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.

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 an HVAC system.

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.

In this article, we presented a hydrogen -based integrated energy and transport system named CaPP. The CaPP concept is a complex sociotechnical system. A large network of players is involved in the development and operation of its technical infrastructure and physical components. To illustrate how the CaPP system will work, we designed a 100% renewable integrated energy and transport system for a smart city area based on wind, solar, hydrogen, and FCEVs inspired by the city of Hamburg, Germany. Using technoeconomic analysis, we have shown that such a design is technically feasible. However, technical feasibility cannot be guaranteed without considering the controllability of the system. So, the next challenge was to maintain the supply -demand balance as well as to minimize the operational costs of the FCEVs, which we have done by using advanced control techniques. We stressed that operation of such an innovative concept should be accompanied by an institutional analysis and by designing an organizational system structure. To this end, we studied the system behavior using different contracts between the system agents, i.e., the owners of FCEVs and the aggregators.New policies to be defined for carbon -free energy transition are manifold, and policymakers require broader knowledge from different disciplines to address the challenges of such system transition. Our framework stresses the need to consider different aspects such as technology, economics, control, institutional, and social perspectives in modeling energy systems. As such, it provides a clearer and more comprehensive insight into the realization of such an energy system to policymakers, compared with the individual models. Moreover, to realize a carbon -free energy system, sector coupling is needed, i.e., the energy and transport sectors should support each other. For this matter, V2G is a promising technology, and FCEVs give more fl exibility than standard battery EVa since, in addition to storage, they can operate as dispatchable power plants independent of the electricity grid. The CaPP system and our combined framework are an example of such a carbon -free energy system offering sector coupling and facilitating the penetration of 100% intermittent renewables without any compromise on reliability of energy supply for power, heat, and transport and, at the same time, reducing system cost. Moreover, our approach will engage consumers to have a more active role in the energy transition as prosumers. The future research will also include grid modeling to explore different system configurations, e.g., hydrogen pipeline grids next to electricity grids, and to investigate whether a combination of battery and FCEVs can reduce total system cost even further.

We investigate trajectory generation algorithms that allow a satellite to autonomously rendezvous and dock with a target satellite to perform maintenance tasks, or transport the target satellite to a new operational location. We propose different path planning strategies for each of the phases of rendezvous. In the first phase, the satellite navigates to a point in the Line of Sight (LOS) region of the target satellite. We show that the satellite's equations of motion are differentially flat in the relative coordinates, hence the rendezvous trajectory can be found efficiently in the flat output space without a need to integrate the full nonlinear dynamics. In the second phase, we use model predictive control (MPC) with linearized dynamics to navigate the spacecraft to the final docking location within a constrained approach envelope. We demonstrate feasibility of this study by simulating a sample docking mission.