CN110752654B - Energy scheduling method for tramcar hybrid energy storage system - Google Patents

Energy scheduling method for tramcar hybrid energy storage system Download PDF

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CN110752654B
CN110752654B CN201910929610.2A CN201910929610A CN110752654B CN 110752654 B CN110752654 B CN 110752654B CN 201910929610 A CN201910929610 A CN 201910929610A CN 110752654 B CN110752654 B CN 110752654B
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lithium battery
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CN110752654A (en
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孙玉坤
丁鹏飞
孟高军
李建林
刘海涛
袁野
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Nanjing Institute of Technology
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    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/14—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries for charging batteries from dynamo-electric generators driven at varying speed, e.g. on vehicle
    • H02J7/1423—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries for charging batteries from dynamo-electric generators driven at varying speed, e.g. on vehicle with multiple batteries
    • 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
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • 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
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
    • Y02T10/92—Energy efficient charging or discharging systems for batteries, ultracapacitors, supercapacitors or double-layer capacitors specially adapted for vehicles

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  • Electric Propulsion And Braking For Vehicles (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention discloses an energy scheduling method of a tramcar hybrid energy storage system, which comprises the following steps: (1) Collecting train running characteristics to determine train traction load energy; the real-time monitored train power and the charge state of the super capacitor are subjected to fuzzy control to obtain the charging multiplying power of the lithium battery energy storage unit, and the maximum charging and discharging power of the two energy storage units is calculated; (2) Calculating the capacity of a hybrid energy storage system, and calculating the pre-allocation energy of each of two energy storage units in the hybrid energy storage system by adopting a low-pass filtering algorithm according to a load energy curve of train start and stop; (3) And (2) carrying out secondary correction on the pre-allocated energy of the two energy storage units based on the respective real-time charge states of the two energy storage units, taking the maximum charge and discharge energy and the maximum charge and discharge power obtained in the step (1) as constraint conditions, multiplying the energy value of the secondary correction by an adjustment coefficient, and determining the energy allocation of the two energy storage units. The invention improves the energy utilization efficiency and the system stability for the energy recovery of the tramcar.

Description

Energy scheduling method for tramcar hybrid energy storage system
Technical Field
The invention relates to an energy scheduling method of an urban rail transit system, in particular to an energy scheduling method of a tramcar hybrid energy storage system.
Background
With the rapid development of economy, urban population is increased, urban process is accelerated, traffic is blocked increasingly, and global energy consumption is increased. In order to solve the problems, urban rail transit has the characteristics of large traffic volume, high environmental friendliness, high density, comfort in quasi-points, safety, reliability and the like, and the rapid rise of the urban rail transit becomes a key for solving the problems of large population and inconvenient traffic in China. Up to now, more than 300 cities exist in rail traffic engineering including subways and light rails built worldwide. In developed countries, urban rail transit is the main body of urban traffic, accounting for about 50% -80% of the total passenger traffic: the total length of the Paris rail transit system exceeds 1200km; a track traffic system of about 2000km is crowded in tokyo; london also exceeded 1000km. The total operating mileage of the railway in China is over 13 ten thousand kilometers, wherein the operating mileage of the high-speed railway reaches 2.9 ten thousand kilometers, and the operating mileage of the urban rail transit reaches 5761 kilometers. It is expected that the operation mileage of the high-speed railway is doubled and the urban rail transit is rapidly developed in 2030, so that the smoothness of the province and the high-speed railway and the interconnection of the ground and the city are basically realized.
Urban rail transit provides great convenience for people to travel, but brings a considerable energy consumption, which is a non-negligible problem. It is counted that the power consumption of urban rail transit for auxiliary services such as lighting and air conditioning is about 10%, the power consumption of traction power supply is about 90%, and the regenerative braking energy is about 40% or more of traction energy. The main reasons for this phenomenon include: (1) The traffic network and the regional energy network have intersection, but can not consume nearby renewable energy; (2) A large amount of renewable energy generated during start-stop of the urban rail transit system is not recovered and reasonably utilized; (3) The demand side response technology is not perfect enough, and the schedulability characteristic of the rail traffic load is not fully excavated. In addition, the regenerative braking energy generated by urban rail transit vehicles during braking is considerable, and the energy can only be used for vehicles which accelerate on line at the same time, if the energy is not utilized, potential safety hazards are brought to a traction network, the voltage of the train traction network is greatly increased, and even the problem of regenerative braking failure is caused.
Disclosure of Invention
The invention aims to: aiming at the problems, the invention provides an energy scheduling method for a tramcar hybrid energy storage system, which can improve the energy utilization efficiency and the system stability of a tramcar energy recovery system.
