A strategy for utilization of regenerative energy in urban railway system by application of smart train scheduling and wayside energy storage system

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1 Available online at ScienceDirect Energy Procedia 138 (2017) International Conference on Alternative Energy in Developing Countries and Emerging Economies 2017 AEDCEE, May 2017, Bangkok, Thailand A strategy for utilization of regenerative energy in urban railway system by application of smart train scheduling and wayside energy storage system Warayut Kampeerawat a, *, Takafumi Koseki a a Department of Electrical Engineering and Information Systems, School of Engineering,The University of Tokyo, Tokyo, , Japan Abstract Nowadays, electric railway system is known as an efficient and environmentally friendly solution for transportation system. Many developing countries have been expanding their railway systems to support the growth of transport demand. Even though railway system can utilize energy efficiently, its operation still requires bulk of energy. Therefore, management of multiple energy sources is an important issue for railway planning and operation. The advent of modern railway technologies provides regenerative braking system which can recover considerable energy from brake operation. Management of regenerative energy becomes a key for enhancing railway operation. This paper presents a strategy for utilizing regenerative energy in urban railway system by adjusting train operating schedule and employing wayside energy storage system. Optimizing train schedule aims to increase the degree of regenerative energy usage and applying super capacitor as temporary energy storage will manage the surplus of regenerative energy. By integrating appropriate train scheduling and energy storage system, energy management can be more flexible and effective leading to improvement of energy-saving operation. The numerical case study of Bangkok Rapid Transit System was performed and evaluated to reflect the improvement of utilizing regenerative energy The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 2017 International Conference on Alternative Energy in Developing Countries and Emerging Economies. Keywords: Regenerative energy; energy storage system; urban railway system. * Corresponding author. Tel.: ; address: kampeeyut@gmail.com The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 2017 International Conference on Alternative Energy in Developing Countries and Emerging Economies /j.egypro

2 796 Warayut Kampeerawat et al. / Energy Procedia 138 (2017) Introduction 1.1 Railway Energy Management and Regenerative Energy Utilization Electric railway system is not only great solution for solving traffic problems but also efficient mass transportation system in the view of energy utilization. Due to the bulk energy amount required by railway system, various energy sources are needed to support the system operation and appropriate energy management becomes the key for effective planning and operation. Nowadays, regenerative braking system is commonly applied for modern electric rail vehicles. When the regenerative braking system is operated, a traction motor will temporarily turn into a generator which generates amount of energy and then feeds the energy back into transmission system. The advent of modern railway technologies provides regenerative braking system which can recover considerable energy from brake operation, for example some urban railway system can recover more than 30% of the energy consumed by traction system [1]. The regenerative energy can be effectively managed by the following ways, Powering vehicles nearby by designing timetable for interchanging energy among running trains. Storing and recycling by application of Energy Storage System (ESS) Feeding into utility grid through a reversible power substation. Basically, the regenerative energy is supposed to be used by train itself and nearby trains as first priority. When the amount of energy exceeds the capability of the system, the excess of energy will be turned into wasted heat or the regenerative system will be cancelled. Due to the advancement of power electronics and energy storage technologies, integrating ESS into railway system becomes an interesting option to manage regenerated braking energy effectively. Regenerative energy can be managed efficiently by Wayside ESS or Onboard ESS. With trackside energy storage system, ESSs collect the regenerative energy that cannot be instantaneously consumed by the system, and deliver it back to the line when the energy demand is needed, e.g. a vehicle is accelerating in its operating section. The stored energy in ESSs can contribute to shaving power peaks during acceleration of vehicles, and it may be used as temporary supplies for vehicles in case of power grid failure. Therefore, such kinds of systems cannot only provide the efficient management of energy consumption but also enhance the ability to stabilize the network.[2] 1.2 Integrated Approach for effective use of regenerative energy There are many researches focusing on effective use of regenerative energy by applying the design of timetable, speed profile, energy storage system, or integrating multiple methods as an integrated optimization problem. Due to the complexity of integrating various factors and parameters into the problem, some metaheuristic methods, e.g. Genetic Algorithm, are applied for solving the problem. An optimal design of speed profiles with consideration of regenerative energy recovery was proposed by [3]. To manage regenerative braking energy, the use of onboard ESS was considered with design of energy-saving speed profile. There are some evaluations of recovery energy by various methods, but wayside ESS and timetable optimization were not mentioned by this work. A two layer optimization including timetable and driving strategy was presented in [4]. The running times of each train are adjusted to minimize energy consumption based on the idea of synchronizing power-time profile by using simple estimation of energy. Moreover, an integrated optimization of driving pattern and timetable was proposed by [5]. They consider optimizing timetable and speed profile in the same problem. However, wayside ESS has not been considered as integrated optimization with other methods. In this paper, the wayside ESS will be considered with the design of smart timetable to improve the utilization of regenerative energy. ESS location and size are included as a part of timetable optimization, and evaluation of network receptivity and energy-saving rate also perform as indicators for measuring achievement of the integrated approach. To express the ability of application on practical railway system, some numerical case studies based on Bangkok Rapid Transit System were performed and evaluated to explain how the proposed method can improvement of utilizing regenerative energy and energy-saving operation.

