Evaluating the performance of a hybrid power plant with P2X using HyDesign

In this notebook we will evaluate a hybrid power plant design in a specific location.

A hybrid power plant design consists on selecting the following parameters:

Wind Plant design:

1. Number of wind turbines in the wind plant [-] (Nwt)

2. Wind power installation density [MW/km2] (wind_MW_per_km2): This parameter controls how closely spaced are the turbines, which in turns affect how much wake losses are present.

PV Plant design:

3. Solar plant power capacity [MW] (solar_MW)

Battery Storage design:

4. Battery power [MW] (b_P)

5. Battery energy capacity in hours [MWh] (b_E_h): Battery storage capacity in hours of full battery power (b_E = b_E_h * b_P).

6. Cost of battery power fluctuations in peak price ratio [-] (cost_of_batt_degr): This parameter controls how much penalty is given to do ramps in battery power in the HPP operation.

Electrolyzer design:

7. Electrolyzer capacity [MW] (ptg_MW)

8. H2 storage capacity [kg] (HSS_kg)

Imports

Install hydesign if needed. Import basic libraries. Import HPP model assembly class. Import the examples file path.

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

import os
import time
import yaml
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from hydesign.assembly.hpp_assembly_P2X import hpp_model_P2X as hpp_model
from hydesign.examples import examples_filepath

Specifying the site

Hydesign, provides example data from several sites in India and Europe.

The site coordinates (longitude, latitude, and altitude) are given in examples_sites.csv.

examples_sites = pd.read_csv(f'{examples_filepath}examples_sites.csv', index_col=0, sep=';')
examples_sites

