A review of optimal control methods for energy storage systems - energy trading, energy balancing and electric vehicles

This paper reviews recent works related to optimal control of energy storage systems. Based on a contextual analysis of more than 250 recent papers we attempt to better understand why certain optimization methods are suitable for different applications, what are the currently open theoretical and numerical challenges in each of the leading applications, and which control strategies will rise in the following years. The reviewed research works are divided to classic methods and advanced methods, in order to highlight the current developments and trends within each of these two groups. The classic methods include linear programming, dynamic programming, stochastic control methods, and Pontryagin's minimum principle, and the advanced methods are further divided into metaheuristic and machine learning techniques.