A method and apparatus for optimizing the discharge voltage of supercapacitors based on the power of trains on the line.

Abstract

This invention discloses a method and apparatus for optimizing the discharge voltage of a supercapacitor based on the power of a train on a railway line. The method includes: constructing an equivalent circuit model based on the line connection between the substation, the train, and the supercapacitor energy storage device; obtaining the train power curve under train operation conditions, inputting the train power curve into the equivalent circuit model, and generating energy-saving rate of the energy storage device, energy-saving rate of the braking resistor, and predicted lifespan of the supercapacitor; constructing a supercapacitor fitness function based on the energy-saving rate of the energy storage device, the energy-saving rate of the braking resistor, and the predicted lifespan of the supercapacitor; and generating the minimum discharge voltage of the supercapacitor using the supercapacitor fitness function. This method optimizes the discharge voltage of the supercapacitor in the energy storage device, thereby improving the energy-saving effect of the energy storage device.

Description

Technical Field

[0001] This invention relates to the field of supercapacitor technology, and more specifically to a method and apparatus for optimizing supercapacitor discharge voltage based on the power of trains on a railway line. Background Technology

[0002] In recent years, energy storage technology has developed rapidly, and energy storage components such as flywheels, supercapacitors, and batteries have been widely used in urban rail transit energy storage systems. Among these energy storage components, supercapacitors have been widely used in urban rail transit energy recovery and utilization due to their higher power density, longer lifespan, and wider operating range.

[0003] To improve the energy-saving effect of energy storage devices, the most widely used strategy is the threshold adjustment strategy. This is because train traction braking is typically controlled by grid voltage, and energy storage devices can recover and utilize regenerative braking energy by judging and controlling the grid voltage. To track the voltage reference value and control charging and discharging power, energy storage devices typically employ dual closed-loop control in their DC / DC converters. The outer loop proportional-integral (PI) controller regulates the line voltage to generate a reference current for the inner loop. This reference current is limited by a current limiter to prevent the current command value from exceeding the supercapacitor's maximum current. The inner loop controller adjusts the duty cycle of the IGBT (Insulated Gate Bipolar Transistor) drive pulses to regulate the supercapacitor current to the reference value. Based on grid voltage, no-load voltage, and other conditions, the charging and discharging thresholds are dynamically optimized to change the charging and discharging power of the energy storage device. This affects the interaction with the train load power, thus influencing the DC grid voltage and the starting status of the braking resistor. However, the effect of threshold optimization has an upper limit because the power of the energy storage device is not only controlled by the charging and discharging thresholds but also limited by the physical conditions of the supercapacitor itself—that is, the output power is affected by the voltage. Therefore, based on threshold adjustments, there is still room for further improvement in the energy-saving effect of the energy storage device. During the charging and discharging process, the SOC (State of Charge) of the supercapacitor continuously changes. The different SOCs determine the amount of energy stored by the supercapacitor and the initial charging and discharging power. Therefore, effective management of the SOC of the energy storage system can improve the energy-saving and voltage-stabilizing effect of the system. The range of SOC variation is controlled by the minimum discharge voltage of the supercapacitor, and the setting of the minimum discharge voltage of the supercapacitor affects the substation voltage, thereby affecting the voltage distribution of the traction network and the energy flow between the energy storage system, the substation, and the train. It also affects the lifespan of the supercapacitor module.

[0004] Therefore, in order to reduce the energy consumption of braking resistors, improve the energy-saving effect of energy storage systems, and extend the lifespan of supercapacitors, it is necessary to dynamically optimize and adjust the minimum discharge voltage of supercapacitors. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the defect in the prior art that the contradiction between the initial output power of the supercapacitor and the stored energy limits the improvement of the energy saving rate of the energy storage system, thereby providing a method and device for optimizing the discharge voltage of the supercapacitor based on the power of the train on the line.

[0006] This invention provides a method for optimizing the discharge voltage of supercapacitors based on the power of trains on the line, comprising the following steps:

[0007] Construct an equivalent circuit model based on the line connections between the substation, train, and supercapacitor energy storage device;

[0008] The train power curve under the train's operating state is obtained, and the train power curve is input into the equivalent circuit model to generate energy storage device energy saving rate, braking resistor energy saving rate and supercapacitor life prediction data.

[0009] A supercapacitor fitness function is constructed based on the energy-saving rate of the energy storage device, the energy-saving rate of the braking resistor, and the predicted lifespan of the supercapacitor.

[0010] The minimum discharge voltage of the supercapacitor is generated using the supercapacitor fitness function.

[0011] Optionally, the step of inputting the train power curve into the equivalent circuit model to generate energy storage device energy saving rate, braking resistor energy saving rate, and supercapacitor lifespan prediction data includes:

[0012] The train power curve is discretized to generate discretized data;

[0013] The discretized data is input into the equivalent circuit model to generate the voltage and current across the substation when the supercapacitor energy storage device is installed, the voltage and current across the substation when the supercapacitor energy storage device is not installed, the voltage and current across the train braking resistor when the supercapacitor energy storage device is installed, the voltage and current across the train braking resistor when the supercapacitor energy storage device is not installed, and the current voltage and temperature of the supercapacitor.

[0014] The energy-saving rate of the energy storage device is generated based on the voltage and current across the substation when the supercapacitor energy storage device is installed and the voltage and current across the substation when the supercapacitor energy storage device is not installed.

[0015] The braking resistor energy saving rate is generated based on the voltage and current across the train braking resistor when the substation is equipped with the supercapacitor energy storage device and the voltage and current across the train braking resistor when the substation is not equipped with the supercapacitor energy storage device.

[0016] Supercapacitor lifetime prediction data is generated based on the current voltage and current temperature of the supercapacitor.

[0017] Optionally, the expression for the supercapacitor fitness function is as follows:

[0018]

[0019] In the above formula, J represents the fitness function of the supercapacitor, uc_min(t) represents the minimum discharge voltage of the supercapacitor, and ω1 represents the weight of the energy saving rate of the energy storage device. ω1 represents the average energy saving rate of the energy storage device, and ω2 represents the weight of the energy saving rate of the braking resistor. ω3 represents the average energy saving rate of the braking resistor, and ω3 represents the weight of the supercapacitor lifespan prediction data. This represents the average value of the predicted supercapacitor lifespan data.

[0020] Optionally, constructing the supercapacitor fitness function includes:

[0021] The node voltage equations, supercapacitor current limits, and supercapacitor voltage limits of the equivalent circuit model are used as constraints on the supercapacitor fitness function.

