Technological Innovation for Cyber-Physical Systems

The alignment of strategies among the enterprises that belong to collaborative networks is of increasing importance due to the influence on the networks operation success in the long term. This paper proposes a Decision-Support Tool for Strategies Alignment (DST-SA) to support SMEs in the selection of strategies that allow higher levels of alignment amongst all the strategies formulated by each partner. The DST-SA includes a mathematical model, a simulation software, and a programmed application, to address the strategies alignment process from a collaborative perspective. The result of the DST-SA is the identification of the strategies that are aligned and the proper time to activate these strategies with the main aim of obtaining higher levels of network performance.

Cloud computing in conjunction with recent advances in Cyber-Physical Systems (CPSs) unravels new opportunities for the European manufacturing industry for high value-added products that can quickly reach the market for sale. The Internet of Things joins the Internet of Services to enact the fourth industrial revolution that digitalises the manufacturing techniques and logistics while pushing forward the development of improved factories with machine-to-machine communication delivering massively customised products tailored to the individualised needs of the customer. Interconnected CPSs using internal and cross-organizational services cooperate in real time increase the business agility and flexibility of manufacturing companies. Using CPS with cloud computing architectures leverage data and services stored and run in cloud environment from different vendors that usually offer different service interfaces to share their services and data. However, when using such architectures data silos appear and different vendors having different service interfaces can easily result in vendor lock-in issues. This paper proposes a multi-cloud marketplace architecture leveraging the existing myriad of different cloud environments at different abstraction levels including the Infrastructure-, Platform-, and Software-as-a-Service cloud models—that is, Everything as a Service or XaaS—delivering services and with different properties that can control the computation happening in multiple cloud environments sharing resources with each other.

In fast changing environments, companies and organizations have to continuously adapt their operating principles in response to new business or collaboration opportunities in order to remain competitive. The growing demand for high-quality services is taking organizations to format their operations to offer more personalized service packages and seek collaboration with other stakeholders. Moreover, provision of personalized services depends on frequent contextual information analysis. In the case of elderly care, the use of assistive technology is expected to have a positive contribution to the diversity of required services that support aging well. Demographic trends show that the percentage of elderly population is increasing, while ageing entails several limitations, calling for assistance services adapted to the specific needs of each person. These needs can evolve along the ageing process, requiring an evolution of the care services. In this paper, we make an overview of related concepts and propose a personalized and evolutionary care services model supported on collaborative networking, context-awareness and Internet of Things.

In this paper, the aim of automatically generating a cyber-physical control system (more precisely, an IEC61499 control system) is discussed. The method is enabled by ontology models, specifically the source plant ontology model and the target control model for the CPS system implemented in the preferred programming language. The transforming of ontologies is enabled by an extension of SWRL (called eSWRL) and it is introduced here. There interpreter of eSWRL is developed using the Prolog language. A case study Baggage Handling System is used to demonstrate how the ontology models are transformed and the corresponding transforming rules that are developed.

Building construction has gone through significant change with the emerging spread of ICT during last decades. “Intelligent buildings” are equipped with building automation systems (BAS) that can be remotely controlled and programmed and that are able to communicate and collaborate. However, BAS aim to facilitate operation of the building and do not provide sufficient support for strategic level decision support. This article presents adaptation of Semantic Sensor Network ontology for use in the field of building operation analysis. The Semantic BMS ontology enriches the SSN with model of building automation datapoints that gather operation data and describe the interconnections between BAS devices, algorithms and influenced or monitored properties of a building. Proposed ontology allows facility managers to query BAS systems in a way that is convenient for tactical and strategic level planning and that is unavailable in current state of the art systems.

Knowledge representation and use are fundamental processes in many areas. The use of a semantic referential (i.e, a domain ontology and a set of related tools to exploit it) to represent knowledge has allowed the development of new mechanisms of semantic search, inferences, and analysis of complex content, but the development of a semantic referential is still a complex task, time-consuming and fundamentally performed by knowledge holders. Taking that into account this work discusses the development of a semantic referential applied to botanical identification process in the Brazilian Amazon area, mainly focused on the mechanisms of interaction and access to a domain ontology (named Onto-AmazonTimber) based on JENA API and SPARQL queries. The main aspects of the development of this work are presented and discussed here. Current challenges and open points are also addressed.

