Abstract

Abstract
The EU megagrid is foreseen to exploit the very large resource of solar energy in southern Europe and of wind power in northern Europe. Microgrids with enhanced control capabilities can integrate and coordinate local distributed resources enhancing the resilience of the EU megagrid and providing local restoration capabilities. The future modeling challenges of microgrids and in particular the shift to distributed control, enhancing further the microgrids resilience, are highlighted. © 2015 IEEE.

Abstract
In the decades to come spatial limitation poses a major challenge for islands, especially where tourism dominates and many heritage and natural conservation sites exist. Simultaneously, alternatives to lower energy import dependency are sought. Since onshore technologies are confronted by spatial limitation, the focus might shift towards integrating offshore technologies. Although most of those technologies lack market maturity, the benefit to be installed (far) off the coast is immense. Yet, it is a challenge to select the most appropriate technology for any given site location. Thereby, it is not pre-dominantly a matter of economic and environmental concerns, rather than an assessment of site conditions and resource characteristics. A set of indicators for, both, conditions and characteristics has been established that allows pre-selecting and comparing the suitability of various offshore technologies. Hence, energy planners will be able to select from the most appropriate technologies when planning for a sustainable future. © 2015 Taylor & Francis Group, London.

Abstract
Photovoltaic (PV) solar power capacity is growing in several countries, either concentrated in medium/large size solar parks or distributed by the medium and low voltage grid. Solar power forecasting is a key input for supporting grid management, participation in the electricity market and maintenance planning. This paper proposes a new forecasting system that is a hybrid of different models, such as electrical and statistical models, as well as different Numerical Weather Prediction (NWP) sources. The time horizon is 48 hours ahead. The proposed model was operationalized and tested in a real PV installation located in North Portugal with 16 kW.

Abstract
The current literature in wind power forecast is focused in generating accurate uncertainty forecasts and communicating this information to the end-user. Multi-temporal decision-making problems require information about the temporal trajectory of wind power for the next hours. Presently, this information is provided through a set of temporal trajectories (or scenarios). This paper aims at contributing with an alternative approach for communicating this information through simultaneous prediction intervals. These intervals include the temporal dependency of forecast errors since they provide information about the probability of having the observed wind power trajectory fully inside the quantiles forming the interval. First, a learning sample of temporal trajectories are generated with the Gaussian copula method and using the marginal prediction intervals. Then, two methods proposed in the literature are used to construct the simultaneous intervals. The quality of these intervals is evaluated for three real wind farms.

Abstract
Demand aggregators are new market players that represent a group of consumers in the electricity market. This paper proposes an aggregator model responsible for gathering residential and commercial consumers, which has the role of managing their flexible consumption in the day-ahead electricity market. A methodology to optimize the aggregator's bids is also presented. It optimizes the scheduling of the flexible loads taking simultaneously into account the consumers' preferences and temporal trajectories of forecasted outdoor temperatures and electricity prices. The proposed methodology was tested using a case study with 200 residential and commercial consumers from the Iberian market.