Control method, device and equipment of super capacitor energy storage device and storage medium

Abstract

The application provides a super capacitor energy storage device control method, device, equipment and storage medium, the method comprises the following steps: collecting the life characteristic parameters of the super capacitor energy storage device and performing life evaluation to obtain a life evaluation result; inputting the life evaluation result into a constructed fuzzy rule base to output a constraint condition adjustment parameter; obtaining a constraint condition according to the constraint condition adjustment parameter, combining an optimization objective function, and using a genetic algorithm to optimize the control parameter to obtain a first control parameter; and using a droop control method to control the charging and discharging current of the super capacitor energy storage device according to the first control parameter. The super capacitor energy storage device control method provided by the application can timely adjust and optimize the control strategy, reduce unreasonable use of the super capacitor energy storage device, improve the energy saving effect, and improve the application benefit in the whole life cycle.

Description

Technical Field

[0001] This invention relates to the field of urban rail transit technology, specifically to a control method, device, equipment, and storage medium for a supercapacitor energy storage device. Background Technology

[0002] In the urban rail transit sector, recovering and reusing train regenerative braking energy is a primary means of reducing system traction energy consumption. Among various technical approaches to train braking energy recovery and utilization, ground-based energy storage technology based on supercapacitors offers advantages such as simple structure and maintenance, no interface with the AC side of the traction power supply system, and power compensation functionality for the traction power supply system, making it a major direction for future applications. With the increasing demands for energy conservation and low carbon emissions in the urban rail transit industry, and the decreasing cost of supercapacitors, supercapacitor energy storage devices will see wider application in the urban rail transit sector. Therefore, reasonable and effective control and management methods are crucial for optimizing their application benefits.

[0003] Currently, the control and management methods for ground-based supercapacitor energy storage devices in urban rail transit are mainly implemented at two levels. The bottom-level control method adopts a voltage-current dual closed-loop control approach. The energy storage device manages charging and discharging based on the DC traction network bus voltage. Specifically, when the DC traction network bus voltage is higher than a set charging threshold, the energy storage device enters charging mode; when the DC traction network bus voltage is lower than a set discharging threshold, the energy storage device enters discharging mode. The charging and discharging current is given by the voltage loop output result. The upper-level management method often employs adaptive management strategies (such as fuzzy control, reinforcement learning algorithms, etc.) to achieve adaptive adjustment of the charging and discharging thresholds. The control and management diagram is shown below. Figure 1 As shown.

[0004] The aforementioned control and management methods can effectively recover and utilize regenerative braking energy from urban rail transit trains, achieving good energy-saving results. However, due to the time-varying nature of urban rail transit operating loads and the collaborative application of multiple energy storage devices, these control and management methods have certain shortcomings and deficiencies in realizing the full life-cycle benefits of supercapacitor energy storage devices. These shortcomings are mainly reflected in the following aspects:

[0005] (1) At the short time scale (second level): The dual closed-loop control method adopted by the bottom control method, due to the characteristics of the PI control method, is prone to DC grid voltage oscillation and instability problems when applied to multiple energy storage devices in the whole line.

[0006] (2) At the medium time scale (hour level): The distribution characteristics of line regenerative energy under different departure intervals were not fully considered, resulting in unreasonable control parameter settings, which in turn affected energy saving benefits;

[0007] (3) At the long-term scale (day level): Due to the degradation of supercapacitors during use, failure to adjust and optimize their control strategies in a timely manner will lead to problems such as abuse of some energy storage devices and poor balance, which will affect the application benefits throughout the entire life cycle. Summary of the Invention

[0008] In view of this, the present invention provides a control method, device, equipment and storage medium for a supercapacitor energy storage device for rail transit, which solves the problem of poor application benefits of multi-ground supercapacitor energy storage devices in urban rail transit throughout their entire life cycle.

[0009] In a first aspect, embodiments of the present invention provide a control method for a supercapacitor energy storage device, comprising:

[0010] In the first timescale period, the lifetime characterization parameters of the supercapacitor energy storage device are collected and the lifetime is assessed to obtain the lifetime assessment results. The lifetime assessment results are then input into the constructed fuzzy rule base and the constraint adjustment parameters are output.

[0011] In the second time scale period, the constraint conditions are obtained by adjusting the quantity parameters according to the constraint conditions. Combined with the optimization objective function, the control parameters are optimized using a genetic algorithm to obtain the first control parameters. The second time scale period is shorter than the first time scale period.

[0012] In the third time scale period, the charging and discharging current of the supercapacitor energy storage device is controlled using a droop control method based on the first control parameter. The third time scale period is shorter than the second time scale period.

[0013] The control method for supercapacitor energy storage devices provided by this invention sets different time scale periods according to different control requirements of the supercapacitor energy storage device, controls and manages the supercapacitor energy storage device on different time scales, and reduces unreasonable use of the supercapacitor energy storage device by timely adjusting and optimizing the control strategy, thereby improving energy saving effect and increasing the application benefits throughout the entire life cycle.

[0014] Optionally, the life assessment result includes a life assessment value and a life assessment difference, wherein the life assessment value is calculated using the following formula:

[0015] life(j) = w1·C sc (j)+w2·R sc (j)

[0016] Where life(j) is the lifespan assessment value at station j, and C sc (j) and R sc (j) represents the real-time status of the capacitance and internal resistance of the supercapacitor, and w1 and w2 represent the evaluation weights of the capacitance and internal resistance of the supercapacitor at station j.

[0017] The formula for calculating the life assessment difference is:

[0018] Δlife(j)=α1·[life(j)-life(j-1)]+α2·[life(j)-life(j+1)]

[0019] Where △life(j) is the life assessment difference, and α1 and α2 are the balance difference between the supercapacitor at station j and the supercapacitor at adjacent stations.

[0020] The lifespan assessment value reflects the lifespan status of station j, and the lifespan assessment difference reflects the lifespan difference with adjacent stations. These two values ​​can reflect the overall lifespan of the supercapacitor energy storage device, facilitating timely adjustments and thus optimizing the lifespan of the supercapacitor energy storage device.

[0021] Optionally, the step of inputting the life assessment results into the constructed fuzzy rule base and outputting constraint adjustment parameters includes:

[0022] The fuzzy rule base determines the lifetime status based on the lifetime assessment value and determines the difference in lifetime status with neighboring stations based on the lifetime assessment difference.

[0023] The constraint adjustment parameters are determined based on the lifespan status and the difference in lifespan status with adjacent stations.

[0024] By assessing the lifespan of supercapacitor energy storage devices, timely adjustments and optimizations to their control strategies can be made to fully utilize their lifespan, improve balance, and ultimately enhance the application benefits throughout their entire lifecycle.

[0025] Optionally, the optimization objective function is:

[0026]

[0027] Where e% represents the energy-saving rate of the supercapacitor energy storage device, E sub_non (j) represents the output energy consumption before the application of the supercapacitor energy storage device in the j-th substation, E sub_ess (j) represents the output energy consumption of the j-th substation after applying the supercapacitor energy storage device.

[0028] By establishing an optimized objective function and using a genetic algorithm to calculate and harmonize intelligent control parameters, the goal of maximizing the energy-saving rate of the objective function is achieved, thus optimizing the energy-saving effect of the supercapacitor energy storage device.

[0029] Optionally, the constraint condition is:

[0030]

[0031] Among them, u dc0The no-load voltage of the DC traction network, u ds and u ch These are the discharge start threshold and the charge start threshold, respectively. min and soc max For the upper and lower limits of the SoC working range, i sc_max These are the limits for charging and discharging current.

[0032] By establishing constraints, the optimal configuration for different operating intervals can be achieved.

[0033] Optionally, the step of using a genetic algorithm to optimize the control parameters to obtain the first control parameters includes:

[0034] The control parameters are initialized to generate a first-generation control parameter population, wherein the control parameters include: the discharge start threshold, the charging start threshold, the charging slope value, and the discharge slope value of the supercapacitor energy storage device;

[0035] Calculate the first fitness of the first generation of control parameter population based on the optimization objective function;

[0036] Based on the constraints and the first fitness, selection, crossover, and mutation operations are performed to generate the next generation of control parameter population;

[0037] The fitness of the control parameter population is calculated iteratively to obtain the first control parameter, wherein the first control parameter includes: the first discharge start threshold, the first charging start threshold, the first charging slope value, and the first discharge slope value of the supercapacitor energy storage device.

[0038] By using genetic algorithms to optimize control parameters, only the objective function and corresponding constraints that affect the search direction are needed, making the solution of complex problems simpler and the first control parameters obtained more accurate.

