Green Hydrogen Hybrid Energy Simulator

Overview

This application is a specialized simulation tool designed to model, analyze, and optimize Off-Grid Wind and Solar Hybrid Systems for Green Hydrogen production.

It was developed to support research into scaling renewable energy systems in regions like Namibia, where maximizing the efficiency of Hydrogen production ($H_2$) is critical. The tool integrates real-world meteorological data from NASA with industry-standard physics models to predict exactly how much energy and hydrogen a specific site can produce.

Tool Workflow

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How to Use the Tool

1. Configuration (Data Ingestion)

• Step 1: Enter the Latitude and Longitude of your site (eg -22.5, 17.0 for Namibia).
• Step 2: Select the Hub Height (eg 100m). This is crucial because wind blows stronger at higher altitudes.
• Step 3: Select the Date Range (eg 2024-01-01 to 2024-12-31).
• Step 4: Click "Generate Plots".
  • What happens: The tool connects to the NASA POWER API and downloads hourly data for Wind Speed, Solar Irradiance, Temperature, and Pressure.

2. Resource Analysis (Plots)

Once data is loaded, explore the tabs to understand the site's potential:

• Time Series: View the raw Wind and Solar availability hour-by-hour.
• Diurnal Average: See the "Daily Profile" (eg Does the wind blow at night? Is the sun strong at noon?).
• Monthly Profile: Check for seasonal shortages (eg Is August a low-wind month?).

3. Turbine Model Selection

• Go to the "Turbine Model" tab.
• Select a real-world machine (eg Vestas V90 3.0 MW).
• The graph will show you the Power Curve:
  • Blue Region: Wind is too low (Cut-in).
  • Green Region: Power increases with wind speed.
  • Orange Region: Maximum rated power (Plateau).
  • Red Region: Storm shutdown (Cut-out).
• Verification: The bottom graph shows exactly how much power this single turbine would have generated at your specific site last year.

4. H2 Simulation (The Core Engine)

• Go to "H2 Simulation".
• System Specs: The tool auto-fills parameters based on your selected turbine.
  • Enter Solar Area ($m^2$).
  • Enter Electrolyzer Capacity (MW).
• Click "Calculate H2 Production".
• Results:
  • Total Hydrogen: Metric Tons produced in the year.
  • Waste (Curtailment): Percentage of energy lost because the electrolyzer was too small to handle the peak power.
  • Specific Period Analysis: You can zoom in to calculate production for a specific week or day.

5. Smart Sizer (Optimization Engine)

• Don't know how many turbines to buy? Use the Algorithm.
• Go to "Smart Sizer".
• Enter your Goal: "I want 100 Tons of H2 per year."
• Enter Costs (optional estimates).
• Click "Auto-Size System".
• The Algorithm will run thousands of simulations to find the cheapest configuration that guarantees reliable production (even at night).
• Click "Load Values" to transfer the Algorithm's best design back to the Simulation tab.

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1. Technology Stack

This tool is built using Python 3, leveraging powerful scientific libraries for precision and speed.

• Language: Python 3.12+
• User Interface (GUI): PyQt5 (Professional desktop framework).
• Data Processing: Pandas & NumPy (Vectorized calculations for handling 8,760 hourly data points instantly).
• Visualization: Matplotlib (Scientific plotting engine).
• Optimization (Algorithm): SciPy (differential_evolution algorithm for multi-objective optimization).
• Data Source: Requests (To fetch data from NASA).

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2. Data Ethics & Sourcing

The meteorological data used in this tool is sourced from the NASA POWER (Prediction of Worldwide Energy Resources) Project.

• Is this Ethical? Yes. The NASA POWER project is an Open Data initiative funded by the US Government. Its specific mission statement is to "improve the nation's and the world's ability to use NASA Earth Observations for... Renewable Energy, Sustainable Buildings, and Agroclimatology."
• Why we use it:
  1. Democratization: It provides developing nations (like Namibia) with high-quality satellite data without requiring expensive physical weather stations.
  2. Sustainability: It directly supports the UN Sustainable Development Goals (SDG 7: Affordable and Clean Energy).
  3. Transparency: The data is public domain, ensuring that our simulation results are reproducible by anyone, anywhere.

