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At least 271 records · Page 15

Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower (DIVERS-H)

U.S. hydropower plants face potential threats from shrinking water supply, rising demands, and warmer stream temperatures from various causes. Power plant owners, operators, and regulators require new tools to take advantage of and interpret the diverse range of scientific data being produced by both observational methods (for example, satellite, radar, stream gauges) and computer modeling methods that evaluate and predict how earth's dynamic systems (atmosphere, oceans, land surface, and sea ice) are changing and interacting. Combining datasets such as these with AI-based analyses introduces a novel decision support system to help users anticipate and address potential impacts on power generation stations. This new technology has been named DIVERS-H for "Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower." In Phase I, technical feasibility was established with the development and demonstration of all the new technologies that are required. Most notably, DIVERS-H will use new artificial intelligence (AI) methods to capture the complex dynamics of water availability, demand, and environmental changes. In addition, new data management software was developed, and a prototype user interface was implemented as the precursor to a full scale decision support system. With technical research complete, the project focus now shifts to development of a commercial software product to provide users with actionable insight into water availability and the risk/resilience of critical systems at their locations of interest. Although DIVER-H was originally conceived as a tool for hydroelectric power applications, the same underlying technology can be readily applied to other water-consuming systems including coal, natural gas, oil, and nuclear power plants.

Chaudhary, Aashish [Kitware, Inc., Clifton Park, N↗

CRiSPPy: An advanced hydropower scheduling tool for the Colorado River Storage Project

The Western Area Power Administration (WAPA) plays a vital role in delivering reliable and cost-effective hydroelectric power to millions of customers across the western United States. The Colorado River Storage Project (CRSP) carries out WAPA’s mission in Arizona, Utah, Colorado, New Mexico, Nevada, Wyoming and Texas. Achieving this mission requires effective management of the Colorado River system, and depends on the use of advanced analytical tools and modeling methodologies. For many years, CRSP has relied on the Generation and Transmission Maximization Superlite (GTMax SL) model for its mid-term and long-term hydroscheduling needs. However, the evolving energy market, power system operations, environmental rules, and hydrology conditions, coupled with advancements in computational capabilities, have necessitated the development of a more modern and robust solution. This report introduces the Colorado River Storage Project Python-based (CRiSPPy) model, a new, advanced hydropower scheduling tool developed to address CRSP ever-evolving challenges. CRiSPPy represents a significant leap forward in our ability to model and optimize the operation of the Colorado River system. It incorporates state-of-the-art optimization algorithms, enhanced data management capabilities, and an advanced graphical user interface, providing WAPA CRSP personnel with unprecedented insights and decision-making support. This document details the development, capabilities, and implementation of CRiSPPy. It is intended to serve as a comprehensive resource for WAPA staff, stakeholders, and anyone interested in the future of hydropower scheduling in the Colorado River Basin. We are confident that CRiSPPy will enhance WAPA's mission while adapting to the challenges of a dynamic and increasingly complex environment. The version of CRiSPPy described in this report is the version 2.3. New versions of CRiSPPy will be developed as the tool keeps evolving to address CRSP challenges.

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Hybrid Bioenergy and Hydropower Systems: Leveraging Heterogeneous Biomass and Wastes from Watersheds

Sandia National Laboratories and INL explore the water-for-energy nexus in this white paper, focusing on the impact of heterogeneous watershed waste materials on hydropower facilities. They identify key challenges—such as wildfires, and debris intercepted at water intakes—that adversely affect both watershed health and hydropower operations. More importantly, SNL and INL highlight opportunities to valorize these bioresources and waste materials by converting them into usable energy.