[0039] Optionally, controlling the charging and discharging current of the supercapacitor energy storage device using a droop control method based on the first control parameter includes:

[0040] Obtain the voltage of the traction network where the supercapacitor energy storage device is located;

[0041] By comparing the traction network voltage value, the first discharge start threshold, and the first charging start threshold, the working area of ​​the supercapacitor energy storage device is determined. When the traction network voltage value is greater than the charging start threshold, the supercapacitor energy storage device enters the charging state. When the traction network voltage value is less than the discharge start threshold, the supercapacitor energy storage device enters the discharging state.

[0042] The charging current or discharging current of the supercapacitor energy storage device is controlled based on the traction network voltage value and the charging slope value or discharging slope value.

[0043] By comparing the voltage of the traction grid where the supercapacitor energy storage device is located with the first control parameter, the energy storage device can enter different working areas in a timely manner, and the charging and discharging current can be accurately controlled by droop control, so as to make the DC grid voltage more stable in the process of multiple energy storage devices in the whole line.

[0044] Secondly, embodiments of the present invention provide a control device for a supercapacitor energy storage device, the device comprising:

[0045] The first timescale management layer module is used to collect lifetime characterization parameters of the supercapacitor energy storage device and perform lifetime assessment in the first timescale period, obtain lifetime assessment results, input the lifetime assessment results into the constructed fuzzy rule base, and output constraint adjustment parameters.

[0046] The second time scale management module is used to adjust the quantity parameters according to the constraint conditions in the second time scale period to obtain the constraint conditions, combine the optimization objective function, and use a genetic algorithm to optimize the control parameters to obtain the first control parameters. The second time scale period is shorter than the first time scale period.

[0047] The third timescale management layer module is used to control the charging and discharging current of the supercapacitor energy storage device using a droop control method according to the first control parameters during the third timescale period, wherein the third timescale period is shorter than the second timescale period.

[0048] The control device for the supercapacitor energy storage device provided by this invention sets up different time-scale management layer modules according to different control requirements of the supercapacitor energy storage device, controls and manages the supercapacitor energy storage device on different time scales, and reduces unreasonable use of the supercapacitor energy storage device by timely adjusting and optimizing the control strategy, thereby improving energy saving effect and increasing the application benefits throughout the entire life cycle.

[0049] Thirdly, embodiments of the present invention provide a computer device, including: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method described in the first aspect, or any optional embodiment of the first aspect.

[0050] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions for causing the computer to perform the method described in the first aspect, or any optional embodiment of the first aspect. Attached Figure Description

[0051] 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.

[0052] Figure 1 This is a schematic diagram of the upper-level control of a supercapacitor energy storage device in the prior art provided by the embodiments of the present invention;

[0053] Figure 2 A flowchart of a control method for a supercapacitor energy storage device is provided in an embodiment of the present invention;

[0054] Figure 3 This is an overall framework diagram of the long-time-scale management layer in a control method for a supercapacitor energy storage device provided in an embodiment of the present invention;

[0055] Figure 4 This is a flowchart of a genetic algorithm in a control method for a supercapacitor energy storage device provided in an embodiment of the present invention;

[0056] Figure 5 This is a control block diagram of the short-timescale management layer in a control method for a supercapacitor energy storage device provided in an embodiment of the present invention;

[0057] Figure 6 In a specific embodiment of a control method for a supercapacitor energy storage device provided by the present invention, a graph showing the relationship between the charging and discharging current of the supercapacitor energy storage device under droop control and the traction network voltage is provided.

[0058] Figure 7 This is a schematic diagram of the structure of a control device for a supercapacitor energy storage device provided in an embodiment of the present invention;

[0059] Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. 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.

[0061] 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.

[0062] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can also refer to the internal connection of two components; and they can refer to a wireless connection or a wired connection. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0063] 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.

[0064] This invention provides a control method for a supercapacitor energy storage device, which controls and manages the supercapacitor energy storage device from different time scales according to different functional layers, such as... Figure 2 As shown, it specifically includes:

[0065] Step S1: In the first time scale period, collect the lifetime characterization parameters of the supercapacitor energy storage device and perform lifetime assessment to obtain the lifetime assessment results. Input the lifetime assessment results into the constructed fuzzy rule base and output the constraint adjustment parameters.