The technical scheme is as follows: the invention adopts the technical scheme that the energy scheduling method of the tramcar hybrid energy storage system is applied to a tramcar energy recovery system, the system comprises a hybrid energy storage system for tramcar energy recovery, a traction transformer substation and a control module, the hybrid energy storage system comprises a lithium battery energy storage unit and a super capacitor energy storage unit which are connected in parallel, the control module executes the energy scheduling method, and the energy scheduling method comprises the following steps:
(1) Collecting train running characteristics and calculating train traction load energy; collecting train power and supercapacitor charge state monitored in real time, obtaining the charging multiplying power of the lithium battery energy storage unit through fuzzy control, and calculating the maximum charging and discharging power of the two energy storage units according to the charging multiplying power;
train traction load energy E described in step (1) S The calculation formula is as follows:
E S ＝E-E L
wherein, the regenerative braking energy E is:
Figure BDA0002217647810000021
f in the formula b Is the total braking force of the train, a' is the average braking deceleration, V is the train speed, V 3 Is the highest running speed of the train;
train air loss E L The method comprises the following steps:
Figure BDA0002217647810000022
v in 2 Is the turning speed from the constant power interval to the characteristic interval, a 2 Is the instantaneous deceleration of a constant torque interval, C 2 Is constant.
When calculating the maximum charge and discharge power of the two energy storage units, a lithium battery 'first and last station' charging mode is preferably adopted, and the super capacitor adopts a 'station' charging mode, wherein the 'first and last station' charging mode is used for charging a train when the train reaches the first and last stations; the station charging mode means that the trains are charged at each stop. This solution is more suitable for practical applications. The specific steps of calculation using this charging mode are:
(21) Collecting power P monitored in real time d And supercapacitor state of charge SOC cap The membership function of the output multiplying power is multiplied by the root punishment coefficient f through fuzzy control, and then multiplied by the maximum multiplying power C of the lithium battery bat，max Obtain the charging rate C of the lithium battery unit bat The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the root penalty coefficient f is:
Figure BDA0002217647810000023
in SOC bat (k)、SOC bat，max And SOC (System on chip) bat，min Respectively a real-time value, a minimum value and a maximum value of the charge state of the lithium battery unit; k is a discrete time series.
(22) Maximum charging power P of super capacitor and lithium battery cap，ch，max 、P bat，ch，max The expressions are respectively:
Figure BDA0002217647810000024
wherein V is cap，rate Rated voltage of super capacitor system, V cap，min For the lowest voltage in its operation, t uc For charging time, V bat，max Charging lithium battery cell to voltage of specified state of charge, C bat Charge rate for lithium battery cell, Q bat Is the rated capacity of the lithium battery unit.
(2) And (3) calculating the capacity of the hybrid energy storage system by the train traction load energy obtained in the step (1), and calculating the pre-distribution energy of each of the two energy storage units in the hybrid energy storage system by adopting a low-pass filtering algorithm according to a load energy curve of the start and stop of the train. The calculation includes the following steps:
(31) The load fluctuation energy during the start-stop of the vehicle is preferentially provided by the hybrid energy storage system, namely the capacity E of the hybrid energy storage system hess The method comprises the following steps:
E hess ＝E S -E grid
wherein E is grid The energy is predicted offline for the traction power plant, and is predicted from the historical operating characteristics of the traction power plant.
(32) According to the load energy curve of the start and stop of the train, adopting a low-pass filtering algorithm to control the capacity E of the hybrid energy storage system hess The pre-distribution of the energy is carried out,
the pre-distribution energy of the lithium battery at the moment t is as follows:
Figure BDA0002217647810000031
wherein, delta T is a sampling time interval, and tau is a time constant;
super capacitor pre-distribution energy E cap (t) is:
E cap_ref (t)＝E hess (t)-E bat_ref (t)
(3) And (2) carrying out secondary correction on the pre-allocated energy of the two energy storage units based on the respective real-time charge states of the two energy storage units, taking the maximum charge and discharge energy and the maximum charge and discharge power obtained in the step (1) as constraint conditions, multiplying the energy value of the secondary correction by an adjustment coefficient, and determining the energy allocation of the two energy storage units.
