M. Fayzul K. Pasha

Strategies to Develop Warm Solutions for Real-Time Pump Scheduling for Water Distribution Systems

An optimal pump operation schedule that maintains satisfactory hydraulics conditions can generally reduce energy consumptions compared to the traditional trial and error based pump operation schedule. Linking an evolutionary based optimization algorithm with a hydraulic simulation model has gained attention for obtaining the optimal schedule. However, this technique requires significant computation time and thus has difficulty in real-time implementation. This paper presents several tactics to generate warm solutions that can be used in the initial population of the evolutionary algorithms to reduce the computation times. Strategies to generate warm solutions include the use of linear programming, surrogate model known as machine learning or meta-model, and historical pump schedule for similar demand pattern. Providing warm solutions from approximate methods or previous day’s results to stochastic search methods can improve solution convergence and offers significant computation time benefits. Results obtained from different strategies are compared.

Stream-Reach Identification for New Run-of-River Hydropower Development through a Merit Matrix–Based Geospatial Algorithm

AbstractEven after a century of development, the total hydropower potential from undeveloped rivers is still considered to be abundant in the United States. However, unlike evaluating hydropower potential at existing hydropower plants or nonpowered dams, locating a feasible new hydropower plant involves many unknowns; hence, the total undeveloped potential is harder to quantify. In light of the rapid development of multiple national geospatial data sets for topography, hydrology, and environmental characteristics, a merit matrix–based geospatial algorithm is proposed to identify possible hydropower stream reaches for future development. These hydropower stream reaches—sections of natural streams with suitable head, flow, and slope for possible future development—are identified and compared by using three different scenarios. A case study was conducted in the Alabama-Coosa-Tallapoosa and Apalachicola-Chattahoochee-Flint hydrologic subregions. It was found that a merit matrix–based algorithm, which is based...

Identifying high energy density stream-reaches through refined geospatial resolution in hydropower resource assessment

AbstractAided by the rapid development of multiple geospatial data sets for topography, hydrology, and existing energy-water infrastructures, reconnaissance-level hydropower resource assessment can now be conducted using geospatial models in all regions of the United States. The updated techniques can be used to estimate the total undeveloped hydropower potential across all regions, and they may eventually help to identify additional hydropower resources that were previously overlooked. To enhance the characterization of higher power–density stream reaches, this paper explored how the degree of geospatial resolution affects the identification of hydropower stream reaches, using the geospatial merit matrix–based hydropower resource assessment (GMM-HRA) model. GMM-HRA model simulation was conducted at eight different spatial resolutions on six USGS eight-digit hydrologic units with terrains classified as flat, mild, and steep. The results showed that more hydropower potential from higher power–density strea...

Uncertainty analysis in geospatial merit matrix–based hydropower resource assessment

AbstractHydraulic head and mean annual streamflow, two main input parameters in hydropower resource assessment, are not measured at every point along the stream. Translation and interpolation are used to derive these parameters, resulting in uncertainties. This study estimates the uncertainties and their effects on model output parameters: the total potential power and the number of potential locations (stream-reach). These parameters are quantified through Monte Carlo simulation (MCS) linking with a geospatial merit matrix–based hydropower resource assessment (GMM-HRA) model. The methodology is applied to flat, mild, and steep terrains. Results show that the uncertainty associated with the hydraulic head is within 20% for mild and steep terrains, and the uncertainty associated with streamflow is around 16% for all three terrains. Output uncertainty increases as input uncertainty increases. However, output uncertainty is around 10–20% of the input uncertainty, demonstrating the robustness of the GMM-HRA m...

Multiobjective Functions to Identify the High-Power-Density Stream Reaches in Hydropower Resource Assessment

AbstractA hydropower resource assessment (HRA) can be considered a reconnaissance survey in which the total hydropower potential in a region is quantified. Hydropower potential for purposes of this...