	case	name	longitude	latitude	altitude	input_ts_fn	sim_pars_fn	price_fn	price_col	H2_demand_col	Unnamed: 11	input_HA_ts_fn	price_up_ts	price_dwn_ts
0	India	Indian_site_good_wind	77.500226	8.334294	679.803454	India/GWA2/input_ts_Indian_site_good_wind.csv	India/hpp_pars.yml	India/Indian_elec_price_t.csv	Price	India/H2_demand.csv	NaN	NaN	NaN	NaN
1	India	Indian_site_good_solar	68.542204	23.542099	29.883557	India/GWA2/input_ts_Indian_site_good_solar.csv	India/hpp_pars.yml	India/Indian_elec_price_t.csv	Price	India/H2_demand.csv	NaN	NaN	NaN	NaN
2	India	Indian_site_bad_solar_bad_wind	77.916878	17.292316	627.424643	India/GWA2/input_ts_Indian_site_bad_solar_bad_...	India/hpp_pars.yml	India/Indian_elec_price_t.csv	Price	India/H2_demand.csv	NaN	NaN	NaN	NaN
3	Europe	France_good_solar	4.229736	44.422011	204.000000	Europe/GWA2/input_ts_France_good_solar.csv	Europe/hpp_pars.yml	Europe/2030-EL_PRICE.csv	FR_R	Europe/H2_demand.csv	NaN	NaN	NaN	NaN
4	Europe	France_good_wind	-0.864258	48.744116	302.000000	Europe/GWA2/input_ts_France_good_wind.csv	Europe/hpp_pars.yml	Europe/2030-EL_PRICE.csv	FR_R	Europe/H2_demand.csv	NaN	NaN	NaN	NaN
5	Europe	France_bad_solar_n_wind	2.167969	47.428087	140.000000	Europe/GWA2/input_ts_France_bad_solar_n_wind.csv	Europe/hpp_pars.yml	Europe/2030-EL_PRICE.csv	FR_R	Europe/H2_demand.csv	NaN	NaN	NaN	NaN
6	Europe	Germany_bad_solar_n_wind	10.766602	49.310798	442.000000	Europe/GWA2/input_ts_Germany_bad_solar_n_wind.csv	Europe/hpp_pars.yml	Europe/2030-EL_PRICE.csv	DE_ME	Europe/H2_demand.csv	NaN	NaN	NaN	NaN
7	Europe	Germany_good_wind	7.873535	53.287111	5.000000	Europe/GWA2/input_ts_Germany_good_wind.csv	Europe/hpp_pars.yml	Europe/2030-EL_PRICE.csv	DE_NW	Europe/H2_demand.csv	NaN	NaN	NaN	NaN
8	Europe	Denmark_good_solar	11.813965	55.397760	42.000000	Europe/GWA2/input_ts_Denmark_good_solar.csv	Europe/hpp_pars.yml	Europe/2030-EL_PRICE.csv	DK_E	Europe/H2_demand.csv	NaN	NaN	NaN	NaN
9	Europe	Denmark_good_wind	8.594398	56.227322	85.000000	Europe/GWA2/input_ts_Denmark_good_wind.csv	Europe/hpp_pars.yml	Europe/2030-EL_PRICE.csv	DK_W	Europe/H2_demand.csv	NaN	NaN	NaN	NaN
10	Europe	Denmark_offshore	7.906111	55.529722	85.000000	Europe/GWA2/input_ts_Denmark_offshore.csv	Europe/hpp_pars_offshore.yml	Europe/2030-EL_PRICE.csv	DK_W	Europe/H2_demand.csv	NaN	NaN	NaN	NaN
11	Europe	Denmark_good_wind_BM	8.594398	56.227322	85.000000	Europe/GWA2_BM/input_ts_Denmark_good_wind_DA.csv	Europe/hpp_pars.yml	Europe/2030-EL_PRICE.csv	DK_W	Europe/H2_demand.csv	NaN	Europe/GWA2_BM/input_ts_Denmark_good_wind_HA.csv	Europe/BM_Prices/Up_reg_price.csv	Europe/BM_Prices/Down_reg_price.csv
12	Europe	Denmark_hybridization_wind_Norhede_Hjortmose	8.366400	56.095400	16.662000	Europe/GWA2/input_ts_Denmark_hybridization_win...	Europe/hpp_pars_Hjortmose.yml	Europe/2030-EL_PRICE.csv	DK_W	Europe/H2_demand.csv	NaN	NaN	NaN	NaN
13	Europe	Denmark_hybridization_solar_Langelinie	11.290641	54.717469	0.042000	Europe/GWA2/input_ts_Denmark_hybridization_sol...	Europe/hpp_pars_Langelinie.yml	Europe/2030-EL_PRICE.csv	DK_E	Europe/H2_demand.csv	NaN	NaN	NaN	NaN

Select a site to run

name = 'Denmark_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]

# Weather data and Price data
input_ts_fn = examples_filepath+ex_site['input_ts_fn'].values[0]

input_ts = pd.read_csv(input_ts_fn, index_col=0, parse_dates=True)

required_cols = [col for col in input_ts.columns if 'WD' not in col]
input_ts = input_ts.loc[:,required_cols]
input_ts

	WS_1	WS_50	WS_100	WS_150	WS_200	temp_air_1	ghi	dni	dhi	Price	Unnamed: 16
2012-01-01 00:00:00	2.803428	6.289076	8.109687	8.777118	10.335076	276.767325	0.035121	0.0	0.035121	35.171	NaN
2012-01-01 01:00:00	2.791624	6.275261	8.093319	8.910806	10.407985	277.374423	0.017560	0.0	0.017560	35.171	NaN
2012-01-01 02:00:00	3.036807	6.656427	8.547709	9.467695	10.864995	278.015879	0.000000	0.0	0.000000	35.171	NaN
2012-01-01 03:00:00	3.405581	7.233561	9.237670	10.435093	11.793017	278.655888	0.000000	0.0	0.000000	35.171	NaN
2012-01-01 04:00:00	3.649363	7.542440	9.584774	10.864283	12.081760	279.290819	0.000000	0.0	0.000000	35.171	NaN
...	...	...	...	...	...	...	...	...	...	...	...
2012-12-30 19:00:00	3.263756	7.267221	9.358232	10.267748	11.455513	277.214861	0.000000	0.0	0.000000	35.446	NaN
2012-12-30 20:00:00	3.368223	7.417605	9.533621	10.482431	11.694345	276.913527	0.000000	0.0	0.000000	33.565	NaN
2012-12-30 21:00:00	3.392954	7.546295	9.715589	10.655855	11.978293	277.356365	0.000000	0.0	0.000000	32.709	NaN
2012-12-30 22:00:00	3.898207	8.486008	10.884241	11.965626	13.407147	278.369005	0.000000	0.0	0.000000	31.392	NaN
2012-12-30 23:00:00	4.359323	9.180988	11.708513	12.752611	14.282134	278.680641	0.000000	0.0	0.000000	30.124	NaN

