Academic Journal of Computing & Information Science, 2026, 9(2); doi: 10.25236/AJCIS.2026.090207.
Peng Wenquan1, Lu Zhi1, Huang Yaokun1, Zheng Kai1, Huang Siling1, Zheng Wenjing1, Zhao Guorui1
1School of Computer Science and Engineering, Guangdong Ocean University, Yangjiang, China
This study innovatively develops a UAV D-LCOE model to quantify the cost reduction of UAV-based operation and maintenance (O&M) for offshore wind farms, using the UK Round 3 project as a case. Analysis shows that UAV O&M, through automated inspection and swarm coordination, significantly lowers costs, reducing the levelized cost of energy (LCOE) by 28% and achieving cumulative savings of ¥120 million. Empirical findings identify swarm efficiency gains (47% in 15-point scenarios) and an average annual equipment learning rate of 7.2% as the core drivers. The study provides a practical quantitative tool and decision support for optimizing offshore wind O&M costs.
offshore wind power; UAV operations and maintenance; levelized cost of energy (LCOE); cost optimization; UAV D-LCOE model
Peng Wenquan, Lu Zhi, Huang Yaokun, Zheng Kai, Huang Siling, Zheng Wenjing, Zhao Guorui. Research on the Benefits of UAVs for Offshore Wind Farm O&M—Analysis Based on the UAV D-LCOE Model. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 2: 46-53. https://doi.org/10.25236/AJCIS.2026.090207.
[1] SHORT W, PACKEY D J, HOLT T. A manual for the economic evaluation of energy efficiency and renewable energy technologies[J]. NASA STI/Recon Technical Report N, 1995, 95
[2] NELSON C R, SIEGEL A F. Parsimonious Modeling of Yield Curves[J]. The Journal of Business, 1987, 60(4): 473–489.
[3] XU L, CAO X, DU W, et al. Cooperative path planning optimization for multiple UAVs with communication constraints[J]. Knowledge-Based Systems, 2023, 260: 110164.
[4] ISMAIL A H, SONG X, OUELHADJ D, et al. Unmanned surface vessel routing and unmanned aerial vehicle swarm scheduling for off-shore wind turbine blade inspection[J]. Expert Systems with Applications, 2025, 284: 127534.
[5] ZHEN L, YANG Z, LAPORTE G, et al. Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management[J]. Engineering, 2024, 36: 223–239.
[6] Ministry of Finance of the People's Republic of China. Official Website of the Ministry of Finance of the People's Republic of China [Z]. Ministry of Finance of the People's Republic of China. 2025.
[7] Board of Directors of Guangdong Electric Power Development Co., Ltd. Guangdong Electric Power Development Co., Ltd. (Yuedianli A): Announcement on Investing in the Construction of the Zhanjiang Wailuo Offshore Wind Power Project (Phase I) [Z]. Sina Finance (stock.finance.sina.com.cn). 2018.
[8] Southern+ (Platform). Guangdong Energy Group’s Zhanjiang Wailuo Offshore Wind Power Phase III Project Achieves Full Capacity Grid Connection [Z]. Southern+ (southcn.com). 2021.
[9] China Communications Construction Company First Harbor Engineering Co., Ltd. (Source). Completion of Guangdong’s First Large-Megawatt Offshore Wind Power Project (Guangdong Yuedian Zhanjiang Wailuo Offshore Wind Power Project Phase I) [Z]. China Communications Construction News Network (cccnews.cn). 2019.
[10] Guangdong Yuedian Qujie Wind Power Co., Ltd. (Tenderer), Guangdong Far East Tendering Agency Co., Ltd. (Tendering Agency). Wailuo Phase I and Phase III Offshore Wind Farm Offshore Substation Foundation Anti-8Scour Research and Treatment Project (Tender Announcement) [Z]. Guangdong Far East Tendering Agency Official Website (gdydzb.com). 2024.
[11] Electric Wind Power (Information Source). First Batch of Wind Turbines Successfully Connected to the Grid in Zhanjiang Wailuo Offshore Wind Power Project Phase II [Z]. Polar Power News Network (bjx.com.cn). 2021.
[12] NAGY B, FARMER J D, BUI Q M, et al. Statistical basis for predicting technological progress[J]. PLoS One, 2013, 8(2): e52669.
[13] WRIGHT T P. Factors affecting the cost of airplanes[J]. Journal of the Aeronautical Sciences, 1936, 3: 122–128.