TODAY’S STUDY: 100 MEGAWATT POWER PLANTS FROM THE OCEAN’S TEMPERATURE GRADIENT

Modeling the Physical and Biochemical Influence of Ocean Thermal Energy Conversion Plant Discharges into their Adjacent Waters

Pat Grandelli, et. al., 29 September 2012 (Makai Ocean Engineering)

Executive Summary

This paper describes the modeling work by Makai Ocean Engineering, Inc. to simulate the biochemical effects of of the nutrient-enhanced seawater plumes that are discharged by one or several 100 megawatt OTEC plants. The modeling is needed to properly design OTEC plants that can operate sustainably with acceptably low biological impact.

Ocean Thermal Energy Conversion (OTEC) uses large flows of warm tropical seawater and cold deep seawater to generate non-polluting electric power. The magnitude of the global OTEC resource dwarfs that of other other marine renewable energy technologies, and OTEC power is non-intermittent, making it suitable for utilities and manufacturing. Small demonstration OTEC plants using commercially-available equipment have generated 50 - 270 kilowatts of electricity.

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Recent advances in offshore design and cold water pipe technologies have renewed interest in developing large 100 megawatt plants that would be cost-competive with local island utilities. Such plants would have several seawater pumps, each equivalent to tugboat engines, that would guide 750 tonnes per second of seawater thorugh the OTEC system.

At most potential OTEC sites, the tropical ocean is thermally stratified into a well-mixed warm upper layer overlying cooler and denser seawater. This stratification hinders the supply of nutrients upwelled into the photic zone, which results in a nutrient-limited "oligotrophic" phytoplankton community having low biological productivity. Discharging deep seawater nutrients (primarily nitrates) into the upper waters from the OTEC plant could potentially enhance phytoplankton growth, shift community species composition, or cause algal blooms. It is desirable to discharge the seawater flows deep enough so that the discharged nutrients are diluted and remain below the photic zone. Thus, the size and depth of the large seawater ducts affect both the overall architecture of an OTEC plant as well as the extent of the perturbation to the ambient phytoplankton populations.

In order to quantify the effect of discharge configuration and phytoplankton response, Makai Ocean Engineering implemented a biological and physical model for the waters surrounding O`ahu, Hawai`i, using the EPA-approved Environmental Fluid Dynamics Code (EFDC). Each EFDC grid cell was approximately 1 square kilometer by 20 meters deep, and used a time step of three hours. The biological model was set up to simulate the biochemical response for three classes of organisms: Picoplankton (< 2 um) such as prochlorococccus, nanoplankton (2-20 um), and microplankton (> 20 um) e.g., diatoms. The dynamic biological phytoplankton model was calibrated using chemical and biological data collected for the Hawaii Ocean Time Series (HOTS) project. Peer review of the biological modeling was performed by Dr. John Hamrick, the author of EFDC, and by Dr. Matt Church of the University of Hawai'i, a leading marine microbiologist.

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The physical oceanography model uses boundary conditions from a surrounding Hawai'I Regional Ocean Model, (ROM) operated by the University of Hawai`i and the National Atmospheric and Oceanic Administration. The ROM provided tides, basin scale circulation, mesoscale variability, and atmospheric forcing into the edges of the EFDC computational domain. This model is the most accurate and sophisticated Hawai'ian Regional Ocean Model presently available, assimilating real time oceanographic observations, as well as model calibration based upon temperature, current and salinity data collected during 2010 near the simulated OTEC site. The ROM program manager, Dr. Brian Powell of the University of Hawai'i, peer-reviewed Makai's implementation of the ROM output into our EFDC model. The supporting oceanographic data was collected for a Naval Facilities Engineering Command / Makai project.

Using these models, the negatively-buoyant discharge flows were simulated by a dynamically coupled Lagrangian jet-plume entrainment model in the near-field, and by dynamic oceanic circulation and turbulence in the far-field. The result is a three-dimensional time-dependent model of the oceanic circulation, nutrient concentration due to natural variability and OTEC operation, with corresponding phytoplankton growth dynamics. This is the most sophisticated and realistic plume model yet developed for OTEC.

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Results:

The model was run for a 100 MW OTEC Plant consisting of four separate ducts, discharging a total combined flow rate of 420 m3/s of warm water and 320 m3/s of cold water in a mixed discharge at 70 meters deep. Each duct was assumed to have a discharge port diameter of 10.5m producing a downward discharge velocity of about 2.18 m/s. The natural system, as measured in the HOTS program, has an average concentration of 10-15 mgC/m3. To calibrate the biological model, we first ran the model with no OTEC plant and varied biological parameters until the simulated data was a good match to the HOTS observations. This modeling showed that phytoplankton concentration were patchy and highly dynamic. The patchiness was a good match with the data variability observed within the HOTS data sets. We then ran the model with simulated OTEC intake and discharge flows and associated nutrients.