A specific secondary correction scheme is as follows:
(41) When the real-time charge state of the energy storage unit is higher than the offline charge state threshold value of the energy storage unit, the part of the super capacitor exceeding the threshold value is born by a battery, and the part of the super capacitor exceeding the threshold value is born by a traction transformer substation; the threshold value takes 10%, and when the real-time charge state of the energy storage unit is higher than the offline charge state of the energy storage unit by more than 10%, the secondary correction of the super capacitor is;
Figure BDA0002217647810000032
in SOC cap (t) is the real-time state of charge, SOC, before the super-capacitor adjusts the strategy CAP (t) is the charge state of the super capacitor when offline, E cap Pre-distributing energy for the super capacitor; Δt is the sampling time interval;
the secondary correction amount of the lithium battery is as follows:
Figure BDA0002217647810000033
in SOC bat (t) real-time state of charge, SOC, before strategy adjustment for lithium batteries BAT (t) is the state of charge of the lithium battery when offline, E bat Pre-distributing energy for lithium batteries.
(42) When the real-time state of charge of the energy storage unit is lower than the offline state of charge threshold of the energy storage unit, the lower threshold part of the super capacitor is born by a battery, the lower threshold part of the battery is corrected by reducing the output power of the traction substation, and if the unabsorbed braking amount occurs, the super capacitor is consumed by a braking resistor; the threshold value takes 10%, and when the real-time charge state of the energy storage unit is lower than the offline charge state of the energy storage unit by less than 10%, the secondary correction amounts of the super capacitor and the lithium battery are respectively;
Figure BDA0002217647810000041
Figure BDA0002217647810000042
in SOC cap (t) is the real-time state of charge, SOC, before the super-capacitor adjusts the strategy CAP (t) is the charge state of the super capacitor when offline, E cap Pre-distributing energy for the super capacitor; SOC (State of Charge) bat (t) real-time state of charge, SOC, before strategy adjustment for lithium batteries BAT (t) is the state of charge of the lithium battery when offline, E bat Pre-distributing energy for lithium batteries.
(43) The charge state of the energy storage unit is divided into 5 sections which are respectively over-discharge, low-charge, normal, high-charge and over-charge, when the energy storage unit is in the low-charge state and is discharged or in the high-charge state and is charged, an adjustment coefficient a is introduced, and the energy storage unit adjustment energy E is calculated ad (t)＝aE ref (t) wherein E ref For regulating the energy of the energy storage unit before. The adjustment coefficientThe value of a is as follows:
if 0 < E bat_ref (t) < Det, then the adjustment factor a=1; if Det < E bat_ref (t)＜E bat_max (t)-Det，a＝Det/E bat_ref (t); if E bat_max (t)-Det＜E bat_ref (t) < ++ infinity the process comprises, at this time, the adjustment coefficient takes a=det/(E) bat_max (t)-Det)；
if-Det < E bat_ref (t) < 0, then the adjustment factor a=1; if-E bat_max (t)+Det＜E bat_ref (t)＜-Det，a＝Det/E bat_ref (t); if- +_infinity < E bat_ref (t)＜-E bat_max (t) +det, where the adjustment factor takes a=det/(E) bat_max (t)-Det)；
Wherein Det is a set value, E bat_max Maximum charge and discharge energy of lithium battery E bat_ref And the energy is real-time energy of the lithium battery.
The beneficial effects are that: compared with the prior art, the invention has the following advantages: 1. the invention is applied to a hybrid energy storage system based on parallel connection of a lithium battery and a super capacitor, and the super capacitor energy storage device absorbs and stores redundant regenerative braking energy, and releases energy for traction of a motor when a locomotive is started and accelerated, so that the aims of saving electric energy and avoiding energy waste are achieved. 2. The dispatching method can stabilize the traction network voltage and reduce voltage fluctuation. When the locomotive starts to accelerate, the traction network voltage can drop sharply, and the energy storage device can release energy to raise the network voltage; meanwhile, the energy generated during the regenerative braking of the locomotive can raise the network voltage and even cause the failure of the regenerative braking, and at the moment, the energy storage energy of the energy storage device reduces the traction network voltage, so that the voltage fluctuation is reduced greatly. 3. The energy scheduling method can adjust the charge and discharge power of the battery energy storage system and the super capacitor energy storage system, optimize the output condition of the two energy storage elements and improve the recycling rate of regenerative braking energy.