This paper proposes a new modeling formalism for the specification of cyber-physical systems, combining the functionality offered by Petri nets and synchronous data flows. Petri nets have been traditionally used to model the behavior of reactive systems, whose state evolves depending on the interaction with external events. On the opposite, data-flow formalisms have been used predominantly to describe data-driven systems that produce output data through mathematical transformations applied to input signals. The proposed formalism covers both kinds of problems, offering support for the design of mixed systems containing linear control and signal processing operations along with event driven elements. Model composition using multiple components communicating through input and output signals and events, enable the implementation of distributed cyber-physical systems. The new formalism and the respective execution semantics are presented, with special attention to the bidirectional interaction between Petri net elements and data-flow nodes.

Available industrial standards do not explicitly consider how to treat sustainability indicators in PS design and its control system. Therefore, this paper proposes a framework to systematize the performance evaluation process for industrial PS considering indicators that qualify and quantify its sustainability. The framework adopts Petri net technique and extensions of the standard ANSI/ISA95. Simulation-based analysis, decision making techniques and a PS´s classification based on product green seal are also considered. Furthermore, the framework considers the processing information, storing and accessing each component using a Cyber Physical Technology due to the trend of PSs to be, in fact, a network for companies that are, in general geographically dispersed.

Input-output place-transition nets (IOPT nets) is a Petri net based formalism targeted for the development of embedded systems controllers. It is an extension to common place-transition Petri nets, introducing constructs to model the communication between the controller and the environment and using an execution semantics assuring a deterministic behavior. However, IOPT nets and the supporting tools framework - the IOPT-Tools - do not have a mechanism to support model structuring. Since models are flat, all the graphical components and annotations are visualized in the same page. Systems with several dozens of nodes become very difficult to manage. In this paper a modular construct for IOPT nets is presented, helping to manage large-scale systems, and the reuse of model components across projects. The algebraic specification of the model is provided and an example illustrating the concept is presented.

Industrial complex systems demand the dynamic adaptation and optimization of their operation to cope with operational and business changes. In order to address such requirements and challenges, cyber-physical systems promotes the development of intelligent production units and products. The realization of such concepts requires, amongst others, advanced data analysis approaches, capable to take advantage of increased availability of data, in order to overcome the inherent dynamics of industrial environments, by providing more modular, adaptable and responsiveness systems. In this context, this work introduces an agent-based data analysis approach to support the supervisory and control levels of industrial processes. It proposes to endow agents with data analysis capabilities and cooperation strategies, enabling them to perform distributed data analysis and dynamically improve their analysis capabilities, based on the aggregation of shared knowledge. Some experiments have been performed in the context of an electric micro grid to validate this approach.

The work presented in this paper demonstrates how flexible manufacturing systems (FMS) combined with context awareness can be used to allow for an improved decision support in manufacturing industry. Thereby manufacturing companies shall be supported in a continuous process of increasing efficiency and availability of their production machines. Such optimization has to be embedded in the processes allowing for run time adaptation of the process to various dynamically changing external conditions. Context awareness, based on the information obtained from cyber physical systems, is a promising approach to allow for efficient building of such embedded optimization solutions. The objective of the research presented is to explore how context awareness, using the information from cyber physical systems integrated in the processes, can be applied to build a solution for self-optimization of discrete, flexible manufacturing processes.

Current trends are showing a technological evolution to an unified Industrial Internet of Things network where smart manufacturing devices are loosely coupled over a cloud to realize comprehensive collaboration and analysis possibilities, and to increase the dynamic and volatile of manufacturing environments. This rising complexity generates also higher ranges of error possibilities and analog a growing demand of new diagnostic approaches to handle also those highly complex systems as manufacturing systems which are following the Industry 4.0 vision. This is an ISA’95 compliant approach of a Big Data analytics methodology for analysis and observation in Industry 4.0 vision following manufacturing systems.

This work is focused on the development of Mixed Reality-based Systems suitable for their use in psychological therapies. Recently, on-going research about Immersive Virtual Environments has shown its applicability in psychological domains. Firstly, we intend to explore the bio signals data for emotion recognition. Secondly, we aim to develop an innovative system that allows the creation and manipulation of experiments oriented to psychological therapy. Finally, these experiments will be tested and evaluated in real scenarios by therapists with clinical patients. The results from this work will enable the assessment of the efficacy of these experiments and its improvement in order to apply in a generalized way to individuals with emotional, behavioural and psychological problems.