8760 rows × 11 columns

# Hydrogen demand data, when H2 offtake is infinite -> make H2_demand values very high (1e6) in H2_demand.csv file and penalty_H2 as '0' in hpp_pars.yml file
H2_demand_fn = examples_filepath+ex_site['H2_demand_col'].values[0]

H2_demand_ts = pd.read_csv(H2_demand_fn, index_col=0, parse_dates=True)
H2_demand_ts

	H2_demand
2012-01-01 00:00:00	10000
2012-01-01 01:00:00	10000
2012-01-01 02:00:00	10000
2012-01-01 03:00:00	10000
2012-01-01 04:00:00	10000
...	...
2012-12-31 19:00:00	10000
2012-12-31 20:00:00	10000
2012-12-31 21:00:00	10000
2012-12-31 22:00:00	10000
2012-12-31 23:00:00	10000

8784 rows × 1 columns

# Input data of technology's cost
sim_pars_fn = examples_filepath+ex_site['sim_pars_fn'].values[0]

with open(sim_pars_fn) as file:
    sim_pars = yaml.load(file, Loader=yaml.FullLoader)

print(sim_pars_fn)
sim_pars

c:/sandbox/repo/topfarm/hydesign/hydesign/examples/Europe/hpp_pars.yml

{'G_MW': 300,
 'year': '2012',
 'N_life': 25,
 'wind_turbine_cost': 640000,
 'wind_civil_works_cost': 260000,
 'wind_fixed_onm_cost': 12600,
 'wind_variable_onm_cost': 1.35,
 'd_ref': 145,
 'hh_ref': 100,
 'p_rated_ref': 5.0,
 'wpp_efficiency': 1,
 'wind_deg_yr': [0, 25],
 'wind_deg': [0, 0.25],
 'share_WT_deg_types': 0.5,
 'solar_PV_cost': 110000,
 'solar_hardware_installation_cost': 100000,
 'solar_inverter_cost': 20000,
 'solar_fixed_onm_cost': 4500,
 'land_use_per_solar_MW': 0.01226,
 'tracking': 'No',
 'pv_deg_yr': [0, 1, 25],
 'pv_deg': [0.03, 0.03, 0.28],
 'latitude': None,
 'longitude': None,
 'altitude': None,
 'battery_energy_cost': 62000,
 'battery_power_cost': 16000,
 'battery_BOP_installation_commissioning_cost': 80000,
 'battery_control_system_cost': 2250,
 'battery_energy_onm_cost': 0,
 'battery_depth_of_discharge': 0.9,
 'battery_charge_efficiency': 0.985,
 'battery_price_reduction_per_year': 0.05,
 'min_LoH': 0.7,
 'n_full_power_hours_expected_per_day_at_peak_price': 0,
 'peak_hr_quantile': 0.9,
 'price_H2': 5,
 'storage_eff': 0.9,
 'ptg_deg': 0.99,
 'hhv': 39.3,
 'water_consumption': 9.4,
 'electrolyzer_capex_cost': 800000,
 'electrolyzer_opex_cost': 16000,
 'electrolyzer_power_electronics_cost': 0,
 'water_cost': 4,
 'water_treatment_cost': 2,
 'H2_storage_capex_cost': 300,
 'H2_storage_opex_cost': 3,
 'H2_transportation_cost': 5,
 'H2_transportation_distance': 0,
 'penalty_factor_H2': 0,
 'electrolyzer_eff_curve_name': 'PEM electrolyzer H2 production',
 'min_power_standby': 0.0,
 'hpp_BOS_soft_cost': 119940,
 'hpp_grid_connection_cost': 50000,
 'land_cost': 300000,
 'wind_WACC': 0.06,
 'solar_WACC': 0.06,
 'battery_WACC': 0.06,
 'tax_rate': 0.22,
 'ptg_WACC': 0.08,
 'phasing_yr': [-1, 0],
 'phasing_CAPEX': [1, 1],
 'inflation_yr': [-3, 0, 1, 25],
 'inflation': [0.1, 0.1, 0.06, 0.06],
 'ref_yr_inflation': 0,
 'depreciation_yr': [0, 25],
 'depreciation': [0, 1],
 'bi_directional_status': 0,
 'penalty_BM': 2000,
 'era5_zarr': '/groups/reanalyses/era5/app/era5.zarr',
 'ratio_gwa_era5': '/groups/INP/era5/ratio_gwa2_era5.nc',
 'era5_ghi_zarr': '/groups/INP/era5/ghi.zarr',
 'elevation_fn': '/groups/INP/era5/SRTMv3_plus_ViewFinder_coarsen.nc'}

