Abstract
The new energy policies adopted by the European Union are set to help in the decarbonization of the energy system. In this context, the share of Variable Renewable Energy Sources is growing, affecting electricity markets, and increasing the need for system flexibility to accommodate their volatility. For this reason, legislation and incentives are being developed to engage consumers in the power sector activities and in providing their potential flexibility in the scope of grid system services. This work identifies energy and cross-sector Business Models (BM) centered on or linked to the provision of distributed flexibility to the DSO and TSO, building on those found in previous research projects or from companies' commercial proposals. These BM are described and classified according to the main actor. The remaining actors, their roles, the interactions among them, how value is created by the BM activities and their value propositions are also described.

Abstract
Nowadays, human operators at grid control centers analyze a large volume of alarm information during outage’s events, and must act fast to restore the service. Currently, after the occurrence of short-circuit faults and its isolation via feeder protection, fault location and isolation is achieved via remotely controlled switching actions defined by operator’s experience. Despite operator’s experience and knowledge, this makes the process sub-optimal and slower. This paper proposes two novel machine learning-based algorithms to assist human operator decisions, aiming to: i) classify the complexity of a fault occurrence (Occurrences Classifier) based on its alarm events; ii) provide fast insights to the operator on how to solve it (Data2Actions). The Occurrences Classifier takes the alarm information of an occurrence and classifies it as a “simple” or “complex” occurrence. The Data2Actions takes a sequence of alarm information from the occurrence and suggests to the operator the more adequate sequence of switching actions to isolate the fault section on the overhead medium voltage line. Both algorithms were tested in real data from a Distribution System Operator between 2017 and 2020, and showed i) an accuracy of 86% for the Data2Actions, and ii) the Occurrences Classifier reached 74% accuracy for “simple” occurrences and 58% for “complex” ones, leading to an overall 65% accuracy. © 2022 IET Conference Proceedings. All rights reserved.

Abstract

Abstract
Decarbonization of energy systems is one of the main tracks in the energy sector, and in this transition, green hydrogen assumes an important role. Considering the variability of renewable energy sources (RES), the flexibility of the hydrogen production could help dealing with imbalances. However, to truly contribute to a greener energy mix, a principle of additivity must be obeyed. In other words, to produce green hydrogen, the energy supplied to the electrolyzers must be renewable and must not entail a decrease in the RES consumed by other loads according to the energy strategic plans. This study integrates power flow tracing (PFT) technique within an optimal power flow (OPF) to determine and maximize the physical flow between the energy from RES generators and the electrolyzer through the existing grid. The proposed method was tested on both radial and meshed IEEE test grids. Simulation results showed that the electrolyzer green supply can be increased by controlling the dispatch of the distributed generators (e.g., CHP) according to the location of the electrolyzer. In addition, installing storage systems nearby load buses allows increasing the amount of green supply by using the RES-based electricity stored. © 2022 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>