CN113326961B - Integrated optimization method for tramcar-mounted energy storage configuration and ground charging scheme - Google Patents
Integrated optimization method for tramcar-mounted energy storage configuration and ground charging scheme Download PDFInfo
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Abstract
The invention discloses an integrated optimization method for a tramcar-mounted energy storage configuration and ground charging scheme. The method comprises the following steps: establishing a basic data module, wherein the basic data module comprises a line data module, a train attribute data module, an ATO parameter module, a super capacitor parameter module and a charging station parameter module; establishing a train operation simulation module which comprises a vehicle-mounted ATO model, a train model, a vehicle-mounted super capacitor model, a platform charging device model, a train state updating calculation model and a train operation energy consumption calculation model; the optimization of the vehicle-mounted energy storage configuration and the ground charging scheme is used as a multi-objective optimization problem, the vehicle-mounted energy storage configuration and the ground charging scheme are optimized in an integrated mode through a genetic algorithm NSGA-II, and the NSGA-II is solved to obtain a Pareto solution set which is distributed evenly, so that the optimal vehicle-mounted energy storage configuration and ground charging scheme is determined. The method provides data and theoretical support for train model selection and line design in engineering projects, and has high use value and application prospect.
Description
Technical Field
The invention relates to the technical field of urban rail transit, in particular to an integrated optimization method for a tramcar vehicle-mounted energy storage configuration and ground charging scheme.
Background
The tramcar circuit of traditional contact net power supply has strict requirement to the headroom, not only has certain potential safety hazard in the downtown highway section, still produces adverse effect to the expansion of future gauze, therefore the hybrid power supply mode that on-vehicle energy memory and contact net collocation have become present development trend. For the power supply mode, the combination of the actual line conditions and the train characteristic parameters is of great importance to reasonably select the vehicle-mounted energy storage configuration and the ground charging scheme.
The existing research on vehicle-mounted energy storage configuration and ground charging scheme is not mature, and the following defects exist: (1) the existing research converts the vehicle-mounted energy storage configuration and ground charging scheme of the modern tramcar into a single-target optimization problem by setting a weighting coefficient, although the research on the problem is simplified, the setting of the coefficient cannot guarantee scientificity, and the vehicle-mounted energy storage configuration and ground charging scheme optimization is not analyzed as a multi-target optimization problem from the perspective of integrated optimization. (2) In the existing research, a traversal method is adopted for optimizing a ground charging scheme, which is not suitable for a long line with multiple stations, and the search speed of a global optimal solution is low. (3) The train runs back and forth on the line, so that the optimization simulation should not only consider the downlink running process or the uplink running process, but consider the uplink running process and the downlink running process as a whole.
Disclosure of Invention
The invention aims to provide a scientific, reliable and efficient integrated optimization method for a tramcar-mounted energy storage configuration and ground charging scheme.
The technical solution for realizing the purpose of the invention is as follows: an integrated optimization method for a tramcar-mounted energy storage configuration and ground charging scheme comprises the following steps:
step 2, establishing a train operation simulation module which comprises a vehicle-mounted ATO model, a train model, a vehicle-mounted super capacitor model, a platform charging device model, a train state updating calculation model and a train operation energy consumption calculation model;
and 3, optimizing the vehicle-mounted energy storage configuration and the ground charging scheme to serve as a multi-objective optimization problem, performing integrated optimization on the vehicle-mounted energy storage configuration and the ground charging scheme by adopting a genetic algorithm NSGA-II, and solving the NSGA-II to obtain a Pareto solution set which is uniformly distributed, so that the optimal vehicle-mounted energy storage configuration and ground charging scheme is determined.
