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In the United States, building sector is responsible for around 40% of total energy consumption and contributes about 40% of carbon emissions since 2012. Within the past several years, various optimization models and control strategies have been studied to improve buildings’ energy efficiency and reduce operational expenses under the constraints of satisfying occupants’ comfort requirements. However, the majority of these studies consider building electricity demand and thermal load being satisfied by unidirectional electricity flow from the power grid or on-site renewable energy generation to electrical and thermal home appliances. Opportunities for leveraging low-grade heat for electricity have largely been overlooked due to impracticality at small scale. In 2016, a modular pumped hydro storage technology was invented in Oak Ridge National Laboratory, named Ground Level Integrated Diverse Energy Storage (GLIDES). In GLIDES, employing high-efficiency hydraulic machinery instead of gas compressor/turbine, liquid is pumped to compress gas inside high-pressure vessel creating head on ground level. This unique design eliminates the geographical limitation associated with the existing state-of-the-art energy storage technologies. It is easy to be scaled for building level, community level, and grid level applications. By using this novel hydro-pneumatic storage technology, opportunities for leveraging low-grade heat in building can be economical. In this research, the potential of utilizing low-grade thermal energy to augment electricity generation of GLIDES is investigated. Since GLIDES relies on gas expansion in the discharge process and the gas temperature drops during this nonisothermal process, available thermal energy, e.g., from thermal storage, combined cooling, heat and power system (CCHP), can be utilized by GLIDES to counter the cooling effect of the expansion process and elevate the gas temperature and pressure and boost the roundtrip efficiency. Several groups of comparison experiments have been conducted, and the experimental results show that a maximum 12.9% cost saving could be achieved with unlimited heat source for GLIDES, and a moderate 3.8% cost improvement can be expected when operated coordinately with CCHP and thermal energy storage in a smart building.

Quantifying excess energy using an energy balance model is the key to designing and operating an energy-efficient water distribution system (WDS). Excess energy, which can be recovered instantly or stored in a water-energy storage is the basis to estimate hydropower potential in the system. For a given WDS with its demand, how the excess energy can be managed efficiently to design a water-energy storage to maximize hydropower generation is the focus of this paper. A single-objective optimization model has been developed to optimize the dimensions for up to six water-energy storages for maximizing hydropower generation while minimizing the pumping energy. While the ratio of total energy recovered to total pumping energy is found to be about 40% for all water-energy configurations, the recovered specific energy ranges from 0.116 kWh/m3 to 0.121 kWh/m3 showing the potential use of WDS as an energy storage. Results show that hydropower generation increases with the increase of number of storages up to a certain number representing the constraints of constant drinking water demand and storage dimensions. In-pipe turbines with pump operation for minimizing pumping energy can offer the optimal solution for WDS energy management. A higher number of storages with in-pipe turbines offers uniformity in pressure distribution resulting increase in system robustness.

Grid integration of the increasing distributed energy resources could be challenging in terms of new infrastructure investment, power grid stability, etc. To resolve more renewables locally and reduce the need for extensive electricity transmission, a community energy transaction market is assumed with market operator as the leader whose responsibility is to generate local energy prices and clear the energy transaction payment among the prosumers (followers). The leader and multi-followers have competitive objectives of revenue maximization and operational cost minimization. This non-cooperative leader-follower (Stackelberg) game is formulated using a bi-level optimization framework, where a novel modular pump hydro storage technology (GLIDES system) is set as an upper level market operator, and the lower level prosumers are nearby commercial buildings. The best responses of the lower level model could be derived by necessary optimality conditions, and thus the bi-level model could be transformed into single level optimization model via replacing the lower level model by its Karush-Kuhn-Tucker (KKT) necessary conditions. Several experiments have been designed to compare the local energy transaction behavior and profit distribution with the different demand response levels and different local price structures. The experimental results indicate that the lower level prosumers could benefit the most when local buying and selling prices are equal, while maximum revenue potential for the upper level agent could be reached with non-equal trading prices.