[0066] For example, the first timescale period is measured in "days," but it is not limited to this and can be adjusted according to actual conditions. This step is a function implemented by the long-term timescale management layer, mainly realizing the generation and adjustment of constraints, and coordinating the management between different energy storage devices. It is the top-level optimization link of the control method for supercapacitor energy storage devices provided in this embodiment of the invention. This layer first evaluates the real-time lifetime status of different supercapacitor energy storage devices, then establishes control rules based on fuzzy algorithms, generates constraint adjustment parameters, and finally updates the optimized constraint adjustment parameters in the second timescale management layer.

[0067] For example, the lifespan of a supercapacitor is primarily characterized by a decrease in capacitance and an increase in internal resistance. Therefore, the lifespan can be assessed by monitoring the capacitance and internal resistance data. The weighting relationship between capacitance and internal resistance depends on the parameter design of different products and can be obtained from the product manual. The lifespan assessment results include a lifespan assessment value and a lifespan assessment difference, where the lifespan assessment value is calculated using the following formula:

[0068] life(j) = w1·C sc (j)+w2·R sc (j)

[0069] Where life(j) is the lifespan assessment value at station j, and C sc (j) and R sc (j) represents the real-time status of the capacitance and internal resistance of the supercapacitor, and w1 and w2 represent the evaluation weights of the capacitance and internal resistance of the supercapacitor at station j.

[0070] The formula for calculating the life assessment difference is:

[0071] Δlife(j)=α1·[life(j)-life(j-1)]+α2·[life(j)-life(j+1)]

[0072] Where △life(j) is the life assessment difference, and α1 and α2 are the balance difference between the supercapacitor at station j and the supercapacitor at adjacent stations.

[0073] The lifespan assessment value reflects the lifespan status of station j, while the lifespan assessment difference reflects the lifespan difference with adjacent stations. These two values ​​can reflect the overall lifespan of the supercapacitor energy storage device, facilitating timely adjustments and thus optimizing the lifespan of the supercapacitor energy storage device.

[0074] For example, such as Figure 3 The diagram shown is an overall framework diagram of the long-term management layer. ESS1-ESSj represent multiple supercapacitors. The capacitance value C of each supercapacitor is obtained. sc (j) and internal resistance R sc(j) The lifetime assessment value is obtained to determine the lifetime state. The fuzzy rule base determines the lifetime state based on the lifetime assessment value, determines the difference in lifetime state with adjacent stations based on the lifetime assessment difference, and determines the constraint adjustment parameters based on the lifetime state and the difference in lifetime state with adjacent stations. Since the lifetime of a supercapacitor is negatively correlated with its operating voltage and charging / discharging current, that is, the higher the operating voltage and the larger the charging / discharging current, the worse its lifetime will be; the lower the operating voltage and the smaller the charging / discharging current, the longer its lifetime will be. The operating voltage is represented by SOC (State of Charge). In one embodiment, firstly, a fuzzy rule base is established based on the correspondence between the lifetime state of the current station, the difference in lifetime state of adjacent stations, and the trends of operating voltage and charging / discharging current; secondly, the fuzzy rule base table is queried through the above assessment results of the lifetime state of the current station and the difference in lifetime state of adjacent stations to find the corresponding operating voltage and charging / discharging current adjustment amounts; finally, this result is used as the output on a long time scale and as the input on a medium time scale.

[0075] In one specific embodiment, the lifetime assessment value is defined as having three states: "good," "average," and "poor," and the lifetime assessment difference is defined as having three states: "large," "medium," and "small." To reduce the lifetime difference between adjacent stations, the rules for adjusting the SOC and charge / discharge current parameters of the current station and adjacent stations are as follows:

[0076] If the lifespan status of a supercapacitor energy storage device at a certain station is "good" and the difference between its lifespan status and that of a neighboring station is "large", it indicates that the neighboring station is being abused to a high degree. Therefore, the corresponding adjustment amount for the upper limit of SOC and the charging and discharging current value of this station should be "large upward adjustment".

[0077] If the lifespan status of a supercapacitor energy storage device at a certain station is "good" and the difference between its lifespan status and that of the adjacent station is "medium", it indicates that the degree of abuse at the adjacent station is generally moderate. Therefore, the corresponding adjustment amount for the upper limit of SOC and the charging and discharging current value of this station is "small upward adjustment".