Initializing the HPP model

Initialize the HPP model (hpp_model class) with the coordinates and the necessary input files.

hpp = hpp_model(
        latitude=latitude,
        longitude=longitude,
        altitude=altitude,
        num_batteries = 3,
        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 = 8.594398
latitude = 56.227322
altitude = 85.0

Evaluating the HPP model

start = time.time()

clearance = 10
sp = 360
p_rated = 4
Nwt = 90
wind_MW_per_km2 = 5
solar_MW = 80
surface_tilt = 50
surface_azimuth = 210
DC_AC_ratio = 1.5
b_P = 50
b_E_h  = 3
cost_of_batt_degr = 10
ptg_MW = 130
HSS_kg = 0

x = [clearance, sp, p_rated, Nwt, wind_MW_per_km2, solar_MW, \
surface_tilt, surface_azimuth, DC_AC_ratio, b_P, b_E_h , cost_of_batt_degr, ptg_MW, HSS_kg]

outs = hpp.evaluate(*x)

hpp.print_design(x, outs)

end = time.time()
print(f'exec. time [min]:', (end - start)/60 )

Design:
---------------
clearance [m]: 10.000
sp [W/m2]: 360.000
p_rated [MW]: 4.000
Nwt: 90.000
wind_MW_per_km2 [MW/km2]: 5.000
solar_MW [MW]: 80.000
surface_tilt [deg]: 50.000
surface_azimuth [deg]: 210.000
DC_AC_ratio: 1.500
b_P [MW]: 50.000
b_E_h [h]: 3.000
cost_of_battery_P_fluct_in_peak_price_ratio: 10.000
ptg_MW [MW]: 130.000
HSS_kg [kg]: 0.000


NPV_over_CAPEX: 0.820
NPV [MEuro]: 393.278
IRR: 0.124
LCOE [Euro/MWh]: 44.828
LCOH [Euro/kg]: 8.247
Revenue [MEuro]: 1320.888
CAPEX [MEuro]: 479.756
OPEX [MEuro]: 8.392
penalty lifetime [MEuro]: 0.000
AEP [GWh]: 177.221
GUF: 0.067
annual_H2 [tons]: 9233.125
annual_P_ptg [GWh]: 607.683
grid [MW]: 300.000
wind [MW]: 360.000
solar [MW]: 80.000
PtG [MW]: 130.000
HSS [kg]: 0.000
Battery Energy [MWh]: 150.000
Battery Power [MW]: 50.000
Total curtailment [GWh]: 0.000
Awpp [km2]: 72.000
Apvp [km2]: 0.981
Rotor diam [m]: 118.942
Hub height [m]: 69.471
Number of batteries used in lifetime: 1.000
Break-even H2 price [Euro/kg]: 2.677
Break-even PPA price [Euro/MWh]: 0.000
Capacity factor wind [-]: 0.210