3 Warayut Kampeerawat et al. / Energy Procedia 138 (2017) Proposed Method This proposed method aims to design timetable and ESS by minimizing energy supplied from substation and capacity of ESS. To maximize regenerative energy, synchronizing between some powering trains and decelerating trains can be achieved by adjusting timetable parameters, e.g. running time and dwell time. Additionally, some amount of braking energy can be recycled effectively by application of ESS. Therefore, an optimization problem with multi-objective function can be expressed by equation (1). min T + E (1) f ( r, T d, Less, Ness ) = w 1E ˆsup w2 where T = T, T,..., T ] = set of dwell time, T = T, T,..., T ] = set of running time d [ d 1 d 2 dn Ê sup = Estimated total energy supplied from substations (kwh) r ess [ r, 1 2 r, 2 3 r, ( n 1 ) n E ess = Total energy capacity of energy storage system (kwh) = Ness L ess = Location for installing ESS, ess Constraints: limit, etc.) Headway limit: x (kwh per module) N = Number of energy storage module, w 1, w 2 = weighting factor Train movement constraint (tractive/ braking effort, motor efficiency, gradient, curvature, speed Running time limit: T T T, Dwell time limit: Tdj, min Tdj Tdj, max h, min h h, max T T T, Trip time limit: Ttrip, min Ttrip Ttrip, max r, i j,min r, i j r, i j, max ESS limitation and control parameters: SOCdis SOC dis, min, Vess, dis Vess, dis, min, Vess, char Vess, char, min Due to the complicated conditions included in the constraints, Genetic Algorithm is selected for solving the problem. The algorithm is shown in fig.1 Fig.1 The flowchart of proposed method for solving the problem by Genetic Algorithm Generally, train movement calculation integrated with power flow calculation entails the large calculation time. To simplify the calculation, power flow section will be performed separately by negligible effect of voltage to train performance. Moreover, train speed profiles and power profiles will be generated by modification of predefined profiles based on an all-out operating mode. The all-out mode is the driving mode controlling a train to a destination with the shortest running time. The power profile from previous process will be used by Power flow calculation implemented based on algorithm proposed by [6]. The power flow results can contribute to estimating regenerative energy, charging and discharging energy of ESS, State of charge (SOC), and Energy supplied by substations. The charging and discharging energy of ESS is estimated based on the control strategy mentioned in [7].

