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Latest results on quiescent and post-disruption runaway electrons mitigation experiments at Frascati Tokamak Upgrade.

Analyses of experimental data collected in the last FTU campaign provide interesting results on Runaway Electrons (REs) suppression by means of large (wrt FTU plasma volume) deuterium pellets on RE quiescent discharges, mainly inducing bursts of MHD activity that expel the RE seeds. This phenomenon was found to be very reproducible on discharges at 0.5 MA and 5.3 T. Pellets injected during current ramp-ups are also shown to lead to a complete suppression of the RE seed; however, they triggered disruptions as well. These promising results on pellet cleaning effects for different RE quiescent scenarios are discussed. Avalanche multiplication of REs after single pellet injection on 0.36 MA RE quiescent discharges is reported. We provide quantitative indications of RE mitigation effects in terms of increased Connor-Hastie (critical) electrical field due to Anomalous Doppler Instabilities (ADI) for RE quiescent scenarios and post-disruption RE beams. Analysis of large fan-like instabilities on post-disruption RE beams, that seem correlated to the applied negative electrical field and the background density drops, revealed their strong capability to dissipate the RE energy increasing their pitch angle, expelling some of them, and transferring a large fraction of their magnetic energy to the background plasma. Then, ADI can lead to efficient RE energy dissipation (on FTU) as well as full conversion from runaway into thermal electrons (on TCV), indicating a new possible strategy for RE mitigation by controlling the onset of large fan-like instabilities. We also show that short pulses of ECRH, combined with D2 gas puffing, generate a density increase that gives way to a density decrease, when ECRH is switched off, triggering ADI and opening the path to a possible alternative RE mitigation strategy.

A standard assumption in control of network dynamical systems is that its nodes interact through pairwise interactions, which can be described by means of a directed graph. However, in several contexts, multibody, directed interactions may occur, thereby requiring the use of directed hypergraphs rather then digraphs. For the first time, we propose a strategy, inspired by the classic pinning control on graphs, that is tailored for controlling network systems coupled through a directed hypergraph. By drawing an analogy with signed graphs, we provide sufficient conditions for controlling the network onto the desired trajectory provided by the pinner, and a dedicated algorithm to design the control hyperedges.

In this paper, a novel model for price management systems in resource allocation problems is proposed. Stochastic customer requests for resource allocations and releases are modelled as constrained parallel Birth–Death Processes (BDP). We address both instant (i.e. the customer requires a resource to be allocated immediately) and advance (i.e. the customer books a resource for future use) reservation requests, the latter with both bounded and unbounded time interval options. Algorithms based on Dynamic Programming (DP) principles are proposed for the calculation of suitable price profiles. At the core of such algorithms, there is the resolution of stochastic optimisation problems. In particular, the maximisation of the expected total revenue is formulated via a constrained Stochastic Dynamic Programming (SDP) approach, which becomes time-variant in case of advance reservation requests. Approximate Dynamic Programming (ADP) techniques are adopted in case of large state spaces. Simulations are performed to show the effectiveness of the proposed models and the related algorithms.

This paper presents a computationally efficient novel heuristic approach for solving the combined heat and power economic dispatch (CHP-ED) problem in residential buildings considering component interconnections. The proposed solution is meant as a substitute for the cutting-edge approaches, such as model predictive control, where the problem is a mixed-integer nonlinear program (MINLP), known to be computationally-intensive, and therefore requiring specialized hardware and sophisticated solvers, not suited for residential use. The proposed heuristic algorithm targets simple embedded hardware with limited computation and memory and, taking as inputs the hourly thermal and electrical demand estimated from daily load profiles, computes a dispatch of the energy vectors including the CHP. The main idea of the heuristic is to have a procedure that initially decomposes the three energy vectors’ requests: electrical, thermal, and hot water. Then, the latter are later combined and dispatched considering interconnection and operational constraints. The proposed algorithm is illustrated using series of simulations on a residential pilot with a nano-cogenerator unit and shows around 25–30% energy savings when compared with a meta-heuristic genetic algorithm approach.

