An urban rail ground energy storage system configuration method based on energy coupling and co-evolution

Abstract

This invention provides a configuration method for urban rail transit ground energy storage systems based on energy coupling and co-evolution. The method includes: establishing an equivalent circuit for the system based on the load and line information of the DC traction power supply system, using the location and installed power of the traction substation where energy storage is installed as decision variables; dividing the daily train operation scenario into peak and off-peak periods based on passenger flow; determining the expression for the system energy saving amount under the daily typical scenario where energy storage is installed relative to the system without energy storage; simultaneously, using the installed energy storage capacity as a decision variable, establishing an optimal configuration model for the urban rail transit ground energy storage system considering energy storage charging and discharging losses; and solving the model using a genetic algorithm combined with a co-evolution strategy to obtain the optimal configuration results for energy storage installation location, installed power, and capacity. This invention considers the energy coupling between traction substations, trains, and energy storage, enabling precise matching of the dynamic energy demand of trains and achieving efficient utilization of overall system resources.

Description

Technical Field

[0001] This invention belongs to the field of energy storage optimization configuration for urban rail transit, and relates to a configuration method for urban rail ground energy storage systems based on energy coupling and co-evolution. Background Technology

[0002] Urban rail transit has developed rapidly due to its advantages of being green, environmentally friendly, and convenient. However, the short distances between subway stations and frequent train starts and stops lead to significant voltage fluctuations in the traction network. Furthermore, trains generate substantial braking energy during braking, which is wasted using traditional braking resistors. Energy storage devices can absorb excess energy during braking and, in conjunction with rectifier units, release electrical energy during train traction, while also providing voltage stabilization. Therefore, researching the optimal configuration of energy storage devices in urban rail transit systems is crucial for improving renewable energy utilization and reducing energy consumption.

[0003] Existing research on energy storage optimization in urban rail transit typically configures energy storage capacity and power based on fixed discrete unit specifications, limiting the range of optimization variables to integer multiples of the units, which restricts configuration flexibility. At the same time, existing research pays little attention to the impact of energy flow characteristics of traction power supply systems on energy storage configuration in multi-train operation scenarios. Furthermore, due to the complexity and strong coupling of energy paths in this scenario, traditional optimization methods are prone to problems such as low search efficiency, insufficient accuracy, and easy getting trapped in local optima.

[0004] In the context of energy conservation and emission reduction, it is essential to incorporate the energy-saving effect of the system after installing energy storage into the configuration scheme when optimizing system energy storage configuration. Based on this, this invention proposes a corresponding configuration method for urban rail ground energy storage systems. This method comprehensively considers the installation space conditions of each traction substation and the dynamic changes of train load over time, continuously optimizing the power and capacity of energy storage. The optimization model takes into account both efficient resource utilization and energy interaction between trains under multi-train operation conditions to improve the operability and scientific nature of the energy storage configuration scheme. Furthermore, a genetic algorithm combined with a co-evolutionary strategy is used to solve the model to improve algorithm efficiency. Summary of the Invention

[0005] To address the shortcomings of existing methods, this invention provides a configuration method for urban rail ground energy storage systems based on energy coupling and co-evolution. This method comprehensively considers the energy interaction between traction substations, trains, and energy storage, and takes into account the flexibility of energy storage power and capacity configuration. This is beneficial to improving the operability and scientific nature of the scheme, realizing the optimization of energy storage configuration under multi-train operation conditions, and improving efficiency compared with traditional genetic algorithms.

[0006] This invention proposes a configuration method for urban rail ground energy storage systems based on energy coupling and co-evolution, comprising the following steps:

[0007] Step 1: Based on the load information and line information of the DC traction power supply system of urban rail transit, construct the equivalent circuit of the parameterized DC traction power supply system containing candidate energy storage nodes, express the energy coupling equation in the form of parameters for the energy storage installation location and power, divide the daily train operation scenario into two typical daily scenarios, peak period and off-peak period, according to the passenger flow, and determine the calculation formula for the energy saving of the system with energy storage installed relative to the system without energy storage under the typical daily scenario.

[0008] Step 2: Based on the above calculation formula, the objective function is to maximize the amount of resources saved throughout the entire life cycle corresponding to the decisions on the energy storage installation location, installed power, and installed capacity of all traction substations included in the system. The constraints include power balance constraints, energy storage SOC constraints, and energy storage power charging and discharging power constraints. An optimal configuration model for the urban rail ground energy storage system considering energy storage charging and discharging losses throughout the entire life cycle is established. The model is solved using a genetic algorithm combined with a co-evolutionary update strategy to obtain the optimal configuration location, rated power, and rated capacity of the energy storage system in each traction substation.

