Welcome to WaRP, NHA’s new Waterpower Research Portal.

The focus of this report is to review the current practices for data analysis techniques, with specific emphasis on contemporary approaches relevant to hydropower systems.

A tool for HOPIG members to use to benchmark their water management departments and to improve operational efficiency in the planning and management of hydroelectric generation and water resources.

As interest rises in pumped storage’s role for integrating variable renewable energy (VRE) resources into the grid, a key resource may be overlooked. Conventional hydropower can, and sometimes does, provide ancillary services that mimic pumped storage’s capabilities.This report describes (1) the development and demonstration of an innovative, water-based methodology for analyzing and evaluating alternative energy and ancillary services operations under different market conditions, (2) the results from analyses using the methodology, and (3) recommendations based on results from the analyses. Results are provided from detailed analyses of a hypothetical three-unit hydropower plant operating (a) under cold weather (February) and hot weather (July) conditions; (b) with low, medium low, medium high, and high water budgets; and (c) in three market regions (CAISO, MISO, and NYISO). Across markets, seasons, and water budgets, the daily revenues from co-optimized energy, regulation, and spinning reserves significantly exceed the daily revenues from energy-only operation.

The matrix includes all activities for Outage Planning including the scheduling and impact evaluation of outages in the forecast mode, the evaluation of actual outage impacts computed after the fact, and issues pertaining to efficient communications within the organization.

The main objectives of this project are to review/describe/classify data screening methods, identify easily applicable and robust techniques, develop a reference guide to assist users in the selection of data screening methods, and identify emerging techniques for discharge/inflow data screening.

A significant reservoir operations need in Great Plains Region is to rapidly evaluate a large number of potential reservoir inflow scenarios in order to make short- and mid-term operational decisions. These scenarios are currently created manually by adjusting inflow timing and volume within a daily operations spreadsheet. Risk is then assessed qualitatively to make a final operational decision.GP reservoir operators do not have a systematic, risk-based methodology for operational decisions, nor are we aware of any within Reclamation. This results in operators using professional judgment on an ad-hoc basis to evaluate risk.This research will provide a case study for automating and rapidly evaluating numerous potential inflow scenarios, significantly improving staff efficiency. The project will also provide clarity to the decision-making process. The current lack of operational clarity leaves Reclamation management and staff open to criticism, or in the extreme, to lawsuits, if operations damage water users in some manner.

Implementation of this project will facilitate implementation of probabilistic modeling and reduce time required to implement them, by several orders of magnitude. Stochastic simulation and representation of modeling results as probabilistic is a growing field and identified as an important and valuable effort in many fields of science and engineering (Romanowicz & Beven,1996, 1998, 2003; Aronica et. al., 1998, 2002; Bates et. al., 2004; Hall et. al., 2005; Pappenberger et. al., 2005, 2006). Probabilistic modeling is required for most risk analyses associated with large infrastructure projects. Development of this tool will allow HEC-RAS modelers from the most sophisticated regional efforts to the most simple project implemented at the most local level to enter their calibrated and validated model into this software tool and produce probabilistic results, by doing nothing more than putting in the location of their model program file. This tool could save on the order of weeks of time for any project to develop probabilistic results.

The project developed advanced user-centric modular visualization tools to help present critical information from a large amount of multidimensional data in an effective way for improved power system state awareness. The project demonstrated the visualization tools through examples from pressing challenges for hydro operations as they integrate 15-minute scheduling. Probabilistic flow and load forecasting was also achieved.