Efficient energy production and consumption are fundamental points for reducing carbon emissions that influence climate change. Alternative resources, such as renewable energy sources (RESs), used in electricity grids, could reduce the environmental impact. Since RESs are inherently unreliable, during the last decades the scientific community addressed research efforts to their integration with the main grid by means of properly designedenergy storage systems(ESSs). In order to highlight the best performance from these hybrid systems, proper design and operations are essential. The purpose of this paper is to present a so-called model predictive controller (MPC) for the optimal operations of grid-connected wind farms with hydrogen-based ESSs and local loads. Such MPC has been designed to take into account the operating and economical costs of the ESS, the local load demand and the participation to the electricity market, and further it enforces the fulfillment of the physical and the system's dynamics constraints. The dynamics of the hydrogen-based ESS have been modeled by means of the mixed-logic dynamic (MLD) framework in order to capture different behaviors according to the possible operating modes. The purpose is to provide a controller able to cope both with all the main physical and operating constraints of a hydrogen-based storage system, including the switching among different modes such as ON, OFF, STAND-BY and, at the same time, reduce the management costs and increase theequipment lifesaving. The case study for this paper is a plant under development in the north Norway. Numerical analysis on the related plant data shows the effectiveness of the proposed strategy, which manages the plant and commits the equipment so as to preserve the given constraints and save them from unnecessary commutation cycles.

In this paper, resource allocation problems are formulated via a set of parallel birth–death processes (BDP). This way, we can model the fact that resources can be allocated to customers at different prices, and that customers can hold them as long as they like. More specifically, a discretisation approach is applied to model resource allocation problems as a set of discrete-time BDPs, which are then integrated into one Markov decision process. The stochastic dynamics of the resulting system are also investigated. As a result, revenue management becomes a stochastic decision-making problem, where price managers can propose suitable prices to the allocation requests such that the maximum expected total revenue is obtained at the end of a predefined finite time horizon. Stochastic Dynamic Programming is employed to solve the related optimisation problem with the support of an ad-hoc Matlab-based application. Several simulations are performed to prove the effectiveness of the proposed model and the optimisation approach.

Results from the last FTU campaigns on RE mitigation strategies for quiescent and post-disruption RE beams are presented. We provide experimental evidence that for some RE quiescent scenarios D-2 solid pellets achieve complete RE suppression capability, mainly due to the induced burst MHD activity expelling RE seed, whereas in other cases we report clear indications of avalanche multiplication of RE. Results on the assimilation of solid deuterium pellets on RE quiescent scenarios are provided. Quantitative indications of dissipative effects of anomalous Doppler instabilities (ADI) and MHD activity, in terms of critical electric field increase, is introduced and supported by experimental evidence. Multiple analysis are provided to show the significant energy conversion/dissipation of large ADI on post-disruption RE beams suggesting new strategies for RE energy suppression. We also demonstrate experimentally that modulated ECRH could be used for ADI pacing.

This work presents the current status of the design of Central Instrumentation and Control Systems for the IFMIF-DONES plant. IFMIF-DONES is the International Fusion Materials Irradiation Facility-DEMO Oriented NEutron Source, an accelerator-based neutron source producing a high-energy deuterons beam focused on a fast flowing liquid lithium jet to generate high-energy neutrons via stripping reactions. The intensity and irradiation volume is suitable to generate material irradiation data for design, licensing, construction and safe operation of the DEMO fusion reactor. After a general overview of the current status of the design of the overall architecture, a particular attention is given to the data management including the EPICS control framework, the general rules and criteria for the design and implementation of the HMI system, and the timing system solution composed of a three-layer distribution network to deliver time at each element of the plant that implements an optimal trade-off between timing performance and cost. Eventually, the next design steps and future work are outlined.

Designing artificial cyber-agents able to interact with human safely, smartly and in a natural way is a current open problem in control. Solving such an issue will allow the design of cyber-agents capable of co-operatively 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 has been proposed of artificial agents interacting and coordinating their movements with those of a patient suffering from social or motor disorders. Specifically, it has been shown that an artificial agent exhibiting certain kinematic properties could provide innovative and efficient rehabilitation strategies for these patients. Moreover, it has also been shown that the level of motor coordination is enhanced if these kinematic properties are similar to those of the individual it is interacting with. In this paper we discuss, first, a new method based on Markov Chains to confer "human motor characteristics" on a virtual agent, so as 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 control architecture based on reinforcement learning to synthesize a cyber-agent able to mimic the behaviour of a specific human performing a joint motor task with one or more individuals., Comment: 16 pages, 13 figures

In many real-word scenarios, humans and robots are required to coordinate their movements in joint tasks to fulfil a common goal. While several examples regarding dyadic human robot interaction exist in the current literature, multi-agent scenarios in which one or more artificial agents need to interact with many humans are still seldom investigated. In this paper we address the problem of synthesizing an autonomous artificial agent to perform a paradigmatic oscillatory joint task in human ensembles while exhibiting some desired human kinematic features. We propose an architecture based on deep reinforcement learning which is flexible enough to make the artificial agent interact with human groups of different sizes. As a paradigmatic coordination task we consider a multi-agent version of the mirror game, an oscillatory motor task largely used in the literature to study human motor coordination.

