Abstract
In this paper it is intended to solve an Economical Dispatch (ED) problem with a new tool, named Sensing Cloud Optimization (SCO). It is a technique based on clouds of particles which allow a dynamic change in search space. It has appropriate heuristic characteristic to solve not convex, not differentiable and highly constrained optimisation problems. It is provided with a statistical analysis which determines the cloud's dimension with dynamic adjustments in search space in order to accelerate the convergence and to avoid to get trapped in local minima. Two case studies are presented in which SCO demonstrated good performances reaching lower cost values where compared with other techniques.

Abstract
In this work is discussed the importance of the renewable production forecast in an island environment. A probabilistic forecast based on kernel density estimators is proposed. The aggregation of these forecasts, allows the determination of thermal generation amount needed to schedule and operating a power grid of an island with high penetration of renewable generation. A case study based on electric system of S. Miguel Island is presented. The results show that the forecast techniques are an imperative tool help the grid management.

Abstract
In this paper a solution to an highly constrained and non-convex economical dispatch (ED) problem with a meta-heuristic technique named Sensing Cloud Optimization (SCO) is presented. The proposed meta-heuristic is based on a cloud of particles whose central point represents the objective function value and the remaining particles act as sensors "to fill" the search space and "guide" the central particle so it moves into the best direction. To demonstrate its performance, a case study with multi-fuel units and valve- point effects is presented.

Abstract
In this paper is studied an approach based on risk assessment to solve the scheduling of a power production system with variable power sources. The spinning reserves resulting from the unit commitment are analyzed too. In this methodology there are no infeasible solutions, only more or less costly solutions associated to the operation risks, such as, load or renewable production curtailment. The uncertainty of forecasted production and load demand are defined by probability distribution functions. The methodology is tested in a real case study, an island with high penetration of renewable power production. Finally, forecasted and measured reserves are compared, once the reserves are strongly linked with the forecasting quality. The results of a real case study are presented and discussed. They show the difficulty to achieve complete robust solutions.

Abstract
In the past two decades, interest in demand response (DR) schemes has grown exponentially. The need for DR has been driven by sustainability (environmental and socioeconomic) and cost-efficiency. The main premise of DR is to influence the timing and magnitude of consumption to match energy supply by sharing the benefits with consumers, ultimately aiming to optimize generation cost. As such, the first and primary enabler to DR was the establishment of contemporary electricity markets. Increased proliferation of Distributed Energy Resources (DER) and microgeneration further motivated the participation of consumers as active players in the market, popularizing DR and the wider category of Demand-Side Management (DSM) programs. Smart Grids (SG) have been an enabler to modern DR schemes, with smart metering data providing input to the underlying optimization and forecasting tools. The more recent emergence of the Internet of Energy (IoE), seen as the evolution of SG, is driven by increased Internet of Things (IoT)-enabling and high penetration of scalable and distributed energy resources. In this IoE paradigm being a fully decentralized network of energy prosumers, DR will continue to be a vital aspect of the grid in future Transactive Energy (TE) schemes, aiming for a more user-centered, energy-efficient, cost-saving, energy management approach. This paper investigates original motives and identifies the first mentions of DR in the legislative and scientific literature. Afterwards, the evolution of DR is tracked over the past four decades, attempting to study the co-influence of legislation and research by performing a thorough statistical analysis of research trends on the IEEE Xplore digital library. Finally, conclusions are made as to the current state of DR and future prospects of DR are discussed.

Abstract
This paper presents novel intraday session models for price forecasts (ISMPF models) for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL) and the analysis of mean absolute percentage errors (MAPEs) obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models) for the day- ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day- ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry.