System energy optimisation strategies for DC railway traction power networks

Energy and environmental sustainability in transportation are becoming ever more important. In Europe, the transportation sector is responsible for about 32% of the final energy consumption. Electrified railway systems play an important role in contributing to the reduction of energy usage and C0$_2$ emissions compared with other transport modes. Previous studies have investigated train driving strategies for traction energy saving. However, few of them consider the overall system energy optimisation. This thesis analyses the energy consumption of urban systems with regenerating trains, including the energy supplied by substations, used in power transmission networks, consumed by monitoring trains, and regenerated by braking trains. This thesis proposes an approach to searching energy-efficient driving strategies with coasting controls. A Driver Advisory System is designed and implemented in a field test on Beijing Yizhuang Subway Line. The driver guided by the DAS achieves 16% of traction energy savings, compared with normal driving. This thesis also proposes an approach to global system energy consumption optimisation, based on a Monte Carlo Algorithm. The case study indicates that the substation energy is reduced by around 38.6% with the system optimised operations. The efficiency of using regenerative braking energy is improved to from 80.6 to 95.5%

Energy-Efficient Train Control with Onboard Energy Storage Systems considering Stochastic Regenerative Braking Energy

With the rapid development of energy storage technology, onboard energy storage systems(OESS) have been applied in modern railway systems to help reduce energy consumption. In addition, regenerative braking energy utilization is becoming increasingly important to avoid energy waste in the railway systems, undermining the sustainability of urban railway transportation. However, the intelligent energy management of the trains equipped with OESSs considering regenerative braking energy utilization is still rare in the field. This paper considers the stochastic characteristics of the regenerative braking power distributed in railway power networks. It concurrently optimizes the train trajectory with OESS and regenerative braking energy utilization. The expected regenerative braking power distribution can be obtained based on the Monte-Carlo simulation of the train timetable. Then, the integrated optimization using mixed integer linear programming (MILP) can be conducted and combined with the expected available regenerative braking energy. A generic four-station railway system powered by one traction substation is modeled and simulated for the study. The results show that by applying the proposed method, 68.8% of the expected regenerative braking energy in the environment will be further utilized. The expected amount of energy from the traction substation is reduced by 22.0% using the proposed train control method to recover more regenerative braking energy from improved energy interactions between trains and OESSs

Traction Power Substation Load Analysis with Various Train Operating Styles and Substation Fault Modes

Simulation of railway systems plays a key role in designing the traction power supply 13 network, managing the train operation and making changes of timetables. Various simulation 14 technologies have been developed to study the railway traction power network and train operation 15 independently. However, the inter-action among load performance, train operation and fault 16 conditions have been fully understood. This paper proposes a mathematical modeling method to 17 simulate the railway traction power network with consideration of multi-train operation, driving 18 controls, under-voltage traction, and substation fault modes. The network voltage, power load 19 demands, energy consumption according to the existing operation are studied. The hotspots of the 20 power supply network are identified based on the evaluation of train operation and power demand. 21 The impact of traction power substation (TPSS) outage and short circuit on the power supply 22 network have been simulated and analyzed. The simulation results have been analyzed and 23 compared with the normal operation. A case study based on a practical metro line in Singapore 24 Metro is developed to illustrate the power network evaluation performance

Bi-level Optimization of Sizing and Control Strategy of Hybrid Energy Storage System in Urban Rail Transit Considering Substation Operation Stability

The hybrid energy storage system (HESS) which consists of battery and ultracapacitor can efficiently reduce the substation energy cost from grid and achieve the peak shaving function, due to its characteristics of high-power density and high-energy density. The sizing of HESS affects the operation cost of whole system. Besides, operation stability (like substation peak power and voltage fluctuations) is rarely considered in urban rail transit (URT) when sizing optimization of HESS is considered. Thus, this research proposes a sizing and control strategy optimization of HESS in URT. First, the mathematic model of URT with HESS is established, which is used to simulate URT and HESS operation state by power flow analysis method. Then, based on the proposed HESS control principle, a bi-level optimization of HESS in URT is proposed. The master level aims to optimize the rated capacity and power of HESS, reducing total operational cost. Then, the HESS control strategy is optimized at slave level, reducing substation peak power and voltage fluctuations of URT. The case study is conducted based on the data of Merseyrail line in Liverpool. A comparison is also conducted, which shows that the proposed method can reduce daily operation cost by 12.68% of the substation, while the grid energy cost is decreased by 57.26%

Energy-Efficient Train Control with Onboard Energy Storage Systems considering Stochastic Regenerative Braking Energy

With the rapid development of energy storage technology, onboard energy storage systems(OESS) have been applied in modern railway systems to help reduce energy consumption. In addition, regenerative braking energy utilization is becoming increasingly important to avoid energy waste in the railway systems, undermining the sustainability of urban railway transportation. However, the intelligent energy management of the trains equipped with OESSs considering regenerative braking energy utilization is still rare in the field. This paper considers the stochastic characteristics of the regenerative braking power distributed in railway power networks. It concurrently optimizes the train trajectory with OESS and regenerative braking energy utilization. The expected regenerative braking power distribution can be obtained based on the Monte-Carlo simulation of the train timetable. Then, the integrated optimization using mixed integer linear programming (MILP) can be conducted and combined with the expected available regenerative braking energy. A generic four-station railway system powered by one traction substation is modeled and simulated for the study. The results show that by applying the proposed method, 68.8% of the expected regenerative braking energy in the environment will be further utilized. The expected amount of energy from the traction substation is reduced by 22.0% using the proposed train control method to recover more regenerative braking energy from improved energy interactions between trains and OESSs