Grid integration of renewable energy (REN) requires efficient and reliable power conversion stages, particularly with an increasing demand for high controllability and flexibility seen from the grid side. Underpinned by advanced control and information technologies, power electronics converters play an essential role in large-scale REN generation. However, the use of power converters has also exposed several challenges in conventional power grids, e.g., reducing the system inertia. In this article, grid integration using power electronics is presented for large-scale REN generation. Technical issues and requirements are discussed with a special focus on grid-connected wind, solar photovoltaic, and energy storage systems. In addition, the core of the energy generation and conversion—control for individual power converters (e.g., general current control) and for the system level (e.g., coordinated operation of large-scale energy systems)—is briefly discussed. Future research perspectives are then presented, which further advance large-scale REN generation technologies by incorporating more power electronics systems.

In this paper, we consider decentralized optimal control for large populations of two-wheeled vehicles via mean field game theory. Specifically, the main objective is that each two-wheeled vehicle follows the average behavior (or mean field) of the whole population while achieving the overall optimal control performance without sharing their state information (position and/or velocity) with other vehicles. We first provide a general modeling framework of the two-wheeled vehicle for its position control by using balances of moments and forces that are obtained from the two electric DC motors and the vehicle chassis structure. Next, we design an optimal control for each vehicle, which is decentralized as it is a function of its own state, and the set of designed decentralized optimal controls constitutes an ϵ-Nash equilibrium. With these decentralized optimal controls, we characterize an approximated average behavior of the vehicles, and show that it is the best estimate of the actual average behavior when the population size becomes arbitrarily large. Finally, these theoretical results are validated through simulation and experiment results of large populations of two-wheeled vehicles.