The research field of Brain-Computer Interfaces (BCI) emerged in an attempt to enable communication between paralyzed patients and technology. Identifying an individual’s mental state, through his brain’s electric activity, a typical BCI system assigns to it a particular action in the computer. It is known that when the visual cortex is stimulated with a certain frequency, it shows activity with the same frequency. This Steady-State Visually Evoked Potential (SSVEP) activity can be used to achieve the aforementioned communication goal. In this work, we first analyze the spontaneous electrical activity of the brain, to distinguish two mental sates (concentration/meditation). Then, following an SSVEP type of approach, we divide the stimulating screen in four areas, each of which flickering at a distinct frequency. By observing the responding frequency from the occipital lobe of the subject, we can then estimate the 2 bit decision he made. We observe that such a setup is efficient for real time BCI, and can be easily integrated in mobile devices. Besides, the user is able to change voluntarily her/his decisions, interacting with the system in a natural manner.

In this paper, a new algorithm is proposed for artifact removing of sleep electroencephalogram (EEG) with application in sleep stage classification. Rather than other works which used artificial noise, in this study real EEG data contaminated with electro-oculogram (EOG) and electromyogram (EMG) are used for evaluating the proposed artifact removal algorithm’s efficiency using classification accuracy. The artifact detection is performed by thresholding the EEG-EOG and EEG-EMG cross correlation coefficients. Then, the segments considered contaminated are denoised by normalized least-mean squares (NLMS) adaptive filtering technique. Using a single EEG channel, four sleep stages consisting of Awake, Stage1 + REM, Stage 2 and Slow Wave Stage (SWS) are classified. A wavelet packet (WP) based feature set together with artificial neural network (ANN) are deployed for sleep stage classification purpose. Simulation results show that artifact removed EEG allows a classification accuracy improvement of around 14 %.

A low-cost inertial measurement unit has been developed for accurate motion capture, allowing real-time spatial position registration (linear and angular) of the user’s whole-body. For this, we implemented a dedicated circuit for 9 degrees of freedom motion sensors, composed of an accelerometer, gyroscope and a magnetometer. We also applied signal processing and data fusion algorithms to prevent the inherent drift of the position signal. This drift is known to exist during the sensor integration process and the implemented algorithms showed promising results. This system is meant to be used in two specific biomedical applications. The first one is linked to the development of a low-cost system for gait analysis of the whole-body, which can be used in home-based rehabilitation systems. The second application is related to the real-time analysis of working postures and the identification of ergonomic risk factors for musculoskeletal disorders.

The objective of this work is of improving the efficacy, acceptance, adaptability and overall performance of Human-Machine Interaction (HMI) applications using a context-based approach. In HMI, we aim to define a general human model that may lead to principles and algorithms allowing more natural and effective interaction between humans and artificial agents. This is paramount for applications in the field of Active and Assisted Living (AAL). The challenge of user acceptance is of vital importance for future solutions, and still one of the major reasons for reluctance to adopt cyber-physical systems in this domain. Our hypothesis is that, we can overcome limitations of current interaction functionalities by integrating contextual information to improve algorithms accuracy when performing under very different conditions and to adapt interfaces and interaction patterns according user intentions and emotional states.

Accuracy and precision are key parameters in the definition of indoor positioning systems. We want to provide a mobile robot with the capacity to autonomously determining its location inside buildings, to allow it to autonomously navigate. The solution developed is based on spreading emitter beacons of Bluetooth Low Energy in the building and use location finding techniques to determine the robot’s location. The main challenge is the capacity to obtain accurate readings of signal strength and the low repeatability of readings even under unchanged conditions. To improve the signal strength measurements it is necessary to deal with this imprecision. Our approach is based on the use of Fuzzy Logic to deal with the accuracy problem. Once better signal strength readings are achieved, using this method, approximate distances are calculated based on signal strength and the trilateration method is implemented to provide the location of the mobile robot.

This paper presents an overview of the PhD thesis “CMOS indoor light energy harvesting system for wireless sensing applications”, whose main goal was designing a micro-power light energy harvesting system for indoor scenarios, addressing the challenges associated with this kind of environment. Light energy was taken in by using an amorphous silicon (a-Si) photovoltaic (PV) cell and conditioned using a switched-capacitor (SC) voltage converter, along with a maximum power point tracking (MPPT) capability. The MPPT method was the Fractional V _OC , put into practice by using an asynchronous state machine (ASM) which automatically establishes and controls the clock signals’ frequency, thus controlling the switches of the voltage converter. To minimize the area of the SC section, MOSFET capacitors were used. A charge reusing scheme was proposed, so as to decrease the loss through the parasitic capacitance of their bottom plate. Laboratorial results, taken from a CMOS solid-state prototype, show that the proposed system can achieve better results than those in the present state of the art.

