Stochastic Optimization of Braking Energy Storage and Ventilation in a Subway Station

Abstract

In the Paris subway system, stations represent about one third of the overall energy consumption. Within stations, ventilation is among the top consuming devices; it is operated at maximum airflow all day long, for air quality reasons. In this paper, we present a concept of energy system that displays comparable air quality while consuming much less energy. The system comprises a battery that makes it possible to recover the trains braking energy, arriving under the form of erratic and strong peaks. We propose an energy management system (EMS) that, at short time scale, controls energy flows and ventilation airflow. By using proper optimization algorithms, we manage to match supply with demand, while minimizing energy daily costs. For this purpose, we have designed algorithms that take into account the braking variability. They are based on the so-called Stochastic Dynamic Programming (SDP) mathematical framework. We fairly compare SDP based algorithms with the widespread Model Predictive Control (MPC) ones. First, both SDP and MPC yield energy/money operating savings of the order of one third, compared to the current management without battery (our figure does not include the cost of the battery). Second, depending on the specific design, we observe that SDP outperforms MPC by a few percent, with an easier online numerical implementation.

Impacts of Subway System Modifications on Air Quality in Subway Platforms and Trains

“…Novel approaches are being explored to increase the efficiency of mechanical ventilation systems to reduce their considerable energy consumption. 33 Strategies involving increasing the rate of particle removal via filtration and magnetism have been explored. Filtration such as train HVAC systems can reduce in-train PM concentrations.…”

“…However, the impact of continual ventilation on platform PM mass and number concentrations for several size fractions can differ depending on factors such as fan speed, flow direction (impulsion/expulsion), station design (single-track/double-track/double-track with separating wall), and presence of PSDs. ,, Cost can be an issue as well. Novel approaches are being explored to increase the efficiency of mechanical ventilation systems to reduce their considerable energy consumption . Strategies involving increasing the rate of particle removal via filtration and magnetism have been explored.…”

On the Use of Supervised Clustering in Stochastic NMPC Design

“…Unfortunately, solving the SDP leads to algorithms that scale exponentially in the dimension of the state. Nevertheless, for small sized problems, nice and elegant solutions can be derived (Rigaut et al, 2018) that might even address realistic real-life problems.…”

Power Management for a DC MicroGrid in a Smart Railway Station including Recovery Braking

“…The control target of the proposed power management model is to provide the needed references to the low level controllers that are dedicated to each physical device in the microgrid [10], [11], considering given references and constraints for the battery energy and the power demanded and provided to the AC grid. Only the power management control level is treated in this work [12], [13]: the lower [14], [15], [16] and higher [6], [17] control levels are supposed to work properly and to meet their targets. This paper extends the preliminary results described in [18], considering power losses and converter efficiency, and is focusing on a specific application as the Smart Railway Station.…”

“…This paper extends the preliminary results described in [18], considering power losses and converter efficiency, and is focusing on a specific application as the Smart Railway Station. The resulting problem is a non linear optimization one, and it is solved by a Model Predictive Control (MPC) formulation for Mixed Integer Quadratic Programming (MIQP) [19], [17], [20].…”