Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables
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energies Article Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line Jiang Liu 1,*, Tian-tian Li 2, Bai-gen Cai 3 and Jiao Zhang 4 1 School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China 2 Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation, Beijing 100044, China; [email protected] 3 School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China; [email protected] 4 Vehicle and Equipment Technology Department, Beijing Subway Operation Technology Centre, Beijing 100044, China; [email protected] * Correspondence: [email protected] Received: 14 February 2020; Accepted: 20 April 2020; Published: 24 April 2020 Abstract: Energy conservation is attracting more attention to achieve a reduced lifecycle system cost level while enabling environmentally friendly characteristics. Conventional research mainly concentrates on energy-saving speed profiles, where the energy level evaluation of the timetable is usually considered separately. This paper integrates the train driving control optimization and the timetable characteristics by analyzing the achievable tractive energy conservation performance and the corresponding boundaries. A calculation method for energy efficient driving control solution is proposed based on the Bacterial Foraging Optimization (BFO) strategy, which is utilized to carry out batch processing with timetable. A boundary identification solution is proposed to detect the range of energy conservation capability by considering the relationships with average interstation speed and the passenger volume condition. A case study is presented using practical data of Beijing Metro Batong Line and two timetable schemes. The results illustrate that the proposed optimized energy efficient driving control approach is capable of saving tractive energy in comparison with the conventional traction calculation-based train operation solution. With the proposed boundary identification method, the capability space of the energy conservation profiles with respect to the energy reduction and energy saving rate is revealed. Moreover, analyses and discussions on effects from different passenger load conditions are given to both the weekday and weekend timetables. Results of this paper may assist the decision making of rail operators and engineers by enhancing the cost effectiveness and energy efficiency. Keywords: energy conservation; urban transit; train driving control; timetable; bacterial foraging optimization; boundary identification; case study 1. Introduction In recent years, by providing safe, efficient and convenient transport services to a large number of passengers in a short period of time, metro systems are playing a more important role in modern transportation systems all over the world [1]. It has been a common sense that urban rail transit systems have great advantages in enhancing the energy utilization efficiency over private cars and other public transportation modes, which enables a greener choice with higher environmental friendliness capabilities [2,3]. However, according to the fast development of the urban rail transit networks in many cities, the extended energy consumption reduction solution is becoming a big concern for Energies 2020, 13, 2111; doi:10.3390/en13082111 www.mdpi.com/journal/energies Energies 2020, 13, 2111 2 of 25 bothEnergies the 2020 research, 13, x FOR and PEER rail REVIEW operation fields. Energy efficient operation solutions are attracting2 of more 25 attention to optimize the system cost in the whole lifecycle, reduce carbon emissions and realize public environmentalattracting more responsibilities. attention to optimize the system cost in the whole lifecycle, reduce carbon emissions andAmong realize public the di ffenvironmentalerent factors ofresponsibilities. energy consumption in the urban rail transit trains, including the trainAmong braking, the different lighting, factors aeration of energy and air consumption condition, the in the train urban traction rail transit plays trains, the most including significant the role,train which braking, reaches lighting, about aeration 52% of and the air total condition, energy the consumption train traction of theplays train the systemmost significant in Beijing role, (see Figurewhich1)[ reaches4]. 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A control collection methods of research [5–8]. works In recent published years, in great the e1990sfforts begun have beento focus made on optimal to develop train advanced control energy-emethodsffi cient[5–8]. traction In recent methods, years, great which efforts calculate have and been suggest made to energy-e developffi cientadvanced driving energy-efficient strategies that aretraction feasible methods, with the which permitted calculate trip and time suggest schedule ener andgy-efficient track conditions, driving strategies and do notthat requireare feasible extra railwaywith the infrastructure permitted investments.trip time schedule Many existingand trac worksk conditions, in the literature and do concentrate not require on extra the calculation railway ofinfrastructure energy-efficient investments. train driving Many control existing strategies works toin etheffectively literature reduce concentrate tractive on energy the calculation consumption of underenergy-efficient specific constraints, train driving including control the strategies traveling to time, effectively running reduce distance tractive within energy adjacent consumption rail stations, trainunder traction specific/braking constraints, force including characteristics, the traveling train parameters time, running and distance the passenger within adjacent distribution rail stations, features, etc.train The traction/braking most typical solution force characteristics, for solving the train condition-constrained parameters and the tractive passenger energy distribution saving problem features, is anetc. intelligent The most optimization typical solution approach, for solving such the as genetic condition-constrained algorithm [9], particle tractive swarm energy optimization saving problem [10 ], simulatedis an intelligent annealing optimization [11], differential approach, evolution such [as12 ],genetic artificial algorithm bee colony [9], method particle [ 13swarm], etc. optimization Some of these methods[10], simulated have been annealing validated [11], with differ fieldential experiences evolution in[12], design artificial and b optimizationee colony method of the [13], train’s etc. driving Some of these methods have been validated with field experiences in design and optimization of the train’s curves for automatic train operation [14,15]. In addition, efforts also have been made to the analysis driving curves for automatic train operation [14,15]. In addition, efforts also have been made to the of the correlation between the energy efficient control and specific issues. Zhang et al. [16] proposed analysis of the correlation between the energy efficient control and specific issues. Zhang et al. [16] the Comprehensive Evaluation Index (CEI) to analyze integrated optimization strategies of urban proposed the Comprehensive Evaluation Index (CEI) to analyze integrated optimization strategies of rail transport by calculating the traction energy cost and the technical operation time. Lin et al. [17] urban rail transport by calculating the traction energy cost and the technical operation time. Lin et al. realized the optimization of the train dwell time with a multiple train operation model for an energy [17] realized the optimization of the train dwell time with a multiple train operation model for an conservation target. Tian et al. [18] proposed a multi-train traction power network modelling method to energy conservation target. Tian et al. [18] proposed a multi-train traction power network modelling determine the system energy flow of the rail system with regenerating braking trains. Hamid et al. [19] method to determine the system energy flow of the rail system with regenerating braking trains. exploredHamid et how al. errors[19] explored in train how position errors data in atrainffect po thesition overall data energy affect consumptionthe overall energy under consumption a single train trajectoryunder a single optimization train trajectory scheme. optimization Gomes et al.scheme. [20] evaluated Gomes et theal. [20] potentials evaluated of thethe energypotentials effi ofciency the fromenergy “fixed efficiency block” tofrom the “fixed “moving block” block” to the type “mov usinging the block” empirical type datausing and the the empirical statistical data approaches. and the Zhoustatistical et al. [approaches.21] take into Zhou account et al. multiple [21] take issues into account in the energymultiple minimization issues in the ofenergy the traction minimization system, includingof the traction train headway,system, including interstation train runtime,headway, coasting interstation on downhillruntime, coasting slopes, and on downhill the variations slopes, of trainand massthe variations due to the of change train mass of on-board due to the passengers. change of Yangon-board et al. passengers. [22] further Ya investigatedng et al. [22] the further effect frominvestigated multi-phase the effect speed from limits mult in optimizingi-phase speed