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Optimal EV Charging Scheduling at Electric Railway Stations Under Peak Load Constraints

G. Pierrou, C. Valero-De La Flor, G. Hug

TL;DR

The paper tackles grid overloading risks from EV charging at railway parking by coordinating EV charging with railway loads, PV generation, and regenerative braking within a peak-load constrained EMS. It advances a scenario-tree based EMS that treats each EV’s departure state of charge as a flexible optimization variable, while integrating PV, wayside energy storage, and RB power. Key contributions include (i) a comprehensive EMS model for an electric railway energy hub, (ii) a peak-load aware objective that balances cost with customer satisfaction, and (iii) a detailed numerical study on a Swiss railway line showing significant improvements in departure SoC and peak reduction under various scenarios. The work demonstrates the practical viability of railway stations as energy hubs and sets the stage for further enhancements like battery degradation modeling and uncertainty updates.

Abstract

In this paper, a novel Energy Management System (EMS) algorithm to achieve optimal Electric Vehicle (EV) charging scheduling at the parking lots of electric railway stations is proposed. The proposed approach uncovers the potential of leveraging EV charging flexibility to prevent overloading in the combined EV charging and railway operation along with renewable generation, railway regenerative capabilities, and energy storage. Specifically, to realize end-user flexibility, each EV state of charge at departure time is introduced as an optimization variable. Peak load constraints are included in the railway EMS to properly adjust EV charging requirements during periods of high railway demand. A comprehensive numerical study using a scenario-tree approach on an actual railway line in Switzerland demonstrates the effectiveness and the feasibility of the proposed method in a practical setting under multiple scenarios.

Optimal EV Charging Scheduling at Electric Railway Stations Under Peak Load Constraints

TL;DR

The paper tackles grid overloading risks from EV charging at railway parking by coordinating EV charging with railway loads, PV generation, and regenerative braking within a peak-load constrained EMS. It advances a scenario-tree based EMS that treats each EV’s departure state of charge as a flexible optimization variable, while integrating PV, wayside energy storage, and RB power. Key contributions include (i) a comprehensive EMS model for an electric railway energy hub, (ii) a peak-load aware objective that balances cost with customer satisfaction, and (iii) a detailed numerical study on a Swiss railway line showing significant improvements in departure SoC and peak reduction under various scenarios. The work demonstrates the practical viability of railway stations as energy hubs and sets the stage for further enhancements like battery degradation modeling and uncertainty updates.

Abstract

In this paper, a novel Energy Management System (EMS) algorithm to achieve optimal Electric Vehicle (EV) charging scheduling at the parking lots of electric railway stations is proposed. The proposed approach uncovers the potential of leveraging EV charging flexibility to prevent overloading in the combined EV charging and railway operation along with renewable generation, railway regenerative capabilities, and energy storage. Specifically, to realize end-user flexibility, each EV state of charge at departure time is introduced as an optimization variable. Peak load constraints are included in the railway EMS to properly adjust EV charging requirements during periods of high railway demand. A comprehensive numerical study using a scenario-tree approach on an actual railway line in Switzerland demonstrates the effectiveness and the feasibility of the proposed method in a practical setting under multiple scenarios.
Paper Structure (22 sections, 16 equations, 8 figures)

This paper contains 22 sections, 16 equations, 8 figures.

Table of Contents

  1. Introduction
  2. Railway EMS Modeling
  3. Solar Generation
  4. Power Balance
  5. Grid Power Exchange
  6. Energy Storage Model
  7. RB Power
  8. EV Charging Modeling
  9. Aggregated EV Charging Power
  10. Individual EV Modeling
  11. The Proposed Railway EMS with Flexible EV Charging Under Peak Load Constraints
  12. Peak Load Constraints
  13. EV Charging Flexibility
  14. Objective Function
  15. The Proposed Algorithm
  16. ...and 7 more sections

Figures (8)

  • Figure 1: Electric railway systems as energy hubs.
  • Figure 2: Flowchart of the proposed railway EMS with EV charging flexibility.
  • Figure 3: The impact of RB power on the flexible EV state of charge at departure time.
  • Figure 4: The impact of PV generation on the flexible EV state of charge at departure time.
  • Figure 5: Railway power consumption and RB power for the selected scenario.
  • ...and 3 more figures