Regional Analysis Tutorial

Learn how to analyze OTEC potential for specific geographic regions using real oceanographic data from CMEMS.

Prerequisites

OTEX installed (pip install otex)
CMEMS credentials configured (see Installation Guide)
Internet connection for data download

Overview

Regional analysis in OTEX:

Downloads temperature profiles from CMEMS for your region
Identifies feasible OTEC sites (adequate water depth)
Sizes plants for each site based on local conditions
Calculates LCOE considering distance to shore
Generates time-resolved power profiles

Available Regions

OTEX includes a bundled database of pre-defined regions covering tropical areas worldwide:

from otex.data import load_regions

regions = load_regions()
print(regions.head(20))

Popular regions include:

Caribbean: Jamaica, Cuba, Dominican Republic, Puerto Rico, Bahamas
Pacific: Hawaii, Philippines, Fiji, Guam, Samoa
Indian Ocean: Mauritius, Maldives, Seychelles, Reunion
Southeast Asia: Indonesia, Malaysia, Vietnam
Africa: Kenya, Tanzania, Mozambique

Basic Usage

Command Line

After installing OTEX via pip, the otex-regional command is available:

# Analyze Jamaica with default settings (136 MW, low_cost, 2020)
otex-regional Jamaica

# Specify plant size and year
otex-regional Jamaica --power -50000 --year 2021

# Use different cycle and cost assumptions
otex-regional Philippines --cycle kalina --cost high_cost

Python API

from otex.regional import run_regional_analysis

# Run analysis
otec_plants, sites_df = run_regional_analysis(
    studied_region='Jamaica',
    p_gross=-50000,          # 50 MW
    cost_level='low_cost',
    year=2020,
    cycle_type='rankine_closed',
    fluid_type='ammonia',
    use_coolprop=True
)

Step-by-Step Guide

Step 1: Choose Your Region

First, verify your region exists in the database:

from otex.data import load_regions

regions = load_regions()
print(regions[regions['region'].str.contains('Jam', case=False)])

Output:

         region   north    east  south    west    demand
Jamaica  19.358 -74.009 14.083 -80.833  3.092992

Step 2: Check Available Sites

View potential OTEC sites in your region:

from otex.data import load_sites

sites = load_sites()
jamaica_sites = sites[sites['region'] == 'Jamaica']

print(f"Total sites in Jamaica: {len(jamaica_sites)}")
print(f"Water depth range: {jamaica_sites['water_depth'].min():.0f} to {jamaica_sites['water_depth'].max():.0f} m")
print(f"Distance to shore: {jamaica_sites.iloc[:, 4].min():.1f} to {jamaica_sites.iloc[:, 4].max():.1f} km")

Step 3: Run the Analysis

from otex.regional import run_regional_analysis

otec_plants, sites_df = run_regional_analysis(
    studied_region='Jamaica',
    p_gross=-50000,
    year=2020,
)

This will:

Download CMEMS temperature data (~5-15 minutes first time)
Process and cache data locally
Run OTEC sizing for all valid sites
Calculate LCOE for each site
Save results to Data_Results/Jamaica/

Step 4: Examine Results

import pandas as pd
import matplotlib.pyplot as plt

# Load results
results = pd.read_csv(
    'Data_Results/Jamaica/Jamaica_2020_50.0_MW_low_cost/OTEC_sites_Jamaica_2020_50.0_MW_low_cost.csv',
    sep=';',
    index_col='id'
)

print(results.head())
print(f"\nNumber of feasible sites: {len(results)}")
print(f"LCOE range: {results['LCOE'].min():.2f} - {results['LCOE'].max():.2f} ct/kWh")
print(f"Best site LCOE: {results['LCOE'].min():.2f} ct/kWh")

Step 5: Visualize Results

import matplotlib.pyplot as plt

fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# LCOE map
ax1 = axes[0, 0]
scatter = ax1.scatter(
    results['longitude'],
    results['latitude'],
    c=results['LCOE'],
    cmap='RdYlGn_r',
    s=50
)
plt.colorbar(scatter, ax=ax1, label='LCOE (ct/kWh)')
ax1.set_xlabel('Longitude')
ax1.set_ylabel('Latitude')
ax1.set_title('LCOE by Location')

# LCOE histogram
ax2 = axes[0, 1]
ax2.hist(results['LCOE'], bins=20, edgecolor='white')
ax2.axvline(results['LCOE'].median(), color='red', linestyle='--', label='Median')
ax2.set_xlabel('LCOE (ct/kWh)')
ax2.set_ylabel('Number of sites')
ax2.set_title('LCOE Distribution')
ax2.legend()