This study presents a scheme to detect and isolate faults in over-actuated electric vehicles. Although this research work is still emerging, it already provides a view of the main challenges on the problem and discusses some possible approaches that can be useful to overcome the key difficulties. This paper intends to present a fault detection algorithm based on Unknown Input Observer (UIO). The residuals are built through the difference of signals between the measured outputs and the output estimations from the observer. The main idea is to detect fault in the electric motors and steering wheel actuator. The algorithm is presented and tested with some fault scenarios using the co-simulation tool between Simulink/MATLAB and the high-fidelity model from Carsim software.

A performance analysis of a synchronous reluctance motor (SynRM) drive, operating under different fault conditions, with a three-level NPC inverter, controlled by a seven-segment Space Vector Modulation (SVM) technique, is presented in this paper. Considering the voltage source inverter, open-circuit faults of different types are introduced and their effects are studied regarding the SynRM and the inverter performance evaluation. The healthy and faulty operating conditions comparison will take into account the evaluation of some variables, such as the motor power factor, electromagnetic torque, efficiency, total waveform distortion values, currents RMS values, and total waveform oscillation values, obtained from simulation results.

Modern embedded microcontrollers used in lift automation can provide the system operation under both static and dynamic strategies. The control system operating under dynamic strategy can change the lift system controls under different passenger behaviours, varying to improve the efficiency of service. However, it is extremely challenging to determine the system conditions leading to the decision to switch the control strategies. The present paper investigates the influence of the passengers’ flow parameters on lift control systems strategies using a hybrid mathematical model combining agent-based and discrete event methods. It has been shown that the operation of the lift control systems, in the context of decision leading conditions, can be effectively assessed through a model analysis. The proposed model has been simulated using AnyLogic systems to estimate the impact of uneven flow of passengers on performance of a ten floor lift system. The results of simulations have been used to determine the optimum intervals between the calls and lift arrival time which lead to strategies that minimise passenger’s waiting time.

Scalar traction control systems can be used in trains, trams and electrical vehicles equipped with induction motors. To increase the versatility and efficiency of such systems compared to conventional solutions, they must enable dynamic links to different types of power grids, i.e., be supplied by multi-frequency and multi-voltage power sources. Behind these solutions, there are control systems based on vectorial controllers. In order to set systems with the above features, in this work it is intended to build a scalar traction control system, in which the speed is controlled via a scalar controller. The system controls the motor speed and also the position of the rotor and stator fields. Furthermore, it allows to clearly setting the operation conditions, i.e., avoiding situations that would change from braking to traction and vice-versa due to operational and/or functional disturbances. To achieve the required speed reference follow up, the proposed solution includes a torque and field orientation filter.

Cyber-Physical Systems are present in many industries such as aerospace, automotive, health-care and transportation, and over time they have become critical and require high levels of resiliency and fault tolerance. Often they are implemented on reconfigurable logic due to IP design reutilisation, high-performance, and low-cost. Nevertheless, the continuous technology shrinking and the increasing demand for systems that operate under different power profiles with high-performance has led to implementations operating below the maximum performance offered by a particular technology. Design tools are conservative in the estimation of the maximum performance that can be achieved by a design when placed on a device, accounting for any variability in the fabrication process of the device. This work takes a new view on the performance improvement of circuit designs by pushing them into the error prone regime, as defined by the synthesis tools, and by investigating methodologies that reduce the impact of timing errors at the output of the system. In this work two novel error reduction techniques are proposed to address this problem. One is based on reduced-precision redundancy and the other on an error optimisation framework that uses information from a prior characterisation of the device. Both of these methods allow to achieve graceful degradation in performance whilst variation increases.

This paper proposes a new tuning approach by which all Model-Free Adaptive Control (MFAC) algorithm parameters are computed using a nonlinear Virtual Reference Feedback Tuning (VRFT) algorithm. This new mixed data-driven control approach, which results in a mixed data-driven tuning algorithm, is advantageous as it offers a systematic way to tune the parameters of MFAC algorithms by VRFT using only the input/output data of the process. The proposed approach is validated by a set of MIMO experiments conducted on a nonlinear twin rotor aerodynamic system laboratory of equipment position control system. The mixed VRFT-MFAC algorithm is compared with a classical MFAC algorithm whose initial parameter values are optimally tuned.