Drawings
FIG. 1 is a schematic diagram of a hybrid energy storage system cogeneration according to the invention;
FIG. 2 is a brake deceleration characteristic of a urban rail train;
FIG. 3 is a fuzzy control framework and fuzzy control rules according to the present invention;
FIG. 4 is a graph of the energy distribution of the hybrid energy storage system according to the present invention;
FIG. 5 is a state of charge profile of the hybrid energy storage system according to the present invention;
FIG. 6 is a schematic diagram of a hybrid energy storage system scheduling strategy according to the present invention;
fig. 7 is a rule chart of the adjustment coefficient a according to the present invention.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
The energy scheduling method of the tramcar hybrid energy storage system is applied to a tramcar energy recovery system, as shown in fig. 1, the tramcar and the hybrid energy storage system are respectively connected with a power grid, other loads are contained in the power grid, and the hybrid energy storage system comprises a lithium battery and a super capacitor which are connected in parallel. The tramcar and the hybrid energy storage system are respectively connected with a control module, and the control module is used for executing the energy scheduling method. The traction substation is reduced by a step-down transformer, alternating current is converted into direct current by an AC/DC converter, and the hybrid energy storage system and the direct current of the traction power station are connected to a direct current bus to jointly provide energy for the tramcar. The method combines fuzzy control, fourier decomposition, correction and adjustment strategy based on the SOC and adjustment coefficient a, and improves the energy scheduling efficiency of the tramcar hybrid energy storage system. The energy scheduling strategy of the hybrid energy storage system is shown in fig. 6.
The invention relates to an energy scheduling method for a tramcar hybrid energy storage system, which comprises the following specific implementation steps:
(1) Collecting train running characteristics and calculating train traction load energy; and acquiring the train power and the charge state of the super capacitor which are monitored in real time, obtaining the charging multiplying power of the lithium battery energy storage unit through fuzzy control, and calculating the maximum charging and discharging power of the two energy storage units according to the charging multiplying power.
Regeneration system for determining train braking based on train running characteristicsEnergy E and consider train air loss E L Determining the traction load energy E of a train S The specific calculation process is as follows:
calculating the instantaneous value of the regenerative braking power P according to the regenerative braking characteristic curve equation and the formula P=Be·V of the train, then calculating the regenerative braking energy E by integration, and considering the air loss E of the train L Thereby obtaining the train traction load energy E S . Where Be is the regenerative braking force, V is the instantaneous speed; the regenerative braking characteristic is shown in fig. 2. Fig. 2 is a braking deceleration characteristic curve of a urban rail train, and the whole deceleration process can be known to comprise three areas: a constant torque zone, a constant power zone, and a natural characteristic zone. In order to fully utilize the regenerative braking force, the braking constant torque region is often extended to the characteristic region, i.e., point B is extended to point C. At present, deceleration is generally adopted for braking a vehicle in rail transit, and when the train brakes in a high-speed area, the regenerative braking is insufficient and air braking force is needed to be supplemented.
Natural characteristic region CD:
Figure BDA0002217647810000061
constant torque zone AC:
P AC ＝Be AC ·V＝C 1 ·V (2)
wherein C is 1 、C 2 Is constant.
Train slave primary speed V 0 The deceleration motion with certain deceleration can be known as follows:
V 2 -V 0 2 ＝2aS (3)
where V is the instantaneous velocity of the particle; v (V) 0 Is the initial velocity of the particle, and is generally constant (5 km/h); a is the acceleration of the particle; s is the displacement of the particles.
Figure BDA0002217647810000062
Figure BDA0002217647810000063
Figure BDA0002217647810000064
The regeneration energy generated by the train in each characteristic section of the regeneration braking curve is as follows:
Figure BDA0002217647810000065
Figure BDA0002217647810000066
wherein V is 1 The turning speed from a constant torque interval to a constant power interval; v (V) 2 Is the turning speed from the constant power interval to the characteristic interval; v (V) 3 Is the highest running speed of the train. a, a 1 Is the constant torque interval instantaneous deceleration; a, a 2 Is the constant torque interval instantaneous deceleration.
The total energy generated by the train during the braking time is:
Figure BDA0002217647810000067
wherein F is b Is the total braking force of the train and a' is the average braking deceleration.
