Optimal charging control of electrified public transportation based on deep reinforcement learning

点击翻译按钮获取中文摘要

Electrification of public transport across Canadian cities reduces Greenhouse Gas (GHG) emissions, improves air quality and decreases noise pollution, lowers maintenance and fuel costs, and provides the public with a more comfortable riding experience. In recent years, the adoption of electric buses has been growing rapidly as Canada's largest cities are working to have fully net-zero fleets by 2050, with some aiming for as early as 2036 and 2040. However, the electrification of urban bus fleets presents significant challenges to current grid capacity, which include a power imbalance between Electric Vehicle (EV)s' charging demand and the power grid's supply, larger voltage fluctuation, and more power loss. Properly coordinated charging control strategies can not only alleviate the above impacts, but also benefit the smart grid by flattening direct load, reducing renewable generation curtailment, and increasing system flexibility. For this purpose, electric buses should be able to charge during off-peak hours and even discharge energy to the grid during peak hours in response to time-varying electricity prices. However, as the electric buses must be sufficiently charged before departure, it is challenging to optimize their charging control in real-time due to the uncertainty in electricity prices and in their arrival/departure time resulting from random traffic conditions. In recent years, Deep Reinforcement Learning (DRL) has made great progress as an efficient and effective framework to make sequential decisions under uncertainty. DRL can directly learn an optimal policy by trial and error from real-world data, and there is no need to model the distribution of the randomness. The goal of this partnership is to develop DRL algorithms for optimal charging control of electric buses that will (1) minimize GHG emissions; (2) minimize charging cost while ensuring reliable operation of buses for public transportation; and (3) flatten charging load and enhance renewable generation dispatchability for power grid. The expected outcome of this partnership will provide innovative solutions and useful insights for a smooth and efficient transition toward an electrified public transit network in Canada.