Charge Control of Regenerative Power for Energy Saving in Railway Systems

From the viewpoint of energy conservation in the railway systems, the effective usage of regenerative power generated during train braking has attracted a lot of attention lately. To utilize regenerative power with balancing the electric power supply-demand, we introduce a storage battery, and propose a charge control method of it. Our proposed algorithm could make not only balance the electric power supply-demand but also suppresses the fluctuation of the charged amount within the storage battery. The smaller amount of charge fluctuation, the smaller capacity battery would be available to use. In several existing methods, the empirical rules have been adopted to secure the balance, without consideration for suppressing the fluctuations of charged amount electricity. However, rule-based control which is based on the human empirical knowledge, has some limitations in electricity supply-demand dynamics in the railway systems. To overcome the limitations, we introduce reinforcement learning with an actor-critic algorithm to acquire the effective control policy which had been difficult to draw from the experts' knowledge as the rules. Through several computational simulations, we verified that the performance of our proposed method shows superior to that of the existing one.