HESS in DC Rail Transit System: Optimal Sizing and ...

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HESS in DC Rail Transit System: Optimal Sizing and System Design Oindrilla Dutta, Student Member, IEEE, Mahmoud Saleh, Student Member, IEEE, and Ahmed Mohamed, Senior Member, IEEE

Abstract—This

paper presents a methodology for optimizing the size of energy storage system (ESS) for maximizing the capture of energy regenerated by a train. Besides, minimization of energy consumption from the substation, in order to obtain a return on investment, has also been explored. A standard structure of rail transit system has been used here for analysis. The optimization process takes into account the limitations of the transit system, such as catenary (third-rail voltage), and limitations in battery chemistry. The optimum size has been obtained using Genetic Algorithm and the results have been validated by developing a control strategy in MATLAB Simulink. Index Terms—Battery; C-rate; Genetic Algorithm; hybrid energy storage system; optimization; regenerative energy; supercapacitor; transit system.

I. INTRODUCTION

R

braking of trains, light rails, and electric vehicles is a well-researched field presently. In the wake of green energy technology and a demand to reduce carbon emission, vehicle technology has received widespread attention. Alongside the improvement in vehicle design and optimized scheduling of trains, there is also progress in the design and chemistry of hybrid energy storage systems (HESS). HESS may comprise of battery, supercapacitor (SC), fuel cells (FC), and flywheels. A combination of these storage technologies is used depending on the application. Thus, finding the sizes of the different types of HESS, to be combined for an application, is an optimization problem. The state-of-the-art topologies for energy storage, comprising battery, SC, and FC, have been studied for Hybrid Electric Vehicle (HEV) and plug-in HEV applications in [1]. Another research shows an improvement in efficiency of Auxiliary Energy System for application in Electric Vehicle using different control strategies. The system designed here, consists of SC bank in combination with lead-acid battery [2]. Alongside on-board energy storage systems, stationary storage technologies have also been thoroughly studied. In [3], a sodium sulphide battery system for Long Island, a SC system for Madrid de Metro, and a flywheel for London Underground have been reviewed for effectiveness and return on investment. There has also been a thorough exploration of battery chemistry and monitoring techniques, to make their EGENERATIVE

applications in automotive systems more controllable [4, 5, 6]. The minimization of cost of energy obtained from the utility by installing an optimum size of stationary storage systems have been thoroughly researched in [7, 8]. However, to the best of our knowledge, there is a lack of study, which includes both the transit system constraints along with limitations in battery chemistry in order to achieve optimization. In this paper, a HESS, consisting of battery and SC, has been optimally sized considering the stabilization of catenary voltage and limitation of battery technology. The constraints on battery technology that have been considered here are minimum/maximum voltages of battery to avoid over charge/discharge, C-rate of charging/discharging, and depth of discharge. The paper has been arranged as follows. Section II illustrates the system under study, followed by Section III, which sheds light on the optimization methodology. Section IV shows the control strategy used in simulation. The results are discussed in Section V followed by conclusion in Section VI. II. ELECTRIC RAIL TRANSIT SYSTEM A DC traction system topology has been developed based on the typical systems implemented worldwide, such as New York City Transit. The topology contains two substations, with a train moving between three passenger stations as demonstrated in Fig. 1. Two energy storage systems, each comprising batteries and super-capacitors, has been connected at the terminals of substations A and B through DC/DC bidirectional converters to harness the regenerative energy. Substations A and B encompass two step-down star-delta and delta-delta transformers, each connected to a capacitor to maintain voltage stability on the AC side of the substations during any transients, followed by two rectifier stations. System parameters can be found in [9]. The variables related to the train motion and vehicle dynamics that has been used here can be found in [10, 11]. The train has been modeled as a current source that represents the current demanded/regenerated by eleven cars of the train. The current corresponding to one complete speed cycle (i.e. during acceleration, coasting and deceleration) has been simplified as shown in Fig. 4, which approximately matches the current profiles in [12].

2

Storage Management System1

Storage Management System2

Bidirectional1

IGBT1 C 2

IGBT2

R L1

Bidirectional2

IGBT3

Battery1

C 1

C 3

IGBT4

R2 L2 C 4

Battery 2

Supercapacitor Unit 2

Supercapacitor Unit 1

Passenger Station 1

Passenger Station 2

Vdc1

Vdc2

Rectifier Substation1

Passenger Station 3 Rectifier Substation2

Current profile of the train

Chopper Fig. 1. Electric Rail Transit System with Energy Storage System (ESS) understudy.

