Scenario Comparison: BAU vs High Demand Growth

This page shows how you can use BICEP to compare BTM infrastructure upgrade costs between two energy scenarios.

Overview

BICEP can compare two energy scenarios that represent different levels of technology adoption:

Business As Usual (BAU)

• Reflects current policies and baseline technology adoption trends
• Lower overall technology adoption rates
• Lower electrical infrastructure upgrade requirements
• Baseline for comparison

High

• Assumes higher technology adoption and increased demand
• Significantly higher technology adoption rates
• Greater electrical infrastructure demands
• Represents a high-demand scenario

Analysis Capabilities

The interactive notebook provides detailed comparisons including:

• Total cost comparison - National and state-level estimates
• Cost by building type - Residential vs commercial differences
• Temporal trends - How costs change over time
• Geographic variation - Top states by upgrade costs
• Capacity requirements - Electrical load impacts by technology

Open the Scenario Comparison Notebook

How to Compare Scenarios

Total Cost Comparison

from bicep.analysis import BicepResults

# Run both scenarios
bau = BicepResults(scenario='bau', mode='local')
high = BicepResults(scenario='high', mode='local')

# Compare total costs
bau_cost = bau.aggregated['cost'].sum()
high_cost = high.aggregated['cost'].sum()
increase = ((high_cost - bau_cost) / bau_cost) * 100

print(f"BAU total cost: ${bau_cost:,.0f}")
print(f"High total cost: ${high_cost:,.0f}")
print(f"Cost increase: {increase:.1f}%")

Cost by Building Type

# Compare residential vs commercial costs across scenarios
for name, results in [('BAU', bau), ('High', high)]:
    res_cost = results.residential['cost'].sum()
    com_cost = results.commercial['cost'].sum()
    print(f"{name} - Residential: ${res_cost:,.0f}, Commercial: ${com_cost:,.0f}")

Temporal Trends

# Get costs by year for each scenario
for name, results in [('BAU', bau), ('High', high)]:
    yearly_costs = results.residential.groupby('year')['cost'].sum()
    peak_year = yearly_costs.idxmax()
    print(f"{name} peak cost year: {peak_year}")

Geographic Variation

# Compare top states by upgrade cost
for name, results in [('BAU', bau), ('High', high)]:
    state_costs = results.buildings.groupby('state')['cost'].sum()
    top_states = state_costs.nlargest(10)
    print(f"\n{name} - Top 10 states by cost:")
    print(top_states)

Capacity Requirements by Technology

# Compare capacity requirements across scenarios
for name, results in [('BAU', bau), ('High', high)]:
    print(f"\n{name} Capacity Requirements:")
    print(results.requirements_by_tech(residential=1))

Filtering by State

from bicep.analysis import BicepResults

# Analyze a specific state under both scenarios
ca_bau = BicepResults(scenario='bau', mode='local', target_states='CA')
ca_high = BicepResults(scenario='high', mode='local', target_states='CA')

# Compare state-level costs
print(f"CA BAU cost: ${ca_bau.total_cost:,.0f}")
print(f"CA High cost: ${ca_high.total_cost:,.0f}")

# Analyze a region (multiple states)
region = BicepResults(
    scenario='high',
    mode='local',
    target_states=['CA', 'OR', 'WA']
)

Technology-Specific Analysis

# Identify buildings needing EV charging infrastructure
ev_upgrades = results.residential[
    results.residential['ev_req_capacity_amp'] > 
    results.residential['spare_capacity_amp']
]

ev_upgrade_cost = ev_upgrades['cost'].sum()
print(f"EV infrastructure costs: ${ev_upgrade_cost:,.0f}")

Next Steps

• Data Requirements - Understand technology adoption assumptions
• Custom Distributions - Explore cost uncertainty and variation
• API Reference - Access all analysis methods