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.

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In this thesis, we design control algorithms for power scheduling of a fleet of fuel cell cars in a microgrid. Fuel cell cars are a relatively new type of vehicles. The driving force of these cars comes from an electrical motor and in order to generate the required electricity for the operation of the motor, the vehicle is equipped with a fuel cell system. The purpose of the fuel cell system is to convert the chemical energy of hydrogen into electricity. By considering the fact that fuel cell cars have the ability to generate electricity from hydrogen, these type of vehicles can be considered as a new type of flexible power plant. The idea of generating electricity inside a parking lot by using fuel cell cars is what we refer to as the Car as Power Plant (CaPP) concept. In this PhD thesis, we consider the power scheduling problem of a fleet of fuel cell cars in the CaPP concept. Several robust model predictive control methods are developed to determine the power generation schedule of the fuel cell cars inside the microgrid. @en

In recent years, the European Union (EU) has set ambitious targets toward a carbon-free energy transition. Many studies show that a drastic reduction in greenhouse gas emissions-at least 90% by 2050-is required. In the transition toward a sustainable energy system, solar (or green) hydrogen plays many important roles, as it is a clean and safe energy carrier that can also be used as a fuel in transportation and in electricity production. To understand and steer the transition from the current energy system toward an integrated hydrogenbased energy and transport system, we propose a framework that integrates a technical and economic feasibility study, a controllability study, and institutional analysis. This framework is applied to the Car as Power Plant (CaPP) concept, which is an integrated energy and transport system. Such a system consists of a power system based on wind and solar power, conversion of renewable energy surpluses to hydrogen using electrolysis, hydrogen storage and distribution, and hydrogen fuel cell vehicles that provide mobility, electricity, heat, and water. Controlling these vehicles in their different roles and designing an appropriate organizational system structure are necessary steps in the feasibility study. Our proposed framework for a future 100% renewable energy system is presented through a case study.@en

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.

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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.

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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.

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Recently, the idea of using fuel cell vehicles as the future way of producing electricity has emerged. A fuel cell car has all the necessary devices on board to convert the chemical energy of hydrogen into electricity. This paper considers a scenario where a parking lot for fuel cell cars acts as a virtual power plant. In order to describe the system behavior from the energy point of view, a hybrid (mixed logical dynamical) model is constructed. With this model, a control system is designed to determine the production profile for both the fuel cell and battery of each car in the parking lot subject to minimizing the operational cost. In order to deal with both the uncertainty in the demand profile and the power balance constraint, a robust min-max model predictive control algorithm is developed. The effectiveness of the proposed approach is illustrated in a numerical example.@en

This addendum contains the Lemmas and their proofs that are used to simplify the optimization problem developed in Section 4 of the manuscript “Fuel cell cars in a microgrid for synergies between hydrogen and electricity networks” by F. Alavi, E. Park Lee, N. van de Wouw, B. De Schutter, and Z. Lukszo, Apr. 2015.@en