Multi-Criteria Decision Making (MCDM) methods use normalization techniques to allow aggregation of criteria with numerical and comparable data. With the advent of Cyber Physical Systems, where big data is collected from heterogeneous sensors and other data sources, finding a suitable normalization technique is also a challenge to enable data fusion (integration). Therefore, data fusion and aggregation of criteria are similar processes of combining values either from criteria or from sensors to obtain a common score. In this study, our aim is to discuss metrics for assessing which are the most appropriate normalization techniques in decision problems, specifically for the Analytical Hierarchy Process (AHP) multi-criteria method. AHP uses a pairwise approach to evaluate the alternatives regarding a set of criteria and then fuses (aggregation) the evaluations to determine the final ratings (scores).

This paper addresses target localization problem in a cooperative 3-D wireless sensor network (WSN). We employ non-traditional methodology which merges distance and angle measurements, respectively withdrawn from the received signal strength (RSS) and angle-of-arrival (AoA) information. Based on RSS measurement model and effortless geometry, a novel non-convex estimator according to the weighted least squares (WLS) criterion is obtained, which closely approximates the maximum likelihood (ML) estimator for small noise. It is shown that the devised estimator is appropriate for distributed implementation. Following the squared range (SR) approach, we propose a suboptimal SR-WLS estimator according to the generalized trust region sub-problem (GTRS) framework, to estimate the locations of all targets in the WSN. According to our simulations, the new estimator has excellent performance in a great variety of considered settings, in which the effectiveness of fusing two radio measurements is confirmed.

Cyber-physical systems often include sensor devices in their structure. These devices may require firmware updates once deployed and these updates must be energy efficient for battery powered, physically inaccessible sensors. The problem of energy saving reprogramming can be split to four tasks– making old and new firmware versions more similar, generating small delta files using differencing algorithms, propagating delta files and applying updates at the end devices. This paper describes existing approaches dealing with this problem, analyzes their power consumption and introduces new optimizations for differencing algorithms. A new approach is presented that requires no external flash memory, device reboot or complex update agent at the sensor device.

This paper focuses on measurement and analysis of the electromagnetic fields generated by wireless power transfer system and their possible interaction on data transmission channel. To measure the levels of electromagnetic fields and spectrum near the wireless power transfer equipment the measurement system in the frequency range 100 kHz to 3 GHz was used. Due to the advances in technology it becomes feasible to apply the wireless power transfer in the electric vehicles charging. Currently, in the Faculty of Science and Technology of the University Nova high power wireless power transfer systems are in development. Those systems need to be controlled by several microcontrollers in order to optimize the energy transmission. Their mutual communication is of extreme importance especially when high intensity fields will generate highly undesired influence. The controllers are supposed to communicate with each other through radio frequency data channels. The wireless power transfer system with the electromagnetic interference may influence or completely disrupt the communication which will be a severe problem.

It is well known that the stochastic nature of the interference deeply impacts on the performance of emerging and future wireless communication systems. In this work we consider an ad hoc network where the mobile nodes adopt the Random Waypoint mobility model. Assuming a time-varying wireless channel due to slow and fast fading and, considering the dynamic path loss caused by the node’s mobility, we start by characterizing the interference caused to a receiver by the moving nodes positioned in a ring. Based on the interference distribution, we evaluate two different methodologies to estimate the interference in real-time. The accuracy of the results achieved with the proposed methodologies in several simulations show that they may be used as an effective tool of interference estimation in future wireless communication systems, being the main contribution of this work.

Regulatory policies applied to traditional energy systems are not sufficient when considering smart grids’ new requirements such as distributed and decentralised coordination. New management techniques are needed in order to shape consumers conducts by prohibiting, sanctioning or incentivising specific behaviours leading to more efficient utilisation of resources. This paper discusses the possibility of regulating demand in smart grids by the application of processes reflecting the utilisation of norms and incentives in order to better adjust supply to demand. Thus we consider this soft-control policy based on the business layer rather than traditional control for cyber-physical systems. The Business Process Modelling (BPM) approach will make easier the design, management and observation of the norms and incentives as flexible artefacts, highlighting decision and coordination processes.

This paper focuses on computational models development and its applications on demand response, within smart grid scope. A prosumer model is presented and the corresponding economic dispatch problem solution is analyzed. The prosumer solar radiation production and energy consumption are forecasted by artificial neural networks. The existing demand response models are studied and a computational tool based on fuzzy clustering algorithm is developed and the results discussed. Consumer energy management applications within the InovGrid pilot project are presented. Computation systems are developed for the acquisition, monitoring, control and supervision of consumption data provided by smart meters, allowing the incorporation of consumer actions on their electrical energy management. An energy management system with integration of smart meters for energy consumers in a smart grid is developed.

The importance of forecasting has become more evident with the appearance of the open electricity market and the restructuring of the national energy sector. This paper presents a new approach to load forecasting in the medium voltage distribution network in Portugal. The forecast horizon is short term, from 24 h up to a week. The forecast method is based on the combined use of a regression model and artificial neural networks (ANN). The study was done with the time series of telemetry data of the DSO (EDP Distribution) and climatic records from IPMA (Portuguese Institute of Sea and Atmosphere), applied for the urban area of Évora - one of the first Smart Cities in Portugal. The performance of the proposed methodology is illustrated by graphical results and evaluated with statistical indicators. The error (MAPE) was lower than 5 %, meaning that chosen methodology clearly validate the feasibility of the test.

This paper is about a PhD thesis and includes the study and analysis of the performance of an onshore wind energy conversion system. First, mathematical models of a variable speed wind turbine with pitch control are studied, followed by the study of different controller types such as integer-order controllers, fractional-order controllers, fuzzy logic controllers, adaptive controllers and predictive controllers and the study of a supervisor based on finite state machines is also studied. The controllers are included in the lower level of a hierarchical structure composed by two levels whose objective is to control the electric output power around the rated power. The supervisor included at the higher level is based on finite state machines whose objective is to analyze the operational states according to the wind speed. The studied mathematical models are integrated into computer simulations for the wind energy conversion system and the obtained numerical results allow for the performance assessment of the system connected to the electric grid. The wind energy conversion system is composed by a variable speed wind turbine, a mechanical transmission system described by a two mass drive train, a gearbox, a doubly fed induction generator rotor and by a two level converter.

Renewable energy capacity has been growing rapidly, exceeding 140 GW of installed power in solar Photovoltaic (PV) power generation. Along with PV installations, the variety of applied power electronics topologies has also increased, resulting in a key point of future Smart Grids, as long as they allow new operation possibilities. This paper reviews the emerging topologies for PV applications that could be used in the generation of new smart inverters. A particular focus is on impedance-source converters and naturally clamped solutions. Pros and cons along with areas of application are summarized.

The increasing penetration of renewable energies in the electrical systems, particularly in small and isolated systems, like the case studied in this paper, Terceira Island in Azores, Portugal, creates challenges in the dispatch related to the variability and the difficulty to forecast the renewable resources. One way to deal with such issues is to use Water Pumping Storage Systems (WPSS) to regulate the system electricity production by storing energy surplus in low load periods and returning it back in high load periods, reducing at the same time the need to curtail wind. This paper describes and compares a deterministic and a metaheuristic methodology, using Particle Swarm Optimization (PSO), used to determine the best configuration of the WPSS in terms of number and unit power of pumps and turbines, and upper and lower reservoir capacity that lead to the best economic value, determined by the Net Present Value (NPV) of the investment.

This paper is on modeling and simulation for an offshore wind system equipped with a semi-submersible floating platform, a wind turbine, a permanent magnet synchronous generator, a multiple point clamped four level or five level full-power converter, a submarine cable and a second order filter. The drive train is modeled by three mass model considering the resistant stiffness torque, structure and tower in deep water due to the moving surface elevation. The system control uses PMW by space vector modulation associated with sliding mode and proportional integral controllers. The electric energy is injected into the electric grid either by an alternated current link or by a direct current link. The model is intend to be a useful tool for unveil the behavior and performance of the offshore wind system, especially for the multiple point clamped full-power converter, under normal operation or under malfunctions.

This paper presents a tool to robustly allocate the allowable operating zones of active and reactive power trading margins for multi energy storage systems (ESSs) without violating typical distribution system constraints. This tool helps the distribution network operator (DNO) to facilitate ESS safe participation in day-ahead active and reactive power markets. It estimates the required ESS reactive power support to keep safe voltage margins. In order to avoid conservative results, an uncertainty budget designed by a fuzzy expert is imposed on the uncertainty domain. Case studies on one hundred different uncertainty scenarios are conducted on a real 41-bus Canadian system. Simulation results have shown that the proposed algorithm provides robust operating zones for ESSs with less conservatism.

In order to support the intensive development of agricultural crops and, in particular the floricultural inside a greenhouse, with the perspective of a quick distribution in the market, increasing the economic benefits and supported on efficient and intelligent management systems energy, it is mandatory to conceive a model based on Cyber-Physical Systems (CPS) This implies, accordingly, increases in renewable primary energy sources utilization coupled with sensing technologies, include developments on Internet of Things (IoT) and Cloud Computing (CC), supported with Information and Communication Technologies (ICT) that will lead to new architectural approach applied to a proposed energy system, based on a sustainable and more engineering autonomous process. This work comes up with a new energy model that retrofits the system of a greenhouse supported with multiple sensors in one grid, to expand into CPS concept to manage sensors and controllers that will improve a profitable energy system.

