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

A parametric study of wave energy converter layouts in real wave models

Ocean wave energy is a broadly accessible renewable energy source; however, it is not fully developed. Further studies on wave energy converter (WEC) technologies are required in order to achieve more commercial developments. In this study, four CETO6 spherical WEC arrangements have been investigated, in which a fully submerged spherical converter is modelled. The numerical model is applied using linear potential theory, frequency-domain analysis, and irregular wave scenario. We investigate a parametric study of the distance influence between WECs and the effect of rotation regarding significant wave direction in each arrangement compared to the pre-defined layout. Moreover, we perform a numerical landscape analysis using a grid search technique to validate the best-found power output of the layout in real wave models of four locations on the southern Australian coast. The results specify the prominent role of the distance between WECs, along with the relative angle of the layout to dominant wave direction, in harnessing more power from the waves. Furthermore, it is observed that a rise in the number of WECs contributed to an increase in the optimum distance between converters. Consequently, the maximum exploited power from each buoy array has been found, indicating the optimum values of the distance between buoys in different real wave scenarios and the relative angle of the designed layout with respect to the dominant in-site wave direction

Open Data and Models for Energy and Environment

This Special Issue aims at providing recent advancements on open data and models. Energy and environment are the fields of application.For all the aforementioned reasons, we encourage researchers and professionals to share their original works. Topics of primary interest include, but are not limited to:Open data and models for energy sustainability;Open data science and environment applications;Open science and open governance for Sustainable Development Goals;Key performance indicators of data-aware energy modelling, planning and policy;Energy, water and sustainability database for building, district and regional systems; andBest practices and case studies

Geometric optimisation of wave energy conversion devices: A survey

Unlike more established renewable energy conversion technologies, such as wind turbines, wave power systems have reached neither commercial maturity, nor technological convergence. The significant variation in device geometries and operating principles has resulted in a diversification of effort, with little coordination or true comparative analysis. The situation is compounded by the relative lack of systematic optimisation applied to the sector, partly explained by the complexity and uncertainty associated with wave energy system models, as well as difficulties in the evaluation of appropriate target function metrics. This review provides a critical overview of the state-of-the-art in wave energy device geometry optimisation, comparing and contrasting various optimisation approaches, and attempting to detail the current limitations preventing further progress, and convergence, in the development of optimal wave energy technology

Modelling and Optimization of Wave Energy Converters

Wave energy offers a promising renewable energy source. This guide presents numerical modelling and optimisation methods for the development of wave energy converter technologies, from principles to applications. It covers oscillating water column technologies, theoretical wave power absorption, heaving point absorbers in single and multi-mode degrees of freedom, and the relatively hitherto unexplored topic of wave energy harvesting farms. It can be used as a specialist student textbook as well as a reference book for the design of wave energy harvesting systems, across a broad range of disciplines, including renewable energy, marine engineering, infrastructure engineering, hydrodynamics, ocean science, and mechatronics engineering. The Open Access version of this book, available at https://www.routledge.com/ has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license