Chapter 6 Sustainable Engineering: Renewal Energy

Chapter 6 Sustainable Engineering: Renewal Energy#

Introduction: Renewal Energy
Simulation: Wind Energy
Simulation: Solar Energy
Simulation: Wave Energy
Self-Assessment

1. Introduction#

Descriptive alt text for accessibility — Fig. 46 **Figure 6.14 **: Renewal Energy#

🌱 Renewable Energy: Definition, Role in Energy Security & Regional Potential#

📘 What Is Renewable Energy?#

Renewable energy refers to power derived from natural sources that are continuously replenished. These include:

Solar energy (sunlight)
Wind energy (air movement)
Wave energy (ocean surface motion)
Hydropower, geothermal, and biomass are also part of the renewable family.

Unlike fossil fuels, renewables do not deplete over time and produce minimal greenhouse gas emissions.

🛡️ How Renewables Enhance Energy Security#

Energy security means reliable, affordable, and uninterrupted access to energy. Renewables contribute by:

Reducing dependence on imported fossil fuels
Diversifying the energy mix, making grids more resilient
Decentralizing generation, which protects against geopolitical and supply chain disruptions
Improving grid stability through distributed systems and storage

🔋 Role of Specific Sources#

Source	Contribution to Energy Security
Solar	Localized generation (rooftops, farms), predictable output
Wind	Large-scale generation, especially in windy regions
Wave	Coastal resilience, consistent energy from ocean motion

🌍 Regional Potential of Wind, Solar & Wave Energy#

🌬️ Wind Energy Potential#

Region	Highlights
United States	Over 136 GW installed; Texas leads with 19,000+ turbines
Europe	Denmark, Germany, and UK are leaders in onshore/offshore wind
China	Largest global wind capacity; aggressive offshore expansion
India	Strong potential in Tamil Nadu and Gujarat

☀️ Solar Energy Potential#

Region	Highlights
Southwest US	Nevada, Arizona, and New Mexico have highest solar irradiance
California	Leading solar installations; rooftop and utility-scale
India	High solar potential across most states; major national initiatives
Middle East	Desert regions ideal for large-scale solar farms

🌊 Wave Energy Potential#

Region	Highlights
Alaska (US)	Highest recoverable wave energy: ~620 TWh/year
West Coast (US)	Oregon, California, Washington have strong wave resources
Hawaii & Puerto Rico	Consistent wave patterns; ideal for island resilience
Europe	UK, Ireland, and Portugal exploring commercial wave projects

🇺🇸 Renewable Energy Potential in the United States#

Source	Estimated Recoverable Potential (TWh/year)
Wind	>10,000 TWh/year (onshore + offshore)
Solar	Varies by region; Southwest exceeds 6 kWh/m²/day
Wave	~1,170 TWh/year (West Coast, Alaska, Hawaii, East Coast)

⚡ Comparative Overview: Wind, Solar & Wave Energy#

Attribute	🌬️ Wind Energy	☀️ Solar Energy	🌊 Wave Energy
Intermittency	Medium — seasonal & diurnal variability	High — affected by clouds/night cycles	Low — ocean motion is fairly consistent
Cost (per kWh)	\(0.02–\)0.05 (onshore)	\(0.03–\)0.06 (utility-scale solar)	~\(0.15–\)0.30 (pilot-scale)
CapEx / OpEx	High upfront, low maintenance	Medium upfront, low operating costs	Very high upfront, emerging maintenance needs
Scalability	Excellent — utility-scale, offshore farms	Excellent — utility & distributed systems	Good — coastal deployments, island grids
Maturity	Mature — proven tech & economies of scale	Mature — widely deployed & improving	Emerging — mostly demo/pilot projects
Land Use	Land-friendly (above crops, offshore)	Moderate — rooftops, solar farms	Minimal — ocean surface use
Grid Integration	Requires storage or backup	Requires storage & smart grid	Compatible with coastal microgrids

