Abstract
The hybrid microgrids (MGs) are introduced to form the future distribution systems that utilize the advantages of both DC and AC grids. In the hybrid MG, renewables, DC and AC loads, storage devices and distributed energy resources (DERs) are integrated and connected through the separated AC and DC buses. The control and management of the hybrid MG are more complicated than an individual AC or DC microgrid. In this paper, first, the two control strategies are implemented in a typical hybrid MG. Then, the performance of these control strategies in the view of the primary control are discussed and compared in response to the occurrence of a defined contingency. Simulation results show the different performance of each control strategy in frequency and voltage control of the hybrid MG in response to the same contingency.

Abstract

Abstract
Electrical energy consumption forecasting is, nowadays, essential in order to deal with the new paradigm of consumers' active participation in the power and energy system. The uncertainty related to the variability of consumption is associated to numerous factors, such as consumers' habits, the environmental temperature, luminosity, etc. Current forecasting methods are not suitable to deal with such a combination of input variables, with often highly variable influence on the outcomes of the actual energy consumption. This paper presents a study on the application of five different methods based on fuzzy rule-based systems. This type of method is able to find associations between the distinct input variables, thus creating rules that support and improve the actual forecasting process. A case study is presented, showing the results of applying these five methods to predict the consumption of a real building: the Thales TRT building, in France.

Abstract

Abstract
This paper presents a comprehensive sensitivity analysis to identify the uncertain parameters which significantly influence the decision-making process in distributed generation (DG) investments and quantify their degree of influence. To perform the analysis, a DG investment planning model is formulated as a novel multistage and multiscenario optimization problem. Moreover, to ensure tractability and make use of exact solution methods, the entire problem is kept as a mixed-integer linear programming optimization. A real-world distribution network system is used to carry out the analysis. The results of the analysis generally show that uncertainty as well as operational variability of the considered parameters have meaningful impacts on investment decisions of DG. The degree of influence varies from one parameter to another. But, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. Hence, the analysismade in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices.

Abstract
Higher education institutions are at this stage, on the one hand, faced with challenges never seen before and, on the other hand, their action is moving very rapidly into digital learning spaces. These challenges are increasingly complex because of the global competition for resources, students and teachers. In addition, the amount of data produced inside and outside higher education institutions has grown exponentially, so more and more institutions are exploring the potential of Big Data to meet these challenges. In this context, higher education institutions and key stakeholders (students, teachers, and governance) can derive multiple benefits from learning analytics using different data analysis strategies to produce summative, real-time and predictive information and recommendations. However, it may be questioned whether institutions, academic administrative staff as well as including those with responsibility for governance, are prepared for learning analytics? As a response to the question raised in this paper is presented an extension of a disruptive conceptual approach to higher education, using information gathered by IoT and based on Big Data & Cloud Computing and Learning Analytics analysis tools, with the main focus on the stakeholder governance.