A voltage threshold optimization method for energy storage devices based on digital twin architecture

Abstract

The present invention relates to the field of electrified railway traction power supply technology, and specifically discloses a method for optimizing the voltage threshold of an energy storage device based on a digital twin architecture. The method comprises: calling real data on the day-ahead state of the urban rail traction power supply system; based on the day-ahead data, correcting the day-ahead model of the urban rail traction power supply system while taking into account the characteristics of each sub-component of the model and the data characteristics; optimizing the day-ahead voltage threshold of the energy storage device of the urban rail traction power supply system based on the established train operation plan and the corrected model; collecting real data on the intraday system state; correcting the intraday model based on the day-ahead corrected model according to the real data on the intraday system state; optimizing the intraday voltage threshold of the energy storage device based on the intraday model correction results and the day-ahead optimization results. Compared with traditional energy storage device voltage threshold optimization methods, the method of the present invention can further improve the utilization rate of regenerative braking energy.

Description

Technical Field

[0001] The present invention relates to the technical field of electrified railway traction power supply technology, and in particular to a method for optimizing the voltage threshold of an energy storage device based on a digital twin architecture. Background Art

[0002] With the rapid increase in newly built railway mileage, the total energy consumption of urban rail transit is also increasing. Electricity costs account for 50% of urban rail transit's total operating costs, of which 55% goes to stations and related operations, and the remaining 45% is fed into the grid via traction substations. Urban rail trains preferentially utilize regenerative braking during braking. Combined with their high operating density and frequent starts, these trains generate a significant amount of regenerative braking energy. Utilizing this regenerative braking energy has been a hot topic of research both domestically and internationally. However, traditional traction power supply system energy consumption models are primarily based on empirical parameters or are limited to simulation systems based on theoretical models. These models lack interaction with the physical world. As systems wear out and under complex operating conditions, these traditional models can differ significantly from the actual system. Consequently, energy consumption simulations and energy-saving optimization control differ significantly from actual results, impacting the utilization of regenerative braking energy. Summary of the Invention

[0003] In order to solve the problems existing in the prior art, the present invention provides a voltage threshold optimization method for an energy storage device based on a digital twin architecture, so as to solve the problem of low regenerative braking energy utilization caused by the traditional theoretical model-based regenerative braking energy management without considering system parameter changes and actual complex working conditions.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the voltage threshold of an energy storage device based on a digital twin architecture, comprising the following steps:

[0005] S1. Call the real data of the status of the urban rail traction power supply system on the previous day;

[0006] S2. Based on the day-ahead data, the day-ahead model of the urban rail traction power supply system is calibrated by taking into account the characteristics of each sub-component of the model and the data features;

[0007] S3. Based on the train's established operation plan and the calibrated model, complete the day-ahead optimization of the voltage threshold of the energy storage device in the urban rail traction power supply system;

[0008] S4. Collect real data on system status within the day;

[0009] S5. Based on the day-ahead calibration model, the intraday model is calibrated according to the real data of the intraday system status;

[0010] S6. Based on the intraday model correction results and combined with the day-ahead optimization results, the intraday voltage threshold of the energy storage device is optimized.

[0011] Preferably, in step S1, the real data of the urban rail traction power supply system status in the day before the call includes: the voltage amplitude U of the high-voltage side of the main substation MA , high voltage active power P MA / Reactive power Q MA , low voltage side voltage amplitude U MB , low voltage side active power P MB / Reactive power Q MB ; Voltage amplitude U on the high side of step-down substation DA , high voltage side active power P DA / Reactive power Q DA , low voltage side voltage amplitude U DB , low voltage side active power P DB / Reactive power Q DB ; Voltage amplitude U at both ends of the cable line S 、U R , active power P S 、P R / Reactive power Q S , Q R ; Traction substation terminal voltage U d , terminal current I d and train schedules.

