4:30 PM - 4:50 PM
[3K2-OS-18b-03] Charge Control of Regenerative Power for Energy-Saving Railway Systems
Keywords:Regenerative Power, Railway Systems, Reinforcement Learning, Charge Control
Recently, utilization of regenerated power from the brake operation has drawn attention to help for energy conservation of railway systems. In this paper, we introduce reinforcement learning with actor-critic to acquire the appropriate rules that decide the amount of charge/discharge so that the fluctuation of the SOC (State of Charge) can be suppressed.In the previous researches, the control rules are hand-crafted based on human empirical heuristics that has limitations on suppressing electricity supply-demand dynamics in the railway system.
We show some empirical results of both previous research and our proposed one, and find that the generated rules via reinforcement learning show the better performance than the ones by the previous researches.
We show some empirical results of both previous research and our proposed one, and find that the generated rules via reinforcement learning show the better performance than the ones by the previous researches.