[0022] Optionally, the node voltage equations of the equivalent circuit model are generated in the following manner:

[0023] Extract the voltage and current of the train, the voltage and current of the substation, and the variable resistance between line nodes from the equivalent circuit model. Based on the voltage and current of the train, the voltage and current of the substation, the variable resistance between line nodes, and the node distance, generate the node voltage equation of the equivalent circuit model.

[0024] Optionally, generating the minimum discharge voltage of the supercapacitor using the supercapacitor fitness function includes:

[0025] The supercapacitor fitness function is initialized to generate initial function values;

[0026] The discharge voltage of the supercapacitor is processed using a genetic algorithm to generate an objective function value;

[0027] Based on the initial function value and the target function value, the minimum discharge voltage of the supercapacitor is calculated using the simulated annealing algorithm.

[0028] Optionally, the step of calculating the minimum discharge voltage of the supercapacitor using a simulated annealing algorithm based on the initial function value and the target function value includes:

[0029] Calculate the difference between the initial function value and the target function value to generate a function difference;

[0030] The acceptance probability of the target function is calculated based on the function difference. When the acceptance probability does not meet a preset threshold, the target function value is iterated.

[0031] Obtain the iteration count, iteration temperature value, and termination condition. Compare the iteration count and iteration temperature value with the termination condition. When the iteration count and temperature value meet the termination condition, output the minimum discharge voltage of the supercapacitor.

[0032] In a second aspect of this application, a supercapacitor discharge voltage optimization device based on line train power is also proposed, comprising:

[0033] The module is used to construct an equivalent circuit model based on the line connections between substations, trains and supercapacitor energy storage devices.

[0034] The generation module is used to obtain the train power curve under the train's operating state, input the train power curve into the equivalent circuit model, and generate energy-saving rate of energy storage device, energy-saving rate of braking resistor and life prediction data of supercapacitor.

[0035] The acquisition module is used to construct a supercapacitor fitness function based on the energy saving rate of the energy storage device, the energy saving rate of the braking resistor, and the supercapacitor lifetime prediction data.

[0036] The solution module is used to generate the minimum discharge voltage of the supercapacitor using the supercapacitor fitness function.

[0037] Optionally, the generation module includes:

[0038] The first generation submodule is used to discretize the train power curve and generate discretized data.

[0039] The second generation submodule is used to input the discretized data into the equivalent circuit model to generate the voltage and current across the substation when the supercapacitor energy storage device is installed, the voltage and current across the substation when the supercapacitor energy storage device is not installed, the voltage and current across the train braking resistor when the supercapacitor energy storage device is installed, the voltage and current across the train braking resistor when the supercapacitor energy storage device is not installed, and the current voltage and temperature of the supercapacitor.

[0040] The third generation submodule is used to generate the energy saving rate of the energy storage device based on the voltage and current at both ends of the substation when the supercapacitor energy storage device is installed and the voltage and current at both ends of the substation when the supercapacitor energy storage device is not installed.

[0041] The fourth generation submodule is used to generate the braking resistor energy saving rate based on the voltage and current across the train braking resistor when the substation is equipped with the supercapacitor energy storage device and the voltage and current across the train braking resistor when the substation is not equipped with the supercapacitor energy storage device.

[0042] The fifth generation submodule is used to generate supercapacitor lifetime prediction data based on the current voltage and current temperature of the supercapacitor.

[0043] Optionally, the expression for the supercapacitor fitness function is as follows:

[0044]

[0045] In the above formula, J represents the fitness function of the supercapacitor, uc_min(t) represents the minimum discharge voltage of the supercapacitor, and ω1 represents the weight of the energy saving rate of the energy storage device. ω1 represents the average energy saving rate of the energy storage device, and ω2 represents the weight of the energy saving rate of the braking resistor. ω3 represents the average energy saving rate of the braking resistor, and ω3 represents the weight of the supercapacitor lifespan prediction data. This represents the average value of the predicted supercapacitor lifespan data.

[0046] Optionally, constructing the supercapacitor fitness function includes:

[0047] The node voltage equations, supercapacitor current limits, and supercapacitor voltage limits of the equivalent circuit model are used as constraints on the supercapacitor fitness function.

[0048] Optionally, the node voltage equations of the equivalent circuit model are generated in the following manner:

[0049] Extract the voltage and current of the train, the voltage and current of the substation, and the variable resistance between line nodes from the equivalent circuit model. Based on the voltage and current of the train, the voltage and current of the substation, the variable resistance between line nodes, and the node distance, generate the node voltage equation of the equivalent circuit model.

[0050] Optionally, the solution module includes:

[0051] An initialization submodule is used to initialize the supercapacitor fitness function and generate initial function values;

[0052] The preprocessing submodule is used to preprocess the initial function values ​​using a genetic algorithm to generate the target function values;

[0053] The calculation submodule is used to calculate the minimum discharge voltage of the supercapacitor using a simulated annealing algorithm based on the initial function value and the target function value.

[0054] Optionally, the computing submodule includes:

[0055] The calculation unit is used to calculate the difference between the initial function value and the target function value, and generate the function difference;

[0056] An iterative unit is used to calculate the acceptance probability of the target function based on the function difference, replace the initial function value with the target function value according to the acceptance probability, and perform iteration based on the target function value;

[0057] The comparison unit is used to obtain the iteration number, iteration temperature value and termination condition, compare the iteration number and iteration temperature value with the termination condition respectively, and output the minimum discharge voltage of the supercapacitor when the iteration number and the temperature value meet the termination condition respectively.

[0058] In a third aspect of this application, a computer device is also provided, comprising a processor and a memory, wherein the memory is used to store a computer program, the computer program including a program, and the processor is configured to invoke the computer program to perform the method described in the first aspect.

[0059] In a fourth aspect of this application, embodiments of the present invention provide a computer-readable storage medium storing a computer program that is executed by a processor to implement the method of the first aspect described above.

