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  1. Understanding Line Losses and Transformer Losses in Rural Isolated Distribution Systems: Preprint

    Rural, isolated power systems in the mainland U.S. and in states like Alaska and Hawaii are powered by assets like diesel generators. These rural, isolated power systems also cannot operate at the higher band of medium voltage (like 69kV). They are primarily in the 12 to 14 kV range to keep the cost of the distribution investments lower. Because of this mid-band medium voltage range, the line losses and distribution transformers losses consume significant diesel consumption (almost 10 percent of the peak load). This work considers one such power system powering an isolated system and presents key findings online losses,more » and transformer losses. Understanding and documenting the impacts is critical for these communities operating their power systems and take actions to reduce expensive diesel consumption. In this paper, we will present one such typical grid and model it in electromagnetic transients (EMT) domain. We used the tower structure, under ground cabling installation to develop high fidelity models of lines. We also used high fidelity models of distribution transformers to present the no-load losses and full load loses. We will also present technical solutions available commercially off-the-shelf to reduce these losses and reduce diesel consumption. This work will be a primer for communities to understand the technical challenges and to understand the possible solution available to solve such challenges for rural, isolated power system operators.« less
  2. Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method

    Accurate subseasonal reservoir inflow forecasts and understanding the influence of hydrometeorological forcings on these forecasts are crucial for improving water resources management. Machine learning (ML) techniques, such as long short-term memory (LSTM) networks, perform well for short-term inflow forecasts but have deficiencies in subseasonal forecasts and lack interpretability. To address these limitations, we propose an explainable ML method that integrates an encoder–decoder LSTM (ED-LSTM) network to improve long-term reservoir inflow forecasts and a gradient-based explanation method to quantify the importance of individual hydrometeorological forcings and their interactions on inflow forecasts. The ED-LSTM model outperforms the standard LSTM in the 30-daymore » inflow forecasts at all 30 reservoirs. At the 1-day lead time, ED-LSTM produces NSEs exceeding 0.75 at 29 reservoirs; at the 15-day lead time, about half of reservoirs maintain this high-accurate performance, and when forecasting 30 days ahead, ED-LSTM achieves NSEs exceeding 0.5 at most reservoirs. The variable importance identifies past inflow and temperature as crucial drivers for predicting inflow dynamics. When considering interactions between hydrometeorological forcings, precipitation contributes significantly to inflow forecasting through its interaction with temperature and historical inflow. The proposed method enhances subseasonal reservoir inflow forecasts and the understanding of the impact of hydrometeorological factors, which supports decision-making in reservoir operations.« less
  3. Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification

    We propose an explainable machine learning (ML) model with uncertainty quantification (UQ) to improve multi-step reservoir inflow forecasting. Traditional ML methods have challenges in forecasting inflows multiple days ahead, and lack explainability and UQ. To address these limitations, we introduce an encoder–decoder long short-term memory (ED-LSTM) network for multi-step forecasting, employ the SHapley Additive exPlanation (SHAP) technique for understanding the influence of hydrometeorological factors on inflow prediction, and develop a novel UQ method for prediction trustworthiness. We apply these methods to forecast 7-day inflow in snow-dominant and rain-driven reservoirs. The results demonstrate the effectiveness of the ED-LSTM model, with highmore » forecasting accuracy for short lead times. Our UQ method provides reliable uncertainty estimates, covering 90% of data with a 90% confidence level. The SHAP analysis reveals the importance of historical inflow and precipitation as influential factors. These findings and methods may support reservoir operators in optimizing water resources management decisions.« less
  4. Energy Transitions Initiative Partnership Project: City and Borough of Sitka, Alaska - Modeling and Controls Assistance and Renewable Energy Resource Assessment [Slides]

    This presentation provides a summary of the ETIPP project objectives and findings for Sitka, Alaska, including sizing of wind penetration, dynamic models, and analysis of efficiency of load control, stability and grid control impacts of wind capacity expansions and locations, and wind-hydro control coordination.
  5. Energy Transitions Initiative Partnership Project: Wainwright, Alaska. Cohort 1 Technical Assistance: Assessment of Energy Efficiency, Renewable Energy, and Energy Storage Options