4 798 Warayut Kampeerawat et al. / Energy Procedia 138 (2017) Numerical Case Studies and Results The railway system for performing case studies is the Bangkok Rapid Transit System, elevated urban electric railway operated in Bangkok, Thailand. There are 13 passenger stations along 13-km-long double track and 7 traction substations shown in fig.2(a). The system parameters for calculation were shown in table 1. Speed limit (km/h) Altitude(m) -2 0 W01 S01 S03 S06 S08 S10 S12* Fig.2 (a) BTS silom line map (b) Gradient profile and speed limit (South bound) Table 1 Basic System Parameters of BTS silom line Parameter Specific data Parameter Specific data Vehicle name BTS CNR Changchun EMU Formation Tc-M-M-Tc Length / Tare Weight m / 153 ton Max.payload (AW4) 85 ton Nominal voltage 750 V Max/Min regenerative voltage 500 V / 900 V Max. speed 80 km/h Max. acceleration /deceleration 0.87 m/s 2 / 1.00 m/s 2 gear, motor, inverter 98%,88%,98% Regenerative brake 70% Max. Auxiliaries constant load 270 kw Train resistance coefficient A=4025, B=118.67, C=0.871 Third-rail resistance 8.23 m Ω /km Running rail resistance m Ω /km 3.1 Case 1: Preliminary evaluation of regenerative energy usage Before designing timetable and ESS, the evaluation of regenerative energy and supplied energy was performed for different traffic condition. Assuming that trains are operating on single track in south bound direction using 5% - time reserve mode with simple coasting control, i.e. a train is controlled by applying maximum effort and using coasting control to keep its running time at 1.05 times of shortest running time. The traffic condition and the number of train operated on the route in the same time (N T,same) were shown in table 2. By employing power flow calculation, relevant energy quantities were estimated in a period of single trip time, i.e. 24 minutes. The network receptivity (E recp) and energy saving (E save) are defined as follows. Ereg ( Esup, case i Esup, base ) % E recp = 100 and % E save = 100 (2) E E brake sup, base Where E reg = Brake energy that can be recovered as regenerative energy (kwh), E brake = Total energy dissipated in brake operation (kwh), E sup,base = Total energy supplied by all substations in case of nominal operation without utilization of regenerative energy (kwh), E sup,case i = Total energy supplied by all substations of case no.i (kwh) As shown in table 2, regenerative energy can be utilized with the highest network receptivity of 68.27% and energy saving of 15.74% in the period of high traffic volume. Utilization of regenerative energy decreases when traffic volume decreases, because the possibility of utilizing regenerative energy relies on the number of train operating in the same time. Table 2 Estimated energy for different traffic volume condition when trains were operated in 5%Time reserve mode Traffic Headway Dwell Payload E N sup,base E brake E sup,case 1 E reg volume (sec) time (tons) T,same (kwh) (kwh) (kwh) (kwh) %E recp %E save High Moderate Low

5 Warayut Kampeerawat et al. / Energy Procedia 138 (2017) Case 2: Effect of installing wayside ESS to regenerative energy receptivity and energy saving Due to the substantial cost of ESS, ESS is not considered as the first priority in managing regenerative energy for general railway system. However, selection of suitable site and size can provide lower cost realizing more active use of ESS. In this case, assuming the operator has a plan to install ESS at one location, we performed a simple sensitivity analysis by varying installing location, capacity, and traffic volume in order to observe the best scenario for employing ESS. To determine the variation of variable, installing location for ESS was specified as the existing substation (TS1-TS7) located and capacity of ESS considered as the number of module to be installed. The network receptivity and energy saving were used to decide the optimal solution for ESS. The specification of ESS was selected as super capacitor with energy capacity of 1 kwh and maximum power of 650 kw per module [8]. From the analysis, the most suitable scenario for each traffic volume was concluded in table 3. Table 3 The best scenarios for installing ESS at a substation with 5%-time reserve mode Traffic ESS ESS capacity E sup,case 2 E reg E brake condition location module kwh kw (kwh) (kwh) (kwh) %E recp %E save High TS Moderate TS Low TS Case 3: Design of timetable and ESS by using the proposed integrated approach In this case, timetable and ESS will be designed for achieving energy-saving operation by using proposed method. For design of timetable, running times from S06 to S12, and dwell times at each station from S06 to S11 are considered as variables because this section has single speed limit that can reduce the complexity for adjusting speed profile in solving process. For design of ESS, location and capacity of ESS are considered in the same way as those of case 2. Therefore, there are 14 variables for optimization problem. The constraints and range for variable are determined as in Table 4. The feasible ranges of timetable parameter for each traffic volume will be different due to relevant traffic condition. For solving the solution, parameters of GA are determined as follows. GA parameters: Crossover probability = 0.8, Mutation probability = 0.2, Population size = 14x100, Number of generation = 500, Stall generation limit = 30 Table 4 Range of variable for designing timetable and ESS Timetable parameters ESS Running time limit Traffic Dwell Dwell limit Running time ( Tr, i ) j Tr, i j,min Volume & Tr, i j, max Trip time Location Ness T d, nom ( T d, nom ± 5 ) W1-CEN-S01-S02-S03-S05-S06 S06-S07-S08-S09-S10-S11-S12 T trip, base High Min Max Moderate Min Max TS1 TS Low Min Max The proposed method provides the solution for improving energy-saving operation as shown in Table 5. When both designing timetable and ESS are applied, in case of high traffic volume, the overall network receptivity and energy saving increase up to 76.4% and 17.76% respectively. In case of high traffic volume, the energy can be utilized efficiently in nominal operation (case 1) leading to less surplus regenerative energy. When the integrated design is applied, the improvement of energy saving is obtained by only 2%. In case of moderate and low traffic volume, the energy saving of nominal operation (case 1) drastically decreases. When the integrated design is applied, the energy saving is improved by 3.6 %.

