CN114498692B - Energy management method of electrified railway energy storage system based on fuzzy control - Google Patents
Energy management method of electrified railway energy storage system based on fuzzy control Download PDFInfo
- Publication number
- CN114498692B CN114498692B CN202210032034.3A CN202210032034A CN114498692B CN 114498692 B CN114498692 B CN 114498692B CN 202210032034 A CN202210032034 A CN 202210032034A CN 114498692 B CN114498692 B CN 114498692B
- Authority
- CN
- China
- Prior art keywords
- fuzzy
- energy storage
- discharge
- power
- life
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L9/00—Electric propulsion with power supply external to the vehicle
- B60L9/16—Electric propulsion with power supply external to the vehicle using ac induction motors
- B60L9/24—Electric propulsion with power supply external to the vehicle using ac induction motors fed from ac supply lines
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60M—POWER SUPPLY LINES, AND DEVICES ALONG RAILS, FOR ELECTRICALLY- PROPELLED VEHICLES
- B60M3/00—Feeding power to supply lines in contact with collector on vehicles; Arrangements for consuming regenerative power
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/30—Reactive power compensation
Abstract
The invention provides an electrified railway energy storage system energy management method based on fuzzy control, which takes a double-layer fuzzy control system as a core, an upper-layer fuzzy control system adjusts a discharge threshold according to external power and the residual service life of an energy storage medium, a lower-layer fuzzy control system distributes power according to the charge state of the energy storage medium and the external power, a rain flow technology method is adopted to extract the charge-discharge depth, equivalent life loss is calculated through a four-order fitting life curve and is fed back to the input end of the upper-layer fuzzy control system, and the service life of the energy storage system can be effectively prolonged while the impact power of a traction network is reduced.
Description
Technical Field
The invention belongs to the technical field of electrified railways, and particularly relates to an energy management method of a hybrid energy storage system of an electrified railway.
Background
With the continuous expansion of national railway network operation mileage and the continuous increase of railway electrification rate, energy and electricity consumption of a traction power supply system becomes one of main expenses of railway operation units, how to reduce energy consumption of the traction power supply system and improve energy utilization efficiency becomes a problem to be solved urgently, and the appearance of an energy storage technology provides a new technical idea. Current energy storage system usually adopts single energy storage medium (like battery, electric capacity, flywheel etc.), but single energy storage medium is difficult to compromise simultaneously high power density, high energy density, long life, the engineering requirement of low acquisition cost, and hybrid energy storage system combines the energy storage medium of difference to compromise characteristics such as power density, energy density and life. On the other hand, the energy management system is a control core for controlling the energy storage system, energy interaction between the energy storage system and the traction power supply system needs to be realized under different working conditions, and regenerative braking energy of the train is absorbed or released. Therefore, how to design a simple and effective energy management strategy of the hybrid energy storage system has practical engineering significance for improving the energy utilization efficiency and prolonging the service life of the energy storage system.
Currently, energy management research aiming at a hybrid energy storage system of an electrified railway is still in a starting stage. The patent "method, device, terminal and storage medium for determining discharge threshold of energy storage device" (publication number: CN 111628514A) proposes a method for determining discharge threshold of energy storage system of electrified railway and a storage medium, but only proposes a method for determining discharge threshold, and does not relate to power distribution of hybrid energy storage system; a patent (publication number: CN 110829435A) proposes a fixed threshold energy management method suitable for a single energy storage medium, the scheme only aims at the single energy storage medium, is not suitable for a hybrid energy storage system, and has insufficient flexibility; the patent application publication No. CN113263920A discloses a vehicle-mounted hybrid energy storage system structure of an electrified railway and an energy management method thereof, and adopts an energy management strategy based on multiple thresholds, and the scheme does not consider the influence of the service life attenuation of an energy storage medium on a charge and discharge strategy, has numerous working conditions and has no self-adaptive control effect.
Disclosure of Invention
The invention aims to solve the technical problem of the background technology, provides an energy management method suitable for a hybrid energy storage system of an electrified railway, and solves the technical problems as follows:
1. establishing an energy management system model based on double-layer fuzzy control;
2. extracting the depth of discharge of the energy storage medium by adopting a raindrop counting method;
3. evaluating the residual life of the energy storage system by an energy storage equivalent life conversion method;
4. and establishing a parameter optimization model of the fuzzy control system.