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3. Guide to Reading the Plots

Tab 1: Time Series

• Purpose: The raw heartbeat of the site.
• X-Axis: Date (Jan 1 to Dec 31).
• Left Y-Axis (Blue): Wind Speed ($m/s$).
• Right Y-Axis (Orange): Solar Irradiance ($Wh/m^2$).
• What to look for: Gaps where the Blue line is low and Orange is zero (Night). These are the "Danger Zones" for an off-grid system.

Tab 2: Diurnal Average

• Purpose: The daily routine.
• X-Axis: Hour of the Day (0 = Midnight, 12 = Noon).
• Y-Axis: Average resource strength.
• What to look for: Does the wind peak at night (good for solar complement) or during the day (bad, causes congestion)?

Tab 3: Wind Duration Curve

• Purpose: Reliability check.
• X-Axis: Percentage of Time (0% to 100%).
• Y-Axis: Wind Speed ($m/s$).
• What to look for: If the line drops below 3 m/s (Cut-in speed) at the 50% mark, it means your turbines will be OFF half the time.

Tab 6: Turbine Model

• Purpose: Real-world machine performance.
• Top Graph (Theoretical): Shows the manufacturer's Power Curve.
  • Red/Orange Zones: Where the turbine makes money.
  • Blue Zone: Wind is too weak.
• Bottom Graph (Actual Generation):
  • Blue Line: Actual wind speed at the site.
  • Orange Line: Actual MW power produced.
  • Note: Observe how the Orange line drops to ZERO whenever the Blue line goes above 25 m/s (Storm Safety).

Tab 8: Hybrid Power Series

• Purpose: System Interaction.
• Blue Line: Wind Power (MW).
• Orange Line: Solar Power (MW).
• Red Dashed Line: Electrolyzer Capacity limit.
• Green Line: Actual Electricity Used.
• Insight: Any Blue/Orange peaks above the Red line represent Wasted Energy.

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4. Backend Logic & Formulas

A. Data Preprocessing (Scaling)

NASA provides wind speed at 10 meters height ($v_{10}$). Turbines stand at 100 meters ($h$). We use the Power Law to scale the wind speed:

$$ v_h = v_{10} \times \left( \frac{h}{10} \right)^\alpha $$

• $\alpha$ (Shear Exponent) is set to 0.143 (Standard for open terrain).

B. Wind Power Calculation

The tool uses a 4-Region Realistic Model, which is more accurate than the theoretical cubic formula found in general textbooks.

• Region 1 (Below Cut-in): If $v < v_{in}$, Power = 0.
• Region 2 (Ramp Up): If $v_{in} \le v < v_{rated}$: $$ P_{wind} = P_{rated} \times \left( \frac{v - v_{in}}{v_{rated} - v_{in}} \right)^3 $$
  • Reference: This is the applied form of Equation 2 ($P = \frac{1}{2}\rho A v^3 C_p$) calibrated to the specific machine's rated curve.
• Region 3 (Rated Plateau): If $v_{rated} \le v < v_{out}$, Power = $P_{rated}$.
• Region 4 (Cut-out): If $v \ge v_{out}$, Power = 0 (Safety Shutdown).

C. Solar Power Calculation

$$ P_{solar} (MW) = \frac{Irradiance (Wh/m^2) \times Area (m^2) \times Efficiency}{1,000,000} $$

D. Hydrogen Production

Based on Equation 3 from the research paper:

$$ H_2 (kg) = \frac{\text{Usable Energy (MWh)} \times 1000}{\text{Specific Energy Consumption (kWh/kg)}} $$

• Usable Energy: This is the lesser of (Wind + Solar Generation) OR (Electrolyzer Capacity).
• Energy Consumption: Defaults to 55 kWh/kg (Standard efficiency for PEM electrolyzers).

E. The "Smart Sizer" Logic (Algorithm)

The Algorithm uses a Differential Evolution (Genetic Algorithm). It does not just look for the cheapest setup; it looks for the most Stable one.

• The "Night Shift" Rule: The Algorithm is penalized if the system produces near-zero hydrogen at night. This forces it to install Wind Turbines (since Solar is useless at night).
• Waste Penalty: It avoids building systems that waste > 35% of energy.
• Target Constraint: It ensures the yearly production meets your requested tonnage exactly.

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