Abstract

Abstract
Microgrids are promising to enhance power distribution systems' efficiency, quality, sustainability, and reliability. However, microgrids operation can impose several challenges to traditional protection schemes, like changes in the power flow direction and an increase in short-circuit currents. Microgrids can include several distributed generation technologies with different behaviours during short-circuit conditions, requiring additional protection schemes and devices. In this way, the optimized coordination of reclosers and fuses in distribution networks with directional overcurrent relays, which operate as interconnection devices, might overcome many imposed protection challenges. Regarding different generation technologies, voltage-restrained overcurrent relays and frequency relays are presented as local microgrid protection for rotative and inverter-based distributed generators, respectively. The optimized coordination of these protection devices maximizes microgrid benefits and minimizes operation drawbacks by reducing interruptions impacts and energy not supplied to consumers. This work proposes, thus, a mathematical model for the optimal coordination of protection devices in distribution networks with distributed energy resources operating in grid-connected and islanded modes. The minimization technique of operating times using an elitist genetic algorithm with variable crossover and mutation processes is proposed, as well. The results show adequate coordination using passive and low-cost protection devices.

Abstract
As the access to consumption data available in household smart meters is now very common in several developed countries, this kind of information is assuming a providential role for different players in the energy sector. The proposed study was applied to data available from the Smart Meter Energy Consumption Data in the London Households dataset, provided by UK Power Networks, containing half-hourly readings from an original sample of 5567 households (71 households were hereby carefully selected after a justified filtering process). The main aim is to forecast the day-ahead load profile, based only on previous load values and some auxiliary variables. During this research different forecasting models are applied, tested and compared to allow comprehensive analyses integrating forecasting accuracy, processing times and the interpretation of the most influential features in each case. The selected models are based on Multivariate Adaptive Regression Splines, Random Forests and Artificial Neural Networks, and the accuracies resulted from each model are compared and confronted with a baseline (Naive model). The different forecasting approaches being evaluated have been revealed to be effective, ensuring a mean reduction of 15% in Mean Absolute Error when compared to the baseline. Artificial Neural Networks proved to be the most accurate model for a major part of the residential consumers.

Abstract
Renewable energy has a key role to play in the transition towards a low-carbon society. Despite its importance, relatively little attention has been focused on the crucial impact of weather and climate on energy demand and supply, or the generation or operational planning of renewable technologies. In particular, to improve the operation and longer-term planning of renewables, it is essential to consider seasonal and subseasonal weather forecasting. Unfortunately, reports that focus on these issues are not common in scientific literature. This paper presents a systematic review of the seasonal forecasting of wind and wind power for the Iberian Peninsula and the Canary Islands, a region leading the world in the development of renewable energies (particularly wind) and thus an important illustration in global terms. To this end, we consider the scientific literature published over the last 13 years (2008–2021). An initial search of this literature produced 14,293 documents, but our review suggests that only around 0.2% are actually relevant to our purposes. The results show that the teleconnection patterns (North Atlantic Oscillation [NAO], East Atlantic [EA] and Scandinavian [SCAND]) and the stratosphere are important sources of predictability of winds in the Iberian Peninsula. We conclude that the existing literature in this crucial area is very limited, which points to the need for increased research efforts, that could lead to great returns. Moreover, the approach and methods developed here could be applied to other areas for which systematic reviews might be either useful or necessary.

Abstract

Abstract
With the increased penetration of renewables, energy storage has become a critical issue in microgrid and small household applications. Accordingly, this paper undertakes a feasability study the varying limitations from conventional batteries in residential buildings, such as capacity-loss over time and aging, as well as the alternative application and challenges of hydrogen-based storage for the domestic sector. The paper considers a test case study where an analysis is performed on the practicality of hydrogen-based storage, in addition to lithium-ion battery storage. Various scenarios are considered based on solar installation sizes, self-consumption, battery capacity, autonomy rates and grid extraction. A detailed analysis is carried out on both thermal and electrical demands of a residential household, which also includes the energy performance and applications of heat pumps. While the obtained results from various scenarios are compared and analysed, these anticipate that the potential integration of hydrogen can improve the autonomy rate of residential buildings, The cost of hydrogen storage is expected to reduce significantly, opening opportunities for hydrogen application.