Core Insights - The photovoltaic operation and maintenance (O&M) sector is crucial in the solar energy industry, directly impacting the safety and profitability of power plants [1][10] - The rapid growth of the photovoltaic industry has led to an increasing demand for O&M services, resulting in a steady market expansion [1][10] Photovoltaic O&M Industry Overview - Photovoltaic O&M refers to the daily monitoring, maintenance, and management of solar power systems to ensure optimal energy output [2] - Key tasks in O&M include daily inspections, fault handling, data monitoring, and equipment maintenance and upgrades [2][10] Market Size and Growth - The Chinese photovoltaic O&M market is projected to reach approximately 36.65 billion yuan in 2024, representing a year-on-year growth of 42% [1][10] - The centralized O&M market is expected to be 19.41 billion yuan, while the distributed O&M market is estimated at 17.24 billion yuan [1][10] O&M Cost Trends - The O&M costs for distributed photovoltaic systems are projected to be 0.046 yuan/(W·year), while centralized ground stations are expected to be 0.038 yuan/(W·year) in 2024 [10][11] O&M Operational Models - The main operational models for photovoltaic O&M include asset entrustment, full-service O&M entrustment, and labor entrustment for O&M [10] Market Competition and Landscape - The O&M service market is characterized by a highly fragmented landscape, with numerous players competing for market share [1][12] - Leading companies in the industry include Yangguang Zhihui, Linyang O&M, Xianlin Smart O&M, and Zhengtai Zhihui, among others [1][12] Bidding and Tendering Situation - The demand for photovoltaic O&M services is robust, with an estimated bidding scale of approximately 116.47 GW in 2024, reflecting a year-on-year increase of around 76% [12] - Over 70% of the bids are for routine maintenance, with component cleaning services accounting for 18.74% and weeding services for 5.3% [12] Technological Trends in O&M - Future trends in photovoltaic O&M include the establishment of a full lifecycle data loop, interconnected maintenance devices, and the integration of AI for predictive maintenance and self-healing capabilities [12][14]