Multistage Robust Optimization of Routing and Scheduling of Mobile Energy Storage in Coupled Transportation and Power Distribution Networks

Mobile energy storage systems (MSSs) manifest a significant potential for enhancing the reliable and economic operations of distribution systems with high photovoltaic (PV) penetrations. This article proposes a robust and dynamic MSS scheduling method, which includes MSS mobility and its power management, in a coupled transportation and power distribution network. A rolling-horizon multistage robust optimization model with integer and linear decisions is proposed to schedule the MSS mobility and its charging and discharging strategies, considering uncertain traffic conditions, PV power output, and load demands, as well as nonanticipativity MSS operation constraints. The proposed model is transformed into a mixed-integer linear programming (MILP) problem to achieve computational tractability based on mixed-linear and binary decision rules, and the duality theory. The proposed dynamic MSS scheduling method is tested on the augmented 33- and 123-bus distribution systems with actual solar irradiance, load, and traffic data; the simulation results are compared with those considering day-ahead MSS scheduling and stationary energy storage systems to verify the effectiveness of the proposed dynamic MSS operation.