This paper presents a stochastic framework for the optimization of microgrids that has the functionality of providing flexibility services to System Operators (SOs) considering uncertainties in the energy forecast. The methodology is developed with the aim of being applied to complex microgrids composed of different distributed energy resources and hybrid energy storage systems (ESS). The associated optimization problem is operated in two stages: the first one performs a stochastic optimization of the microgrid in order to reserve an up/down regulation capacity with which to deal with the energy forecast uncertainties of the microgrid. The different microgrid devices are optimized by considering their operational costs in order to achieve their optimal operation in the Day-Ahead Market (DM). The second stage is used to re-schedule the initial planning according to the signal request and an economic offer from the SO. The control problem is developed using Stochastic Model Predictive Control (SMPC) techniques and Mixed-Integer Quadratic Programming (MIQP), owing to the presence of logic, integer, mixed and probabilistic variables. The simulation results show that the proposed methodology reduces the risk of undergoing up/down-penalty deviations in the Regulation Service Market (RM), also being able to provide flexibility services to the SOs, despite being subject to uncertainties in the energy forecast carried out for the microgrid.

Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.

The proliferation of photovoltaic (PV) power generation in power distribution grids induces increasing safety and service quality concerns for grid operators. The inherent variability, essentially due to meteorological conditions, of PV power generation affects the power grid reliability. In order to develop efficient monitoring and control schemes for distribution grids, reliable forecasting of the solar resource at several time horizons that are related to regulation, scheduling, dispatching, and unit commitment, is necessary. PV power generation forecasting can result from forecasting global horizontal irradiance (GHI), which is the total amount of shortwave radiation received from above by a surface horizontal to the ground. A comparative study of machine learning methods is given in this paper, with a focus on the most widely used: Gaussian process regression (GPR), support vector regression (SVR), and artificial neural networks (ANN). Two years of GHI data with a time step of 10 $\min$ are used to train the models and forecast GHI at varying time horizons, ranging from 10 $\min$ to 4 $\mathrm{h}$. Persistence on the clear-sky index, also known as scaled persistence model, is included in this paper as a reference model. Three criteria are used for in-depth performance estimation: normalized root mean square error (nRMSE), dynamic mean absolute error (DMAE) and coverage width-based criterion (CWC). Results confirm that machine learning-based methods outperform the scaled persistence model. The best-performing machine learning-based methods included in this comparative study are the long short-term memory (LSTM) neural network and the GPR model using a rational quadratic kernel with automatic relevance determination.

This paper introduces a new empirical formulation of the clear-sky intensity distribution based on images acquired with a sky imager developed at the PROMES-CNRS laboratory (Perpignan, France). Both the formulation and image processing methodology are detailed and stand for key steps in the development of a high quality cloud detection algorithm. The work presented in this paper is a part of a research project which aims at improving solar plant control procedures using direct normal irradiance forecasts under various sky conditions at short-term horizon (5-30 min) and high spatial resolution (similar to 1 km(2)). Modelling the clear-sky intensity distribution in real time allows clear-sky images to be generated. These clear-sky images can then be used to remove the clear-sky background anisotropy on images and so improve cloud detection algorithms significantly. Cloud detection is essential in short-term solar resource forecasting. The new formulation is especially designed for improving performance of the existing models in the circumsolar area. When tested over more than 2200 clear-sky images, corresponding to a solar zenith angle spanning from 24 degrees to 85 degrees, the new formulation outperforms a standard approach based on the All-Weather model (Perez et al., 1993) by 15% on the whole sky and more than 20% in the circumsolar area. Application of the methodology for the real-time cloud detection purpose is discussed at the end of the paper. (C) 2015 Elsevier Ltd. All rights reserved.

Microgrids (MGs) and networked (interconnected) microgrids (NMGs) are emerging as an efficient way for integrating distributed energy resources (DERs) into power distribution systems. MGs and NMGs can disconnect from the main grid and operate autonomously, strengthen grid resilience, and help mitigate grid disturbances and maintain power quality. In addition, when supported by sophisticated and efficient management strategies, MGs and NMGs have the ability to enhance power supply reliability. However, their deployment comes with many challenges, in particular regarding the efficient management of DERs. That is why a survey of recent advances in the smart management—the term refers to a variety of planning and control tasks—of MGs and NMGs is presented in this paper. It aims at establishing a picture of strategies and identifying trends in methods. The reader is provided with an in-depth analysis of a variety of papers recently published in peer-reviewed journals: the way the methods are used and the common issues addressed by the scientific community are discussed. Following this analysis, one can especially observe that (1) model-based predictive control (MPC) is emerging as a competitive alternative to conventional methods, in particular in voltage and frequency regulation and DER management (2) due to their ability to handle complex tasks, data-driven strategies are getting more and more attention from the scientific community (3) game theory (GT) is a very good candidate for efficient management of complex systems as NMGs (4) MPC and artificial intelligence are increasingly being used for proper MG islanded operation or to manage electric vehicles (EVs) efficiently.