Module 10 of 12

Renewable Energy & Smart Grid Systems

Advanced Renewable Energy Technologies and Smart Grid Integration

1. Solar Photovoltaic Systems

🎯 Learning Objectives

Understand photovoltaic cell operation and characteristics
Design residential and commercial solar PV systems
Analyze solar resource assessment and performance modeling
Implement grid-tied and off-grid PV system configurations

🌱 Renewable Energy & Smart Grid Systems

This module explores the integration of renewable energy technologies with smart grid systems, covering advanced concepts in distributed energy resources, grid modernization, and sustainable energy management.

1.1 Photovoltaic Cell Technology

Solar photovoltaic (PV) cells convert sunlight directly into electricity through the photovoltaic effect. Understanding cell technology is fundamental to PV system design.

Smart grid system showing renewable energy integration and communication networks

Figure 1.1: Smart grid infrastructure showing renewable energy integration and advanced communication protocols

🔬 PV Cell Technologies

Technology	Efficiency (%)	Cost ($/W)	Lifespan (years)	Applications
Monocrystalline Silicon	20-24	1.50-2.00	25-30	Residential, Commercial
Polycrystalline Silicon	16-20	1.20-1.50	20-25	Residential, Large-scale
Thin Film (CdTe)	18-22	0.80-1.20	20-25	Utility-scale, Building-Integrated
Thin Film (CIGS)	14-19	1.00-1.40	20-25	Commercial, Flexible Applications
Multi-junction (GaAs)	30-47	25.00+	20-30	Space, Concentrated PV

📐 PV Cell Electrical Characteristics

PV Cell I-V Relationship:

$$I = I_{ph} - I_0 \left[ e^{\frac{q(V + I R_s)}{n k T}} - 1 \right] - \frac{V + I R_s}{R_{sh}}$$

Parameters:

$I_{ph}$ = Photogenerated current (A)
$I_0$ = Saturation current (A)
$q$ = Electron charge ($1.602 \times 10^{-19}$ C)
$V$ = Cell voltage (V)
$I$ = Cell current (A)
$R_s$ = Series resistance (Ω)
$R_{sh}$ = Shunt resistance (Ω)
$n$ = Ideality factor
$k$ = Boltzmann constant ($1.381 \times 10^{-23}$ J/K)
$T$ = Cell temperature (K)

⚡ PV Performance Factors

🌡️ Temperature Effects

Temperature Coefficient: -0.3% to -0.5%/°C

$$P_{temp} = P_{STC} \left[ 1 + \gamma (T_c - 25) \right]$$

Higher temperature reduces efficiency
Optimal operating range: 15-35°C
Requires adequate ventilation

☀️ Irradiance Effects

Linear Relationship:

$$I_{ph} = I_{ph,STC} \times \frac{G}{1000}$$

Current proportional to irradiance
Voltage less dependent on irradiance
Standard Test Condition (STC): 1000 W/m²

🏗️ Cell Degradation

Annual Degradation: 0.5% to 0.8%

First year: 2-3%
Subsequent years: 0.5-0.8%
Warranty: 80% after 25 years

🎯 Spectral Response

Silicon: 300-1100 nm
GaAs: 300-1700 nm
Multi-junction: 300-2000 nm
Affects performance in different weather

1.2 PV System Design and Sizing

Proper system design ensures optimal performance, safety, and economic viability. Key design considerations include energy requirements, available space, and grid connection.

🔧 PV System Design Steps

Step 1: Energy Load Analysis

Appliance	Power (W)	Hours/Day	Energy (kWh/day)
LED Lights	60	4	0.24
Refrigerator	150	24	3.6
TV	100	3	0.3
Computer	200	6	1.2
Total Daily Load			5.34 kWh

Considerations:

Peak power requirements vs. total energy
Load diversity factors
Efficiency losses (inverter, wiring)
Future load growth

Step 2: Solar Resource Assessment

Global Horizontal Irradiance (GHI) Data

Location	GHI (kWh/m²/day)	DNI (kWh/m²/day)	Solar Hours	Optimal Tilt
Phoenix, AZ	6.5	7.2	6.5	30-35°
Los Angeles, CA	5.4	5.8	5.4	30-35°
Miami, FL	5.0	5.3	5.0	25-30°
New York, NY	4.2	4.8	4.2	35-40°
Anchorage, AK	3.2	3.5	3.2	40-45°

Key Metrics:

Peak Sun Hours: Hours at 1000 W/m² equivalent
Capacity Factor: Actual output / Nameplate capacity
Seasonal Variation: Peak summer vs. winter

Step 3: Array Sizing Calculations

Daily Energy Requirement: 5.34 kWh/day

System Losses:

Inverter efficiency: 95%
Shading losses: 5%
Soiling losses: 2%
Temperature losses: 5%
Mismatch losses: 2%

Total System Efficiency: 0.95 × 0.95 × 0.98 × 0.95 × 0.98 = 85%

Required Array Size:

$$P_{array} = \frac{E_{daily}}{PSH \times \eta_{system}}$$ $$P_{array} = \frac{5.34}{5.4 \times 0.85} = 1.16 \, kW$$

Number of Panels:

Assume 350W panels: N = 1.16 kW / 0.35 kW = 3.3 ≈ 4 panels

Final Array Size: 1.4 kW (4 × 350W)

1.3 PV System Components and Configurations

🔧 Essential PV System Components

📱 Solar Panels

Configuration Options:

String Connection: Modules in series (higher voltage)
Parallel Connection: Modules in parallel (higher current)
Series-Parallel: Combines benefits of both

Key Specifications:

