This is a cost calculator for a datacenter powered by solar, batteries, and gas generation.

It can simulate a datacenter of any load anywhere in the world, with any combination of solar, battery, and gas generation. The output is a Levelized Cost of Energy (LCOE) in $/MWh, and a yearly financial model.

The code calculates the LCOE using the following steps:

1. It pulls weather data for the speciifed (lat, long)
2. It simulates the solar power from the weather data
3. It simulates the powerflow of the system between the solar, battery, generator, and datacenter.
4. It calculates the annual cashflows and the LCOE of the system.

There are three ways to use this code:

streamlit run app.py

This simulates a single case.

python calculate_lcoe_one_shot.py --lat 31.9 --long -106.2 --solar-mw 250 --bess-mw 100 --generator-mw 125 --datacenter-load-mw 100

(See calculate_lcoe_one_shot.py for all possible args)

This simulates a range of cases and saves the results to a CSV file. The "raw results" for every case are saved as a CSV, as well as the Pareto-optimal frontier on LCOE vs renewable-percentage.

python run_ensemble.py

You can define the test cases in run_ensemble.py.

"""There are three steps to calculate the LCOE:
1. Get solar weather data
2. Simulate powerflow
3. Calculate LCOE
"""
# 1. Get solar weather data
solar_ac_dataframe = get_solar_ac_dataframe(lat, long)
# 2. Simulate powerflow
powerflow_results = simulate_system(lat, long, solar_ac_dataframe, ...)
# 3. Create DataCenter instance and calculate LCOE
datacenter = DataCenter(
    powerflow_results=powerflow_results,
    solar=100, 
    bess=100, 
    generator=125, 
    generator_type="Gas Engine",
    # CAPEX rates
    solar_capex_total_dollar_per_w=0.25,
    bess_capex_total_dollar_per_kwh=0.10,
    # O&M rates
    solar_om_fixed_dollar_per_kw=0.01,
    bess_om_fixed_dollar_per_kw=0.01,
    ... # See `datacenter.py` for all options and defaults
)
lcoe = datacenter.calculate_lcoe()

• Ben James