🧭 Use Case Matching by Region (U.S. Context)#

Region	Best Fit Source(s)	Notes
Southwest (AZ, NM)	☀️ Solar	Highest solar irradiance in U.S.
Great Plains (TX, OK, KS)	🌬️ Wind	Strong, consistent wind patterns
Pacific Coast (CA, OR, WA)	🌬️ Wind + 🌊 Wave	Wind-rich plus viable wave testing areas
Alaska	🌊 Wave + 🌬️ Wind	Wave energy potential > 600 TWh/year
Southeast (FL, GA)	☀️ Solar + 🌬️ Wind (offshore)	Solar coverage year-round; coastal wind
Urban Northeast (NY, MA)	☀️ Rooftop Solar + 🌬️ Small Wind	Urban mix with community grid integration
Islands (HI, PR)	🌊 Wave + ☀️ Solar	Resilience from multiple local sources

🔮 Scenario Prompts for Planning Worksheets#

Urban Planning: What renewable mix optimizes roof space and grid resilience?
Rural Utility: Should community wind or solar farms lead the development?
Island Systems: What is the role of wave energy in grid-independent regions?
Disaster Preparedness: Which renewable source offers most reliability in storms?
Seasonal Optimization: How do regional wind and solar cycles affect design?

🔮 Future of Renewable Energy#

📈 Global Outlook#

Renewables expected to supply nearly 50% of global electricity by 2030
Solar and wind dominate new capacity additions
Battery storage and smart grids will enable 24/7 clean power

🇺🇸 U.S. Outlook#

Renewables already supply 44% of electricity (including nuclear)
Over 160 clean energy manufacturing facilities announced since 2022
EVs, data centers, and AI driving demand for clean electricity

🌐 Strategic Trends#

Decentralization: Rooftop solar, microgrids, community wind
Electrification: Transport, heating, industry shifting to electric
Resilience: Renewables reduce vulnerability to fuel price shocks and cyber threats
Innovation: AI, green hydrogen, and advanced storage accelerating adoption

2. Simulation#

💡 Summary: Wind Turbine Power Calculator (Interactive Notebook)#

This Python-based tool simulates wind power generation from a turbine based on user inputs, incorporating realistic parameters for U.S. regions. It uses ipywidgets for interactivity and calculates both theoretical and practical power outputs.

⚙️ How It Works#

🛠️ Inputs:#

Region: Dropdown with average wind speed (at 10 m) and air density values for 7 U.S. regions.
Turbine Type: Preset power coefficient (Cp) values for HAWT, VAWT, and urban turbines.
Rotor Diameter (m): Determines swept area.
Hub Height (m): Adjusts wind speed using power-law scaling.

⚡ Computations:#

Adjusted Wind Speed: Wind speed at hub height using power law with default exponent (α = 0.14).
Swept Area: \(( A = \pi \cdot (D/2)^2 \))
Theoretical Power: \(( P = \frac{1}{2} \cdot \rho \cdot A \cdot v^3 \))
Actual Output: \(( P_{\text{actual}} = P \cdot C_p \))

📊 How to Interpret Results#

Output Metric	Meaning
Adjusted Wind Speed	Wind speed at selected hub height
Air Density	Affects energy content of wind
Swept Area	Larger area captures more wind
Power Coefficient (Cp)	Efficiency of turbine at converting wind energy
Theoretical Power	Max extractable power from wind (Betz limit ≈ 0.59)
Actual Power Output	Realistic power based on turbine type & efficiency

This tool is ideal for exploring turbine design tradeoffs, regional feasibility, and instructional demonstrations of wind energy physics.

import numpy as np
import ipywidgets as widgets
from IPython.display import display, Markdown

# Regional wind data (average conditions)
region_data = {
    'Great Plains': {'v_10m': 7.5, 'rho': 1.22},
    'Midwest': {'v_10m': 6.5, 'rho': 1.225},
    'Pacific Northwest': {'v_10m': 6.0, 'rho': 1.20},
    'California': {'v_10m': 6.7, 'rho': 1.18},
    'Southwest': {'v_10m': 5.8, 'rho': 1.19},
    'Southeast': {'v_10m': 5.5, 'rho': 1.21},
    'Northeast': {'v_10m': 6.2, 'rho': 1.22}
}