[0009] Furthermore, in step 1 of this invention, the line information includes the number and location of traction substations, the distance between substations, the impedance per unit length of the overhead contact line, and the impedance per unit length of the rail. The load information includes the number of trains on the same power supply zone, the train location, and the train power.

[0010] Furthermore, in step 1, based on the line information and the load information at each sampling time, a parameterized DC traction power supply system equivalent circuit containing candidate energy storage nodes is constructed. In this circuit, the negative terminal of the equivalent circuit of the uncontrolled rectifier units of all traction substations is grounded. There are a total of N+2M nodes in the system equivalent circuit, where N and M are the number of traction substations and the number of trains on the line, respectively.

[0011] Furthermore, in step 1, the energy storage installation location and power are used as decision variables. Based on the equivalent circuit of the parameterized DC traction power supply system containing candidate energy storage nodes, a model of traction substation-train-energy storage energy coupling is established, showing the flow of regenerative braking energy between adjacent power supply sections under the through-power supply technology. The energy coupling equation is shown below:

[0012] (1)

[0013] in, and These refer to the number of traction substations and the number of trains on the line, respectively. It is a node in the system's equivalent circuit. With nodes The admittance between, where, , , It is the first in the system's equivalent circuit. Each traction substation corresponds to a node The node voltage, where , It is installed in the first The current corresponding to the energy storage equivalent branch of each traction substation. It is installed in the first The node voltage of energy storage in a traction substation. It is the corresponding node of the online train. The node voltage, where, , It is the first in the system's equivalent circuit. Each traction substation corresponds to a branch injection node. The current, It is the train branch line injection node Given the current and the train power, the train branch current is... . Indicates the first Whether a traction substation is equipped with energy storage is indicated by a value of 1 (equivalent to 0) or 0 (equivalent to 0).

[0014] Furthermore, when the voltage at the energy storage node is higher than the energy storage charging threshold... Energy storage is primarily equivalent to a constant voltage source, with the energy storage node voltage... Equal to the charging threshold, the energy storage output power obtained after solving the equation using Newton's method. Exceeding rated power When energy storage is equivalent to a constant power source, during energy storage charging... equal to charging power divided by The energy coupling equations were solved again using Newton's method.

[0015] When the voltage at the energy storage node is lower than the discharge threshold, the energy storage is initially equivalent to a constant voltage source, and the energy storage node voltage... equal to the discharge threshold The energy storage output power obtained after solving the equation using Newton's method Exceeding rated power At that time, the energy storage is equivalent to a constant power source, and the current of the energy storage branch is... equal to the discharge power divided by The energy coupling equations were solved again using Newton's method.

[0016] In other cases, energy storage does not involve charging or discharging;

[0017] The termination condition for solving the iteration using Newton's method is that the values ​​of the voltage or current variables to be solved are within the allowable range.

[0018] Furthermore, typical daily scenarios include peak and off-peak periods. The formula for calculating the daily energy savings of the system with energy storage installed relative to the system without energy storage under these two typical daily scenarios is as follows:

[0019] (2)

[0020] (3)

[0021] in, and They represent the first Output power of a traction substation before and after energy storage installation and They represent the first Output voltage of a traction substation before and after energy storage installation and They represent the first Output current of a traction substation before and after energy storage installation Indicates the first day after energy storage installation The traction substation at the first The energy of the day, and They are respectively in the 1st The first day In a typical daily scenario, the first The traction energy provided by each traction substation before and after the installation of energy storage. and They are respectively in the 1st In a typical daily scenario, the first The traction power provided by each traction substation before and after the installation of energy storage Number of typical scenes on this day It is the first The frequency of a typical daily scenario within a day. It is the first Typical daily driving time for each vehicle.