In this letter, we study the problem of controlling via pinning the motion of nonlinear network systems of any order whose dynamics are in controllable canonical form. Different from existing works that either focus on spontaneous synchronization, assume linear dynamics or rely on dynamics cancellation, here we provide a constructive method to prove pinning controllability towards the desired trajectory selected by the pinner. We introduce an algorithmic procedure that associates to any connected topology a suitable Lyapunov function for the network system. The approach is demonstrated on an illustrative example.

PROTO-SPHERA is a magnetic confinement device for controlled fusion experiments which forms a plasma spherical torus not around a metal centerpost (as in spherical tokamaks), but around a plasma centerpost. Thanks to a vacuum vessel in polymethylmethacrylate (PMMA) transparent to visible light, a system of six fast visible light cameras has been implemented around the PROTO-SPHERA experiment so as to perform an optical tomography. To do so, synchronous triggered frames acquisition and exposure time control have been performed using the MARTe2 software framework. Also, a proper calibration/reconstruction of each line of sight for all the cameras has been implemented, so as to compute a 3D representation of the vessel. A 2D tomography inversion of the plasma column equatorial plane has been performed under the consideration that the value of each pixel represents the integrated brightness along its line of sight. The camera acquisition system, the experimental setup and the obtained results will be presented.

This paper proposes an output power smoothing strategy for a grid-connected wind-hydrogen plant. An Energy Storage System (ESS) composed of an electrolyzer and a fuel cell is used to smooth the fluctuating output power of the wind plant. The aim of this study is to propose a multi-objective optimization model for joint wind farm and energy storage operation, to smooth the wind power output, and to track a load demand subject to a variety of constraints on the system model. Based on a modeling adopting mixed-integer constraints and dynamics, the problem of output power fluctuations and smooth tracking of load demand is solved through the implementation of an optimal controller which follows a sequential optimization technique. We illustrate the effectiveness of the proposed controller with a simulation example, employing real equipment data and wind profiles.

In many joint-action scenarios, humans and robots have to coordinate their movements to accomplish a given shared task. Lifting an object together, sawing a wood log, transferring objects from a point to another are all examples where motor coordination between humans and machines is a crucial requirement. While the 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 able to coordinate its motion in human ensembles. Driven by a control architecture based on deep reinforcement learning, such an artificial agent will be able to autonomously move itself in order to synchronise its motion with that of the group while exhibiting human-like kinematic features. As a paradigmatic coordination task we take a group version of the so-called mirror-game which is highlighted as a good benchmark in the human movement literature.

The intermittent nature of wind energy combined with the penalty deviations adopted in several electricity regulation markets explains the difficulty of this clean energy in playing a major role in the energy system. Coupling the wind farm with advanced energy storage systems represents, in principle, a good solution for these problems. To date, several researches have been conducted on storage technology, but the problem of finding the best ESS solution is still open. Indeed, every storage technology has its own constraints and limitations in terms of capital cost, response time, operational, maintenance and degradation issues. This highlights the importance of advanced control algorithms for energy storage management systems to mitigate the problems outlined. In this paper, we model a hydrogen-based energy storage system in terms of operating constraints and cost/degradation features. Via a MPC based controller and mixed-integer linear constraints and dynamics, we address the problem of satisfying a forecasted power demand. The paper collects the preliminary ideas for the EU-FCH 2 JU (European Union Fuel Cells and Hydrogen 2 Joint Undertaking) founded project HAEOLUS aiming at building and integrating advanced control strategies for a hydrogen based ESS within a wind farm fence. Numerical simulations corroborate the feasibility and the effectiveness of the proposed approach.

