Optimization of maintenance scheduling for offshore wind turbines considering the wake effect of arbitrary wind direction

•The coupling of wake effect and maintenance status is firstly formulated.•A bi-objective maintenance scheduling model is proposed with the consideration of stochastic wind speed and direction.•A mixed integer second-order cone programming model is built and linearized to deal with the nonlinearity...

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Bibliographic Details
Published in:Electric power systems research Vol. 184; p. 106298
Main Authors: Ge, Xiaolin, Chen, Quan, Fu, Yang, Chung, C.Y., Mi, Yang
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.07.2020
Elsevier Science Ltd
Subjects:
Electric power generation
Electricity generation
Integer programming
Linear programming
Maintenance
Maintenance costs
Maintenance management
Maximum power
Mixed integer
Mixed integer linear programming
Multi-objective
Multiple objective analysis
Offshore energy sources
Offshore wind farm
Optimization
Scheduling
Turbines
Wake effect
Wind direction
Wind effects
Wind power
Wind power generation
Wind speed
Wind turbines
Maintenance
Mixed integer linear programming
Multi-objective
Offshore wind farm
Wake effect
ISSN:0378-7796, 1873-2046
Online Access:Get full text
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Summary:•The coupling of wake effect and maintenance status is firstly formulated.•A bi-objective maintenance scheduling model is proposed with the consideration of stochastic wind speed and direction.•A mixed integer second-order cone programming model is built and linearized to deal with the nonlinearity in the above problem. An optimization model for offshore wind farm maintenance scheduling is presented, considering minimum maintenance costs and maximum power generation. For power generation, the wind speed at each tower site plays an important role that is impacted not only by the dynamically changing wake overlap area and the consequence of variation of wind direction but also by the relative position of the wake affected by the maintenance status. This paper combines the wake model with the maintenance status to accurately express the input wind speed of the wind turbine (WT) in each period. Because the optimization model includes complex dynamical coupling relationships and a number of nonlinear constraints, mixed integer second-order cone programming (MISOCP) are employed to address these issues. The MISOCP model is relaxed as a mixed integer linear programming (MILP) model to improve computational efficiency and the ε-constraint method is utilized to handle the multi-objective function. The proposed model and method are tested in a short-term maintenance case of an offshore wind farm. The numerical results demonstrate that the proposed approach can achieve sound economic benefits and provide comprehensive decision support.
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ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2020.106298