[0022] Furthermore, in step 2, in the urban rail ground energy storage system optimization configuration model, each traction substation in the system corresponds to three decision variables, namely whether or not to install energy storage. , Energy storage installation power and energy storage installation capacity The overall objective function of the system is to maximize the total life-cycle resource savings corresponding to the energy storage installation decisions of all traction substations included in the system. The total life-cycle resource savings of each traction substation is obtained by subtracting the sum of the resource input of the energy storage system during the production phase and the resource input of the maintenance during the entire life-cycle from the total resource savings converted from the electricity saved during the entire life-cycle. The total resource savings converted from the electricity saved during the entire life-cycle is obtained by multiplying the electricity saved during the entire life-cycle by the resource consumption intensity coefficient of the electricity saved per unit of electricity. The resource input of the energy storage system during the production phase is obtained by multiplying the resource consumption intensity coefficient of the energy storage power per unit of energy storage power produced by the energy storage power, plus the product of the resource consumption intensity coefficient of the energy storage capacity per unit of energy storage produced by the energy storage capacity. The resource input of the maintenance during the entire life-cycle is obtained by multiplying the resource consumption intensity coefficient of the energy storage per unit of power maintained per year by the energy storage power, plus the resource consumption intensity coefficient of the energy storage per unit of capacity maintained per year by the energy storage capacity.

[0023] The optimized configuration model for urban rail ground energy storage system, considering energy storage charging and discharging losses throughout its entire life cycle, is shown below:

[0024] (4)

[0025] (5)

[0026] (6)

[0027] , (7)

[0028] (8)

[0029] (9)

[0030] in, It refers to energy storage lifespan; It is the resource consumption intensity coefficient for saving a unit of electricity. According to engineering calculations and experience, the value range is usually [0.6, 1.5]. It is the first The traction substation at the first Annual electricity savings; It is the time value coefficient, and according to engineering calculations and experience, its value usually ranges from [0.015, 0.1]. It is the saturation upper limit coefficient of resource consumption of power-related components of energy storage equipment, based on the preset benchmark energy storage system at rated power. The power resource consumption is obtained by dividing the saturation ratio determined by the rated power of the reference system. The saturation ratio is determined by... Calculated; It is the scale effect saturation rate coefficient that characterizes the rate at which the resource consumption of an energy storage power system tends to saturate as the power increases. It can usually be taken as the reciprocal of the saturation power corresponding to the point when the scale benefits of the energy storage power components begin to weaken significantly. It is the saturation limit coefficient of resource consumption of components related to the capacity of energy storage equipment, based on the energy storage unit reaching its maximum design capacity. The total resource consumption at that time is obtained by dividing the saturation ratio determined by the maximum design capacity. The saturation ratio is calculated using the formula... Calculated; It is the capacity marginal efficiency decay coefficient of an energy storage capacity system, which is the rate at which resource consumption tends to saturate as capacity increases. It can usually be taken as the reciprocal of the saturated capacity corresponding to a significant decrease in the marginal resource consumption of energy storage capacity components. and These are the resource consumption intensity coefficients for maintaining energy storage units of power and capacity per year, respectively, based on engineering calculations and experience. The typical value range is 2%-4% of the resources consumed in producing the corresponding energy storage power, divided by the energy storage power. The typical value range is 2%-4% of the resources consumed in producing the corresponding energy storage capacity divided by the energy storage capacity. It is the first The energy storage capacity installed in each traction substation It is the sampling interval. When the first... When installing energy storage in a traction substation , and These are the current value, minimum value, and maximum value of SOC, respectively. , and These are system topology nodes The current, minimum, and maximum voltage values. and They are the first The actual charging power and actual discharging power of the energy storage installed in each traction substation. and They are the first The maximum charging power and maximum discharging power of the energy storage installed in each traction substation. and These are energy storage discharge efficiency and charging efficiency, respectively.

[0031] Furthermore, the optimization process of the genetic algorithm combined with the co-evolutionary update strategy is as follows: For each traction substation, an initial population is generated at the initial moment. The fitness value of each traction substation depends on the strategies of other traction substations, namely, whether energy storage is installed, the installed energy storage power, and the installed energy storage capacity. In each iteration, when updating the population, for each traction substation, the current optimal strategies of other traction substations are retained, and only the strategy of the current traction substation is changed. In each iteration, the crossover rate and mutation rate of each population are adjusted accordingly based on the fitness value of each traction substation and the fitness value of the system. The adaptive crossover probability and mutation probability are expressed as follows:

[0032] (10)

[0033] in, and They are populations The crossover probability and mutation probability, ; and They are populations The minimum and maximum fitness; It is a population The maximum fitness of the two individuals in the crossover; It is a population Fitness of individuals with variants; It represents the maximum fitness of all populations.

