The Application of Nature-inspired Metaheuristic Methods for Optimising Renewable Energy Problems and the Design of Water Distribution Networks

This work explores the technical challenges that emerge when applying bio-inspired optimisation methods to real-world engineering problems. A number of new heuristic algorithms were proposed and tested to deal with these challenges. The work is divided into three main dimensions: i) One of the most significant industrial optimisation problems is optimising renewable energy systems. Ocean wave energy is a promising technology for helping to meet future growth in global energy demand. However, the current technologies of wave energy converters (WECs) are not fully developed because of technical engineering and design challenges. This work proposes new hybrid heuristics consisting of cooperative coevolutionary frameworks and neuro-surrogate optimisation methods for optimising WECs problem in three domains, including position, control parameters, and geometric parameters. Our problem-specific algorithms perform better than existing approaches in terms of higher quality results and the speed of convergence. ii) The second part applies search methods to the optimization of energy output in wind farms. Wind energy has key advantages in terms of technological maturity, cost, and life-cycle greenhouse gas emissions. However, designing an accurate local wind speed and power prediction is challenging. We propose two models for wind speed and power forecasting for two wind farms located in Sweden and the Baltic Sea by a combination of recurrent neural networks and evolutionary search algorithms. The proposed models are superior to other applied machine learning methods. iii) Finally, we investigate the design of water distribution systems (WDS) as another challenging real-world optimisation problem. WDS optimisation is demanding because it has a high-dimensional discrete search space and complex constraints. A hybrid evolutionary algorithm is suggested for minimising the cost of various water distribution networks and for speeding up the convergence rate of search.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

Multi-faceted Methodology for Coastal Vegetation Drag Coefficient Calibration: Implications for Wave Height Attenuation

The accurate prediction of wave height attenuation due to vegetation is crucial for designing effective and efficient natural and nature-based solutions for flood mitigation, shoreline protection, and coastal ecosystem preservation. Central to these predictions is the estimation of the vegetation drag coefficient. The present study undertakes a comprehensive evaluation of three distinct methodologies for estimating the drag coefficient: traditional manual calibration, calibration using a novel application of state-of-the-art metaheuristic optimization algorithms, and the integration of an established empirical bulk drag coefficient formula (Tanino and Nepf, 2008) into the XBeach non-hydrostatic wave model. These methodologies were tested using a series of existing laboratory experiments involving nearshore vegetation on a sloping beach. A key innovation of the study is the first application of metaheuristic optimization algorithms for calibrating the drag coefficient, which enables efficient automated searches to identify optimal values aligning with measurements. We found that the optimization algorithms rapidly converge to precise drag coefficients, enhancing accuracy and overcoming limitations in manual calibration which can be laborious and inconsistent. While the integrated empirical formula also demonstrates reasonable performance, the optimization approach exemplifies the potential of computational techniques to transform traditional practices of model calibration. Comparing these strategies provides a framework to determine the most effective methodology based on constraints in determining the vegetation drag coefficient

A Novel Hybrid Algorithm for Optimized Solutions in Ocean Renewable Energy Industry: Enhancing Power Take-Off Parameters and Site Selection Procedure of Wave Energy Converters

Ocean renewable energy, particularly wave energy, has emerged as a pivotal component for diversifying the global energy portfolio, reducing dependence on fossil fuels, and mitigating climate change impacts. This study delves into the optimization of power take-off (PTO) parameters and the site selection process for an offshore oscillating surge wave energy converter (OSWEC). However, the intrinsic dynamics of these interactions, coupled with the multi-modal nature of the optimization landscape, make this a daunting challenge. Addressing this, we introduce the novel Hill Climb - Explorative Gray Wolf Optimizer (HC-EGWO). This new methodology blends a local search method with a global optimizer, incorporating dynamic control over exploration and exploitation rates. This balance paves the way for an enhanced exploration of the solution space, ensuring the identification of superior-quality solutions. Further anchoring our approach, a feasibility landscape analysis based on linear water wave theory assumptions and the flap's maximum angular motion is conducted. This ensures the optimized OSWEC consistently operates within safety and efficiency parameters. Our findings hold significant promise for the development of more streamlined OSWEC power take-off systems. They provide insights for selecting the prime offshore site, optimizing power output, and bolstering the overall adoption of ocean renewable energy sources. Impressively, by employing the HC-EGWO method, we achieved an upswing of up to 3.31% in power output compared to other methods. This substantial increment underscores the efficacy of our proposed optimization approach. Conclusively, the outcomes offer invaluable knowledge for deploying OSWECs in the South Caspian Sea, where unique environmental conditions intersect with considerable energy potential.Comment: 35 pages, 22 Figures, 7 Table