Air brake energy consumption E of train in high-speed area L The method comprises the following steps:
Figure BDA0002217647810000068
train traction load energy E S The method comprises the following steps:
E S ＝E-E L (11)
power P to be monitored in real time d And supercapacitor state of charge SOC cap As a fuzzy functionAnd inputting the number, blurring the charge and discharge multiplying power, outputting a membership function of the multiplying power, and determining the maximum charge and discharge power of the two energy storage units. The specific calculation process is as follows:
considering the characteristics of high energy density of the lithium battery, high power density of the super capacitor, quick charge and discharge time and safety, the lithium battery adopts a 'head and tail station' charging mode, and the super capacitor adopts a 'station' charging mode. The 'first and last station' charging mode is to charge the train when the train arrives at the first and last station; the station charging mode means that the train is charged every time the train stops at the station. Thus, the train power P to be monitored in real time d And supercapacitor state of charge SOC cap As input to the blur function. Triggering corresponding power control according to different running states such as train traction, braking, parking charging and the like; when the energy storage device is in the charging mode, the energy storage system will no longer need to be controlled. Calculating the maximum charge and discharge power of the energy storage element comprises the following steps:
(21) According to the fuzzy control rule shown in FIG. 3, the charge-discharge multiplying power of the fuzzy energy storage system, the membership function of the output multiplying power, the root-type punishment coefficient f and the maximum multiplying power C are multiplied bat，max Obtaining the final multiplying power given C bat . The root type penalty coefficient f aims to adjust the amplitude of the battery multiplying power so as to realize the balance of an optimization model among energy storage system configuration, charging station capacity and battery system charging time, and the specific form is as follows:
Figure BDA0002217647810000071
in SOC bat (k)、SOC bat，max And SOC (System on chip) bat，min Respectively real-time value, minimum value and maximum value of the SOC of the lithium battery system; k is a discrete time series.
(22) Maximum charging power P of super capacitor in two modes of station charging and lithium battery charging cap，ch，max 、P bat，ch，max The expression is:
Figure BDA0002217647810000072
wherein V is cap，rate Rated voltage of super capacitor system, V cap，min For the lowest voltage in its operation, t uc For charging time, V bat，max Charging a lithium battery system to a voltage of a specified SOC, C bat Charging multiplying power for lithium battery system, Q bat Is the rated capacity of the lithium battery system.
(2) And according to typical characteristics of train start and stop, adopting a low-pass filtering algorithm to distribute energy of the hybrid energy storage system, wherein the distribution is a pre-distribution process and needs subsequent correction. The frequency cut-off point of the low-pass filtering algorithm is the frequency division point f obtained by Fourier decomposition hess . The specific distribution process is as follows:
(31) From equation (11), the train traction load energy E s ＝E-E L To minimize energy fluctuations in the traction plant, the load fluctuation energy at start-stop of the vehicle is preferentially provided by the hybrid energy storage system, i.e., the hybrid energy storage system capacity E hess The method comprises the following steps:
E hess ＝E S -E grid (14)
wherein E is grid The off-line prediction of energy for a traction power station is based on historical operating characteristics of the traction power station, which may be the energy provided by a rail train.
(32) The start and stop of the train are relatively typical, and the known load energy curve during the start and stop of the train is decomposed by using DFT and IDFT to obtain the frequency division point f of the lithium battery and the super capacitor hess As the frequency cut-off point of the low-pass filtering algorithm, the real-time fluctuation part E of the hybrid energy storage system is subjected to the low-pass filtering algorithm hess The energy is pre-distributed.
The pre-distribution of energy is performed according to the algorithm shown in fig. 4, and the time t can be deduced from fig. 4, and the energy E of the hybrid energy storage system is obtained hess Real time energy E with lithium battery bat_ref Is the relation of:
Figure BDA0002217647810000081
where Δt is the sampling time interval and τ is the time constant.
The real-time energy of the lithium battery can be obtained by converting the formula (15):
Figure BDA0002217647810000082
so super capacitor real-time energy E cap (t) is:
E cap_ref (t)＝E hess (t)-E bat_ref (t) (17)
it can be seen that E increases with increasing time constant τ bat_ref (t) near E bat_ref (T-DeltaT) the smoother the lithium battery energy curve, at the same time E cap_ref (t) will gradually increase. E when the time constant τ gradually decreases bat_ref (t) will approach E hess_ref The value of (t) will fluctuate more and more.
(4) The method comprises the steps of considering errors of real-time load and off-line compensation load energy, adopting an adjustment strategy based on SOC correction to carry out secondary correction on an energy distribution result, and combining an SOC state and an adjustment coefficient a to determine the energy of an energy storage element so as to prevent overcharge and overdischarge and ensure safe operation of the energy storage element.
The real-time load energy fluctuation is compensated by the energy storage system, and due to the difference between the real-time load energy fluctuation and the off-line load energy compensation, the off-line load energy deviation and the actual energy deviation of the energy storage system are continuously accumulated, so that the adjustment amount of the scheduling plan of the energy storage system is increased. To reduce this deviation, it is necessary to correct the amount of deviation caused by accumulation of real-time fluctuation of traction load energy according to the SOC of the energy storage system. The specific adjustment strategy is as follows:
(41) When the real-time charge state of the hybrid energy storage system is higher than the offline charge state of the hybrid energy storage system by more than 10%, the exceeding part of the super capacitor is born by the battery, and the exceeding part of the battery is born by the traction substation.