Directly under the OTEC plant, the near-field plume has an average terminal depth of 172 meters, with a volumetric dilution of 13:1. The average terminal plume temperature was 19.8oC. Nitrate concentrations are 1 to 2 umol/kg above ambient. The advecting plume then further dilutes to less than 1 umol/kg above ambient within a few kilometers downstream, while remaining at depth.

Because this terminal near-field plume is well below the 1% light limited depths (~120m), no immediate biological utilization of the nutrients occurs. The figure below shows a typical model result at 1440 on May 25th, 2010. Phytoplankton concentration at 100 meters depth is shown by the green patches that denote natural variations of between 0.010 and 0.020 milligrams carbon per cubic meter. The light blue patches delineate the slightly elevated picoplankton levels caused by the 100 MW OTEC discharge plume, raising the concentration by 0.001 or 0.0015 milligrams carbon per cubic meter. Due to varying ocean circulation, this zone of perturbation at 100 meters depth will dynamically shift location, increase in concentration, or vanish. No perturbation occurs in the upper 40 meters of the ocean.

As the nitrate is advected and dispersed downstream, a fraction of the deep ocean nutrients (< 0.5 umol/kg perturbation) mix upward where they are utilized by the ambient phytoplankton population. This occurs approximately twenty-five kilometers downstream from the plant at 110 - 70 meters depth. For pico-phytoplankton, modeling results indicate that this nutrient perturbation causes a phytoplankton perturbation of approximately 1 mgC/m3 (~10% of average ambient concentrations) that covers an area 10x5 km in size at the 70 to 90m depth. Thus, the perturbations are well within the natural variability of the system, generally corresponding to a 10 to 15% increase above the average pico-phytoplankton biomass. This perturbation exhibits a meandering horizontal plume trajectory and spatial extent, but remains similar in magnitude (generally 1-2 mgC/m3).

The diatom perturbations become more noticeable after three weeks of the simulation period, when the nearshore diatom population trends towards a greater concentration of 1 to 3 mgC/m3 . Relative to the background concentrations, this increased response is a fraction of the ambient, with perturbations remaining within fluctuations of the existing system. The perturbations were quantified by post-processing each time-step of model simulations without OTEC plants, with identical simulations that included OTEC plumes. Without this post processing, the 10-25% perturbations were obscured by the larger dynamic variations naturally caused by ocean circulation. Convenient summaries of the data can be viewed in Table 2 and Figure 100 of this report.

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Accomplishments Compared to Goals & Objectives:

This modeling effort has successfully attained its goals and objectives, as listed below.

1. Process data from an oceanographic array measuring continuous ocean current profiles, temperatures and conductivity at the Makai/Lockheed Martin initial OTEC site in West Oahu. Completed.

2. Use these data to improve the accuracy and calibrate the local output from a newly developed regional ocean model, HIROM, which is part of an integrated global program funded by NOAA and other government agencies. Completed.

3. Use the newly calibrated HIROM boundary conditions to force Makai’s OTEC Hydrodynamic Model, developed with State of Hawaii funds (CEROS), under various OTEC design configurations and operating conditions. Completed.

4. Implement eutrophication modeling within the OHM to define the effect of the nutrient-rich and low oxygen deep seawater on increased productivity of phytoplankton. Completed.

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Summary:

This study has showed that the biochemical response of OTEC discharges can be modeled, quantified, and dynamically visualized for OTEC plants having different discharge configurations. We now have an extremely useful tool for use by OTEC regulators and designers.

In all cases modeled (discharge at 70 meters depth or more), no perturbation occurs in the upper 40 meters of the ocean's surface. The picoplankton response in the 110 - 70 meter depth layer is approximately a 10-25% increase, which is well within naturally occurring variability.

The nanoplankton response is negligible. The enhanced productivity of diatoms (microplankton) is small, but this additional "standing stock" may potentially enhance growth if the plume water subsequently advects into nearshore water.

Another significant finding is that detecting the plume of an OTEC pilot plant, as envisioned for the "NAVFAC OTEC Pilot Plant", will require many more samples in time and space than was originally envisioned, because ocean variability is greater than anticipated.

Finally, the model does not attempt to calculate the higher order trophic levels where fauna consume the phytoplankton, but these results could be readily extended to this purpose. The subtle phytoplankton increase of our baseline design suggests that higher-order effects will be very small.

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