Abstract
This paper proposes a new model for short-term forecasting of electric energy production in a photovoltaic (PV) plant. The model is called HIstorical SImilar MIning (HISIMI) model; its final structure is optimized by using a genetic algorithm, based on data mining techniques applied to historical cases composed by past forecasted values of weather variables, obtained from numerical tools for weather prediction, and by past production of electric power in a PV plant. The HISIMI model is able to supply spot values of power forecasts, and also the uncertainty, or probabilities, associated with those spot values, providing new useful information to users with respect to traditional forecasting models for PV plants. Such probabilities enable analysis and evaluation of risk associated with those spot forecasts, for example, in offers of energy sale for electricity markets. The results of spot forecasting of an illustrative example obtained with the HISIMI model for a real-life grid-connected PV plant, which shows high intra-hour variability of its actual power output, with forecasting horizons covering the following day, have improved those obtained with other two power spot forecasting models, which are a persistence model and an artificial neural network model.

Abstract
This paper presents an original short-term forecasting model for hourly average electric power production of small-hydro power plants (SHPPs). The model consists of three modules: the first one gives an estimation of the "daily average" power production; the second one provides the final forecast of the hourly average power production taking into account operation strategies of the SHPPs; and the third one allows a dynamic adjustment of the first module estimation by assimilating recent historical production data. The model uses, as inputs, forecasted precipitation values from Numerical Weather Prediction tools and past recorded values of hourly electric power production in the SHPPs. The structure of the model avoids crossed-influences between the adjustments of such model due to meteorological effects and those due to the operation strategies of the SHPPs. The forecast horizon of the proposed model is seven days. which allows the use of the final forecast of the power production in Power System operations, in electricity markets, and in maintenance scheduling of SHPPs. The model has been applied in the forecasting of the aggregated hourly average power production for a real-life set of 130 SHPPs in Portugal achieving satisfactory results, maintaining the forecasting errors delimited in a narrow band with low values.

Abstract
Storing energy on wind farms could improve the power generation curve, avoiding the problems associated with abrupt variations and the random nature of wind power. New batteries such as flow batteries or NaS batteries are suitable to be used in storing energy on wind farms in intervals of some hours. A new methodology for the optimization of the management of wind farms, including energy storage, is shown. The objective is to maximize the benefits of selling electricity to the grid within 24 hours. The genetic algorithm technique was used for the optimization. © (2013) Trans Tech Publications, Switzerland.

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 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
We present and compare two short-term statistical forecasting models for hourly average electric power production forecasts of photovoltaic (PV) plants: the analytical PV power forecasting model (APVF) and the multiplayer perceptron PV forecasting model (MPVF). Both models use forecasts from numerical weather prediction (NWP) tools at the location of the PV plant as well as the past recorded values of PV hourly electric power production. The APVF model consists of an original modeling for adjusting irradiation data of clear sky by an irradiation attenuation index, combined with a PV power production attenuation index. The MPVF model consists of an artificial neural network based model (selected among a large set of ANN optimized with genetic algorithms, GAs). The two models use forecasts from the same NWP tool as inputs. The APVF and MPVF models have been applied to a real-life case study of a grid-connected PV plant using the same data. Despite the fact that both models are quite different, they achieve very similar results, with forecast horizons covering all the daylight hours of the following day, which give a good perspective of their applicability for PV electric production sale bids to electricity markets.