Abstract
Protection and control systems represent an essential part of distribution networks by ensuring the physical integrity of components and by improving system reliability. Protection devices isolate a portion of the network affected by a fault, while control devices reduce the number of de-energized loads by transferring loads to neighboring feeders. The integration of distributed generation has the potential to enhance the continuity of energy services through islanding operation during outage conditions. In this context, this study presents a multi-objective optimization approach for sizing and allocating protection and control devices in distribution networks with microgrids supplied by renewable energy sources. Reclosers, fuses, remote-controlled switches, and directional relays are considered in the formulation. Demand and generation uncertainties define the islanding operation and the load transfer possibilities. A non-dominated sorting genetic algorithm is applied in the solution of the allocation problem considering two conflicting objectives: cost of energy not supplied and equipment cost. The compromise programming is then performed to achieve the best solution from the Pareto front. The results show interesting setups for the protection system and viability of islanding operation.

Abstract
Modern power grids have high levels of distributed energy resources, automation, and inherent flexibility. Those characteristics have been proven to be favorable from an environmental, social and economic perspective. Despite the increased versatility, modern grids are becoming more vulnerable to high-impact low-probability (HILP) threats, particularly for the distribution networks. On one hand, this is due to the increasing frequency and severity of weather events and natural disasters. On the other hand, it is aggravated by the increased complexity of smart grids. Resilience is broadly defined as the capability of a system to mitigate the effects of and recover from HILP events, which is often confused with reliability that is concerned with low-impact high-probability (LIHP) ones. In this paper, a distribution system in Portugal is simulated to showcase how the utilization of flexibility and mobile energy resources (MERs) should be considered differently relative to HILP vs LIHP threats.

Abstract
In this paper, a multi-temporal optimal power flow (OPF) model for radial networks is proposed. The mathematical problem formulation is presented as a mixedinteger quadratically constrained programming (MIQCP) problem. The main core of the developed OPF problem is benefiting from the second-order conic programming (SOCP) approach while the quadratic constraints of the power flow equations have been efficiently handled. In the developed model, the dynamic behaviour of the electrical energy storage (EES) has been addressed for the day-ahead operation problem. In addition, the developed model is tested and verified for both normal and contingent events and the obtained results are satisfactory in terms of feasibility and optimality. In the islanded operation, a grid-forming unit is the main responsible for maintaining the voltage reference while other units behave as slave. The model is tested on the modified IEEE 33-bus network to verify the performance of the developed tool.

Abstract
Protection and control systems represent an essential part of distribution networks, ensuring the physical integrity of components and improving system reliability. Protection devices isolate a portion of the network affected by a fault, while control devices reduce the number of de-energized loads by transferring loads to neighboring feeders. The integration of distributed generation has the potential to improve the continuity of energy services through islanding operation during outage conditions. In this context, this paper presents a multi-objective optimization approach for the size and allocation of protection and control devices in distribution networks with microgrids supplied by renewable energy sources. Reclosers, fuses, remote-controlled switches, and directional relays are considered in the formulation. The demand and generation uncertainties define the islanding operation and the load transfer possibilities. A genetic algorithm is presented to solve the allocation problem. The compromise programming is performed to choose the best solution from the Pareto front. Results show interesting setups for the protection system and viability of islanding operation.

Abstract
With a still high rate of use of energy from non-renewable sources, it is crucial that new energy generation solutions are adopted to reach greenhouse gas reduction targets. The integration of renewable energy sources in buildings is an interesting solution that allows reducing the need for energy from the power grid, contributing to a significant increase in the energy efficiency of buildings. The main aim of this paper is to evaluate the impact that the aerodynamics of the buildings in particular the roof shape has considering the integration of wind energy systems. The results of Computational Fluid Dynamics (CFD) simulations are presented in order to identify the effect of the two roof shapes on energy production by wind turbines (WT). For this purpose, the factor matrices (FM) that gives information about the wind profile around the building taking into account the building's roof profile were calculated. Comparing the results for the wind flow obtained by the FM and the CFD simulations for the flat and gabled roofs, similarities are observed for them, allowing to conclude that the CFD analysis results in a methodology with great accuracy for the aerodynamic study of buildings roof shape.

Abstract
There are different clean energy production technologies, including wind energy production. This type of energy, among renewable energies, is one of the least predictable due to the unpredictability of the wind. The wind prediction has been a deeply analysed field since has a considerable share on the green energy production, and the investments on this sector are growing. The efficiency and stability of power production can be increased with a better prediction of the main source of energy, in our case the wind. In this paper, some techniques inspired by Biological Inspired Optimization Techniques applied to wind forecast are compared. The wind forecast is very important to be able to estimate the electric energy production in the wind farms. As you know, the energy balance must be checked in the electrical system at every moment. In this study we are going to analyse different methodologies of wind and power prediction for wind farms to understand the method with best results.