[0034] The beneficial effects of this invention are as follows:

[0035] This invention integrates and globally optimizes the installation location, power, and capacity of an energy storage system. This method fully utilizes the cross-regional flow characteristics of regenerative braking energy under continuous power supply technology, considering the energy coupling between traction substations, trains, and energy storage under typical train operating conditions. In particular, it addresses the varying spatiotemporal distribution characteristics of regenerative energy at different installation locations, incorporating location into the optimization scope. This helps discover power and capacity combinations that better match specific energy flow characteristics, thereby seeking a superior energy utilization scheme at the system level. Simultaneously, it continuously optimizes energy storage power and capacity based on load demand, overcoming the limitations of traditional fixed-point configuration. This allows for precise matching of system requirements, maximizing overall resource utilization and making the configuration scheme more scientific and rational. Furthermore, in constructing the equivalent circuit of a parameterized DC traction power supply system including candidate energy storage nodes, the negative terminals of the branches of the equivalent circuits of all traction substations are grounded, simulating the ground current sharing effect, and the load distribution ratio of each traction substation is closer to reality. The proposed co-evolution strategy can adapt to the strong coupling of multiple energy sources when optimizing energy storage configuration, reducing the situation of "individual optimal superposition but global inefficiency", reducing the search dimension and oscillation, and improving optimization efficiency. Attached Figure Description

[0036] Figure 1 This is a flowchart of a genetic algorithm that incorporates a co-evolutionary strategy.

[0037] Figure 2 This is an overall flowchart of the method of the present invention.

[0038] Figure 3 This is a schematic diagram of the equivalent topology of a DC traction power supply system according to an embodiment of the present invention.

[0039] Figure 4 This is a schematic diagram of the optimization process according to an embodiment of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0041] This embodiment presents a configuration method for urban rail ground energy storage systems based on energy coupling and co-evolution, such as... Figure 2 As shown, the method includes the following steps:

[0042] Step 1: Based on the load information and line information of the DC traction power supply system of urban rail transit, construct the equivalent circuit of the parameterized DC traction power supply system containing candidate energy storage nodes, express the energy coupling equation in the form of parameters for the energy storage installation location and power, divide the daily train operation scenario into two typical daily scenarios, peak period and off-peak period, according to the passenger flow, and determine the calculation formula for the energy saving of the system with energy storage installed relative to the system without energy storage under the typical daily scenario.

[0043] The line information includes the number and location of traction substations, the distance between substations, the impedance per unit length of the overhead contact line, and the impedance per unit length of the rail. The load information includes the number of trains on the same power supply zone, the train location, and the train power.

[0044] Based on the line information and load information at each sampling time, a parameterized equivalent circuit of the DC traction power supply system including candidate energy storage nodes is constructed. The equivalent topology of the DC traction power supply system with energy storage in the embodiment is as follows: Figure 3As shown, the negative terminals of the equivalent circuits of the uncontrolled rectifier units of all traction substations are grounded. The equivalent circuit contains N+2M nodes, where N and M represent the number of traction substations and trains on the line, respectively. By grounding the equivalent negative terminals of all traction substations uniformly in the circuit model, the model can reflect the system-level electrical characteristics of the earth as a common return path. Furthermore, it merges multiple independent equivalent negative terminal nodes into a single reference node. This reduces the solution order of the station-train-energy storage coupling equations and, by constructing a multi-point grounded topology, provides a more realistic model basis for analyzing the energy flow distribution across the entire network.

[0045] By representing the energy storage installation location and power as decision variables, an energy coupling model of traction substation-train-energy storage is established, and the energy coupling equation is written as follows:

[0046] (1)

[0047] in, and These refer to the number of traction substations and the number of trains on the line, respectively. It is a node in the system's equivalent circuit. With nodes The admittance between, where, , , It is the first in the system's equivalent circuit. Each traction substation corresponds to a node The node voltage, where , It is installed in the first The current corresponding to the energy storage equivalent branch of each traction substation. It is the corresponding node of the online train. The node voltage, where, , It is the first in the system's equivalent circuit. Each traction substation corresponds to a branch injection node. The current, It is the train branch line injection node Given the current and the train power, the train branch current is... . Indicates the first Whether a traction substation is equipped with energy storage is indicated by a value of 1 (equivalent to 0) or 0 (equivalent to 0). , , , and They are installed in the first The charging threshold, discharging threshold, rated power, node voltage, and actual output power of the energy storage of each traction substation.

[0048] When the voltage at the energy storage node is higher than the energy storage charging threshold Energy storage is primarily equivalent to a constant voltage source, with the energy storage node voltage... Equal to the charging threshold, the energy storage output power obtained after solving the equation using Newton's method. Exceeding rated power When energy storage is equivalent to a constant power source, during energy storage charging... equal to charging power divided by The energy coupling equations were solved again using Newton's method.

