Layouts for ocean wave energy farms: Models, properties, and optimization. (January 2017)

• Record Type: Journal Article
• Authors: Moarefdoost, M. Mohsen; Snyder, Lawrence V.; Alnajjab, Basel
• Is Part Of: Omega. Volume 66:Part B(2017:Jan.)
• Page Start: 185
• Page End: 194
• Publication Date: 2017-01
• Subjects: Wave Energy Farm -- Optimal Layout Configuration -- Optimization; Management -- Periodicals; 658.4005
• DOI: 10.1016/j.omega.2016.06.004
• Languages: English
• ISSNs: 0305-0483

Abstract

Abstract: Renewable energy, such as wave energy, plays a significant role in sustainable energy development. Wave energy represents a large untapped source of energy worldwide and potentially offers a vast source of sustainable energy. We present models and a heuristic algorithm for choosing optimal locations of wave energy conversion (WEC) devices within an array, or wave farm. The location problem can have a significant impact on the total power of the farm due to the interactions among the incident ocean waves and the scattered and radiated waves produced by the WECs. Depending on the nature of the interference (constructive or destructive) among these waves, the wave energy entering multiple devices, and thus the power output of the farm, may be significantly larger or smaller than the energy that would be seen if the devices were operating in isolation. Our algorithm chooses WEC locations to maximize the performance of a wave farm as measured by a well known performance measure called the q -factor, which is the ratio of the power from an array of N WECs to the power from N WECs operating independently, under the point absorber approximation . We prove an analytical optimal solution for the 2-WEC problem and, based on the properties of the 2-WEC solution, we propose an iterative heuristic optimization algorithm for the general problem. Abstract : Highlights: We propose optimization models and algorithms for the optimal configuration of wave farms. We drive the optimal analytical solution for farms with 2 devices. We develop efficient and effective algorithm to optimally configure wave farms. We find the essential properties of optimal configuration of wave farms. We provide computational results to evaluate the proposed algorithm.