David Emanuel Rua

Abstract
The increasing number of IoT devices and digital services offers cross-domain sensing and control opportunities to a growing set of stakeholders. The provision of cross-domain digital services requires interoperability as a key enabler to bridge domain specifics, while inferring knowledge and allowing new data-driven services. This work addresses H2020 InterConnect project's Interoperability Framework, highlighting the use of semantic web technologies. The interoperability framework layering is presented, particularly addressing the Semantic Interoperability layer as its cornerstone to build an interoperable ecosystem of cross-domain digital services via a federation of distributed knowledge bases. Departing from a generic, ontology-agnostic approach that can fit any cross-domain use case, it validates the approach by considering the SAREF family of ontologies, showcasing an IoT and energy cross-domain use case.

Abstract
Electricity demand may vary significantly and consequently the generation side must be adapted to fully supply it. However, the increased penetration of variable renewable energy sources is changing the game by leading to an increase need of load response and load flexibility to face these changes from the generation side. Flexibility is highly related to the viability of Demand Response actions that can allow the participation of loads from buildings, clusters of communities, industry in market-driven energy services. Policymakers and energy stakeholders are beginning to prepare for a reality in which many consumers are also producers (prosumers) and operate with a significantly decentralized electricity grid. Also, the increased use of information and communication technologies is creating new opportunities for smarter control and load management schemes, interconnecting multiple demand-side stakeholders, where prosumers can leverage the potential for energy flexibility in demand-response programs. This chapter presents an overview of strategies to enable end-user participation in energy services, including building optimization schemes that provide load flexibility for the grid, as single users or as aggregated communities.

Abstract
The evolution of the electrical power sector due to the advances in digitalization, decarbonization and decentralization has led to the increase in challenges within the current distribution network. Therefore, there is an increased need to analyze the impact of the smart grid and its implemented solutions in order to address these challenges at the earliest stage, i.e., during the pilot phase and before large-scale deployment and mass adoption. Therefore, this paper presents the scalability and replicability analysis conducted within the European project InteGrid. Within the project, innovative solutions are proposed and tested in real demonstration sites (Portugal, Slovenia, and Sweden) to enable the DSO as a market facilitator and to assess the impact of the scalability and replicability of these solutions when integrated into the network. The analysis presents a total of three clusters where the impact of several integrated smart tools is analyzed alongside future large scale scenarios. These large scale scenarios envision significant penetration of distributed energy resources, increased network dimensions, large pools of flexibility, and prosumers. The replicability is analyzed through different types of networks, locations (country-wise), or time (daily). In addition, a simple replication path based on a step by step approach is proposed as a guideline to replicate the smart functions associated with each of the clusters.

Abstract
Home energy management systems (HEMSs) are important platforms to allow consumers the use of flexibility in their consumption to optimise the total energy cost. The optimisation procedure embedded in these systems takes advantage of the nature of the existing loads and the generation equipment while complying with user preferences such as air temperature comfort configurations. The complexity in finding the best schedule for the appliances within an acceptable execution time for practical applications is leading not only to the development of different formulations for this optimisation problem, but also to the exploitation of non-deterministic optimisation methods as an alternative to traditional deterministic solvers. This study proposes the use of genetic algorithms (GAs) and the cross-entropy method (CEM) in low-power HEMS to solve a conventional mixed-integer linear programming formulation to optimise the total energy cost. Different scenarios for different countries are considered as well as different types of devices to assess the HEMS operation performance, namely, in terms of outputting fast and feasible schedules for the existing devices and systems. Simulation results in low-power HEMS show that GAs and the CEM can produce comparable solutions with the traditional deterministic solver requiring considerably less execution time.

Abstract
In this article, we present a holistic framework for the integration of electric vehicles (EVs) in electric power systems. Their charging management and control methodologies must be optimized to minimize the negative impact of the charging process on the grid and maximize the benefits that charging controllability may bring to their owners, energy retailers, and system operators. We have assessed the performance of these methods initially through steady-state computational simulations, and then we validated them in a microgrid (MG) laboratory environment.