Figure BDA0002217647810000091
In SOC cap (t) is the real-time SOC state before the super capacitor adjusts the strategy, SOC CAP (t) is the SOC state of the super capacitor when offline, ΔE cap Is the secondary correction quantity of the super capacitor, E cap Pre-distributing energy for the super capacitor.
The secondary correction amount of the lithium battery is as follows:
Figure BDA0002217647810000092
in SOC bat (t) is the real-time SOC state before strategy adjustment of the lithium battery, SOC BAT (t) is the SOC state of the lithium battery when offline, ΔE bat Is the secondary correction amount of the lithium battery, E bat Pre-distributing energy for lithium batteries.
(42) When the real-time charge state of the hybrid energy storage system is lower than the offline charge state of the hybrid energy storage system by less than 10%, the lower part of the super capacitor is born by a battery, the lower part of the battery can be corrected by reducing the output power of the traction substation, and if the unabsorbed braking quantity occurs, the super capacitor can be consumed by a braking resistor.
Figure BDA0002217647810000093
Figure BDA0002217647810000094
(43) Because the capacity of the hybrid energy storage system is limited, the hybrid energy storage energy distribution obtained by directly utilizing the low-pass filtering and the SOC-based energy storage system control strategy is likely to lead to the overcharge and overdischarge of the battery or the super capacitor because the charge and discharge power of the battery and the super capacitor is larger than the rated charge and discharge power of the battery or the super capacitor or because the residual capacity of the battery or the super capacitor does not meet the charge and discharge power instructions of the battery and the super capacitor, and the service life of the energy storage system is reduced. In order to avoid overcharge and overdischarge of the battery and the super capacitor, secondary adjustment of the charge and discharge power of the hybrid energy storage system is required according to the SOC of the hybrid energy storage system and the maximum charge and discharge power of the hybrid energy storage system. The charge state of the energy storage system is divided into 5 sections averagely, and the charging and discharging energy of different sections is calculated by introducing an adjustment coefficient a in combination with the maximum charging and discharging power.
The distribution diagram of the state of charge of the energy storage system is shown in FIG. 5, and S1, S2, S3, S4 and S5 are five states of the SOC of the energy storage system min And SOC (System on chip) max For the upper and lower limits of the energy storage device SOC, the energy storage system SOC should be prohibited from out of limit when it reaches the upper and lower limits. SOC (State of Charge) high And SOC (System on chip) low An upper overcharge warning limit and a lower overdischarge warning limit. Taking a lithium battery as an example, an over-charge and over-discharge power protection strategy of the energy storage system based on the SOC is described as follows:
(a) The SOC state of the lithium battery is located in the S1 region. At this time, the charging and discharging energy of the lithium battery only needs to be satisfied within the maximum charging and discharging energy range, and no additional adjustment is needed, namely:
Figure BDA0002217647810000101
in E bat_ad And (t) is the energy of the lithium battery after adjustment. E (E) bat_ref And (t) is the energy before the lithium battery is regulated. E (E) bat_max And the maximum charge and discharge energy of the lithium battery is obtained.
(b) The SOC state of the lithium battery is located in the S2 region. At this time, the SOC of the lithium battery is in a low state, and if the maximum charge/discharge energy range is satisfied, the lithium battery may not be adjusted if the lithium battery is discharged, and if the lithium battery is charged, the charge/discharge energy needs to be adjusted, and the adjustment coefficient is a.
Figure BDA0002217647810000102
(c) The SOC state of the lithium battery is located in the S3 region. At this time, the SOC of the lithium battery is in a high state, and for the range satisfying the maximum charge and discharge energy, the charging of the lithium battery may not be adjusted, and if the lithium battery is discharged, the charge and discharge energy needs to be adjusted, and the adjustment coefficient is a.
Figure BDA0002217647810000103
(d) The SOC state of the lithium battery is located in the S4 region. The lithium battery is already in an overcharged state, and the lithium battery only allows discharging and does not allow charging.
Figure BDA0002217647810000104
(e) The SOC state of the lithium battery is located in the S5 region. The super capacitor is in an overdischarge state, and only charge and discharge are allowed by the super capacitor.
Figure BDA0002217647810000105
In the above-described overcharge and overdischarge power protection strategy, the value of the adjustment coefficient a is shown in fig. 7.
