Optimization and prediction modeling of solar module considering environmental parameters

Abstract

Photovoltaic (PV) solar cells are the main components of solar energy generation that convert sunlight's irradiation into electricity. Solar cell efficiency has increased over time due to improvement in its material structure, optical and electrical properties. However, environmental factors such as wind, temperature, relative humidity and irradiance can also majorly influence the rate of solar cell's power conversion. The objective of this proposal is to study the effect of stochastics and environmental factors on the efficiency and degradation of solar modules. Mathematical models will be developed to estimate how electrical parameters are influenced by the environmental factors. The work is a collaborative effort between Energy, Control and Optimization (ECO) lab of Texas A&M University directed by Dr. Shima Hajimirza, and the Computational Analysis in Electric Power Systems Lab, directed by Dr. Elmer Pablo Tito Cari at the University of Sao Paulo with ongoing FAPESP sponsored research project on parameter estimation of PV power stations. While Dr. Cari's present method uses a hybrid mixture of Mean Variance Mapping Optimization (MVMO) and Trajectory Sensitivity, Dr. Hajimirza will present a surrogate based Neural Network alternative. A hybrid model will be developed based on the combination of the two approaches. (AU)