Electric infrastructures have been pushed forward to handle tasks they were not originally designed to perform. To improve reliability and efficiency, state-of-the-art power grids include improved security, reduced peak loads, increased integration of renewable sources, and lower operational costs. In this framework“, smart grids” are built around bidirectional communication technologies, where “smart meters” communicate with all other entities and collect data from the power grid, offering specific features to each actor playing in the energy marketplace. In this paper, to overcome some of the challenges raised by smart grids and smart meters, we propose a distributed metering infrastructure which provides bidirectional communication, self-configuration, and auto-update capabilities. Our 3-phase smart meters follow the basics Internet-of-Things principles and have the ability to run, either on-board or distributed on the network, multiple algorithms for smart grid management. These algorithms can be freely added, updated, or removed on-the-fly thanks to the auto-update feature of the system. Moreover, to reduce costs and improve scalability, we prove that it is possible to implement our smart meters using only off-the-shelf and inexpensive hardware devices. A digital real-time simulator (i.e., Opal-RT) has been used to assess the capabilities of both the infrastructure and the meter. Our experimental analysis shows that the latency introduced by the data transmission over the Internet is compliant with the limits imposed by the IEC 61850 standard. As a consequence, our architecture does not affect the operational status of the smart grid, making it a viable solution to support the deployment of novel services.

The range of operations of electric vehicles (EVs) is a critical aspect that may affect the user's attitude toward them. For manned EVs, range anxiety is still perceived as a major issue and recent surveys have shown that one-third of potential European users are deterred by this problem when considering the move to an EV. A similar consideration applies to aerial EVs for commercial use, where a careful planning of the flying range is essential not only to guarantee the service but also to avoid the loss of the EVs due to charge depletion during the flight. Therefore, route planning for EVs for different purposes (range estimation, route optimization) and/or application scenarios (terrestrial, aerial EVs) is an essential element to foster the acceptance of EVs as a replacement of traditional vehicles. One essential element to enable such accurate planning is an accurate model of the actual power consumption. While very elaborate models for the electrical motors of EVs do exist, the motor power does not perfectly match the power drawn from the battery because of battery non-idealities. In this paper, we propose a general methodology that allows to predict and/or optimize the operation range of EVs, by allowing different accuracy/complexity tradeoffs for the models describing the route, the vehicle, and the battery, and taking into account the decoupling between motor and battery power. We demonstrate our method on two use cases. The first one is a traditional driving range prediction for a terrestrial EV; the second one concerns an unmanned aerial vehicle, for which the methodology will be used to determine the energy-optimal flying speed for a set of parcel delivery tasks.

In recent years, the research about energy waste and CO₂ emission reduction has gained a strong momentum, also pushed by European and national funding initiatives. The main purpose of this large effort is to reduce the effects of greenhouse emission, climate change to head for a sustainable society. In this scenario, Information and Communication Technologies (ICT) play a key role. From one side, advances in physical and environmental information sensing, communication and processing, enabled the monitoring of energy behaviour of buildings in real-time. The access to this information has been made easy and ubiquitous thank to Internet-of-Things (IoT) devices and protocols. From the other side, the creation of digital repositories of buildings and districts (i.e. Building Information Models - BIM) enabled the development of complex and rich energy models that can be used for simulation and prediction purposes. As such, an opportunity is emerging of mixing these two information categories to either create better models and to detect unwanted or inefficient energy behaviours. In this paper, we present a software architecture for management and simulation of energy behaviours in buildings that integrates heterogeneous data such as BIM, IoT, GIS (Geographical Information System) and meteorological services. This integration allows: i) (near-) real-time visualisation of energy consumption information in the building context and ii) building performance evaluation through energy modelling and simulation exploiting data from the field and real weather conditions. Finally, we discuss the experimental results obtained in a real-world case-study.

Counterbalancing climate change is one of the biggest challenges for engineers around the world. One of the areas in which optimization techniques can be used to reduce energy needs, and with that the pollution derived from its production, is building design. With this study of a generic office located both in a northern country and in a temperate/Mediterranean site, we want to introduce a coding approach to dynamic energy simulation, able to suggest, from the early-design phases when the main building forms are defined, optimal configurations considering the energy needs for heating, cooling and lighting. Generally, early-design considerations of energy need reduction focus on the winter season only, in line with the current regulations; nevertheless a more holistic approach is needed to include other high consumption voices, e.g., for space cooling and lighting. The main considered design parameter is the WWR (window-to-wall ratio), even if further variables are considered in a set of parallel analyses (level of insulation, orientation, activation of low-cooling strategies including shading devices and ventilative cooling). Finally, the effect of different levels of occupancy was included in the analysis to regress results and compare the WWR with corresponding heating and cooling needs. This approach is adapted to Passivhaus design optimization, working on energy need minimisation acting on envelope design choices. The results demonstrate that it is essential to include, from the early-design configurations, a larger set of variables in order to optimize the expected energy needs on the basis of different aspects (cooling, heating, lighting, design choices). Coding is performed using Python scripting, while dynamic energy simulations are based on EnergyPlus.

In recent years, the introduction and exploitation of innovative information technologies in industrial contexts have led to the continuous growth of digital shop floor environments. The new Industry 4.0 model allows smart factories to become very advanced IT industries, generating an ever-increasing amount of valuable data. As a consequence, the necessity of powerful and reliable software architectures is becoming prominent along with data-driven methodologies to extract useful and hidden knowledge supporting the decision-making process. This article discusses the latest software technologies needed to collect, manage, and elaborate all data generated through innovative Internet-of-Things (IoT) architectures deployed over the production line, with the aim of extracting useful knowledge for the orchestration of high-level control services that can generate added business value. This survey covers the entire data life cycle in manufacturing environments, discussing key functional and methodological aspects along with a rich and properly classified set of technologies and tools, useful to add intelligence to data-driven services. Therefore, it serves both as a first guided step toward the rich landscape of the literature for readers approaching this field and as a global yet detailed overview of the current state of the art in the Industry 4.0 domain for experts. As a case study, we discuss, in detail, the deployment of the proposed solutions for two research project demonstrators, showing their ability to mitigate manufacturing line interruptions and reduce the corresponding impacts and costs.