OPTIMAL DESIGN OF SPV SYSTEM AND APPLICATIONS

Abstract

The demand for grid power is increasing significantly due to the ever-increasing population and increasing comfort zone and usage of modern household equipment. Climate change and greenhouse gas emissions from conventional power plants are a major concern and it invigorated the use of renewable and green energy resources for power generation. In addition, the energy sector worldwide faces rising challenges related to rising demand, efficiency, changing supply and demand patterns, and a lack of analytics needed for optimal management. The power sector in a developed nation has incorporated artificial intelligence and related technologies for sizing and communication between modern grid with renewables, smart meters, and other connected devices. These intelligent approaches and their integration in the electrical systems have the potential to improve energy efficiency, cost reduction, and user comfort. Therefore, it is of key importance to develop intelligent optimization-based techniques for their applications in different solar photovoltaic systems. It is essential to develop an optimization-based solar photovoltaic power forecasting model with improved performance. A number of mathematical models have been developed for the assessment of solar power and energy under cloudless skies and

cloudy conditions. However, there is a scope for developing a novel artificial intelligence- based solar power forecasting considering aerosol particles. A comparative performance

of the developed forecasting model is done with other techniques considering various performance indices. Various methods have been proposed for the optimal capacity of SPV, wind turbines, and other energy storage devices. A novel approach to integrate the various

xviii aspects and characterization of energy consumption in a deregulated electricity environment is presented. This approach not only focuses on the economic aspects for designing an SPV-based microgrid but, also on the performance of the batteries and its interaction with the grid power at different times of the day. The stochastic nature of SPV hampers the I-V and P-V characteristics in varying meteorological conditions. This stochastic nature results in a mismatch of power produced from panels especially during different shading conditions. Hence, in this research the performance of the SPV system is enhanced by two methods firstly, an array reconfiguration method and secondly, using maximum power point tracking algorithms. The developed grey wolf optimizer-based array reconfiguration and artificial neural fuzzy inference-based maximum power point tracking (MPPT) algorithm perform significantly well under varying meteorological conditions.