ABSTRACT
In this paper, an attempt is made to build an effective solar photovoltaic (PV) system considering geographical aspects such as irradiance and temperature using the single-diode equation model. Furthermore, a comparative analysis of various DC-DC converters (buck, boost, inverting buck-boost, non-inverting buck-boost (NIBB), Cuk, and single-ended primary inductance converter (SEPIC)) connected to a solar photovoltaic (PV) module was conducted in order to determine the optimal combination of DC-DC converter and solar PV. Moreover, the R, L, and C parameters for the converters have also been proposed to gain optimum efficiency from the solar PV system, and it has been shown that increasing the resistance decreases the ripple value. Furthermore, it has been shown that Ns and Np values of 36 and 1, respectively, result in 199 W of output power from a solar PV module at the maximum power point (48 V). The obtained results show that NIBB and SEPIC simulations gave the best results, with efficiencies of 93.27% and 92.35%, respectively.
Subject(s)
Electric Power Supplies , Models, Theoretical , TemperatureABSTRACT
In this paper, a methodology and optimization using the artificial bee colony (ABC) algorithm for life cycle cost (LCC) assessment of photovoltaic (PV) system for some Indian smart cities have been presented. Smart cities are the urban areas that utilize solar power PV system with other advanced innovations. The life cycle cost with and without carbon credit earned due to solar power generation in particular smart city should be known. Smart cities aim to assist local bodies in assessing their present energy consumption and future demand. This paper also focuses on PV energy generation, savings, and efficiency with cost. Four smart cities have been selected for LCC estimation on different field condition basis. The results show that in Srinagar, energy production is maximum, i.e., 435MWh, and LCC is minimum INR4125 million with an 8.9% rate of return on it. Therefore, net carbon dioxide emission reduction per annum for smart PV system over a lifetime of 20 years is 76.5 tCO2e on the overall thermal energy and 17.7 tCO2e on the overall exergy gain. LCC of smart PV system for different years has been estimated and optimized using the ABC algorithm, and the results show a 50.24% reduction in LCC. With the proposed approach, future opportunities for smart cities with smart PV system can be revealed.
Subject(s)
Solar Energy , Cities , Costs and Cost Analysis , ForecastingABSTRACT
The paper aims to develop a model using adaptive neuro-fuzzy inference system (ANFIS) architecture for enhancing output power of semitransparent photovoltaic thermal (PV/T) air collector by predicting the failure of PV panels for different weather conditions and different climate zones. Increased temperature of the photovoltaic module is a big problem which reduces its working life. The working and hotspot temperatures of photovoltaic (PV) modules have been reduced using ANFIS-based thermal design with optimal placement of PV cells which increase their life and reduce the failure rate which in turn increase the output power. The overall analysis reveals that output power is enhanced using ANFIS-based model by minimizing absolute error to 1.4% in 100 epochs by predicting accurate parameters.
