CN113629742A - Capacity configuration method for ground hybrid energy storage system of electrified railway - Google Patents
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Abstract
The invention discloses a capacity configuration method of a ground hybrid energy storage system of an electrified railway, which aims at the problem that the capacity configuration of the railway hybrid energy storage system is difficult, takes the serial-parallel connection number of energy storage media as a control variable, takes investment cost and economic benefit as optimization targets, sets various electric and non-electric constraints, adopts a multi-target genetic algorithm to iterate to obtain a pareto solution set, and optimizes the solution by using cost recovery years.
Description
Technical Field
The invention relates to the technical field of electrified railways, in particular to a capacity configuration method of a ground hybrid energy storage system of an electrified railway.
Background
With the continuous enlargement of the scale of a road network, the electrification rate of railways is continuously improved, the huge traction energy consumption of electrified railways becomes one of the main expenses of daily operation and maintenance of railway systems, the problem of how to reduce the traction energy consumption of the electrified railways and improve the utilization efficiency of energy becomes urgent to be solved, and the appearance of the energy storage technology provides a new technical idea. The main technical route of the existing electrified energy storage technology is to install a hybrid energy storage system on a traction substation, wherein the hybrid energy storage system generally comprises two energy storage media, a feeder line is respectively led out from a traction substation contact line and a steel rail, and the hybrid energy storage system performs power exchange after voltage reduction, rectification and direct-direct conversion, so that regenerative braking energy is absorbed when a train is in a regenerative braking state, the stored energy is released when the train is in a traction state, and the utilization efficiency of the regenerative braking energy and the quality of electric energy are improved. However, the energy storage system is high in manufacturing cost, and the physical characteristics and purchase price difference between different energy storage media are large, so that how to configure the capacity of the hybrid energy storage system under the condition of meeting various actual constraint conditions has practical engineering significance for reducing the system investment and shortening the cost recovery period.
At present, the capacity allocation of an energy storage system is mainly concentrated in the field of power distribution networks such as wind power and photovoltaic power generation, and the capacity allocation of the energy storage system of the electrified railway is still in a starting stage. Chinese patent publication No. CN111628514A, publication No. 2020.09.04, entitled method, apparatus, terminal and storage medium for determining a discharge threshold of an energy storage device, discloses a method for determining a discharge threshold of an energy storage system of an electrified railway and a storage medium, but the description of the storage medium in the scheme is macroscopic and lacks a detailed and quantized capacity configuration process; chinese patent publication No. CN111864774A, publication No. 2020.10.30, the name of the invention is a peak clipping and valley filling control method for in-phase hybrid energy storage power supply structure of an electrified railway, publication No. CN110829435A, publication No. 2019.10.09, the name of the invention is an energy storage type traction power supply system of an electrified railway and a control method thereof, which respectively describe two structures and control methods of energy storage systems suitable for electrified railways, but both of them use the basic premise of completing capacity configuration, and only use a single energy storage medium, and are not as economical as the hybrid energy storage system, and lack description of the configuration process.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a capacity configuration method of a ground hybrid energy storage system of an electrified railway aiming at the defects of the background technology, and the technical problem to be solved is as follows:
1. establishing a capacity configuration multi-objective optimization model of the ground hybrid energy storage system of the electrified railway;
2. quantifying various electrical and non-electrical constraint conditions of the traction power supply system;
3. solving a capacity configuration multi-objective optimization model by using a genetic algorithm;
4. and optimizing the optimized pareto solution set according to the return on investment years.
The invention adopts the following technical scheme for solving the technical problems:
the invention provides a capacity allocation method of a ground hybrid energy storage system of an electrified railway, which takes the serial number and the parallel number of energy storage media of each energy storage subsystem as control variables, takes a minimum equal-year-value investment function and a maximum monthly economic benefit function as optimization objective functions, takes voltage, current, charge state, power and capacity as electric constraints, takes volume and mass as non-electric constraints, establishes a multi-target capacity allocation model of the hybrid energy storage system, solves a pareto solution set through a genetic algorithm, obtains cost recovery time through the equal-year-value investment value corresponding to each solution and monthly economic benefits, and optimizes the solution set to obtain an optimal allocation scheme.
The ground hybrid energy storage system of the electrified railway is characterized in that the traction power supply system adopts a single-phase alternating-current power-frequency power supply system, the ground hybrid energy storage system is connected with contact lines of power supply arms on two sides of a traction substation and a steel rail, the contact lines pass through a single-phase step-down transformer and then are connected with an LCL (lower control limit) type filter, then are connected with a railway power regulator device, a feeder line is led out from a direct-current bus of the railway power regulator and is connected with a direct-current bus of the energy storage system, two feeder lines are respectively led out, and are respectively connected with a half-bridge type DC/DC converter and finally are connected with a high-power energy storage subsystem and a high-capacity energy storage subsystem, and the structure of the ground hybrid energy storage system is shown in figure 1.