[0078] If the lifespan status of a supercapacitor energy storage device at a certain station is "good" and the difference between its lifespan status and that of the adjacent station is "small", it indicates that there is no abuse at the adjacent station. Therefore, the corresponding adjustment amount for the upper limit of SOC and the charging and discharging current value of this station is "no adjustment".

[0079] If the lifespan status of a supercapacitor energy storage device at a certain station is "normal" and its lifespan status differs "large" from that of adjacent stations, it indicates that the station is being abused to a high degree. Therefore, the corresponding adjustment amount for the upper limit of SOC and the charging and discharging current value of this station should be "large downward adjustment".

[0080] If the lifespan status of a supercapacitor energy storage device at a certain station is "normal" and the difference between its lifespan status and that of adjacent stations is "medium", it indicates that the degree of abuse at this station is average. Therefore, the corresponding adjustment amount for the upper limit of SOC and the charging and discharging current value at this station is "small downward adjustment".

[0081] If the lifespan status of a supercapacitor energy storage device at a certain station is "normal" and the difference between its lifespan status and that of adjacent stations is "small", it indicates that there is no abuse at the adjacent stations. Therefore, the corresponding adjustment amount for the upper limit of SOC and the charging and discharging current value of this station is "no adjustment".

[0082] If the lifespan status of a supercapacitor energy storage device at a certain station is "poor" and its lifespan status differs "large" from that of adjacent stations, it indicates that the station is being abused to a high degree. Therefore, the corresponding adjustment amount for the upper limit of SOC and the charging and discharging current value of this station is "large downward adjustment".

[0083] If the lifespan status of a supercapacitor energy storage device at a certain station is "poor" and its lifespan status difference with that of adjacent stations is "medium", it indicates that the degree of abuse at this station is generally moderate. Therefore, the corresponding adjustment amount for the upper limit of SOC and the charging and discharging current value at this station is "small downward adjustment".

[0084] If the lifespan status of a supercapacitor energy storage device at a certain station is "poor" and the difference between its lifespan status and that of adjacent stations is "small", it indicates that there is no high degree of abuse at this station. Therefore, the corresponding adjustment amount for the upper limit of SOC and the charging and discharging current value of this station is "no adjustment".

[0085] By assessing the lifespan of supercapacitor energy storage devices, timely adjustments and optimizations to their control strategies can be made to fully utilize their lifespan, improve balance, and ultimately enhance the application benefits throughout their entire lifecycle.

[0086] Step S2: In the second time scale period, adjust the quantity parameters according to the constraint conditions to obtain the constraint conditions. Combined with the optimization objective function, use the genetic algorithm to optimize the control parameters and obtain the first control parameters. The second time scale period is shorter than the first time scale period.

[0087] For example, the second time scale period is measured in "hours," but is not limited to this and can be adjusted according to actual conditions. This step is a function implemented by the mid-time scale management layer, mainly realizing the generation and optimization of control parameters, as well as achieving optimal matching with different operating intervals. It is the key decision layer of the method proposed in this invention. Figure 4 The diagram shows a flowchart of a genetic algorithm. Genetic algorithms are existing technologies and will not be described in detail here.

[0088] Specifically, in one embodiment, the objective function to be optimized is:

[0089]

[0090] Where e% represents the energy-saving rate of the supercapacitor energy storage device, E sub_non (j) represents the output energy consumption before the application of the supercapacitor energy storage device in the j-th substation, E sub_ess (j) represents the output energy consumption of the j-th substation after applying the supercapacitor energy storage device.

[0091] By establishing an optimization objective function and using a genetic algorithm to calculate and harmonize control parameters, the goal of maximizing the energy saving rate of the objective function is achieved, thus optimizing the energy-saving effect of the supercapacitor energy storage device.

[0092] Specifically, in one embodiment, the constraint condition is:

[0093]

[0094] Among them, u dc0 The no-load voltage of the DC traction network, u ds and u ch These are the discharge start threshold and the charge start threshold, respectively. min and soc max These are the upper and lower limits of the SOC's working range, typically for a SOC. min i is a fixed value of 0. sc_max These are the limits for charging and discharging current.

[0095] By establishing constraints and using the constraints obtained from the long-term management layer to adjust and update parameters, the optimal configuration for different operating intervals can be achieved.