09 BIOMASS FUELS↗

Existing Methods for Grid Strength Assessment and Role of Hydropower in Future Grids

The power system is undergoing rapid evolution with increasing penetration of inverter-based resources (IBRs), large loads, microgrids, and power electronic devices. Ensuring reliable and stable operation of the modern power grid is a multi-faceted challenge that requires detailed understanding of this complex system. Dynamic stability is a major concern in maintaining the security of power grids as the generation mixes and large loads transitions to include high shares of power electronic devices. The operation of IBRs in regions with low system strength and higher grid impedances has been found to be the main reason behind many of the power system instabilities that manifest themselves in various types of oscillations and interactions that, if not properly addressed and damped, can jeopardize the reliable operation of the power system. Weak grid conditions compound such stability problems, particularly when many IBRs operate in proximity to and connect to weak power grids. Therefore, it is important to assess grid strength for planning, integration and operation of IBRs for a given power system. In this report, we aim to understand and classify existing methodologies for grid strength assessment, along with their limitations and future needs. Further, we aim to utilize the huge untapped potential in utilizing hydro energy resources in addressing some of the pertinent challenges of grid strength and reliable operation of modern power systems. Hydropower, historically valued for its flexibility and dispatchability, now faces new constraints due to reduced share of synchronous machines in the generation mix and seasonal variability of available water resources. Yet, these same plants present untapped potential beyond energy generation - notably, as providers of critical grid services. This report explores how hydropower plants, particularly through operation as synchronous condensers, can play a pivotal role in strengthening the grid amid evolving system dynamics.

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Hydropower Value Drivers

This report presents a framework developed to identify system value drivers and quantify their relative impact on several different value streams, with a specific focus on implications for conventional hydropower and pumped storage hydropower (PSH) resources.

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Updated Baseline Cost Model for Hydropower (2023)

The "Updated Baseline Cost Model (BCM) for Hydropower" is an empirical model for estimating the costs of US hydropower projects in six categories (Non-Powered Dam (NPD), New Stream Development (NSD), Canal/Conduit, Pumped Storage Hydro (PSH), Capacity Expansion, Generator Rewind). The model equations have been estimated with data on existing or planned projects obtained from various sources. NOTE: This is a macro-enabled workbook, so Excel may block it on the first opening. In that case: 1. Save the workbook to a local directory 2. Right-click on the workbook in the local directory and select “Properties” 3. Then check “Unblock” at the bottom of the “General” tab.

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Cost of Fish Exclusion Technologies for Hydropower

This dataset compiles data on the costs of environmental mitigations related to fish exclusion for 35 hydropower projects based on documents obtained from the U.S Federal Energy Regulatory Commission (FERC). Data for all measures and their associated project-specific information, including installed and hydraulic capacities, were manually extracted from environmental impact statements and/or environmental assessment (EIS/EA) documents. All data were consolidated in a comma-separated (.csv) file, referred to as the Cost of Fish Exclusion Technologies for Hydropower Database.

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Hydropower Energy Storage Capacity Dataset

The Hydropower Energy Storage Capacity (HESC) Dataset catalogues estimates of nominal energy storage capacity based on varying levels of detail. Dams and reservoirs selected were selected based on those reported in the National Inventory of Dams (NID 2019) and/or the Global Reservoir and Dam (GRanD v1.3) datasets. These data provide a foundation for understanding available resources at existing hydropower facilities and their potential to provide storage of energy and more flexible generation. Current estimates include Level 1 (based on maximum storage capacities and hydraulic head) and Level 2 (based on historical models or observations of reservoir volume and hydraulic head). For facilities where installed capacity is known, there are also estimates for discharge duration or the length of time when a facility could provide generation at a given capacity. Essential information used to calculate the energy storage capacity and discharge duration (volume, hydraulic head, and details about the sources or records used to obtain those parameters) and summaries of historical generation (for context) are also included.

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Hydropower Infrastructure - LAkes, Reservoirs, and RIvers (HILARRI)

HILARRI is a database of links between major datasets of operational hydropower dams and powerplants (National Inventory of Dams (2019), Global Reservoir and Dam Database (GRanD v1.3), Existing Hydropower Assets (EHA 2020), and inland water bodies (NHDPlusV2 river network, NHD water bodies, NHD Watershed Boundary Dataset, HydroLAKES).

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Hydropower Infrastructure - LAkes, Reservoirs, and RIvers (HILARRI), V2

HILARRI Version 2 is a database of links between major datasets of operational hydropower dams and powerplants (National Inventory of Dams (2021), Global Reservoir and Dam Database (GRanD v1.3), Existing Hydropower Assets (EHA 2022), and inland water bodies (NHDPlusV2 river network, NHD water bodies, NHD Watershed Boundary Dataset, HydroLAKES).

