Abstract
Pacific Energy Ventures has partnered with utility industry and variable energy resource characterization experts, Ken Dragoon (Ecofys), Jeff King and Gordon Reikard to provide the Oregon Wave Energy Trust (OWET) a comprehensive analysis aimed at further validating wave energy as an economical and viable part of the Northwest’s energy portfolio. This project relied on advanced analytical and evaluation techniques to 1) develop an improved understanding of the time-dependent power of waves along the Northwest coast, 2) determine hypothetical electrical output of representative wave energy converter designs deployed at prospective development sites along the Oregon coast, and 3) evaluate the magnitude of balancing capacity reserves needed to integrate wave-generated electricity into the Northwest electrical grid. The overall purpose of this project was to provide an analysis of wave energy resources so as to inform utilities and balancing authorities about potential integration issues and costs, overall resource characterization, and methods for managing them. The rapid and large-scale development of wind generation in the Northwest has necessitated changes in the utilities’ load balancing procedures to accommodate wind’s greater variability. Although it is generally agreed that wave energy is more predictable than wind, it will likely face similar integration challenges as the industry matures. This project attempts to develop tools to help quantify the cost and value of introducing wave energy into the Northwest’s energy mix. This study provides evidence that wave energy integration is manageable, and that wave energy has the potential to contribute a great deal of electricity to the power grid. It shows areas where additional study is warranted (See Section V. Conclusions and Recommendations) and the potential for substantially more accurate resource forecasts by incorporating data from distant observation sites. The study broke new ground in using data from buoy observations, and the use of advanced computational models to develop sub-hourly data from hourly observations. Finally, the embedded analysis emphasizes the need for improvements in wave forecasting models to better capture energy aspects of wave behavior.