Break-even price and power purchase agreement

HyDesign calculates the price of electricity and/or hydrogen that would break-even i.e. result in NPV of zero. These results are based on the current hybrid configuration and operation. This means that the results will vary depending on which input parameters you are using, but will generally converge when the input price parameters are approaching the resulting break-even prices.

Break-even price of H2

The break-even price of H2 can be obtained as following.

[28]:

# Install hydesign if needed
import importlib
if not importlib.util.find_spec("hydesign"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git

[29]:

%%capture
import pandas as pd
from hydesign.assembly.hpp_assembly import hpp_model
from hydesign.assembly.hpp_assembly_P2X import hpp_model_P2X
from hydesign.examples import examples_filepath

[30]:

examples_sites = pd.read_csv(f'{examples_filepath}examples_sites.csv', index_col=0, sep=';')
name = 'France_good_wind'
ex_site = examples_sites.loc[examples_sites.name == name]
longitude = ex_site['longitude'].values[0]
latitude = ex_site['latitude'].values[0]
altitude = ex_site['altitude'].values[0]
input_ts_fn = examples_filepath+ex_site['input_ts_fn'].values[0]
sim_pars_fn = examples_filepath+ex_site['sim_pars_fn'].values[0]
H2_demand_fn = examples_filepath+ex_site['H2_demand_col'].values[0]
hpp = hpp_model_P2X(
    latitude=latitude,
    longitude=longitude,
    altitude=altitude,
    num_batteries = 1,
    work_dir = './',
    sim_pars_fn = sim_pars_fn,
    input_ts_fn = input_ts_fn,
    H2_demand_fn=H2_demand_fn)


Fixed parameters on the site
-------------------------------
longitude = -0.864258
latitude = 48.744116
altitude = 302.0

[31]:

x = [10.0, 350.0, 5.0, 20.0, 7.0, 100.0, 50.0, 180.0, 1.5, 0.0, 3.0, 5.0, 100.0, 2500.0]
outs = hpp.evaluate(*x)

[32]:

hpp.print_design(x, outs)


Design:
---------------
clearance [m]: 10.000
sp [W/m2]: 350.000
p_rated [MW]: 5.000
Nwt: 20.000
wind_MW_per_km2 [MW/km2]: 7.000
solar_MW [MW]: 100.000
surface_tilt [deg]: 50.000
surface_azimuth [deg]: 180.000
DC_AC_ratio: 1.500
b_P [MW]: 0.000
b_E_h [h]: 3.000
cost_of_battery_P_fluct_in_peak_price_ratio: 5.000
ptg_MW [MW]: 100.000
HSS_kg [kg]: 2500.000


NPV_over_CAPEX: 1.022
NPV [MEuro]: 251.401
IRR: 0.140
LCOE [Euro/MWh]: 36.981
LCOH [Euro/kg]: 7.363
Revenue [MEuro]: 764.257
CAPEX [MEuro]: 245.948
OPEX [MEuro]: 4.221
penalty lifetime [MEuro]: 0.000
AEP [GWh]: 38.281
GUF: 0.015
annual_H2 [tons]: 5678.523
annual_P_ptg [GWh]: 373.183
grid [MW]: 300.000
wind [MW]: 100.000
solar [MW]: 100.000
PtG [MW]: 100.000
HSS [kg]: 2500.000
Battery Energy [MWh]: 0.000
Battery Power [MW]: 0.000
Total curtailment [GWh]: 0.000
Awpp [km2]: 14.286
Apvp [km2]: 1.226
Rotor diam [m]: 134.867
Hub height [m]: 77.434
Number of batteries used in lifetime: 0.000
Break-even H2 price [Euro/kg]: 2.509
Break-even PPA price [Euro/MWh]: 0.000
Capacity factor wind [-]: 0.303

The break-even H2 price is seen in the above table and can also be extracted directly:

[33]:

print(f'Input H2 price: {float(hpp.prob["price_H2"][0]):.2f}')
print(f'Break-even H2 price: {float(hpp.prob["break_even_H2_price"][0]):.2f}')

Input H2 price: 5.00
Break-even H2 price: 2.51

Power Purchase Agreement (PPA)

Constant price can be introduced by supplying a time series of constant prices or setting the PPA price directly when instantiating the hpp model:

[34]:

PPA = 40 # Euro/MWh
hpp = hpp_model(
    latitude=latitude,
    longitude=longitude,
    altitude=altitude,
    num_batteries = 1,
    work_dir = './',
    sim_pars_fn = sim_pars_fn,
    input_ts_fn = input_ts_fn,
    ppa_price=PPA,)


Fixed parameters on the site
-------------------------------
longitude = -0.864258
latitude = 48.744116
altitude = 302.0

[35]:

x = [10.0, 350.0, 5.0, 20.0, 7.0, 100.0, 50.0, 180.0, 1.5, 0.0, 3.0, 5.0]
outs = hpp.evaluate(*x)

[36]:

hpp.print_design(x, outs)


Design:
---------------
clearance [m]: 10.000
sp [W/m2]: 350.000
p_rated [MW]: 5.000
Nwt: 20.000
wind_MW_per_km2 [MW/km2]: 7.000
solar_MW [MW]: 100.000
surface_tilt [deg]: 50.000
surface_azimuth [deg]: 180.000
DC_AC_ratio: 1.500
b_P [MW]: 0.000
b_E_h [h]: 3.000
cost_of_battery_P_fluct_in_peak_price_ratio: 5.000


NPV_over_CAPEX: 0.540
NPV [MEuro]: 89.279
IRR: 0.103
LCOE [Euro/MWh]: 41.288
Revenues [MEuro]: 14.254
CAPEX [MEuro]: 165.198
OPEX [MEuro]: 2.293
Wind CAPEX [MEuro]: 76.430
Wind OPEX [MEuro]: 1.618
PV CAPEX [MEuro]: 33.500
PV OPEX [MEuro]: 0.675
Batt CAPEX [MEuro]: 0.000
Batt OPEX [MEuro]: 0.000
Shared CAPEX [MEuro]: 55.268
Shared Opex [MEuro]: 0.000
penalty lifetime [MEuro]: 0.000
AEP [GWh]: 356.345
GUF: 0.136
grid [MW]: 300.000
wind [MW]: 100.000
solar [MW]: 100.000
Battery Energy [MWh]: 0.000
Battery Power [MW]: 0.000
Total curtailment [GWh]: 0.000
Total curtailment with deg [GWh]: 0.000
Awpp [km2]: 14.286
Apvp [km2]: 1.226
Plant area [km2]: 14.286
Rotor diam [m]: 134.867
Hub height [m]: 77.434
Number of batteries used in lifetime: 0.000
Break-even PPA price [Euro/MWh]: 27.152
Capacity factor wind [-]: 0.265

The break-even PPA price is seen in the above table and can also be extracted directly:

[37]:

print(f'Input H2 price: {float(PPA):.2f}')
print(f'Break-even H2 price: {float(hpp.prob["break_even_PPA_price"][0]):.2f}')

Input H2 price: 40.00
Break-even H2 price: 27.15

LCOE is a measure of lifetime cost of producing power, but comes out somewhat different from the break-even price. This is because break-even price is based on NPV calculations that takes into account e.g. the corporate tax rate as well as penalties incurred from not meeting expected loads.