[0012] Preferably, in step S2, the sub-models in the day-ahead model of the urban rail traction power supply system that need to be calibrated are:

[0013] τ-type equivalent circuit model of the main substation: equivalent impedance Z M =R M +jX M , equivalent admittance Y M =G M -jB M ;

[0014] τ-type equivalent circuit model of step-down substation: equivalent impedance Z D =R D +jX D , equivalent admittance Y D =G D -jB D ;

[0015] π-type equivalent circuit model of cable line: equivalent impedance Z = R + jX, equivalent admittance Y;

[0016] External characteristic model of 24-pulse rectifier unit in DC traction substation: U D =φ(X)

[0017] X=[U dc1 ,U dc2 ,U dc3,U dc4 ,R eq1 ,R eq2 ,R eq3 ,R eq4 ,R F1 ,R F2 ,R F3 ,R F4 ,b0,b1,b2,b3,b4,b5];

[0018] For the part with high linearity in the working range of the 24-pulse rectifier unit, it is equivalent to a constant voltage source in series with resistance, U dc1 Indicates the equivalent voltage of working interval 1, R eq1 Indicates the equivalent resistance of working interval 1; U dc2 Indicates the equivalent voltage of working interval 2, R eq2 Indicates the equivalent resistance of working range 2; U dc3 Indicates the equivalent voltage of working range 3, R eq3 Indicates the equivalent resistance of working range 3; U dc4 Indicates the equivalent voltage of working range 4, R eq4 Indicates the equivalent resistance of working range 4; the part with high nonlinearity in the working range, its output voltage U d and output current I d Perform fitting, b0, b1, b2, b3, b4, b5 represent fitting factors, and the fitting curve can be expressed as: R F1 ,R F2 ,R F3 ,R F4 The critical current conditions are represented as intervals 1-2, 2-3, 3-4, and 4-5 respectively;

[0019] Equivalent circuit model of DC traction network: upstream unit resistance R of the contact network u , contact network downstream unit resistance R d , rail unit resistance R t ;

[0020] For linear models, including main substation, step-down substation, cable and DC traction network models, the recursive iterative least squares method is used to correct their parameters based on data processing and error analysis of the numerical matrix. For nonlinear and complex models, namely the external characteristic model of the 24-pulse rectifier unit in the traction substation, the multi-strategy improved dung beetle optimization algorithm is used to correct the external characteristics.

[0021] Preferably, in step S3, the day-ahead optimization process of the voltage threshold of the energy storage device of the urban rail traction power supply system is specifically as follows:

[0022] According to the model correction results in S2, the configuration update of the day-ahead optimization model is completed, and the decision variables for day-ahead optimization are selected as: X1={ΔU 1-Bchar ,ΔU 1-Bdis ,,ΔU 2-Bchar ,ΔU 2-Bdis ,......};

[0023] Among them, ΔU 1-Bchar Indicates the difference between the charging voltage threshold and the no-load voltage of the energy storage device at traction substation 1, ΔU 1-Bdis Indicates the difference between the no-load voltage at traction substation 1 and the discharge voltage threshold of the energy storage device; ΔU 2-Bchar Indicates the difference between the charging voltage threshold and the no-load voltage of the energy storage device at traction substation 2, ΔU 2-Bdis Indicates the difference between the no-load voltage at traction substation 2 and the discharge voltage threshold of the energy storage device;

[0024] Taking system energy consumption as the optimization objective and system grid voltage, energy storage device charge and discharge power, and SOC status as constraints, a multi-objective dung beetle optimization algorithm based on non-dominated sorting was used to optimize the energy storage device voltage threshold using the calibrated urban rail traction power supply system model. The energy storage device voltage threshold configuration result after day-ahead optimization is:

[0025]

[0026] Among them, U0 represents the no-load voltage set on the DC side of the traction substation in the day-ahead optimization, U char ′ represents the energy storage charging voltage threshold after day-ahead optimization, U dis ' represents the discharge voltage threshold of the energy storage device after day-ahead optimization.

[0027] Preferably, in step S4, the collected real data of the system status during the day include: 110kV AC side network voltage effective value U S_real ; Energy storage device terminal voltage U e , current I e .