[0096] Specifically, in one embodiment, a genetic algorithm is used to optimize the control parameters to obtain the first control parameters. The specific steps include:

[0097] Step S21: Initialize the control parameters to generate the first generation of control parameter population. The formula for optimization using a genetic algorithm is as follows:

[0098] X(j)=[u ch ,λ ch ,u ds ,λ ds ]

[0099] Among them, u ds u ch , λ ch and λ ds For control parameters, u ds and u ch These are the discharge start threshold and the charge start threshold, respectively, λ ch and λ ds These are the charging slope value and the discharging slope value, respectively.

[0100] Step S22: Calculate the first fitness of the first generation of control parameter population based on the optimization objective function.

[0101] Step S23: Based on the constraints and the first fitness, perform selection, crossover, and mutation operations to generate the next generation of control parameter population.

[0102] Step S24: Iteratively calculate the fitness of the control parameter population to obtain the first control parameters, wherein the first control parameters include: the first discharge start threshold, the first charging start threshold, the first charging slope value, and the first discharge slope value of the supercapacitor energy storage device.

[0103] Specifically, the objective function is an evaluation of fitness. Taking two 1.5MW supercapacitor energy storage devices as an example, each supercapacitor energy storage device has a set of control parameters. If the energy saving rate is 9% when the first set of data is X(1)=[870,0.3,780,0.4],X(2)=[875,0.2,785,0.2], and the energy saving rate is 10% when the second set of data is X(1)=[860,0.3,790,0.4],X(2)=[870,0.2,775,0.1], then the second set of data has a better control effect and a higher fitness in the genetic algorithm. Therefore, the second set of data is used as the first control parameter.

[0104] By using genetic algorithms to optimize control parameters, only the objective function and corresponding constraints that affect the search direction are needed, making the solution of complex problems simpler and the first control parameters obtained more accurate.

[0105] Step S3: In the third time scale period, based on the first control parameter, the charging and discharging current of the supercapacitor energy storage device is controlled using a droop control method. The third time scale period is shorter than the second time scale period. For example, the third time scale period is measured in "seconds," but it is not limited to this and can be adjusted according to actual conditions. This step is a function implemented by the short-time scale management layer, mainly realizing the real-time control of the supercapacitor energy storage device. It is the basic control layer of the method proposed in this invention, and its control block diagram is as follows: Figure 5 As shown.

[0106] Specifically, in one embodiment, the specific steps of controlling the charging and discharging current of the supercapacitor energy storage device using the droop control method according to the first control parameter include:

[0107] Step S31: Obtain the voltage of the traction network where the supercapacitor energy storage device is located.

[0108] Step S32: Compare the traction network voltage value, the first discharge start threshold, and the first charge start threshold to determine the working area of ​​the supercapacitor energy storage device. When the traction network voltage value is greater than the charge start threshold, the supercapacitor energy storage device enters the charging state. When the traction network voltage value is less than the discharge start threshold, the supercapacitor energy storage device enters the discharging state. When the traction network voltage value is less than the charge start threshold and greater than the discharge start threshold, the supercapacitor energy storage device enters the standby state.

[0109] Step S33: Control the charging current or discharging current of the supercapacitor energy storage device based on the traction network voltage and the charging slope or discharging slope. For example... Figure 6 The figure shown is a graph illustrating the relationship between the charging and discharging current of the supercapacitor energy storage device and the traction grid voltage during droop control. Figure 6 The horizontal axis represents the supercapacitor charging and discharging current, and the vertical axis represents the traction network voltage. λ ch and λ ds λ and λ represent the charge / discharge rate, i.e., the speed at which maximum power is reached. The steeper the slope, the slower the speed. ch_max λ represents the minimum charging rate. ch_min Represents the maximum charging rate; λ ds_max λ represents the minimum discharge rate. ds_min This represents the maximum discharge rate. The left quadrant represents the supercapacitor discharge region, and the right quadrant represents the supercapacitor charging region. When the supercapacitor energy storage device is operating in the charging region, the detected traction grid voltage... Figure 6 The supercapacitor charging current is obtained in the right quadrant; when the supercapacitor energy storage device is operating in the discharge region, the traction network voltage is detected... Figure 6 The supercapacitor discharge current is obtained in the left quadrant.

[0110] By comparing the voltage of the traction grid where the supercapacitor energy storage device is located with the first control parameter, the energy storage device can enter different working areas in a timely manner, and the charging and discharging current can be accurately controlled by droop control, so as to make the DC grid voltage more stable in the process of multiple energy storage devices in the whole line.

