Energy storage device voltage threshold optimization method based on digital twin architecture

Abstract

The invention relates to the technical field of electrified railway traction power supply, and particularly discloses an energy storage device voltage threshold optimization method based on a digital twin architecture, and the method comprises the steps: calling the day-ahead state real data of an urban rail traction power supply system; on the basis of day-ahead data, realizing the day-ahead model correction of the urban rail traction power supply system on the basis of considering the characteristics of each sub-part of the model and the data characteristics; on the basis of a set operation plan of the train and the corrected model, current optimization of the voltage threshold value of the energy storage device of the urban rail traction power supply system is completed; collecting the real data of the intraday system state; on the basis of the day-ahead correction model, completing the correction of the intra-day model according to the real data of the intra-day system state; and based on the intra-day model correction result, combining with the day-ahead optimization result to realize the intra-day voltage threshold optimization of the energy storage device. Compared with a traditional energy storage device voltage threshold optimization method, the method 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, 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 the mileage of newly built railways, the total energy consumption of urban rail transit is also growing. In the total operating cost of urban rail transit, its electricity costs account for 50%, of which 55% flows to stations and related links, and the other 45% is input into the power grid through traction substations. Urban rail trains give priority to regenerative braking during braking. In addition, due to their high operating density and frequent starts, a large amount of regenerative braking energy will be generated. How to utilize this part of regenerative braking energy has always been a research hotspot at home and abroad. However, the traditional energy consumption model of the traction power supply system is mainly derived from empirical parameters or limited to the simulation system of the theoretical model, lacking interaction with the physical entity. With the wear of the system and under complex working conditions, there is a large difference between the traditional model and the actual system, which makes the effect of its energy consumption simulation and energy-saving optimization control different from the actual one, affecting the utilization rate 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 not considering system parameter changes and actual complex working conditions when traditional regenerative braking energy management is based on theoretical models.

[0004] To achieve the above object, 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 corrected by taking into account the characteristics of each sub-part of the model and the data features;

[0007] S3. Based on the established train operation plan and the corrected model, the voltage threshold of the energy storage device of the urban rail traction power supply system is optimized on the day before;

[0008] S4, collect real data of system status within the day;

[0009] S5. Based on the day-ahead correction model, the intraday model is corrected 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 previous day that is called 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 S , P R / Reactive power Q S , Q R ; Traction substation terminal voltage U d , terminal current I d and train operation diagram.

[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 corrected are:

[0013] τ-type equivalent circuit model of 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 ,b 0 ,b 1 ,b 2 ,b 3 ,b 4 ,b 5 ];

[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 series resistor, 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 Fitting, b 0 ,b 1 ,b 2 ,b 3 ,b 4 ,b 5 represents the fitting factor, and the fitting curve can be expressed as: R F1 ,R F2 ,R F3 ,R F4 They are respectively expressed as critical current conditions of intervals 1-2, 2-3, 3-4, and 4-5;

[0019] Equivalent circuit model of DC traction network: upstream unit resistance R of 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 numerical matrices; 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: X 1 ={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 target, system grid voltage, energy storage device charging and discharging power, and SOC state as constraints, a multi-objective dung beetle optimization algorithm based on non-dominated sorting is used to optimize the energy storage device voltage threshold on the day-ahead using the corrected urban rail traction power supply system model. The voltage threshold configuration result of the energy storage device after the day-ahead optimization is:

[0025]

[0026] Among them, U 0 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 model that needs to be further corrected includes:

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

[0030] The corrected external characteristic model of the intraday 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 energy storage device: Ohmic internal resistance R 0 , polarization resistance R 1 , R 2 , polarization capacitance C 1 , C 2 ; 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, and 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 in the intraday optimization process is corrected;

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

[0041] X 2 = {b 1 ,b 2 ,a 1 ,a 2 ,k 1 ,k 2}

[0042] b 1 ,b 2 ,,k 1 ,k 2 represents the voltage threshold adjustment coefficient of the energy storage device optimized during the day, a 1 ,a 2 Indicates a certain SOC state of the energy storage device, which is used as a control quantity for adjusting the threshold voltage;

