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Igiugig, Alaska received technical assistance through the Energy Technology Innovation Partnership Project (ETIPP) to implement high-priority strategies detailed in their 2022 Community Energy Plan.
In 2023, the National Laboratory of the Rockies (NLR) partnered with the Renewable Energy Alaska Project (REAP) to provide technical assistance to Igiugig through the US Department of Energy's Energy Transitions Initiative Partnership Project (ETIPP). The overall objective of this ETIPP project was to provide technical support to help the community achieve their energy goal of reducing diesel fuel for electricity by 50% by 2030 through the implementation of high priority strategies defined in their 2022 Community Energy Plan. Technical support focused on evaluating additional renewables and renewable heating scenarios, identifying heat recovery system design options for high penetration renewables, investigation power quality and line loss issues, and supporting integration of battery energy storage.
Marine energy-including wave, tidal, and river current energy-can provide a local energy source for rural and remote communities. Marine energy has the potential to bolster self-sufficiency and create economic opportunities while preserving ecological integrity. Communities may be interested in deploying, testing, and advancing these early-stage technologies to meet their needs. However, limited capacity, workforce constraints, and other barriers can challenge development. To better understand a community's interest in and preparedness for marine energy, we developed a suite of 150 'metrics of readiness.' Organized across seven categories-technical, social, environmental, strategic, governance, economic, financial-and 29 subcategories, the metrics provide a holistic perspective beyond the technical aspects of an energy device. We conducted a desktop application of the metrics of readiness for Igiugig, Alaska. The metrics were applied retrospectively for two points in time: before (2009) and after (2018) in-stream testing of a river current energy device. By documenting changes among categories and subcategories of the metrics, our results show the evolving nature of community readiness for river current energy. They also illustrate how our interdisciplinary framework captures the investment in environmental effects research and commitment to strategic planning that occurred in Igiugig. In future applications, we envision the framework could be used to foster public engagement in marine energy, collaborate with communities in project development, shape capacity building activities, prioritize investments, and inform research needs. While our study focuses on enabling river current energy in Alaska, the metrics of readiness have the potential to inform implementation of other renewable technologies with communities in new geographies.
This repository contains data and processing scripts necessary to train the object detection models utilized in the underwater target detection software demonstration on the RivGen turbine project and to produce performance metrics (precision, recall, mAP50, mAP50-95). - Contents - Data consist of "images" and "labels". Each image has an associated label, both share the same time string in its file name (e.g., 2024_05_25_09_01_57.98.jpg and 2024_05_25_09_01_57.98.txt). Time strings have the format %yyyy_%mm_%dd_%HH_%MM_%SS.%3f. Images and labels were curated from 2021 and 2024 smolt outmigration periods at the project site in Igiugig, AK. Images are monochrome 8-bit images of objects (smolt, debris, and other) passing through the field of view of the deployed cameras during various operational stages of the RivGen turbine. Labels are text files indicating the class and bounding polygon of each object in an image. The provided labels use the "YOLO" label format. - Requirements - Python3.8+ is required to install and run the train and validation script. The README.md provides instruction for installing the requirements from the requirements.py file. - Instructions - The "example_train.py" file ingests the provided data, trains a model, and produces model performance metrics at completion. NOTE: model performance metrics will vary from run to run as a consequence of the random selection of training and validation data.
In this study, we characterize the sound generated by a cross-flow riverine turbine in the Kvichak River near Igiugig, Alaska, United States. To do this, we follow the International Electro-technical Commission (IEC) technical specification for characterization of the acoustic emissions from marine energy converters. While marine mammals do not inhabit the test site, the U.S. National Marine Fisheries Service (NMFS) guidelines for assessing the effects of anthropogenic sound on marine mammal hearing were implemented to provide context for the deployment of similar turbines in other areas. The results indicate that turbine sound at the measurement locations was not predicted to be harmful to marine mammals. Results also provide insight into the acoustic characteristics of current energy converters and the complex acoustic propagation in rivers.