[0060] The technical solution of this invention has the following advantages:

[0061] The present invention provides a method for optimizing the discharge voltage of supercapacitors based on the power of trains on the line. This method generates energy-saving rate data for energy storage devices, braking resistors, and supercapacitor lifetime prediction data based on equivalent circuits. It then constructs a supercapacitor fitness function based on these data. By considering the supercapacitor lifetime prediction data, the method improves the energy-saving rate of both the energy storage device and the braking resistor. Furthermore, by solving the supercapacitor fitness function, it optimizes the discharge voltage of the supercapacitor in the energy storage device, thereby constraining the optimal discharge capacity of the supercapacitor and improving the energy-saving effect of the energy storage device. Attached Figure Description

[0062] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0063] Figure 1 This is a flowchart of the supercapacitor discharge voltage optimization method based on the power of the train on the line in Embodiment 1 of the present invention;

[0064] Figure 2 This is a schematic diagram of the equivalent circuit model in Embodiment 1 of the present invention;

[0065] Figure 3 This is a schematic diagram of the line voltage control strategy in Embodiment 1 of the present invention;

[0066] Figure 4 This is a flowchart of step S102 in Embodiment 1 of the present invention;

[0067] Figure 5 This is a flowchart of step S104 in Embodiment 1 of the present invention;

[0068] Figure 6 This is a schematic diagram illustrating the combination of genetic algorithm and simulated annealing algorithm in Embodiment 1 of the present invention;

[0069] Figure 7 This is a schematic diagram of the evolution curve of the supercapacitor capacity configuration optimization process in Embodiment 1 of the present invention;

[0070] Figure 8 This is a flowchart of step S1043 in Embodiment 1 of the present invention;

[0071] Figure 9 This is a schematic diagram showing the voltage variation of the supercapacitor when the charging and discharging voltage thresholds are different in Embodiment 1 of the present invention;

[0072] Figure 10 This is a schematic diagram showing the relationship between the minimum discharge voltage of the supercapacitor and the braking resistor before optimization of the minimum discharge voltage of the supercapacitor in Embodiment 1 of the present invention.

[0073] Figure 11 This is a schematic diagram showing the relationship between the minimum discharge voltage of the supercapacitor and the braking resistor after optimization of the minimum discharge voltage of the supercapacitor in Embodiment 1 of the present invention.

[0074] Figure 12 This is a schematic diagram of the supercapacitor discharge voltage optimization device based on the power of the train line in Embodiment 2 of the present invention. Detailed Implementation

[0075] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0077] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0078] Example 1

[0079] This embodiment provides a method for optimizing the discharge voltage of supercapacitors based on the power of trains on the line, such as... Figure 1 As shown, it includes the following steps:

[0080] S101. Construct an equivalent circuit model based on the line connections between the substation, train, and supercapacitor energy storage device.

[0081] Among them, such as Figure 2 As shown, due to train movement, the network topology and the line resistance between the train and the substation are time-varying. The SCESS (Super-capacitor Energy Storage System) consists of a DC / DC converter and supercapacitor modules. The charging and discharging of the SCESS is based on a line voltage control strategy (e.g., ...). Figure 3 As shown in the diagram, when a train brakes near the SCESS, the substation voltage rises above the charging threshold, and the SCESS operates in charging mode, with excess braking energy being recovered by the supercapacitor. When the train accelerates, the substation voltage drops below the discharge threshold, and the SCESS discharges to stabilize the network voltage at the discharge threshold.

[0082] Furthermore, in order to track the voltage reference value and achieve charging and discharging power control, the DC / DC converter adopts a dual closed-loop control structure. The outer loop proportional-integral (PI) controller adjusts the line voltage to generate a reference current for the inner loop. The reference current is limited by a current limiter to prevent the current command value from exceeding the maximum current of the supercapacitor. The inner loop controller adjusts the supercapacitor current to the reference value by adjusting the duty cycle of the IGBT drive pulse.

[0083] S102. Obtain the train power curve under the train's operating state, input the train power curve into the equivalent circuit model, and generate energy storage device energy saving rate, braking resistor energy saving rate and supercapacitor life prediction data.

[0084] S103. Construct a supercapacitor fitness function based on the energy saving rate of the above-mentioned energy storage device, the energy saving rate of the above-mentioned braking resistor, and the above-mentioned supercapacitor lifetime prediction data.

[0085] The expression for the fitness function of the supercapacitor is as follows:

[0086]

[0087] In the above formula, J represents the fitness function of the supercapacitor, uc_min(t) represents the minimum discharge voltage of the supercapacitor, and ω1 represents the weight of the energy saving rate of the energy storage device. ω represents the average energy saving rate of the energy storage device (i.e., the average energy saving rate of the energy storage device), and ω2 represents the weight of the energy saving rate of the braking resistor. ω3 represents the average value of the braking resistor (i.e., the average braking resistor energy saving rate), and ω3 represents the weight of the supercapacitor life prediction data. This represents the average value of the predicted supercapacitor lifespan data.

[0088] Furthermore, the nodal voltage equations, supercapacitor current limits, and supercapacitor voltage limits of the above equivalent circuit model are used as constraints on the supercapacitor fitness function, where the expressions for the constraints are as follows:

[0089]

[0090] In the above equation, KVLequations represents the node voltage equations, i sc I represents the real-time current of the supercapacitor. lim U represents the maximum current limit of a supercapacitor. c_min ≤'u c_min '≤U c_max This indicates the discharge voltage limit of the supercapacitor, where U c_min =0 indicates the minimum rated voltage of the supercapacitor, U c_max This indicates the maximum rated voltage of the supercapacitor.

[0091] in, This represents the lowest discharge voltage of the supercapacitor within S calculation cycles T.

[0092] Furthermore, the node voltage equations of the above equivalent circuit model are generated in the following manner: the voltage and current of the train, the voltage and current of the substation, and the variable resistance between line nodes are extracted from the above equivalent circuit model, and the node voltage equations of the above equivalent circuit model are generated based on the voltage and current of the train, the voltage and current of the substation, the variable resistance between line nodes, and the distance between the nodes.

[0093] Furthermore, the node voltage equations of the equivalent circuit model are shown below:

[0094]

[0095] In the above formula, u ss Indicates the voltage of the substation, i ss U represents the current in the substation. t Indicates train voltage, i t The train current is represented by r, and the resistance between line nodes is represented by l1, l2, and L. a and L b Indicates the distance between line nodes (e.g.) Figure 2 (as shown); whereby, the distance between the train position and the position of each substation is obtained, and the distance is multiplied by the unit resistance to generate the variable resistance between the line nodes.

[0096] S104. Use the above-mentioned supercapacitor fitness function to generate the minimum discharge voltage of the supercapacitor.

[0097] Furthermore, a combination of genetic algorithm and simulated annealing algorithm is used to solve the fitness function of the supercapacitor. The system state variable (i.e. the minimum discharge voltage of the supercapacitor) generated in the first T time period is used as the initial state variable for the second T time period. Steps S101, S102 and S103 are repeated until all S T time periods have been calculated.