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The Interactive Hydropower Generation Map

This interactive map shows the spatial distribution and temporal trends of net generation for hydropower facilities in the U.S., as included in the Existing Hydropower Assets (EHA) Net Generation Plant Database.

Schmidt, Erik [Oak Ridge National Laboratory (ORNL↗

The 2018 National Hydropower Map, Version 1

This map poster uses Oak Ridge National Laboratory’s 2018 Existing Hydropower Assets Plant Dataset to visualize the geospatial distribution and characteristics of existing hydropower plants in the United States.

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RectifHyd Version 2.0: Historical and counterfactual-climatological hydropower monthly generation totals for CONUS plants, 1980 – 2019

This dataset will contain the following files: - RectifHydV2.zip – the RectifHydV2 dataset, split into two files: o RectifHydV2_Actual_MWh.csv: Estimated actual monthly net generation from 590 Hydropower Plants (>10MW nameplate) in CONUS, 1980 – 2019 o RectifHydV2_Counterfactual_MWh.csv: Counterfactual (climate-only) monthly net generation from 590 Hydropower Plants (>10MW nameplate) in CONUS, 1980 – 2019 - RectifHydV2_code.zip: Full data processing pipeline, coded using R {targets} framework. This is a snapshot release (v1.0) of the code repository stored at https://code.ornl.gov/turnersw/rectifhydv2 - RectifHydV2_inputs.zip: Complete set of input data used to create RectifHydV2, organized for direct entry into “/data/” directory of the RectifHydV2 reproducible data pipeline - RectifHydV2_misc.zip: - RectifHydV2_dailyRelease.csv

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Hydropower Licensing Timeline and Cost Dataset

This dataset provides timeline dates, lengths, project characteristics, license characteristics, and costs of hydropower licensing and relicensing for 107 randomly selected hydropower projects that have been issued licenses since 1 October 2005. Projects included in this dataset were licensed but not necessarily constructed. These data were analyzed and presented in Chapters 3 (timeline data) and 5 (cost data) of the Levine et al. (2021) report

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Puerto Rico Observed Hydropower Generation

This dataset contains compiled observed hydropower generation for hydropower plants in Puerto Rico. Data have been compiled from data provided to the Energy Information Administration by asset owners. This dataset provides available generation data in monthly and annual files, with quality flags.

Broman, Daniel P [Pacific Northwest National Labor↗

Hawaii Observed Hydropower Generation

This dataset contains compiled observed hydropower generation for hydropower plants in Hawaii. Data have been compiled from data provided to the Energy Information Administration by asset owners. This dataset provides available generation data in monthly and annual files, with quality flags.

Broman, Daniel P [Pacific Northwest National Labor↗

Alaska Observed Hydropower Generation

This dataset contains compiled observed hydropower generation for hydropower plants in Alaska. Data have been compiled from data provided to the Energy Information Administration by asset owners, data contained in annual reports produced by the Institute of Social and Economic Research at the University of Alaska Anchorage (Alaska Electric Power Statistics and Alaska Energy Statistics) and data provided to the Federal Energy Regulatory Commission by asset owners. This dataset provides available generation data from all sources in monthly and annual files, with quality flags, and generation data identifying the highest quality source in monthly and annual files.

Broman, Daniel [Pacific Northwest National Laborat↗

Physics-informed Data-driven Degradation and Prognostic For Hydropower System

The hydropower system is a cyber-physical system, and the industry is experiencing high operation and maintenance (O&M) costs along with increasing unplanned outages, primarily due to aging equipment and new asset loading patterns. Current (O&M) practices in asset management are predominantly reactive rather than predictive and systematic. To address these challenges, a proposed physics-informed data-driven degradation model aims to simulate the health of components in various environments. Its purpose is to accurately characterize wear and fatigue in the pilot components of hydropower systems. Additionally, the model provides informed confidence in prognostic predictions concerning asset degradation and remaining life.

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