The invention has proposed a energy management method of electrified railway energy storage system based on fuzzy control, this method carries on the smoothing treatment to the traction power at first, and regard form and energy storage system remaining life of power difference as the input of the upper fuzzy control system together, output the threshold value after the fuzzy treatment and adjust the discharge threshold value of the energy storage system in real time through the adjustment parameter of the threshold value, input power difference, state of charge of each energy storage medium, and discharge threshold value to the lower fuzzy control system, the lower fuzzy control system outputs the power and distributes the weight parameter, calculate and distribute the power through the parameter, reuse the rain flow counting method to withdraw the depth of discharge and frequency of the energy storage system of each sampling cycle, and calculate the life loss of each discharge, feed back to the upper fuzzy control system, thus realize the real-time control;
the traction power supply system of the electrified railway energy storage system adopts a single-phase alternating-current power frequency power supply system, contact lines of power supply arms on two sides of a traction substation are connected with a steel rail, and are connected with an LCL type filter after passing through a single-phase step-down transformer, and then are connected with a railway power regulator, a feeder line is led out from a direct-current bus of the railway power regulator, is connected with the direct-current bus of the energy storage system, and then two paths of feeder lines are respectively led out, are respectively connected with a half-bridge type DC/DC converter, and finally are connected with a high-power energy storage subsystem and a high-capacity energy storage subsystem; the structure is shown in figure 1;
the power difference is the difference value between the active part of the traction power and the power of the energy storage system, namely, the active part of the traction power is calculated by utilizing the voltage, the current and the power factor angle measured by the traction network, then the difference value is made with the maximum output power of the energy storage system to obtain the power input difference value, and the voltage of the traction network is set asU c (t) The current of the traction network isI c (t) power factor angle ofφ t The maximum output power of the energy storage system isP ESS_en Then the power difference isP d (t) the following:
the smoothing treatment adopts a first-order filtering algorithm to smooth the power difference, and the sampling interval is set astWith a time constant ofτThen smoothed input power differenceP in (t) The following were used:
the residual life of the energy storage system is obtained by extracting each discharge depth and converting the life loss under different discharge depths into the life loss under 100 percent of the discharge depth, and the range of the residual life of the energy storage system is [0,1 ]]When the service life is 1, the service life is not lost, when the service life is 0, the energy storage system reaches the maximum service life, and the rated discharge times under different discharge depths are set asN pls (DOD(t) Rated number of discharges at 100% depth of discharge isN pls (DOD 100% ) Then remaining life of the energy storage systemLCan be expressed as:
the upper-layer fuzzy control system consists of three parts, namely fuzzification, fuzzy reasoning and defuzzification, as shown in a basic structure diagram 2, the fuzzification projects an input power difference from a physical discourse domain to a fuzzy discourse domain, the fuzzy reasoning carries out fuzzy operation on a fuzzy quantity according to a fuzzy rule, and the defuzzification projects an output fuzzy quantity from the fuzzy discourse domain to the physical discourse domain according to a result of the fuzzy reasoning;
the fuzzy quantity of the upper-layer fuzzy control system comprises an input fuzzy quantity and an output fuzzy quantity, wherein the input fuzzy quantity comprises a service life fuzzy quantity and a power difference fuzzy quantity, the output fuzzy quantity is a threshold adjusting weight fuzzy quantity, a triangular membership function is selected as a membership function of each fuzzy quantity, three fuzzy subsets of { S, M and B } are selected for the service life fuzzy quantity for description, five fuzzy subsets of { SS, S, M, B and BB } are selected for description for the power difference fuzzy quantity, five fuzzy subsets of { SS, S, M, B and BB } are selected for description for the threshold adjusting weight fuzzy quantity, and the membership function of each fuzzy subset is shown in FIG. 3;
the fuzzification process of the upper fuzzy control system projects the input power difference to [0,1 ]]The interval is 1 or 0 when the interval exceeds the upper limit or the lower limit respectively, and the discharge threshold of the energy storage system is set asP thr Then the amount of ambiguity of the upper layer power differenceP up Can be expressed as:
the fuzzy inference part of the upper-layer fuzzy control system comprises a fuzzy inference method and a fuzzy rule design part, wherein the fuzzy inference method adopts an if-then dual-input single-output structure in classical fuzzy control, namely two conditions are met, an output task is executed, and the total number of rules is set to bexThe power difference fuzzy value isD l The condition of the life fuzzy value isE l Threshold adjusted weight blur amountUIs provided thatG l Then to the firstlThe bar rule may be expressed as:
the fuzzy rule design of the upper-layer fuzzy control system is to complete the input and output corresponding relation of the if-then structure under all rules, and all the rules are summarized into a table as follows:
TABLE 1 fuzzy rule Table of upper fuzzy control system
The fuzzy reasoning process is that the reasoning calculation is carried out according to the fuzzy calculation rule, if O is a fuzzy relation operator and X is a Cartesian product, the fuzzy quantity of the upper-layer output threshold adjusting weight is obtainedUCan be expressed as:
in the defuzzification process of the upper-layer fuzzy control system, the gravity center method is adopted to perform defuzzification operation, and an output threshold value is set to adjust the weight fuzzy quantityUIs a membership function ofμ U (U i ) Then the de-fuzzified threshold is used to adjust the weightU avr Comprises the following steps:
