Co-phase traction power supply system provides the insights for solving the existing power quality and electrical sectioning issues in high-speed railways, and the flexible control of co-phase traction substation (CTSS) with the integration of photovoltaic (PV) and hybrid energy storage system (HESS) attracts widespread attention. However, the strong volatilities and uncertainties of PV and traction load introduce challenges to the reliable and economic operation of CTSS. In this study, the energy management of CTSS aims at minimizing the operating cost of CTSS by coordinating PV, HESS, power flow controller (PFC) and the energy transactions with the power grid, where the three-phase voltage unbalance constraint is taken into account in particular. Furthermore, to handle the uncertainties of PV and traction load, a two-stage robust optimization model is proposed. The first stage determines the operating status of HESS and energy transaction status with grid, and the second stage aims at finding the optimal scheduling power of CTSS and the worst-case scenario with maximized operating cost of CTSS. A column-and-constraint generation algorithm is employed to solve this robust optimization model, along with efficient linearization approaches for the formulation of mixed-integer linear programming. The proposed methodology is tested with a real high-speed railway line case in China.

The integration of hybrid energy storage systems (HESS) in alternating current (AC) electrified railway systems is attracting widespread interest. However, little attention has been paid to the interaction of optimal size and daily dispatch of HESS within the entire project period. Therefore, a novel bi-level model of railway traction substation energy management (RTSEM) system is developed, which includes a slave level of diurnal HESS dispatch and a master level of HESS sizing. The slave level is formulated as a mixed integer linear programming (MILP) model by coordinating HESS, traction load, regenerative braking energy and renewable energy. As for the master level model, comprehensive cost study within the project period is conducted, with batteries degradation and replacement cost taken into account. Grey wolf optimization technique with embedded CPLEX solver is utilized to solve this RTSEM problem. The proposed model is tested with a real high-speed railway line case in China. The simulation results of several cases with different system elements are presented, and the sensitivity analyses of several parameters are also performed. The obtained results reveal that it shows significant economic-saving potentials with the integration of HESS and renewable energy.

To foster the utilization of regeneration braking energy and suppress voltage unbalance (VU), a railway electrical smart grid (RESG), intergraded with power flow controller (PFC) and energy storage (ES), is proposed as an important part of next-generation electrified railways. However, under the uncertain traction load, how to design the optimal size of PFC-ES is a challenge during the planning period. Hence, this paper proposes a chance-constrained two-stage programming approach. The first-stage aims to minimising the overall cost of RESG’s devices. The second-stage aims to arrange the energy flow of the PFC-ES with the objective of minimising the expected operation cost under the dynamic VU restriction, and the stochastics characteristics of traction load are transformed into a chance constraint by using a scenario approach. Then, traction power predictions are combined with multivariate Gaussian Mixture Model (multi-GMM) model to generate correlated traction power flow scenarios and to assess VU probabilistic metrics distribution with different confidence levels. Finally, a novel algorithm is designed to select the confidence level and violation probability so that the capacity planning results can ensure the high-efficient and high-quality operation of the RESG. Case studies based on an actual electrified railway demonstrate that the proposed PFC-ES sizing approach can reduce the overall cost by up to 13%.

The cophase traction power supply system (CTPSS) is a promising solution in the smart electrified railways to effectively eliminate the neutral section, improve regeneration braking energy (RBE) utilization and mitigate voltage unbalance (VU). It works by coordinating the power flow distribution (PFD) among the energy storage (ES), power flow controller, and traction transformer. However, the conventional dispatch considering VU mitigation may result in increasing extra operation costs and accelerating ES aging. Therefore, driven by the day-ahead forecasted information, a dynamic VU regulation strategy is proposed to draw up the VU compensation schedule (VU-CS), which can satisfy the probabilistic limitations of power quality standards. Consequently, a Copula-based VU dependence model is developed to transform the VU-CS into the PFD constraint set of CTPSS. Meanwhile, an ES degradation model is introduced to quantify the system operation cost within different scenarios. Taking the PFD as a VU regulation constraint, an economic dispatch scheme of CTPSS is established to obtain a minimum operation cost and maximum RBE utilization. Finally, case study tests verify the efficiency of the proposed approach. The results show that scheme can improve ES cycle life and decrease cost under the probabilistic limitations of VU.

Single-phase traction power supply systems (TPSSs) have caused severe voltage unbalance (VU) in the three-phase grid. VU pre-assessment provides a powerful approach to forecast the possible VU emission level in electrified railway planning. However, the uncertainty of traction load poses stringent challenges to the validity of the result. In this study, a modeling method including confidence interval prediction (CIP) and scenario generation is introduced to describe the power uncertainty of traction load. Furthermore, a probabilistic power flow (PPF) algorithm is proposed for the train–network system, especially considering the influence of the power supply scheme. Additionally, uniform VU propagation from TPSS to the power grid model is analyzed in detail. Finally, a novel VU probability pre-assessment process is developed to estimate the statistical features of VU. A real railway line case validates the effectiveness of the proposed methods and provides useful design information for electrified railway planning. The whole VU probability pre-assessment approach can be helpful for future VU assessment and compensation in electrified railway planning and operation.

In order to solve the problem of the serious stray current existing in DC urban rail transit, and to improve the utilization rate of the regenerative braking energy (RBE) generated by locomotive, an AC traction power supply system (TPSS) for long-distance urban rail transit is proposed, in which the network power flow distribution is affected by the train trajectory, the parameters of network and hybrid energy storage system (HESS) configuration. For achieving the minimum comprehensive cost including the electricity cost and the life cycle cost (LCC) of HESS, this paper aims to develop a Train-Network-HESS integrated model to optimize network power flow distribution. The train trajectory is illustrated based on time-space conversion method and HESS control strategy is discussed to increase the utilization of the RBE. Furthermore, the optimized configuration of the AC TPSS for urban rail transit is obtained by the hybrid GA-PSO algorithm. Finally, the effectiveness of the proposed model is verified by detailed case studies and the comprehensive cost can be reduced up to 39.08% compared with conventional TPSS.