[0098] Furthermore, the optimal discharge capacity (SOC) of a supercapacitor can be calculated using its lowest discharge voltage, as shown in the following formula:

[0099]

[0100] In the above formula, U sc_min U represents the minimum discharge voltage of a supercapacitor. sc_rate This indicates the rated voltage of the supercapacitor.

[0101] The aforementioned method for optimizing the discharge voltage of supercapacitors based on the power of trains on the line generates energy-saving rate of energy storage devices, energy-saving rate of braking resistors, and predicted lifespan of supercapacitors based on equivalent circuits. It then constructs a supercapacitor fitness function based on these data. By considering the predicted lifespan of supercapacitors, the method improves the energy-saving rate of energy storage devices and the energy-saving rate of braking resistors. Furthermore, by solving the supercapacitor fitness function, it optimizes the discharge voltage of the supercapacitors in the energy storage device, thereby constraining the optimal discharge capacity of the supercapacitors and improving the energy-saving effect of the energy storage device.

[0102] Preferably, such as Figure 4 As shown, in step S102, the above-mentioned train power curve is input into the above-mentioned equivalent circuit model to generate energy-saving rate of energy storage device, energy-saving rate of braking resistor and predicted life of supercapacitor, including:

[0103] S1021. Discretize the above train power curve (discretize according to the calculation step size Ts) to generate discretized data.

[0104] S1022. Input the above discretized data into the above equivalent circuit model to generate the voltage and current across the substation when the above supercapacitor energy storage device is installed, the voltage and current across the substation when the above supercapacitor energy storage device is not installed, the voltage and current across the train braking resistor when the above supercapacitor energy storage device is installed, the voltage and current across the train braking resistor when the above supercapacitor energy storage device is not installed, and the current voltage and temperature of the supercapacitor.

[0105] S1023. The energy saving rate of the energy storage device is generated based on the voltage and current at both ends of the substation when the supercapacitor energy storage device is installed in the substation and the voltage and current at both ends of the substation when the supercapacitor energy storage device is not installed in the substation.

[0106] Specifically, the expression for the energy storage device's energy saving rate e% is as follows:

[0107]

[0108] In the above formula, This represents the voltage across the j-th substation when a supercapacitor energy storage device is installed. This represents the voltage across the j-th substation when there is no supercapacitor energy storage device. This represents the current across the j-th substation when a supercapacitor energy storage device is installed. This represents the current across the j-th substation when there is no supercapacitor energy storage device, and n represents the number of substations.

[0109] in, This indicates that the average energy saving rate of the energy storage device is obtained by averaging the energy saving rates of the energy storage devices obtained over S calculation periods T.

[0110] S1024. Based on the voltage and current across the train braking resistor when the substation is equipped with the supercapacitor energy storage device and the voltage and current across the train braking resistor when the substation is not equipped with the supercapacitor energy storage device, the braking resistor energy saving rate is generated.

[0111] Specifically, the calculation formula for the braking resistor is as follows:

[0112]

[0113] In the above formula, This represents the voltage across the braking resistor of the j-th train when the substation has a supercapacitor energy storage device. This represents the voltage across the braking resistor of the j-th train when the substation has no supercapacitor energy storage device. This represents the current flowing across the braking resistor of the j-th train when the substation has a supercapacitor energy storage device. This represents the current flowing across the braking resistor of the j-th train when the substation has no supercapacitor energy storage device, and m represents the number of trains.

[0114] in, The average braking resistor energy saving rate is obtained by averaging the braking resistor energy saving rates obtained over S calculation periods T.

[0115] S1025. Generate supercapacitor lifetime prediction data based on the current voltage and temperature of the supercapacitor.

[0116] Specifically, the formula for predicting the lifespan of a supercapacitor is shown below:

[0117] MSL x (T x U x )=MSL(T0,U0)(U x / U0) -n ·exp[-E a / k·(1 / T0-1 / T x )]exp[-m·(U x / U0) α ·(T x / T0) β ]

[0118] In the above formula, MSL(T0,U0) represents the rated capacitance life, and E aU represents the activation energy of an electrochemical capacitor, n represents the voltage power-law exponent, k represents the Boltzmann constant, m represents the gain of the cross-coupling factor, α represents the voltage factor power-law coefficient, β represents the temperature factor power-law coefficient, and U represents the voltage factor power-law coefficient. x T represents the current voltage of the supercapacitor. x This indicates the current temperature of the supercapacitor.

[0119] in, The average value of the supercapacitor lifetime prediction data is obtained by averaging the lifetimes obtained over S calculation cycles T.

[0120] Preferably, such as Figure 5 As shown, step S104, which involves generating the minimum discharge voltage of the supercapacitor using the supercapacitor fitness function, includes:

[0121] S1041. Initialize the supercapacitor fitness function to generate initial function values.

[0122] Specifically, the initial energy storage device energy saving rate, initial braking resistor energy saving rate, and initial supercapacitor lifetime prediction data are input into the supercapacitor fitness function to calculate and generate the initial supercapacitor fitness function, which is then used as the initialization function value.

[0123] Furthermore, initialization control parameters are set, namely, the iteration number gen = 0, and the preset temperature value T. end .

[0124] S1042. Use a genetic algorithm to preprocess the initial function values ​​to generate the target function values.

[0125] Specifically, such as Figure 6 As shown, the genetic algorithm is an adaptive probabilistic search method based on the theory of natural evolution and the mechanisms of genetics. It consists of three main modules: encoding / decoding, fitness evaluation, and genetic operations. Genetic operations include chromosome selection, crossover, and mutation. The basic steps of the genetic algorithm are as follows:

[0126] (1) Encoding: 24 binary codes are used to represent the 24 minimum discharge voltage values ​​of the supercapacitor with a data length of 24 * 150 s = 3600 s. Each X chromosome is treated as an individual in the population. The initial population size is NIND, and the individual length is PRECI. For example, the X chromosome within 24 calculation cycles T is represented as:

[0127] X = [X1, X2, X3, ... X 24 ]

[0128] In the above formula, X1 represents the minimum discharge voltage value of the supercapacitor in the first calculation cycle, X24 This represents the minimum discharge voltage value of the supercapacitor in the 24th calculation cycle.

[0129] (2) Fitness assessment: Find the maximum value of the supercapacitor fitness function J. The larger the function value, the better the individual. The fitness F is calculated as follows:

[0130] F(J[X])=J[X]

[0131] (3) Selection operation: Using roulette wheel selection, if the population size is N and the fitness value of individual i is F i The probability that individual i is selected is:

[0132]

[0133] In the above formula, F j This represents the fitness value of the j-th individual.

