To effectively generate commercial-scale power for an electric grid, Wave Energy Conversion (WEC) devices need to be installed in arrays comprising multiple devices to efficiently convert wave energy into electrical power onshore. The deployment of WEC arrays will begin small (pilot-scale or ~10 devices) but could feasibly number in the hundreds of individual devices at commercial-scale. As the industry progresses from pilot- to commercial-scale it is important to understand and quantify the relationship between the number of devices installed and the potential to effect the natural nearshore processes that support a local, healthy ecosystem. WEC arrays have the potential to alter near-shore wave propagation and circulation patterns, possibly modifying sediment transport patterns and ecosystem processes. As WEC arrays sizes grow, there is a potential for negative environmental impacts which could be detrimental to local coastal ecology, and social and economic services. To help accelerate the realization of commercial-scale wave power, predictive modeling tools are developed and utilized to investigate ranges of anticipated scenarios and evaluate the potential for negative (or positive) environmental impact.
The present study incorporates an industry standard wave modeling tool, SWAN (Simulating WAves Near-shore), to simulate wave propagation through a hypothetical WEC array deployment site on the California coast. Specifically, various sizes of WEC arrays are simulated to examine the changes to wave propagation properties (e.g. wave heights, periods and directions) in lee of the array in both the near- and far-field. Using and building upon results from a previous SWAN model sensitivity analysis (SNL, 2011), the study focuses on the change in wave properties resulting from variation in the ranges of SWAN model parameters, array geometries and array deployment locations (water depths).
At present, direct measurements of the effects of arrays on wave properties for a prototype scale WEC site are not available; therefore, the effects of varying model parameters on the model results must be evaluated before environmental assessments can be completed. The present study provides the groundwork for completing such assessments by investigating the sensitivity of the predictive model results to prescribed model parameters over a range of anticipated wave conditions. The understanding developed here will allow investigators to conduct predictive environmental assessments with increased confidence and reduced uncertainty in future phases.