Optimisation of Large Wave Farms using a Multi-strategy Evolutionary Framework (2003.09594v1)

Abstract: Wave energy is a fast-developing and promising renewable energy resource. The primary goal of this research is to maximise the total harnessed power of a large wave farm consisting of fully-submerged three-tether wave energy converters (WECs). Energy maximisation for large farms is a challenging search problem due to the costly calculations of the hydrodynamic interactions between WECs in a large wave farm and the high dimensionality of the search space. To address this problem, we propose a new hybrid multi-strategy evolutionary framework combining smart initialisation, binary population-based evolutionary algorithm, discrete local search and continuous global optimisation. For assessing the performance of the proposed hybrid method, we compare it with a wide variety of state-of-the-art optimisation approaches, including six continuous evolutionary algorithms, four discrete search techniques and three hybrid optimisation methods. The results show that the proposed method performs considerably better in terms of convergence speed and farm output.

• The paper presents a hybrid multi-strategy evolutionary framework to optimize the complex layout of large wave farms.
• It evaluates 17 algorithmic approaches, finding proposed multi-strategy methods significantly outperform existing ones for power output and convergence.
• The research provides a viable methodology for improving wave farm economic efficiency and contributes insights into high-complexity optimization problems.

Overview of "Optimisation of Large Wave Farms using a Multi-strategy Evolutionary Framework"

The paper, "Optimisation of Large Wave Farms using a Multi-strategy Evolutionary Framework," presents an innovative approach to maximizing energy output in large wave farms. The paper focuses on the arrangement of fully-submerged, three-tether wave energy converters (WECs) to harness ocean wave energy, a promising renewable source with high energy density, predictability, and potential capacity factors. The primary challenge addressed is the computational complexity arising from hydrodynamic interactions among numerous WECs, leading to a high-dimensional and multi-modal search landscape.

Methodology

To tackle these challenges, the authors propose a hybrid multi-strategy evolutionary framework incorporating several strategies:

1. Smart Initialization: Utilizes local symmetrical search and Nelder-Mead optimization to configure smaller sub-layouts that inform the initial population for larger farm arrangements.
2. Discrete and Continuous Optimization: The framework integrates discrete local search techniques with continuous global optimization methods. The discrete search helps in handling the problem's high dimensionality while the continuous optimization refines layouts.
3. Multiple Optimizers: The research evaluates a variety of state-of-the-art optimization algorithms, including variations of genetic algorithms, differential evolution, CMA-ES, and particle swarm optimization, specifically tailored or hybridized for wave farm layout optimization.
4. Hybrid Backtracking Mechanisms: The authors introduce hybrid backtracking strategies involving discrete local search (DLS) and continuous local search (CLS) to further refine solutions found using binary-enhanced EAs during optimization.

Results

This paper provides a comprehensive comparative analysis across 17 algorithmic approaches, including both established and newly proposed methodologies, over two geographical locations (Sydney and Perth) and two farm sizes (49 and 100 WECs). Key findings indicate that the proposed multi-strategy evolutionary algorithms significantly outperform existing methods in both convergence speed and total power output. Notably, the performance of the "SLS-NM + Improved binary DE + Rotate" combination was exemplary, achieving the highest energy output and fastest convergence rates.

Implications and Future Work

The implications of these findings are twofold. Practically, the paper suggests a viable methodology for enhancing the economic efficiency of wave farms, which could expedite commercialization and deployment of wave energy technology. Theoretically, it underscores the importance of hybrid strategies in solving high-complexity, high-dimension optimization problems, contributing valuable insights into evolutionary computation methodologies.

Future avenues for research might include investigating alternative wave energy converter designs, exploring different power take-off system settings, and examining additional environmental or economic constraints that could further influence farm optimization strategies. Additionally, adapting these hybrid strategies to other fields with similar optimization challenges could further validate their utility and efficacy.

The paper is a substantive academic contribution to both renewable energy resource optimization and the broader field of evolutionary algorithms. As ocean wave energy continues to grow as a renewable energy solution, the methods and insights presented in this research could play an integral role in advancing this field.