Strategic Engineering

In the state of New Mexico, electricity is generated from a variety of renewable options, including a single commercial geothermal plant to the southwest of the state known as Lightning Dock. This facility operates on a known conventional hydrothermal system associated with a deep normal fault, generating 10MW of electricity after recent capacity upgrades. Geothermal additions to the energy grid fulfill a state mandate for renewable diversification beyond just wind and solar power, so the additional capacity upgrades would have market interest from local utility companies.

This report investigates applying flexibility with real options to the design of a modular geothermal power plant extension at Lightning Dock. The modeled expansion amounts to 5 MW of additional generation from an offset enhanced geothermal system (EGS), targeting hot, dry reservoir rock with small-scale power plant modules. Each module is fully self-contained, comprising a single injector-producer pair connected to a binary cycle generator rated at ~1 MW based on a present-day commercial analog.

The initial cost model provides a static assessment of capital expenses, operating and maintenance costs, and income from power sales to determine the net present value (NPV) for a 30-year useful life of the plant expansion. However, deeper consideration of the uncertainties in the subsurface resource, the impact of climate change, and potential disruptions to the electricity market highlight several variables that can greatly impact model results depending on the choice of their representative values.

To adequately address this, variables are assigned probability functions and randomly sampled many times over in a Monte Carlo simulation to produce an ensemble of NPV estimates. In addition, three real option decision rules allow the project design to adjust as operating conditions change over time. The first scenario implements well redevelopment in response to degrading subsurface thermal conditions that reduce the productivity of power plant modules. This results in a negative NPV, but less downside risk compared to the no-flexibility base case scenario. Adding a decision rule for plant expansion when electricity prices surge effectively captures upside potential, making the ensemble averaged NPV (ENPV) both positive and attractive. By comparison, adding the option to remove modules during a price downturn lowers ENPV due to the dominant factor of income loss as capacity decreases.

An exception is observed when plant reductions are limited to only 10% of existing modules at a time. This preferred model integrates all three flexibilities to achieve the greatest ENPV, upside capture, and downside risk mitigation, with the caveat that module removal in response to downturns must be performed slowly and with care to preserve maximum value for the power plant expansion.