Charging Scenarios

Warning

This documentation is under active development. Some sections may be incomplete.

Charging Scenarios#

This section describes the various electric vehicle charging scenarios supported by the Fleet Electrification BC framework.

Scenario Overview#

The framework supports multiple charging strategies to analyze different grid integration approaches:

  • Coordinated Charging: Optimized scheduling for grid benefits

  • Uncoordinated Charging: User-driven charging patterns

  • Vehicle-to-Grid (V2G): Bidirectional energy flow

  • Hybrid Strategies: Mixed coordinated/uncoordinated approaches

Fleet Workflow High Level

Hybrid Coordinated-Uncoordinated Strategy#

🔄 Configuration#

The hybrid strategy allows simulation of mixed charging behaviors with varying coordination levels:

# Example hybrid configuration
hybrid_scenarios = [
    (10, 90),   # 10% coordinated, 90% uncoordinated
    (30, 70),   # 30% coordinated, 70% uncoordinated  
    (50, 50),   # 50% coordinated, 50% uncoordinated
    (70, 30),   # 70% coordinated, 30% uncoordinated
    (90, 10),   # 90% coordinated, 10% uncoordinated
]

Assumptions#

  • 100% of EV load is active in the simulation

  • The load is split between coordinated (x%) and uncoordinated (y%) portions

  • The condition x + y = 100% must always hold

  • Each scenario represents a different market penetration of smart charging

Expected System Response#

📈 As Coordinated Charging Increases#

Metric

Behavior

Investigation Focus

🧱 Required Firm Capacity

↓ Decreases

Less peaking capacity needed due to load shifting

☀️ Renewable Utilization

↑ Increases

Better alignment with solar/wind generation

💰 System Cost

↓ Decreases

Lower operational and investment costs

🧮 Flexibility Value

↑ Increases

Enhanced grid stability and price arbitrage

Coordinated Charging Benefits#

Smart Charging Advantages

✅ Load Shifting: Optimizer moves EV charging to optimal periods
✅ Renewable Integration: Charging aligns with solar/wind availability
✅ Peak Reduction: Avoids expensive peaking generation
✅ Grid Support: Provides flexibility services to the power system

Uncoordinated Charging Challenges#

Uncoordinated Charging Issues

❌ Peak Demand: Sharp evening peaks (6-9 PM) increase system stress
❌ Infrastructure Strain: Higher peak demand requires more capacity
❌ Renewable Curtailment: Poor alignment with renewable generation
❌ Higher Costs: Increased need for expensive peaking resources

Elbow Curve Analysis#

🔮 Diminishing Returns Pattern#

The relationship between coordination level and required Variable Renewable Energy (VRE) capacity typically shows:

Expected Coordination Phases#

  1. Initial Gains (0% → 40%)

    • Significant drop in required VRE overbuild

    • Efficient use of existing renewable capacity

    • Major system cost reductions

  2. Middle Range (40% → 70%)

    • Continued benefits but slower improvement rate

    • Some VRE redundancy still present

    • Moderate additional cost savings

  3. High Coordination (70% → 100%)

    • Diminishing returns on additional coordination

    • Flexibility nearly maximized

    • VRE capacity requirements level off

Factors Affecting the Elbow Point#

  • Temporal Overlap: Alignment between EV charging needs and VRE generation

  • Grid Flexibility: Existing system flexibility resources

  • Curtailment Levels: Amount of renewable energy currently curtailed

  • Load Patterns: Regional demand characteristics

Charging Strategy Configurations#

Coordinated Charging#

fleet_EV_args = {
    'ev_charging': "coordinated",
    'ev_population': 0.8,
    # Additional coordinated charging parameters
    'optimization_horizon': 24,  # hours
    'price_signals': True,
    'grid_constraints': True,
}

Characteristics:

  • Centralized optimization

  • Grid-aware scheduling

  • Price-responsive charging

  • Renewable energy alignment

Uncoordinated Charging#

fleet_EV_args = {
    'ev_charging': "uncoordinated", 
    'ev_population': 0.8,
    # Uncoordinated charging follows natural patterns
    'user_behavior': "immediate",  # or "convenience"
    'location_preference': "home_dominant",
}

Characteristics:

  • User-driven timing

  • Immediate charging preference

  • Evening peak loading

  • No grid optimization

Vehicle-to-Grid (V2G)#

fleet_EV_args = {
    'ev_charging': "v2g",
    'ev_population': 0.8,
    # V2G specific parameters
    'discharge_capability': 0.5,  # 50% of vehicles can discharge
    'min_soc': 0.2,  # Minimum state of charge
    'grid_services': True,
}

Characteristics:

  • Bidirectional energy flow

  • Grid support services

  • Enhanced flexibility

  • Battery degradation considerations

Scenario Implementation#

Running Multiple Scenarios#

import itertools

# Define scenario matrix
coordination_levels = [10, 30, 50, 70, 90]
ev_penetrations = [0.5, 0.8, 1.0]

# Run scenario combinations
for coord, penetration in itertools.product(coordination_levels, ev_penetrations):
    scenario_args = {
        'coordination_percent': coord,
        'ev_population': penetration,
        'scenario_name': f'coord_{coord}_pen_{int(penetration*100)}'
    }
    # Run simulation with scenario_args

Scenario Comparison#

The framework automatically generates comparison visualizations:

  • Capacity Requirements: Generation capacity by scenario

  • Load Profiles: Temporal demand patterns

  • Cost Analysis: System cost breakdown

  • Renewable Integration: VRE utilization rates

Research Questions#

The scenario framework is designed to answer key research questions:

🔍 Primary Questions#

  1. How does increasing smart charging reduce the need to overbuild VRE?

  2. At what coordination level do diminishing returns occur?

  3. What is the optimal balance between coordination complexity and system benefits?

  4. How do different EV penetration rates affect the value of coordination?

🔬 Analysis Metrics#

  • VRE Capacity Requirements: Total renewable capacity needed

  • System Flexibility: Grid response capability

  • Peak Demand Reduction: Maximum load reduction

  • Cost-Benefit Ratio: Economic efficiency of coordination

  • Grid Stability Metrics: Frequency response and voltage stability

Visualization Outputs#

The scenario analysis generates comprehensive visualizations:

Elbow Curve Analysis Load Duration Curves

These visualizations help identify:

  • Optimal coordination levels

  • System planning requirements

  • Economic trade-offs

  • Grid integration challenges

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