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Review on Electric Railway System Optimization: Train Dynamic Scheduling, Energy Management, and Storage Integration
Fei Liu, Can Wan, Stefan Östlund, Qianwen Xu
TL;DR
Electric railway research must coordinate operational efficiency, sustainability, and intelligent control as scheduling, train trajectories, and ESS decisions become increasingly interdependent. This review synthesizes advances in dynamic scheduling, operation control, AI, and ESS applications across energy management, peak shaving, load balancing, and voltage and frequency regulation. It concludes that integrated optimization can improve energy efficiency, delay performance, power reliability, and resilience, while unified multi-timescale coordination remains an open challenge.
Problem
Existing reviews treat scheduling, train control, storage, power-flow, and AI largely as separate topics, leaving their cross-layer interactions insufficiently addressed.
Method
The review systematically analyzes dynamic scheduling, operation control, AI-driven strategies, and ESS integration across railway energy-management and regulation applications.
Results
Advanced optimization and ESS integration support timetable adjustment, coordinated train operation, energy-efficient trajectories, regenerative-energy recovery, and improved power-supply reliability.
Takeaways & Limitations
Effective railway-system performance depends on integrating scheduling, operation control, and ESS decisions across architectures and operating timescales.
Takeaways & Limitations
A unified framework coordinating ESS dispatch, train speed profiles, and frequency response across multiple timescales remains an open challenge.
Abstract
from arXiv · showhide
The modernization of railway systems is being driven by the need for greater efficiency, sustainability, and intelligent operation. In this review, recent advancements in dynamic scheduling, energy management, and energy storage systems (ESSs) integration within electric railway networks are analyzed. Optimization strategies for train scheduling and operation control are reviewed, with a focus on methods that reduce energy consumption and improve overall system performance. The role of energy storage technologies is analyzed in terms of energy management, peak shaving, and voltage and frequency control for practical application. The integration of artificial intelligence and advanced control strategies is also reviewed in electrified railway systems. This review provides a comprehensive overview of current research trends and outlines future directions for the development of resilient, energy efficient, and intelligent railway systems with ESSs integration.
1. Introduction
Electric railway modernization seeks efficient, sustainable, and intelligent operation under rising transport and electrical-infrastructure demands. This review connects dynamic scheduling, operation control, and ESS integration while addressing limited cross-layer treatment in prior surveys.
- 1. Introduction: Dynamic scheduling and automated control use real-time monitoring, train tracking, route setting, and rescheduling to maintain efficient and reliable operations during disruptions.These functions coordinate train movements and schedules in response to changing operating conditions.
- 1. Introduction: ESS operation is tightly coupled with train timetables and speed trajectories, making integrated scheduling, control, and storage decisions necessary for railway optimization.Charging and discharging decisions depend on train operation and timetable conditions.
- 1. Introduction: ESSs recover regenerative braking energy, reduce losses and peak power, and support grid stability and railway resilience.Onboard and wayside ESSs address mismatches between regenerated energy and network demand or grid capacity.
- 1. Introduction: Existing reviews separately address rescheduling, energy-efficient control, regenerative braking, storage, power-flow control, and AI, but provide limited systematic treatment of their interactions.The review identifies timetable decisions, speed trajectories, and ESS operation as insufficiently studied as an integrated subject.
- 1. Introduction: The review analyzes dynamic scheduling, operation control, ESS integration, and related applications including peak shaving, load balancing, energy management, and voltage and frequency regulation.It also organizes recent optimization algorithms and AI-driven methods alongside storage-control applications and future challenges.
2. Train Power Supply System Structure
The train power supply system delivers utility-grid electricity to trains through interconnected traction and electrification components. Its architecture also incorporates ESSs to improve energy efficiency.
- 2. Train Power Supply System Structure: The TPSS uses traction substations and transformers to draw utility-grid power and convert it to suitable levels for train operation.Power is then supplied to trains through overhead contact lines.
- 2. Train Power Supply System Structure: Overhead contact lines transmit the conditioned electrical power from the traction supply system to trains.
- 2. Train Power Supply System Structure: ESSs are incorporated into the TPSS architecture to enhance railway energy efficiency.
3. Dynamic Scheduling and Operation Control in Railway Systems
Dynamic scheduling and operation control jointly adapt railway timetables and train trajectories to disruptions, operational constraints, passenger needs, and energy objectives. Recent approaches combine optimization, simulation, AI, and robustness modeling, while integrated ESS control remains an important unresolved challenge.
- 3.1. Adaptive Train Scheduling and Timetable Optimization: Dynamic scheduling adjusts train timetables in response to disruptions, fluctuating demand, and congestion, including departure times, arrival times, dwell durations, routes, and sequencing.
- 3.1.1. Objective functions: Timetable optimization balances delay reduction, affected-train minimization, passenger travel time, energy efficiency, robustness, and service reliability under operational and safety constraints.
- 3.1.1. Objective functions: Regenerative braking recovery can improve energy utilization when acceleration and braking are synchronized across trains, but effectiveness depends on a receiving train being available at the right time.
- 3.1.2. Constraints: Scheduling constraints enforce minimum running and dwell times, safe headways, track capacity, section occupancy, and robustness buffers against delays.
