Medium-term electric energy demand forecasting is an essential tool for power system planning and operation, mainly in those countries whose power systems operate in a deregulated environment. This paper proposes a novel approach to monthly electric energy demand time series forecasting, in which it is split into two new series: the trend and the fluctuation around it. Then two neural networks are trained to forecast them separately. These predictions are added up to obtain an overall forecasting. Several methods have been tested to find out which of them provides the best performance in the trend extraction. The proposed technique has been applied to the Spanish peninsular monthly electric consumption. The results obtained are better than those reached when only one neural network was used to forecast the original consumption series and also than those obtained with the ARIMA method

Integration of renewable energy sources, active role of consumers and energy management systems are currently among research priorities in energy systems. This paper proposes an innovative coordinated energy scheduling for a microgrid of neighbor prosumers with different consumption patterns. All prosumers have photovoltaic generation systems, Li-ion batteries as energy storage systems and regular household loads. A genetic algorithm is used to schedule each prosumer's battery charge/discharge, with the aim of reducing energy exchange losses by minimizing the power in the point of interconnection of the microgrid with the main grid, with several advantages compared to classical optimization objectives, and without worsening battery lifespan degradation. Individual and coordinated strategies are compared and self-consumption and self-sufficiency of the prosumers' set are evaluated with the aim of showing the advantage of coordination. The paper concludes that coordinated operation can contribute to improve the exploitation of energy resources in the prosumer microgrid, reducing the amount of energy interchanged with the distribution grid by approximately 13%, and, at the same time, avoiding increasing the battery cycling and consequent degradation.

This paper presents a new topology for the power injection system that is based on the parallel association of two voltage source inverters. One operates using a quasi-square voltage waveform strategy, and the other operates with a pulsewidth-modulation (PWM)-based strategy. The aims of this topology are to inject the power from the photovoltaic generation system using the quasi-square inverter and to control the current quality using the PWM inverter. The proposal optimizes the system design, permitting the reduction of system losses and an increase of the energy injected into the grid.