the discharge threshold is adjusted based on the basic threshold value and the weightU avr The magnitude of the discharge voltage is controlled in real time to realize the dynamic regulation of the threshold value, and the discharge basic threshold value is set asP base The dynamic threshold adjustment range isP range Then real-time dynamic thresholdP thu (t) Can be expressed as:
the lower-layer fuzzy control system consists of three parts, namely fuzzification, fuzzy reasoning and defuzzification, and a basic structure diagram is shown in a figure 4;
the fuzzy quantity of the lower fuzzy control system comprises an input fuzzy quantity and an output fuzzy quantity, and is characterized in that: wherein the input fuzzy quantity comprises power difference fuzzy quantity and each energy storage medium state-of-charge fuzzy quantity, the output fuzzy quantity is power weight fuzzy quantity, and the membership function of each fuzzy quantity is a triangular membership function, wherein the selection is performed according to the power difference fuzzy quantity
Seven fuzzy subsets of { NB, NM, NS, ZO, PS, PB, PB }, five fuzzy subsets of { SS, S, M, B, BB } are selected for description according to the fuzzy quantity of the state of charge of each energy storage medium, five fuzzy subsets of { SS, S, M, B, BB } are selected for description according to the fuzzy quantity of the power weight, and the membership function of each fuzzy subset is shown in FIG. 5;
the power difference fuzzification process of the lower layer fuzzy control system comprises the following steps: projecting the input power difference to [ -3,3 [)]The interval is 3 or-3 when the interval exceeds the upper limit or the lower limit respectively, and the discharge threshold of the energy storage system is set asP thr Then the amount of ambiguity of the lower layer power differenceP down Can be expressed as:
the charge state of the energy storage medium is calculated by adopting an ampere-hour integration method, and the rated electric quantity of the energy storage medium is set asQ R The charging and discharging current of the energy storage medium isI ESM If the discharge sign is positive and the charge sign is negative, then (t+1) State of charge at time of daySOC(t+1) Can be expressed as:
the charge state fuzzification process of the lower fuzzy control system comprises the following steps: the upper limit of the state of charge is set toSOC up The lower limit of the state of charge isSOC down Then the amount of state of charge blurSCan be expressed as:
the fuzzy inference part of the lower layer fuzzy control system comprises a fuzzy inference method and a fuzzy rule design, wherein the fuzzy inference method adopts an if-then dual-input single-output structure in classical fuzzy control, namelyIf two conditions are satisfied, an output task is executed, and the total number of the rules is set asxThe power difference fuzzy value isM l The condition of the fuzzy quantity of the charge state of each energy storage medium isN 1 l 、N 2 l The output power weight ambiguity quantity isη 1 、η 2 The corresponding conditions are respectivelyk 1 、k 2 Then the lower layer fuzzy control systemlRule of stripR 2 l() Can be expressed as:
the fuzzy rule design of the lower fuzzy control system is to complete the input and output corresponding relation of if-then structure under all rules, thereby establishing a fuzzy rule matrixR 2；
The fuzzy reasoning process is that the reasoning calculation is carried out according to the fuzzy calculation rule, if O is a fuzzy relation operator, and x is a Cartesian product, the lower layer is the second layeriOutput power weight ambiguity quantityη i Can be expressed as:
the lower layer fuzzy control system comprises a defuzzification process: performing defuzzification operation by gravity center methodiOutput power weight ambiguity quantityη i Is a membership function ofμ η (η i ) Then the de-fuzzified threshold adjusts the weightη i_avr Comprises the following steps:
the output power weightThe fuzzy quantity is used for completing the distribution of external power under the traction working condition and the regenerative braking working condition of the electric locomotive, and the train traction power is set asP tr (t) Adaptive power of each energy storage subsystem under traction working conditionP i out (t) Comprises the following steps:
setting the regenerative braking power of the train asP re (t) Adaptive power of each energy storage subsystem under regenerative braking conditionP i in (t) Comprises the following steps:
the residual life algorithm of the energy storage system comprises three steps of discharge depth extraction, discharge depth-life curve fitting and life conversion, the discharge depth extraction is completed through a rain flow counting method, a discharge period is decomposed into a combination of a half period and a full period, a fourth-order curve equation is fitted according to a scattered point set of the discharge depth-life, the cycle life under different discharge depths is solved according to the equation, the life loss caused by each charge and discharge is converted into the life loss under the discharge depth of 100%, and therefore the estimation of the residual life of the energy storage system is completed;
the full cycle is a complete discharge cycle, comprises two physical processes of charging and discharging, can be charged firstly and then discharged, and can also be discharged firstly and then charged, and the initial SOC is the same as the ending SOC, and the half cycle is an incomplete discharge process and only comprises a discharge process or a charge process, and the initial SOC is different from the ending SOC;
the depth of discharge extraction is realized by a rain flow counting method in a sampling period of charge and discharge depth measurement, the process is shown in fig. 6, and the specific flow is as follows:
1) rotating the SOC curve by 90 degrees clockwise, and enabling rain flow to start flowing from the topmost end;
2) the rain flow falls at the extreme value of the SOC until the rain flow reaches the maximum value point of the SOC curve;
3) when the rain flow falls from the most extreme point, the first round of counting is completed, and meanwhile, a new rain flow starts to flow in the opposite direction at the most extreme point;
