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.

Mini-drones have a wide range of applications, including weather monitoring, parcel delivery, search and rescue, and entertainment. As their functionality, safety, and performance heavily relies on ubiquitously reliable positioning and navigation, their applications are relatively limited to outdoor environments where Global Positioning System (GPS) and/or similar are available. Indoor localization is improving, e.g, using Visual Simultaneous Localization and Mapping (V-SLAM). However, for the applications of mini-drone navigation with a higher safety requirements, further improvements are still needed. This paper proposes a novel approach to improve the localization performance for mini-drone indoor navigation. The proposed approach enhances V-SLAM techniques by combining Oriented Rotated Brief SLAM (ORB-SLAM2) and Semi-direct monocular Visual Odometry (SVO) algorithms along with an Adaptive Complementary Filter (ACF). The results show that the proposed approach improvement in position estimation in the different conditions (low light, low texture and dynamic environments) in comparison with other commonly used indoor positioning approaches.

A Nonlinear Model Predictive Control (NMPC) strategy aimed at controlling a small-scale car model for autonomous racing competitions is presented in this paper. The proposed control strategy is concerned with minimizing the lap time while keeping the vehicle within track boundaries. The optimization problem considers both the vehicle’s actuation limits and the lateral and longitudinal forces acting on the car modeled through the Pacejka’s magic formula and a simple drivetrain model. Furthermore, the approach allows to safely race on a track populated by static obstacles generating collision-free trajectories and tracking them while enhancing the lap timing performance. Gazebo simulations using the F1/10 simulator showcase the feasibility and validity of the proposed control strategy. The code is released as open-source making it possible to replicate the obtained results.

Due to the increasingly dominant climate change, "green" energy sources that do not contribute to further damage of the environment play an emerging and relevant role. A big advantage is that this kind of energy can be generated not only on a large scale, such as by wind turbines or hydroelectric power plants, but also by individual households through (among others) so-called Photovoltaic-Battery (PVB) systems which are based on solar energy. Compared with energy from the power grid, such systems naturally involve higher acquisition costs, which can, however, potentially be amortized. This raises the question of how large such systems should be designed, what the optimal strategy of usage is, and how relevant typical sizes of the individual elements available on the market are to the design strategy. Against this background, we investigate the problems addressed, taking into account different pricing strategies, and show, based on a simulation study, that PVB systems can bring major benefits in the long term. Additionally, our results indicate, that – to a certain extent – efficient energy management is able to compensate for limitations in sizing.

Complexity of manufacturing systems and variability in anomalous operations make fault detection and diagnosis in industrial systems a challenging task. In steel industries characterized by high temperatures and pressures, elevated production speeds, and intense throughput, the early diagnosis of an incoming fault is highly relevant for both safety and economic reasons. However, expensive preventive maintenance and early substitution of equipment are largely adopted, hence strongly limiting the availability of data related to fault events and the applicability of standard machine learning methods. In this work, we present a one class-support vector machine (OC-SVM) approach to early detect anomalies in steel making plants; we validate our method using production data gathered from a steel making industry placed in the South of Italy and compare performance with a multivariate statistical method recently designed for the fault detection of the same plant. The study revealed that OC-SVM outperforms the statistical method, and also is able to predict breakdowns.

In the reverse Stackelberg mechanism, by considering a decision function for the leader rather than a decision value in the conventional Stackelberg game, the leader can explore a wider decision space. This flexibility can result in realizing the globally optimal solution of the leader's objective function, while controlling the reaction function of the followers, simultaneously. We consider an aggregator who purchases energy from the wholesale energy market. The aggregator acts as the leader for a group of plugged in electric vehicles (PEVs) and determines the price of energy versus consumption at each hour a day as its decision function. In the followers level, since the optimal charging strategies of the PEVs are coupled through the electricity price, the PEVs in a group are considered to cooperate in finding their Nash-Pareto-optimal charging strategy, by minimizing a social cost function. For a large number of PEVs, the cooperative cost minimization of PEVs can be modeled as a cooperative mean-field (MF) game. We propose a decentralized MF optimal control algorithm and prove that the algorithm converges to leader-follower MF εN-Nash equilibrium point of the game. Furthermore, a decentralized reverse Stackelberg algorithm is implemented to achieve the optimal linear price function of the leader. Simulation results and comparison with benchmark methods are performed to demonstrate the advantages of the proposed method.

This article presents a novel solution technique for scheduling multi-energy system (MES) in a commercial urban building to perform price-based demand response and reduce energy costs. The MES scheduling problem is formulated as a mixed integer nonlinear program (MINLP), a nonconvex NP-hard problem with uncertainties due to renewable generation and demand. A model predictive control approach is used to handle the uncertainties and price variations. This in-turn requires solving a time-coupled multitime step MINLP during each time-epoch, which is computationally intensive. This investigation proposes an approach called the scenario-based branch-and-bound (SB3), a light-weight solver to reduce the computational complexity. It combines the simplicity of convex programs with the ability of meta-heuristic techniques to handle complex nonlinear problems. The performance of the SB3 solver is validated in the Cleantech building, Singapore and the results demonstrate that the proposed algorithm reduces energy cost by about 17.26% and 22.46% as against solving a multi-time step heuristic optimization model.

