The technique of using renewable resources as the source of generating electrical energy leads to the development of sustainable ecosystem. Hybridizing, (combining more than two renewable energy sources) these resources for the efficient generation of energy in power system is rapidly increasing in the present scenario. For this generation of electrical energy, there are certain factors are to be maintained in particular levels. Finding the factors are by going through some predefined algorithms. This paper leads to summarization of Hybrid renewable energy sources (HRES) usage and co-relative study of optimization techniques.

Generation expansion planning (GEP) is a highly constrained, non-linear optimization problem. The selection of the best expansion alternative for long-term planning horizon is commonly referred as GEP problem. In this paper, the GEP problem is solved for Tamil Nadu, an Indian state for the period of 30 years from 2012 to 2041 using a state-of-the-art computer model Wien Automatic System Planning IV package. In the year 2012, the peak load for Tamil Nadu was approximately 12 000 MW and had a peak load shortage of 3500–4000 MW. A load growth rate of 6.65% per annum is assumed on the basis of past data. Raising coal costs, pollution impacts associated conventional plants make renewable sources an attractive proposition for development. Renewable energies such as wind and solar are considered as expansion candidates in addition to conventional plants. The impact of incorporating renewable energy resources into an electric utility's generation expansion plan has been studied with different penetration level. The reliability criteria, loss of load probability, and energy not served are also calculated. Copyright © 2014 John Wiley & Sons, Ltd.

Power generation is challenged to meet energy demand during peak hours. As a result of limited non-renewable energy resources, power utilities heavily rely on fossil fuels. Therefore, scientists and researchers are looking for some distributed generators to provide additional power during peak hours. During such period, load demand is solved using solar power. As a consequence, grid-connected solar Photovoltaic (PV) systems are catching the attention owing to their ability to significantly reduce the use of fossil fuels. Under Partial Shading Condition (PSC), this paper utilizes Luo Converter along with Cascaded Artificial Neural Network (ANN), which is a Machine Learning Based Maximum Power Point Tracking (ML-MPPT) approach for tracking optimal power from PV system. The gained DC supply is converted into AC voltage using VSI attached to the system. In addition, PI controller engaged controls the voltage at grid side and results in effective grid synchronization. Furthermore, MATLAB Simulink analysis is carried out and the outcomes reveal the effectiveness of proposed system with 98% efficiency under different PV circumstances.