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This paper presents a day-ahead energy management strategy for a DC smart railway grid integrating a photovoltaic (PV) power generator and energy storage systems (ESS) at the traction substation (TSS). The proposed energy management method relies on the forecasted power consumption profile of the TSS for the upcoming day and a non-linear optimization approach to manage the ESS in an economically efficient way while respecting physical constraints and maintaining power balance within the power system. An adequate forecast method is used to address the unique characteristics of TSS power consumption, particularly the occurrence of multiple short-duration peak loads during the day. The non-linear optimization approach accounts for both the system’s behavior and the inherent non-linearity of the problem. The forecasted TSS consumption profile is combined with a PV production profile as input to the optimization algorithm, which determines the optimal ESS schedule for the next 24 h. The impact of forecast accuracy on optimization outcomes is evaluated using key performance indicators. To demonstrate the superiority of the proposed optimization method in scheduling the ESS, its results are compared against those of a basic baseline case.

This paper proposes a real time energy supervision tool to manage the operation of a wayside energy storage system (ESS) connected in a DC railway smart grid. This energy management system (EMS) is adapted to the unique nature of railway substation load demand and the uncertain behavior of renewable energy production. It aims to solicit the ESS in a way that ensures the services it provides are well obtained while respecting economic and technical criteria. The EMS in this paper is composed of two supervision levels, dayahead and real time. The day-ahead is a predictive mode that finds the optimal economic ESS schedule, and the real time is a fuzzy logic model that adjusts the power reference set point provided by the day-ahead in real time based on the current state of the electrical network and actual measurement.