# Turbine type presets for Cp values
turbine_cp_map = {
    'Horizontal Axis (HAWT)': 0.45,
    'Vertical Axis (VAWT)': 0.30,
    'Small Urban Turbine': 0.25
}

def adjust_wind_speed(v_ref, h_ref, h_target, alpha=0.14):
    return v_ref * (h_target / h_ref) ** alpha  # Power law adjustment

def calculate_wind_power(region, diameter, turbine_type, hub_height):
    v_ref = region_data[region]['v_10m']
    rho = region_data[region]['rho']
    Cp = turbine_cp_map[turbine_type]
    
    # Adjust wind speed to user-defined hub height
    v = adjust_wind_speed(v_ref, h_ref=10, h_target=hub_height)
    A = np.pi * (diameter / 2) ** 2
    
    P_theoretical = 0.5 * rho * A * v**3
    P_actual = P_theoretical * Cp
    
    result = f"""
### 🌬️ Wind Turbine Power Estimate

- **Region**: {region}  
- **Turbine Type**: {turbine_type}  
- **Hub Height**: {hub_height} m  
- **Adjusted Wind Speed**: {v:.2f} m/s  
- **Air Density**: {rho:.3f} kg/m³  
- **Rotor Diameter**: {diameter:.1f} m  
- **Swept Area**: {A:.1f} m²  
- **Power Coefficient (Cp)**: {Cp}  
- **⚡ Theoretical Power**: {P_theoretical:.2f} W  
- **✅ Estimated Actual Power Output**: {P_actual:.2f} W
"""
    display(Markdown(result))

# Interactive Widgets
region_dropdown = widgets.Dropdown(
    options=list(region_data.keys()),
    value='Midwest',
    description='Region:'
)

turbine_dropdown = widgets.Dropdown(
    options=list(turbine_cp_map.keys()),
    value='Horizontal Axis (HAWT)',
    description='Turbine Type:'
)

diameter_slider = widgets.FloatSlider(
    value=40, min=5, max=120, step=5, description='Rotor Diameter (m):'
)

height_slider = widgets.FloatSlider(
    value=80, min=10, max=150, step=5, description='Hub Height (m):'
)

run_button = widgets.Button(description='Calculate Wind Power')

def on_click(b):
    calculate_wind_power(region_dropdown.value, diameter_slider.value,
                         turbine_dropdown.value, height_slider.value)

run_button.on_click(on_click)

# Display interface
display(region_dropdown, turbine_dropdown, diameter_slider, height_slider, run_button)

3. Simulation#

☀️ Summary: Interactive Solar Energy Potential Calculator#

📘 What It Is#

This Jupyter-based Python tool estimates potential solar energy generation using region-specific irradiance data. It’s designed for educational modeling or quick feasibility assessments using adjustable inputs via ipywidgets.

⚙️ How It Works#

🔧 Inputs#

Region: Selected from U.S. regions with average irradiance values (kWh/m²/day)
Panel Area: Total surface area available for solar panels (m²)
System Efficiency: Conversion efficiency of solar panels (typically 10–25%)
Duration: Time period in days for which energy output is calculated

🔢 Calculation Logic#

Retrieves irradiance for the selected region
Computes daily output:
\(( \text{Daily Output} = \text{Irradiance} \times \text{Area} \times \text{Efficiency} \))
Computes total output over selected duration:
\(( \text{Total Output} = \text{Daily Output} \times \text{Days} \))

📊 How to Interpret Results#

Output Field	Meaning
Solar Irradiance	Average sunlight energy available per m² per day
Estimated Daily Output	Energy produced daily (kWh/day) based on current inputs
Estimated Total Output	Cumulative energy over time (kWh) — useful for budgeting, forecasting
Efficiency	Percentage of sunlight converted into usable electricity

This tool helps visualize how geographic location and system design affect solar yield. It’s great for scenario testing, comparing installation scale, and understanding regional solar performance.

import numpy as np
import ipywidgets as widgets
from IPython.display import display, Markdown