Compared with the prior art, the invention has the remarkable advantages that: (1) from the perspective of integrated optimization, the vehicle-mounted energy storage configuration and ground charging scheme optimization are used as a multi-objective optimization problem to be analyzed; (2) the genetic algorithm NSGA-II is applied to the integrated optimization of the vehicle-mounted energy storage configuration and the ground charging scheme, a rapid non-dominated sorting algorithm, a crowding distance and crowding degree comparison operator and an elite and fitness sharing strategy are adopted, so that the finally designed vehicle-mounted energy storage configuration and the ground charging scheme meet the requirements of non-dominated standards, and meanwhile, the NSGA-II is solved to obtain a uniformly distributed Pareto solution set, so that the most appropriate vehicle-mounted energy storage configuration and ground charging scheme can be conveniently selected based on actual conditions; (3) the train up-and-down running process is considered as a whole, the optimization is not only carried out on the up-running process or the down-running process, and the finally obtained vehicle-mounted energy storage configuration and ground charging scheme is scientific and reliable.
Drawings
Fig. 1 is a structural diagram of an integrated optimization method of a tramcar-mounted energy storage configuration and a ground charging scheme according to the invention.
Fig. 2 is a schematic diagram of the general structure of the train operation simulation module in the invention.
FIG. 3 is a flow chart of the NSGA-II solution for the vehicle-mounted energy storage configuration and the ground charging scheme Pareto solution in the present invention.
Detailed Description
The invention discloses an integrated optimization method of a tramcar-mounted energy storage configuration and ground charging scheme, which comprises the following steps of:
step 2, establishing a train operation simulation module which comprises a vehicle-mounted ATO model, a train model, a vehicle-mounted super capacitor model, a platform charging device model, a train state updating calculation model and a train operation energy consumption calculation model;
and 3, optimizing the vehicle-mounted energy storage configuration and the ground charging scheme to serve as a multi-objective optimization problem, performing integrated optimization on the vehicle-mounted energy storage configuration and the ground charging scheme by adopting a genetic algorithm NSGA-II, and solving by the NSGA-II to obtain a uniformly distributed Pareto solution set so as to determine the optimal vehicle-mounted energy storage configuration and ground charging scheme.
Further, the basic data module in step 1 includes a line data module, a train attribute data module, an ATO parameter module, a super capacitor parameter module, and a charging station parameter module, and these five modules are data input modules, and provide initial parameters for the train operation simulation module, wherein:
the line data module is divided into station data, ramp data, curve data, speed limit data, contact network layout and charging station layout;
the train attribute data module is used for providing basic operation parameters of train operation, including train marshalling, passenger capacity, basic resistance parameters, inverter efficiency and traction braking characteristics;
the ATO configuration module is used for configuring basic characteristic quantities of the ATO system, including maximum traction acceleration, maximum braking acceleration, maximum impact limit and maximum running speed;
the super-capacitor parameter module is used for providing basic electrical parameters of the vehicle-mounted super-capacitor, and comprises a super-capacitor capacitance value, the highest working voltage, the lowest working voltage and energy conversion efficiency;
and the charging station parameter module provides basic electrical parameters of the platform charging device, including charging current and energy conversion efficiency.
Further, the establishing of the train operation simulation module in the step 2 includes a vehicle-mounted ATO model, a tramcar mechanics model, a vehicle-mounted super capacitor model, a platform charging device model, a train state updating calculation model and a train operation energy consumption calculation model, wherein:
vehicle ATO model: calculating the current train acceleration, realizing the maintenance or transfer of the train working condition, and transmitting the acceleration value to the tramcar mechanical model;
the tramcar mechanical model is as follows: calculating the traction or braking force of the train according to the acceleration data provided by the vehicle-mounted ATO model, and transmitting the traction or braking force value to a train operation energy consumption calculation model and a train state updating calculation model;
vehicle-mounted super capacitor model: calculating the voltage, the current and the power of the current super capacitor according to the super capacitor parameters, the train running power requirement, the contact network segment layout and the layout of the platform charging device;
platform charging device model: charging the vehicle-mounted super capacitor according to the charging device parameters;
updating a calculation model of the train state: performing dynamic operation according to data provided by the tramcar mechanical model, determining the current speed, the running distance and the running time of the train, and transmitting a calculation result to the vehicle-mounted ATO model;
the train operation energy consumption calculation model is as follows: and calculating the interval running time and the traction energy consumption of the train according to data provided by the tramcar mechanical model.
