Abstract
This paper proposes a Local Energy Market using a P2P blockchain-powered marketplace where agents bilaterally trade energy after the consumption and production period, and not before, as usual in electricity market design. The EU and MIBEL regulatory framework for Renewable Energy Communities potentially creates space for such a market, but some improvements in the settlement procedures and agent's participation must be met. © 2020 IEEE.

Abstract
<p>Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The International Energy Agency (IEA) Wind Task on Wind Power Forecasting organises international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, ...), forecast vendors and forecast users.<br>Collaboration is open to IEA Wind member states, 12 countries are already therein.</p><p>The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks for NWP models. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions.</p><p>The main result is the IEA Recommended Practice for Selecting Renewable Power Forecasting Solutions. This document in three parts (Forecast solution selection process, and Designing and executing forecasting benchmarks and trials, and their Evaluation) takes its outset from the recurrent problem at forecast user companies of how to choose a forecast vendor. The first report describes how to tackle the general situation, while the second report specifically describes how to set up a forecasting trial so that the result is what the client intended. Many of the pitfalls which we have seen over the years, are avoided. <br><br>Other results include a paper on possible uses of uncertainty forecasts, an assessment of the uncertainty chain within the forecasts, and meteorological data on an information portal for wind power forecasting. This meteorological data is used for a benchmark exercise, to be announced at the conference. The poster will present the latest developments from the Task, and announce the next activities.</p>

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.