[0134] (4) Crossover operation: the k-th chromosome X k and the m-th chromosome X m The expression for the crossover operation on the j-th individual is:

[0135]

[0136] Among them, P c denoted by , where r represents the crossover probability and r represents a random number in the interval [0, 1].

[0137] (5) Mutation operation: X of the j-th individual on the i-th chromosome ij The formula for calculating mutations is:

[0138]

[0139] Among them, P m denoted by , where s represents the mutation probability and s represents a random number in the interval [0, 1].

[0140] S1043. Based on the above initial function value and the above objective function value, the minimum discharge voltage of the above supercapacitor is calculated using the simulated annealing algorithm.

[0141] Specifically, simulated annealing uses the physical picture and statistical properties of the solid annealing process as its physical background. The Metropolis criterion (which accepts new states with probability rather than using completely deterministic rules) helps the algorithm avoid local optima.

[0142] In existing technologies, optimizing the minimum discharge state of a supercapacitor (SOC) based on line power requires optimization algorithms based on the line model. Due to the numerous constraints and limitations of supercapacitors and lines, traditional intelligent algorithms are often used for optimization. Among traditional intelligent algorithms, particle swarm optimization (PSO) is easier to implement, more accurate, and faster than other evolutionary algorithms. However, during its operation, if a particle finds a current optimal position, other particles will quickly move towards it. If this optimal position is a local optimum, or two particles are simultaneously in a local optimum, the search ability of the particle swarm in the solution space deteriorates. Therefore, PSO is prone to getting trapped in local optima and premature convergence when solving complex optimization problems. Genetic algorithms, through crossover and mutation, exchange information among individuals, effectively avoiding information overlap and escaping local convergence. However, genetic algorithms have relatively low computational efficiency and slow convergence speed. Simulated annealing is a heuristic algorithm and a greedy algorithm, but its search process introduces random factors. When iteratively updating feasible solutions, genetic algorithms accept a worse solution with a certain probability, thus potentially escaping the local optimum and reaching the global optimum. Compared with traditional optimization algorithms, genetic algorithms have better convergence, shorter computation time, and higher robustness. However, when using genetic algorithms for optimization, the optimization results often converge to a local optimum rather than the global optimum, a phenomenon known as premature convergence. To address this issue, simulated annealing is incorporated into the genetic algorithm. In the early stages of the genetic algorithm, when the temperature T is relatively high, the stretching effect of simulated annealing on the fitness of the genetic algorithm is not strong, and individuals with similar fitness have similar probabilities of producing offspring. As the temperature T decreases, i.e., in the later stages of the genetic algorithm, the stretching effect strengthens, increasing the fitness differences among individuals with similar fitness, making the advantages of superior individuals more pronounced, thereby avoiding the problem of convergence to a local optimum. Figure 7 The figure shows the evolution curve of the supercapacitor capacity configuration optimization process. The fitness function of the supercapacitor is solved by combining the simulated annealing algorithm and the genetic algorithm. The optimization curve is oscillating and tends to be stable when the optimization reaches 70 generations, and the minΦ value is obtained. The individual in the population at this time is the optimal solution for capacity configuration.

[0143] Preferably, refer to Figure 8 As shown, in step S1043 above, the calculation of the minimum discharge voltage of the supercapacitor using the simulated annealing algorithm based on the initial function value and the target function value includes:

[0144] S10431. Calculate the difference between the initial function value and the objective function value to generate the function difference.

[0145] Specifically, such as Figure 6As shown, based on the initial function value X of the supercapacitor fitness function J... i The objective function value X is generated by a genetic algorithm. j Calculate the function difference before and after the genetic algorithm operation. The formula for calculating the function difference ΔΦ is as follows:

[0146] ΔΦ=Φ(X j )-Φ(X i )

[0147] S10432. Calculate the acceptance probability of the objective function based on the above function difference, replace the above initial function value with the above objective function value according to the above acceptance probability, and iterate based on the above objective function value.

[0148] Specifically, the objective function value X j The probability P of acceptance, i.e., the Metropolis acceptance probability, is calculated using the following formula:

[0149]

[0150] S10433. Obtain the iteration number, iteration temperature value and termination condition. Compare the iteration number and iteration temperature value with the termination condition. When the iteration number and temperature value meet the termination condition, output the minimum discharge voltage of the supercapacitor.

[0151] Specifically, the number of iterations gen is counted. When the number of iterations gen is greater than the maximum number of iterations MAXGEN, the temperature value T in the i-th iteration is compared. i With preset temperature value T end When the temperature value T in the i-th iteration i Greater than the preset temperature value T end When, calculate T i+1 =T i *q represents a temperature value that decreases over time. q is a positive number less than 1, typically between 0.8 and 0.99, and then the genetic operation is repeated.

[0152] Furthermore, when the number of iterations and the temperature value meet the termination condition, the objective function value of the current iteration is used as the minimum discharge voltage output of the supercapacitor.

[0153] The following specific embodiment illustrates the technical effectiveness of the supercapacitor discharge voltage optimization method based on the power of trains on the line.

[0154] Since the grid voltage is also affected by the charging and discharging threshold of the energy storage system, the optimization results of the minimum discharge voltage of the supercapacitor are not the same when different charging and discharging thresholds are selected. The simulation was carried out in the time period of T=1 (0-150s).

[0155] The charge / discharge voltage thresholds are respectively ①U char =860V,U dis =850V, ②U char =850V,U dis =840V, ③U char =850V,U dis When the voltage is 825V, the voltage change of the supercapacitor can be obtained as follows: Figure 9 As shown, since the supercapacitor is basically not discharged in case ③, there is no need to optimize the minimum discharge voltage in this case. For cases ① and ②, the optimal discharge voltage values ​​U can be obtained by optimizing the minimum discharge voltage control strategy of the supercapacitor. c_min =201V and U c_min =194V, the corresponding energy saving rates of the energy storage device and the braking resistor are e% = 93.13%, 98.46% and e%, respectively. bra_loss % = 61.17%, 63.12%, therefore, in the optimization process of the minimum discharge voltage of supercapacitors, strategies (constraints) such as dynamic threshold control can be combined to obtain better energy-saving effects;

[0156] To further compare the effectiveness of the optimization strategy using the lowest discharge voltage of supercapacitors, the optimized indicators were compared with those before optimization, such as... Figure 10-11 As shown in the figure, the rectangle represents the braking resistor and the line represents the minimum discharge voltage of the supercapacitor. After adopting the optimization strategy, the energy consumption of the braking resistor decreased during the start-up period. The average energy consumption of the braking resistor decreased from 9.15 kWh to 4.18 kWh.