4) recording a full cycle every time the rain flow falls at the extreme value, recording a half cycle every time the rain flow reaches the maximum value from the beginning to the end, and repeating the above processes;
the fitting process of the discharge depth-life curve comprises the following steps: obtaining rated discharge times under different discharge depths by using a fourth-order fitting curve according to maximum cycle life data under different discharge depths obtained by manufacturers or experimentsa 0 ，a 1 ，a 2 ，a 3 ，a 4 As fitting parameters, the rated discharge times at different discharge depthsN pls (DOD(t) Can be expressed as:
the service life conversion process comprises the following steps: obtaining the depth of discharge of each full cycle and half cycle by rain flow counting methodN pls (DOD(t) Obtaining rated discharge times under different discharge depths, and converting the life loss under different discharge depths to 100% discharge depth according to the rated discharge times, and setting the discharge coefficient as
Taking the value 1 at full cycle and 0.5 at half cycle, the equivalent life loss at different depths of dischargeN equ Comprises the following steps:
then (a)tAt +1) timeRemaining life of the energy storage systemL(t+1) may be expressed as:
n is the number of energy storage subsystems;
the optimization process of the left and right end point abscissas and the middle vertex abscissas of the triangular membership functions of each fuzzy subset comprises three steps of target function establishment, control variable selection and constraint condition setting, the target function considers two factors of energy utilization efficiency and energy storage system service life attenuation, the control variable is selected as a matrix formed by characteristic variables of the membership functions of each fuzzy subset, the constraint condition considers two types of constraints of internal constraint and external constraint, and the optimal solution of the optimization model is obtained through a solver;
establishing an objective function: the optimization function consists of an energy utilization target function and a life attenuation target function, the energy utilization target function needs to improve the energy utilization efficiency of the energy storage system under the energy feeding working condition and the energy storage working condition, and the weight of the energy utilization target function is set asαCan be fed with power ofP fe (t) The stored energy power isP ab (t) With a sampling period of ΔtThen the energy utilizes the objective functionf(t) Can be expressed as:
the life attenuation objective function needs to reduce the life loss of the energy storage system as much as possible, and the weight of the life attenuation objective function is set asβThen life decay objective functiong(t) Can be expressed as:
selecting control variables: the control variable is a matrix composed of characteristic variables of the triangular membership function of each fuzzy subset, wherein the characteristic variables areSetting p as fuzzy quantity label, q as fuzzy subset label corresponding to fuzzy quantity, each fuzzy subset triangular membership function characteristic variable matrix as mu p （x q ) And m and n are the number of maximum membership functions and the number of fuzzy subsets, the control variable matrix X can be expressed as:
let the left and right end abscissas and the middle vertex abscissas of the triangular membership function of each fuzzy subset be a p （x q ），b p （x q ），c p （x q ) Then the triangular membership function matrix mu for each fuzzy subset p （x q ) Can be expressed as:
the schematic diagram is shown in FIG. 7;
constraint conditions are as follows: considering two types of constraints of internal constraint and external constraint, the internal constraint, namely the abscissa of the left end point, the right end point and the middle vertex of the triangular membership function cannot be crossed, the external constraint, namely the middle vertex of the triangular membership function of the adjacent fuzzy subsets, needs to keep a proper distance, and the minimum distance is set to be deltaDThen the constraint can be expressed as:
in summary, the flow of the method for managing energy of the energy storage system of the electrified railway based on the fuzzy control is shown in the attached fig. 8 in the specification.
Advantageous effects
1. The method can effectively and dynamically adjust the discharge threshold, has better adaptability under different power scenes, and can better play the effect of peak clipping and valley filling.
2. The invention takes the service life of the energy storage system as one of the parameters to adjust the discharge threshold of the energy storage in real time, can effectively prolong the service life of the energy storage system and slow down the frequency and the depth of discharge.
3. According to the invention, the distribution weight can be dynamically adjusted according to the real-time charge state of the energy storage system, the cooperative operation of the hybrid energy storage system is realized, and the occurrence probability of over-discharge of the energy storage system is reduced.
Drawings
FIG. 1 is a hybrid energy storage system configuration for an electrified railway ground.
Fig. 2 is a schematic structural diagram of an upper-layer fuzzy control system.
FIG. 3 is a diagram of fuzzy subset membership functions of upper layer service life, power difference and threshold adjustment weight.
FIG. 4 is a schematic diagram of a lower fuzzy control system.
FIG. 5 is a graph of membership functions of fuzzy subsets of lower power difference, state of charge and power distribution weights.
Fig. 6 is a schematic flow chart of the rain flow counting method.
FIG. 7 is a schematic diagram of an optimization function control variable matrix element.
Fig. 8 is a flow chart of the fuzzy control.
Fig. 9 is a test power diagram.
Detailed Description
For the ground hybrid energy storage system structure of the electrified railway shown in fig. 1, it is assumed that the energy storage subsystem 1 adopts a super capacitor as an energy storage medium, the energy storage subsystem 2 adopts a lithium titanate battery as an energy storage medium, and parameters of the hybrid energy storage system are shown in table 1.