Further, in step 3, the optimization of the vehicle-mounted energy storage configuration and the ground charging scheme is used as a multi-objective optimization problem, a genetic algorithm NSGA-II is adopted to perform integrated optimization of the vehicle-mounted energy storage configuration and the ground charging scheme, and the NSGA-II is solved to obtain a Pareto solution set which is uniformly distributed, so that the optimal vehicle-mounted energy storage configuration and ground charging scheme is determined, and the method specifically comprises the following steps:
(1) and (3) encoding: real number coding is adopted, and a coded object is a vehicle-mounted energy storage configuration and ground charging scheme;
(2) determining the population quantity: determining the size of the population and an iterative algebra according to the interval length;
(3) setting a population fitness equation: max { f } total1 ,f total2 In which f total1 Configuring an optimized evaluation function, f, for a vehicle-mounted energy storage total2 An evaluation function is optimized for the ground charging scheme, and the specific form is as follows:
wherein p is i I is more than or equal to 1 and less than or equal to n for the passing condition of the train in the interval i section If the train can normally run through the section, p i Value 1, otherwise p i A value of 0; n is section The number of the unidirectional intervals of the line is; x is the total rated effective energy of the super capacitor, the unit is kWh, and the range is E min ≤x≤E max ；E min And E max Respectively representing the minimum value and the maximum value of the rated effective energy total value of the super capacitor; y is the number of sets of installed platform charging devices, and y is more than or equal to 0 and less than or equal to n section +1；E extra The lowest point of the super capacitor SOE in the running process of the train is shown as percentage;
(4) calculating the individual fitness value of the parent population: calculating the individual fitness value of the parent population by the train operation simulation module in the step 2;
(5) genetic manipulation: the genetic operation comprises selection, crossing and variation, the selection operation adopts championship selection operators, the crossing operation adopts simulated binary crossing, and the variation operation adopts polynomial variation to generate a sub-population;
(6) calculating the individual fitness value of the sub population: calculating an individual fitness value of the sub-population by the train operation simulation module in the step 2;
(7) generating the next generation of father population: the father population and the child population participate in competition together, and a next generation father population is obtained by adopting an elite and fitness value sharing strategy;
(8) judging whether the iteration meets the termination condition: judging whether the iteration algebra reaches the maximum iteration algebra, if so, ending and entering (9), and if not, returning to (5);
(9) outputting an optimal vehicle-mounted energy storage configuration and ground charging scheme: and selecting a vehicle-mounted energy storage configuration and ground charging scheme by adopting a non-dominant standard, an energy consumption sensitivity standard and a time uniform distribution standard.
Further, the object of the coding in the step (1) is a vehicle-mounted energy storage configuration and ground charging scheme, and specifically comprises the following steps: a real number coding mode is adopted for vehicle-mounted energy storage configuration, and the total value of rated effective energy is directly used as a real number coding value; a binary coding mode is adopted for a ground charging scheme, and because each station has installation or uninstallation conditions, the installation is directly represented by 0, and the installation is represented by 1.
Further, the determining of the population size and the iterative algebra according to the interval length in the step (2) specifically includes: when the interval length is less than 600m, the population size is set to be 60; when the interval length is more than 600m and less than 1000m, the population size is set to 80; when the interval length is more than 1000m, the population size is set to be 100; the iteration algebra is set to 100.