Value function approximation has a central role in Approximate Dynamic Programming (ADP) to overcome the so-called curse of dimensionality associated to real stochastic processes. In this regard, we propose a novel Least-Squares Temporal Difference (LSTD) based method: the ‘‘Multi-trajectory Greedy LSTD’’ (MG-LSTD). It is an exploration-enhanced recursive LSTD algorithm with the policy improvement embedded within the LSTD iterations. It makes use of multi-trajectories Monte Carlo simulations in order to enhance the system state space exploration. This method is applied for solving resource allocation problems modeled via a constrained Stochastic Dynamic Programming (SDP) based framework. In particular, such problems are formulated as a set of parallel Birth–Death Processes (BDPs). Some operational scenarios are defined and solved to show the effectiveness of the proposed approach. Finally, we provide some experimental evidence on the MG-LSTD algorithm convergence properties in function of its key-parameters. © 2020 Elsevier Ltd. All right

The use of electric power by wind generation in actual grids is hampered by its inherent stochastic nature and the penalty deviations adopted in several electricity regulation markets with respect to power quality requirements. Coupling wind farms with advanced Energy Storage Systems (ESS) can help their integration within grids. In this direction, several studies have been conducted, but the problem is still open due to the constraints and limitations regarding the ESSs time autonomy, time response, degradation issues and overall costs. In order to take into account these relevant aspects, advanced control algorithms are needed. In this paper, a Model-Based Predictive Controller (MPC) is presented. Such a controller minimizes the degradation of the ESS and the load tracking error while fulfilling the operational constraints and dynamics. The ESS considered is hydrogen-based and the study has been developed within the EU-FCH 2 JU (European Union Fuel Cells and Hydrogen 2 Joint Undertaking) funded project HAEOLUS aiming at building and integrating advanced control strategies for a hydrogen-based ESS within a wind farm fence. Numerical simulations show the feasibility and the effectiveness of the proposed approach.

The interaction of robots or physical/virtual avatars with humans will be increasingly common in a number of different scenarios intended to improve the quality of human life. For example, in the domain of healthcare, they can support therapists aiding patients in need of motor rehabilitation and so on. In this context, a fundamental control problem is to synthesize strategies to make the artificial agents interact with humans in a “natural” and human-like fashion. This is particularly relevant when artificial agents are required to coordinate their motion with humans performing joint tasks. It has been shown that for rehabilitation purposes, virtual agents in motor coordination tasks must exhibit certain kinematic properties (or Individual Motor Signature) that are characteristic of human motion. In this paper we discuss a method based on the use of Markov chain to generate artificial individual motor signatures that can be used to provide online reference signals for the control of virtual agents. The methodology is also used to classify and identify individual motor signatures belonging to individuals affected by social disorders.

This paper investigates the problem of event-triggered pinning control for the synchronization of networks of nonlinear dynamical agents onto a desired reference trajectory. The pinned agents are those that have access to the reference trajectory. We consider both static and switching topologies. We prove that the system is well posed and identify conditions under which the network achieves exponential convergence. A lower bound for the rate of convergence is also derived. Numerical examples demonstrating the effectiveness of the results are provided.

This study proposes a multi-level model predictive control (MPC) for a grid-connected wind farm paired to a hydrogen-based storage system (HESS) to produce hydrogen as a fuel for commercial road vehicles while meeting electric and contractual loads at the same time. In particular, the integrated system (wind farm þ HESS) should comply with the “fuel production” use case as per the IEA-HIA report, where the hydrogen production for fuel cell electric vehicles (FCEVs) has the highest unconditional priority among all the objectives. Based on models adopting mixed-integer constraints and dynamics, the problem of external hydrogen consumer requests, optimal load demand tracking, and electricity market participation is solved at different timescales to achieve a long-term plan based on forecasts that then are adjusted at real-time. The developed controller will be deployed onto the management platform of the HESS which is paired to a wind farm established in North Norway within the EU funded project HAEOLUS. Numerical analysis shows that the proposed controller efficiently manages the integrated system and commits the equipment so as to comply with the requirements of the addressed scenario. The operating costs of the devices are reduced by 5%, which corresponds to roughly 300 commutations saved per year for devices.

The COVID-19 epidemic hit Italy particularly hard, yielding the implementation of strict national lockdown rules. Previous modelling studies at the national level overlooked the fact that Italy is divided into administrative regions which can independently oversee their own share of the Italian National Health Service. Here, we show that heterogeneity between regions is essential to understand the spread of the epidemic and to design effective strategies to control the disease. We model Italy as a network of regions and parameterize the model of each region on real data spanning over two months from the initial outbreak. We confirm the effectiveness at the regional level of the national lockdown strategy and propose coordinated regional interventions to prevent future national lockdowns, while avoiding saturation of the regional health systems and mitigating impact on costs. Our study and methodology can be easily extended to other levels of granularity to support policy- and decision-makers., An ongoing global debate concerns effective and sustainable lockdown release strategies in the current pandemic. Here, the authors implement a network model at healthcare-relevant spatial scale to show that coordinated local strategies can be effective in containing further resurgence of the disease.