Wave Energy and Ocean-Thermal Energy: Resource Assessment ...

Wave Energy and Ocean-Thermal Energy: Resource Assessment and Modeling

Luis A. Vega, Ph.D.

Hawaii National Marine Renewable Energy Center (HINMREC) Hawaii Natural Energy Institute (HNEI)

School of Ocean and Earth Science and Technology (SOEST) University of Hawaii at Manoa (UH)

Energy Ocean International

June 10, 2013

http://hinmrec.hnei.hawaii.edu/

Table of Contents

•Ocean Thermal Resources; •OTEC Sustainable Resources.

•Fine Resolution Wind-Wave Model; •Extreme Wave Analysis for Survival Design; •WETS Waverider Data Analysis; •On-Line Wave Forecasting •WEC Array Interactions.

2 HINMREC

Ocean-Thermal Resources

• Mapping of world-wide theoretical resource (∆T) available through webpage;

• Will display technical resource (annual electricity

generation with 100 MW OTEC plant) at user-selected location.

3 HINMREC

Theoretical Resource → Technical

Resource → Practical Resource

ηconversion ηconversionsocial, economic, regulatory, environment filters

Technical Resource: 100 MW OTEC Plant Annual Electricity Generation (GWh) Baseline: 877 GWh/year @ ∆T = 20 °C

Color palette 15°C to 25°C

Theoretical Resource: Annual Average ∆T (T20m – T1000m)

Ocean Thermal (OTEC) Resource

4 HINMREC

HOTS Data at Station ALOHA (22°45'N, 158°W)HYCOM Computations (22.7°N, 158°W)

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2007 2008 2009 2010 2011 2012 2013

Year

Tem

pera

ture

Diff

eren

ce (°

C)

HOTS Data

HYCOM { 20 m - 1000 m } 1/12° resolution

5 HINMREC

∆T Model Verification

Extractable Global OTEC Resources • Goal: use Ocean-General-Circulation-Model to

assess global OTEC resources;

– [Could ocean thermal energy be extracted on a global scale at unsustainable rates?]

– [Could ocean thermal energy be extracted world-wide at environmentally detrimental rates?]

• Global OTEC development modeled with sources and sinks in simulations of increasing resolution (4° x 4°, 15 layers; 1° x 1°, 23 layers).

6 HINMREC

Global OTEC Net Power (TW)

0

10

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30

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0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80w cw (m/yr)

No feedback (zero-flow) - 4 degree resolutionNo feedback (zero-flow) - 1 degree resolution1-D Model (Nihous, JERT 129, 2007)MITgcm - 4 degree resolutionMITgcm - 1 degree resolution

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Full Deployment: 14 TW from 250,000 Plants throughout OTEC Region (Currently world wide: 18 TW)

HINMREC 8

Resource Perspective:

Average OTEC Power Density (kW/km2)

wcw = Qcw/Area = 20 m/year

(1° grid)

Year: 0 Current resource & all 250,000 plants operational

Resource degradation stable after 200 years

Environment Perspective:

Temperature Changes (°C ) wcw = 20 m/yr; 1° grid

200 years after full deployment (OTEC region boundary shown)

Surface Layer

Deep Water Intake Layer

9 HINMREC

Wind-Wave Model Validation Global Wave Data (10-years)

- 1°×1.25° (~110 km × 100 km) resolution

- Measurements at 19 Buoys Root-mean-square error = 0.48 m 90% Confidence Interval = ±0.71 m

- Altimetry (satellite) data

Root-mean-square error = 0.39 m 90% Confidence Interval = ±0.62 m

Hawaii Wave Data (10-years)

- 5.5 km × 5.5 km resolution

- Measurements at 12 Buoys Root-mean-square error = 0.37 m 90% Confidence Interval = ±0.51 m

- Altimetry (satellite) data

Root-mean-square error = 0.40 m 90% Confidence Interval = ±0.56 m

20 years Hindcast: Wave Power Flux (kW/m)

High-resolution data including coastal processes

• 0.55 km for Oahu and Kauai • 1.1 km for Maui and Hawaii Island

• Examples: - Occurrence of events larger

than 15 kW/m - Statistics at potential

deployment sites (input to performance analysis)

Wave Power Flux Probability Density Function

SWAN (nearshore) Calibration: Oahu

Swell event 2005 February (before WETS) • 270 m resolution

• Subtle differences at 51201 and 51202 with SWAN implementation of diffraction

1. SWAN’s implementation of diffraction needs improvement 2. Dissipation mechanism needs calibration

• Further analysis is needed to determine the cause large discrepancies for isolated

events

Hs Validation Waverider at WETS

•5 months of data at WETS • RMSE ~0.26 m • Swell events peaks are underestimated • Overestimate small waves • Underestimate large waves • 80% of the data is within ±10%

Hs-Wave Statistics at WETS • Bi-modal seas: Swells from N (1 to 2.5 m) Wind waves from the ENE (1.5 to 4 m) • Largest waves are sheltered at site

Tp- Wave Statistics WETS • Bi-modal seas: Swells N (12 to 18 s) Wind waves from the ENE (6 to 8 s) • Model predicts E wind waves to be more Easterly than the buoy

Real-time Monitoring/Forecast at WETS

Current forecast and previous forecast runs (oceanforecast.org) -5 days of previous runs and 7.5 day forecast

Wave Array Interference Model

Computer code to determine the site-specific power extracted by arbitrary arrays of WEC devices (and estimate ocean area requirements);

Status: Given wave climate and basic Oscillating Water Column (OWC) system properties, array wave power extraction can be predicted;

Ongoing: Conduct interference studies with different systems (e.g., Point Absorber, Pelamis).

17 HINMREC

Circular Array: Power (2009)

30 OWC WEC Devices in Array

Rectangular Array: Pneumatic power (kW)

[Mechanical power ~ 50%]

Kilauea (2009)

Kaneohe (2009)

30 OWC WEC Devices 200 m x 60 m

meters 19 HINMREC

Hawaii Electricity Demand: Contribution Potential Island Wave Farm Challenge OTEC Challenge

Oahu < 17% Siting: requires all shoreline segments;

Storage: intermittent resource

>> 100% No prototype operational data

Maui < 75%

“ >> 100%

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Hawaii < 150%

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>> 100%

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Kauai < 300%

Siting: requires 30% shoreline segments;

Storage: intermittent resource

>> 100%

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Molokai < 2000%

Storage: intermittent resource

>> 100%

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