Further, the calculating of the individual fitness value in step (4) and step (6) comprises:
(a) taking the ith individual in the population and decoding the vehicle-mounted energy storage configuration and ground charging scheme corresponding to the individual, wherein the initial value of i is 0;
(b) transmitting the vehicle-mounted energy storage configuration and ground charging scheme decoded by the individual chromosome to a train operation simulation module;
(c) carrying out train operation simulation: the train operation simulation module carries out operation simulation and calculates an individual fitness value according to the train operation state information;
(d) saving fitness value of individuals: the individual fitness 1 is an optimized evaluation function value of vehicle-mounted energy storage configuration, and the individual fitness 2 is an optimized evaluation function value of a ground charging scheme;
(e) judging whether the current individual is the last individual in the population: if the last individual is the same, the calculation is finished; otherwise, i equals i +1, jumping to (a).
Further, in the step (7), a sharing strategy of elite and fitness value is adopted to obtain a next generation father population, specifically: combining the father population with the offspring population generated by the father population to compete together to generate a next generation father population, so that excellent individuals in the father generation enter the next generation, and the optimal individuals cannot be lost.
The invention is described in further detail below with reference to the following figures and detailed description.
With reference to fig. 1, the invention discloses an integrated optimization method of a vehicle-mounted energy storage configuration and a ground charging scheme of a modern tram, which comprises the following steps:
the basic data module comprises a line data module, a train attribute data module, an ATO parameter module, a super capacitor parameter module and a charging station parameter module, wherein the five modules are data input modules and provide initial parameters for the integrated optimization of vehicle-mounted energy storage configuration and a ground charging scheme, and the method comprises the following steps of:
the train attribute data module is used for providing basic operation parameters of train operation, including parameters such as train marshalling, passenger capacity, basic resistance parameters, inverter efficiency and traction brake characteristics;
the ATO configuration module is used for configuring basic characteristic quantities of the ATO system, wherein the basic characteristic quantities comprise parameters such as maximum traction acceleration, maximum braking acceleration, maximum impact limit and maximum running speed;
the super-capacitor parameter module is used for providing basic electrical parameters of the vehicle-mounted super-capacitor, and comprises parameters such as a super-capacitor capacitance value, a highest working voltage, a lowest working voltage and energy conversion efficiency;
and the charging station parameter module is used for providing basic electrical parameters of the platform charging device, including parameters such as charging current and energy conversion efficiency.
Step 2, establishing a vehicle-mounted energy storage configuration and ground charging scheme evaluation module, and evaluating the advantages and disadvantages of the current vehicle-mounted energy storage configuration and ground charging scheme;
with reference to fig. 2, the establishing of the vehicle-mounted energy storage configuration and ground charging scheme evaluation module, that is, the establishing of the train operation simulation module, includes:
vehicle ATO model: calculating the current train acceleration, realizing the maintenance or transfer of the train working condition, and transmitting the acceleration value to a train model and a running calculation model;
a train model: calculating the traction or braking force of the train according to the acceleration data provided by the vehicle-mounted ATO model, and transmitting the traction or braking force value to the operation calculation model;
vehicle-mounted super capacitor model: calculating the voltage, the current and the power of the current super capacitor according to the current running power requirement of the train, the layout of the contact network segment and the layout of the platform charging device;
platform charging device model: charging the vehicle-mounted super capacitor according to the parameter setting of the current charging device;
updating a calculation model of the train state: performing dynamic operation according to data provided by the vehicle-mounted ATO model and the train model, determining the current speed, the running distance and the running time of the train, and transmitting the calculation result to the energy consumption and time calculation model;
a train operation energy consumption calculation model: and calculating the interval running time and the traction energy consumption of the train according to the data provided by the running calculation model.
Step 3, establishing a multi-objective genetic algorithm NSGA-II-based vehicle-mounted energy storage configuration and ground charging scheme integrated optimization method, and determining an optimal vehicle-mounted energy storage configuration and ground charging scheme, as shown in FIG. 3, the specific steps are as follows:
(1) and (3) encoding: real number coding is adopted, and a coded object is a vehicle-mounted energy storage configuration and ground charging scheme;
(2) determining the population quantity: determining the size of the population and an iterative algebra according to the interval length;
(3) setting a population fitness equation: max { f } total1 ,f total2 In which f total1 Configuring an optimized evaluation function, f, for a vehicle-mounted energy storage total2 An evaluation function is optimized for the ground charging scheme, and the specific form is as follows:
wherein p is i For the train in the interval i (i is more than or equal to 1 and less than or equal to n) section ) If the train can normally run through the interval, the value is 1, otherwise the value is 0;
n section the number of the unidirectional intervals of the line;
x is the total rated effective energy of the super capacitor, with the unit of kWh and the range of E min ≤x≤E max ，E min And E max Respectively representing the minimum value and the maximum value of the rated effective energy total value of the super capacitor;
y is the number of sets of installed platform charging devices, and y is more than or equal to 0 and less than or equal to n section +1；
E extra The lowest point of the super capacitor SOE in the running process of the train is shown as percentage;
(4) calculating the individual fitness value of the parent population: calculating the individual fitness value of the parent population by the vehicle-mounted energy storage configuration and ground charging scheme evaluation module in the step 2;
(5) genetic manipulation: the genetic operation comprises selection, crossing and mutation, the selection operation adopts a championship selection operator, the crossing operation adopts simulated binary crossing, and the mutation operation adopts polynomial mutation to generate a sub-population;
(6) calculating an individual fitness value of the sub-population: calculating individual fitness values of the sub-populations by the vehicle-mounted energy storage configuration and ground charging scheme evaluation module in the step 2, wherein the individual fitness value calculation steps in the step (4) and the step (6) comprise:
(a) taking the ith individual in the population and calculating a vehicle-mounted energy storage configuration and ground charging scheme corresponding to the individual, wherein the initial value of i is 0;
(b) transmitting the vehicle-mounted energy storage configuration and ground charging scheme converted from the individual chromosome to a train operation simulation module;
(c) carrying out train operation simulation: calling a vehicle-mounted energy storage configuration and ground charging scheme evaluation module to perform operation simulation, and calculating an individual fitness value according to train operation state information;
(d) saving fitness value of individuals: the individual fitness 1 is an optimized evaluation function value of vehicle-mounted energy storage configuration, and the individual fitness 2 is an optimized evaluation function value of a ground charging scheme;
(e) judging whether the current individual is the last individual in the population: if the last individual is the same, the calculation is finished; otherwise, i equals i +1, jumping to (a).
(7) Generating the next generation of father population: the father population and the child population participate in competition together, and a sharing strategy of elite and fitness value is adopted to obtain the next generation father population, which specifically comprises the following steps: the father population and the offspring population generated by the father population are combined to compete together to generate a next generation father population, so that excellent individuals in the father generation enter the next generation, and the optimal individuals cannot be lost.
(8) Judging whether the iteration meets the termination condition: and judging whether the iteration algebra reaches the maximum iteration algebra, if so, ending and entering (9), and if not, returning to (5).
(9) Outputting an optimal vehicle-mounted energy storage configuration and ground charging scheme: and selecting a vehicle-mounted energy storage configuration and ground charging scheme by adopting a non-dominant standard, an energy consumption sensitivity standard and a time uniform distribution standard.
Example 1
Taking a certain modern tramcar line in urban rail transit as an example, the design steps of the vehicle-mounted energy storage configuration and ground charging scheme are as follows:
firstly, inputting line data, train attribute data, ATO parameters, super-capacitor parameters and charging station parameters, determining a simulation interval, and if the data is correct, entering a vehicle-mounted energy storage configuration and ground charging scheme design module by a computer;
secondly, get into on-vehicle energy storage configuration and ground charge scheme design module, concrete step includes:
the method comprises the following steps: and (4) encoding, namely encoding each population. A real number coding mode is adopted for vehicle-mounted energy storage configuration, and the total value of the rated effective energy is directly used as a real number coding value; a binary coding mode is adopted for the ground charging scheme, and because each station has installation or non-installation, the installation is directly represented by 0, and the installation is represented by 1.