TABLE 1 hybrid system parameter Table
The energy storage medium parameter information is shown in table 2.
TABLE 2 energy storage Medium parameter Table
Information on the depth of discharge-maximum number of cycles of a known lithium titanate battery is shown in table 3.
TABLE 3 information table of lithium titanate battery discharge depth-maximum cycle number
The fourth order fit curve is:
a traction power curve of 20000 seconds was used as a test case, sampled every 120s, as shown in fig. 9.
The power simulation data are shown in table 4, and the results show that, compared with the threshold-based energy management method, the average traction power of the traction network of the unoptimized fuzzy energy management system is reduced by 12.14%, the average traction power of the optimized fuzzy energy management system is reduced by 5.39%, and the average traction power of the traction network is reduced.
TABLE 4 numerical simulation power parameter information table
The life simulation test data are shown in table 5, and the results show that, compared with the threshold-based energy management method, the battery life loss of the unoptimized fuzzy energy management system is reduced by 37.41%, the battery life loss of the optimized fuzzy energy management system is reduced by 56.77%, and the service life of the energy storage system is effectively prolonged.
TABLE 5 numerical simulation Life parameter information Table
The above embodiments are merely illustrative of the technical ideas of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like based on the technical ideas of the present invention should be included in the scope of the present invention.
Claims (1)
1. An electrified railway energy storage system energy management method based on fuzzy control is characterized in that: firstly, carrying out smoothing treatment on traction power, taking a power difference form and the residual life of an energy storage system as the input of an upper-layer fuzzy control system, outputting a threshold adjustment parameter after fuzzy treatment, adjusting a discharge threshold of the energy storage system in real time through the threshold adjustment parameter, inputting the power difference, the charge state of each energy storage medium and the discharge threshold into a lower-layer fuzzy control system, outputting a power distribution weight parameter by the lower-layer fuzzy control system, calculating and distributing power through the parameter, extracting the discharge depth and frequency of the energy storage system in each sampling period by using a rain flow counting method, calculating the life loss of each discharge, and feeding back the life loss to the upper-layer fuzzy control system, thereby realizing real-time control;
the traction power supply system of the electrified railway energy storage system adopts a single-phase alternating-current power frequency power supply system, contact lines of power supply arms on two sides of a traction substation are connected with a steel rail, and are connected with an LCL type filter after passing through a single-phase step-down transformer, and then are connected with a railway power regulator, a feeder line is led out from a direct-current bus of the railway power regulator, is connected with the direct-current bus of the energy storage system, and then two paths of feeder lines are respectively led out, are respectively connected with a half-bridge type DC/DC converter, and finally are connected with a high-power energy storage subsystem and a high-capacity energy storage subsystem;
the power difference is the difference value between the active part of the traction power and the power of the energy storage system, namely, the active part of the traction power is calculated by utilizing the voltage, the current and the power factor angle measured by a traction network, and then the difference is made with the maximum output power of the energy storage system to obtain a power input difference value; set the voltage of the traction network asU c (t) The current of the traction network isI c (t) power factor angle ofφ t The maximum output power of the energy storage system isP ESS_en Then the power difference isP d (t) the following:
the smoothing treatment adopts a first-order filtering algorithm to smooth the power difference, and the sampling interval is set astWith a time constant ofτThen smoothed input power differenceP in (t) The following were used:
the residual life of the energy storage system is obtained by extracting each discharge depth and converting the life loss under different discharge depths into the life loss under 100 percent of the discharge depth, and the range of the residual life of the energy storage system is [0,1 ]]When the service life is 1, the service life is not lost, when the service life is 0, the energy storage system reaches the maximum service life, and the rated discharge times under different discharge depths are set asN pls (DOD(t) Rated number of discharges at 100% depth of discharge isN pls (DOD 100% ) Then remaining life of the energy storage systemLCan be expressed as:
the upper-layer fuzzy control system consists of three parts, namely fuzzification, fuzzy reasoning and defuzzification, wherein the fuzzification projects an input power difference from a physical theory domain to the fuzzy theory domain, the fuzzy reasoning carries out fuzzy operation on a fuzzy quantity according to a fuzzy rule, and the defuzzification projects an output fuzzy quantity from the fuzzy theory domain to the physical theory domain according to a result of the fuzzy reasoning;
the fuzzy quantity of the upper-layer fuzzy control system comprises an input fuzzy quantity and an output fuzzy quantity, wherein the input fuzzy quantity comprises a service life fuzzy quantity and a power difference fuzzy quantity, the output fuzzy quantity is a threshold adjusting weight fuzzy quantity, and a triangular membership function is adopted as a membership function of each fuzzy quantity, wherein three fuzzy subsets of { S, M and B } are selected for the service life fuzzy quantity for description, five fuzzy subsets of { SS, S, M, B and BB } are selected for description for the power difference fuzzy quantity, and five fuzzy subsets of { SS, S, M, B and BB } are selected for description for the threshold adjusting weight fuzzy quantity;
