Estimation of an Optimal PV Panel Cleaning Strategy Based on Both Annual Radiation Profile and Module Degradation

Abstract

This paper addresses a methodology to optimize PV panel cleaning schedule. PV system yield is impacted due to soiling on panel surface. This soiling changes during the system operation as a consequence of environmental conditions. In order to reduce the impact of soiling accumulation, the panel surface must be cleaned. However, cost and frequency of cleaning can affect plant revenue. Therefore, to enhance financial performance, an optimal cleaning strategy must be selected. This strategy depends mostly on energy cost, soiling rate, cleaning costs, and system efficiency. Hence, the model proposed in this work takes into account these variables in order to support the decisions during the assessment of PV yield. The methodology was evaluated through two irradiation profiles with different associated costs. As a result, the methodology made it possible to estimate a cleaning strategy optimizing the incomes under different scenarios. INDEX TERMS Cleaning schedule optimization, fourier series, solar energy yield, soling ratio. I. INTRODUCTION Photovoltaic (PV) generation is considered as a solution to face the issues related to climate change. The use of PV is expected to reduce the emissions produced by electricity generation. Traditional power generation using coal, gas, water, among others, as primary sources is considered as mature and optimized technology. However, the integration of new renewable generation including PV posses challenges during their planning, operation, and maintenance [1]. Accurate estimation of return on investment is a concern of the current planning of PV projects to minimize financial risk. The assessment of return on investment depends on the prediction of power delivery which is influenced by the performance of PV-system. In [2], the factors that influence the system performance are classified by environment, PV system, installation, cost, and miscellaneous. The environmental factors include the solar irradiance, temperature, shades, dust and soiling. As for PV-system factors the I-V characteristics, inverter efficiency, battery efficiency, PV technology, The associate editor coordinating the review of this manuscript and approving it for publication was Dwarkadas Pralhaddas Kothari.

Low-Capacity Exploitation of Distribution Networks and Its Effect on the Planning of Distribution Networks

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Research on optimum tilt angle of photovoltaic module based on regional clustering of influencing factors of power generation

“…Taking Jordan as an example, the optimum cleaning cycle was calculated as 12 to 15 days depending on the analytic model and the duration of exposure. Alvarez et al 26 established a mathematical model based on Fourier irradiation prediction model, cleaning technology and cell efficiency to estimate the optimum cleaning cycle. Similarly in Reference 27, an analytic model for calculating optimum cleaning cycle based on the cost evaluation of power loss cost, dust residual cost and maintenance and cleaning cost is addressed.…”

Photovoltaic cleaning frequency optimization under different degradation rate patterns

“…The cleaning frequency that minimizes the LCOE and maximizes the NPV for each year of the system's lifetime is calculated. Compared to the previous studies [19,21,23], where fixed numbers of cleanings throughout the lifetime of the system were assumed, in this case, the optimum cleaning frequency can vary with time due to degradation, electricity price, and O&M costs. The results of this analysis for the two metrics considered in this study are shown in Fig.…”

“…A similar analysis was conducted on a PV system in Chile [19] taking into account fixed values for electricity price and cleaning costs whereas the degradation rate was based on a fixed performance loss value extracted from a 2-year period. A model to optimize the optimal cleaning schedule also based on linear degradation and fixed electricity price and cleaning costs was recently presented by Alvarez et al [21]. In the present work, these economic parameters are realistically modeled to vary annually, and the effects of their variation is thoroughly discussed.…”