[0049] When the voltage at the energy storage node is lower than the discharge threshold, the energy storage is initially equivalent to a constant voltage source, and the energy storage node voltage... equal to the discharge threshold The energy storage output power obtained after solving the equation using Newton's method Exceeding rated power At that time, the energy storage is equivalent to a constant power source, and the current of the energy storage branch is... equal to the discharge power divided by The energy coupling equations were solved again using Newton's method.

[0050] In other cases, energy storage does not involve charging or discharging;

[0051] The termination condition for solving the iteration using Newton's method is that the values ​​of the voltage or current variables to be solved are within the allowable range.

[0052] Typical daily scenarios include peak and off-peak periods. The formula for calculating the daily energy savings of the system with energy storage installed relative to the system without energy storage under these two typical daily scenarios is as follows:

[0053] (2)

[0054] (3)

[0055] in, and They represent the first Output power of a traction substation before and after energy storage installation and They represent the first Output voltage of a traction substation before and after energy storage installation and They represent the first Output current of a traction substation before and after energy storage installation Indicates the first day after energy storage installation The traction substation at the first The energy of the day, and They are respectively in the 1st The first day In a typical daily scenario, the first The traction energy provided by each traction substation before and after the installation of energy storage. and They are respectively in the 1st In a typical daily scenario, the first The traction power provided by each traction substation before and after the installation of energy storage Number of typical scenes on this day It is the first The frequency of a typical daily scenario within a day. It is the first Typical daily driving time for each vehicle.

[0056] Step 2: Based on the above calculation formula, the objective function is to maximize the resource savings throughout the entire lifecycle corresponding to the decisions on the energy storage installation location, installed power, and installed capacity of all traction substations included in the system. Constraints include power balance constraints, energy storage SOC constraints, and energy storage power charging and discharging constraints. An optimal configuration model for the urban rail ground energy storage system, considering energy storage charging and discharging losses throughout its entire lifecycle, is established. A genetic algorithm combined with a co-evolutionary update strategy is used to solve this model, obtaining the optimal configuration location, rated power, and rated capacity of the energy storage system in each traction substation. In this scheme, the energy storage installation location, rated power, and rated capacity are all used as decision variables for solving the problem. Compared to strategies that fix only some variables (such as optimizing only power and capacity), this method can effectively account for the inherent coupling effects between these three core variables. Since the spatiotemporal distribution characteristics of regenerative energy vary at different installation locations, including location in the optimization scope helps to discover power and capacity combinations that better match specific energy flow characteristics, thereby seeking a better energy utilization scheme at the system level.

[0057] In the optimization configuration model of urban rail ground energy storage system, each traction substation in the system corresponds to three decision variables, namely whether or not to install energy storage. , Energy storage installation power and energy storage installation capacity The overall objective function of the system is to maximize the total life-cycle resource savings corresponding to the energy storage installation decisions of all traction substations included in the system. The total life-cycle resource savings of each traction substation is obtained by subtracting the sum of the resource input of the energy storage system during the production phase and the resource input of the maintenance during the entire life-cycle from the total resource savings converted from the electricity saved during the entire life-cycle. The total resource savings converted from the electricity saved during the entire life-cycle is obtained by multiplying the electricity saved during the entire life-cycle by the resource consumption intensity coefficient of the electricity saved per unit of electricity. The resource input of the energy storage system during the production phase is obtained by multiplying the resource consumption intensity coefficient of the energy storage power per unit of energy storage power produced by the energy storage power, plus the product of the resource consumption intensity coefficient of the energy storage capacity per unit of energy storage produced by the energy storage capacity. The resource input of the maintenance during the entire life-cycle is obtained by multiplying the resource consumption intensity coefficient of the energy storage per unit of power maintained per year by the energy storage power, plus the resource consumption intensity coefficient of the energy storage per unit of capacity maintained per year by the energy storage capacity.

[0058] The optimized configuration model for urban rail ground energy storage system, considering energy storage charging and discharging losses throughout its entire life cycle, is shown below:

[0059] (4)

[0060] (5)

[0061] (6)

[0062] , (7)

[0063] (8)

[0064] (9)

[0065] in, It refers to energy storage lifespan; It is the resource consumption intensity coefficient for saving a unit of electricity. According to engineering calculations and experience, the value range is usually [0.6, 1.5]. It is the first The traction substation at the first Annual electricity savings; It is the time value coefficient, and according to engineering calculations and experience, its value usually ranges from [0.015, 0.1]. It is the saturation upper limit coefficient of resource consumption of power-related components of energy storage equipment, based on the preset benchmark energy storage system at rated power. The power resource consumption is obtained by dividing the saturation ratio determined by the rated power of the reference system. The saturation ratio is determined by... Calculated; It is the scale effect saturation rate coefficient that characterizes the rate at which the resource consumption of an energy storage power system tends to saturate as the power increases. It can usually be taken as the reciprocal of the saturation power corresponding to the point when the scale benefits of the energy storage power components begin to weaken significantly. It is the saturation limit coefficient of resource consumption of components related to the capacity of energy storage equipment, based on the energy storage unit reaching its maximum design capacity. The total resource consumption at that time is obtained by dividing the saturation ratio determined by the maximum design capacity. The saturation ratio is calculated using the formula... Calculated; It is the capacity marginal efficiency decay coefficient of an energy storage capacity system, which is the rate at which resource consumption tends to saturate as capacity increases. It can usually be taken as the reciprocal of the saturated capacity corresponding to a significant decrease in the marginal resource consumption of energy storage capacity components. and These are the resource consumption intensity coefficients for maintaining energy storage units of power and capacity per year, respectively, based on engineering calculations and experience. The typical value range is 2%-4% of the resources consumed in producing the corresponding energy storage power, divided by the energy storage power. The typical value range is 2%-4% of the resources consumed in producing the corresponding energy storage capacity divided by the energy storage capacity. It is the first The energy storage capacity installed in each traction substation It is the sampling interval. When the first... When installing energy storage in a traction substation , and These are the current value, minimum value, and maximum value of SOC, respectively. , and These are system topology nodes The current, minimum, and maximum voltage values. and They are the first The actual charging power and actual discharging power of the energy storage installed in each traction substation. and They are the first The maximum charging power and maximum discharging power of the energy storage installed in each traction substation. and These are energy storage discharge efficiency and charging efficiency, respectively.

[0066] The optimization process of the genetic algorithm combined with the co-evolutionary update strategy is as follows: For each traction substation, an initial population is generated at the initial moment. The fitness value of each traction substation depends on the strategies of other traction substations, namely, whether energy storage is installed, the installed energy storage power, and the installed energy storage capacity. In each iteration, when updating the population, for each traction substation, the current optimal strategies of other traction substations are retained, and only the strategy of the current traction substation is changed. In each iteration, the crossover rate and mutation rate of each population are adjusted accordingly based on the fitness value of each traction substation and the fitness value of the system. The adaptive crossover probability and mutation probability are expressed as follows:

[0067] (10)

[0068] in, and They are populations The crossover probability and mutation probability, ; and They are populations The minimum and maximum fitness; It is a population The maximum fitness of the two individuals in the crossover; It is a population Fitness of individuals with variants; It represents the maximum fitness of all populations.

[0069] Furthermore, to verify the effectiveness of the present invention, a subway line section in a certain city is used as an example. This section has 3 traction substations and 6 subway stations. The station spacing data is shown in Table 1, and the parameters in the energy coupling equation calculation and flywheel energy storage configuration model are shown in Table 2. The example includes train data from five train trips, with 5 minutes and 10 minutes selected as typical scenarios during peak and off-peak periods, respectively.

[0070] Table 1. Station spacing data

[0071]

[0072] Table 2 Parameters in the calculation of energy coupling equations and flywheel energy storage configuration model

[0073]

[0074] Using the above data and the proposed optimization configuration method for urban rail transit ground energy storage systems, the flywheel energy storage configuration results for each traction substation are shown in Table 3. A comparison is made between the optimization process of the genetic algorithm using a co-evolutionary strategy and the traditional genetic algorithm. Figure 4 As shown.

[0075] Table 3. Flywheel energy storage configuration results of the next traction substation using the method proposed in this invention.

[0076]

[0077] The optimization results of the proposed method and the traditional fixed-installation method for optimizing the number of fixed modules (selecting a common flywheel energy storage unit with a rated power of 0.5MW and a rated capacity of 10MJ as the benchmark) are shown in Table 4. The static payback period is obtained by dividing the initial investment cost by the annual net cash flow, and the net rate of return is obtained by dividing the total life-cycle net present value by the initial investment cost and then by the energy storage lifespan.