This paper proposes enabling intelligence for cyber-physical system of intelligent collaborating energy storages. Two intelligent batteries coordinate their behaviour in a dynamic electricity price scenario, accumulating the energy when the electricity price is low, and replacing the grid when the price is high. Both batteries directly exchange their state information with each other without any centralized processing agent, following the coordination algorithm developed in this paper. This simplifies their integration and enables achieving more optimal behaviour with regards to state of their charge. When one battery is depleted, the other one immediately compensates the losses by a higher discharge rate. Such a distributed coordination approach enables plug-and-play formation of system of batteries, demonstrates the efficiency of such formation and allows for reduction of costs due to longer discharge time of the batteries.

After liberalization of the electric sector and due to the expansion of distributed generation with the appearing of new kinds of producers and consumers, a new power player emerged taking an major role in the commercialization of electricity - the commercial agent or aggregator. The aggregator thus enables small and medium clients to access to market prices that were impossible to obtain by themselves, since scale is an important factor in the electric energy market. This paper is focused on analyzing how the variation of prices in the energy market affects the aggregator’s customer portfolio energy sold and its total profits. The weekly market prices considered showed different levels of volatility. The effect of market price variation, both in terms of average value and variance, was analysed for a typical clients’ portfolio in terms of profitability and risk. As it was expected the highest levels of profitability were attained in the weeks of lowest average prices that also correspond to the highest price volatilities increasing also the risk of the aggregator.

A paradigm shift is taking place in Low Voltage (LV) distribution networks, motivated by progressive implementation of renewable micro-generation (µG), mainly Photovoltaic (PV), near household consumers. The concept of self-consumption linked to battery storage is emerging as a way to enhance the quality of electrical network. Smart-Grid (SG) environment comes close to this approach and may have a crucial relevance on management of intelligent power distribution networks, in the framework of a Smart Environment. This paper proposes an additional contribution on the subject by investigating the economic profitability of PV battery systems being analyzed with respect to its impact and economic feasibility, taking into consideration their initial investment and operation costs. The purpose is to verify if prosumer’s investment is financially more interesting than purchasing all electricity needed for consumption from the LV grid. The results of the performed economic analysis show that self-consumption with storage is a potential solution.

This paper is focused on the development of a demand side management control method in a distributed network, aiming the creation of greater flexibility in demand and better ease the integration of renewable technologies. In particular, this work presents a novel multi-agent model-based predictive control method to manage distributed energy systems from the demand side, in presence of limited energy sources with fluctuating output and with energy storage in house-hold or car batteries. Specifically, here is presented a solution for thermal comfort which manages a limited shared energy resource via a demand side management perspective, using an integrated approach that includes an auction and a shifting load strategy. The control is applied individually to a set of Thermal Control Areas, demand units, where the objective is to minimize the energy usage and not exceed the limited and shared energy resource, while simultaneously indoor temperatures are maintained within a comfort frame. The developed solution is explained and applied to different scenarios wherein the results illustrate the benefits of the proposed approach.

This paper presents a computer application (CoA) for wind energy (WEn) bidding strategies (BStr) in a pool-based electricity market (EMar) to better accommodate the variability of the renewable energy (ReEn) source. The CoA is based in a stochastic linear mathematical programming (SLPr) problem. The goal is to obtain the optimal wind bidding strategy (OWBS) so as to maximize the revenue (MRev). Electricity prices (EPr) and financial penalties (FiPen) for shortfall or surplus energy deliver are modeled. Finally, conclusions are addressed from a case study, using data from the pool-based EMar of the Iberian Peninsula.

This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feedforward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three datasets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models.

This paper addresses a stochastic mixed-integer linear programming model for solving the self-scheduling problem of a thermal and wind power producer acting in an electricity market. Uncertainty on market prices and on wind power is modelled via a scenarios set. The mathematical formulation of thermal units takes into account variable and start-up costs and operational constraints like: ramp up/down limits and minimum up/down time limits. A mixed-integer linear formulation is used to obtain the offering strategies of the coordinated production of thermal and wind energy generation, aiming the profit maximization. Finally, a case study is presented and results are discussed.

