A Hybrid Cleaning Scheduling Framework for Operations and Maintenance of Photovoltaic Systems

Abstract

Dust deposition on the surface of photovoltaic (PV) modules is a nonnegligible factor that reduces a PV system’s efficiency and reliability. Cleaning can remove dust, and the effect of cleaning on PV performance resembles that of maintenance. In this article, we propose a hybrid cleaning scheduling policy with periodic planning and dynamic adjustment for refining the operations and maintenance of PV systems. Specifically, the periodic planning stage aims for medium-term scheduling while the dynamic adjustment stage is tailed for short-term fine-tuning. In the former stage, we show that when the number of cleaning actions is fixed, a periodic cleaning strategy is optimal. Moreover, we derive the optimality condition under which the optimal cleaning interval can be determined. In the latter stage, based on the determined cleaning interval, we dynamically adjust the cleaning schedule with the forecast of meteorological parameters, PV power generation, and dust deposition in order to further minimize economic losses. In addition, we take the forecasting uncertainty into account and propose a new custom parameter called risk-taking tendency (RTT), which is able to quantify the risk preference of decision makers and analyze its influence on the scheduling policy. A case study is provided to illustrate the proposed strategy.