The electric railway electric charge metering mode adopts a maximum demand charging mode, namely, the monthly electric charge of the electric railway is divided into a maximum demand electric charge part and an actually used electric charge part, the maximum demand electric charge part is equal to the maximum demand power multiplied by the unit power price, and the actually used electric charge part is equal to the monthly actually used electric charge value multiplied by the unit electric charge price.
The capacity configuration method of the ground hybrid energy storage system of the electrified railway is characterized by comprising the following steps:
step 1: the serial number and the parallel number of the energy storage media of the energy storage subsystem are used as control variables, and the serial number of the high-power energy storage subsystem is set as Ns1And the number of parallel connections is Np1The number of series-connected high-capacity energy storage subsystems is Ns2And the number of parallel connections is Np2Then the control variables are as follows:
X＝[Ns1,Np1,Ns2,Np2]； (1)
step 2: the method takes an objective function of the ground hybrid energy storage system of the electrified railway as a minimum equal-annual-value investment function and a maximum monthly economic benefit function as an optimization objective function, and comprises the following steps:
2-1 calculating a minimum equal annual value investment function, and setting the related equipment investment of the energy storage system as C1Annual average maintenance cost C2The system residual value rate is S, the loan interest rate is I, the service life is n, and the minimum equal-annual-value investment function is as follows:
2-2 calculating related equipment investment C of energy storage system1Setting the investment of energy storage medium as CessCivil engineering costInvestment is CbuildThe investment of the unit power step-down transformer is CtransThe investment of the railway power regulator with unit power is CrpcInvestment of unit power DC/DC converter is CdcThe investment of unit power system integration cost is CintLet total power of energy storage system be PsysInvestment C on equipment related to energy storage system1The following were used:
C1＝Cess+Cbuild+Psys(Ctrans+Cdc+Crpc+Cint)； (3)
2-3, calculating a maximum monthly economic benefit function, and setting the maximum power demand reduction amount to be P after the hybrid energy storage system is installedrd_powerThe monthly active power saving degree is WsaveThe maximum demand electric charge per unit power is CkWAnd the unit industrial electricity consumption electric charge is CkWhThe maximum menstrual economic benefit function is then as follows:
maxg2(x)＝Prd_powerCkW+WsaveCkWh； (4)
2-4 calculating the maximum power demand reduction amount, and setting the maximum power demand P reduced every monthrd_powerAnd hybrid energy storage system power PsysFor a linear relationship, let krdFor the maximum power demand reduction coefficient, the relationship is as follows:
Prd_power＝krdPsys； (5)
2-5, calculating the monthly active power saving degree, and setting the monthly active power saving degree WsaveWith the average traction power P of the locomotiveqy_pjHybrid energy storage system capacity WsysTotal traction electric quantity WqyTotal quantity of regenerative braking electric power WzdAnd short-time regenerative braking electric quantity Wzd_dsRelated, the total loss coefficient of each link of the system is set as ktotalPower coefficient of kpCoefficient of capacity loss of kwThen the monthly reduced maximum power demand formula is as follows:
and step 3: setting a constraint function of the electrified railway ground hybrid energy storage system, comprising:
3-1 power constraint, and setting the maximum traction power of the energy storage subsystem 1 as Psub1_maxMaximum regenerative braking power of Ps′ub1_maxThe maximum traction power of the energy storage subsystem 2 is Psub2_maxMaximum regenerative braking power of Ps′ub2_maxThe locomotive traction power is PtractThe regenerative braking power of the locomotive is PbreakLine loss power of PlossThe power constraint function is as follows:
3-2 capacity constraint, and setting the kth work starting time of the energy storage subsystem 1 as t1_st(k)The end time is t1_end(k)The k-th work starting time of the energy storage subsystem 2 is t2_st(k)The end time is t2_end(k)The short-time traction energy of the train is Etract_minAnd the short-time regenerative braking energy of the train is Ebreak_minLine loss energy of ElossThe capacity constraint function is as follows:
3-3 voltage, current, charge state constraints, set Usingle_mediumFor storing the voltage of the medium, U, alones_lowerAnd Us_upperRespectively, the lower limit and the upper limit of the operating voltage, Csingle_mediumFor the monomer energy storage medium current, C is the rated output current under the rated voltage, SOCsingle_mediumIs a monomer pure medium state of charge, SOCs_lowerAnd SOCs_upperThe lower and upper charge state limits, respectively, then the voltage, current, and charge state constraint functions are as follows:
3-4 mass, volume constraint, setting Msub1And Msub2Respectively mass of the energy storage subsystem, MequipmentFor the relevant equipment quality, MmaxAs a quality threshold, Vsub1And Vsub2Respectively, the volume of the energy storage subsystem, VequipmentFor the relevant apparatus volume, VmaxFor the volume threshold, the mass constraint and volume constraint functions are as follows:
and 4, step 4: solving a capacity allocation model of a ground hybrid energy storage system of the electrified railway by using a genetic algorithm, setting four variables to represent the serial and parallel quantity of energy storage media of each subsystem respectively by the genetic algorithm for solving the capacity allocation model of the multi-target hybrid energy storage system, setting power constraint and capacity constraint as inequality constraint of the genetic algorithm, setting voltage constraint, current constraint and charge state constraint as upper and lower limit constraints of the genetic algorithm, taking a minimum equal-year-value investment function and a maximum menstrual economic function as target functions of the genetic algorithm, setting an optimal individual coefficient, a population size, a maximum evolution algebra, a stopped algebra and a fitness function deviation, carrying out initialization operation on the population to obtain a first-generation population, calculating the individual equal-year-value investment function value and the monthly economic benefit value to obtain the fitness, and carrying out selection, crossing and variation operation according to the fitness, generating a next generation population, and circulating the process until the evolution algebra reaches the maximum evolution algebra to obtain a pareto solution set of the multi-target model;
and 5: optimizing the pareto solution set obtained by the genetic algorithm, calculating the equal annual value investment amount and monthly economic benefit corresponding to each group of solutions according to the pareto solution set obtained by the genetic algorithm after rounding up, calculating the investment cost recovery time limit of each solution set, screening out solutions with the cost recovery time limit larger than the equipment service time limit according to the equipment service time limit as a basic criterion, and sequencing the remaining solutions to obtain the optimal solution of the cost recovery time limit meeting all constraint conditions.
In summary, the flow of the capacity allocation method of the ground hybrid energy storage system of the electrified railway is shown in fig. 4.
Compared with the prior art, the invention adopting the technical scheme has the following beneficial effects:
1. the capacity configuration process of the hybrid energy storage system of the electrified railway is quantized, the economical efficiency of the investment cost of the system and the monthly economic benefit can be effectively considered, and the year of the investment cost is shortened as much as possible;
2. the capacity configuration method provided by the invention has wide application range, only needs to modify corresponding parameters for different line conditions, has no strict limitation on the type of the energy storage medium, and can meet the capacity configuration under different requirements;
3. the method has the advantages that the required information is simple and easy to obtain, the capacity configuration of the hybrid energy storage system can be carried out according to the related data during railway planning, the calculation can be carried out by using a computer, and the labor time is saved.
Drawings
FIG. 1 is a hybrid energy storage system configuration for an electrified railway ground.
Fig. 2 is a schematic diagram of series-parallel connection of energy storage media.
FIG. 3 is a flow chart of a genetic algorithm.
FIG. 4 is a flow chart of a capacity allocation method of a ground hybrid energy storage system of an electrified railway.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art
Aiming at the structure of the ground hybrid energy storage system of the electrified railway shown in the figure 1, a traction power supply system adopts a single-phase alternating-current power frequency power supply system, the ground hybrid energy storage system is connected with contact lines of power supply arms on two sides of a traction substation and a steel rail, the ground hybrid energy storage system is connected with an LCL (lower control limit) type filter after passing through a single-phase step-down transformer and then is connected with a railway power regulator device, a feeder line is led out from a direct-current bus of the railway power regulator and is connected with a direct-current bus of the energy storage system, two feeder lines are led out respectively and are connected with a half-bridge type DC/DC converter respectively, and finally the high-power energy storage subsystem is connected with a large-capacity energy storage subsystem, the energy storage subsystem 1 adopts a super capacitor as an energy storage medium, the energy storage subsystem 2 adopts a lithium titanate battery as the energy storage medium, and the parameter information of the energy storage medium is shown in a table 1.
TABLE 1 energy storage Medium parameter Table
Assuming that only one train of electric locomotives is in operation on the power supply arms on both sides of the traction power supply system, the basic parameters of the traction power supply system are shown in table 2.
TABLE 2 parameter table of traction power supply system
The various economic parameters of the energy storage system are shown in table 3.