[0028] Preferably, in step S5, based on the day-ahead correction model, the intraday models that need to be further corrected include:

[0029] DC traction substation external characteristic model: U D =φ(X);

[0030] The corrected daily external characteristic model of the traction substation is:

[0031] U D_rn =U D_rq +(U S_real / 110-1)*U 0_110

[0032] U D_rq represents the external characteristic of the traction substation after day-ahead correction, U 0_110 Indicates the no-load voltage of the traction substation corresponding to the grid voltage of 110kV;

[0033] Second-order dual-polarization model of the energy storage device: ohmic internal resistance R0, polarization resistors R1 and R2, polarization capacitors C1 and C2; the state equation of the energy storage device is expressed as follows:

[0034]

[0035] Among them, y k Indicates the terminal voltage output by the energy storage device; is the observation vector; θ k is the parameter matrix to be determined; U oc Represents the open circuit voltage of the energy storage system; θ k The parameters in and the parameters to be identified in the equivalent model have the following relationship:

[0036]

[0037] Combined with the above formula, the parameters of the energy storage device are corrected online based on the genetic factor least squares method FFRLS.

[0038] Preferably, in step S6, the intraday voltage threshold optimization process of the energy storage device of the urban rail traction power supply system is specifically as follows:

[0039] According to the correction result of the external characteristics of the traction substation in step S5, the configuration update of the traction substation model is completed. According to the online correction result of the energy storage device parameters combined with the EKF algorithm, the initial value of the SOC state of the energy storage device during the intraday optimization process is corrected;

[0040] The decision variables selected for intraday optimization are:

[0041] X2={b1,b2,a1,a2,k1,k2}

[0042] b1, b2,, k1, k2 represent the daily optimization voltage threshold adjustment coefficients of the energy storage device, a1, a2 represent a certain SOC state of the energy storage device, which serves as the control quantity for threshold voltage adjustment;

[0043] The system energy consumption and the SOC equilibrium state of each energy storage device are selected as the optimization objectives, and the system grid voltage, energy storage device charge and discharge power, and SOC state are selected as the constraints. Based on the equivalent model of the energy storage device corrected online, the EKF algorithm is combined to correct the initial value of the SOC state of the energy storage device in the non-dominated sorting multi-objective dung beetle optimization algorithm. The intraday optimization of the energy storage device voltage threshold is further realized based on the intraday correction model. The configuration result of the energy storage device voltage threshold after intraday optimization is as follows:

[0044]

[0045]

[0046] Among them, U0' represents the actual no-load voltage within one optimization cycle in intraday optimization, U char Indicates the charging voltage threshold of the energy storage device after daily optimization, U dis Indicates the discharge voltage threshold of the energy storage device after intraday optimization.

[0047] The beneficial effects of the present invention are as follows: The present invention uses the traction power supply system's main transformer substation, step-down transformer substation, cables, traction substation (24-pulse rectifier unit), DC-side traction network, and energy storage system as physical objects to establish their virtual equivalent models. Based on the data interaction characteristics of the digital twin system's physical entities and virtual systems, the day-ahead model is corrected using real-world system state data, and day-ahead optimization is performed based on the corrected model. Within the day, the model and initial algorithm state are further corrected based on intraday collected data, and in this case, intraday optimization is performed in conjunction with the day-ahead optimization results. Compared to traditional energy storage device voltage threshold optimization methods, voltage threshold optimization control implemented within this digital twin architecture can further improve the utilization rate of regenerative braking energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of the flow of a method for optimizing the voltage threshold of an energy storage device based on a digital twin architecture in an embodiment of the present invention;

[0049] Figure 2 Schematic diagram of the architecture of a voltage threshold optimization method for an energy storage device based on a digital twin architecture in an embodiment of the present invention;

[0050] Figure 3 Schematic diagram of an equivalent model of an urban rail traction power supply system in an embodiment of the present invention;

[0051] Figure 4 This is a flow chart of calibration of equivalent model parameters and characteristics of the traction power supply system in an embodiment of the present invention;

