Energy Cost Minimization Based on Decentralized Reinforcement Learning With Feedback for Stable Operation of Wireless Charging Electric Tram Network

The convenience and energy efficiency of electric trams have resulted in their rapid adoption in many areas of the world. Despite trams' many advantages, overhead lines installed along the track cause severe problems, such as safety issues, maintenance problems, and difficulty in renovating the urban road system. To solve these problems, a wireless charging electric tram system has been developed to replace the overhead lines, catenary, and pantograph. With wireless charging segments installed underground, a tram can operate freely without any support from overhead lines. In order to successfully commercialize a wireless charging tram system, it is essential to minimize the energy cost of the wireless charging tram system. In this article, a model of a wireless charging electric tram system and an energy cost minimization algorithm are proposed. The Manchester Metrolink tram network is modeled using the Google Maps API, the Google Transit API, and the Metrolink API to simulate the wireless charging system under massive public transportation network. To determine the minimum energy cost, an energy cost minimization algorithm for the wireless charging tram system is proposed based on decentralized multiagent reinforcement learning with feedback. The numerical results show that the proposed algorithm can reduce costs while ensuring stable operation.