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This work focuses on using the full potential of PV inverters in order to improve the efficiency of low voltage networks. More specifically, the independent per-phase control capability of PV threephase four-wire inverters, which are able to inject different active and reactive powers in each phase, in order to reduce the system phase unbalance is considered. This new operational procedure is analyzed by raising an optimization problem which uses a very accurate modelling of European low voltage networks. The paper includes a comprehensive quantitative comparison of the proposed strategy with two state-ofthe-art methodologies to highlight the obtained benefits. The achieved results evidence that the proposed independent per-phase control of three-phase PV inverters improves considerably the network performance contributing to increase the penetration of renewable energy sources.INDEX TERMS Low voltage systems, minimization of unbalances, smart grid optimization, three-phase balancing photovoltaic inverters. NOMENCLATUREThe following notation has been considered within the paper: D: complex variable. D:vector of complex variables. D:RMS magnitude of a complex variable. d:real variable or parameter. d:vector of real variables or real functions. i, j, k, l: indexes associated to buses. q:index associated to each of the network phases (a, b, c) and the neutral n. p:index associated to each of the network phases (a, b, c) but excluding the neutral n.

The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms' performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field. INDEX TERMS Time series forecasting, electric vehicle, power consumption, ensemble learning.

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