[0078] Table 4 Comparison of optimization results between the optimization algorithm of this invention and the traditional optimization algorithm

[0079]

[0080] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0082] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0083] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0084] The above-described embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions of the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A configuration method for urban rail ground energy storage systems based on energy coupling and co-evolution, characterized in that, Includes the following steps: Step 1: Based on the load information and line information of the urban rail transit DC traction power supply system, construct a parameterized DC traction power supply system equivalent circuit including candidate energy storage nodes. In this circuit, the negative pole of the equivalent circuit of the uncontrolled rectifier units of all traction substations is grounded. There are a total of N+2M nodes in the system equivalent circuit, where N and M are the number of traction substations and the number of trains on the line, respectively. Express the energy coupling equation in parameter form for the energy storage installation location and power. Divide the daily train operation scenario into two typical daily scenarios, peak and off-peak, based on passenger flow. Determine the calculation formula for the energy saving of the system under the typical daily scenario with energy storage installed compared to when no energy storage is installed. Step 2: Based on the above calculation formula, the objective function is to maximize the amount of resources saved throughout the entire life cycle corresponding to the decisions on the energy storage installation location, installed power, and installed capacity of all traction substations included in the system. The constraints include power balance constraints, energy storage SOC constraints, and energy storage power charging and discharging power constraints. An optimal configuration model for the urban rail ground energy storage system considering energy storage charging and discharging losses throughout the entire life cycle is established. The model is solved using a genetic algorithm combined with a co-evolutionary update strategy to obtain the optimal configuration location, rated power, and rated capacity of the energy storage system in each traction substation.

2. The configuration method for urban rail ground energy storage system based on energy coupling and co-evolution according to claim 1, characterized in that, In step 1, the load information includes the number of trains on the same power supply zone, the train location and the train power, and the line information includes the number and location of traction substations, the distance between stations, the impedance per unit length of the contact wire and the impedance per unit length of the rail.

3. The configuration method for urban rail ground energy storage system based on energy coupling and co-evolution according to claim 1, characterized in that, Using the energy storage installation location and power as decision variables, and based on the equivalent circuit of the parameterized DC traction power supply system including candidate energy storage nodes, a model of traction substation-train-energy storage energy coupling is established for the flow of regenerative braking energy of trains in adjacent power supply sections under through-power supply technology. The energy coupling equation is shown below: (1) in, and These refer to the number of traction substations and the number of trains on the line, respectively. It is a node in the system's equivalent circuit. With nodes The admittance between, where, , , It is the first in the system's equivalent circuit. The node voltage of each traction substation corresponding to the node, among which , It is installed in the first The current corresponding to the energy storage equivalent branch of each traction substation. It is installed in the first The node voltage of energy storage in a traction substation. It is the corresponding node of the online train. The node voltage, where, , It is the first in the system's equivalent circuit. Each traction substation corresponds to a branch injection node. The current, It is the train branch line injection node Current, train power Given that the train branch current is... , Indicates the first Whether a traction substation is equipped with energy storage is indicated by a value of 1 (equivalent to 0) or 0 (equivalent to 0).

4. The configuration method for urban rail ground energy storage system based on energy coupling and co-evolution according to claim 3, characterized in that, When the voltage at the energy storage node is higher than the energy storage charging threshold Energy storage is primarily equivalent to a constant voltage source, with the energy storage node voltage... Equal to the charging threshold, the energy storage output power obtained after solving the equation using Newton's method. Exceeding rated power When energy storage is equivalent to a constant power source, during energy storage charging... equal to charging power divided by The energy coupling equations were solved again using Newton's method. When the voltage at the energy storage node is lower than the discharge threshold, the energy storage is initially equivalent to a constant voltage source, and the energy storage node voltage... equal to the discharge threshold The energy storage output power obtained after solving the equation using Newton's method Exceeding rated power At that time, the energy storage is equivalent to a constant power source, and the current of the energy storage branch is... equal to the discharge power divided by The energy coupling equations were solved again using Newton's method. The termination condition for solving the iteration using Newton's method is that the values ​​of the voltage or current variables to be solved are within the allowable range.