When the SOC state is high and the lithium battery is charged, if 0 < E bat_ref (t) < Det, the adjustment coefficient a=1, that is, the error is small, and the introduction of the adjustment coefficient is not needed; if Det < E bat_ref (t)＜E bat_max (t)-Det，a＝Det/E bat_ref (t); if E bat_max (t)-Det＜E bat_ref (t) < ++ infinity the process comprises, the adjustment coefficient of the lithium battery in the time period can only be a=det/(E) bat_max (t) -Det). Wherein, the liquid crystal display device comprises a liquid crystal display device,
when the SOC state is low and the lithium battery is discharged, if-Det < E bat_ref (t) < 0, the adjustment coefficient a=1, that is, the error is small, and the introduction of the adjustment coefficient is unnecessary; if-E bat_max (t)+Det＜E bat_ref (t)＜-Det，a＝Det/E bat_ref (t); if- +_infinity < E bat_ref (t)＜-E bat_max (t) +det, in which case the adjustment coefficient of the lithium battery can only be a=det/(E) bat_max (t) -Det). Det is the adjustment coefficient set value; lithium battery and super powerThe capacitors together form a hybrid energy storage system power and capacity distribution. And determining the energy of the lithium battery, and determining the super capacitor. Only lithium batteries are calculated here.

Claims (6)

1. The utility model provides a tramcar hybrid energy storage system energy scheduling method, is applied to tramcar energy recovery system, the system includes hybrid energy storage system, traction substation and the control module that is used for tramcar energy recovery, hybrid energy storage system includes parallelly connected lithium cell energy storage unit and super capacitor energy storage unit, control module carries out energy scheduling method, characterized in that, energy scheduling method includes the following steps:
(1) Collecting train running characteristics and calculating train traction load energy; collecting train power and supercapacitor charge state monitored in real time, obtaining the charging multiplying power of the lithium battery energy storage unit through fuzzy control, and calculating the maximum charging and discharging power of the two energy storage units according to the charging multiplying power; calculating the maximum charge and discharge power of the two energy storage units comprises the following steps:
(21) Collecting power P monitored in real time d And supercapacitor state of charge SOC cap The membership function of the output multiplying power is multiplied by the root punishment coefficient f through fuzzy control, and then multiplied by the maximum multiplying power C of the lithium battery bat，max Obtaining the charging multiplying power of the lithium battery unit
Figure QLYQS_1
Wherein, the root penalty coefficient f is:
Figure QLYQS_2
in SOC bat (k)、SOC bat，max And SOC (System on chip) bat，min Respectively a real-time value, a minimum value and a maximum value of the charge state of the lithium battery unit; k is a discrete time series;
(22) Maximum charging power P of super capacitor and lithium battery cap，ch，max 、P bat，ch，max Expressions respectivelyThe method comprises the following steps:
Figure QLYQS_3
wherein V is cap，rate Rated voltage of super capacitor system, V cap，min For the lowest voltage in its operation, t uc For charging time, V bat，max The lithium battery cell is charged to a voltage at a specified state of charge,
Figure QLYQS_4
charge rate for lithium battery cell, Q bat Rated capacity for lithium battery cells;
(2) Calculating the capacity of a hybrid energy storage system according to the train traction load energy obtained in the step (1), and calculating the pre-allocation energy of each of two energy storage units in the hybrid energy storage system by adopting a low-pass filtering algorithm according to a load energy curve of the start and stop of the train; the pre-allocation energy of each of two energy storage units in the hybrid energy storage system is calculated by adopting a low-pass filtering algorithm, and the method comprises the following steps:
(31) The load fluctuation energy during the start-stop of the vehicle is preferentially provided by the hybrid energy storage system, namely the capacity E of the hybrid energy storage system hess The method comprises the following steps:
E hess ＝E S -E grid
wherein E is grid The method comprises the steps of predicting energy offline for a traction power station, and predicting historical operating characteristics of the traction power station;
(32) According to the load energy curve of the start and stop of the train, adopting a low-pass filtering algorithm to control the capacity E of the hybrid energy storage system hess The pre-distribution of the energy is carried out,
pre-allocation energy E of lithium battery at t moment bat_ref (t) is:
Figure QLYQS_5
wherein, delta T is a sampling time interval, and tau is a time constant;
super capacitor pre-distribution energyE cap (t) is:
E cap (t)＝E hess (t)-E bat_ref (t)；
(3) Performing secondary correction on the pre-allocated energy of the two energy storage units based on the respective real-time charge states of the two energy storage units, taking the maximum charge and discharge energy and the maximum charge and discharge power obtained in the step (1) as constraint conditions, multiplying the energy value of the secondary correction by an adjustment coefficient, and determining the energy allocation of the two energy storage units; the method specifically comprises the following steps:
(41) When the real-time charge state of the energy storage unit is higher than the offline charge state threshold value of the energy storage unit, the part of the super capacitor exceeding the threshold value is born by a battery, and the part of the super capacitor exceeding the threshold value is born by a traction transformer substation;
(42) When the real-time state of charge of the energy storage unit is lower than the offline state of charge threshold of the energy storage unit, the lower threshold part of the super capacitor is born by a battery, the lower threshold part of the battery is corrected by reducing the output power of the traction substation, and if the unabsorbed braking amount occurs, the super capacitor is consumed by a braking resistor;
(43) The charge state of the energy storage unit is divided into 5 sections which are respectively over-discharge, low-charge, normal, high-charge and over-charge, when the energy storage unit is in the low-charge state and is discharged or in the high-charge state and is charged, an adjustment coefficient a is introduced, and the energy storage unit adjustment energy E is calculated ad (t)＝aE ref (t) wherein E ref For regulating the energy of the energy storage unit before.
