Hydrogen is a promising energy vector for achieving renewable integration into the grid, thus fostering the decarbonization of the energy sector. This paper presents the control platform architecture of a real hydrogen-based energy production, storage, and re-electrification system (HESS) paired to a wind farm located in north Norway and connected to the main grid. The HESS consists of an electrolyser, a hydrogen tank, and a fuel cell. The control platform includes the management software, the control algorithms, and the automation technologies operating the HESS in order to address the three use cases (electricity storage, mini-grid, and fuel production) identified in the IEA-HIA Task24 final report, that promote the integration of wind energy into the main grid. The control algorithms have been already developed by the same authors in other papers using mixed-logical dynamical modeling, and implemented via a two-layer model predictive control scheme for each use case, and are quickly introduced in order to make evident their integration into the presented architecture. Simulation test runs with real equipment data, wind generation, load profiles, and market prices are also reported so as to highlight the control platform performances.Note to Practitioners—The paper develops the integration between the management platform of a HESS, paired to a real wind farm in northern Norway, and the control algorithms aimed at scheduling hydrogen production and re-electrification on the basis of several forecast streams about exogenous conditions and different possible operating modes of the wind-hydrogen system. The control algorithms address the three use cases identified by the IEA-HIA in the final report of Task 24 about the integration of wind energy into the grid, namely i) electricity storage, where the HESS is operated in order to enable the wind farm to power smoothing; ii) mini-grid, where the wind farm and the HESS form a mini-grid with a local load (small town) and the HESS is therefore operated in order to fulfill it without and with grid support (in this case buying and selling electricity to the market is also handled); and iii) fuel production, where the HESS is operated in order to fulfill a hydrogen demand (e.g., due to fuel cell vehicles). In addition to the specific objectives of each use case, the developed control algorithms also optimize the HESS operating costs and typically address two time-scale behaviors to appropriately handle corresponding long and short terms dynamics. The management platform of the HESS is arranged in three layers (physical, control, and supervision layers), and located in the cloud. The physical layer targets the physical components, sensors, and actuators. The automation layer includes all local controllers and modules used for measurement, and several servers for interactions between the higher and lower layers of the control architecture and databases. In the supervision layer, the execution of control algorithms and clients for remote diagnoses, monitoring, and top-management activities are located. Since each layer performs specific functionalities, a multi-tier architecture is implemented and the communications among the layers occur through services and microservices.

Designing artificial avatars able to interact with humans in a safe, smart, and natural way is a current open problem in control. Solving such an issue will allow the design of cyber-agents capable of cooperatively interacting with people in order to fulfil common joint tasks in a multitude of different applications. This is particularly relevant in the context of healthcare applications. Indeed, the use for rehabilitation has been proposed of artificial agents able to interact and coordinate their movements with those of patients suffering from social or motor disorders. Moreover, it has also been shown that the level of motor coordination between the avatar and the human patient is enhanced if the kinematic properties of the avatar’s motion are similar to those of the individual it is interacting with. In this article, we discuss, first, a new method based on Markov chains to confer “human motor characteristics” on the motion of a virtual agent so that it can coordinate its motion with that of a target individual while exhibiting specific kinematic properties. Then, we embed such synthetic model in a novel control architecture based on reinforcement learning to synthesize a cyber-agent able to mimic the behavior of a specific human performing a joint motor task with one or more individuals.

In many joint-action scenarios, humans and robots have to coordinate their movements to accomplish a given shared task. Examples include lifting an object together, sawing a wood log, transferring objects from a point to another. While dyadic coordination between a human and a robot has been studied in previous investigations, the multi-agent scenario in which a robot has to be integrated into a human group still remains a less explored field of research. In this paper we discuss how to synthesise an artificial agent, driven by a control architecture based on deep reinforcement learning, able to coordinate its motion in human ensembles. As a paradigmatic coordination task we take a group version of the so called mirror game from the human movement literature.

This paper addresses the problem of synchronization of piecewise smooth systems. We introduce conditions to guarantee bounded synchronization of two coupled PWS systems as a preliminary work for a future study of a huger network of discontinuous agents. Numerical examples are used to confirm theoretical results.