5. The configuration method for urban rail ground energy storage system based on energy coupling and co-evolution according to claim 1, characterized in that, Typical daily scenarios include peak and off-peak periods. The formula for calculating the daily energy savings of the system with energy storage installed relative to the system without energy storage under these two typical daily scenarios is as follows: (2) (3) in, and They represent the first Output power of a traction substation before and after energy storage installation and They represent the first Output voltage of a traction substation before and after energy storage installation and They represent the first Output current of a traction substation before and after energy storage installation Indicates the first day after energy storage installation The traction substation at the first The energy of the day, and They are respectively in the 1st The first day In a typical daily scenario, the first The traction energy provided by each traction substation before and after the installation of energy storage. and They are respectively in the 1st In a typical daily scenario, the first The traction power provided by each traction substation before and after the installation of energy storage Number of typical scenes on this day It is the first The frequency of a typical daily scenario within a day. It is the first Typical daily driving time for each vehicle.

6. The configuration method for urban rail ground energy storage system based on energy coupling and co-evolution according to claim 5, characterized in that, In step 2, in the urban rail ground energy storage system optimization configuration model, each traction substation in the system corresponds to three decision variables, namely whether or not to install energy storage. , Rated power and energy storage installation capacity The overall objective function of the system is to maximize the total life-cycle resource savings corresponding to the energy storage installation decisions of all traction substations included in the system. The total life-cycle resource savings of each traction substation is obtained by subtracting the sum of the resource input of the energy storage system during the production phase and the resource input of the maintenance during the entire life-cycle from the total resource savings converted from the electricity saved during the entire life-cycle. The total resource savings converted from the electricity saved during the entire life-cycle is obtained by multiplying the electricity saved during the entire life-cycle by the resource consumption intensity coefficient of the electricity saved per unit of electricity. The resource input of the energy storage system during the production phase is obtained by multiplying the resource consumption intensity coefficient of the energy storage power per unit of energy storage power produced by the energy storage power, plus the product of the resource consumption intensity coefficient of the energy storage capacity per unit of energy storage produced by the energy storage capacity. The resource input of the maintenance during the entire life-cycle is obtained by multiplying the resource consumption intensity coefficient of the energy storage per unit of power maintained per year by the energy storage power, plus the resource consumption intensity coefficient of the energy storage per unit of capacity maintained per year by the energy storage capacity.

7. The configuration method for urban rail ground energy storage system based on energy coupling and co-evolution according to claim 6, characterized in that, The optimized configuration model for urban rail ground energy storage system, considering energy storage charging and discharging losses throughout its entire life cycle, is shown below: (4) (5) ， (6) (7) (8) (9) in, It refers to energy storage lifespan; It is the resource consumption intensity coefficient for saving a unit of electricity; It is the first The traction substation at the first Annual electricity savings; It is the time value coefficient; It is the saturation upper limit coefficient of resource consumption of power-related components of energy storage equipment; It is the scale effect saturation rate coefficient that characterizes the rate at which the resource consumption of an energy storage power system tends to saturate as power increases; It is the saturation upper limit coefficient of resource consumption of energy storage equipment capacity-related components; It is the capacity marginal efficiency decay coefficient, which represents the rate at which resource consumption of an energy storage system tends to saturate as capacity increases. and These are the resource consumption intensity coefficients for maintaining energy storage per unit power and per unit capacity per year, respectively. It is the first The energy storage capacity installed in each traction substation It is the sampling interval; when the first When installing energy storage in a traction substation , and These are the current value, minimum value, and maximum value of SOC, respectively. , and These are system topology nodes The current, minimum, and maximum voltage values. and They are the first The actual charging power and actual discharging power of the energy storage installed in each traction substation. and They are the first The maximum charging power and maximum discharging power of the energy storage installed in each traction substation. and These are energy storage discharge efficiency and charging efficiency, respectively.

8. The configuration method for urban rail ground energy storage system based on energy coupling and co-evolution according to claim 7, characterized in that, The optimization process of the genetic algorithm combined with the co-evolutionary update strategy is as follows: For each traction substation, an initial population is generated at the initial moment. The fitness value of each traction substation depends on the strategies of other traction substations, namely, whether energy storage is installed, the installed energy storage power, and the installed energy storage capacity. In each iteration, when updating the population, for each traction substation, the current optimal strategies of other traction substations are retained, and only the strategy of the current traction substation is changed. In each iteration, the crossover rate and mutation rate of each population are adjusted accordingly based on the fitness value of each traction substation and the fitness value of the system. The adaptive crossover probability and mutation probability are expressed as follows: (10) in, and They are populations The crossover probability and mutation probability, ; and They are populations The minimum and maximum fitness; It is a population The maximum fitness of the two individuals in the crossover; It is a population Fitness of individuals with variants; It represents the maximum fitness of all populations.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-8.

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