2. The tramcar hybrid energy storage system energy scheduling method of claim 1, wherein: train traction load energy E described in step (1) S The calculation formula is as follows:
E S ＝E-E L
wherein, the regenerative braking energy E is:
Figure QLYQS_6
f in the formula b Is a train mainBraking force, a', is the average braking deceleration, V is the train speed, V 3 Is the highest running speed of the train;
train air loss E L The method comprises the following steps:
Figure QLYQS_7
v in 2 Is the turning speed from the constant power interval to the characteristic interval, a 2 Is the instantaneous deceleration of a constant torque interval, C 2 Is constant.
3. The tramcar hybrid energy storage system energy scheduling method of claim 1, wherein: calculating the maximum charge and discharge power of the two energy storage units in the step (1), wherein a lithium battery adopts a first station and a last station charging mode during calculation, and a supercapacitor adopts a station charging mode, and the first station and the last station charging mode are used for charging a train when the train arrives at a first station and a last station; the station charging mode means that the trains are charged at each stop.
4. The tramcar hybrid energy storage system energy scheduling method of claim 1, wherein: the threshold value in the step (41) takes 10 percent, and when the real-time charge state of the energy storage unit is higher than the offline charge state of the energy storage unit by more than 10 percent, the secondary correction amount of the super capacitor is;
Figure QLYQS_8
in SOC cap (t) is the real-time state of charge, SOC, before the super-capacitor adjusts the strategy CAP (t) is the charge state of the super capacitor when offline, E cap Pre-distributing energy for the super capacitor; Δt is the sampling time interval;
the secondary correction amount of the lithium battery is as follows:
Figure QLYQS_9
in SOC bat (t) real-time state of charge, SOC, before strategy adjustment for lithium batteries BAT (t) is the state of charge of the lithium battery when offline, E bat Pre-distributing energy for lithium batteries.
5. The tramcar hybrid energy storage system energy scheduling method of claim 1, wherein: the threshold value in the step (42) takes 10 percent, and when the real-time charge state of the energy storage unit is lower than the offline charge state of the energy storage unit by less than 10 percent, the secondary correction amounts of the super capacitor and the lithium battery are respectively;
Figure QLYQS_10
Figure QLYQS_11
in SOC cap (t) is the real-time state of charge, SOC, before the super-capacitor adjusts the strategy CAP (t) is the charge state of the super capacitor when offline, E cap Pre-distributing energy for the super capacitor; SOC (State of Charge) bat (t) real-time state of charge, SOC, before strategy adjustment for lithium batteries BAT (t) is the state of charge of the lithium battery when offline, E bat Pre-distributing energy for lithium batteries.
6. The tramcar hybrid energy storage system energy scheduling method of claim 1, wherein: the adjustment coefficient a in the step (43) has the following value:
if 0 < E bat_ref (t) < Det, then the adjustment factor a=1; if Det < E bat_ref (t)＜E bat_max (t)-Det，a＝Det/E bat_ref (t); if E bat_max (t)-Det＜E bat_ref (t) < ++ infinity the process comprises, at this time, the adjustment coefficient takes a=det/(E) bat_max (t)-Det)；
If it is-Det＜E bat_ref (t) < 0, then the adjustment factor a=1; if-E bat_max (t)+Det＜E bat_ref (t)＜-Det，a＝Det/E bAt_ref (t); if- +_infinity < E bat_ref (t)＜-E bat_max (t) +det, where the adjustment factor takes a=det/(E) bat_max (t)-Det)；
Wherein Det is a set value, E bat_max Maximum charge and discharge energy of lithium battery E bat_ref And the energy is real-time energy of the lithium battery.
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