Regenerative braking in a traction system takes place when the train starts decelerating. The kinetic energy of the motor is sent back to the supply in case of reversible substations, or stored in wayside energy storage systems. This energy can be captured using various storage techniques, such as super capacitors and/or batteries. The wayside energy storage devices require bidirectional DC-DC interface to control their charging and discharging processes. The DC/DC converters topology that operates between two fixed voltages, the third rail voltage and the energy storage system voltage, has been adopted from [13]. However, the values of the capacitance and inductances were altered to withstand higher voltage and current. The values used to design the DC/DC converter are shown in Table I. III. OPTIMIZATION OF SIZE OF HESS The capacity of HESS, for installation in a Rail Transit System, is influenced by various factors. These factors can be primarily categorized as two sets. The first set consists of the TABLE I PARAMETER VALUES FOR BIDIRECTIONAL

Parameters

Description

Values

Vin

Substation voltage Battery voltage

645 V

Average duty cycle Voltage ripple

0.44

V0 D (V0/ (V0+ Vin)) Vr f

500 V

0.05 %

L1

Switching frequency Inductor

5 kHz ≥ 1.01e-4 H

C1

Capacitor

≥ 5.4e-4 F

limiting parameters in the railway system, such as, the minimum and maximum values of the third rail voltage, which is system specific. For instance, the New York City Transit has a minimum and maximum third rail voltage limit of about 580 V and 720 V, respectively [14]. The second set constitutes the elements that limit the State of Health (SoH) of the battery and SC. The SoH of a battery, for automotive applications, are determined by the following parameters: a. Ambient temperature and temperature of the battery unit b. Discharging rate c. Charging rate d. Depth of Discharge (DoD) e. Minimum/maximum allowable battery voltage f. Time intervals between full charge cycles These aforementioned parameters are responsible for significant degradation and consequently ageing of the battery [4, 14]. In this paper, an attempt has been made to include the parameters b, c, d, and e in the equations for finding an optimal size of HESS. Besides, the limiting parameters specific to the transit system have also been taken into consideration. The aim and scope of the optimization methodology used in this paper have been elaborated in the subsequent discussions. A. Maximization of the capture of Regenerative Energy The cost of installation of HESS will be reasonable only if the capture of regenerative energy can be maximized. Table II and III demonstrate the constants and variables used in the optimization process, respectively. It has been claimed that, a battery attains minimum degradation if its state of charge (SOC) remains between 30% and 80% of its capacity during charging/discharging [6]. Moreover, for a NiMH battery, the

3 voltage of the battery shares a nearly linear relationship with SOC when the latter varies from 30% to 80% of the full capacity. The voltage sharply drops/rises beyond these lower/upper limits of SOC, resulting in overcharge/overdischarge [6]. Considering these aforementioned constraints, the amount of power (in kW), which can be captured by a battery and SC, is expressed in (1). It is to be noted that, unlike batteries, the SCs are absent of multiple degradation factors. The only parameter that limits its performance is the charging/discharging efficiency. A

kW 10-3V + c reg b, max n 3600n b, ch arging c 0, i.e the train is decelerating, the HESS gets charged from the energy regenerated by the train. The storage devices start charging at a rate determined by the magnitude of current supplied by the train. According to this magnitude the duty cycle of IGBT1 in Fig. 1 is controlled. However, the SC

Symbol

Description

Iref & Vref 𝑡 𝐵𝑆𝑂𝐶

Current and Voltage reference State of charge of battery at time t

𝑡 𝐴ℎ𝑏𝑎𝑡𝑡𝑒𝑟𝑦

Capacity (Ah) of the battery at time t

𝑉𝐵 𝑉𝐵𝑚𝑖𝑛 & 𝑉𝐵𝑚𝑖𝑛 𝑁𝐵 𝑈𝑈 & 𝑈𝐿 𝐵𝐷𝑅 & 𝐵𝐶𝑅 𝐵 𝜀𝐷𝑂𝐷 nDOD

Open circuit voltage of the battery Minimum & maximum permissible battery voltage respectively Number of battery cycles Predefined limits for rate of change of current Maximum rate of discharge and charge of the battery respectively Depth of discharge of the battery Depth of discharge of battery (%)

can take up a higher rate of change of current, but the charging of the battery is restricted by its chemistry. Thus a predefined limit for the rate of change of current has been determined. If the rate of change of current is greater than this limit, then the SC gets charged, otherwise the battery gets charged. There is also a lower limit for the rate of change of current, below which neither the battery nor the SC gets charged. Besides these, there are some other constraints, which are checked before charging the battery. These are: a. The present SOC of the battery should be less than 80%. b. The open circuit voltage of the battery should be less than the maximum allowable voltage. c. The number of cycle of the battery should be less than the maximum allowable cycles per day. The battery starts charging only if the above-mentioned conditions are satisfied. Besides, although the current supplied by the train determines the rate of charge of the battery, the maximum rate of charge is limited by a predefined permissible value. The chopper starts operating when the third rail voltage

Fig. 2. Flowchart for the control of ESS operation.