the fuzzification process of the upper layer fuzzy control system comprises the following steps: projecting the input power difference to [0,1]The interval is 1 or 0 when the interval exceeds the upper limit or the lower limit respectively, and the discharge threshold of the energy storage system is set asP thr Then the amount of blurring of the upper layer power differenceP up Can be expressed as:
the fuzzy inference part of the upper-layer fuzzy control system comprises a fuzzy inference method and a fuzzy rule design part, wherein the fuzzy inference method adopts an if-then dual-input single-output structure in classical fuzzy control, namely two conditions are met, an output task is executed, and the total number of rules is set to bexThe power difference fuzzy value isD l The condition of the lifetime ambiguity value isE l Threshold adjusted weight blur amountUUnder the condition thatG l Then it is firstlThe bar rule may be expressed as:
the fuzzy rule design of the upper-layer fuzzy control system is to complete the input and output corresponding relation of the if-then structure under all rules, and all the rules are summarized into a table as follows:
TABLE 1 fuzzy rule Table of upper fuzzy control system
The fuzzy reasoning process is that the reasoning calculation is carried out according to a fuzzy calculation rule, if O is a fuzzy relation operator, and x is a Cartesian product, the fuzzy quantity of the threshold adjustment weight output by the upper layerUCan be expressed as:
in the defuzzification process of the upper-layer fuzzy control system, the gravity center method is adopted to perform defuzzification operation, and a threshold value is set to adjust the weight fuzzy quantityUIs a membership function ofμ U (U i ) Then the de-fuzzified threshold is used to adjust the weightU avr Comprises the following steps:
the discharge threshold is adjusted based on the basic threshold value and the weightU avr The magnitude of the discharge voltage is controlled in real time to realize the dynamic regulation of the threshold value, and the discharge basic threshold value is set asP base The dynamic threshold adjustment range isP range Then real-time dynamic thresholdP thu (t) Can be expressed as:
the lower-layer fuzzy control system consists of three parts, namely fuzzification, fuzzy inference and defuzzification, wherein the fuzzy quantity of the lower-layer fuzzy control system comprises an input fuzzy quantity and an output fuzzy quantity, the input fuzzy quantity comprises a power difference fuzzy quantity and a state-of-charge fuzzy quantity of each energy storage medium, the output fuzzy quantity is a power weight fuzzy quantity, a membership function of each fuzzy quantity is a triangular membership function, seven fuzzy subsets { NB, NM, NS, ZO, PS, PB and PB } are selected for describing the power difference fuzzy quantity, five fuzzy subsets { SS, S, M, B and BB } are selected for describing the state-of-charge fuzzy quantity of each energy storage medium, and five fuzzy subsets { SS, S, M, B and BB } are selected for describing the power weight fuzzy quantity;
the power difference fuzzification process of the lower fuzzy control system comprises the following steps: projecting the input power difference to [ -3,3 [)]The interval is 3 or-3 when the interval exceeds the upper limit or the lower limit respectively, and the discharge threshold of the energy storage system is set asP thr Then the amount of ambiguity of the lower layer power differenceP down Can be expressed as:
the charge state of the energy storage medium is calculated by adopting an ampere-hour integration method, and the rated electric quantity of the energy storage medium is set asQ R The charging and discharging current of the energy storage medium isI ESM If the discharge sign is positive and the charge sign is negative, then (t+1) State of charge at time of daySOC(t+1) Can be expressed as:
the charge state fuzzification process of the lower fuzzy control system comprises the following steps: the upper limit of the state of charge is set toSOC up The lower limit of the state of charge isSOC down Then the amount of state of charge blurSCan be expressed as:
the fuzzy inference part of the lower fuzzy control systemThe fuzzy inference method adopts an if-then dual-input single-output structure in classical fuzzy control, namely two conditions are met, an output task is executed, and the total number of rules is set asxThe power difference fuzzy value isM l The condition of the fuzzy quantity of the charge state of each energy storage medium isN 1 l 、N 2 l The output power weight ambiguity quantity isη 1 、η 2 Respectively corresponding to the conditionsk 1 、k 2 Then the lower layer fuzzy control systemlRule of stripR 2 l() Can be expressed as:
the fuzzy rule design of the lower fuzzy control system is to complete the input and output corresponding relation of if-then structure under all rules, thereby establishing a fuzzy rule matrixR 2 ；
The fuzzy reasoning process is that the reasoning calculation is carried out according to the fuzzy calculation rule, if O is a fuzzy relation operator, and x is a Cartesian product, the lower layer is the second layeriOutput power weight ambiguity quantityη i Can be expressed as:
the lower fuzzy control system defuzzification process adopts a gravity center method to perform defuzzification operation, and sets the second stepiOutput power weight ambiguity quantityη i Is a membership function ofμ η (η i ) Then the de-fuzzified threshold is used to adjust the weightη i_avr Comprises the following steps:
the fuzzy quantity of the output power weight is used for completing the distribution of the external power under the traction working condition and the regenerative braking working condition of the electric locomotive, and the train traction power is set asP tr (t) Adaptive power of each energy storage subsystem under traction working conditionP i out (t) Comprises the following steps:
