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In recent years, the solar energy has been considered as one of principal renewable energy sources for electric power generation. However, the maximization of extracted power from PV system is a matter of concern as its conversion efficiency is low. Therefore, a maximum power point tracking (MPPT) controller is necessary in a PV system for maximum power extraction. In this paper, several MPPT methods have been studied and implemented in MATLAB/Simulink environment. Based on the generation of control signal, the MPPT methods have been innovatively proposed to be categorized into three classes i.e. conventional, artificial intelligence (AI) based and hybrid methods. Further, the considered MPPT methods are modeled and compared on the basis of various parameters. For achieving this purpose, MATLAB/Simulink modeling of a double diode equivalent circuit based PV panel is developed and validated with commercially available solar panel. Then, the designed MPPT methods are implemented on this PV system under varying solar irradiation conditions to study their dynamic response for tracking the maximum power point. Based on this study, a novel comparison of various class of MPPT method is carried out in terms of output voltage, current, power, rise time, fall time, tracking efficiency etc. [ABSTRACT FROM AUTHOR]

Renewable hybrid systems with small capacities are widely installed for joining demand response in modern communities. Based on hybrid systems, renewable energy generation can be stored and used for power supply during peak load period. It is impractical to control power flow in such small-scale systems, because power flow dispatching asks for extra investment of expensive regulators and adaptors. In this paper, a switching grid connected photovoltaic system is studied for simplifying system installation. Optimal switching control model is proposed to sufficiently utilize the solar energy and to minimize electricity cost under the time-of-use program. As shown in results, optimal scheduling of the PV system can achieve promising cost savings. [ABSTRACT FROM AUTHOR]

Abstract: During the last few years the photovoltaic energy market has seen an outstanding growth. According to the new directive on renewable energies of the European Commission (2009/28/EC), the European Union should reach a 20% share of the total energy consumption from renewable sources by 2020. The national overall targets impose for Italy a 17% renewable share: in case of failure the gap would be filled by importation of renewable energy from non-UE countries. The ambitious national targets and thus the continuously increasing interest on renewable fuels, require simple but reliable methods for the energy potential assessment over large-scale territories. Considering roof-top integrated PV systems, the assessment of the PV energy potential passes through the evaluation of the roof surface area available for installations. In the present paper a methodology for estimating the PV solar energy potential is presented, together with its application to Piedmont Region (North-Western Italy). The roof area suitable for solar applications, is calculated through the analysis of available GIS data. The solar radiation maps are taken from the database of the Joint Research Centre of the European Commission. Different solar energy exploitation scenarios are proposed with the relative perspective results and confidence interval. Further developments and applications of the presented methodology are finally discussed. [Copyright &y& Elsevier]

Abstract: The package r.sun within the open source Geographical Resources Analysis Support System (GRASS) can be used to compute insolation including temporal and spatial variation of albedo and solar photovoltaic yield. A complete algorithm is presented covering the steps of data acquisition and preprocessing to post-simulation whereby candidate lands for incoming solar farms projects are identified. The optimal resolution to acquire reliable solar energy outputs to be integrated into PV system design software was determined to be 1square km. A case study using the algorithm developed here was performed on a North American region encompassing fourteen counties in South-eastern Ontario. It was confirmed for the case study that Ontario has a large potential for solar electricity. This region is found to possess over 935,000 acres appropriate for solar farm development, which could provide 90GW of PV. This is nearly 60% of Ontario’s projected peak electricity demand in 2025. The algorithm developed and tested in this paper can be generalized to any region in the world in order to foster the most environmentally-responsible development of large-scale solar farms. [Copyright &y& Elsevier]