A two-stage model for asynchronously scheduling offshore wind farm maintenance tasks and power productions. (September 2021)

• Record Type: Journal Article
• Authors: Zhang, Bingying; Zhang, Zijun
• Is Part Of: International journal of electrical power & energy systems. Volume 130(2021)
• Publication Date: 2021-09
• Subjects: Wind farms -- Power production -- Robust optimization -- Scheduling model -- Operations and maintenance; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.107013
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Simultaneously scheduling power productions and maintenance activities of an offshore wind farm is studied. A two-stage model is developed to offer different time-scale schedule of power productions and maintenance. Uncertainty of wind power and tide conditions on scheduling is considered. Robust schedules of the two-stage model tackle uncertainties while optimize operations and maintenance. Abstract: This paper studies the model for scheduling both offshore wind farm maintenance tasks and power productions in consideration of their distinct scheduling timescales and the wind power uncertainty. A two-stage adaptive robust optimization model is formulated to derive the optimal schedule, which well handles wind turbine maintenance tasks and maximizes wind farm power productions by anticipating the worst wind power output scenario based on a wind power uncertainty set and a set of metocean conditions. In two decision stages, we schedule maintenance tasks daily and power productions hourly respectively. The proposed model is equivalently transformed to a form which can be efficiently solved by the column-and-constraint generation algorithm. Numerical experiments demonstrate the effectiveness and applicability of the proposed model considering wind farms of different large sizes. Results show that the developed model can maximize the total power production of the wind farm and enhance the robustness of obtained maintenance schedules against the wind power uncertainty.