A microgrid in the islanded mode is considered where a fleet of fuel cell cars is used as a distributed power generation system. The objective of the proposed control system is to minimize the operational cost of the system, subject to the physical and operational constraints of the system. In order to deal with uncertainty in the prediction of the microgrid's load, two model predictive control methods, a min-max (MM) approach and disturbance feedback MM approach, are proposed. We develop three distributed control algorithms and we show that by using these algorithms, the driving patterns of the fuel cell cars can be kept private. In other words, no privacy sensitive data on the usage of the cars are collected by a central control agent. Numerical case studies are presented to demonstrate the excellent performance of the proposed control methods.

We consider power scheduling in a microgrid operated in the islanded mode. It is assumed that at any time all the renewable energy sources are generating the maximum achievable electrical power based on the weather conditions and the power balance of the microgrid is exclusively done by a fleet of fuel cell cars. As a result, the uncertainty in the prediction of the load will also make the future power generation of the fuel cell cars uncertain and, hence, a robust control method should be used to operate the fuel cell cars. We develop a min-max model predictive control approach to schedule the power generation profile of the fuel cell cars. Furthermore, we develop an alternative approach, a min-max disturbance feedback approach, in order to reduce the conservatism of the min-max approach. Finally, an illustrative case study shows the performance of the proposed approaches.

A parking lot for fuel cell cars is considered inside a microgrid where the fuel cell cars are exploited to generate power inside the microgrid. A central control unit is considered in the microgrid in order to guarantee the power balance of the microgrid by means of scheduling the power generation of fuel cell cars. To compensate the uncertainty in the prediction of the load, three robust model predictive control methods are designed. Simulation of a case study compares the developed control methods and the performance of each method is evaluated.

Fuel cell electric vehicles convert chemical energy of hydrogen into electricity to power their motor. Since cars are used for transport only during a small part of the time, energy stored in the on-board hydrogen tanks of fuel cell vehicles can be used to provide power when cars are parked. In this paper, we present a community microgrid with photovoltaic systems, wind turbines, and fuel cell electric vehicles that are used to provide vehicle-to-grid power when renewable power generation is scarce. Excess renewable power generation is used to produce hydrogen, which is stored in a refilling station. A central control system is designed to operate the system in such a way that the operational costs are minimized. To this end, a hybrid model for the system is derived, in which both the characteristics of the fuel cell vehicles and their traveling schedules are considered. The operational costs of the system are formulated considering the presence of uncertainty in the prediction of the load and renewable energy generation. A robust min-max model predictive control scheme is developed and finally, a case study illustrates the performance of the designed system.