Capacity Expansion Optimizer

Capacity Expansion Optimization

Let the algorithm find the optimal mix of generation and storage capacity to meet your CO2 emissions target at minimum cost.

CO2 Emissions Target (million metric tons/year)

Current baseline: 38.09 million MT

Emissions acceptance rule

A solved portfolio can be above your requested target when tolerance is allowed. The result reports the actual difference. This model uses 12 representative days (288 hours), weighted to a year; it does not establish resource adequacy or retail bills.

Allow Direct Air Capture (DAC) - Enable CO2 removal technology if needed Limit curtailment to % - Prevents excessive renewable overbuild

📖 Optimizer Documentation

Technology Cost Assumptions

Discount Rate (decimal)

Technology	CAPEX $/kW	Fixed O&M $/kW-yr	Var O&M $/MWh	Life yrs

* Storage CAPEX is $/kWh. DAC CAPEX is $/ton/yr. NG Variable O&M shows start-end range implies ramp.

Supply Curves (Resource-Constrained Technologies)

CAPEX multipliers increase as deployment grows. Base CAPEX (from table above) × multiplier = effective CAPEX.

Geothermal Supply Curve

0-2 GW Multiplier

2-4 GW Multiplier

4-5 GW Multiplier

Biomass Supply Curve

0-1.5 GW Multiplier

1.5-3 GW Multiplier

Capacity Bounds (Max New Additions)

Maximum capacity additions for each technology. Generation in GW, Storage in GWh.

Solar Max (GW)

Wind Max (GW)

Offshore Max (GW)

Nuclear Max (GW)

Geothermal Max (GW)

Biomass Max (GW)

RNG Max (GW)

Hydrogen Max (GW)

Battery 4hr Max (GWh)

Battery 8hr Max (GWh)

Long-Duration Max (GWh)

DAC Max (tons/hr)

Bounds represent maximum new capacity additions (not total installed capacity).

How the Optimizer Uses These Parameters

Base CAPEX & O&M: Used to calculate annualized costs for each technology. The optimizer minimizes total system cost while meeting demand and CO2 targets.

Supply Curves: For geothermal and biomass, costs increase with deployment. The optimizer sees: Base CAPEX × multiplier = effective cost. This reflects resource quality degradation as the best sites are used first.

Capacity Bounds: Hard constraints on maximum deployments. When a technology hits its bound, the optimizer explores alternative solutions. Tighter bounds force more diverse technology portfolios.

💡 Tip: Geothermal and biomass are resource-constrained (tightened bounds + supply curves) to reflect realistic deployment limits in California.

Capacity Expansion Optimizer

Hourly Electricity Generation (GWh)

Hourly Marginal Energy Price ($/MWh)

CO₂ Emissions (Metric Tons)

Installed Capacity Mix (%)

Yearly Generation Mix (%)

Policy Levers

Marginal Price

System Cost

Government Incentives

Hourly Capacity Factors

Capacity Expansion Optimization

Technology Cost Assumptions

Supply Curves (Resource-Constrained Technologies)

Capacity Bounds (Max New Additions)

How the Optimizer Uses These Parameters

Optimization Progress

Re-run Optimization?

Capacity Expansion Optimizer

Hourly Electricity Generation (GWh)

Hourly Marginal Energy Price ($/MWh)

CO2 Emissions (Metric Tons)

Installed Capacity Mix (%)

Yearly Generation Mix (%)

Policy Levers

Marginal Price

System Cost

Government Incentives

Hourly Capacity Factors

Capacity Expansion Optimization

Technology Cost Assumptions

Supply Curves (Resource-Constrained Technologies)

Capacity Bounds (Max New Additions)

How the Optimizer Uses These Parameters

Optimization Progress

CO₂ Emissions (Metric Tons)