دانلود رایگان مقاله طرح بندی برای مزارع انرژی موج اقیانوس: مدل ها، خواص، و بهینه سازی

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.

6 Conclusion

In this work, we study optimization models and algorithms for the optimal configuration of wave farms. We develop theoretical properties for the q-factor under the point-absorber approximation and derived an analytical optimal solution for the 2-WEC problem. The analytical solution for the 2-WEC problem provides insights into the relative spacing among the WECs in near-optimal solutions. Based on these insights, we propose a heuristic optimization algorithm for choosing layouts to maximize the q-factor. Our results demonstrate that the heuristic generally outperforms the WECLP-specific genetic algorithm by Mao (2013) and the general-purpose global solvers PSWARM and NOMAD.