Development of a Multi-Objective Genetic Algorithm for the Design of Offshore Renewable Energy Systems
Pillai, A; Thies, P; Johanning, L
Date: 1 June 2017
International Society for Structural and Multidisciplinary Optimisation (ISSMO)
Optimization algorithms have been deployed for a range of renewable energy problems and can successfully be applied to aid in the design of devices, farms, control strategies, and operations and maintenance strategies. Building on this, the present work makes use of a multi-objective genetic algorithm (GA) in order to develop a framework ...
Optimization algorithms have been deployed for a range of renewable energy problems and can successfully be applied to aid in the design of devices, farms, control strategies, and operations and maintenance strategies. Building on this, the present work makes use of a multi-objective genetic algorithm (GA) in order to develop a framework that can further aid in the design and development of offshore renewable energy systems by explicitly taking into account reliability considerations. Though the reliability-based design optimization approach has previously been used in offshore renewable energy applications and multi-objective optimization applications, it has not previously been applied to multi-objective offshore renewable energy design optimization. As the offshore renewable energy sectors grows it is important for the industry to explore more sophisticated methods of designing devices in order to ensure that the device reliability and lifetime can be maximized while downtime and cost are minimized. This paper describes the development of a framework using a GA in order to aid in the design of a mooring system for offshore renewable energy devices. This framework couples numerical models of the mooring system and structural response to both stress-life cumulative damage models and cost models in order for the GA to effectively operate considering the multiple objectives. The use of this multi-objective optimization approach allows multiple design objectives such as system lifetime and cost to be satisfied simultaneously using an automated mathematical approach. From the outputs of this approach, a designer can then select a solution which appropriately balances the different objectives. The developed framework will be applicable to any offshore technology subsystem allowing multi-objective optimization and reliability to be considered from the design stage in order to improve the design efficiency and aid the industry in using more systematic design approaches.
College of Engineering, Mathematics and Physical Sciences
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