This study evaluated wastewaters reuse in the production of perennial crops Arundo donax and Miscanthus x giganteus. The trials were conducted in pots under controlled conditions, with different water regimes (950, 475 and 238 mm) in two growing cycles. The results indicated that irrigation with wastewaters did not affect biomass productivity but the amount of irrigation did. Yet, biomass obtained from pots irrigated with wastewaters presented higher levels of ash and nitrogen content than biomass from control pots. The soil-plant system retained over 90 % of pollutant load resulting in wastewater depuration. Furthermore, the produced biomass can be economically valorized for energy or biomaterials, once irrigation with wastewater did not influence the contents in fiber and the calorific value. Still, the higher ash and nitrogen contents in the biomass can be detrimental especially when biomass is for combustion purposes.

Refuse derived fuel (RDF) was subject to torrefaction in order to produce a char with higher homogeneity and lower moisture content than the RDF raw materials. The resulting product, RDF char, showed increased fixed carbon and ash contents, decreased moisture and volatile matter contents, and a very significant increase in density. The torrefaction of RDF may therefore contribute to reduce the landfill volume needed to accommodate these materials to one third of the presently used. This new char material was also tested for its adsorption capacities and the results show that it could be used for the removal of chromium and aluminum from aqueous solutions.

Two microalgae species (Chlorella vulgaris and Chlorella protothecoides) were tested at lab scale in order to select the optimal conditions for biomass production and the efficient remediation of effluents from poultry and pig industries. Both microalgae showed biomass productivities in the agro-industrial effluents that were comparable to the Chlorella synthetic medium used as control. C. protothecoides presented the higher productivities both for poultry effluents (46.13 and 41.75 mg.L−1.day−1 for raw and flocculated effluents) and for pig manure (95.86 mg.L−1.day−1). The supplementation of pig effluents with biomass ash increased by 50 % the microalgae productivity with the highest results obtained for C. protothecoides and C. vulgaris at ash concentrations of 1.5 g/L and 3.0 g/L, respectively. The optical density of both effluents was efficiently reduced by both microalgae but particularly by C. protothecoides and in the presence of added ash, indicating that significant reductions of suspended solids and organic matter occurred. The results showed that poultry and pig effluents may be efficiently remediated with microalgae and the fortification with biomass ash benefits the process.

Thin-film transistors (TFTs) employing oxide semiconductors have recently emerged in electronics, offering excellent performance and stability, low processing temperature and large area processing, being indium-gallium-zinc oxide (IGZO) the most popular amorphous oxide semiconductor. In this work it is shown how IGZO TFTs can be integrated with multilayer high-κ dielectrics to obtain low operating voltages, both on glass and flexible PEN substrates. Then, the electrical properties extracted from these devices are used to design and simulate a 2nd-order Sigma-Delta ($$\Sigma\Delta$$ΣΔ) analog-to-digital converter (ADC), showing superior performance (e.g. SNDR ≈ 57 dB, and DR ≈ 65 dB) over ADCs using competing thin-film technologies.

This paper reports the integration of an electrochromic inorganic oxide semiconductor (WO₃) into an electrolyte gated transistor device. The resulting electrochromic transistor (EC-T) is a novel optoelectronic device, exhibiting simultaneous optical and electrical modulation. These devices show an On-Off ratio of $$ 5\times 10^{ 6} $$5×106 and a transconductance (g_m) of 3.59 mS, for gate voltages (V_G) between −2 and 2 V, which, to the authors knowledge, are one of the best values ever reported for this type of electrochemical transistors. The simple and low-cost processing together with the electrical/optical performances, well supported into a comprehensive analysis of device physics, opens doors for a wide range of new applications in display technologies, biosensors, fuel cells or electrochemical logic circuits.

Indium-gallium-zinc oxide (IGZO) thin-film transistors (TFTs) are simulated using TCAD software. Nonlinearities observed in fabricated devices are obtained through simulation and corresponding physical characteristics are further investigated. For small channel length (below 1 µm) TFTs’ simulations show short channel effects, namely drain-induced barrier lowering (DIBL), and effectively source-channel barrier is shown to decrease with drain bias. Simulations with increasing shallow donor-like states result in transfer characteristics presenting hump-like behavior as typically observed after gate bias stress. Additionally, dual-gate architecture is simulated, exhibiting threshold voltage modulation by the second gate biasing.

This work presents an experimental characterization of electrochemically gated graphene field-effect transistors (EGFETs) to measure extracellular cell signals. The performance of the EGFETs was evaluated using cardiomyocytes cells. Extracellular signals with a peak value of 0.4 pico-amperes (pA) embedded in a noise level of 0.1 pA were recorded. Signals in current mode were compared with signals recorded as a voltage. Signals below 28 µV of magnitude can be detected in a noise floor of 7 µV with a signal-to-noise ratio of 4.