setting the regenerative braking power of the train asP re (t) Adaptive power of each energy storage subsystem under regenerative braking conditionP i in (t) Comprises the following steps:
the residual life algorithm of the energy storage system comprises three steps of depth of discharge extraction, depth of discharge-life curve fitting and life conversion, wherein the depth of discharge extraction is completed through a rain flow counting method, a discharge period is decomposed into a combination of a half period and a full period, a fourth-order curve equation is fitted according to a scattered point set of the depth of discharge and the life, the cycle life under different depth of discharge is worked out according to the equation, and the life loss caused by each charge and discharge is converted into the life loss under the depth of discharge of 100%, so that the estimation of the residual life of the energy storage system is completed; the full cycle is a complete discharge cycle, comprises two physical processes of charging and discharging, can be charged firstly and then discharged, and can also be discharged firstly and then charged, and the initial SOC is the same as the ending SOC, and the half cycle is an incomplete discharge process and only comprises a discharge process or a charge process, and the initial SOC is different from the ending SOC;
and (3) extracting the depth of discharge, namely extracting the depth of discharge in a sampling period of measuring the depth of discharge by a rain flow counting method, wherein the specific flow is as follows:
1) rotating the SOC curve by 90 degrees clockwise, and enabling rain flow to start flowing from the topmost end;
2) the rain flow falls at the extreme value of the SOC until the rain flow reaches the maximum value point of the SOC curve;
3) when the rain flow falls from the most extreme point, the first round of counting is completed, and simultaneously the new rain flow starts to flow in the opposite direction at the most extreme point;
4) recording a full cycle every time the rain flow falls at the extreme value, recording a half cycle every time the rain flow reaches the maximum value from the beginning to the end, and repeating the above processes;
according to maximum cycle life data under different discharge depths obtained by manufacturers or experiments, a fourth-order fitting curve is used for obtaining rated discharge times under different discharge depths, and the fitting process of the discharge depth-life curve is designeda 0 ，a 1 ，a 2 ，a 3 ，a 4 As fitting parameters, the rated discharge times at different discharge depthsN pls (DOD(t) Can be expressed as:
and (3) a life conversion process: obtaining the depth of discharge of each full cycle and half cycle by rain flow counting methodN pls (DOD(t) Obtaining rated discharge times under different discharge depths, and converting the life loss under different discharge depths to 100% discharge depth according to the rated discharge times, and setting the discharge coefficient as
Taking the value 1 at full cycle and 0.5 at half cycle, the equivalent life loss at different depths of dischargeN equ Comprises the following steps:
then (1)t+1) time remaining life of the energy storage systemL(t+1) may be expressed as:
n is the number of energy storage subsystems;
the optimization process of the left and right end point abscissas and the middle vertex abscissas of the triangular membership functions of each fuzzy subset comprises three steps of target function establishment, control variable selection and constraint condition setting, the target function considers two factors of energy utilization efficiency and energy storage system service life attenuation, the control variable is selected as a matrix formed by characteristic variables of the membership functions of each fuzzy subset, the constraint condition considers two types of constraints of internal constraint and external constraint, and the optimal solution of the optimization model is obtained through a solver;
an objective function is set, an optimization function consists of an energy utilization objective function and a life attenuation objective function, the energy utilization objective function needs to improve the energy utilization efficiency of the energy storage system under the energy feeding working condition and the energy storage working condition, and the weight of the energy utilization objective function is set asαCan feed power intoP fe (t) The stored energy power isP ab (t) With a sampling period of ΔtThen the energy utilizes the objective functionf(t) Can be expressed as:
the life attenuation objective function needs to reduce the life loss of the energy storage system as much as possible, and the weight of the life attenuation objective function is set asβThen life decay objective functiong(t) Can be expressed as:
selecting control variables: the control variable is a matrix formed by characteristic variables of the triangular membership function of each fuzzy subset, wherein the characteristic variables are the horizontal coordinates of the left and right end points of the triangular membership function and the horizontal coordinate of the middle vertex, p is a fuzzy quantity label, q is a fuzzy subset label corresponding to the fuzzy quantity, and the characteristic variable matrix of the triangular membership function of each fuzzy subset is mu p （x q ) And m and n are the number of maximum membership functions and the number of fuzzy subsets, the control variable matrix X can be expressed as:
let the left and right end abscissas and the middle vertex abscissas of the triangular membership function of each fuzzy subset be a p （x q ），b p （x q ），c p （x q ) Then the triangular membership function matrix mu for each fuzzy subset p （x q ) Can be expressed as:
constraint conditions are as follows: considering two types of constraints of internal constraint and external constraint, the internal constraint, namely the abscissa of the left end point, the right end point and the middle vertex of the triangular membership function cannot be crossed, the external constraint, namely the middle vertex of the triangular membership function of the adjacent fuzzy subsets, needs to keep a proper distance, and the minimum distance is set to be deltaDThen the constraint can be expressed as:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210032034.3A CN114498692B (en) | 2022-01-12 | 2022-01-12 | Energy management method of electrified railway energy storage system based on fuzzy control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210032034.3A CN114498692B (en) | 2022-01-12 | 2022-01-12 | Energy management method of electrified railway energy storage system based on fuzzy control |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114498692A CN114498692A (en) | 2022-05-13 |
| CN114498692B true CN114498692B (en) | 2022-08-16 |
Family
ID=81511706
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210032034.3A Active CN114498692B (en) | 2022-01-12 | 2022-01-12 | Energy management method of electrified railway energy storage system based on fuzzy control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114498692B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116111645A (en) * | 2023-01-06 | 2023-05-12 | 国网山东省电力公司潍坊供电公司 | Energy storage system suitable for low-voltage grid connection and control method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104242329A (en) * | 2014-09-12 | 2014-12-24 | 上海电力学院 | Micro-grid hybrid energy storage system power distribution method based on fuzzy control rules |
| CN109217343A (en) * | 2018-08-02 | 2019-01-15 | 江苏大学 | A kind of wind-powered electricity generation hybrid energy-storing power slide control based on fuzzy control |
| CN111900745A (en) * | 2020-07-17 | 2020-11-06 | 江苏科能电力工程咨询有限公司 | Hybrid energy storage frequency division coordination control system for stabilizing wind power fluctuation |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130169232A1 (en) * | 2011-12-30 | 2013-07-04 | Caterpillar Inc. | Methods and systems for monitoring and using an electrical energy-storage device |
-
2022
- 2022-01-12 CN CN202210032034.3A patent/CN114498692B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104242329A (en) * | 2014-09-12 | 2014-12-24 | 上海电力学院 | Micro-grid hybrid energy storage system power distribution method based on fuzzy control rules |
| CN109217343A (en) * | 2018-08-02 | 2019-01-15 | 江苏大学 | A kind of wind-powered electricity generation hybrid energy-storing power slide control based on fuzzy control |
| CN111900745A (en) * | 2020-07-17 | 2020-11-06 | 江苏科能电力工程咨询有限公司 | Hybrid energy storage frequency division coordination control system for stabilizing wind power fluctuation |
Non-Patent Citations (1)
| Title |
|---|
| 储能式有轨电车车地一体化配置模型;韦绍远 等;《电工技术学报》;20190125;第34卷(第2期);第427-436页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114498692A (en) | 2022-05-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Qin et al. | Energy transfer strategy for urban rail transit battery energy storage system to reduce peak power of traction substation | |
| CN109599856B (en) | Electric vehicle charging and discharging management optimization method and device in micro-grid multi-building | |
| CN109146201A (en) | Filling based on cooperative game changes the integrated power station micro-capacitance sensor Optimization Scheduling of storage | |
| CN104917173B (en) | Adapt to the distribution optimization method of power distribution network Large Copacity load transfer | |
| CN107947231A (en) | A kind of mixed energy storage system control method towards power distribution network optimization operation | |
| CN108062619B (en) | Rail vehicle-ground integrated capacity configuration method and device | |
| CN110311396A (en) | A kind of alternating current-direct current mixing micro-capacitance sensor hybrid energy-storing capacity configuration optimizing method | |
| CN109948823B (en) | Self-adaptive robust day-ahead optimization scheduling method for light storage charging tower | |
| Huangfu et al. | An Improved Energy Management Strategy for Fuel Cell Hybrid Vehicles Based on Pontryagin's Minimum Principle | |
| CN107294102B (en) | Electrified railway energy storage and power supply device and control method thereof | |
| CN111806253A (en) | Ground type battery and super capacitor hybrid energy storage system and management method thereof | |
| CN106427607A (en) | Energy distribution method of electric vehicle hybrid energy storage system | |
| CN111845453B (en) | Electric vehicle charging station double-layer optimized charging and discharging strategy considering flexible control | |
| CN113300395B (en) | Hybrid energy storage optimal capacity configuration method for flexible traction power supply system | |
| CN109849694B (en) | Hybrid energy storage type tramcar energy management method based on online convex programming | |
| CN105406481A (en) | Charging-discharging control method of electric vehicle | |
| CN111244988A (en) | Electric automobile considering distributed power supply and energy storage optimization scheduling method | |
| CN114498692B (en) | Energy management method of electrified railway energy storage system based on fuzzy control | |
| CN105141000A (en) | Electric vehicle charging/discharging control method | |
| CN115811074A (en) | Hybrid energy storage capacity configuration method and device for traction substation and related medium | |
| CN109523079B (en) | Optimization method for combined auxiliary peak shaving by utilizing electric automobile and heat accumulating electric boiler | |
| CN107776433A (en) | A kind of discharge and recharge optimal control method of electric automobile group | |
| CN104283224B (en) | A kind of energy-storage system smooth wind power Poewr control method limiting wind-powered electricity generation stability bandwidth | |
| CN115000985A (en) | Aggregation control method and system for user-side distributed energy storage facilities | |
| CN110896246B (en) | Configuration optimization method of hybrid energy storage type tramcar system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |