CN113987930B - Optimization method of urban rail regenerative braking energy inversion feedback system - Google Patents
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Abstract
The application discloses an optimization method of an urban rail regenerative braking energy inversion feedback system, which comprises the following steps: collecting basic data related to train operation; carrying out simulation calculation on the basic data to obtain a capacity and address selection key parameter of the inversion feedback device; and carrying out iterative processing on the basic data according to the capacity and address selecting key parameters, and generating a constant volume addressing optimization scheme of the inversion feedback system after simulation calculation of the iterative processing result. The method forms a perfect inversion feedback simulation method, and has important significance for utilization of the urban rail renewable energy absorption device, energy-saving effect evaluation and reasonable system design to reduce subway operation cost.
Description
Technical Field
The application belongs to the technical field of urban rail transit, and particularly relates to an optimization method of an urban rail regenerative braking energy inversion feedback system.
Background
The rail transit large environment faces high requirements on energy conservation, emission reduction and sustainable development, and the design concept that a regenerative electric energy absorption device is arranged on a traction substation, the regenerative braking efficiency of a train is improved, the operation cost is reduced by less operation electric energy, and meanwhile, the air pollution is reduced is increasingly emphasized in rail transit construction and operation at home and abroad. How to reasonably select the constant volume and site selection scheme of the inversion feedback system by combining the actual line conditions and the train characteristic parameters is very important.
At present, from the initial theoretical research to the wide-range use of the inverter feedback device, the inverter feedback device is gradually improved from the perspective of technical maturity, product functionalization and modularization, and therefore, the requirement for the optimal design of the inverter feedback device in a power supply system is increasingly prominent. Although relatively detailed algorithm models and relatively mature simulation software products exist in the urban rail power supply calculation simulation field in China, a simulation system for perfecting inversion feedback related modules is almost blank, and reasonable analysis is not performed on the energy feedback efficiency of a feedback type renewable energy utilization device at present, so that the method has important significance for utilization of an urban rail renewable energy absorption device, energy-saving effect evaluation and reasonable system design to reduce subway operation cost.
Disclosure of Invention
The application provides an optimization method of an urban rail regenerative braking energy inversion feedback system, which starts from relevant basic data of train operation, and searches an optimal capacity selection and address selection scheme of the inversion feedback system through simulation calculation, thereby improving the utilization of regenerative energy, improving the energy-saving effect and reducing the energy consumption.
In order to achieve the above purpose, the present application provides the following solutions:
an optimization method of an urban rail regenerative braking energy inversion feedback system comprises the following steps:
collecting basic data related to train operation;
carrying out simulation calculation on the basic data to obtain capacity and address selection key parameters of the inversion feedback device;
and performing iterative processing on the basic data according to the capacity selection and address selection key parameters, and generating a capacity-fixed address optimization scheme of the inversion feedback system after the result of the iterative processing is subjected to simulation calculation.
Preferably, the basic data comprises basic line parameters, basic train attribute parameters, ATO system parameters, basic electric parameters of the substation and basic electric parameters of the inversion feedback device.
Preferably, the capacity and site selection key parameters include train operation energy consumption data and energy consumption data of a whole-line substation.
Preferably, the iterative process is performed using the genetic algorithm NSGA-II.
Preferably, the iterative processing method includes:
coding a rated value of the inversion feedback system, wherein the capacity adopts real number coding, and the address selection adopts binary coding;
establishing a genetic algorithm population fitness function according to an investment cost optimization evaluation function and an energy saving rate optimization evaluation function of the inversion feedback device;
and calculating a population fitness value and carrying out genetic operation, and finishing iteration when the iteration algebra reaches the maximum iteration algebra.
Preferably, the fitness equation is maxf (x) ═ f1(X)，f2(X)]Wherein f is1(x) Optimizing an evaluation function, f, for the investment cost of an inverter feedback device2(x) The evaluation function is optimized for the system level energy saving rate,
wherein W is the total power saved by train operation in the reference system, costinvInvestment cost of an inversion feedback device in the ith traction station is saved; n is the total input quantity of the full-line inversion feedback device; c0The price component of the inversion feedback device irrelevant to the capacity is obtained; e is the capacity of the inverter feedback device, CMIs the unit volume, unit cell/kVA; wT1Is the sum of traction energy consumption, W, of the whole-line substation in a reference systemT2Is the sum of the traction energy consumption of the whole line substation after the input of the inversion feedback device, WEFSIs the sum of the regenerative braking energy absorbed by the inversion feedback device on the whole line.
Preferably, the method for calculating the fitness value includes:
selecting an ith individual, decoding an inversion feedback system constant volume location scheme corresponding to the ith individual, and performing simulation calculation on the inversion feedback system constant volume location scheme;
and obtaining the fitness value of the individual according to the result of the simulation calculation.
Preferably, after the iterative processing is finished, the constant volume addressing optimization scheme is obtained by adopting a non-dominant standard, an energy consumption sensitivity standard and a time uniform distribution standard.
The beneficial effect of this application does:
the application discloses an optimization method of an urban rail regenerative braking energy inversion feedback system, which analyzes the inversion feedback device location and capacity setting optimization as a multi-objective optimization problem, so that the finally designed inversion feedback system constant volume location meets the requirement of a non-dominant standard, and simultaneously NSGA-II solution is convenient for selecting the most appropriate inversion feedback system constant volume location based on actual conditions; the method forms a perfect inversion feedback simulation method, and has important significance for utilization of the urban rail renewable energy absorption device, energy-saving effect evaluation and reasonable system design to reduce subway operation cost.
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In order to more clearly illustrate the technical solutions of the present application, the drawings required to be used in the embodiments are briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
Fig. 1 is a schematic flow chart of an optimization method of an urban rail regenerative braking energy inversion feedback system according to an embodiment of the application;
FIG. 2 is a schematic diagram of a system designed for the embodiments of the present application;
FIG. 3 is a schematic diagram of a general flow of a simulation calculation process in an embodiment of the present application;
fig. 4 is a schematic view of a process of solving the Pareto solution of the constant volume location scheme of the inverse feedback system by the NSGA-II in the embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description.
As shown in fig. 1, a schematic flow chart of an optimization method of an urban rail regenerative braking energy inverter feedback system according to an embodiment of the present application is shown, and for convenience of understanding, a method process of each step can be accurately implemented, a system structure diagram as shown in fig. 2 is designed in the present application.
The optimization method of the urban rail regenerative braking energy inversion feedback system mainly comprises the following steps:
s1, collecting basic data related to train operation. In the embodiment, a basic data module is arranged for implementation.
In this embodiment, the basic data module is configured to collect basic data related to train operation, where the basic data to be collected includes basic line parameters, basic train attribute parameters, ATO system parameters, basic electrical parameters of a substation, and basic electrical parameters of an inverter feedback device.
Specifically, the basic data module comprises a line data unit, a train attribute data unit, an ATO configuration unit, a substation parameter unit and an inversion feedback device parameter unit.
The line data unit is used for acquiring basic line parameters including station data, ramp data, curve data, speed limit data, contact network layout and the like.
The train attribute data unit is used for acquiring basic train attribute parameters of a train and providing basic operation parameters of train operation, including train marshalling, passenger capacity, basic resistance parameters, inverter efficiency and traction brake characteristics.
And the ATO configuration unit is used for configuring basic characteristic quantities of the ATO system and acquiring ATO system parameters of train operation, including maximum traction acceleration, maximum braking acceleration, maximum impact limit and maximum operation speed.
The substation parameter unit provides basic electrical parameters and layout of the substation, and obtains the basic electrical parameters of the substation, including rectifier capacity, highest working voltage, lowest working voltage and energy conversion efficiency.
The parameter unit of the inversion feedback device is used for providing basic electrical parameters and layout of the inversion feedback device and obtaining the basic electrical parameters of the inversion feedback device, including starting voltage, energy conversion efficiency, capacity and power.
And S2, carrying out train operation simulation calculation on the basic data to obtain a capacity and address selecting key parameter of the inversion feedback device. Fig. 3 is a schematic diagram of the overall flow of the simulation calculation process. In this embodiment, this step is designed to be performed by the simulation calculation module.
In this embodiment, a simulation calculation module is configured to perform train operation simulation calculation on the basic data to obtain capacity selection and site selection key parameters of the inversion feedback device, where the capacity selection and site selection key parameters include train operation energy consumption data, energy consumption data of the whole-line substation, and regenerative braking energy consumption data.
Specifically, the simulation calculation module comprises an ATO unit, a train mechanics unit, a substation unit, an inversion feedback device unit, a train state updating calculation unit and a train operation energy consumption calculation unit.
The ATO unit is used for calculating the current train acceleration according to the ATO system parameters and the basic line parameters to obtain a train acceleration value, keeping or transferring the train working condition and sending the train acceleration value to the train mechanics unit.
And the train mechanics unit is used for calculating the traction or braking force of the train according to the train acceleration value, the basic line parameter and the basic train attribute parameter provided by the ATO unit, respectively obtaining a traction value or a braking force value, and transmitting the traction value or the braking force value to the train state updating calculation unit and the train operation energy consumption calculation unit.
And the train state updating calculation unit performs mechanical calculation on the traction force value or the braking force value to obtain the current running data of the train, including the current speed, the running distance and the running time, and sends the current running data to the ATO unit for cyclic optimization.
And the inversion feedback device unit obtains regenerative braking energy consumption data fed back to the inversion feedback device according to the basic electrical parameters of the inversion feedback device.
The train operation energy consumption calculation unit calculates energy consumption according to the traction force value or the braking force value provided by the train mechanics unit, and calculates the interval operation time and the traction energy consumption of the train.
And the substation unit is used for calculating the energy consumption data of the current whole-line substation, including the voltage, the current and the power at the direct current side, according to the basic electrical parameters of the substation, including the train operation power requirement, the contact network segment layout, the layout of the traction substation and the traction force value or the braking force value.
And S3, carrying out iterative processing on the basic data according to the capacity selection and address selection key parameters, and generating a constant volume addressing optimization scheme of the inversion feedback system after simulation calculation of the iterative processing result. In this embodiment, the design is implemented by an iterative optimization module.
In this embodiment, the inversion feedback device location and capacity setting optimization is used as a multi-objective optimization problem, a genetic algorithm NSGA-II is used to perform constant volume location optimization, and the NSGA-II is solved to obtain a Pareto solution set which is uniformly distributed, so as to determine an optimal constant volume location scheme of the inversion feedback system, as shown in fig. 4. The method comprises the following specific steps:
(1) and (3) encoding: the coded object is an inversion feedback system constant volume location scheme; in this embodiment, the capacity coding of the inverting feedback device adopts real number coding, and the coded value directly represents the capacity E of the inverting feedback device; the address selection code of the inversion feedback device adopts binary coding, the code of 0 indicates that the inversion feedback device is not installed, and the code of 1 indicates that the inversion feedback device is installed.
(2) Determining the population quantity: determining the size of the population and an iterative algebra according to the interval length; in this embodiment, the population size and the iteration algebra are determined according to the interval length, for example: 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.
(3) Is provided withA population fitness setting equation: maxf (x) ═ f1(X)，f2(X)]Wherein f is1(x) Optimizing an evaluation function, f, for the investment cost of an inverter feedback device2(x) Optimizing an evaluation function for the system-level energy saving rate, wherein the specific form is as follows:
wherein W is the total power saved by train operation in the reference system, costinvInvestment cost of an inversion feedback device in the ith traction station is saved; n is the total input quantity of the full-line inversion feedback device; c0The price component of the inversion feedback device irrelevant to the capacity is obtained; e is the capacity of the inverter feedback device, CMIs the unit volume, unit cell/kVA; wT1Is the sum of traction energy consumption, W, of the whole-line substation in a reference systemT2Is the sum of the traction energy consumption of the whole line substation after the input of the inversion feedback device, WEFSIs the sum of the regenerative braking energy absorbed by the inversion feedback device on the whole line.
(4) Calculating the individual fitness value of the parent population: calculating the individual fitness value of the parent population by a simulation calculation module;
(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 an individual fitness value of the sub-population by a simulation calculation module;
(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; for example, a parent population and a child population generated by the parent population are combined to compete together to generate a next generation parent population, so that excellent individuals in the parent population enter the next generation, and the optimal individuals cannot be lost.
(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 inversion feedback system constant volume location scheme: and selecting an inversion feedback system constant volume site selection scheme by adopting a non-dominant standard, an energy consumption sensitivity standard and a time uniform distribution standard.
In this embodiment, the calculating of the individual fitness value in step (4) and step (6) includes:
(a) taking the ith individual in the population, decoding an inversion feedback system constant volume addressing scheme corresponding to the individual, wherein the initial value of i is 0;
(b) transmitting the inversion feedback system constant volume location scheme of the individual chromosome decoding 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 investment cost optimization evaluation function of the inversion feedback device, and the individual fitness 2 is a system-level energy-saving rate optimization evaluation function;
(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).
The above-described embodiments are merely illustrative of the preferred embodiments of the present application, and do not limit the scope of the present application, and various modifications and improvements made to the technical solutions of the present application by those skilled in the art without departing from the spirit of the present application should fall within the protection scope defined by the claims of the present application.
Claims (3)
1. An optimization method of an urban rail regenerative braking energy inversion feedback system is characterized by comprising the following steps:
collecting basic data related to train operation, wherein the basic data comprises basic line parameters, basic train attribute parameters, ATO system parameters, basic electric parameters of a substation and basic electric parameters of an inversion feedback device;
carrying out train operation simulation calculation on the basic data to obtain capacity and address selection key parameters of the inversion feedback device, wherein the capacity and address selection key parameters comprise train operation energy consumption data and whole-line substation energy consumption data;
performing iterative processing on the basic data according to the capacity selection and address selection key parameters, and generating a capacity-fixed addressing optimization scheme of the inversion feedback system after the result of the iterative processing is subjected to the simulation calculation;
performing the iterative processing by adopting a genetic algorithm NSGA-II;
the method of iterative processing comprises:
coding a rated value of the inversion feedback system, wherein the capacity adopts real number coding, and the address selection adopts binary coding;
establishing a genetic algorithm population fitness function according to an investment cost optimization evaluation function and an energy saving rate optimization evaluation function of the inversion feedback device;
calculating a population fitness value and carrying out genetic operation, and finishing iteration when an iteration algebra reaches a maximum iteration algebra;
the fitness equation is max f (X) ═ f1(X)，f2(X)]Wherein f is1(x) Optimizing an evaluation function, f, for the investment cost of an inverter feedback device2(x) The evaluation function is optimized for the system level energy saving rate,
wherein W is the total power saved by train operation in the reference system, costinvInvestment cost of an inversion feedback device in the ith traction station is saved; n is the total input quantity of the full-line inversion feedback device; c0The price component of the inversion feedback device irrelevant to the capacity is obtained; e is the capacity of the inverter feedback device, CMIs the unit volume, unit cell/kVA; wT1Is the sum of traction energy consumption, W, of the whole-line substation in a reference systemT2Is the sum of the traction energy consumption of the whole line substation after the input of the inversion feedback device, WEFSIs the sum of the regenerative braking energy absorbed by the inversion feedback device on the whole line.
2. The method for optimizing the urban rail regenerative braking energy inverter feedback system according to claim 1, wherein the method for calculating the fitness value comprises:
selecting an ith individual, decoding an inversion feedback system constant volume location scheme corresponding to the ith individual, and performing simulation calculation on the inversion feedback system constant volume location scheme;
and obtaining the fitness value of the individual according to the result of the simulation calculation.
3. The optimization method of the urban rail regenerative braking energy inversion feedback system according to claim 1, wherein the constant volume addressing optimization scheme is obtained by adopting a non-dominated standard, an energy consumption sensitivity standard and a time uniform distribution standard after iteration processing is finished.
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