Wave Energy Converter Array Optimization: A Genetic Algorithm Approach and Minimum Separation Distance Study

With the need to integrate renewable energy sources into the current energy portfolio and the proximity of power consumers to ocean coastlines, it is important to evaluate marine energy systems, specifically wave energy converters (WECs), as potential solutions for meeting electricity needs. The ability to model these systems computationally is vital to their eventual deployment. The power development, economics, grid integration requirements, operations and maintenance requirements, and ecological impacts must be understood before these devices are physically installed. However, the research area of WEC array optimization is young, and the few available results of previously implemented optimization methods are preliminary. The purpose of this work is to introduce a new WEC array optimization framework to explore systems-level concerns, specifically WEC layout and device spacing. A genetic algorithm approach that utilizes an analytical hydrodynamic model and includes an array cost model is presented, and the resulting optimal layouts for a preliminary test case are discussed. This initial work is integral in providing an understanding of device layout and spacing and is a foundational starting point for subsequent and more advanced WEC array optimization research

Array Modeling and Testing of Fixed OWC Type Wave Energy Converters

If wave energy technology is to mature to commercial success, array optimization could play a key role in that process. This paper outlines physical and numerical modeling of an array of five oscillating water column wave energy converters. Numerical model simulations are compared with experimental tank test data for a non-optimal and optimal array layout. Results show a max increase of 12% in average power for regular waves, and 7% for irregular waves between the non-optimized and optimized layouts. The numerical model matches well under many conditions; however, improvement is needed to adjust for phase errors. This paper outlines the process of numerical and physical array testing, providing methodology and results helpful for researchers and developers working with wave energy converter arrays

An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended pattern search algorithm

This paper presents a system of modeling advances that can be applied in the computational optimization of wind plants. These modeling advances include accurate cost and power modeling, partial wake interaction, and the effects of varying atmospheric stability. To validate the use of this advanced modeling system, it is employed within an Extended Pattern Search (EPS)-Multi-Agent System (MAS) optimization approach for multiple wind scenarios. The wind farm layout optimization problem involves optimizing the position and size of wind turbines such that the aerodynamic effects of upstream turbines are reduced, which increases the effective wind speed and resultant power at each turbine. The EPS-MAS optimization algorithm employs a profit objective, and an overarching search determines individual turbine positions, with a concurrent EPS-MAS determining the optimal hub height and rotor diameter for each turbine. Two wind cases are considered: (1) constant, unidirectional wind, and (2) three discrete wind speeds and varying wind directions, each of which have a probability of occurrence. Results show the advantages of applying the series of advanced models compared to previous application of an EPS with less advanced models to wind farm layout optimization, and imply best practices for computational optimization of wind farms with improved accuracy.Keywords: Extended pattern search algorithm, Wind farm modeling, Systems optimization, Wind farm optimizatio

The Wave Energy Converter Design Process: Methods Applied in Industry and Shortcomings of Current Practices

Wave energy is among the many renewable energy technologies being researched and developed to address the increasing demand for low-emissions energy. The unique design challenges for wave energy converter design—integrating complex and uncertain technological, economic, and ecological systems, overcoming the structural challenges of ocean deployment, and dealing with complex system dynamics—have lead to a disjointed progression of research and development. There is no common design practice across the wave energy industry and there is no published synthesis of the practices that are used by developers. In this paper, we summarize the methods being employed in WEC design as well as promising methods that have yet to be applied. We contextualize these methods within an overarching design process. We present results from a survey of WEC developers to identify methods that are common in industry. From the review and survey results, we conclude that the most common methods of WEC design are iterative methods in which design parameters are defined, evaluated, and then changed based on evaluation results. This leaves a significant space for improvement of methods that help designers make better-informed decisions prior to sophisticated evaluation, and methods of using the evaluation results to make better design decisions during iteration. Despite the popularity of optimization methods in academic research, they are less common in industry development. We end this paper with a summary of the areas of WEC design in which the testing and development of new methods is necessary, and where more research is required to fully understand the influence of design decisions on WEC performance

An Extended Pattern Search Approach for Optimizing Offshore Floating Wind Farms

Figures from 2017 publication (Miller, Forinash, and DuPont). Floating offshore wind farm optimization using an Extended Pattern Search (EPS) algorithm. Unidirectional, constant wind speed test case. Comparison to EPS for onshore wind farm optimization, and comparable literature

A study of graphical representations of uncertainty in LCA guide

This study user-tested different data visualizations for highly uncertain life cycle assessments (LCAs) to determine what best supported decision-making. Precise LCAs can only be performed once designs are finalized, due to the information necessary to complete them, but design changes in such late stages are costly. If designers could have environmental impact data earlier in the process, sustainable design choices could instead be built into the initial designs. We compiled LCAs for various product categories, finding the best means of visualizing the data for online and printable dissemination. Because this LCA data varied widely within each product category, it was necessary to display uncertainty and require users to acknowledge the uncertainty. Here, four different data visualizations were tested with engineering, design, and STEM students and professionals; both quantitative and qualitative analysis determined what visualizations were most favored and forced users to consider uncertainty. We hope that this research helps LCA data be more accessible to designers and engineers in the early phases of design, allowing those without the resources or ability to perform LCA to benefit from it and design more sustainably.Circular Product Desig