# ☀️ Regional solar irradiance (approximate values from NREL data)
region_irradiance = {
    'Southwest (AZ, NM)': 6.5,
    'California': 6.2,
    'Midwest': 5.0,
    'Northeast': 4.5,
    'Southeast': 5.2,
    'Pacific Northwest': 4.0,
    'Texas': 5.8,
    'Florida': 5.7,
    'Great Plains': 5.4
}

def estimate_solar_energy(region, area, efficiency, days):
    irradiance = region_irradiance[region]  # kWh/m²/day
    daily_output = irradiance * area * efficiency
    total_output = daily_output * days
    
    result = f"""
### ☀️ Solar Energy Potential

- **Region**: {region}  
- **Solar Irradiance**: {irradiance:.2f} kWh/m²/day  
- **Panel Area**: {area:.1f} m²  
- **System Efficiency**: {efficiency:.2f}  
- **Time Frame**: {days} days  

⚡ **Estimated Daily Output**: {daily_output:.2f} kWh/day  
🔋 **Estimated Total Output**: {total_output:.2f} kWh
"""
    display(Markdown(result))

# 🎛️ Widgets
region_dropdown = widgets.Dropdown(
    options=list(region_irradiance.keys()),
    value='Southwest (AZ, NM)',
    description='Region:'
)

area_slider = widgets.FloatSlider(
    value=10, min=1, max=100, step=1, description='Panel Area (m²):'
)

efficiency_slider = widgets.FloatSlider(
    value=0.18, min=0.10, max=0.25, step=0.01, description='Efficiency:'
)

days_slider = widgets.IntSlider(
    value=30, min=1, max=365, step=1, description='Duration (days):'
)

run_button = widgets.Button(description='Calculate Solar Potential')

def on_click(b):
    estimate_solar_energy(region_dropdown.value,
                          area_slider.value,
                          efficiency_slider.value,
                          days_slider.value)

run_button.on_click(on_click)

# 🔧 Display interface
display(region_dropdown, area_slider, efficiency_slider, days_slider, run_button)

4. Simulation#

🌊 Summary: Interactive Ocean Wave Energy Converter (U.S. Coastal Estimator)#

This Python-based widget simulates wave energy conversion for selected U.S. coastal regions, using average wave data or custom inputs. It allows learners and planners to explore how wave parameters—such as height, period, density, and converter width—affect the energy output of a wave energy system.

🔧 Inputs & Controls#

Input Widget	Purpose
`Region Dropdown`	Selects a coastal location with typical NOAA wave data
`Width (m)`	Physical width of the wave energy converter system
`Efficiency`	Conversion efficiency (0.10 to 0.60)
`Use Custom H & T`	Checkbox to override regional data with manual input
`Wave Height (H)`	Custom wave height in meters
`Wave Period (T)`	Custom wave period in seconds
`ρ (kg/m³)`	Water density (can be adjusted for salinity or education)
`g (m/s²)`	Gravitational acceleration (defaults to Earth value)
`Units`	Choose whether output is shown in kW or MW
`Calculate Wave Power`	Executes the computation and displays results

🧮 Computation Logic#

Wave Energy Flux per Meter (kW/m):
\(( P = \frac{ρg^2 H^2 T}{64π \cdot 1000} \))
Total Power Output:
\(( P_{\text{total}} = P_{\text{wave}} \times \text{Width} \times \text{Efficiency} \))

📊 Interpreting Outputs#

Output Field	What It Tells You
Wave Height (H)	Amplitude of the wave; higher waves contain more energy
Wave Period (T)	Time between wave crests; affects energy flux
Energy Flux	Theoretical energy available per meter of wave front
Estimated Extracted Power	Realistic power accounting for system width and efficiency

✅ Example Insights#

Switching from default regional data to custom wave conditions lets you model seasonal or storm scenarios.
Higher H and T = more energy potential, but also design challenges.
Efficiency and width directly scale output—these are key for economic feasibility.

This estimator is ideal for conceptual design, site selection, or classroom exploration. You can adapt it to show plots over time, compare device designs, or layer in environmental constraints for real-world scenarios.

import numpy as np
import ipywidgets as widgets
from IPython.display import display, Markdown

# Default wave data from NOAA & DOE reports
coastal_wave_data = {
    'Pacific Northwest (OR, WA)': {'H': 2.5, 'T': 8.5},
    'California': {'H': 2.0, 'T': 7.5},
    'Hawaii': {'H': 2.2, 'T': 8.0},
    'Alaska': {'H': 3.0, 'T': 9.0},
    'Puerto Rico': {'H': 1.8, 'T': 6.5},
    'East Coast (NY, MA)': {'H': 1.5, 'T': 6.0},
    'Gulf Coast (FL, TX)': {'H': 1.2, 'T': 5.5}
}

def estimate_wave_energy(region, width, efficiency, rho, g, use_custom, H_custom, T_custom, unit):
    H = H_custom if use_custom else coastal_wave_data[region]['H']
    T = T_custom if use_custom else coastal_wave_data[region]['T']

