Electrified railway vehicle-mounted energy storage configuration method, system, device and medium

Abstract

This invention discloses a method, system, equipment, and medium for configuring on-board energy storage in electrified railways. The method includes: acquiring historical operating data from traction substations; constructing a scenario reduction strategy based on filtered noise scenarios and synchronous back-substitution reduction; reducing train load power curve scenarios using a clustering reduction algorithm; calculating the power matching parameters of the traction substations, setting variables such as the capacity of the on-board energy storage device and the power purchased by the tie line, with low daily operating cost and high source-load power self-matching degree as the objective function; sequentially solving the multi-objective function for N traction substations using a solver to calculate the optimal solution for the train on-board energy storage configuration power and capacity for each traction substation; and establishing and outputting an on-board energy storage configuration model based on the optimal solution. This method can meet the planning requirements for optimal on-board energy storage, exhibiting high reliability and practicality.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching, and in particular to a method, system, equipment and medium for configuring onboard energy storage for electrified railways. Background Technology

[0002] With the advancement of the "dual-carbon" policy and the development of science and technology, large-scale grid connection of clean energy sources such as photovoltaics and wind power has become a future trend in energy supply. Existing research shows that qualitative analysis techniques for wind and solar power output are becoming increasingly mature, and distributed energy multi-energy supply models for high-speed rail and railway transit energy systems are becoming the general trend. However, considering the randomness and volatility of new energy supply, as well as the drastic fluctuations in locomotive load power in the traction network, existing methods for configuring on-board energy storage in electrified railways suffer from low reliability and poor practicality, failing to meet the planning requirements for optimal on-board energy storage. Summary of the Invention

[0003] This invention aims to solve at least one of the technical problems existing in the prior art. To this end, this invention proposes a method, system, equipment, and medium for configuring on-board energy storage in electrified railways, which can meet the planning requirements for optimal on-board energy storage, and has high reliability and good practicality.

[0004] A method for configuring on-board energy storage for electrified railways according to a first aspect of the present invention includes the following steps:

[0005] S100: Obtain historical operating data of the traction substation, and obtain wind and solar power output curve scenarios and train load power curve scenarios based on the historical operating data;

[0006] S200, reduces the scene of wind and solar power output curve by filtering noise scene and synchronous back-substitution reduction method;

[0007] S300, Reduce the train load power curve scenario through clustering reduction algorithm;

[0008] S400. Calculate the power matching parameters of the traction substation, and use the capacity and power of the on-board energy storage device and the power purchased by the tie line as the solution variables to construct an objective function with low daily operating cost and high source-load power self-matching degree.

[0009] S500, iterate through the total number of N traction substations on the train line, and solve the multi-objective function of N traction substations in sequence to calculate the optimal solution of the train on-board energy storage configuration power and capacity for each traction substation.

[0010] S600: Establish and output a vehicle-mounted energy storage configuration model with low daily operating costs and high source-load power matching based on the optimal solution.

[0011] According to some embodiments of the present invention, the historical operating data in step S100 includes historical parameters of regional wind speed, historical parameters of light intensity environment, train operation data map, and power grid purchase price.

[0012] According to some embodiments of the present invention, the specific steps of step S100 are as follows:

[0013] S101. Calculate the wind turbine output power for the corresponding time period based on the historical wind speed parameters of the region. The calculation formula is as follows:

[0014]

[0015] P wind (t) represents the power generation of the wind turbine unit at the i-th traction substation during time period t; This is the rated power of the wind turbine unit; The per-unit value of wind speed at time t is as follows: v in,i and v out,i These are the cut-in and cut-out wind speeds of the wind turbine; v Ni It is the rated operating wind speed of the wind turbine; τ i Whether the i-th traction substation is equipped with wind power is a 0-1 state variable;

[0016] S102. Calculate the output power of the photovoltaic power generation equipment for the corresponding time period based on historical environmental parameters of light intensity. The calculation formula is as follows:

[0017]

[0018] P PV,i I(t) represents the power generation of the photovoltaic power generation equipment at the i-th traction substation during time period t, and I(t) represents the solar irradiance of the area. max The threshold of light intensity required for normal power generation by photovoltaic power generation equipment; The rated power output of the photovoltaic power generation equipment; λ i Whether the i-th traction substation is equipped with photovoltaic power generation equipment is a 0-1 state variable.

[0019] S103. Calculate the daily load power of the traction substation based on train operation data. The calculation formula is as follows:

[0020]

[0021] n(t) = n1(t) + n2(t) + n3(t)

[0022] Among them, P G γ(t) represents the daily load power of the train at the traction substation, and γ(t) represents the train operation state variable, which is a 0-1 state variable, where 1 indicates the train is accelerating and 0 indicates the train is braking. This refers to the power supply required for the train to operate normally at its rated speed. n is the power supply of the traction substation under normal braking conditions; n(t) is the total number of train trips on the i-th traction line at time t; n1(t) is the number of train trips in variable speed operation at time t; n2(t) is the number of train trips in constant speed operation (below rated speed); n3(t) is the number of train trips at rated speed; v i (t), v G,N Let t be the actual speed of the train and the normal rated operating speed of the train.

[0023] S104. Obtain the wind and solar power output curve scenario based on the wind turbine output power calculated in step S101 and the photovoltaic power generation equipment output power calculated in step S102. Obtain the train load power curve scenario based on the daily train load power of the traction substation calculated in step S103.

[0024] According to some embodiments of the present invention, the specific steps of step S200 are as follows:

[0025] S201. Construct the original scenarios for wind power and photovoltaic power, calculate the Euclidean distance between each original scenario for photovoltaic and wind power, form a clustered scenario set, and divide the scenario set into important scenarios, general scenarios and noise scenarios.

[0026] S202. Eliminate noisy scenes and retain important and general scenes as the original scenes;

[0027] S203. The original scene is reduced by synchronous back-substitution reduction method to obtain the power curve of the reduced scene and the occurrence probability of the corresponding scene.

[0028] According to some embodiments of the present invention, the specific steps of the clustering reduction algorithm in step S300 are as follows:

[0029] S301. Select m train load power curves as initial cluster centers;

[0030] S302. Calculate the Euclidean distance between the initial cluster centers of the load power curves for all trains at each time point, and classify each load power curve into the curve with the nearest cluster center. The calculation formula is as follows:

[0031]

[0032] Wherein d(p i,t ,o j,t ) represents the Euclidean distance between the power value at time t of the i-th train load curve and the power value at time t of the curve containing the j-th initial cluster center; p i,t For the load curve data of the i-th train; o jThe train load curve for the j-th cluster center;

[0033] S303. Update the cluster center curve using the cluster center curve update calculation formula to obtain the updated cluster center curve. The cluster center curve update calculation formula is as follows:

[0034]

[0035] o i The updated train load curve for the i-th cluster center; Let be the daily load curve data of the i-th train when pi,t = j; |Ci,t| is the number of train load curve scenarios retained in the i-th cluster.

[0036] S304. The updated cluster center curve is validated using a distortion function, wherein the distortion function is SSE(p i,t ,o j,t The formula for ) is

[0037]

[0038] If the verification passes (SSE≤SSE) min If the condition is met, then proceed to the next steps;

[0039] If the verification fails (SSE > SSE) min If the cluster center curve is not updated, return to step S303 to update it again.

[0040] Wherein, SSEmin is the minimum threshold for distortion function verification;

[0041] S305. Two typical train operation scenarios are obtained through clustering reduction as the train heavy load scenario and the train normal load scenario of the traction substation.

[0042] According to some embodiments of the present invention, step S400 calculates the "source-grid-load" power matching parameters of the traction substation during the day, sets the variable on-board energy storage device capacity and tie-line power purchase capacity, and takes low daily operating cost and high source-load power self-matching degree as the objective function.

[0043] S401. Construct an expression with high source-load power self-matching degree.

[0044]

[0045] This is the per-unit value of the total daily operating cost of the traction substation. Per-unit value for power interaction with the public power grid;

[0046] S402. Construct an expression for the purchase and maintenance costs of on-board lithium battery energy storage:

[0047]

[0048] C BAT C is the total purchase cost of onboard lithium battery energy storage; EBAT The cost per unit capacity of lithium batteries; C PBAT Cost per unit power of lithium battery; C om Maintenance cost per unit capacity of lithium battery; Purchase capacity and power for lithium batteries; N G This refers to the total number of train cars on the route from the starting point to the end point.

[0049] S403. Calculate the electricity revenue from the train's onboard energy storage configuration. The calculation formula is as follows:

[0050]

[0051] Where, N life The total operating cycle of onboard lithium battery energy storage; I e,t Let E be the unit price of electricity in the power grid at time t; save Saves energy per hour for lithium batteries;

[0052] S404. Calculate the daily operating cost of the traction substation. The calculation formula is as follows:

[0053]

[0054] Among them, C grid,day,n The daily operating cost of electricity purchased from the power grid for the traction substation;

[0055] S405. Based on the power conservation principle of the i-th traction substation, and considering the total number of n(t) trains on the i-th traction line at time t, construct the energy interaction formula between the traction substation and the power grid.

[0056]

[0057] in, P represents the energy exchange between traction substation i and the public power grid at time t; wind (t) represents the output power of the wind turbine unit at the traction substation at time t, P PV (t) represents the photovoltaic output power of the traction substation at time t. Let t be the energy storage discharge of the traction substation. Let t be the charging power of the traction substation at time t.

[0058] According to some embodiments of the present invention, in step S500, the multi-objective function solution for N traction substations is completed by the Cplex solver.

[0059] According to a second aspect of the present invention, an electrified railway on-board energy storage configuration system includes:

[0060] The data acquisition unit is used to acquire historical operating data of the traction substation and obtain wind and solar power output curve scenarios and train load power curve scenarios based on the historical operating data.

[0061] The first reduction unit is used to reduce the power output curve scene of wind and solar power by filtering noise scene and synchronous back-substitution reduction method.

[0062] The second reduction unit is used to reduce the train load power curve scenario through a clustering reduction algorithm;

[0063] The function construction unit is used to calculate the power matching parameters of the traction substation, set the variable on-board energy storage device capacity and tie line power purchase, and construct the objective function with low daily operating cost and high source-load power self-matching degree.

[0064] The function solving unit is used to iteratively solve the multi-objective function of all N traction substations on the train line, and calculate the optimal solution of the train on-board energy storage configuration power and capacity for each traction substation.

[0065] The model output unit is used to establish and output a vehicle-mounted energy storage configuration model with low daily operating costs and high source-load power matching based on the optimal solution.

[0066] According to a third aspect of the present invention, the electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for implementing connection communication between the processor and the memory, wherein the program, when executed by the processor, implements the steps of the method described above.

[0067] According to a fourth aspect of the present invention, the storage medium is a computer-readable storage medium for computer-readable storage, wherein the storage medium stores one or more programs that can be executed by one or more processors to implement the steps of the above-described method.

[0068] The electrified railway on-board energy storage configuration method, system, equipment, and medium according to embodiments of the present invention have at least the following features:

[0069] Beneficial effects:

[0070] This invention provides a method for configuring on-board energy storage in electrified railways considering multiple uncertainties in source and load. First, historical operating data of traction substations is acquired. A scenario reduction strategy is constructed based on noise filtering scenarios and synchronous back-substitution reduction. A clustering reduction algorithm is used to reduce train load power curve scenarios. Daily "source-grid-load" power matching parameters for traction substations are calculated, and variables such as the on-board energy storage device capacity and the power purchased by the tie line are set. The objective function is low daily operating cost and high source-load power self-matching degree. The method iteratively solves the multi-objective function for all N traction substations on the train line, calculating the optimal solution for the train's on-board energy storage configuration power and capacity for each traction substation. Based on the optimal solution, an on-board energy storage configuration model with low daily operating cost and high source-load power matching is established and output. This method constructs a multi-objective function that achieves the lowest operating cost and highest source-load power matching, considering factors such as energy storage configuration cost and grid power purchase cost. It meets the planning requirements for optimal on-board energy storage, exhibiting high reliability and practicality.

[0071] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0072] The present invention will be further described below with reference to the accompanying drawings and embodiments, wherein:

[0073] Figure 1 This is a schematic diagram of the operation of a traction substation for an electrified railway in an embodiment of the present invention;

[0074] Figure 2 This is a flowchart of the method for configuring onboard energy storage in electrified railways according to an embodiment of the present invention. Detailed Implementation

[0075] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0076] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, etc., are based on the orientation or positional relationship shown in the drawings and are only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0077] In the description of this invention, "multiple" refers to two or more. The use of "first" and "second" is for distinguishing technical features only and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features or their sequential relationship.

[0078] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0079] Reference Figure 1 and Figure 2 As shown, a method for configuring on-board energy storage in electrified railways includes the following steps:

[0080] S100: Obtain historical operating data of the traction substation, and obtain wind and solar power output curve scenarios and train load power curve scenarios based on the historical operating data;

[0081] It should be noted that traction substations refer to all traction substations along the railway line from the starting point to the end point. Suppose there are N traction substations on a line. Obtain the historical operating data of the i-th traction substation for the previous month, where 1≤i≤N. The historical operating data includes the regional wind speed historical parameters, light intensity environmental historical parameters, train operation data, and grid electricity purchase price of the traction substation.

[0082] First, combining the historical wind speed and solar irradiance parameters of the traction substation area from the previous month, a scenario set of wind and solar power output is constructed, obtaining 30 daily intraday photovoltaic and wind power curves. Wind speed is correlated with wind turbine output, and solar irradiance is correlated with photovoltaic output. The specific steps of step S100 are as follows:

[0083] S101. Calculate the wind turbine output power for the corresponding time period based on the historical wind speed parameters of the region. The calculation formula is as follows:

[0084]

[0085] Among them, P wind (t) represents the power generation of the wind turbine unit at the i-th traction substation during time period t; This is the rated power of the wind turbine unit; The per-unit value of wind speed at time t is as follows: v in,i and v out,i These are the cut-in and cut-out wind speeds of the wind turbine; v Ni It is the rated operating wind speed of the wind turbine; τ i Is the i-th traction substation equipped with wind power? τ iThe state variables are 0-1;

[0086] S102. Calculate the output power of the photovoltaic power generation equipment for the corresponding time period based on historical environmental parameters of light intensity. The calculation formulas for photovoltaic power generation equipment output and light intensity are as follows:

[0087]

[0088] Among them, P PV,i I(t) represents the power generation of the photovoltaic power generation equipment at the i-th traction substation during time period t, and I(t) represents the solar irradiance of the area. max The threshold of light intensity required for normal power generation by photovoltaic power generation equipment; The rated power output of the photovoltaic power generation equipment; λ i Is the i-th traction substation equipped with photovoltaic power generation equipment? λ i The state variables are 0-1;

[0089] S103. Calculate the daily load power of the traction substation based on the train operation data.

[0090] Specifically, train operation data is obtained through the train timetable. Assuming that all trains passing through the traction line on a given day are of the same type, the daily load power of the i-th traction substation can be calculated based on the train times and train numbers passing through the traction line that day, according to the daily train timetable. The calculation formula is as follows:

[0091]

[0092] n(t) = n1(t) + n2(t) + n3(t)

[0093] Among them, P G γ(t) represents the daily load power of the train at the traction substation, and γ(t) represents the train operation state variable. The state variable of γ(t) is 0-1, where 1 represents the train accelerating and 0 represents the train braking. This refers to the power supply required for the train to operate normally at its rated speed. t is the power supply of the traction substation under normal braking conditions; n(t) is the total number of train trips on the i-th traction line at time t; n1(t), n2(t), and n3(t) are the number of train trips under the three operating conditions at time t, where n1(t) is the number of train trips in the variable speed operation state at time t, which refers to the operation state where the speed is between 0 and the rated speed, accelerating / decelerating; n2(t) is the number of train trips in the uniform speed operation state where the speed is the same as the speed at the previous time, but not reaching the rated speed; and n3(t) is the number of train trips at the rated operating speed; v i (t), v G,N These represent the train's actual speed at time t and its normal rated operating speed, respectively.

[0094] Among them, the constraints for train operation in step S300 are:

[0095]

[0096] S104. Obtain the wind and solar power output curve scenario based on the wind turbine output power calculated in step S101 and the photovoltaic power generation equipment output power calculated in step S102. Obtain the train load power curve scenario based on the daily train load power of the traction substation calculated in step S103.

[0097] S200 reduces the power output curve scene by filtering noise scenes and using synchronous back-substitution reduction method.

[0098] Specifically, step S200 first constructs a scenario reduction strategy based on the filtered noise scenario and the synchronous back-substitution reduction method, and then reduces the wind and solar power output curve scenario using the scenario reduction strategy. Because wind and solar power have significant uncertainties and fluctuations, in order to consider these power uncertainties and incorporate them into the subsequent objective function formula, it is necessary to reduce the wind and solar power output curve scenario and then calculate based on the remaining typical wind and solar power output scenario curves. Step S200 can obtain the typical wind and solar power output scenario power curves.

[0099] It should be noted that, because 30 scenarios for wind and solar power are being reduced, it is necessary to obtain wind and solar power output curves under three typical scenarios. To address the issue of reducing large-scale solar and wind power output across multiple scenarios, typical wind and solar power output scenarios need to be retained to improve the accuracy of the reduction. Therefore, this invention constructs a reduction strategy associated with filtering noise scenarios and the synchronous back-substitution reduction method, specifically including the following steps:

[0100] S201. Construct the original scenarios for wind power and photovoltaic power, calculate the Euclidean distance between each original scenario for photovoltaic and wind power, form a clustered scenario set, and divide the scenario set into important scenarios, general scenarios and noise scenarios. The formula for calculating the Euclidean distance is:

[0101]

[0102] The expression for the scene set is

[0103]

[0104] Wherein, D(c i ,c j θ represents the Euclidean distance between scenes ci and cj; i Let ci be the judgment metric for scene ci, where 1 indicates importance, -1 indicates normal, and 0 indicates noise; ε represents the neighborhood judgment threshold; N minThe minimum threshold for determining the number of important scenario domains; To count the number of scenarios that meet the criteria of the relevant field.

[0105] S202. Eliminate noisy scenes, retain important scenes and general scenes, and use the important scenes and general scenes as the original scenes for the synchronous back-substitution reduction method.

[0106] The synchronous back-substitution reduction method is implemented through steps S203-S206, as follows:

[0107] S203. Let the total number of remaining scenes after removing noisy scenes be N', and the number of iterations be k. Iteratively calculate the distance set D' of N' scenes, which serves as an N'×N' order scene distance matrix. The formula is as follows:

[0108] D'(c i ,c j )=||c t,i -c t,j ||2

[0109] S204. Reduction and Retention of Scenarios: Filtering the scenarios that need to be retained. Scenarios that need to be reduced Let the initial value of the iteration number k = 0, where for The formula for calculating the probability of selecting the nearest scene and choosing the scene to retain is as follows:

[0110]

[0111] S205, Iterate k = k + 1, reduce the scene. And append its probability to the nearest scene. The calculation formula is

[0112]

[0113] S206. Let the remaining typical scenario sample matrix be S, and update it after synchronous back-substitution reduction to...

[0114] S207. Determine whether the number of retained scenes meets the expected requirement N. retain The calculation is repeated iteratively. If the number of retained scenarios exceeds the expected number, the iteration continues in step S204. If it equals the expected number of scenarios, the power curve of the reduced scenario and the probability of occurrence of the corresponding scenario are obtained, which is the probability that different power curves will occur.

[0115] It should be noted that step S207 is expected to require N. retain The judgment is made by setting a threshold value manually.

[0116] S300: The train load power curve scenario is reduced using a clustering reduction algorithm.

[0117] Because the railway locomotive load curve fluctuates greatly, it is necessary to reduce the train load power curve scenario to obtain train power curves for several typical scenarios. The specific steps of step S300 are as follows:

[0118] S301. Select m train load power curves as initial cluster centers;

[0119] S302. Calculate the Euclidean distance between the initial cluster centers of the load power curves for all trains at each time point, and classify each load power curve into the curve with the nearest cluster center. The calculation formula is as follows:

[0120]

[0121] Wherein d(p i,t ,o j,t ) represents the Euclidean distance between the power value at time t of the i-th train load curve and the power value at time t of the curve containing the j-th initial cluster center; p i,t For the load curve data of the i-th train; o j Let m be the train load curve for the j-th cluster center. Since m train load power curves are selected in step S301, m in the above formula is the total number of load scenarios. In this embodiment, m is set to 30, but it can also be set to other values ​​according to actual needs.

[0122] S303. Update the cluster center curve to obtain the updated cluster center curve. The formula for calculating the updated cluster center curve is as follows:

[0123]

[0124] o i The updated train load curve for the i-th cluster center; Let be the daily load curve data of the i-th train when pi,t = j; |Ci,t| is the number of train load curve scenarios retained in the i-th cluster.

[0125] S304. The updated cluster center curve is validated using a distortion function, SSE(p). i,t ,o j,t The formula for ) is

[0126]

[0127] If the verification passes (SSE≤SSE) min If the condition is met, then proceed to the next steps;

[0128] If the verification fails (SSE > SSE)min If the cluster center curve is not updated, return to step S303 to update it again.

[0129] Wherein, SSEmin is the minimum threshold for distortion function verification;

[0130] S305. Two typical train operation scenarios are obtained through clustering reduction as the train heavy load scenario and the train normal load scenario of the traction substation.

[0131] S400. Calculate the power matching parameters of the traction substation, using the capacity and power of the on-board energy storage device and the power purchased from the tie line as solution variables, and construct an objective function that achieves low daily operating cost and high source-load power self-matching degree.

[0132] It should be noted that the power matching parameters of the traction substation are the "source-grid-load" power matching parameters of the traction substation within the day. To ensure the reliable and stable operation of the traction substation, this invention constructs an objective function with low daily operating cost and high source-load power self-matching degree based on the typical scenario of low wind and solar power output obtained in step S200 and the heavy train load scenario obtained in step S300, as follows:

[0133] S401. Construct an expression for high source-load power self-matching degree, where "high source-load power self-matching degree" is represented by "low interaction energy with the public power grid," as detailed below.

[0134]

[0135] in, This is the per-unit value of the total daily operating cost of the traction substation. This refers to the per-unit value of the power interacting with the public power grid.

[0136] S402. Construct an expression for the purchase and maintenance cost of onboard energy storage equipment. To better promote peak shaving and valley filling in the traction energy system, improve the low-carbon and clean energy supply, and reduce daily operating costs, all trains are assumed to be equipped with energy storage devices of equal capacity, using lithium batteries. The expression for the purchase and maintenance cost of onboard lithium battery energy storage for trains is as follows:

[0137]

[0138] Among them, C BAT C is the total purchase cost of onboard lithium battery energy storage; EBAT The cost per unit capacity of lithium batteries; C PBAT Cost per unit power of lithium battery; C om Maintenance cost per unit capacity of lithium battery; Purchase capacity and power for lithium batteries; N G This refers to the total number of train cars on the route from the starting point to the end point.

[0139] S403. Calculate the electricity revenue from the onboard energy storage configuration of the train. The onboard energy storage configuration brings revenue because the lithium battery recovers energy during braking, reducing energy loss and indirectly converting it into revenue. Therefore, the formula for calculating the electricity revenue is as follows:

[0140]

[0141] Where, N life The total operating cycle of onboard lithium battery energy storage; I e,t Let E be the unit price of electricity in the power grid at time t; save Saves energy per hour for lithium batteries.

[0142] S404. Calculate the daily operating cost of the traction substation;

[0143] Specifically, the daily operating cost of the traction substation is calculated based on the entire lifecycle of the on-board energy storage, combined with the investment cost and returns of the on-board energy storage. The calculation formula is as follows:

[0144]

[0145] Among them, C grid,day,n The daily operating cost of electricity purchased from the power grid for the traction substation;

[0146] S405. Based on the power conservation principle of the i-th traction substation, and considering the total number of n(t) trains on the i-th traction line at time t, construct an expression for the interaction energy between the traction substation and the power grid.

[0147]

[0148] in, P represents the energy exchange between traction substation i and the public power grid at time t; wind (t) represents the output power of the wind turbine unit at the traction substation at time t, P PV (t) represents the photovoltaic output power of the traction substation at time t. Let t be the energy storage discharge of the traction substation. Let t be the charging power of the traction substation at time t.

[0149] It should be noted that the operational constraints of the train-mounted energy storage device in this invention are as follows:

[0150]

[0151] in, and These represent the charging / discharging power of the on-board energy storage at time t; and 0-1 variables; and These represent the charge / discharge output coefficients, respectively; SOC(t) represents the state of charge of the onboard energy storage at time t; SOC max and SOC min These represent the upper and lower limits of the state of charge, respectively.

[0152] S500, iterates through the total number of N traction substations on the train line, and uses a solver to solve the multi-objective function of N traction substations in sequence, and calculates the optimal solution for the train's on-board energy storage configuration power and capacity for each traction substation.

[0153] It should be noted that in this embodiment, the Cplex solver is used to sequentially solve the multi-objective function of the i = {1,2,3,......,N}th traction substation of the train line.

[0154] Specifically, this embodiment uses the MATLAB platform to call the Cplex solver for calculation and analysis. The optimization solution cycle is 24 hours per day, with a step size of 1 hour. Iterative calculations are performed sequentially for all N traction substations from the starting station to the terminal station. The objective function of low daily operating cost and high source-load power self-matching degree is solved, obtaining the optimal solutions for the onboard energy storage configuration and grid power purchase for each of the N traction substation lines. Onboard energy storage configuration is then performed based on the optimal solutions.

[0155] After configuring onboard energy storage, to ensure the safety and reliability of the entire train's power supply operation on the line, it is necessary to select the maximum power and capacity solution of the onboard energy storage, and assign N... G Each train is equipped with onboard energy storage, and the calculation formula is as follows.

[0156]

[0157] in, The optimal purchase capacity and power for vehicle-mounted lithium batteries.

[0158] S600: Based on the optimal solution of train on-board energy storage configuration power and capacity calculated in step S500, establish and output the on-board energy storage configuration model with low daily operating cost and high source-load power matching.

[0159] The present invention also relates to an onboard energy storage configuration system for electrified railways, comprising: a data acquisition unit, a strategy construction unit, a scenario reduction unit, a function construction unit, a function solving unit, and a model output unit.

[0160] Specifically, the data acquisition unit acquires historical operating data of traction substations and obtains wind and solar power output curve scenarios and train load power curve scenarios based on the historical operating data; the first reduction unit reduces the wind and solar power output curve scenarios by filtering noise scenarios and using the synchronous back-substitution reduction method; the second reduction unit reduces the train load power curve scenarios by using a clustering reduction algorithm; the function construction unit calculates the power matching parameters of traction substations, sets variables such as the capacity of on-board energy storage devices and the power purchased by tie lines, and constructs an objective function with low daily operating costs and high source-load power self-matching degree; the function solving unit iterates through the total number N of all traction substations on the train line, sequentially solves the multi-objective function for N traction substations, and calculates the optimal solution for the power and capacity of the on-board energy storage configuration for each traction substation; the model output unit establishes and outputs an on-board energy storage configuration model with low daily operating costs and high source-load power matching based on the optimal solution.

[0161] The present invention also relates to an electronic device comprising a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for implementing communication between the processor and the memory, wherein the program, when executed by the processor, implements the steps of the method as described in the above embodiments.

[0162] The present invention also relates to a storage medium, which is a computer-readable storage medium for computer-readable storage, wherein the storage medium stores one or more programs that can be executed by one or more processors to implement the steps of the methods described in the above embodiments.

[0163] This invention provides a method for configuring on-board energy storage in electrified railways considering multiple uncertainties in source and load. First, historical operating data of traction substations is acquired. A scenario reduction strategy is constructed based on noise filtering scenarios and synchronous back-substitution reduction. A clustering reduction algorithm is used to reduce train load power curve scenarios. Daily "source-grid-load" power matching parameters for traction substations are calculated, and variables such as the on-board energy storage device capacity and the power purchased by the tie line are set. The objective function is low daily operating cost and high source-load power self-matching degree. The method iteratively solves the multi-objective function for all N traction substations on the train line, calculating the optimal solution for the train's on-board energy storage configuration power and capacity for each traction substation. Based on the optimal solution, an on-board energy storage configuration model with low daily operating cost and high source-load power matching is established and output. This method constructs a multi-objective function that achieves the lowest operating cost and highest source-load power matching, considering factors such as energy storage configuration cost and grid power purchase cost. It meets the planning requirements for optimal on-board energy storage, exhibiting high reliability and practicality.

[0164] This invention constructs a multi-objective function with the lowest operating cost and highest source-load power matching, comprehensively considering factors such as energy storage configuration costs and grid power purchase costs. Combining the photovoltaic and wind power storage capacity and monthly power generation curve characteristics of the traction substation area, and considering the typical operating load curves of trains by clustering train timetables, the multi-objective optimization model is solved using the MATLAB solver to obtain the optimal onboard energy storage planning scheme. This approach is highly replicable and has significant potential for widespread application.

[0165] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for on-board energy storage configuration of electrified railways, characterized by, Includes the following steps: S100: Obtain historical operating data of the traction substation, and obtain wind and solar power output curve scenarios and train load power curve scenarios based on the historical operating data; S200, reduces the scene of wind and solar power output curve by filtering noise scene and synchronous back-substitution reduction method; S300, Reduce the train load power curve scenario through clustering reduction algorithm; S400. Calculate the power matching parameters of the traction substation, and use the capacity and power of the on-board energy storage device and the power purchased by the tie line as the solution variables to construct an objective function with low daily operating cost and high source-load power self-matching degree. S500, iterate through the total number of N traction substations on the train line, and solve the multi-objective function of N traction substations in sequence to calculate the optimal solution of the train on-board energy storage configuration power and capacity for each traction substation. S600. Establish and output a vehicle-mounted energy storage configuration model with low daily operating cost and high source-load power matching based on the optimal solution. The specific steps of step S400 are as follows: S401. Construct an expression with high source-load power self-matching degree: ； wherein is the unit of the total daily operating cost of the traction substation, is the unit of the interaction power with the public grid; S402. Construct an expression for the purchase and maintenance costs of on-board lithium battery energy storage: ； C BAT C is the total purchase cost of onboard lithium battery energy storage; EBAT The cost per unit capacity of lithium batteries; C PBAT Cost per unit power of lithium battery; C om Maintenance cost per unit capacity of lithium battery; Purchase capacity for lithium batteries, Purchase power for lithium batteries; N G This refers to the total number of train cars on the route from the starting point to the end point. S403. Calculate the electricity revenue from the train's onboard energy storage configuration. The calculation formula is as follows: ； Where, N life The total operating cycle of onboard lithium battery energy storage; I e,t Let E be the unit price of electricity in the power grid at time t; save Saves energy per hour for lithium batteries; S404. Calculate the daily operating cost of the traction substation. The calculation formula is as follows: ； wherein is the daily operating grid purchase cost for the traction substation; S405. Based on the power conservation principle of the i-th traction substation, and considering the total number of n(t) trains on the i-th traction line at time t, construct an expression for the interaction energy between the traction substation and the power grid: ； in, The energy exchanged between traction substation i and the public power grid at time t; Let t be the output power of the wind turbine unit in the traction substation. The photovoltaic output power of the traction substation at time t, Let t be the energy storage discharge of the traction substation. Let t be the charging power of the traction substation at time t.

2. The electrified railway on-board energy storage configuration method according to claim 1, characterized in that, The historical operating data of the traction substation in step S100 includes historical parameters of regional wind speed, historical parameters of light intensity environment, train operation data map, and power grid purchase price.

3. The electrified railway on-board energy storage configuration method according to claim 2, characterized in that, The specific steps of step S100 are as follows: S101. Calculate the wind turbine output power for the corresponding time period based on the historical wind speed parameters of the region. The calculation formula is as follows: ； in, It is the power generation of the wind turbine unit in the i-th traction substation during time period t; This is the rated power of the wind turbine unit; The wind speed per unit value at time t is as follows: It is the cut-in wind speed of the wind turbine. It is the cut-out wind speed of the wind turbine; This is the rated operating wind speed of the wind turbine unit; This indicates whether the i-th traction substation is equipped with wind power. The state variables are 0-1; S102. Calculate the output power of photovoltaic power generation equipment for the corresponding time period based on historical environmental parameters of light intensity. The calculation formula is as follows: ； Let be the power generation capacity of the photovoltaic power generation equipment in the i-th traction substation during time period t. The light intensity of the area; The threshold of light intensity required for normal power generation by photovoltaic power generation equipment; The rated power output of the photovoltaic power generation equipment; Is the i-th traction substation equipped with photovoltaic power generation equipment? The state variables are 0-1; S103. Calculate the daily load power of the traction substation based on train operation data. The calculation formula is as follows: ； ； in, This refers to the daily train load power of the traction substation. These are the state variables of the train operation; This refers to the power supply required for the train to operate normally at its rated speed. This refers to the power supply of the traction substation under normal braking conditions. Let be the total number of train trips on the i-th traction line at time t; The number of trains under the three operating conditions at time t; Let be the train number in the variable speed operation state at time t. This refers to the number of times the vehicle operates at a constant speed without reaching its rated speed. The number of times the train runs at its rated operating speed; Let be the actual speed of the train at time t. This refers to the train's normal rated operating speed. S104. Obtain the wind and solar power output curve scenario based on the wind turbine output power calculated in step S101 and the photovoltaic power generation equipment output power calculated in step S102. Obtain the train load power curve scenario based on the daily train load power of the traction substation calculated in step S103.

4. The electrified railway on-board energy storage configuration method of claim 1, wherein, The specific steps of step S200 are as follows: S201. Construct the original scenarios for wind power and photovoltaic power, calculate the Euclidean distance between each original scenario for photovoltaic and wind power, form a clustered scenario set, and divide the scenario set into important scenarios, general scenarios and noise scenarios. S202. Eliminate noisy scenes and retain important and general scenes as the original scenes; S203. The original scene is reduced by synchronous back-substitution reduction method to obtain the power curve of the reduced scene and the occurrence probability of the corresponding scene.

5. The electrified railway onboard energy storage configuration method of claim 1, wherein, The specific steps of the clustering reduction algorithm in step S300 are as follows: S301. Select m train load power curves as initial cluster centers; S302. Calculate the Euclidean distance between the initial cluster centers of the load power curves for all trains at each time point, and classify each load power curve into the curve with the nearest cluster center. The calculation formula is as follows: ； in, The Euclidean distance between the power value of the i-th train load curve at time t and the power value of the j-th initial cluster center curve at time t; This refers to the load curve data for the i-th train. The train load curve for the j-th cluster center; S303. Update the cluster center curve using the cluster center curve update calculation formula to obtain the updated cluster center curve. The cluster center curve update calculation formula is as follows: ； the train load curve of the i-th cluster center after updating; the train load curve of the i-th cluster center after updating; i,t the train load curve of the i-th cluster center after updating; S304, verifying the updated cluster center curve by a distortion function, the distortion function The formula is: ； If the verification passes ( If so, continue with the subsequent steps; If the check fails (YES in S302) , the process returns to step S303 to update the cluster center curve again. Wherein, SSEmin is the minimum threshold for distortion function verification; S305. Two typical train operation scenarios are obtained through clustering reduction as the train heavy load scenario and the train normal load scenario of the traction substation.

6. The electrified railway on-board energy storage configuration method of claim 1, wherein, In step S500, the multi-objective function solution for N traction substations is completed using the Cplex solver.

7. An on-board energy storage configuration system for an electrified railway, for use in the method of any one of claims 1 to 6, characterized in that, include: The data acquisition unit is used to acquire historical operating data of the traction substation and obtain wind and solar power output curve scenarios and train load power curve scenarios based on the historical operating data. The first reduction unit is used to reduce the power output curve scene of wind and solar power by filtering noise scene and synchronous back-substitution reduction method. The second reduction unit is used to reduce the train load power curve scenario through a clustering reduction algorithm; The function construction unit is used to calculate the power matching parameters of the traction substation, set the variable on-board energy storage device capacity and tie line power purchase, and construct the objective function with low daily operating cost and high source-load power self-matching degree. The function solving unit is used to iteratively solve the multi-objective function of all N traction substations on the train line, and calculate the optimal solution of the train on-board energy storage configuration power and capacity for each traction substation. The model output unit is used to establish and output a vehicle-mounted energy storage configuration model with low daily operating costs and high source-load power matching based on the optimal solution.

8. An electronic device, comprising: The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory, wherein the program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 6.

9. A storage medium, said storage medium being a computer-readable storage medium for computer-readable storage, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the steps of the method according to any one of claims 1 to 6.

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