[0052] Figure 5 This is a flowchart of the day-ahead and day-intraday optimization of the voltage threshold of the energy storage device in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] The present invention originates from the idea of ​​mutual optimization of physical entities and virtual systems under the digital twin architecture and the method of optimizing and controlling the voltage threshold of the energy storage device. The digital twin system can perceive the system status in real time through actual measurement, data interaction and other methods, thereby realizing the dynamic correction of the virtual system, ensuring high-precision mapping of the real system in the virtual space, and can provide a more reliable basis for system optimization decisions by perceiving the real system status in real time. Therefore, optimizing the voltage threshold of the energy storage device based on the digital twin architecture can further improve the utilization rate of regenerative braking energy under the digital twin model. However, in the current research field of regenerative braking energy utilization in urban rail traction power supply systems, there is no specific application solution for digital twin technology.

[0055] Therefore, the present invention provides a technical solution: a voltage threshold optimization method for energy storage devices based on digital twin architecture, such as Figure 1 and Figure 2 As shown, the following steps are included:

[0056] S1. Call the real data of the status of the urban rail traction power supply system on the previous day.

[0057] Furthermore, the real data of the urban rail traction power supply system status today include: the voltage amplitude on the high-voltage side of the main substation U MA , high voltage active power P MA / Reactive power Q MA , low voltage side voltage amplitude U MB , low voltage side active power P MB / Reactive power Q MB ; Voltage amplitude U on the high side of step-down substation DA , high voltage side active power P DA / Reactive power Q DA , low voltage side voltage amplitude U DB , low voltage side active power P DB / Reactive power Q DB ; Voltage amplitude U at both ends of the cable line S 、U R , active power P S 、P R / Reactive power Q S , Q R ; Traction substation (24-pulse rectifier unit) terminal voltage U d , terminal current I d and train schedules.

[0058] S2. Based on the day-ahead data, the day-ahead model of the urban rail traction power supply system is calibrated by taking into account the characteristics of each sub-part of the model and the data features.

[0059] The sub-models in the day-ahead model of the urban rail traction power supply system that need to be corrected, such as Figure 3 As shown, they are:

[0060] τ-type equivalent circuit model of the main substation: equivalent impedance Z M =R M +jX M , equivalent admittance Y M =G M -jB M The matrix equation used for correction is in the form of Hx=z, which is specifically expressed as:

[0061]

[0062] τ-type equivalent circuit model of step-down substation: equivalent impedance Z D =R D +jX D , equivalent admittance Y D =G D -jB D The matrix equation used for correction is the same as that used for correction of the main substation, and will not be described in detail here.

[0063] π-type equivalent circuit model of the cable line: equivalent impedance Z = R + jX, equivalent admittance Y; the matrix equation used for correction is in the form of Hx = z, specifically expressed as:

[0064]

[0065]

[0066] External characteristic model of 24-pulse rectifier unit in DC traction substation: U D =φ(X), X=[U dc1 ,U dc2 ,U dc3 ,U dc4 ,R eq1 ,R eq2 ,R eq3 ,R eq4 ,R F1 ,R F2 ,R F3 ,R F4 ,b0,b1,b2,b3,b4,b5]; the correction function can be expressed as:

[0067]

[0068] in represents the voltage at the traction substation terminal obtained by the i-th measurement, represents the voltage at the traction substation terminal calculated according to the model for the i-th time, I d,irepresents the traction substation terminal current obtained by the i-th measurement.

[0069] For the part with high linearity in the working range of the 24-pulse rectifier unit, it is equivalent to a constant voltage source in series with resistance, U dc1 Indicates the equivalent voltage of working interval 1, R eq1 Indicates the equivalent resistance of working interval 1; U dc2 Indicates the equivalent voltage of working interval 2, R eq2 Indicates the equivalent resistance of working range 2; U dc3 Indicates the equivalent voltage of working range 3, R eq3 Indicates the equivalent resistance of working range 3; U dc4 Indicates the equivalent voltage of working range 4, R eq4 Indicates the equivalent resistance of working range 4; the part with high nonlinearity in the working range, its output voltage U d and output current I d Perform fitting, b0, b1, b2, b3, b4, b5 represent fitting factors, and the fitting curve can be expressed as: R F1 ,R F2 ,R F3 ,R F4 The critical current conditions are represented as intervals 1-2, 2-3, 3-4, and 4-5 respectively;

[0070] Equivalent circuit model of DC traction network: upstream unit resistance R of the contact network u , contact network downstream unit resistance R d , rail unit resistance R t The matrix equation used for correction is in the form of Hx=z, which is specifically expressed as:

[0071]

[0072] Where x1, x2, and L are from the train operation diagram; U2 and U1 represent the voltages at the traction substations on both sides of the correction section traction network; I d1 , I d2 , I d3 , I d4 Indicates the upstream and downstream feeder currents at the traction substation ports on both sides of the correction section traction network. oscd Correction method is the same as R oscu , I will not go into details here.

[0073] like Figure 4 As shown in the figure, for linear models, including main substation, step-down substation, cable and DC traction network models, after processing the data and performing error analysis on the numerical matrix, the recursive iterative least squares method is used to calibrate / optimally estimate their parameters; the optimal estimate form is: Where R is the covariance matrix of the measurement error e. For a nonlinear and complex model, namely the external characteristic model of a traction substation (24-pulse rectifier unit), a multi-strategy improved dung beetle optimization algorithm is used to correct the external characteristics.

[0074] S3. Based on the train's established operation plan and the corrected model, complete the day-ahead optimization of the voltage threshold of the energy storage device in the urban rail traction power supply system.

[0075] like Figure 5 As shown in Figure 2, the day-ahead optimization process for the voltage threshold of the energy storage device in the urban rail traction power supply system is as follows:

[0076] According to the model correction results in S2, the configuration update of the day-ahead optimization model is completed, and the decision variables for day-ahead optimization are selected as: X1={ΔU 1-Bchar ,ΔU 1-Bdis ,,ΔU 2-Bchar ,ΔU 2-Bdis ,......};

[0077] Among them, ΔU 1-Bchar Indicates the difference between the charging voltage threshold and the no-load voltage of the energy storage device at traction substation 1, ΔU 1-Bdis Indicates the difference between the no-load voltage at traction substation 1 and the discharge voltage threshold of the energy storage device; ΔU 2-Bchar Indicates the difference between the charging voltage threshold and the no-load voltage of the energy storage device at traction substation 2, ΔU 2-Bdis Indicates the difference between the no-load voltage at traction substation 2 and the discharge voltage threshold of the energy storage device;

[0078] Taking system energy consumption as the optimization target, specifically, the optimization target of the energy storage device voltage threshold day-ahead optimization is:

[0079]

[0080] Among them, P T,i represents the output energy consumption of the i-th traction substation.

[0081] The constraints are the system grid voltage, energy storage device charge and discharge power, and SOC state. The constraints are:

[0082]

[0083] U max , U min Indicates upper and lower limit constraints of traction; SOC max , SOC min represents the state of charge constraint of the energy storage device; P max , P min Indicates the charging and discharging power constraints of the energy storage device.

[0084] The multi-objective dung beetle optimization algorithm based on non-dominated sorting is used to optimize the voltage threshold of the energy storage device using the calibrated urban rail traction power supply system model. The voltage threshold configuration result of the energy storage device after day-ahead optimization is:

[0085]

[0086] Among them, U0 represents the no-load voltage set on the DC side of the traction substation in the day-ahead optimization, U char ′ represents the energy storage charging voltage threshold after day-ahead optimization, U dis ' represents the discharge voltage threshold of the energy storage device after day-ahead optimization.

[0087] S4. Collect real data on the system status during the day.

[0088] The collected daily system status data include: 110kV AC side network voltage effective value U S_real ; Energy storage device terminal voltage U e , current I e .

[0089] S5. Based on the day-ahead correction model, the intraday model is calibrated according to the real data of the intraday system status.

