Scheduled charging offers the potential for electric vehicles (EVs) to use renewable energy more efficiently, lowering costs and improving the stability of the electricity grid. Many studies related to EV charge scheduling found in the literature assume perfect or highly accurate knowledge of energy demand for EVs expected to arrive after the scheduling is performed. However, in practice, there is always a degree of uncertainty related to future EV charging demands. In this work, a Model Predictive Control (MPC) based smart charging strategy is developed, which takes this uncertainty into account, both in terms of the timing of the EV arrival as well as the magnitude of energy demand. The objective of the strategy is to reduce the peak electricity demand at an EV parking lot with PVarrays. The developed strategy is compared with both conventional EV charging as well as smart charging with an assumption of perfect knowledge of uncertain future events. The comparison reveals that the inclusion of a 24 h forecast of EV demand has a considerable effect on the improvement of the performance of the system. Further, strategies that are able to robustly consider uncertainty across many possible forecasts can reduce the peak electricity demand by as much as 39% at an office parking space. The reduction of peak electricity demand can lead to increased flexibility for system design, planning for EV charging facilities, deferral or avoidance of the upgrade of grid capacity as well as its better utilization@en

The current focus on the massive CO2 reduction highlights the need for the rapid development of technology for the production, storage, transportation and distribution of renewable energy. In addition to electricity, we need other forms of energy carriers that are more suitable for energy storage and transportation. Hydrogen is one of the main candidates for this purpose, since it can be produced from solar or wind energy and then stored; once needed, it can be converted back to electricity using fuel cells. Another important aspect of future energy systems is sector coupling, where different sectors, e.g. mobility and energy, work together to provide better services. In such an integrated system, electric vehicles – both battery and hydrogen-based fuel cell – can provide, when parked, electricity services, such as backup power and balancing; when driving they produce no emissions. In this paper we present the concept design and energy management of such an integrated energy and mobility system in a real-life environment at the Shell Technology Centre in Amsterdam. Our results show that storage using hydrogen and salt caverns is much cheaper than using large battery storage systems. We also show that the integration of electric vehicles into the electricity network is technically and economically feasible and that they can provide a flexible energy buffer. Ultimately, the results of this study show that using both electricity and hydrogen as energy carriers can create a more flexible, reliable and cheaper energy system at an office building.

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The objective of this paper is to provide a concise introduction to the max-plus algebra and to max-plus linear discrete-event systems. We present the basic concepts of the max-plus algebra and explain how it can be used to model a specific class of discrete-event systems with synchronization but no concurrency. Such systems are called max-plus linear discrete-event systems because they can be described by a model that is “linear” in the max-plus algebra. We discuss some key properties of the max-plus algebra and indicate how these properties can be used to analyze the behavior of max-plus linear discrete-event systems. Next, some control approaches for max-plus linear discrete-event systems, including residuation-based control and model predictive control, are presented briefly. Finally, we discuss some extensions of the max-plus algebra and of max-plus linear systems.

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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 present shrinking horizon model predictive control for discrete-time linear systems under stochastic disturbances with constraints encoded as signal temporal logic (STL) specification. The control objective is to satisfy a given STL specification with high probability against stochastic uncertainties while maximizing the robust satisfaction of an STL specification with minimum control effort. We formulate a general solution, which does not require precise knowledge of probability distributions of (possibly dependent) stochastic disturbances; only the bounded support of the density functions and moment intervals are used. For the specific case of disturbances that are normally distributed, we optimize the controllers by utilizing knowledge of the probability distribution of the disturbance. We show that in both cases, the control law can be obtained by solving optimization problems with linear constraints at each step. We experimentally demonstrate effectiveness of this approach by synthesizing a controller for a heating, ventilation, and air conditioning system.

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The focus of this paper is on modeling and control of Smart Thermal Grids (STGs) in which the uncertainties in the demand and/or supply are included. We solve the corresponding robust model predictive control (MPC) optimization problem using mixed-integer-linear programming techniques to provide a day-ahead prediction for the heat production in the grid. In an example, we compare the robust MPC approach with the robust optimal control approach, in which the day-ahead production plan is obtained by optimizing the objective function for entire day at once. There, we show that the robust MPC approach successfully keeps the supply-demand balance in the STG while satisfying the constraints of the production units in the presence of uncertainties in the heat demand. Moreover, we see that despite the longer computation time, the performance of the robust MPC controller is considerably better than the one of the robust optimal controller.@en