In the U.S., building sector is responsible for around 40% of total energy consumption and contributes about 40% of carbon emissions since 2012. Within the past several years, various optimization models and control strategies have been studied to improve buildings energy efficiency and reduce operational expenses under the constraints of satisfying occupants’ comfort requirements. However, the majority of these studies consider building electricity demand and thermal load being satisfied by unidirectional electricity flow from the power grid or on-site renewable energy generation to electrical and thermal home appliances. Opportunities for leveraging low grade heat for electricity have largely been overlooked due to impracticality at small scale. In 2016, a modular pumped hydro storage technology was invented in Oak Ridge National Laboratory, named Ground Level Integrated Diverse Energy Storage (GLIDES). In GLIDES, employing high efficiency hydraulic machinery instead of gas compressor/turbine, liquid is pumped to compress gas inside high-pressure vessel creating head on ground-level. This unique design eliminates the geographical limitation associated with existing state of the art energy storage technologies. It is easy to be scaled for building level, community level and grid level applications. Using this novel hydro-pneumatic storage technology, opportunities for leveraging low-grade heat in building can be economical. In this research, the potential of utilizing low-grade thermal energy to augment electricity generation of GLIDES is investigated. Since GLIDES relies on gas expansion in the discharge process and the gas temperature drops during this non-isothermal process, available thermal energy, e.g. from thermal storage, Combined Cooling, Heat and Power system (CCHP), can be utilized by GLIDES to counter the cooling effect of the expansion process and elevate the gas temperature and pressure and boost the roundtrip efficiency. Several groups of comparison experiments have been conducted and the experimental results show that a maximum 12.9% cost saving could be achieved with unlimited heat source for GLIDES, and a moderate 3.8% cost improvement can be expected when operated coordinately with CCHP and thermal energy storage in a smart building.

Hydropower generation has advantages for societies that seek low-carbon, renewable energy alternatives, but sustainable hydropower production will require an explicit consideration of potential tradeoffs between socioeconomic and environmental priorities. These tradeoffs are often explored during a formal environmental impact assessment process that can be complex and controversial. The steps taken to address stakeholder concerns through impact hypotheses and field studies are not always transparent. We have created a Checklist of River Function Indicators to facilitate stakeholder discussions during hydropower licensing and to support more transparent, holistic, and scientifically informed hydropower environmental analyses. Based on a database of environmental metrics collected from hydropower project studies documented by the Federal Energy Regulatory Commission (FERC), the International Hydropower Association, the Low Impact Hydropower Institute, and peer-reviewed scientific literature, our proposed Checklist of River Function Indicators contains 51 indicators in six categories. We have tested the usefulness of the Indicators by applying them to seven hydropower projects documented by FERC. Among the case study projects, 44 of the 51 Indicators were assessed according to the FERC documentation. Even though each hydropower project presents unique natural resource issues and stakeholder priorities, the proposed Indicators can provide a transparent starting point for stakeholder discussions about which ecological impacts should be considered in hydropower planning and relicensing assessments.

With the increasing share of renewable energy in the power grid, energy storage is considered a viable and effective solution to mitigate intermittency and improve power system stability. To quantify and estimate market potential for a recently developed Ground-Level Integrated Diverse Energy Storage (GLIDES) technology, the first operation model is developed for this hydropneumatic system and the bilinear pressure-volume terms are then linearized using the binary McCormick Relaxation method to improve solving efficiency. Based on up-to-date 5-min price data in four different real-time markets, several sets of experiments have been designed with three objective scenarios: maximize arbitrage, maximize arbitrage + reserve, maximize arbitrage + reserve + regulation. The experimental results show that the monetary value of GLIDES could be boosted by participation in ancillary markets and that regulation service might contribute more than half of the total revenue, even as high as 90% in PJM and ISONE markets. In addition, a case study for 1-day operation is used to illustrate the volume control of the GLIDES system.

•Hydropower literature review produced database of over 3000 environmental metrics.•Metrics varied by project location, life cycle stage, size and literature type.•Emergent properties of the metrics can help stakeholders evaluate sustainability.•Measurable, repeatable & understandable metrics will improve licensing efficiency.