Step two: determining the size and the algebra of the population, and initializing a first generation parent population. Determining the size of the population according to the interval length, and when the interval length is less than 600m, setting the size of the population to be 60; when the interval length is more than 600m and less than 1000m, the population size is set to 80; when the interval length is larger than 1000m, the population size is set to be 100; the iteration algebra is set to 100.
Step three: and setting a fitness equation of the population, wherein the aim is to minimize the vehicle-mounted energy storage configuration and minimize the number of sets of platform charging devices. The integrated optimization of the vehicle-mounted energy storage configuration and the ground charging scheme is a problem of two-target optimization, and a mathematical model of the integrated optimization is as follows: max { f } total1 ,f total2 In which f total1 Configuring an optimized evaluation function, f, for a vehicle-mounted energy storage total2 An evaluation function is optimized for the ground charging scheme, and the specific form is as follows:
wherein p is i The train is in an interval i (i is more than or equal to 1 and less than or equal to n) section ) If the train can normally run through the interval, the value is 1, otherwise the value is 0;
n section the number of the unidirectional intervals of the line is;
x is the total rated effective energy of the super capacitor, the unit is kWh, and the range is E min ≤x≤E max ，E min And E max Respectively representing the minimum value and the maximum value of the rated effective energy total value of the super capacitor;
y is the number of sets of installed platform charging devices, and y is more than or equal to 0 and less than or equal to n section +1；
E extra The lowest point of the super capacitor SOE in the running process of the train is the value of percentage.
Step four: and transmitting the father population to a fitness calculation model, and calculating the fitness value of each individual of the population by the fitness calculation model.
Step five: genetic manipulation: the sub-populations are generated from the parent population by genetic manipulation, which mainly includes selection, crossover and mutation. The selection operation adopts a championship selection operator, the crossover operation adopts analog binary crossover, and the variation operation adopts polynomial variation to generate a sub-population.
Step six: calculating a sub-population fitness function: and transmitting the sub-population to a fitness calculation model, and calculating the fitness value of each individual of the population by using the sub-population.
Step seven: the father population and the child population participate in competition together, and the father population of the next generation is obtained by adopting an elite and fitness value sharing strategy, so that excellent individuals in the father generation can be favorably ensured to enter the next generation, and the optimal individuals cannot be lost by storing all the individuals in the population in a grading way;
step eight: and judging whether the iteration meets the termination condition.
Step nine: and obtaining an optimal vehicle-mounted energy storage configuration and ground charging scheme solution set by adopting a non-dominant standard.
In conclusion, the method can obtain the optimal vehicle-mounted energy storage configuration and ground charging scheme solution set of the line, provides data and theoretical support for the model selection of the train and the design of the line in the engineering project, and has higher use value and application prospect.