6 800 Warayut Kampeerawat et al. / Energy Procedia 138 (2017) Table 5 Results of designing timetable and ESS Traffic Optimal Timetable Trip ESS scenarios condition Dwell time Running time time location module S06 S07 S08 S9 S10 S11 S06 S07 S08 S09 S10 S11 %E recp %E save S07 S08 S09 S10 S11 S12 High Moderate Low Conclusions and Discussions This paper proposes a strategy for utilizing regenerative energy in urban railway system by integrating train scheduling and active use of wayside energy storage system. Basically, optimizing train schedule is sensitive to traffic condition. By integrating a suitable scenario of ESS, energy management is supposed to be more stable and effective in various traffic conditions. The Bangkok Rapid Transit System was selected as a practical example of application of timetable and ESS to improve energy-saving operation. Numerical case studies were performed to express the effect of ESS and adjusting timetable to network receptivity of regenerative energy and energy saving rate. From preliminary estimation of regenerative energy, regenerative energy can be used effectively for high traffic condition. However, network receptivity drastically decreases in moderate and low traffic condition. The application of adjusting timetable and installing ESS are supposed to improve energy-saving operation. By performing sensitivity analysis, the suitable location and capacity for installing an ESS can be decided. Finally, the integrated design approach provides examples of smart timetable and suitable ESS capacity to achieve energy-saving operation in difference traffic scenarios. When the proposed integrated design is applied, the energy saving can be improved up to 3.6 % compared with nominal operation without ESS. For the future works, distribution of ESS along the route will be considered. Furthermore, design of driving strategy in complicated speed limit section will be included. However, application of ESS deals with substantial investment cost. The investment return period of installing ESS should be evaluated to express the feasible condition for making decision. References [1] Yang, Xin, et al. "A survey on energy-efficient train operation for urban rail transit." IEEE Transactions on Intelligent Transportation Systems 17.1 (2016): [2] Barrero, Ricardo, Xavier Tackoen, and Joeri Van Mierlo. "Stationary or onboard energy storage systems for energy consumption reduction in a metro network." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit (2010): [3] Domínguez, María, et al. "Energy savings in metropolitan railway substations through regenerative energy recovery and optimal design of ATO speed profiles." IEEE Transactions on automation science and engineering 9.3 (2012): [4] Cao, Fang, et al. "A two-layer construction of energy optimization approach for timetable." Intelligent Rail Transportation (ICIRT), 2016 IEEE International Conference on. IEEE, [5] Zhao, Ning, et al. "An integrated metro operation optimization to minimize energy consumption." Transportation Research Part C: Emerging Technologies 75 (2017): [6] Cai, Y., M. R. Irving, and S. H. Case. "Iterative techniques for the solution of complex DC-rail-traction systems including regenerative braking." IEE Proceedings-Generation, Transmission and Distribution (1995): [7] Zongyu, G. A. O., et al. "Control strategy for wayside supercapacitor energy storage system in railway transit network." Journal of Modern Power Systems and Clean Energy 2.2 (2014): [8]