[0043] The system energy consumption and the SOC equilibrium state of each energy storage device are selected as the optimization targets, and the system grid voltage, the charging and discharging power of the energy storage device and the SOC state are selected as the constraints. According to the equivalent model of the energy storage device corrected online, 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, and further according to the intra-day correction model, the voltage threshold of the energy storage device is optimized intra-day. The configuration result of the voltage threshold of the energy storage device after intra-day optimization is as follows:

[0044]

[0045]

[0046] Among them, U 0 ' represents the actual no-load voltage within an optimization cycle in intraday optimization, U char It represents 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 takes the main transformer substation, step-down transformer substation, cable, traction substation (24-pulse rectifier unit), DC side traction network and energy storage system of the traction power supply system as physical objects, establishes its virtual equivalent model, and based on the characteristics of data interaction between the physical entity and virtual system of the digital twin system, corrects the day-ahead model with the real data of the day-ahead system state, and optimizes the day-ahead based on the corrected model; within the day, further corrects the model and the initial state of the algorithm based on the data collected within the day, and in this case, combines the day-ahead optimization results to perform intraday optimization. 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. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0049] Figure 2 It is a 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 It is a 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 the correction of equivalent model parameters and characteristics of the traction power supply system in the embodiment of the present invention;

[0052] Figure 5 This is a flow chart 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 be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0054] The present invention is derived from the idea of ​​mutual optimization of physical entities and virtual systems under the digital twin architecture and the method of optimizing 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 the 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 an energy storage device based on a digital twin architecture, such as Figure 1 and Figure 2 As shown, the following steps are included:

[0056] S1. Call up 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 current state of the urban rail traction power supply system called include: the voltage amplitude 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 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 operation diagram.

[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 are as follows: Figure 3 As shown, they are:

[0060] τ-type equivalent circuit model of 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 the matrix equation used for correction of the main substation, and will not be described in detail here.

[0063] π-type equivalent circuit model of cable line: equivalent impedance Z = R + jX, equivalent admittance Y; the matrix equation used for correction is in the form of Hx = z, which is 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 ,b 0 ,b 1 ,b 2 ,b 3 ,b 4 ,b 5 ]; the correction function can be expressed as:

[0067]

[0068] in represents the voltage at the traction substation obtained by the i-th measurement, represents the traction substation terminal voltage calculated according to the model for the i-th time, I d,i Represents 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 series resistor, 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 Fitting, b 0 ,b 1 ,b 2 ,b 3 ,b 4 ,b 5represents the fitting factor, and the fitting curve can be expressed as: R F1 ,R F2 ,R F3 ,R F4 They are respectively expressed as critical current conditions of intervals 1-2, 2-3, 3-4, and 4-5;

[0070] Equivalent circuit model of DC traction network: upstream unit resistance R of 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 x 1 、x 2 , L comes from the train operation diagram; U 2 , U 1 Indicates the voltage at the traction substations on both sides of the traction network in the correction section; I d1 ,I d2 ,I d3 ,I d4 Indicates the upstream and downstream feeder currents at the ports of the traction substations on both sides of the traction network in the correction section. oscd Correction method is the same as R oscu , which will not be elaborated 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, on the basis of data processing and error analysis of numerical matrices, the idea of ​​recursive iterative least squares method is used to correct / optimally estimate their parameters; the optimal estimation form is: Where R is the covariance matrix of the measurement error e. For the nonlinear and complex model, namely the external characteristic model of the traction substation (24-pulse rectifier unit), the multi-strategy improved dung beetle optimization algorithm is used to correct the external characteristics.

[0074] S3. Based on the established train operation plan and the corrected model, the voltage threshold of the energy storage device of the urban rail traction power supply system is optimized in advance.

[0075] like Figure 5 As shown in the figure, the specific process of optimizing the voltage threshold of the energy storage device of 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: X 1 ={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 voltage threshold of the energy storage device is selected as follows:

[0079]

[0080] Where P T,i Represents the output energy consumption of the i-th traction substation.

[0081] The constraints are the system grid voltage, the charging and discharging power of the energy storage device, and the 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 Represents the charging and discharging power constraints of the energy storage device.

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

[0085]

[0086] Among them, U 0 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 system status during the day.