Behind-meter application for a recently invented hydropneumatic Ground-Level Integrated Diverse Energy Storage (GLIDES) is focused in this research. Considering possible uncertainties from solar radiation and electricity load in a building microgrid, two-stage stochastic programming is adopted for 15-min operation and a mixed-integer nonlinear stochastic model is built with integrated energy systems of GLIDES, solar panel and power generating unit. In order to make the model computationally tractable, we use sample average approximate method to decrease associated complexity with sample size and improve solving efficiency. The experimental results in deterministic and stochastic operation indicate that best candidate solution from SAA are superior to the solution from expected value problem in terms of optimality gap and variance.

Due to the promising potential for environmental sustain-ability, there has been a significant increase of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV) in the market. To support this increasing demand for EVs and PHEVs, challenges related to capacity planning and investment costs of public charging infrastructure must be addressed. Hence, in this paper, a capacity planning problem for EV charging stations is developed and aims to balance current capital investment costs and future operational revenue. The charging station considered in this work is assumed to be equipped with solar photovoltaic panel (PV) and an energy storage system which could be electric battery or the recently invented hydro-pneumatic energy storage (GLIDES, Ground-Level Integrated Diverse Energy Storage) system. A co-optimization model that minimizes investment and operation cost is established to determine the global optimal solution while combining the capacity and operational decision making. The operational decision making considers EV mobility which is modeled as an Erlang-loss system. Meanwhile, stochastic programming is adopted to capture uncertainties from solar radiation and charging demand of the EV fleet. To provide a more general and computationally efficient model, main configuration parameters are sampled in the design space and then fixed in solving the co-optimization model. The model can be used to provide insights for charging station placement in different practical situations. The sampled parameters include: the total number of EV charging slots, the PV area, the maximum capacity of the energy storage system, and daily mean EV arrival number in the Erlang-loss system. Based on the sampled parameter combinations and its responses, black-box mappings are then constructed using surrogate models (RBF, Kriging etc). The effectiveness of proposed surrogate modeling approach is demonstrated in the numerical experiments.

Due to the promising potential for environmental sustainability, there has been a significant increase of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV) in the market. To support this increasing demand for EVs and PHEVs, challenges related to capacity planning and investment costs of public charging infrastructure must be addressed. Hence, in this paper, a capacity planning problem for charging stations is developed and aims to balance the current capital investment costs and future operational revenue. The charging station is assumed to be equipped with the solar photovoltaic (PV) panel and an energy storage system, which could be electric battery or recently invented hydropneumatic energy storage (ground-level integrated diverse energy storage (GLIDES)) system. A co-optimization model that minimizes investment and operation cost is established to determine optimal solution while considering capacity planning and following operations. EV mobility is modeled as an Erlang-loss system. Meanwhile, stochastic programming is adopted to capture uncertainties from solar radiation and charging demand of EV fleet. To provide a more general and computationally efficient model, main configuration parameters are sampled in design space and then fixed in solving the co-optimization model. Sampled parameters include EV charging slots number, PV area, capacity of energy storage system, and daily mean EV arrival number. Based on the sampled parameter combinations and its responses, black-box mappings are then constructed using surrogate models, which could provide insights for charging station placement in different practical situations. The effectiveness of the proposed surrogate modeling approach is demonstrated in numerical experiments. The results indicate better profit advantage of GLIDES over battery system with the increased power capacity

This paper demonstrates a new method by which the energy storage density of compressed air systems is increased by 56.8% by changing the composition of the compressed gas to include a condensable component. A higher storage density of 7.33 MJ/m3 is possible using a mixture of 88% CO2 and 12% N2 compared to 4.67 MJ/m3 using pure N2. This ratio of gases representing an optimum mixture was determined through computer simulations that considered a variety of different proportions from pure CO2 to pure N2. The computer simulations are based on a thermodynamic equilibrium model that predicts the mixture composition as a function of volume and pressure under progressive compression to ultimately identify the optimal mixture composition (88% CO2 + 12% N2). The model and simulations predict that the optimal gas mixture attains a higher energy storage density than using either of the pure gases.

The original version of this article unfortunately contains mistake introduced during the publishing process. The author name “M. Fayzul” should be deleted in the author group. The corrected author group is shown above.

