Optimize photovoltaic panels cleaning scheduling framework based on variations of hourly-based active electricity pricing in the market

• Published in: Solar energy 2024-06, Vol.275, p.112633, Article 112633
• Main Authors: Yaghoubi, Ali Akbar, Gandomzadeh, Mahdi, Gholami, Aslan, Gavagsaz Ghoachani, Roghayeh, Zandi, Majid, A. Kazem, Hussein
• Format: Article
• Language: English
• Subjects: Cleaning frequency; Cleaning schedule; Dust accumulation; Levelized Costs of Energy (LCOE); Pricing mechanism
• ISSN: 0038-092X
• DOI: 10.1016/j.solener.2024.112633

•Experiential investigation of dust accumulation and cleaning impact on PV systems.•Provide a decision-making framework for a real-time cleaning schedule.•Thecno-economic simulation of various manual and machine-based cleaning scenarios.•Introducing an hourly-based electricity pricing using Cost of Preparation Factors.•Decreasing levelized costs of electricity for large-scale power plants by 4–8%.•Suggesting a cost-saving technique for demand charges drop and peak shaving. Through one-year experimental measurements, the current study investigated the soiling effects and proposed a decision-making framework for a cleaning schedule based on a real-time long-term output dataset of a PV plant. The framework uses a dust accumulation threshold-based strategy for cleaning decision-making along with consideration of hourly-based electricity prices. This framework was applied to a case study of electricity prices in the Iranian market to offer optimum cleaning planning as well as pricing policy. The detailed techno-economic investigation results indicated that by applying the proposed framework for either manual or machine-based cleaning strategies, consideration of a 3 gr.m−2 dust accumulation density threshold for cleaning results in the lowest levelized costs of electricity. If the proposed method is applied, the levelized costs of electricity for large-scale power plants on average will be decreased by 8% reaching 0.066 $.kWh−1 from 0.077 $.kWh−1 by reducing an average of 2 $.kWp-1 to 3 $.kWp-1 annual revenue loss, while for roof-top or small-scale systems, it decreased by about 4.3% reaching to 0.067 $.kWh−1 from 0.070 $.kWh−1. Furthermore, it was shown that the introduction of an hourly-based pricing mechanism by using Cost of Preparation Factors would both better motivate the power plant stakeholders to optimize the plant cleaning schedule as well as benefit the electricity providers and governments by providing a cost-saving technique for definite demand charges drop and even peak shaving if applied throughout the whole country.