[0157] Example 2

[0158] This embodiment provides a supercapacitor discharge voltage optimization device based on the power of the train on the line, such as Figure 12 As shown, it includes:

[0159] Module 121 is used to construct an equivalent circuit model based on the line connections between the substation, train and supercapacitor energy storage device.

[0160] Due to train movement, the network topology and line resistance between the train and the substation are time-varying. The SCESS (Super-capacitor Energy Storage System) consists of a DC / DC converter and supercapacitor modules. SCESS charging and discharging is based on a line voltage control strategy. When a train brakes near the SCESS, and the substation voltage rises above the charging threshold, the SCESS operates in charging mode, and excess braking energy is recovered by the supercapacitor. When the train accelerates, and the substation voltage drops below the discharging threshold, the SCESS discharges, stabilizing the network voltage at the discharge threshold.

[0161] Furthermore, in order to track the voltage reference value and achieve charging and discharging power control, the DC / DC converter adopts a dual closed-loop control structure. The outer loop proportional-integral (PI) controller adjusts the line voltage to generate a reference current for the inner loop. The reference current is limited by a current limiter to prevent the current command value from exceeding the maximum current of the supercapacitor. The inner loop controller adjusts the supercapacitor current to the reference value by adjusting the duty cycle of the IGBT drive pulse.

[0162] The generation module 122 is used to obtain the train power curve under the train's operating state, input the train power curve into the equivalent circuit model, and generate energy-saving rate of energy storage device, energy-saving rate of braking resistor and life prediction data of supercapacitor.

[0163] The acquisition module 123 is used to construct a supercapacitor fitness function based on the energy saving rate of the energy storage device, the energy saving rate of the braking resistor, and the supercapacitor lifetime prediction data.

[0164] The expression for the fitness function of the supercapacitor is as follows:

[0165]

[0166] In the above formula, J represents the fitness function of the supercapacitor, uc_min(t) represents the minimum discharge voltage of the supercapacitor, and ω1 represents the weight of the energy saving rate of the energy storage device. ω represents the average energy saving rate of the energy storage device (i.e., the average energy saving rate of the energy storage device), and ω2 represents the weight of the energy saving rate of the braking resistor. ω3 represents the average value of the braking resistor (i.e., the average braking resistor energy saving rate), and ω3 represents the weight of the supercapacitor life prediction data. This represents the average value of the predicted supercapacitor lifespan data.

[0167] Furthermore, the nodal voltage equations, supercapacitor current limits, and supercapacitor voltage limits of the above equivalent circuit model are used as constraints on the supercapacitor fitness function, where the expressions for the constraints are as follows:

[0168]

[0169] In the above equation, KVLequations represents the node voltage equations, i sc I represents the real-time current of the supercapacitor. lim U represents the maximum current limit of a supercapacitor. c_min ≤'u c_min '≤U c_max This indicates the discharge voltage limit of the supercapacitor, where U c_min =0 indicates the minimum rated voltage of the supercapacitor, U c_max This indicates the maximum rated voltage of the supercapacitor.

[0170] in, This represents the lowest discharge voltage of the supercapacitor within S calculation cycles T.

[0171] Furthermore, the node voltage equations of the above equivalent circuit model are generated in the following manner: the voltage and current of the train, the voltage and current of the substation, and the variable resistance between line nodes are extracted from the above equivalent circuit model, and the node voltage equations of the above equivalent circuit model are generated based on the voltage and current of the train, the voltage and current of the substation, the variable resistance between line nodes, and the distance between the nodes.

[0172] Furthermore, the node voltage equations of the equivalent circuit model are shown below:

[0173]

[0174] In the above formula, u ss Indicates the voltage of the substation, i ss U represents the current in the substation. t Indicates train voltage, i t The train current is represented by r, and the resistance between line nodes is represented by l1, l2, and L. a and L b This represents the distance between line nodes; specifically, it obtains the distance between the train location and each substation location, multiplies this distance by the unit resistance, and generates the variable resistance between line nodes.

[0175] Solver module 124 is used to generate the minimum discharge voltage of the supercapacitor using the supercapacitor fitness function described above.

[0176] Furthermore, a combination of genetic algorithm and simulated annealing algorithm is used to solve the fitness function of the supercapacitor. The system state variable (i.e. the minimum discharge voltage of the supercapacitor) generated in the first T time period is used as the initial state variable for the second T time period. Steps S101, S102 and S103 are repeated until all S T time periods have been calculated.

[0177] Furthermore, the optimal discharge capacity (SOC) of a supercapacitor can be calculated using its lowest discharge voltage, as shown in the following formula:

[0178]

[0179] In the above formula, U sc_min U represents the minimum discharge voltage of a supercapacitor. sc_rate This indicates the rated voltage of the supercapacitor.

[0180] The aforementioned supercapacitor discharge voltage optimization device based on the power of the train on the line generates energy-saving rate of the energy storage device, energy-saving rate of the braking resistor, and predicted lifespan of the supercapacitor based on the equivalent circuit. It then constructs a supercapacitor fitness function based on these data. By considering the predicted lifespan of the supercapacitor, it improves the energy-saving rate of the energy storage device and the energy-saving rate of the braking resistor. Furthermore, by solving the supercapacitor fitness function, it optimizes the supercapacitor discharge voltage of the energy storage device, thereby constraining the optimal discharge capacity of the supercapacitor and improving the energy-saving effect of the energy storage device.

[0181] Preferably, the above-mentioned generation module 122 includes:

[0182] The first generation submodule 1221 is used to discretize the above-mentioned train power curve and generate discretized data.

[0183] The second generation submodule 1222 is used to input the above discretized data into the above equivalent circuit model to generate the voltage and current across the substation when the above supercapacitor energy storage device is installed, the voltage and current across the substation when the above supercapacitor energy storage device is not installed, the voltage and current across the train braking resistor when the above supercapacitor energy storage device is installed, the voltage and current across the train braking resistor when the above supercapacitor energy storage device is not installed, and the current voltage and current temperature of the supercapacitor.

[0184] The third generation submodule 1223 is used to generate the energy saving rate of the energy storage device based on the voltage and current at both ends of the substation when the supercapacitor energy storage device is installed and the voltage and current at both ends of the substation when the supercapacitor energy storage device is not installed.

[0185] Specifically, the expression for the energy storage device's energy saving rate e% is as follows:

[0186]

[0187] In the above formula, This represents the voltage across the j-th substation when a supercapacitor energy storage device is installed. This represents the voltage across the j-th substation when there is no supercapacitor energy storage device. This represents the current across the j-th substation when a supercapacitor energy storage device is installed. This represents the current across the j-th substation when there is no supercapacitor energy storage device, and n represents the number of substations.