[0111] The control method for supercapacitor energy storage devices provided by this invention sets different time scale periods according to different control requirements of the supercapacitor energy storage device, controls and manages the supercapacitor energy storage device on different time scales, and reduces unreasonable use of the supercapacitor energy storage device by timely adjusting and optimizing the control strategy, thereby improving energy saving effect and increasing the application benefits throughout the entire life cycle.

[0112] This invention provides a control device for a supercapacitor energy storage device, such as... Figure 7 As shown, it includes:

[0113] The first timescale management layer module 1 is used to collect lifetime characterization parameters of the supercapacitor energy storage device and perform lifetime assessment during the first timescale period, obtain the lifetime assessment results, input the lifetime assessment results into the constructed fuzzy rule base, and output constraint adjustment parameters. For details, please refer to the relevant description of step S1 in the above method embodiment, which will not be repeated here.

[0114] The second time-scale management module 2 is used to adjust the quantity parameters according to the constraint conditions in the second time-scale period to obtain the constraint conditions. Combined with the optimization objective function, a genetic algorithm is used to optimize the control parameters to obtain the first control parameters. The second time-scale period is shorter than the first time-scale period. For details, please refer to the relevant description of step S2 in the above method embodiment, which will not be repeated here.

[0115] The third timescale management layer module 3 is used to control the charging and discharging current of the supercapacitor energy storage device using a droop control method according to the first control parameters during the third timescale period. The third timescale period is shorter than the second timescale period. For details, please refer to the relevant description of step S3 in the above method embodiment, which will not be repeated here.

[0116] The control device for the supercapacitor energy storage device provided by this invention sets up different time-scale management layer modules according to different control requirements of the supercapacitor energy storage device, controls and manages the supercapacitor energy storage device on different time scales, and reduces unreasonable use of the supercapacitor energy storage device by timely adjusting and optimizing the control strategy, thereby improving energy saving effect and increasing the application benefits throughout the entire life cycle.

[0117] Figure 8 A schematic diagram of the structure of a computer device according to an embodiment of the present invention is shown, including: a processor 901 and a memory 902, wherein the processor 901 and the memory 902 can be connected via a bus or other means. Figure 8 Taking the example of a connection between China and Israel via a bus.

[0118] Processor 901 can be a Central Processing Unit (CPU). Processor 901 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0119] The memory 902, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the above method embodiments. The processor 901 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory 902, thereby implementing the methods in the above method embodiments.

[0120] The memory 902 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor 901, etc. Furthermore, the memory 902 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 902 may optionally include memory remotely located relative to the processor 901, and these remote memories may be connected to the processor 901 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0121] One or more modules are stored in memory 902, and when executed by processor 901, they perform the methods described in the above method embodiments.

[0122] The specific details of the aforementioned computer equipment can be understood by referring to the relevant descriptions and effects in the above method embodiments, and will not be repeated here.

[0123] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The implemented program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. 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 can also include combinations of the above types of memory.

[0124] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A control method for a supercapacitor energy storage device, characterized in that, include: In the first timescale period, lifetime characterization parameters of the supercapacitor energy storage device are collected and lifetime assessment is performed to obtain lifetime assessment results. The lifetime assessment results are input into the constructed fuzzy rule base, and constraint adjustment parameters are output. The lifetime assessment results include lifetime assessment values ​​and lifetime assessment differences, wherein the calculation formula for the lifetime assessment value is: life(j)=w1·C sc (j)+w2·R sc (j) Where life(j) is the lifespan assessment value at station j, and C sc (j) and R sc (j) represents the real-time status of the capacitance and internal resistance of the supercapacitor, and w1 and w2 represent the evaluation weights of the capacitance and internal resistance of the supercapacitor at station j. The formula for calculating the life assessment difference is: Δlife(j)=α1·[life(j)-life(j-1)]+α2·[life(j)-life(j+1)] Where △life(j) is the life assessment difference, and α1 and α2 are the balance difference between the supercapacitor at station j and the supercapacitor at adjacent stations; In the second time scale period, the constraint conditions are obtained by adjusting the quantity parameters according to the constraint conditions. Combined with the optimization objective function, the control parameters are optimized using a genetic algorithm to obtain the first control parameters. The second time scale period is shorter than the first time scale period. In the third time scale period, the charging and discharging current of the supercapacitor energy storage device is controlled using a droop control method based on the first control parameter. The third time scale period is shorter than the second time scale period.