exec. time [min]: 1.1957433700561524

Plot the HPP operation

b_E_SOC_t = hpp.prob.get_val('ems.b_E_SOC_t')
b_t = hpp.prob.get_val('ems.b_t')
price_t = hpp.prob.get_val('ems.price_t')

wind_t = hpp.prob.get_val('ems.wind_t')
solar_t = hpp.prob.get_val('ems.solar_t')
hpp_t = hpp.prob.get_val('ems.hpp_t')
hpp_curt_t = hpp.prob.get_val('ems.hpp_curt_t')
P_ptg_t = hpp.prob.get_val('ems.P_ptg_t')
P_ptg_SB_t = hpp.prob.get_val('ems.P_ptg_SB_t')
grid_MW = hpp.prob.get_val('ems.G_MW')

m_H2_t = hpp.prob.get_val('ems.m_H2_t')
m_H2_demand_t = hpp.prob.get_val('ems.m_H2_demand_t_ext')
m_H2_offtake_t = hpp.prob.get_val('ems.m_H2_offtake_t')
LoS_H2_t = hpp.prob.get_val('ems.LoS_H2_t')

n_days_plot = 14
plt.figure(figsize=[12,4])
plt.plot(price_t[:24*n_days_plot], label='price')
plt.plot(b_E_SOC_t[:24*n_days_plot], label='SoC [MWh]')
plt.plot(b_t[:24*n_days_plot], label='Battery P [MW]')
plt.xlabel('time [hours]')
plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15),
           ncol=3, fancybox=0, shadow=0)

plt.figure(figsize=[12,4])
plt.plot(wind_t[:24*n_days_plot], label='wind')
plt.plot(solar_t[:24*n_days_plot], label='PV')
plt.plot(hpp_t[:24*n_days_plot], label='HPP')
plt.plot(hpp_curt_t[:24*n_days_plot], label='HPP curtailed')
plt.plot(b_t[:24*n_days_plot], label='Battery P [MW]')
plt.plot(P_ptg_t[:24*n_days_plot], label='PtG')
plt.plot(P_ptg_SB_t[:24*n_days_plot], label='PtG_SB')
plt.axhline(grid_MW, label='Grid MW', color='k')
plt.xlabel('time [hours]')
plt.ylabel('Power [MW]')
plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15),
           ncol=7, fancybox=0, shadow=0)

<matplotlib.legend.Legend at 0x1c72df51d50>

results = {'Wind': wind_t, 'PV': solar_t, 'HPP': hpp_t[:8760], 'Curtailment': hpp_curt_t[:8760],\
                    'Battery power': b_t[:8760], 'Energy Level': b_E_SOC_t[:8760], 'Prices': price_t, 'PtG': P_ptg_t[:8760], 'Mass H2': m_H2_t[:8760], 'PtG_SB': P_ptg_SB_t[:8760],}
df = pd.DataFrame(results)
df.to_csv('EMS_out_9_P2X.csv')

plt.figure(figsize=[12,4])
plt.plot(m_H2_t[:24*n_days_plot], label='H2 produced')
plt.plot(m_H2_offtake_t[:24*n_days_plot], label='H2 offtake')
#plt.plot(m_H2_demand_t[:24*n_days_plot], label='H2 demand')
plt.plot(LoS_H2_t[:24*n_days_plot], label='LoS')
plt.xlabel('time [hours]')
plt.ylabel('Hydrogen [kg]')
plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15),
           ncol=5, fancybox=0, shadow=0)

<matplotlib.legend.Legend at 0x1c72e086290>