Utility companies are an integral part of the smart grid, providing consumers with a broad range of energy management programs. The quality of service is based on the measurements obtained from smart metering infrastructures, which can further be improved by sensing at finer resolutions. However, sensing at higher resolutions poses serious challenges both in terms of storage and communication overload due to overgrowing traffic. Compressive sensing is a data compression technique that accounts for the sparsity of electricity consumption pattern in a transformation basis and achieves subNyquist compression. To the best of the authors' knowledge, this is the first study to use the semi-tensor product (STP) for compressed sensing (CS) of power consumption data in the smart grid. In contrast to the conventional CS, the proposed approach has the advantage of reducing the dimension of the sensing matrix needed to sense the signal, thereby significantly lowering the storage requirements. In this regard, we present a comparative study highlighting the difference in compression performance with the conventional CS and STP based CS, where the transformation basis used is Haar and Hankel. We present the results on three publicly available datasets at different sampling rates and outline the key findings of the study.

Probabilistic Boolean control network (PBCN) is a discrete-time dynamical system comprised of a collection of Boolean control networks (BCNs) and switching among them in a stochastic manner. In this paper, the output tracking control of PBCNs is investigated via state feedback and output feedback control. By resorting to the algebraic state-space representation of BCNs, necessary and sufficient conditions for the solvability of the output tracking control problem are presented. A constructive procedure is given to obtain all possible state feedback and output feedback controllers such that the output of PBCNs tracks a constant reference signal. Finally, a PBCN model of a simple manufacturing system is considered to illustrate the effectiveness of the proposed results.

Energy management in electric grids with multiple energy sources, generators, storage devices, and interacting loads along with their complex behaviors requires grid wide control. Communication infrastructure that aggregates information from heterogeneous devices in the electric grid making the applications completely independent of physical connectivity is essential for building in the context of control applications. This investigation presents a semantic middleware that is used to implement a receding-horizon-based optimal power flow (OPF) in smart grids. The presence of renewable energy sources, storage systems, and loads dispersed all along the grid necessitates the use of grid wide control and a communication infrastructure to support it. To this extent, the proposed middleware will serve as the basis for representing various components of the power grid. It is enriched with intelligence by semantic annotation and ontologies that provide situation awareness and context discovery. The middleware deployment is demonstrated by implementing the receding horizon OPF in a network in Steinkjer, Norway. Our results demonstrate the advantages of both the middleware and the algorithm. Furthermore, the results prove the added flexibility obtained in the grid due to the addition of renewable energy and storage systems. The significant advantage of the proposed approach is that the real-time monitoring infrastructure is used for improving the flexibility, reliability, and efficiency of the grid.

Two major challenges in securing reliable Optimal Power Flow (OPF) operations are: (i) fluctuations induced due to renewable generators and energy demand, and (ii) interaction and interoperability among the different entities. Addressing these issues requires handling both physical (e.g., power flows) and cyber aspects (computing and communication) of the energy grids, i.e, a cyber-physical systems (CPS) approach is necessitated. First, this investigation proposes a receding horizon control (RHC) based approach for solving OPF to deal with the uncertainties. It uses forecasts on renewable generation and demand and an optimization model solving a predictive control problem to secure energy balance while meeting the network constraints. Second, to handle the interoperability issues, a middleware using common information model (CIM) for exchanging information among applications and the associated profiles are presented. CIM profiles modelling various components and aspects of the RHC based OPF is proposed. In addition, a middleware architecture and services to collect information is discussed. The proposed CPS approach is illustrated in a distribution grid in Steinkjer, Norway having 85 nodes, 700 customers, three hydrogenerators, and various industrial loads. Our results demonstrate the benefits of CPS approach to implement OPF addressing also the interoperability issues.

Operating heating power plant (DHPP) with fluctuating load is a complex problem. Thermal energy storage (TES), flexible loads, and operating constraints compound this complexity further. This investigation focuses on the design of a model predictive controller (MPC) that reduces the operating and maintenance cost in a DHPP, considering TES and flexible loads. The MPC accomplishes this task by scheduling boilers, TES units, and flexible loads. To handle the fluctuating demand, the MPC uses forecasts and combines it with a constrained optimization problem. The objective function reflects the cost, whereas the generator limits, TES dynamics, thermal loads, including supply temperature, power plant layout, and reliability, are the constraints. The resulting optimization problem is modeled as a mixed-integer linear program with both continuous and logic variables. Here the logic variables model the operating modes of the boiler and storage units. The use of receding horizon approach enhances the robustness to the forecast errors. The constraints modeling plant layout, supply temperature, and grid reliability lead to a more realistic solution. The MPC is illustrated using simulation on historical data and experiments on a DHPP at Ylivieska, Finland. Our results demonstrate the cost benefits of the proposed approach.

This paper presents an algorithm for managing the demand and supply in a shared transportation system. In particular we present a method, independent from the Geographic Information System (GIS), which processes drivers and passengers requests and ranks them in order to encourage matching and to propose the solution profitable for all. The basic idea is to give priority to the requests of passengers with more common route and avoid those with greater excess path. In the end, we propose a solution for the distribution of costs among the participants of shared travel based on the application of the Shapley value.