As hydroelectric utilities move toward probabilistic risk assessments that capture the increased degradation resulting from an increase in unit starts, there is a need to capture how reliably a hydroelectric facility is being operated. Traditional statistics like EAF and EFOR are still necessary but do not capture intra-facility decisions made on a yearly or sub-yearly time frame, as it is possible to operate in the least reliable dispatch case for small periods of time and still avoid major unit outages. Our proposed Reliability Index fills this need and provides asset management decision makers with another tool for benchmarking facilities.

A 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...

There is a general need to change hydropower operational regimes to balance the growing contribution of variable renewable energy sources in power systems. Quantifying the burden on generation equipment is increasingly uncertain and difficult. Here, we propose a framework combining technical and economic indicators to analyze primary frequency control (PFC) on a timescale of seconds. We develop a model integrating hydraulic, mechanical, and electrical subsystems to characterize efficiency loss, wear and fatigue, regulation mileage, and frequency quality. We evaluate burden relief strategies under three idealized remuneration schemes for PFC, inspired by those used in Sweden, the USA, and China, respectively. We show how burden and compensation vary under future scenarios of renewable power systems. Our framework can be used by producers to develop favorable operation strategies that reduce burden and increase economic value, and by transmission system operators to provide insights on the relation between incentive structures and regulating performance., Quantifying burden on hydropower units for balancing variable renewable energy sources has been uncertain and difficult. Herein Yang et al. propose a framework and characterize the burden, performance and compensation of hydropower regulation of renewable power systems.

In this paper, a techno-economic model / cost reduction analysis of a low-cost, dispatchable / scalable, efficient Ground-Level Integrated Diverse Energy Storage (GLIDES) system is analyzed, along with a review of existing state-of-the-art energy storage technologies. The introduced technology, GLIDES, which was invented at the Oak Ridge National Laboratory (ORNL), stores energy by compression and expansion of air using water as a liquid piston inside high-pressure reservoirs. GLIDES is introduced by combining Pumped Hydroelectric Storage (PHS) and Compressed Air Energy Storage (CAES) technologies. By replacing inefficient gas compressor and turbine with higher efficiency liquid turbomachines for liquid-piston compression/expansion, GLIDES achieves higher roundtrip efficiency than other gas compression-based energy storage technologies and is a scalable energy storage system. For cost reduction purposes, various pressure reservoirs including steel vessels, carbon fiber vessels, pipe segments, and underground pressure reservoirs are analyzed in this paper. Based on the analyzed data using the models discussed in this paper, energy storage costs as low as ∼$14/kWh and ∼$346/kWh (roundtrip efficiency (RTE) ∼80%) can be achieved for a grid-scale GLIDES using depleted oil/gas reservoirs and high-pressure pipe segments respectively.

Hydraulic 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...

Aided 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...

A geodatabase of tidal constituents is developed to present the regional assessment of tidal stream power resource in the USA. Tidal currents are numerically modeled with the Regional Ocean Modeling System (ROMS) and calibrated with the available measurements of tidal current speeds and water level surfaces. The performance of the numerical model in predicting the tidal currents and water levels is assessed by an independent validation. The geodatabase is published on a public domain via a spatial database engine with interactive tools to select, query and download the data. Regions with the maximum average kinetic power density exceeding 500 W/m 2 (corresponding to a current speed of ∼1 m/s), total surface area larger than 0.5 km 2 and depth greater than 5 m are defined as hotspots and documented. The regional assessment indicates that the state of Alaska (AK) has the largest number of locations with considerably high kinetic power density, followed by, Maine (ME), Washington (WA), Oregon (OR), California (CA), New Hampshire (NH), Massachusetts (MA), New York (NY), New Jersey (NJ), North and South Carolina (NC, SC), Georgia (GA), and Florida (FL).

