The environmental impact on the energy sector has become a significant concern, necessitating the implementation of Home Energy Management Systems (HEMS) to enhance the energy efficiency of buildings, reduce costs and greenhouse gas emissions, and ensure user comfort. This paper presents a novel approach to provide optimal day-ahead energy management plans in smart homes with Photovoltaic/Thermal (PVT) systems, aiming to achieve a balance between energy cost and user comfort. This multi-objective problem employs the Non-dominated Sorting Genetic Algorithm III as the optimization algorithm and the Nonlinear Auto-regressive with External Input to forecast the day-ahead meteorological variables, which serve as inputs to predict the PVT electrical and heat production in the thermal resistance model. The HEMS benefits from the time-of-use tariff due to the flexibility provided by the energy storage from a battery bank and a boiler. Furthermore, it performs a load scheduling for 10 controllable loads based on three feature parameters to characterize occupant behavior. A study case analysis revealed a cost reduction of approximately 66% in the solution close to the knee of the Pareto curve (S3 solution). The environmental impact on the energy sector has become a The PVT heat production was sufficient to meet the thermal demand of the showers. The proposed hybrid battery management model effectively eliminated the export of electricity to the grid, reducing consumption during peak periods and the maximum peak demand.

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.

This paper presents how the InterConnect project is enhancing the relationship between smart buildings, energy communities and grids, enabling the potential of interoperable flexibility mechanisms and the offer of new energy and non-energy services. Within this framework DSO will leverage its role of neutral market facilitator acting as key enabler for new business models. The paper presents the first technical definition of the DSO Interface of the H2020 InterConnect project that will ensure interoperable integration of flexibility services between DSOs and the different market parties to support the grid operation towards an increasingly decentralized, digitalized and decarbonized electric system.

Flexibility is key for the decarbonization of the energy sector, contributing to decrease uncertainty in the operation of distribution networks, due to the connection of renewable energy sources and electric vehicles. However, effective deployment requires interoperable and replicable solutions, technologically agnostic and independent from the role of each actor and market models adopted. This paper presents an overview of ongoing projects that aim to deliver and demonstrate interoperable solutions across the full value chain of the energy sector. The main objective and expected results of the H2020 InterConnect, EUniversal and OneNet projects will be presented.

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.

The growing digitalisation, grid complexity and the number of digitally connected devices that communicate with systems in the distribution grid are enabling the continuous development of automation and intelligence, acquisition of data from sensors, meters and devices for monitoring and managing the distribution network, to achieve an enhanced, preventive, resilient and flexible network operation philosophy. This study presents a set of use cases towards the demonstration of the benefits of implementing a platform that collects, aggregates and facilitates horizontal integration and data correlation from various sources, enabling these use cases across the distribution grid. The adequacy analysis of current distribution network architecture considered derived requirements on the characterisation of its evolution taking advantage of key digital technologies, towards the implementation of distributed control and management strategies. It is also presented a benefit analysis of implementing a centralised common data and service platform, i.e. an internet of things (IoT) platform, regarding new functionalities and applications.

This study offers an overview of the H2020 InterConnect project, which targets the relation between smart homes and distribution grids. The project vision is to produce a digital marketplace, using an interoperable marketplace toolbox and Smart appliances REference Ontology (SAREF) compliant Internet of Things (IoT) reference architecture as the main backbone, through which all SAREF-ized services, compliant devices, platform enablers and applications can be downloaded onto IoT and smart grid digital platforms. Energy users in buildings, either residential or non-residential, manufacturers, distribution grid operators and the energy retailers will work together towards the demonstration of the smart energy management solutions in seven connected large-scale test-sites in Portugal, Belgium, Germany, the Netherlands, Italy, Greece and France. This study depicts how InterConnect project will enhance the relation and the interconnectivity between smart buildings and grids safeguarding the definition of the role of each stakeholder in energy and non-energy services.

This study presents Integrid's project framework to manage low voltage (LV) electrical networks, aiming to avoid both technical and quality constraints, induced by the ever-increasing amount of flexible resources spread all over the grid. These assets cover a large amount of renewable-based energy generation to electrical vehicles and energy storage units. For this to be possible, new advanced tools were developed to exploit the benefits of the so-called distributed energy resources, while overcoming limitations on the metering and communication infrastructures. Hence, this study describes the approach taken to perform the active management of LV networks, without a perfect level of observability, exploiting the flexibility provided by the distribution system operator's resources combined with the one offered by private consumers through the home energy management systems. Additionally, some results followed by a brief discussion are presented, enforcing the success of the developed tools. The algorithms within these tools allow to forecast both microgeneration, available flexibility and load profiles, as well as to estimate the network's state, at different time frames.