[0188] in, This indicates that the average energy saving rate of the energy storage device is obtained by averaging the energy saving rates of the energy storage devices obtained over S calculation periods T.

[0189] The fourth generation submodule 1224 is used to generate the braking resistor energy saving rate based on the voltage and current across the train braking resistor when the substation is equipped with the supercapacitor energy storage device and the voltage and current across the train braking resistor when the substation is not equipped with the supercapacitor energy storage device.

[0190] Specifically, the calculation formula for the braking resistor is as follows:

[0191]

[0192] In the above formula, This represents the voltage across the braking resistor of the j-th train when the substation has a supercapacitor energy storage device. This represents the voltage across the braking resistor of the j-th train when the substation has no supercapacitor energy storage device. This represents the current flowing across the braking resistor of the j-th train when the substation has a supercapacitor energy storage device. This represents the current flowing across the braking resistor of the j-th train when the substation has no supercapacitor energy storage device, and m represents the number of trains.

[0193] in, The average braking resistor energy saving rate is obtained by averaging the braking resistor energy saving rates obtained over S calculation periods T.

[0194] The fifth generation submodule 1225 is used to generate supercapacitor lifetime prediction data based on the current voltage and current temperature of the supercapacitor.

[0195] Specifically, the formula for predicting the lifespan of a supercapacitor is shown below:

[0196] MSL x (T x U x )=MSL(T0,U0)(U x / U0) -n ·exp[-E a / k·(1 / T0-1 / T x )]exp[-m·(U x / U0) α ·(T x / T0) β ]

[0197] In the above formula, MSL(T0,U0) represents the rated capacitance life, and E a U represents the activation energy of an electrochemical capacitor, n represents the voltage power-law exponent, k represents the Boltzmann constant, m represents the gain of the cross-coupling factor, α represents the voltage factor power-law coefficient, β represents the temperature factor power-law coefficient, and U represents the voltage factor power-law coefficient. x T represents the current voltage of the supercapacitor. x This indicates the current temperature of the supercapacitor.

[0198] in, The average value of the supercapacitor lifetime prediction data is obtained by averaging the lifetimes obtained over S calculation cycles T.

[0199] Preferably, the solution module 124 includes:

[0200] The initialization submodule 1241 is used to initialize the supercapacitor fitness function and generate initial function values.

[0201] Specifically, the initial energy storage device energy saving rate, initial braking resistor energy saving rate, and initial supercapacitor lifetime prediction data are input into the supercapacitor fitness function to calculate and generate the initial supercapacitor fitness function, which is then used as the initialization function value.

[0202] Furthermore, initialization control parameters are set, namely, the iteration number gen = 0, and the preset temperature value T. end .

[0203] The preprocessing submodule 1242 is used to preprocess the initial function values ​​using a genetic algorithm to generate the target function values.

[0204] Specifically, such as Figure 6 As shown, the genetic algorithm is an adaptive probabilistic search method based on the theory of natural evolution and the mechanisms of genetics. It consists of three main modules: encoding / decoding, fitness evaluation, and genetic operations. Genetic operations include chromosome selection, crossover, and mutation. The basic steps of the genetic algorithm are as follows:

[0205] (1) Encoding: This paper uses 24 binary codes to represent the 24 minimum discharge voltage values ​​of the supercapacitor with a data length of 24*150s=3600s. Each X chromosome is used as an individual in the population. The initial population size is NIND and the individual length is PRECI. For example, the X chromosome in 24 calculation cycles T is represented as:

[0206] X = [X1, X2, X3, ... X 24 ]

[0207] In the above formula, X1 represents the minimum discharge voltage value of the supercapacitor in the first calculation cycle, X 24 This represents the minimum discharge voltage value of the supercapacitor in the 24th calculation cycle.

[0208] (2) Fitness assessment: Find the maximum value of the supercapacitor fitness function J. The larger the function value, the better the individual. The fitness F is calculated as follows:

[0209] F(J[X])=J[X]

[0210] (3) Selection operation: Using roulette wheel selection, if the population size is N and the fitness value of individual i is F i The probability that individual i is selected is:

[0211]

[0212] In the above formula, F j This represents the fitness value of the j-th individual.

[0213] (4) Crossover operation: the k-th chromosome X k and the m-th chromosome X m The expression for the crossover operation on the j-th individual is:

[0214]

[0215] Among them, P c denoted by , where r represents the crossover probability and r represents a random number in the interval [0, 1].

[0216] (5) Mutation operation: X of the j-th individual on the i-th chromosome ij The formula for calculating mutations is:

[0217]

[0218] Among them, P m denoted by , where s represents the mutation probability and s represents a random number in the interval [0, 1].

[0219] The calculation submodule 1243 is used to calculate the minimum discharge voltage of the supercapacitor based on the above initial function value and the above objective function value using the simulated annealing algorithm.

[0220] Specifically, simulated annealing uses the physical picture and statistical properties of the solid annealing process as its physical background. The Metropolis criterion (which accepts new states with probability rather than using completely deterministic rules) helps the algorithm avoid local optima.

[0221] Preferably, the above-mentioned calculation submodule 1243 includes:

[0222] The calculation unit 12431 is used to calculate the difference between the initial function value and the target function value, and generate the function difference.

[0223] Specifically, based on the initial function value X of the supercapacitor fitness function J... i The objective function value X is generated by a genetic algorithm. j Calculate the function difference before and after the genetic algorithm operation. The formula for calculating the function difference ΔΦ is as follows:

[0224] ΔΦ=Φ(X j )-Φ(X i )

[0225] The iteration unit 12432 is used to calculate the acceptance probability of the objective function based on the function difference, replace the initial function value with the objective function value according to the acceptance probability, and perform iteration based on the objective function value.

[0226] Specifically, the objective function value X j The probability P of acceptance, i.e., the Metropolis acceptance probability, is calculated using the following formula:

[0227]

[0228] The comparison unit 12433 is used to obtain the number of iterations, the iteration temperature value and the termination condition, and compares the number of iterations and the iteration temperature value with the termination condition respectively. When the number of iterations and the temperature value meet the termination condition respectively, the minimum discharge voltage of the supercapacitor is output.

[0229] Specifically, the number of iterations gen is counted. When the number of iterations gen is greater than the maximum number of iterations MAXGEN, the temperature value T in the i-th iteration is compared. i With preset temperature value T end When the temperature value T in the i-th iteration i Greater than the preset temperature value T endWhen, calculate T i+1 =T i *q represents a temperature value that decreases over time. q is a positive number less than 1, typically between 0.8 and 0.99, and then the genetic operation is repeated.