Power rating (Wp)
Voltage (Voc, Vmp)
Current (Isc, Imp)
Temperature coefficient

⚡ Inverters

Types:

String Inverters: Most common, cost-effective
Microinverters: Module-level optimization
Power Optimizers: Module MPPT with string inverter
Hybrid Inverters: Include battery integration

Specifications:

Maximum Power Point Tracking (MPPT)
Efficiency (95-98%)
Grid compliance (IEEE 1547)
Safety features (anti-islanding)

🔋 Mounting Systems

Types:

Roof-mounted: Residential applications
Ground-mounted: Large commercial systems
Building-integrated (BIPV): Architectural
Tracking systems: Single-axis or dual-axis

Design Considerations:

Structural load capacity
Wind and snow loads
Optimal tilt angle
Maintenance access

🛡️ BOS Components

Balance of System:

DC Disconnects: Safety isolation
AC Disconnects: Grid disconnection
Combiner Boxes: DC wiring integration
Monitoring Systems: Performance tracking
Surge Protection: Lightning and transient protection

⚙️ PV System Configurations

🏠 Grid-Tied System

Connected directly to the utility grid with net metering capability.

✅ Advantages:

No battery storage needed
Utility grid provides backup
Net metering credits
Lower initial cost
Higher overall efficiency

❌ Disadvantages:

No power during grid outages
Grid dependency
Net metering policy dependent
Potential interconnection fees

Net Energy Calculation:

$$E_{net} = E_{generated} - E_{consumed}$$

Economic Value:

$$Value = E_{net} \times Rate_{feed-in}$$

🔋 Grid-Tied with Battery

Hybrid system with both grid connection and energy storage.

✅ Advantages:

Backup power capability
Peak demand management
Time-of-use optimization
Grid services participation

❌ Disadvantages:

Higher initial cost
Additional maintenance
Battery replacement costs
Complex system design

🏝️ Off-Grid System

Standalone system with battery storage for remote applications.

✅ Advantages:

Complete energy independence
Remote location capability
Backup reliability
No grid connection required

❌ Disadvantages:

High battery cost
System sizing complexity
Maintenance requirements
Limited power availability

🛠️ Interactive Solar PV System Designer

System Requirements

Daily Energy Need (kWh):

Location (Peak Sun Hours):

System Efficiency (%):

Panel Wattage (W):

System Type:

Design Results

Enter requirements to calculate system design.

5. Microgrid Design and Control

🎯 Learning Objectives

Understand microgrid architecture and components
Design microgrid control systems
Implement islanding and grid synchronization
Optimize microgrid performance and reliability

5.1 Microgrid Architecture

Microgrids are localized energy systems that can operate independently from the main power grid, providing enhanced reliability and resilience for critical loads.

Microgrid architecture showing distributed generation, storage, and loads

Figure 5.1: Microgrid architecture with distributed generation, energy storage, and intelligent control systems

📐 Microgrid Power Balance

Real Power Balance:

$$P_{load} = P_{gen} + P_{storage} - P_{grid}$$

Reactive Power Balance:

$$Q_{load} = Q_{gen} + Q_{storage} - Q_{grid}$$

Frequency Control:

$$f = f_0 + K_p (P_{ref} - P_{actual})$$

Voltage Control:

$$V = V_0 + K_q (Q_{ref} - Q_{actual})$$

🏗️ Microgrid Design Tool

Peak Load (kW):

Solar PV Capacity (kW):

Wind Capacity (kW):

Battery Capacity (kWh):

Diesel Generator (kW):

Grid Connection:

⚡ Microgrid Design Results

Enter system specifications to analyze microgrid performance.

7. Demand Response and Energy Management

🎯 Learning Objectives

Understand demand response programs and mechanisms
Implement smart energy management systems
Optimize load scheduling and peak shaving
Design automated demand response strategies

7.1 Demand Response Programs

Demand response allows utilities to manage electricity consumption by offering incentives or signals to reduce load during peak periods or emergencies.

💰 Price-based Programs

Time-of-Use (TOU): Different rates for peak/off-peak
Real-time Pricing (RTP): Hourly pricing signals
Critical Peak Pricing: High prices during critical periods
Inclining Block Rates: Higher rates for higher usage

⚡ Incentive-based Programs

Direct Load Control: Utility controls customer equipment
Interruptible/Curtailable Service: Load reduction incentives
Emergency Demand Response: Critical situation response
Capacity Market Programs: Capacity contribution payments

📐 Demand Response Calculations

Load Reduction:

$$\Delta P = P_{baseline} - P_{actual}$$

Peak Demand Reduction:

$$DR_{peak} = \frac{\Delta P_{max}}{P_{total}} \times 100\%$$

Cost Savings:

$$Savings = P_{load\_reduction} \times (Rate_{peak} - Rate_{off-peak})$$

📊 Demand Response Analysis Tool

Baseline Load (kW):

Peak Rate ($/kWh):

Off-peak Rate ($/kWh):

Load Reduction (%):

Peak Hours per Day:

DR Events per Month:

💰 Demand Response Benefits

Enter load and rate information to calculate demand response benefits.

📝 Module Assessment

Test your understanding of Renewable Energy & Smart Grid Systems with this comprehensive assessment.

Question 1: PV System Sizing

A residential system requires 20 kWh/day. The location has 5.5 peak sun hours and the system efficiency is 80%. Calculate the required PV array size using 400W panels.

Solution:

Required array power = Daily energy / (Peak sun hours × Efficiency)

P_array = 20 kWh / (5.5 hrs × 0.80) = 4.55 kW

Number of panels = 4550W / 400W = 11.4 ≈ 12 panels

Final system: 12 × 400W = 4.8 kW