Abstract
The increase in the world's population combined with the development of new economies has led to a large scale increase in the demand for energy resources. New technologies have emerged that allow for the maintenance of the energy supply. Renewable sources and energy storage systems (ESS) are emerging as a crucial option for the development of Smart Grids. Using a Mixed Integer Linear Programming (MILP) optimization model, the effects of renewable production sources and storage systems on an electrical grid were studied, in order to maximize the profit of a Virtual Power Plant (VPP). The obtained results allowed us to verify the efficiency of the proposed method. The placement of renewable producers and the ESS, as well as the management optimization of the purchase and transfer process of the stored energy definitely increased the profit of VPP. The use of these technologies also improves the voltage profile and decreased the active power losses by 84% along with the network.

Abstract
Nowadays, power distribution systems face some challenges related to power losses minimization and voltage stability improvement along the networks. These challenges can also be a big opportunity to develop smarter and more efficient distribution networks, ensuring the continuity of service and the power quality supplied to consumers. At the same time, there are several international standards that regulate the power quality levels required for distribution networks.This paper addresses deeply these issues and provide a solution to solve both of these problems using Distributed Generators (DG) on optimal locations of the grid. The proposed method will analyze the injection of both real and reactive power in a regulated IEEE-69 bus system. In the first stage, the voltage and loss sensitivity of the load flow analysis is calculated using a MatLab algorithm. In a second stage, the methodology uses the voltage stability index (VSI) to obtain the optimal location of the DGs to ensure the best results of both, power loss and voltage stability for the grid. The obtained results show the good effectiveness of the proposed method.

Abstract

Abstract
Online incremental models for recommendation are nowadays pervasive in both the industry and the academia. However, there is not yet a standard evaluation methodology for the algorithms that maintain such models. Moreover, online evaluation methodologies available in the literature generally fall short on the statistical validation of results, since this validation is not trivially applicable to stream-based algorithms. We propose a k-fold validation framework for the pairwise comparison of recommendation algorithms that learn from user feedback streams, using prequential evaluation. Our proposal enables continuous statistical testing on adaptive-size sliding windows over the outcome of the prequential process, allowing practitioners and researchers to make decisions in real time based on solid statistical evidence. We present a set of experiments to gain insights on the sensitivity and robustness of two statistical tests-McNemar's and Wilcoxon signed rank-in a streaming data environment. Our results show that besides allowing a real-time, fine-grained online assessment, the online versions of the statistical tests are at least as robust as the batch versions, and definitely more robust than a simple prequential single-fold approach.

Abstract

Abstract
Smart4RES is a European Horizon2020 project developing next generation solutions for renewable energy forecasting. This paper presents highlight results obtained during the first year of the project. Data science is used throughout the proposed solutions in order to process the large amount of heterogeneous data available to forecasters, and derive model-free approaches of forecasting and decision-aid tasks. This paper presents a series of solutions addressing relevant for Photovoltaics (PV) and storage applications. High-resolution Numerical Weather Predictions and regional solar irradiance forecasting provide detailed information on local weather conditions and their variability. PV power forecasting benefits from such new data sources, but also the proposed collaborative data exchange. Finally, data-driven methods simplify decision-making for trading in short-term markets and for grid management. © 2021 Energynautics GMBH.