[0230] Furthermore, when the number of iterations and the temperature value meet the termination condition, the objective function value of the current iteration is used as the minimum discharge voltage output of the supercapacitor.

[0231] Example 3

[0232] This embodiment provides a computer device including a memory and a processor, the processor being configured to read instructions stored in the memory to execute the supercapacitor discharge voltage optimization method based on line train power in any of the above method embodiments.

[0233] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0234] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0235] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0236] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0237] Example 4

[0238] This embodiment provides a computer-readable storage medium storing computer-executable instructions that can execute the supercapacitor discharge voltage optimization method based on line train power in any of the above method embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above types of memory.

[0239] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for optimizing the discharge voltage of supercapacitors based on the power of trains on a railway line, characterized in that, Includes the following steps: Construct an equivalent circuit model based on the line connections between the substation, train, and supercapacitor energy storage device; The train power curve under the train's operating state is obtained, and the train power curve is input into the equivalent circuit model to generate energy storage device energy saving rate, braking resistor energy saving rate and supercapacitor life prediction data. A supercapacitor fitness function is constructed based on the energy saving rate of the energy storage device, the energy saving rate of the braking resistor, and the predicted lifespan of the supercapacitor; the expression of the supercapacitor fitness function is as follows: In the above formula, J represents the fitness function of the supercapacitor, uc_min(t) represents the minimum discharge voltage of the supercapacitor, and ω1 represents the weight of the energy saving rate of the energy storage device. ω1 represents the average energy saving rate of the energy storage device, and ω2 represents the weight of the energy saving rate of the braking resistor. ω3 represents the average energy saving rate of the braking resistor, and ω3 represents the weight of the supercapacitor lifespan prediction data. This represents the average value of the predicted supercapacitor lifespan data. The minimum discharge voltage of the supercapacitor is generated using the supercapacitor fitness function.

2. The method for optimizing the discharge voltage of a supercapacitor based on the power of a train line, as described in claim 1, is characterized in that... The step of inputting the train power curve into the equivalent circuit model to generate energy storage device energy saving rate, braking resistor energy saving rate, and supercapacitor lifespan prediction data includes: The train power curve is discretized to generate discretized data; The discretized data is input into the equivalent circuit model to generate the voltage and current across the substation when the supercapacitor energy storage device is installed, the voltage and current across the substation when the supercapacitor energy storage device is not installed, the voltage and current across the train braking resistor when the supercapacitor energy storage device is installed, the voltage and current across the train braking resistor when the supercapacitor energy storage device is not installed, and the current voltage and temperature of the supercapacitor. The energy-saving rate of the energy storage device is generated based on the voltage and current across the substation when the supercapacitor energy storage device is installed and the voltage and current across the substation when the supercapacitor energy storage device is not installed. The braking resistor energy saving rate is generated based on the voltage and current across the train braking resistor when the substation is equipped with the supercapacitor energy storage device and the voltage and current across the train braking resistor when the substation is not equipped with the supercapacitor energy storage device. Supercapacitor lifetime prediction data is generated based on the current voltage and current temperature of the supercapacitor.

3. The method for optimizing the discharge voltage of a supercapacitor based on the power of a train line according to claim 1, characterized in that, Constructing the supercapacitor fitness function includes: The node voltage equations, supercapacitor current limits, and supercapacitor voltage limits of the equivalent circuit model are used as constraints on the supercapacitor fitness function.

4. The method for optimizing the discharge voltage of a supercapacitor based on the power of a train line according to claim 3, characterized in that, The nodal voltage equations of the equivalent circuit model are generated in the following manner: Extract the voltage and current of the train, the voltage and current of the substation, and the variable resistance between line nodes from the equivalent circuit model, and generate the node voltage equations of the equivalent circuit model based on the voltage and current of the train, the voltage and current of the substation, and the variable resistance between line nodes.

5. The method for optimizing the discharge voltage of a supercapacitor based on the power of a train line according to claim 1, characterized in that, The step of generating the minimum discharge voltage of the supercapacitor using the supercapacitor fitness function includes: The supercapacitor fitness function is initialized to generate initial function values; The initial function values ​​are preprocessed using a genetic algorithm to generate the target function values; Based on the initial function value and the target function value, the minimum discharge voltage of the supercapacitor is calculated using the simulated annealing algorithm.

6. The method for optimizing the discharge voltage of a supercapacitor based on the power of a train line according to claim 5, characterized in that, The step of calculating the minimum discharge voltage of the supercapacitor using a simulated annealing algorithm based on the initial function value and the target function value includes: Calculate the difference between the initial function value and the target function value to generate a function difference; The acceptance probability of the target function is calculated based on the function difference, the target function value is used to replace the initial function value according to the acceptance probability, and the iteration is performed based on the target function value. Obtain the iteration count, iteration temperature value, and termination condition. Compare the iteration count and iteration temperature value with the termination condition. When the iteration count and temperature value meet the termination condition, output the minimum discharge voltage of the supercapacitor.

7. A supercapacitor discharge voltage optimization device based on train power, characterized in that, include: The module is used to construct an equivalent circuit model based on the line connections between substations, trains and supercapacitor energy storage devices. The generation module is used to obtain the train power curve under the train's operating state, input the train power curve into the equivalent circuit model, and generate energy-saving rate of energy storage device, energy-saving rate of braking resistor and life prediction data of supercapacitor. The acquisition module is used to construct a supercapacitor fitness function based on the energy saving rate of the energy storage device, the energy saving rate of the braking resistor, and the predicted lifespan of the supercapacitor; the expression of the supercapacitor fitness function is as follows: In the above formula, J represents the fitness function of the supercapacitor, uc_min(t) represents the minimum discharge voltage of the supercapacitor, and ω1 represents the weight of the energy saving rate of the energy storage device. ω1 represents the average energy saving rate of the energy storage device, and ω2 represents the weight of the energy saving rate of the braking resistor. ω3 represents the average energy saving rate of the braking resistor, and ω3 represents the weight of the supercapacitor lifespan prediction data. This represents the average value of the predicted supercapacitor lifespan data. The solution module is used to generate the minimum discharge voltage of the supercapacitor using the supercapacitor fitness function.

8. A computer device, characterized in that, It includes a processor and a memory, wherein the memory is used to store a computer program, and the processor is configured to invoke the computer program to perform the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the method as described in any one of claims 1-6.

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