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

Investigation of new layout design concepts of an array-on-device WaveSub device

Wave Energy Converters (WECs) have not yet proven their competitiveness in the mainstream energy market. Research and development of this technology are necessary to find optimal solutions in terms both of energy produced and reduced cost. A WEC farm is expected to have reduced Levelized Cost of Energy (LCoE) compared to individual devices due to shared installation and grid connection costs. Studies show that energy yield of a WEC array is highly dependent on spacing and layout of the WECs. A method for selecting an optimal array layout is desirable.Here we show a comparison between 4 different design layouts of a WaveSub device with six floats. A six float configuration has been chosen because the LCoE reduces with increasing floats per device as shown in previous research. An optimal configuration in terms of LCoE and rated power is found for linear, rectangle, triangle and circular multi-float configurations. Parameters optimised are float spacing and Power Take Off (PTO) stiffness, damping and rated power. The optimisation algorithm uses a genetic algorithm combined with a Kriging surrogate model. Numerical simulations are solved in the time-domain in WEC-Sim while the hydrodynamic coefficients are calculated in Nemoh using a linear potential flow theory.For all geometric configurations, the smallest float spacing was the most promising because of the lower cost of the structure. In fact, the influence of the float spacing on the power produced by the device is shown to be less significant than the influence of float spacing on the capital cost. Overall, the circular configuration outperformed the other configurations. This study shows that layout configurations can be investigated with optimisation and this could be applied to other configurations and other WEC concepts in future

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