
step 3.3, establishing a charging station optimal layout optimization objective function with the vehicle capable of driving through all intervals, the minimum number of charging stations and the maximum lowest energy point of the super capacitor, wherein the optimal layout optimization objective function specifically comprises the following steps:
the maximum value point of the default optimization objective function is an optimal solution, and the optimization objective function is formed by three parts as follows because the optimization objective is that a train can travel through all intervals, the number of charging stations is minimum, and the energy minimum point of the super capacitor is maximum:
①
②-x
③Eextra
the first part is used for ensuring that the train can run through all sections of the upper and lower rows, wherein nsectionThe number of sections is 2n since the train is continuously running in the up-down directionsection；nstationFor the number of charging stations to be set, PiThe vehicle endurance condition of the interval i is set;
the second part is used for ensuring that as few charging stations as possible are arranged, wherein x is the number of the arranged charging stations;
the third part is used to ensure that the train has the highest possible redundancy for endurance, wherein EextraIs the percentage of the lowest energy point of the super capacitor;
in summary, the optimization objective function is shown as follows:
when the function f (x) obtains the maximum value, namely the vehicle can run through all the intervals, the number of the charging stations is minimum, and the energy lowest point of the super capacitor is maximum, the optimal layout of the charging stations is obtained.
5. The method for optimizing the layout of the charging station of the energy storage type tram based on the genetic algorithm as claimed in claim 4, wherein the layout of the charging station in step 4 is encoded genetically, and the genetic algorithm is adopted to optimize the layout of the charging station of the energy storage type tram, specifically as follows:
step 4.1, determining the coding rule, the fitness function, the selection factor, the crossover operator and the mutation probability of the genetic algorithm, wherein t is the iteration number:
and (3) encoding rules: setting n stations in total on the current line, and counting n bits in a binary coding mode, wherein 1 is set and 0 is not set;
fitness function: selecting the optimized objective function in the step 3.3 as a fitness function, as follows:
selecting a factor: determining a selection factor by adopting a betting rotation method;
and (3) a crossover operator: adopting single-point crossing, wherein the crossing rate is 0.5-0.95;
the mutation probability: single-point variation is adopted, and the variation rate is 0.01-0.2;
4.2, optimizing the layout of the energy storage type tramcar charging station by adopting a genetic algorithm:
step 4.2.1, initialize father group Pt，t＝0；
Step 4.2.2, calculating the father group PtAn individual fitness value of (a);
4.2.3, judging whether t reaches the maximum iteration times, outputting an optimization result if t reaches the maximum iteration times, and entering the next step if t does not reach the maximum iteration times;
step 4.2.4, genetic manipulation to generate a sub-population Qt；
Step 4.2.5, meterOperator population QtAn individual fitness value of (a);
step 4.2.6, generating P using business strategyt+1；
And step 4.2.7, when t is t +1, accumulating the iteration times for 1 time, and returning to step 4.2.3.
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| CN117688968A (en) * | 2024-02-04 | 2024-03-12 | 中国铁建电气化局集团有限公司 | Tramcar layout method based on particle swarm optimization |
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