TABLE 3 energy storage System economic parameters
Setting the variable of the genetic algorithm as 4, setting the power constraint and the capacity constraint as inequality constraints of the genetic algorithm, setting the voltage constraint, the current constraint and the charge state constraint as upper and lower limits constraints of the genetic algorithm, taking the minimum equal-year-number investment function and the maximum economic benefit function as target functions of the genetic algorithm, setting the optimal individual coefficient as 0.3, the population size as 200, the maximum evolution algebra as 300, the stop algebra as 200 and the fitness function deviation as infinitesimal, and initializing the population to obtain a first generation population. After the genetic algorithm completes iteration, a pareto solution set with 50 groups of solutions is obtained, and part of original solutions are as follows:
TABLE 4 genetic Algorithm pareto solution set (part)
After rounding up and rounding up, respectively calculating the annual value investment value and the monthly economic benefit corresponding to each pareto solution, calculating the cost recovery years, sorting according to ascending order, and eliminating invalid solutions with the cost recovery years larger than the service life years, wherein the results are as follows:
TABLE 5 Capacity allocation results Table
According to the optimization result, when the number of the super capacitors is 741 in series, the number of the parallel capacitors is 3, the number of the lithium titanate batteries is 870 in series, and the number of the parallel capacitors is 5, the hybrid energy storage system can simultaneously reduce the system investment and improve the monthly economic benefit under the condition of meeting various constraints, and the lowest cost recovery period is realized, so that the hybrid energy storage system is an optimal solution.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A capacity allocation method for a ground hybrid energy storage system of an electrified railway is characterized in that the method takes the serial number and the parallel number of energy storage media of each energy storage subsystem as control variables, takes a minimum equal-year-value investment function and a maximum monthly economic benefit function as optimization objective functions, takes voltage, current, charge state, power and capacity as electric constraints, takes volume and mass as non-electric constraints, establishes a multi-target capacity allocation model of the hybrid energy storage system, obtains a pareto solution set through a genetic algorithm, obtains cost recovery time through the equal-year-value investment value corresponding to each solution and monthly economic benefits, and optimizes the solution set to obtain an optimal allocation scheme.
2. The method as claimed in claim 1, wherein the traction power supply system in the ground hybrid energy storage system of the electric railway adopts a single-phase ac power frequency power supply system, the ground hybrid energy storage system is connected with the contact lines of the power supply arms on both sides of the traction substation and the steel rail, and is connected to the LCL filter after passing through the single-phase step-down transformer, and then is connected to the railway power regulator apparatus, and the feeder line is led out from the DC bus of the railway power regulator, and is connected to the DC bus of the energy storage system, and then is led out to two feeder lines respectively, and is connected to the half-bridge DC/DC converter respectively, and finally is connected to the high-power energy storage subsystem and the high-capacity energy storage subsystem.
3. The method as claimed in claim 1, wherein the electric railway ground hybrid energy storage system capacity allocation method in the maximum monthly economic benefit function is characterized in that the electric railway electricity charge metering mode adopts a maximum demand charging mode, that is, the electric railway monthly electricity charge is divided into a maximum demand electricity charge part and an actual use electricity charge part, the maximum demand electricity charge part is equal to the maximum demand power multiplied by the unit power price, and the actual use electricity charge part is equal to the monthly actual use electricity charge multiplied by the unit electricity price.
4. The method of configuring capacity of an electrified railway ground hybrid energy storage system of claim 1, comprising:
step 1: the serial number and the parallel number of the energy storage media of the energy storage subsystem are used as control variables, and the serial number of the high-power energy storage subsystem is set as Ns1And the number of parallel connections is Np1The number of series-connected high-capacity energy storage subsystems is Ns2And the number of parallel connections is Np2Then the control variables are as follows:
X＝[Ns1,Np1,Ns2,Np2]； (1)
step 2: the method takes an objective function of the ground hybrid energy storage system of the electrified railway as a minimum equal-annual-value investment function and a maximum monthly economic benefit function as an optimization objective function, and comprises the following steps:
2-1 calculating a minimum equal annual value investment function, and setting the related equipment investment of the energy storage system as C1Annual average maintenance cost C2The system residual value rate is S, the loan interest rate is I, the service life is n, and the minimum equal-annual-value investment function is as follows:
2-2 calculating related equipment investment C of energy storage system1Setting the investment of energy storage medium as CessThe investment of civil engineering cost is CbuildThe investment of the unit power step-down transformer is CtransThe investment of the railway power regulator with unit power is CrpcInvestment of unit power DC/DC converter is CdcThe investment of unit power system integration cost is CintLet total power of energy storage system be PsysInvestment C on equipment related to energy storage system1The following were used:
C1＝Cess+Cbuild+Psys(Ctrans+Cdc+Crpc+Cint)； (3)
2-3, calculating a maximum monthly economic benefit function, and setting the maximum power demand reduction amount to be P after the hybrid energy storage system is installedrd_powerSaving the active power per monthIs WsaveThe maximum demand electric charge per unit power is CkWAnd the unit industrial electricity consumption electric charge is CkWhThe maximum menstrual economic benefit function is then as follows:
max g2(x)＝Prd_powerCkW+WsaveCkWh； (4)
2-4 calculating the maximum power demand reduction amount, and setting the maximum power demand P reduced every monthrd_powerAnd hybrid energy storage system power PsysFor a linear relationship, let krdFor the maximum power demand reduction coefficient, the relationship is as follows:
Prd_power＝krdPsys； (5)
2-5, calculating the monthly active power saving degree, and setting the monthly active power saving degree WsaveWith the average traction power P of the locomotiveqy_pjHybrid energy storage system capacity WsysTotal traction electric quantity WqyTotal quantity of regenerative braking electric power WzdAnd short-time regenerative braking electric quantity Wzd_dsRelated, the total loss coefficient of each link of the system is set as ktotalPower coefficient of kpCoefficient of capacity loss of kwThen the monthly reduced maximum power demand formula is as follows:
Wsave＝Wqy-ktotalkpkwWzd
and step 3: setting a constraint function of the electrified railway ground hybrid energy storage system, comprising:
3-1 power constraint, and setting the maximum traction power of the energy storage subsystem 1 as Psub1_maxMaximum regenerative braking power of Ps′ub1_maxThe maximum traction power of the energy storage subsystem 2 is Psub2_maxMaximum regenerative braking power of Ps′ub2_maxThe locomotive traction power is PtractThe regenerative braking power of the locomotive is PbreakLoss of power in the lineA rate of PlossThe power constraint function is as follows:
3-2 capacity constraint, and setting the kth work starting time of the energy storage subsystem 1 as t1_st(k)The end time is t1_end(k)The k-th work starting time of the energy storage subsystem 2 is t2_st(k)The end time is t2_end(k)The short-time traction energy of the train is Etract_minAnd the short-time regenerative braking energy of the train is Ebreak_minLine loss energy of ElossThe capacity constraint function is as follows:
3-3 voltage, current, charge state constraints, set Usingle_mediumFor storing the voltage of the medium, U, alones_lowerAnd Us_upperRespectively, the lower limit and the upper limit of the operating voltage, Csingle_mediumFor the monomer energy storage medium current, C is the rated output current under the rated voltage, SOCsingle_mediumIs a monomer pure medium state of charge, SOCs_lowerAnd SOCs_upperThe lower and upper charge state limits, respectively, then the voltage, current, and charge state constraint functions are as follows:
3-4 mass, volume constraint, setting Msub1And Msub2Respectively mass of the energy storage subsystem, MequipmentFor the relevant equipment quality, MmaxAs a quality threshold, Vsub1And Vsub2Respectively, the volume of the energy storage subsystem, VequipmentFor the relevant apparatus volume, VmaxFor the volume threshold, the mass constraint and volume constraint functions are as follows:
and 4, step 4: solving a capacity allocation model of a ground hybrid energy storage system of the electrified railway by using a genetic algorithm, setting four variables to represent the serial and parallel quantity of energy storage media of each subsystem respectively by the genetic algorithm for solving the capacity allocation model of the multi-target hybrid energy storage system, setting power constraint and capacity constraint as inequality constraint of the genetic algorithm, setting voltage constraint, current constraint and charge state constraint as upper and lower limit constraints of the genetic algorithm, taking a minimum equal-year-value investment function and a maximum menstrual economic function as target functions of the genetic algorithm, setting an optimal individual coefficient, a population size, a maximum evolution algebra, a stopped algebra and a fitness function deviation, carrying out initialization operation on the population to obtain a first-generation population, calculating the individual equal-year-value investment function value and the monthly economic benefit value to obtain the fitness, and carrying out selection, crossing and variation operation according to the fitness, generating a next generation population, and circulating the process until the evolution algebra reaches the maximum evolution algebra to obtain a pareto solution set of the multi-target model;
and 5: optimizing the pareto solution set obtained by the genetic algorithm, calculating the equal annual value investment amount and monthly economic benefit corresponding to each group of solutions according to the pareto solution set obtained by the genetic algorithm after rounding up, calculating the investment cost recovery time limit of each solution set, screening out solutions with the cost recovery time limit larger than the equipment service time limit according to the equipment service time limit as a basic criterion, and sequencing the remaining solutions to obtain the optimal solution of the cost recovery time limit meeting all constraint conditions.
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