# Net power vs LCOE
ax3 = axes[1, 0]
ax3.scatter(results['p_net_nom'], results['LCOE'], alpha=0.6)
ax3.set_xlabel('Net Power (MW)')
ax3.set_ylabel('LCOE (ct/kWh)')
ax3.set_title('Net Power vs LCOE')

# Temperature difference
ax4 = axes[1, 1]
delta_T = results['T_WW_med'] - results['T_CW_med']
ax4.scatter(delta_T, results['LCOE'], alpha=0.6)
ax4.set_xlabel('Temperature Difference (°C)')
ax4.set_ylabel('LCOE (ct/kWh)')
ax4.set_title('ΔT vs LCOE')

plt.tight_layout()
plt.savefig('jamaica_analysis.png', dpi=150)
plt.show()

Understanding the Output Files

OTEC Sites CSV

Column	Description	Unit
id	Site identifier	-
longitude	Site longitude	degrees
latitude	Site latitude	degrees
p_net_nom	Nominal net power	MW
AEP	Annual Energy Production	GWh
CAPEX	Capital expenditure	$M
LCOE	Levelized cost of energy	ct/kWh
Configuration	Optimal ΔT configuration	-
T_WW_min/med/max	Warm water temperature stats	°C
T_CW_min/med/max	Cold water temperature stats	°C

Power Profiles CSV

Daily average net power output over the year:

profiles = pd.read_csv(
    'Data_Results/Jamaica/.../net_power_profiles_per_day_Jamaica_2020_50.0_MW_low_cost.csv',
    sep=';',
    index_col=0,
    parse_dates=True
)

# Plot annual profile
profiles.plot(figsize=(12, 4))
plt.ylabel('Net Power (kW)')
plt.title('Average Daily Net Power Output')
plt.show()

Advanced Options

Custom Plant Size

from otex.regional import run_regional_analysis

# Analyze different plant sizes
for size_mw in [20, 50, 100, 200]:
    run_regional_analysis(
        studied_region='Jamaica',
        p_gross=-size_mw * 1000,
        year=2020
    )

Different Thermodynamic Cycles

from otex.regional import run_regional_analysis

# Compare cycles
cycles = ['rankine_closed', 'kalina', 'uehara']
for cycle in cycles:
    run_regional_analysis(
        studied_region='Jamaica',
        p_gross=-50000,
        cycle_type=cycle
    )

Multi-Year Analysis

# Analyze multiple years via CLI
for year in 2018 2019 2020 2021; do
    otex-regional Jamaica --year $year
done

Combining with Uncertainty Analysis

After regional analysis, run uncertainty analysis on the best site:

import pandas as pd
from otex.analysis import MonteCarloAnalysis, UncertaintyConfig

# Load regional results
results = pd.read_csv('...OTEC_sites_Jamaica_2020_50.0_MW_low_cost.csv', sep=';')

# Find best site
best_site = results.loc[results['LCOE'].idxmin()]
print(f"Best site: ({best_site['longitude']}, {best_site['latitude']})")
print(f"T_WW: {best_site['T_WW_med']:.1f}°C, T_CW: {best_site['T_CW_med']:.1f}°C")

# Run uncertainty analysis for this site
config = UncertaintyConfig(n_samples=500, seed=42)
mc = MonteCarloAnalysis(
    T_WW=best_site['T_WW_med'],
    T_CW=best_site['T_CW_med'],
    config=config,
    p_gross=-50000
)
ua_results = mc.run()

stats = ua_results.compute_statistics()
print(f"\nLCOE with uncertainty:")
print(f"Mean: {stats['lcoe']['lcoe_mean']:.2f} ct/kWh")
print(f"90% CI: [{stats['lcoe']['lcoe_p5']:.2f}, {stats['lcoe']['lcoe_p95']:.2f}]")

Performance Tips

First run is slower: Data download and processing takes 5-15 minutes
Subsequent runs are faster: Processed data is cached in HDF5 files
Reduce memory usage: Use smaller regions or reduce spatial resolution
Parallel processing: Enabled by default for Monte Carlo

Troubleshooting

“No valid sites found”

Check that your region has sufficient water depth:

from otex.data import load_sites

sites = load_sites()
region_sites = sites[sites['region'] == 'YourRegion']
print(f"Depths: {region_sites['water_depth'].describe()}")

Sites need water depth of at least 600-1000m.

Download failures

Verify CMEMS credentials: copernicusmarine login --check
Check internet connection
Try again later (CMEMS servers may be busy)

Memory errors

Reduce plant size or use a smaller region.