- 3.2. Trajectory Optimization in Railway Networks: Mathematical programming, simulation, heuristics, and reinforcement learning address scheduling and control, but RL requires substantial training data and validation, while ESS SOC and speed–storage interactions are rarely modeled together.
- 3. Dynamic Scheduling and Operation Control in Railway Systems: Predictive control, cooperative optimization, and AI-driven adaptive strategies can improve operational efficiency, passenger experience, and sustainability.
4. Energy Storage System Management and Control for Railway Systems
Railway ESS management coordinates storage with scheduling and real-time train control to capture regenerative braking energy and support efficient, reliable operation. Wayside, onboard, and hybrid configurations offer different trade-offs in centralized capacity, localized responsiveness, and physical constraints.
- Integrated management and control: Timetable decisions and train speed profiles shape regenerative-energy mismatches, influencing ESS sizing and control requirements.Downhill trains can generate surplus braking energy that is underutilized or rejected when grid absorption capacity is limited or train movements are unsynchronized.
- Integrated management and control: ESS management uses minutes-ahead scheduling information and second-level control signals to coordinate charging, discharging, and state-of-charge feedback.Inputs include regenerative braking timing, charging opportunities, peak demand, braking power, and traction demand.
- ESS configurations: Wayside ESSs centralize braking-energy storage at substations for energy savings and voltage stabilization, whereas onboard ESSs manage regenerative energy locally without traction-grid transmission losses.Onboard storage remains constrained by train-car space and weight limits, restricting capacity and scalability.
- Hybrid ground-onboard ESSs: Hybrid ground-onboard ESSs combine high-power onboard support with high-capacity wayside storage for cooperative railway energy management.Bilevel sizing and control can consider both operating cost and substation stability.
- Hybrid ground-onboard ESSs: Hybrid systems can dynamically adjust wayside–onboard storage relationships during faults to enhance emergency power supply capabilities.Wayside systems emphasize long-term energy management and grid stability, while onboard systems provide localized high-power support.
5. Application Scenarios and Advanced Functionalities for Railway Systems
The review organizes railway applications around energy management, peak shaving, load balancing, and voltage/frequency regulation, comparing ESS coupling and control timescales. Across these applications, methods range from coordinated power control to joint SOC-aware optimization, with scalability and real-time deployment remaining challenges.
- 5. Application Scenarios and Advanced Functionalities for Railway Systems: ESS applications span peak clipping and regenerative-braking-energy utilization, voltage stabilization, inertia support, and primary frequency regulation.The reviewed table distinguishes coordinated, joint, and decoupled ESS coupling across real-time and day-ahead strategies.
- 5.1. Energy Management in Railway Systems: Energy-management frameworks coordinate trains, wayside storage, and distributed sources for energy savings and load balancing, but multi-line scalability remains unresolved.A two-level framework combines centralized day-ahead scheduling with decentralized real-time control, while hierarchical strategies extend coordination across substations.
- 5.1. Energy Management in Railway Systems: Wayside strategies simplify implementation by coordinating ESS with scheduling signals, whereas onboard and hybrid methods jointly optimize SOC with train operation at higher modeling complexity.The review identifies DRL as an intermediate approach because SOC is represented implicitly through the reward structure.
- 5.2. Peak Shaving and Load Balancing in Railway Systems: Three-stage MPC combines day-ahead MILP scheduling, intra-day forecast correction, and real-time power-quality control for joint SOC and power optimization.The framework coordinates ESS charging and discharging with negative-sequence-current suppression using remaining power-conditioner capacity.
- 5. Application Scenarios and Advanced Functionalities for Railway Systems: The reviewed applications indicate a progression from passive ESS buffering toward SOC-aware joint optimization, while unified multi-timescale coordination remains an open challenge.The progression involves coordinated early strategies, tighter demand-response coupling, and joint SOC decisions in DQN and MPC approaches.
- 5.3. Voltage and Frequency Regulation in Railway Systems: Voltage-regulation methods use ESS and power electronics to absorb regenerative energy and prevent voltage drops, while frequency-regulation methods rely on train-load flexibility or coordinated train participation.The review notes that most voltage methods use real-time coordinated control without explicit scheduling-layer SOC optimization.
6. Conclusion
The review concludes that optimization algorithms and ESS integration support energy-efficient, reliable, and increasingly intelligent electrified railway operation. System performance depends strongly on ESS sizing, placement, and coordinated control, while future work should address holistic and scalable integration.
- 6. Conclusion: Advanced optimization enables timetable adjustment, multi-train coordination, and energy-efficient trajectory planning under complex railway operating constraints.These methods contribute to reducing total energy consumption and delays while improving operational efficiency.
- 6. Conclusion: ESS integration captures regenerative braking energy and supports load leveling, peak shaving, voltage regulation, and frequency regulation across diverse railway scenarios.These functions are linked to power-supply reliability, operational efficiency, and grid stability.
- 6. Conclusion: Wayside, onboard, and hybrid ESS architectures require precise sizing, placement, and control coordination, particularly under dynamic load and voltage conditions.