A novel stochastic dynamic modeling for photovoltaic systems considering dust and cleaning

Abstract

Stochastic photovoltaic (PV) modeling that can be used for long-term planning of power systems is essential for future renewable power generation. One of the most prevalent problems that PV systems face is the accumulation of dust on the PV panel surface that negatively impacts the output power. Wind speed along with other weather variables including relative humidity, temperature, and precipitation are some of the major factors that contribute to dust accumulation. This paper presents a novel dynamic model of the PV output power profile including the dust accumulation using a Markov chain model. The proposed model incorporates the seasonal variations in ambient temperature, solar irradiance, dust accumulation, and rate of dust accumulation as well as the desired cleaning frequency, which affect the overall energy yield of the PV system. The outcome of the model is virtually generated scenarios that can be used by the investors to decide on the optimal size of the PV system and the optimal cleaning frequency or each season. The model outcome shows an error of less than 5% when compared to actual data collected from the field without cleaning. This error can be reduced by increasing the number of states, which affects the computational time. Various case studies are presented to show the effectiveness of the proposed model and its benefits.