Abstract
Smart grid (SG) technologies are playing a key role in the electric grid transformation, bringing out promising benefits for different actors and empowering customers. However, this transition imposes new challenges concerning the operation and management of energy, particularly at the distribution level of the electric grid. This chapter provides an overview of achieved advances toward the widespread implementation of SG, including technological and infrastructure developments. Transactive energy presents a distributed decision-making coordination approach using automated energy transactions that is enabled by the intelligence and connectivity benefits of SG. The way in which these transactions can be integrated in a local market environment and how advances in transactive energy, supported by the infrastructure already developed to enable SG, are leading to the emergence of local energy markets is discussed in this chapter. © 2021 Elsevier Inc.

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The remote maintenance of home appliances, like washing machines, air conditioning, and heating system is a complex problem, but with the help of the ongoing developments on Internet of Things, Data Analysis and Artificial Intelligence, the problem can now be tackled with success. This paper mostly focus in presenting the architecture developed within the aim of the SMART-PDM project for the acquisition of data on the operation of home appliances and then it also shows some preliminary results for washing machines, which give some hints on how to fine tune the system to achieve predictive maintenance and condition monitoring.

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The electricity field is facing major challenges in the implementation of Renewable Energy Sources (RES) at a large scale. End users are taking on the role of electricity producers and consumers simultaneously (prosumers), acting like Distributed Energy Resources (DER), injecting their excess electricity into the grid. This challenges the management of grid load balance, increases running costs, and is later reflected in the tariffs paid by consumers, thus threatening the widespread of RES. The Flexigy project explores a solution to this topic by proposing a smart-grid architecture for day-ahead flexibility scheduling of individual and Renewable Energy Community (REC) resources. Our solution is prepared to allow Transmission System Operators (TSO) to request Demand Response (DR) services in emergency situations. This paper overviews the grid balance problematic, introduces the main concepts of energy flexibility and DR, and focuses its content on explaining the Flexigy architecture.

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Networking equipment that connects households to an operator network, such as home gateways and routers, are major victims of cyber-attacks, being exposed to a number of threats, from misappropriation of user accounts by malicious agents to access to personal information and data, threatening users’ privacy and security. The exposure surface to threats is even wider when the growing ecosystem of Internet-of-Things devices is considered. Thus, it is beneficial for the operator and customer that a security service is provided to protect this ecosystem. The service should be tailored to the particular needs and Internet usage profile of the customer network. For this purpose, Machine Learning methods can be explored to learn typical behaviours and identify anomalies. In this paper, we present preliminary insights into the architecture and mechanisms of a security service offered by an Internet Service Provider. We focus on Distributed Denial-of-Service kind of attacks and define the system requirements. Finally, we analyse the trade-offs of distributing the service between operator equipment deployed at the customer premises and cloud-hosted servers.