2. The control method for the supercapacitor energy storage device according to claim 1, characterized in that, The process of inputting the life assessment results into the constructed fuzzy rule base and outputting constraint adjustment parameters includes: The fuzzy rule base determines the lifetime status based on the lifetime assessment value and determines the difference in lifetime status with neighboring stations based on the lifetime assessment difference. The constraint adjustment parameters are determined based on the lifespan status and the difference in lifespan status with adjacent stations.

3. The control method for the supercapacitor energy storage device according to claim 1, characterized in that, The optimization objective function is: Where e% represents the energy-saving rate of the supercapacitor energy storage device, E sub_non (j) represents the output energy consumption before the application of the supercapacitor energy storage device in the j-th substation, E sub_ess (j) represents the output energy consumption of the j-th substation after applying the supercapacitor energy storage device, and n represents the total number of substations participating in the calculation of the energy saving rate.

4. The control method for the supercapacitor energy storage device according to claim 3, characterized in that, The constraints are as follows: Among them, u dc0 The no-load voltage of the DC traction network, u ds and u ch These are the discharge start threshold and the charge start threshold, respectively. min and soc max For the upper and lower limits of the SoC working range, i sc_max These are the limits for charging and discharging current.

5. The control method for the supercapacitor energy storage device according to claim 4, characterized in that, The first control parameter is obtained by optimizing the control parameters using a genetic algorithm, including: The control parameters are initialized to generate a first-generation control parameter population, wherein the control parameters include: the discharge start threshold, the charging start threshold, the charging slope value, and the discharge slope value of the supercapacitor energy storage device; Calculate the first fitness of the first generation of control parameter population based on the optimization objective function; Based on the constraints and the first fitness, selection, crossover, and mutation operations are performed to generate the next generation of control parameter population; The fitness of the control parameter population is calculated iteratively to obtain the first control parameter, wherein the first control parameter includes: the first discharge start threshold, the first charging start threshold, the first charging slope value, and the first discharge slope value of the supercapacitor energy storage device.

6. The control method for the supercapacitor energy storage device according to claim 5, characterized in that, The step of controlling the charging and discharging current of the supercapacitor energy storage device using a droop control method based on the first control parameter includes: Obtain the voltage value of the traction network where the supercapacitor energy storage device is located; The working area of ​​the supercapacitor energy storage device is determined by comparing the traction network voltage value, the first discharge start threshold, and the first charge start threshold. When the traction network voltage value is greater than the first charge start threshold, the supercapacitor energy storage device enters the charging state. When the traction network voltage value is less than the first discharge start threshold, the supercapacitor energy storage device enters the discharging state. The charging current of the supercapacitor energy storage device is controlled according to the traction network voltage value and the first charging slope value; the discharging current of the supercapacitor energy storage device is controlled according to the traction network voltage value and the first discharging slope value.

7. A control device for a supercapacitor energy storage device, characterized in that, The device includes: The first timescale management layer module is used to collect lifetime characterization parameters of the supercapacitor energy storage device and perform lifetime assessment during the first timescale period, obtain lifetime assessment results, input the lifetime assessment results into the constructed fuzzy rule base, and output constraint adjustment parameters. The lifetime assessment results include lifetime assessment values ​​and lifetime assessment differences, wherein the calculation formula for the lifetime assessment value is: Where life(j) is the lifespan assessment value at station j, and C sc (j) and R sc (j) represents the real-time status of the capacitance and internal resistance of the supercapacitor, and w1 and w2 represent the evaluation weights of the capacitance and internal resistance of the supercapacitor at station j. The formula for calculating the life assessment difference is: Where △life(j) is the life assessment difference, and α1 and α2 are the balance difference between the supercapacitor at station j and the supercapacitor at adjacent stations; The second time scale management module is used to adjust the quantity parameters according to the constraint conditions in the second time scale period to obtain the constraint conditions, combine the optimization objective function, and use a genetic algorithm to optimize the control parameters to obtain the first control parameters. The second time scale period is shorter than the first time scale period. The third timescale management layer module is used to control the charging and discharging current of the supercapacitor energy storage device using a droop control method according to the first control parameters during the third timescale period, wherein the third timescale period is shorter than the second timescale period.

8. A computer device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method as described in any one of claims 1-6.

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