A Hybrid Evolutionary Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters (1904.07043v1)

Abstract: Ocean wave energy is a source of renewable energy that has gained much attention for its potential to contribute significantly to meeting the global energy demand. In this research, we investigate the problem of maximising the energy delivered by farms of wave energy converters (WEC's). We consider state-of-the-art fully submerged three-tether converters deployed in arrays. The goal of this work is to use heuristic search to optimise the power output of arrays in a size-constrained environment by configuring WEC locations and the power-take-off (PTO) settings for each WEC. Modelling the complex hydrodynamic interactions in wave farms is expensive, which constrains search to only a few thousand model evaluations. We explore a variety of heuristic approaches including cooperative and hybrid methods. The effectiveness of these approaches is assessed in two real wave scenarios (Sydney and Perth) with farms of two different scales. We find that a combination of symmetric local search with Nelder-Mead Simplex direct search combined with a back-tracking optimization strategy is able to outperform previously defined search techniques by up to 3\%.

• The paper introduces a hybrid evolutionary algorithm framework to optimize wave energy converter layouts and power-take-off settings for improved energy capture.
• The proposed framework, particularly a hybrid local search method, demonstrated superior performance, achieving up to 3% higher power output and faster convergence compared to traditional algorithms.
• This research provides a robust method for enhancing wave energy farm efficiency and offers a methodological framework applicable to other complex, real-world optimization challenges.

A Hybrid Evolutionary Algorithm Framework for Wave Energy Converter Optimisation

The paper "A Hybrid Evolutionary Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters" introduces a sophisticated approach for maximising the energy output from wave energy converter (WEC) farms using a hybrid evolutionary algorithm. This research aims to address the complexity inherent in the design and optimization of WEC layouts and power-take-off (PTO) settings, crucial for improving the efficiency of energy harvested from ocean waves.

Research Motivation and Approach

Wave energy converters are a promising technology within the renewable energy sector due to their potential high capacity factors and energy densities. However, their relatively novel status presents challenges, particularly in optimizing the configuration of WEC arrays and the PTO parameters for each converter. The presented research addresses these challenges through the application of heuristic search techniques that navigate the intricate, non-linear, and multi-modal optimization space that characterizes WEC farm design.

Key Methodologies

The core contribution of this work lies in the development and evaluation of several heuristic strategies, including cooperative and hybrid methods. The researchers tested twelve algorithms subdivided into three categories: standalone evolutionary algorithms (EAs), alternating cooperative algorithms, and hybrid algorithms integrating local search techniques.

• Standalone EAs: The paper utilized five common EAs, including CMA-ES and Differential Evolution, among others, to optimize WEC positions and PTO settings as a unified problem.
• Alternating Optimization Strategies: Methods like CMA-ES combined with Nelder-Mead were employed, applying EAs to optimize WEC positions and Nelder-Mead for PTO in an alternating manner.
• Hybrid Algorithms: These leveraged insights from previous studies, using local and symmetric local search in combination with Nelder-Mead, enhanced by a novel backtracking strategy to refine sub-optimal layouts.

The algorithmic approaches were rigorously evaluated under real wave conditions at two Australian sites, Sydney and Perth, with experimental setups for different WEC array scales.

Observations and Results

The hybrid framework, particularly the Symmetric Local Search with Nelder-Mead and Backtracking (SLS-NM-B), demonstrated superior performance over traditional EAs and alternation-based methods, particularly in larger array configurations. The hybrid approach achieved a power output increase of up to 3% over existing techniques and showed faster convergence rates. This method effectively exploited constructive hydrodynamic interactions between WECs, a critical factor in maximizing array energy output.

Additionally, the paper provided valuable insights into the power amplification factor (q-factor), demonstrating configurations where hydrodynamic interference effects were constructive, thereby enhancing the total power capture beyond the sum of individual buoy outputs. It became evident that optimizing PTO parameters and buoy placements could significantly influence energy absconsion—a non-trivial task due to the complex and dynamic nature of wave interactions.

Implications and Future Directions

The implications of this research are twofold. Practically, the developed algorithms offer a robust approach to improving the efficiency of wave energy farms, potentially lowering the cost per unit of electricity generated and enhancing the viability of wave energy as a significant contributor to renewable energy portfolios. Theoretically, the integration of local refinement strategies and backtracking into evolutionary optimization provides a potent methodological framework applicable to other complex, real-world optimization problems in energy systems and beyond.

Looking forward, the paper suggests several avenues for future research. These include the exploration of more diverse WEC designs and configurations, incorporating real-time adaptive control strategies, and developing multi-objective optimization techniques that encompass additional factors such as installation costs and environmental impact. Such advancements will further refine the deployment strategies and operational efficiencies necessary for the large-scale adoption of wave energy technologies globally.