5 reaches its maximum permissible value of 720 V. V. RESULTS AND DISCUSSION The results have been obtained in two steps. In the first step, the optimization has been performed to obtain the sizes of battery and SC. In the second step, the results obtained from step 1 have been simulated in MATLAB using the control technique explained in Section IV. A. Result of Optimization The equations mentioned in Section III have been optimized using Genetic Algorithm. The values of the constants used are shown in Table V. The capacity of battery and SC obtained after 200 iterations are 80 Ah and 153 kW, respectively. It can be inferred from (1-3) that the size is a function of the nominal voltage of the HESS. Also, the bidirectional cost and switching losses are sensitive to this voltage. Thus, the opencircuit voltage of the battery and SC is itself an optimization parameter. It is beyond the scope of this paper to explore this feature. Simulation Results The simulation results for the sizes obtained in subsection A are illustrated here. The simulation is performed for a scenario where a train is acceleration from in a passenger station close to Substation1 and decelerating into another passenger station close to Substation 2. Fig. 3 illustrates the power curve of the train for a current profile as shown in Fig. 4. Fig. 5 exhibits the current drawn from the substation and the third rail voltage. As can be seen from the voltage curve in Fig. 5 that the maximum voltage is around 730 V and the minimum is around 600 V. Thus the third rail voltage limit has not been violated. Fig. 6 demonstrates the power being drawn from the two substations. It can be observed by comparing Figs. 3 and 6, that the difference between the peak power demand by train

Fig. 3. Power curve of the train.

B.

Fig. 4. Current profile of a train with eleven cars.

TABLE V CONSTANTS AND DESCRIPTION

Symbol

Values

C1 C2

15.3 $/kW 13.45 $/kW

Cutility

124.6 $/kWh

Cb,kwh

275.50 $/kWh

Csc,kwh

3112.31 kWh

nc nb,charging nb,dis treg Vb,max Vth,max ipeak Crate,charg

99.5 % 98.5 % 99 25.61 s 583.3 V 720 V 213 A 1C

Crate,dis Vb,nom nDOD ncycle

0.6C 500 V 70% 1500

Fig. 5. Substation current and third rail voltage curve.

6 This work can be elaborated by studying a system for 24 hours with actual train scheduling. Besides, an improvement in the percentage of utilization of the SC can be attempted. Also, sensitivity of the system to the nominal voltage of the battery and SC can be explored. REFERENCES [1]

[2]

Fig. 7. Change in SOC of battery. [3]

[4]

[5]

[6]

Fig. 8. Change in SOC of SC.

and that supplied by the two substation is around 1000 kW. The rest of the power is provided by the HESS. Thus the peak power saving in one cycle is around 1000 kW. The cost of installation of HESS becomes profitable after nearly 1425 such cycles. Besides, Fig. 7 shows the change in SOC of the battery that varies from 55% to 80%. Thus the SOC of the battery, which was limited between 30% to 80%, has not been violated. However, Fig. 8 shows that the change in SOC of the supercapacitor is between 55% to 100%. As there are no limits on the SOC of the SC, this utilization should be improved by modifying the control. Moreover, a further utilization of the SC will also improve the capture of regenerative energy by the storage system. Presently, a portion of the regenerated energy is getting dumped in the chopper as heat (I2R) loss in the chopper resistance. Hence only a part of it is getting stored in the battery and SC. Although the battery is getting utilized up to a limit of 80%, but the SC remains unutilized by around 30%.

[7]

[8]

[9]

[10] [11]

[12] [13] [14]

VI. CONCLUSION AND FUTURE WORK This research work has successfully quantized the size of battery and SC, which is required to be installed in a DC Traction system. The Ah of the battery and the kW of the capacitor, obtained using Genetic Algorithm, is able to provide a peak power shaving of 33% for one cycle of the train movement. The cost of installation of the HESS becomes reasonable after 1425 similar cycles.

[15]

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