[0088] The real data collected during the day include: 110kV AC side grid 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 corrected 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 corrected include:

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

[0092] The corrected external characteristic model of the intraday 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 energy storage device: Ohmic internal resistance R 0 , polarization resistance R 1 , R 2 , polarization capacitance C 1 , C 2 ; 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 the figure, the optimization process of the intraday voltage threshold of the energy storage device of 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, and 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 in the intraday optimization process is corrected;

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

[0105] X 2 = {b 1 ,b 2 ,a 1 ,a 2 ,k 1 ,k 2}

[0106] b 1 ,b 2 ,,k 1 ,k 2 represents the voltage threshold adjustment coefficient of the energy storage device optimized during the day, a 1 ,a 2 Indicates a certain SOC state of the energy storage device, which is used as a control quantity for adjusting the threshold voltage;

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

[0108]

[0109]

[0110] Where f(X 2 ) represents the system energy consumption and measures the energy saving effect of the system; Indicates the SOC equilibrium state of each energy storage device; a indicates the weight coefficient. i Represents the SOC state of the i-th energy storage device.

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

[0112]

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

[0114] According to the equivalent model of the energy storage device corrected online, 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, and further according to the intra-day correction model, the intra-day optimization of the voltage threshold of the energy storage device is realized. After the intra-day optimization, the voltage threshold configuration result of the energy storage device is:

[0115]

[0116]

[0117] Among them, U 0 ' represents the actual no-load voltage within an optimization cycle in intraday optimization, U char It represents 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 article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including 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", "said" 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 other meanings.

[0121] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after 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 determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0123] The "first\second" mentioned in the embodiments is only to distinguish similar objects, and does not represent a specific order for the objects. It is understandable that the "first\second" can be interchanged with the specific order or sequence where permitted. It should be understood that the objects distinguished by "first\second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than those 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 protection scope of the present invention.

Claims

1. A voltage threshold optimization method for 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 corrected by taking into account the characteristics of each sub-part of the model and the data features; S3. Based on the established train operation plan and the corrected model, the voltage threshold of the energy storage device of the urban rail traction power supply system is optimized on the day before; S4, collect real data of system status within the day; S5. Based on the day-ahead correction model, the intraday model is corrected 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, characterized in that: In step S1, the real data of the current state of the urban rail traction power supply system called includes: the voltage amplitude 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 S , P R / Reactive power Q S , Q R ; Traction substation terminal voltage U d , terminal current I d and train operation diagram.

3. The method for optimizing voltage threshold of an energy storage device based on a digital twin architecture according to claim 1, characterized in that: 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: τ-type equivalent circuit model of 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 series resistor, 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 Fitting is performed, 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 They are respectively expressed as critical current conditions of intervals 1-2, 2-3, 3-4, and 4-5; Equivalent circuit model of DC traction network: upstream unit resistance R of contact network u , contact network downstream unit resistance R d , rail unit resistance R t ; 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 numerical matrices; 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.

4. The method for optimizing voltage threshold of an energy storage device based on a digital twin architecture according to claim 1, characterized in that: 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: 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 It represents 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 target, system grid voltage, energy storage device charging and discharging power, and SOC state as constraints, a multi-objective dung beetle optimization algorithm based on non-dominated sorting is used to optimize the energy storage device voltage threshold on the day-ahead using the corrected urban rail traction power supply system model. The voltage threshold configuration result of the energy storage device after the 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, and 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.

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

6. The method for optimizing voltage threshold of an energy storage device based on a digital twin architecture according to claim 1, characterized in that: In step S5, based on the day-ahead correction model, the intraday models that need to be further corrected include: External characteristic model of DC traction substation: U D =φ(X); The corrected external characteristic model of the intraday 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 energy storage device: ohmic internal resistance R0, polarization resistance R1, R2, polarization capacitance C1, C2; the state equation of 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.

7. The method for optimizing voltage threshold of an energy storage device based on a digital twin architecture according to claim 1, characterized in that: 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, and 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 in 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, a1, a2 represent a certain SOC state of the energy storage device, which is used 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 targets, and the system grid voltage, the charging and discharging power of the energy storage device and the SOC state are selected as the constraints. According to the equivalent model of the energy storage device corrected online, 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, and further according to the intra-day correction model, the voltage threshold of the energy storage device is optimized intra-day. The configuration result of the voltage threshold of the energy storage device after intra-day optimization is as follows: Among them, U0' represents the actual no-load voltage within an optimization cycle in intraday optimization, U char It represents 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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