Hydroelectric power provides a cheap source of electricity with few carbon emissions. Yet, reservoirs are not operated sustainably, whichwedefineasmeetingsocietalneedsforwaterandpowerwhileprotectinglong-termhealthoftheriverecosystem.Reservoirs that generate hydropower are typically operated with the goal of maximizing energy revenue, while meeting other legal water requirements. Reservoir optimization schemes used in practice do not seek flow regimes that maximize aquatic ecosystem health. Here, wereviewoptimizationstudiesthatconsideredenvironmental goalsinone ofthree approaches.Thefirstapproachseeksflow regimes that maximize hydropower generation, while satisfying legal requirements, including environmental (or minimum) flows. Solutionsfromthisapproachareoftenusedinpracticetooperatehydropowerprojects.Inthesecondapproach,flowreleasesfroma dam are timed to meet water quality constraints on dissolved oxygen (DO), temperature and nutrients. In the third approach, flow releases are timed to improve the health of fish populations. We conclude by suggesting three steps for bringing multi-objective reservoir operation closer to the goal of ecological sustainability: (1) conduct research to identify which features of flow variation are essential for river health and to quantify these relationships, (2) develop valuation methods to assess the total value of river health and (3) develop optimal control softwares that combine water balance modelling with models that predict ecosystem responses to flow. Published in 2008 by John Wiley & Sons, Ltd.

Even 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...

The rapid development of multiple national geospatial datasets related to topography, hydrology, and environmental characteristics in the past decade have provided new opportunities for the refinement of hydropower resource potential from undeveloped stream-reaches. Through 2011 to 2013, the Oak Ridge National Laboratory (ORNL) was tasked by the Department of Energy (DOE) Water Power Program to evaluate the new stream-reach development (NSD) resource potential for more than 3 million US streams. A methodology was designed that contains three main components: (1) identification of stream-reaches with high energy density, (2) topographical analysis of stream-reaches to estimate inundated surface area and reservoir storage, and (3) environmental attribution to spatially join information related to the natural ecological systems, social and cultural settings, policies, management, and legal constraints to stream-reaches of energy potential. An initial report on methodology (Hadjerioua et al., 2013) was later reviewed and revised based on the comments gathered from two peer review workshops. After implementing the assessment across the entire United States, major findings were summarized in this final report. The estimated NSD capacity and generation, including both higher-energy-density (>1 MW per reach) and lower-energy-density (

Laser-Doppler-velocimetry mappings of open-water and covered dune-bed flows show how cover presence and roughness influence overall channel resistance and flow structure, including turbulence quantities. The mappings show that skin friction along the upstream slope of dunes does not decrease, and may even increase initially, in response to cover presence and increasing cover roughness. Such an increase may be the immediate mechanism whereby an ice cover causes dunes to flatten. The mappings also show that dunes modify distributions of velocity and Reynolds stress from their flat-bed shapes to the extent that the two-layer hypothesis (which suggests ice-covered flows can be treated as a sandwich of two free-surface flows) for analysis of icecovered flow is not accurate for dune-bed channels.

This paper describes the influences of floating level ice covers on flow and bed-load transport in alluvial channels and proposes a semi-empirical procedure for calculating flow resistance in such channels. The procedure can be applied in conjunction with several existing methods for estimating flow resistance in alluvial channels under open-water conditions. Previously, cover influence was poorly understood, with the consequence that flow-resistance calculations for ice-covered alluvial channels entailed an inordinate amount of guesswork. The cover influences described herein were determined from the results of experiments conducted with a laboratory flume. The main findings of the flume experiments are that ice covers increase flow depth, decrease bulk flow velocity, increase dune length, and significantly decrease bed-load transport rate. Additionally, it was found that sediment transport relationships developed for open-water flow are applicable to ice-covered flow provided they are used in conjunctio...

Spatially accurate geospatial information is required to support decision-making regarding sustainable future hydropower development. Under a memorandum of understanding among several federal agencies, a pilot study was conducted to map a subset of National Wild and Scenic Rivers (WSRs) at a higher resolution and provide a consistent methodology for mapping WSRs across the United States and across agency jurisdictions. A subset of rivers (segments falling under the jurisdiction of the National Park Service) were mapped at a high resolution using the National Hydrography Dataset (NHD). The spatial extent and representation of river segments mapped at NHD scale were compared with the prevailing geospatial coverage mapped at a coarser scale. Accurately digitized river segments were linked to environmental attribution datasets housed within the Oak Ridge National Laboratory s National Hydropower Asset Assessment Program database to characterize the environmental context of WSR segments. The results suggest that both the spatial scale of hydrography datasets and the adherence to written policy descriptions are critical to accurately mapping WSRs. The environmental characterization provided information to deduce generalized trends in either the uniqueness or the commonness of environmental variables associated with WSRs. Although WSRs occur in a wide range of human-modified landscapes, environmental data layers more » suggest that they provide habitats important to terrestrial and aquatic organisms and recreation important to humans. Ultimately, the research findings herein suggest that there is a need for accurate, consistent, mapping of the National WSRs across the agencies responsible for administering each river. Geospatial applications examining potential landscape and energy development require accurate sources of information, such as data layers that portray realistic spatial representations. « less