[0090] Based on the day-ahead correction model, the intraday models that need to be further revised include:

[0091] DC traction substation external characteristic model: U D =φ(X);

[0092] The corrected daily external characteristic model of the traction substation is:

[0093] U D_rn =U D_rq +(U S_real / 110-1)*U 0_110

[0094] U D_rq represents the external characteristic of the traction substation after day-ahead correction, U 0_110 Indicates the no-load voltage of the traction substation corresponding to the grid voltage of 110kV;

[0095] Second-order dual-polarization model of the energy storage device: ohmic internal resistance R0, polarization resistors R1 and R2, polarization capacitors C1 and C2; the state equation of the energy storage device is expressed as follows:

[0096]

[0097]

[0098] Among them, y k Indicates the terminal voltage output by the energy storage device; is the observation vector; θ k is the parameter matrix to be determined; U oc Represents the open circuit voltage of the energy storage system, which is related to the SOC value; θ k The parameters in and the parameters to be identified in the equivalent model have the following relationship:

[0099]

[0100] Combined with the above formula, the parameters of the energy storage device are corrected online based on the genetic factor least squares method FFRLS.

[0101] S6. Based on the intraday model correction results and combined with the day-ahead optimization results, the intraday voltage threshold of the energy storage device is optimized.

[0102] like Figure 5 As shown in Figure 2, the optimization process of the intraday voltage threshold of the energy storage device in the urban rail traction power supply system is as follows:

[0103] According to the correction result of the external characteristics of the traction substation in step S5, the configuration update of the traction substation model is completed. According to the online correction result of the energy storage device parameters combined with the EKF algorithm, the initial value of the SOC state of the energy storage device during the intraday optimization process is corrected;

[0104] The decision variables selected for intraday optimization are:

[0105] X2={b1,b2,a1,a2,k1,k2}

[0106] b1, b2,, k1, k2 represent the daily optimization voltage threshold adjustment coefficients of the energy storage device, a1, a2 represent a certain SOC state of the energy storage device, which serves as the control quantity for threshold voltage adjustment;

[0107] The system energy consumption and the SOC equilibrium state of each energy storage device are selected as the optimization targets. Specifically, the optimization target of the energy storage device voltage threshold intraday optimization is selected as follows:

[0108]

[0109]

[0110] Where f(X2) represents the system energy consumption, which measures the energy saving effect of the system; Indicates the SOC equilibrium state of each energy storage device; a represents the weight coefficient. i Represents the SOC state of the i-th energy storage device.

[0111] The system grid voltage, energy storage device charge and discharge power, and SOC state are constraints. The constraints are:

[0112]

[0113] U max , U min Indicates upper and lower limit constraints of traction; SOC max , SOC min represents the state of charge constraint of the energy storage device; P max , P min Indicates the charging and discharging power constraints of the energy storage device.

[0114] According to the equivalent model of the energy storage device with online correction, the EKF algorithm is combined to realize the correction of the initial value of the SOC state of the energy storage device in the multi-objective dung beetle optimization algorithm of non-dominated sorting. Further, the intra-day optimization of the voltage threshold of the energy storage device is realized according to the intra-day correction model. The voltage threshold configuration result of the energy storage device after intra-day optimization is as follows:

[0115]

[0116]

[0117] Among them, U0' represents the actual no-load voltage within one optimization cycle in intraday optimization, U char Indicates the charging voltage threshold of the energy storage device after daily optimization, U dis Indicates the discharge voltage threshold of the energy storage device after intraday optimization.

[0118] Compared with the traditional energy storage device voltage threshold optimization method, the voltage threshold optimization control carried out under this digital twin architecture can further improve the utilization rate of regenerative braking energy.

[0119] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0120] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "an", "the" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0121] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0122] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0123] The references to "first" and "second" in the embodiments merely distinguish similar objects and do not represent a specific ordering of the objects. It is understood that the specific order or precedence of "first" and "second" can be interchanged where appropriate. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein.