Claims (6)
1. An integrated optimization method for a tramcar-mounted energy storage configuration and ground charging scheme is characterized by comprising the following steps:
step 1, establishing a basic data module, wherein the basic data module comprises a line data module, a train attribute data module, an ATO parameter module, a super capacitor parameter module and a charging station parameter module;
step 2, establishing a train operation simulation module which comprises a vehicle-mounted ATO model, a train model, a vehicle-mounted super capacitor model, a platform charging device model, a train state updating calculation model and a train operation energy consumption calculation model;
step 3, optimizing the vehicle-mounted energy storage configuration and the ground charging scheme to serve as a multi-objective optimization problem, performing integrated optimization on the vehicle-mounted energy storage configuration and the ground charging scheme by adopting a genetic algorithm NSGA-II, and solving the NSGA-II to obtain a uniformly distributed Pareto solution set so as to determine the optimal vehicle-mounted energy storage configuration and ground charging scheme;
the basic data module in step 1 comprises a line data module, a train attribute data module, an ATO parameter module, a super capacitor parameter module and a charging station parameter module, wherein the five modules are data input modules and provide initial parameters for a train operation simulation module, and the method comprises the following steps of:
the line data module is divided into station data, ramp data, curve data, speed limit data, contact network layout and charging station layout;
the train attribute data module is used for providing basic operation parameters of train operation, including train marshalling, passenger capacity, basic resistance parameters, inverter efficiency and traction braking characteristics;
the ATO configuration module is used for configuring basic characteristic quantities of the ATO system, including maximum traction acceleration, maximum braking acceleration, maximum impact limit and maximum running speed;
the super-capacitor parameter module is used for providing basic electrical parameters of the vehicle-mounted super-capacitor, and comprises a super-capacitor capacitance value, the highest working voltage, the lowest working voltage and energy conversion efficiency;
the charging station parameter module is used for providing basic electrical parameters of the platform charging device, including charging current and energy conversion efficiency;
step 2, establishing a train operation simulation module, which comprises a vehicle-mounted ATO model, a tramcar mechanics model, a vehicle-mounted super capacitor model, a platform charging device model, a train state updating calculation model and a train operation energy consumption calculation model, wherein:
vehicle ATO model: calculating the current train acceleration, realizing the maintenance or transfer of the train working condition, and transmitting the acceleration value to the tramcar mechanical model;
the tramcar mechanical model is as follows: calculating the traction or braking force of the train according to the acceleration data provided by the vehicle-mounted ATO model, and transmitting the traction or braking force value to a train operation energy consumption calculation model and a train state updating calculation model;
vehicle-mounted super capacitor model: calculating the voltage, the current and the power of the current super capacitor according to the super capacitor parameters, the train running power requirement, the contact network segment layout and the layout of the platform charging device;
platform charging device model: charging the vehicle-mounted super capacitor according to the charging device parameters;
updating a calculation model of the train state: performing dynamic operation according to data provided by the tramcar mechanical model, determining the current speed, the running distance and the running time of the train, and transmitting the calculation result to the vehicle-mounted ATO model;
a train operation energy consumption calculation model: and calculating the interval running time and the traction energy consumption of the train according to data provided by the tramcar mechanical model.
2. The integrated optimization method of the tram vehicle-mounted energy storage configuration and ground charging scheme according to claim 1, wherein in step 3, the optimization of the vehicle-mounted energy storage configuration and the ground charging scheme is used as a multi-objective optimization problem, a genetic algorithm NSGA-II is adopted to perform integrated optimization of the vehicle-mounted energy storage configuration and the ground charging scheme, and the NSGA-II is solved to obtain a Pareto solution set which is uniformly distributed, so that the optimal vehicle-mounted energy storage configuration and ground charging scheme is determined, and the specific steps are as follows:
(1) and (3) encoding: real number coding is adopted, and a coded object is a vehicle-mounted energy storage configuration and ground charging scheme;
(2) determining the population quantity: determining the size of the population and an iterative algebra according to the interval length;
(3) setting a population fitness equation: max { f } total1 ,f total2 In which f total1 Configuring an optimized evaluation function, f, for a vehicle-mounted energy storage total2 An evaluation function is optimized for the ground charging scheme, and the specific form is as follows:
wherein p is i I is more than or equal to 1 and less than or equal to n for the passing condition of the train in the interval i section If the train can normally run through the section, p i Value 1, otherwise p i A value of 0; n is a radical of an alkyl radical section The number of the unidirectional intervals of the line is; x is the total rated effective energy of the super capacitor, the unit is kWh, and the range is E min ≤x≤E max ；E min And E max Respectively representing the minimum value and the maximum value of the rated effective energy total value of the super capacitor; y is the number of sets of installed platform charging devices, and y is more than or equal to 0 and less than or equal to n section +1；E extra The lowest point of the super capacitor SOE in the running process of the train is shown as percentage;
(4) calculating the individual fitness value of the parent population: calculating the individual fitness value of the parent population by the train operation simulation module in the step 2;
(5) genetic manipulation: the genetic operation comprises selection, crossing and mutation, the selection operation adopts a championship selection operator, the crossing operation adopts simulated binary crossing, and the mutation operation adopts polynomial mutation to generate a sub-population;
(6) calculating an individual fitness value of the sub-population: calculating the individual fitness value of the sub population by the train operation simulation module in the step 2;
(7) generating the next generation of father population: the father population and the child population participate in competition together, and a next generation father population is obtained by adopting an elite and fitness value sharing strategy;
(8) judging whether the iteration meets the termination condition: judging whether the iteration algebra reaches the maximum iteration algebra, if so, ending and entering (9), and if not, returning to (5);
(9) outputting an optimal vehicle-mounted energy storage configuration and ground charging scheme: and selecting a vehicle-mounted energy storage configuration and ground charging scheme by adopting a non-dominant standard, an energy consumption sensitivity standard and a time uniform distribution standard.
3. The integrated optimization method for the tramcar-mounted energy storage configuration and ground charging scheme according to claim 2, wherein the coded object in the step (1) is the vehicle-mounted energy storage configuration and ground charging scheme, and specifically comprises the following steps: a real number coding mode is adopted for vehicle-mounted energy storage configuration, and the total value of rated effective energy is directly used as a real number coding value; a binary coding mode is adopted for a ground charging scheme, and because each station has installation or uninstallation conditions, the installation is directly represented by 0, and the installation is represented by 1.
4. The integrated optimization method for the tramcar-mounted energy storage configuration and ground charging scheme according to claim 2, wherein in the step (2), the population size and the iteration algebra are determined according to the interval length, and specifically: when the interval length is less than 600m, the population size is set to be 60; when the interval length is more than 600m and less than 1000m, the population size is set to 80; when the interval length is more than 1000m, the population size is set to be 100; the iteration algebra is set to 100.
5. The method for optimizing the integration of the energy storage configuration on board the tram and the ground charging scheme according to claim 2, wherein the step of calculating the individual fitness value in the steps (4) and (6) comprises:
(a) taking the ith individual in the population and decoding the vehicle-mounted energy storage configuration and ground charging scheme corresponding to the individual, wherein the initial value of i is 0;
(b) transmitting the vehicle-mounted energy storage configuration and ground charging scheme decoded by the individual chromosome to a train operation simulation module;
(c) carrying out train operation simulation: the train operation simulation module carries out operation simulation and calculates an individual fitness value according to the train operation state information;
(d) saving fitness value of individuals: the individual fitness 1 is an optimized evaluation function value of vehicle-mounted energy storage configuration, and the individual fitness 2 is an optimized evaluation function value of a ground charging scheme;
(e) judging whether the current individual is the last individual in the population: if the last individual is the same, the calculation is finished; otherwise, i equals i +1, jumping to (a).
6. The integrated optimization method for the tramcar-mounted energy storage configuration and ground charging scheme according to claim 2, wherein in the step (7), a next generation parent population is obtained by adopting an elite and fitness value sharing strategy, and specifically comprises the following steps: the father population and the offspring population generated by the father population are combined to compete together to generate a next generation father population, so that excellent individuals in the father generation enter the next generation, and the optimal individuals cannot be lost.
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| CN109606204A (en) * | 2018-11-05 | 2019-04-12 | 北京交通大学 | The power supply system and method for the vehicle-mounted energy storage device mobile charging of municipal rail train can be achieved |
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| CN109606204A (en) * | 2018-11-05 | 2019-04-12 | 北京交通大学 | The power supply system and method for the vehicle-mounted energy storage device mobile charging of municipal rail train can be achieved |
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