Abstract

Abstract
MicroGrid (MG) is becoming an important system for a massive and reliable integration of renewable energy sources. Hence, due to its benefits, photovoltaic (PV) systems are the most suitable and widely used in MGs. However, for a reliable integration of PV panels, they must be interfaced by smart power electronics devices that allow the implementation of advanced control solutions. In addition to operating as active power generator with maximum power point tracking (MPPT), the new PV systems (PVS) should behave as ancillary services providers by participating to the grid regulation, such as frequency stabilization, voltage profile control, harmonics compensation and so forth. This paper aims at reviewing the control and operation of PVS in AC MG. Control structures, active power control, MPPT and inner loops are presented and discussed. Design, analysis and control of single-stage three phase PVS in AC MG that is able to operate at MPP or as a dispatchable source are made. The control structure is validated through dynamic simulations. © 2016 IEEE.

Abstract
Photovoltaic systems (PVS) based MicroGrid (MG) should be able to operate at the maximum power point (MPP) in order to extract the maximum available power during atmospheric conditions changes. In addition to this, the PVSs should be able also to operate below the MPP in order to participate to the MG regulation such as grid frequency stabilization, voltage profile control and grid management support. This paper discusses the state of the art of operation and control of PVS in MG. Design and control of a single stage single phase PVS connected to a low voltage AC MG are presented and analyzed. A comparison between two current control methods, Proportional integral with grid voltage feed forward (PI+FF) and proportional resonant with selective harmonics compensation (PR+HC) is also made. The control structures are validated through dynamic simulation. © 2016 IEEE.

Abstract
The deployment of smart grids and renewable energy dispatch centers motivates the development of forecasting techniques that take advantage of near real-time measurements collected from geographically distributed sensors. This paper describes a forecasting methodology that explores a set of different sparse structures for the vector autoregression (VAR) model using the least absolute shrinkage and selection operator (LASSO) framework. The alternating direction method of multipliers is applied to fit the different LASSO-VAR variants and create a scalable forecasting method supported by parallel computing and fast convergence, which can be used by system operators and renewable power plant operators. A test case with 66 wind power plants is used to show the improvement in forecasting skill from exploring distributed sparse structures. The proposed solution outperformed the conventional autoregressive and vector autoregressive models, as well as a sparse VAR model from the state of the art. Copyright (c) 2016 John Wiley & Sons, Ltd.

Abstract
A recent research trend is driven to increase the monitoring and control capabilities of low voltage networks. This paper describes a probabilistic forecasting methodology based on kernel density estimation and that makes use of distributed computing techniques to create a highly scalable forecasting system for LV networks. The results show that the proposed algorithm outperforms three benchmark models (one for point forecast and two for probabilistic forecasts) and demonstrate the applicability of the distributed in-memory computing solution for a practical operational scenario. The ultimate goal is to integrate information about net-load forecasts in power flow optimization frameworks for low voltage networks in order to solve technical constraints with the available home energy management system flexibility.

Abstract
Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. This paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. A set of recommendations for standardization and improved training of operators are provided along with examples of best practices.