    # Deep water wave energy flux per meter crest length
    P_wave = (rho * g**2 / (64 * np.pi)) * H**2 * T / 1000  # in kW/m
    P_total = P_wave * width * efficiency

    # Unit conversion
    if unit == 'MW':
        P_wave /= 1000
        P_total /= 1000

    result_md = f"""
### 🌊 Wave Energy Conversion Estimate

- **Region**: {region}  
- {'**Custom Input Enabled**' if use_custom else '**Using Default NOAA/DOE Parameters**'}  
- **Wave Height (H)**: {H:.2f} m  
- **Wave Period (T)**: {T:.2f} s  
- **Water Density (ρ)**: {rho:.1f} kg/m³  
- **Gravity (g)**: {g:.2f} m/s²  
- **Converter Width**: {width:.1f} m  
- **Efficiency**: {efficiency:.2f}  
- **Energy Flux**: {P_wave:.4f} {unit}/m  
- ⚡ **Estimated Extracted Power**: {P_total:.4f} {unit}
"""
    display(Markdown(result_md))

# Interactive inputs
region_dropdown = widgets.Dropdown(
    options=list(coastal_wave_data.keys()),
    value='Pacific Northwest (OR, WA)',
    description='Region:'
)

width_slider = widgets.FloatSlider(value=20, min=5, max=100, step=5, description='Width (m):')
eff_slider = widgets.FloatSlider(value=0.30, min=0.1, max=0.6, step=0.05, description='Efficiency:')

use_custom_toggle = widgets.Checkbox(value=False, description='Use Custom H & T')

H_input = widgets.FloatSlider(value=2.0, min=0.5, max=5.0, step=0.1, description='Wave H (m):')
T_input = widgets.FloatSlider(value=7.0, min=3.0, max=12.0, step=0.5, description='Wave T (s):')

rho_input = widgets.FloatSlider(value=1025, min=1000, max=1060, step=1, description='ρ (kg/m³):')
g_input = widgets.FloatSlider(value=9.81, min=9.0, max=10.0, step=0.01, description='g (m/s²):')

unit_dropdown = widgets.Dropdown(options=['kW', 'MW'], value='kW', description='Units:')

run_button = widgets.Button(description='Calculate Wave Power')

def on_click(b):
    estimate_wave_energy(region_dropdown.value,
                         width_slider.value,
                         eff_slider.value,
                         rho_input.value,
                         g_input.value,
                         use_custom_toggle.value,
                         H_input.value,
                         T_input.value,
                         unit_dropdown.value)

run_button.on_click(on_click)

# Display layout
display(region_dropdown, width_slider, eff_slider, use_custom_toggle,
        H_input, T_input, rho_input, g_input, unit_dropdown, run_button)

5. Self-Assessment#

Wind Physics & Turbine Design#

What is the mathematical relationship between wind speed and power output in a wind turbine?
How does rotor diameter influence the energy captured by a turbine?
Why is the power coefficient (Cp) limited by physics (Betz limit) and design constraints?
How does air density vary by region and altitude—and why does this matter for turbine output?

Hub Height Adjustment#

What assumptions underlie the use of the power law for wind speed adjustment?
How does increasing hub height affect turbine performance in regions with moderate wind speeds?

💭 Self-Reflective Questions#

How would you explain the trade-off between rotor size and hub height to a non-technical audience?
Which region’s wind characteristics surprised you most during interactive exploration?
If tasked with placing a wind turbine in your region, which turbine type and hub height would you choose? Why?
Reflect on how regional wind data informs renewable infrastructure decisions—and what data gaps could affect accuracy.

🧪 Quiz Questions#

Power Equation & Sensitivity#

Q1. Wind power output is proportional to which expression?

