Abstract
<p>The aim of this paper is to present the <strong>objectives, research directions and first highlight results</strong> of the <strong>Smart4RES</strong> project, which was launched in November 2019, under the <strong>Horizon 2020</strong> Framework Programme. Smart4RES is a research project that aims to bring substantial performance improvements to the whole model and value chain in r<strong>enewable energy (RES) forecasting</strong>, with particular emphasis placed on optimizing <strong>synergies with storage and to support power system operation and participation in electricity markets</strong>. For that, it concentrates on a number of disruptive proposals to support ambitious objectives for the future of renewable energy forecasting. This is thought of in a context with steady increase in the quantity of data being collected and computational capabilities. And, this comes in combination with recent advances in <strong>data science</strong> and approaches to <strong>meteorological forecasting</strong>. Smart4RES concentrates on novel developments towards <strong>very high-resolution and dedicated weather forecasting solutions</strong>. It makes <strong>optimal use of varied and distributed sources of data</strong> e.g. remote sensing (sky imagers, satellites, etc), power and meteorological measurements, as well as high-resolution weather forecasts, to yield high-quality and seamless approaches to renewable energy forecasting. The project accommodates the fact that all these sources of data are distributed geographically and in terms of ownership, with current restrictions preventing sharing. Novel alternative approaches are to be developed and evaluated to reach optimal forecast accuracy in that context, including <strong>distributed and privacy-preserving learning and forecasting methods</strong>, as well as the advent of platform-enabled <strong>data-markets</strong>, with associated pricing strategies. Smart4RES places a strong emphasis on <strong>maximizing the value from the use of forecasts in applications</strong> through advanced decision making and optimization approaches. This also goes through approaches to streamline the definition of new forecasting products balancing the complexity of forecast information and the need of forecast users. Focus is on developing models for applications involving storage, the provision of ancillary services, as well as market participation.</p>

Abstract
The increasing integration of Distributed Energy Resources (DER) in electricity networks has required an improvement in the network management procedures. While the operation paradigm is evolving and adapting to the new network features, the planning approach is rather inefficient as network assets are usually oversized to meet the worst-case scenario. In this regard, this paper presents an innovative methodology that integrates the potential flexibility of DER into the planning process, in an attempt to bridge the gap between current network operation approaches and the planning methods. It includes an analysis of future scenarios, providing different reinforcement plans considering the realistic network operation for those scenarios. The proposed optimal design of the reinforcement plans has two complementary processes: First to optimize flexible resources in their owner's perspective and second to reschedule the flexible resources' operation when the DSO needs to solve technical problems. The model has been tested in a typical Portuguese medium voltage network using future scenarios of DER integration from ENTSO-E. The results conclude that the proposed methodology leads to cost-effective solutions, which provide a better use of flexible resources, deferring high capital investments in network reinforcement.

Abstract
Office buildings consume a significant amount of energy that can be reduced through behavioral change. Gamification offers the means to influence the energy consumption related to the activities of the office users. This paper presents a new mobile gamification platform to foster the adoption of energy efficient behaviors in office buildings. The gamification platform is a mobile application with multiple types of dashboards, such as (1) an information dashboard to increase the awareness of the users about their energy consumption and footprint, (2) a gaming dashboard to engage users in real-time energy efficiency competitions, (3) a leaderboard to promote peer competition and comparison, and (4) a message dashboard to send tailor-made messages about energy efficiency opportunities. The engagement and gamification strategies embedded in these dashboards exploit economic, environmental, and social motivations to stimulate office users to adopt energy efficient behaviors without compromising their comfort and autonomy levels. The gamification platform was demonstrated in an office building environment. The results suggest electricity savings of 20%. © 2020 Elsevier B.V.

Abstract
The smart home will bring many challenges. One of the challenges is how to design a smart home that satisfies the needs of the residents in a cost-effective way. This paper addresses this challenge by proposing an optimization model to define the optimal portfolio of smart home technologies and electricity tariffs that minimize the overall investment and operation costs of the house owner. The smart home technologies include electric vehicle charging stations, battery energy storage systems, home energy management systems, and photovoltaic systems. A case study of a real house in Portugal was used to evaluate the performance of the planning optimization model. The numerical results show that the optimization model selects the combination of smart home technologies and electricity tariffs that best meets the needs of the household owner in a cost-effective way.

Abstract
This paper proposes a near to real-time local market to provide reactive power to the transmission system operator (TSO), using the resources connected to a distribution grid managed by a distribution system operator (DSO). The TSO publishes a requested reactive power profile at the TSO-DSO interface for each time-interval of the next delivery period, so that market agents (managing resources of the distribution grid) can prepare and send their bids accordingly. DSO resources are the first to be mobilized, and the remaining residual reactive power is supplied by the reactive power flexibility offered in the local reactive market. Complex bids (with non-curtailability conditions) are supported to provide flexible ways of bidding fewer flexible assets (such as capacitor banks). An alternating current (AC) optimal power flow (OPF) is used to clear the bids by maximizing the social welfare to supply the TSO required reactive power profile, subject to the DSO grid constraints. A rolling window mechanism allows a continuous dispatching of reactive power, and the possibility of adapting assigned schedules to real time constraints. A simplified TSO-DSO cost assignment of the flexible reactive power used is proposed to share for settlement purposes.

Abstract
EU-SysFlex H2020 project aims at developing and testing innovative tools for the integration of high levels (above 50%) of renewable energy sources (RES) in the pan-European Electricity system. Those high levels of RES will increase the need for new sources of flexibility to support the system services, since there will be a decreasing number of conventional power plants connected to the grid, traditionally the main providers of these services. In this context, this study describes the Flexibility Hub (FlexHub) concept, part of the EU-SysFlex Portuguese demonstrator, consisting of a platform of tools managed by the distribution system operator to facilitate market-based flexibility provision to the transmission system operator (TSO) using resources connected to the distribution system. The FlexHub includes the provision of active power flexibility to the TSO (with an extended restoration reserve market concept), an essential tool to help the TSO balancing the grid, being a replicable concept that will become increasingly relevant in a future with a high share of decentralised assets connected to the distribution grids.