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.

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.

Energy management is a key tool that will enable consumers to optimize their energy use according to different objectives. Allow users to insert their energy use preferences combined with the effective configuration and control of existing devices (loads and micro generation) is the basis, in this paper, to design adaptable energy optimization algorithms that are capable of outputting feasible, understandable and useful actions, automated and/or manual, for the activation of the existing portfolio of flexible devices. This paper presents an advanced energy management system as an innovative platform that intends to accomplish real energy optimization schemes to support demand response, promote the energy efficiency and contribute towards renewable integration.

Within the scope of H2020 program, InteGrid aims to demonstrate intelligent grid technologies for renewables integration and interactive consumer participation, enabling interoperable market solutions and interconnected stakeholders, while addressing the challenges derived from this goal from a technological, market and regulatory and societal perspective. The Portuguese demonstrator, which intends to innovatively boost the management of the distribution network using new DERs (including, here, consumers’ flexibility) with already-implemented assets, in a predictive and integrated way, fostering the implementation of cost-effective, replicable and scalable solutions, will be the place where the herein described function will be demonstrated. This paper presents the architecture of one of the abovementioned solutions – the LV network control -, which, by merging both a predictive and real-time operational mode, will enable the DSO to exploit self and consumerowned available flexibility resources in order to deploy a decision support tool to assist the active managing of the grid, avoiding technical problems.

Smart grids technologies are enablers of new business models for domestic consumers with local flexibility (generation, loads, storage) and where access to data is a key requirement in the value stream. However, legislation on personal data privacy and protection imposes the need to develop local models for flexibility modeling and forecasting and exchange models instead of personal data. This paper describes the functional architecture of an home energy management system (HEMS) and its optimization functions. A set of data-driven models, embedded in the HEMS, are discussed for improving renewable energy forecasting skill and modeling multi-period flexibility of distributed energy resources.

This paper describes an energy management system that is being developed in the AnyPLACE project to support new energy services, like demand response, in residential buildings. In the project end-user interfaces are designed and implemented to allow the input of preferences regarding the flexible use of shiftable and thermal appliances. Monitoring and self-learning algorithm are used to allow additional information to be collected and an automation platform is available for the management and control of appliances. An energy management algorithm is presented that processes end-user preferences and devices characteristics to produce an optimal dispatch considering demand response incentives. Results show the successful implementation of an optimized energy scheduling.

Large-scale integration of variable Renewable Energy Sources (RES) brings significant challenges to grid operation that require new approaches and tools for distribution system management, particularly concerning voltage control. Therefore, an innovative approach for voltage control at the MV level is presented. It is based on a preventive day-ahead analysis that uses data from load/RES forecasting tools to establish a plan for operation of the different Distributed Energy Resources (DER) for the next day. The approach is formulated as a multi-temporal Optimal Power Flow (OPF) solved by a meta-heuristic, used to tackle complex multi-dimensional problems. The tuning of the meta-heuristic parameters was performed to ensure the robustness of the proposed approach and enhance the performance of the algorithm. It was tested through simulation in a large scale test network with good results. (C) 2017 Elsevier B.V. All rights reserved.

This paper presents the development framework for an energy management platform that is being developed within the AnyPLACE project. In order to ensure that end-users become active participants in services like demand response, a combined approach is necessary in terms of monitoring, automation, and user interfacing. The success in engaging the end-user, as the centerpiece of the energy management challenge, is vital in taking advantage of a more efficient use of energy, as it is shown in this paper. The proposed framework can be run in a single board computer.

Smart grids aim at ensuring a secure, reliable and efficient operation of power systems and for that purpose they need communications infrastructures capable of meeting different requirements. Current and emerging wireless multi-hop solutions based on standard technologies are strong candidates for communications networks associated and integrated with electric distribution grids but a suitable methodology to evaluate and deploy them is missing. This paper presents a holistic methodology supported by contextual information used to generate different scenarios of distribution grids and to evaluate and deploy wireless communications networks for smart grids. Simulation results show that the methodology is suitable for the evaluation of wireless multi-hop networks in the smart grid context and prove that the performance of such networks meets the expected requirements of different applications.