    This project identified pathways to reduce energy consumption and enhance indoor environmental quality and resilience of a 1,500-square-foot former federal armory building owned by the tribe as it is renovated as a community multipurpose facility. The scope included thermal evaluation of multiple building efficiency measures, and a techno-economic exploration of an on-site renewable energy and integrated energy storage systems.
  6. Eastport Energy Resilience Opportunities [Slides]

    This presentation offers a high-level overview of the Energy Transitions Initiative Partnership Project's (ETIPP's) work in Eastport, Maine. It covers the project's work to explore microgrid options for the island community; opportunities for incorporating renewable energy technology such as tidal power, solar power, and battery storage; potential benefits and costs for the community; and an estimated timeline for implementing different options.
  7. On the Morphodynamics of a Wide Class of Large‐Scale Meandering Rivers: Insights Gained by Coupling LES With Sediment‐Dynamics

    Abstract In meandering rivers, interactions between flow, sediment transport, and bed topography affect diverse processes, including bedform development and channel migration. Predicting how these interactions affect the spatial patterns and magnitudes of bed deformation in meandering rivers is essential for various river engineering and geoscience problems. Computational fluid dynamics simulations can predict river morphodynamics at fine temporal and spatial scales but have traditionally been challenged by the large scale of natural rivers. We conducted coupled large‐eddy simulation and bed morphodynamics simulations to create a unique database of hydro‐morphodynamic data sets for 42 meandering rivers with a variety of planform shapesmore » and large‐scale geometrical features that mimic natural meanders. For each simulated river, the database includes (a) bed morphology, (b) three‐dimensional mean velocity field, and (c) bed shear stress distribution under bankfull flow conditions. The calculated morphodynamics results at dynamic equilibrium revealed the formation of scour and deposition patterns near the outer and inner banks, respectively, while the location of point bars and scour regions around the apexes of the meander bends is found to vary as a function of the radius of curvature of the bends to the width ratio. A new mechanism is proposed that explains this seemingly paradoxical finding. The high‐fidelity simulation results generated in this work provide researchers and scientists with a rich numerical database for morphodynamics and bed shear stress distributions in large‐scale meandering rivers to enable systematic investigation of the underlying phenomena and support a range of river engineering applications.« less
  8. Energy Transitions Initiative Partnership Project (ETIPP) [Slides]

    These slides describe the U.S. Department of Energy's Energy Transitions Initiative Partnership Project (ETIPP). They include the ETIPP goals, partner network, technical assistance types, project timeline, and quotations by some communities selected for ETIPP in FY21.
  9. The State and Local Planning for Energy (SLOPE) Platform and Additional DOE/NREL Resources for Clean Energy Planning

    The State and Local Planning for Energy (SLOPE) Platform is a tool to enable more data-driven state and local energy planning by integrating dozens of distinct sources of energy efficiency, renewable energy, and (coming in 2020) sustainable transportation data and analyses.
  10. Ocean Deacidification Technologies for Marine Aquaculture

    The increase in partial pressure of CO2 in the oceans directly affects the productivity and survival of coastal industries and ecosystems. For marine aquaculture, the decreased alkalinity of seawater results in reduced availability of carbonates for marine organisms to build their shells, leading to decreased aquaculture quality and productivity. The industry has been implementing recirculating aquaculture systems (RASs) to reduce CO2 in feedwaters, but recent interest in ocean-based CO2 capture has led to additional strategies that may be relevant. The new methods in addition to CO2 removal offer capture options for enhanced aquaculture sustainability. Here, we review and compare early-stagemore » and commercially available technologies for deacidification of seawater and their suitability for aquaculture. Most methods considered rely on a voltage-induced pH swing to shift the carbonate/bicarbonate equilibrium toward the release of CO2, with subsequent capture of the released CO2 as a gas or as solid mineral carbonates. The modular design and distributed deployment potential of these systems offers promise, but current demonstrations are limited to bench scale, highlighting the need for sustained research and development before they can be implemented for marine aquaculture.« less
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