[0124] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing the voltage threshold of an energy storage device based on a digital twin architecture, characterized in that: The steps include: S1. Call the real data of the status of the urban rail traction power supply system on the previous day; S2. Based on the day-ahead data, the day-ahead model of the urban rail traction power supply system is calibrated by taking into account the characteristics of each sub-part of the model and the data features. The sub-models in the day-ahead model of the urban rail traction power supply system that need to be calibrated are: τ-type equivalent circuit model of the main substation: equivalent impedance Z M =R M +jX M , equivalent admittance Y M =G M -jB M ; τ-type equivalent circuit model of step-down substation: equivalent impedance Z D =R D +jX D , equivalent admittance Y D =G D -jB D ; π-type equivalent circuit model of cable line: equivalent impedance Z = R + jX, equivalent admittance Y; External characteristic model of 24-pulse rectifier unit in DC traction substation: U D =φ(X) X=[U dc1 ,U dc2 ,U dc3 ,U dc4 ,R eq1 ,R eq2 ,R eq3 ,R eq4 ,R F1 ,R F2 ,R F3 ,R F4 ,b0,b1,b2,b3,b4,b5]; For the part with high linearity in the working range of the 24-pulse rectifier unit, it is equivalent to a constant voltage source in series with resistance, U dc1 Indicates the equivalent voltage of working interval 1, R eq1 Indicates the equivalent resistance of working range 1; U dc2 Indicates the equivalent voltage of working interval 2, R eq2 Indicates the equivalent resistance of working range 2; U dc3 Indicates the equivalent voltage of working range 3, R eq3 Indicates the equivalent resistance of working range 3; U dc4 Indicates the equivalent voltage of working range 4, R eq4 Indicates the equivalent resistance of working range 4; the part with high nonlinearity in the working range, its output voltage U d and output current I d Perform fitting, b0, b1, b2, b3, b4, b5 represent fitting factors, and the fitting curve can be expressed as: R F1 ,R F2 ,R F3 ,R F4 The critical current conditions are represented as intervals 1-2, 2-3, 3-4, and 4-5 respectively; Equivalent circuit model of DC traction network: upstream unit resistance R of the contact network u , contact network downstream unit resistance R d , rail unit resistance R t ; For linear models, including those for the main substation, step-down substation, cable, and DC traction network, recursive iterative least squares methods were used to calibrate parameters based on data processing and error analysis of the numerical matrix. For nonlinear and complex models, such as the external characteristic model of a 24-pulse rectifier unit in a traction substation, a multi-strategy improved dung beetle optimization algorithm was used to calibrate the external characteristics. S3. Based on the train's established operation plan and the calibrated model, complete the day-ahead optimization of the voltage threshold of the energy storage device in the urban rail traction power supply system; S4. Collect real data on system status within the day; S5. Based on the day-ahead calibration model, the intraday model is calibrated according to the real data of the intraday system status; S6. Based on the intraday model correction results and combined with the day-ahead optimization results, the intraday voltage threshold of the energy storage device is optimized.

2. The method for optimizing voltage threshold of an energy storage device based on a digital twin architecture according to claim 1, wherein: In step S1, the real data of the urban rail traction power supply system status today includes: the voltage amplitude U of the high-voltage side of the main substation MA , high voltage active power P MA / Reactive power Q MA , low voltage side voltage amplitude U MB , low voltage side active power P MB / Reactive power Q MB ; Voltage amplitude U on the high side of step-down substation DA , high voltage side active power P DA / Reactive power Q DA , low voltage side voltage amplitude U DB , low voltage side active power P DB / Reactive power Q DB ; Voltage amplitude U at both ends of the cable line S 、U R , active power P S 、P R / Reactive power Q S , Q R ; Traction substation terminal voltage U d , terminal current I d and train schedules.

3. The method for optimizing voltage threshold of an energy storage device based on a digital twin architecture according to claim 1, wherein: In step S3, the day-ahead optimization process of the voltage threshold of the energy storage device of the urban rail traction power supply system is as follows: According to the model correction results in S2, the configuration update of the day-ahead optimization model is completed, and the decision variables for day-ahead optimization are selected as: X1={ΔU 1-Bchar ,ΔU 1-Bdis ,ΔU 2-Bchar ,ΔU 2-Bdis ,......}; Among them, ΔU 1-Bchar Indicates the difference between the charging voltage threshold and the no-load voltage of the energy storage device at traction substation 1, ΔU 1-Bdis Indicates the difference between the no-load voltage at traction substation 1 and the discharge voltage threshold of the energy storage device; ΔU 2-Bchar Indicates the difference between the charging voltage threshold and the no-load voltage of the energy storage device at traction substation 2, ΔU 2-Bdis Indicates the difference between the no-load voltage at traction substation 2 and the discharge voltage threshold of the energy storage device; Taking system energy consumption as the optimization objective and system grid voltage, energy storage device charge and discharge power, and SOC status as constraints, a multi-objective dung beetle optimization algorithm based on non-dominated sorting was used to optimize the energy storage device voltage threshold using the calibrated urban rail traction power supply system model. The energy storage device voltage threshold configuration result after day-ahead optimization is: Among them, U0 represents the no-load voltage set on the DC side of the traction substation in the day-ahead optimization, U char ′ represents the energy storage charging voltage threshold after day-ahead optimization, U dis ′ represents the discharge voltage threshold of the energy storage device after day-ahead optimization.

4. The method for optimizing voltage threshold of an energy storage device based on a digital twin architecture according to claim 1, wherein: In step S4, the collected real data of the system status during the day include: 110kV AC side network voltage effective value U S_real ; Energy storage device terminal voltage U e , current I e .

5. The method for optimizing voltage threshold of an energy storage device based on a digital twin architecture according to claim 1, wherein: In step S5, based on the day-ahead correction model, the intraday models that need to be further corrected include: DC traction substation external characteristic model: U D =φ(X); The corrected daily external characteristic model of the traction substation is: IN D_rn =U D_rq +(In S_real / 110-1)*U 0_110 U D_rq represents the external characteristic of the traction substation after day-ahead correction, U 0_110 Indicates the no-load voltage of the traction substation corresponding to the grid voltage of 110kV; Second-order dual-polarization model of the energy storage device: ohmic internal resistance R0, polarization resistors R1 and R2, polarization capacitors C1 and C2; the state equation of the energy storage device is expressed as follows: Among them, y k Indicates the terminal voltage output by the energy storage device; is the observation vector; θ k is the parameter matrix to be determined; U oc Represents the open circuit voltage of the energy storage system; θ k The parameters in and the parameters to be identified in the equivalent model have the following relationship: Combined with the above formula, the parameters of the energy storage device are corrected online based on the genetic factor least squares method FFRLS.

6. The method for optimizing voltage threshold of an energy storage device based on a digital twin architecture according to claim 1, wherein: In step S6, the intraday voltage threshold optimization process of the energy storage device of the urban rail traction power supply system is specifically as follows: According to the correction result of the external characteristics of the traction substation in step S5, the configuration update of the traction substation model is completed. According to the online correction result of the energy storage device parameters combined with the EKF algorithm, the initial value of the SOC state of the energy storage device during the intraday optimization process is corrected; The decision variables selected for intraday optimization are: X2={b1,b2,a1,a2,k1,k2} b1, b2, k1, k2 represent the voltage threshold adjustment coefficients of the energy storage device optimized within a day, and a1, a2 represent a certain SOC state of the energy storage device, which serves as the control quantity for threshold voltage adjustment; The system energy consumption and the SOC equilibrium state of each energy storage device are selected as the optimization objectives, and the system grid voltage, energy storage device charge and discharge power, and SOC state are selected as the constraints. Based on the equivalent model of the energy storage device corrected online, the EKF algorithm is combined to correct the initial value of the SOC state of the energy storage device in the non-dominated sorting multi-objective dung beetle optimization algorithm. Further, the intraday optimization of the energy storage device voltage threshold is realized based on the intraday correction model. The configuration result of the energy storage device voltage threshold after intraday optimization is as follows: Among them, U0' represents the actual no-load voltage within one optimization cycle in intraday optimization, U char Indicates the charging voltage threshold of the energy storage device after daily optimization, U dis Indicates the discharge voltage threshold of the energy storage device after intraday optimization.

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