Tidal stream energy is one of the alternative energy sources that are renewable and clean. With the constantly increasing effort in promoting alternative energy, tidal streams have become one of the more promising energy sources due to their continuous, predictable and spatially-concentrated characteristics. However, the present lack of a full spatial-temporal assessment of tidal currents for the U.S. coastline down to the scale of individual devices is a barrier to the comprehensive development of tidal current energy technology. This project created a national database of tidal stream energy potential, as well as a GIS tool usable by industry in order to accelerate the market for tidal energy conversion technology. Tidal currents are numerically modeled with the Regional Ocean Modeling System and calibrated with the available measurements of tidal current speed and water level surface. The performance of the model in predicting the tidal currents and water levels is assessed with an independent validation. The geodatabase is published at a public domain via a spatial database engine and interactive tools to select, query and download the data are provided. Regions with the maximum of the average kinetic power density larger than 500 W/m2 (corresponding to a current speed of ~1 m/s), surface area larger than 0.5 km2 and depth larger than 5 m are defined as hotspots and list of hotspots along the USA coast is documented. The results of the regional assessment show that the state of Alaska (AK) contains the largest number of locations with considerably high kinetic power density, and is followed by, Maine (ME), Washington (WA), Oregon (OR), California (CA), New Hampshire (NH), Massachusetts (MA), New York (NY), New Jersey (NJ), North and South Carolina (NC, SC), Georgia (GA), and Florida (FL). The average tidal stream power density at some of these locations can be larger than 8 kW/m2 with surface areas on the order of few hundred kilometers squared, and depths larger than 100 meters. The Cook Inlet in AK is found to have a substantially large tidal stream power density sustained over a very large area.

The TVA Browns Ferry Nuclear Power Plant (BFNPP) withdraws its condenser cooling water from Wheeler Reservoir via an intake pumping station. Waste heat from the plant increases the condenser cooling water temperature. The heated water returns to the reservoir through three multiport diffusers containing a total of about 22,500 downstream facing ports. The ports are 2 inch diameter holes situated 6 inches apart both vertically and horizontally. The numerous small diffuser ports, the buoyancy of the heated effluent, and the large scale of the discharge area to be simulated present major challenges to developing a reliable three-dimensional computational fluid dynamics (CFD) method for predicting temperature distributions and flow patterns in Wheeler Reservoir. This paper presents an innovative CFD method and the results for numerically simulating under steady conditions the thermal discharge from the BFNPP. A finite-volume CFD package, FLUENT, was used to develop the required numerical models. The realizable k-ε turbulence model was selected for closure of the Reynolds-Averaged Navier-Stokes Equations. Second-order accurate schemes were used to discretize the governing equations. To simulate the discharge from thousands of diffuser ports, the CFD method features an innovative two-zone modeling approach, consisting of a multiple jet sectional model and an overall river model. The latter contains about 2 million computational cells for simulating the cooling water discharged from the thousands of multiport diffuser ports. The multiple jet sectional model, which simulates the flow from the individual discharge ports over a one foot slice of the diffuser pipe in great detail, provides the information for developing sectional boundary conditions for the diffuser effluent. These boundary conditions are then used in the overall river model for the whole study area. Validation and verification of the CFD models were performed using data from field measurements, hydraulic model tests, and other experiments. Comparisons between the CFD results and these data showed that the overall river model, based on the two-zone approach, can reproduce the major features of temperature and flow in the diffuser mixing zone. Model limitations exist, however, for low river flows, where requirements for steady behavior impede solution convergence.