A. \(( v \))
B. \(( v^2 \))
C. \(( v^3 \)) ✅
D. \(( \sqrt{v} \))

Q2. Increasing rotor diameter from 40 m to 80 m will approximately:

A. Double the swept area
B. Quadruple the swept area ✅
C. Halve the output
D. Not change power generation

Air Properties & Turbine Types#

Q3. Air density is typically highest in which U.S. region?

A. California
B. Great Plains
C. Southeast
D. Midwest ✅

Q4. Which turbine type generally has the lowest Cp value?

A. Horizontal Axis (HAWT)
B. Small Urban Turbine ✅
C. Vertical Axis (VAWT)
D. Offshore Turbine

Q5. The power law exponent (α = 0.14) is commonly used for:

A. Forested terrain
B. Rural open land ✅
C. Mountainous zones
D. Urban canyons

Q6. Solar power output is proportional to which combination of inputs?

A. Irradiance × Area × Efficiency ✅
B. Wind speed × Efficiency
C. Temperature × Efficiency
D. Air density × Panel angle

Q7. If a solar panel has an area of 10 m², irradiance of 5.5 kWh/m²/day, and efficiency of 20%, estimated daily energy is:

A. 5.5 kWh
B. 11.0 kWh
C. 8.8 kWh ✅
D. 20.0 kWh

🌍 Regional Properties#

Q8. Air density is typically highest in which U.S. region?

A. California
B. Great Plains
C. Southeast
D. Midwest ✅

Q9. Which U.S. region has the highest solar irradiance on average?

A. Northeast
B. Pacific Northwest
C. Southwest (AZ, NM) ✅
D. Midwest

⚙️ Technology & Adjustment#

Q10. Which turbine type generally has the lowest Cp (efficiency) value?

A. Horizontal Axis (HAWT)
B. Small Urban Turbine ✅
C. Vertical Axis (VAWT)
D. Offshore Turbine

Q11. The power law exponent (α = 0.14) is commonly used for:

A. Forested terrain
B. Rural open land ✅
C. Mountainous zones
D. Urban canyons

Q12. Solar panel efficiency is typically affected by which factor?

A. Air pressure
B. Cloud cover ✅
C. Wind direction
D. Rotor diameter

Q13. Which of the following conditions would decrease solar output most significantly?

A. High elevation
B. Low air density
C. Overcast skies ✅
D. Tilt angle of 25°

Chapter 6 Sustainable Engineering: Renewal Energy

Contents

Chapter 6 Sustainable Engineering: Renewal Energy#

1. Introduction#

🌱 Renewable Energy: Definition, Role in Energy Security & Regional Potential#

📘 What Is Renewable Energy?#

🛡️ How Renewables Enhance Energy Security#

🔋 Role of Specific Sources#

🌍 Regional Potential of Wind, Solar & Wave Energy#

🌬️ Wind Energy Potential#

☀️ Solar Energy Potential#

🌊 Wave Energy Potential#

🇺🇸 Renewable Energy Potential in the United States#

⚡ Comparative Overview: Wind, Solar & Wave Energy#

🧭 Use Case Matching by Region (U.S. Context)#

🔮 Scenario Prompts for Planning Worksheets#

🔮 Future of Renewable Energy#

📈 Global Outlook#

🇺🇸 U.S. Outlook#

🌐 Strategic Trends#

2. Simulation#

💡 Summary: Wind Turbine Power Calculator (Interactive Notebook)#

⚙️ How It Works#

🛠️ Inputs:#

⚡ Computations:#

📊 How to Interpret Results#

3. Simulation#

☀️ Summary: Interactive Solar Energy Potential Calculator#

📘 What It Is#

⚙️ How It Works#

🔧 Inputs#

🔢 Calculation Logic#

📊 How to Interpret Results#

4. Simulation#

🌊 Summary: Interactive Ocean Wave Energy Converter (U.S. Coastal Estimator)#

🔧 Inputs & Controls#

🧮 Computation Logic#

📊 Interpreting Outputs#

✅ Example Insights#

5. Self-Assessment#

Wind Physics & Turbine Design#

Hub Height Adjustment#

💭 Self-Reflective Questions#

🧪 Quiz Questions#

Power Equation & Sensitivity#

Air Properties & Turbine Types#

🌍 Regional Properties#

⚙️ Technology & Adjustment#