# '2030Gelderland_laag': {'id': 815716, 'latlon': (52, 6)},
'2030RES_Achterhoek': {'id': 815753, 'latlon': (52, 6.4)},
# '2030RES_ArnhemNijmegen': {'id': 815754, 'latlon': (51.9, 5.9)},
# '2030RES_Cleantech': {'id': 815755, 'latlon': (52.2, 6.1)},
# '2030RES_Foodvalley ': {'id': 815756, 'latlon': (52.1, 5.7)},
# '2030RES_NoordVeluwe': {'id': 815757, 'latlon': (52.4, 5.8)},
# '2030RES_Rivierenland': {'id': 815758, 'latlon': (51.9, 5.3)},
}
for modelname, value in scenarios_gelderland.items():
scenario_id = value['id']
lat, lon = value['latlon']
# ==========System-setup=======================================================
system = System(lat, lon, model_name=modelname)
# =============================================================================
pva1 = PhotoVoltaic('PV Full south', dof = True)
wind1 = Wind('Onshore turbine', lat=lat, lon=lon, dof = True)
bat1 = Lithium('Li-ion EES', dof = True)
h2 = Hydrogen('H2 storage', dof = True)
demand = ETMdemand('NoordVeluwe2030', scenario_id=scenario_id)
grid = Grid('Grid connection', variable_income=2.5e-6)
deficit = FinalBalance()
# =============================================================================
# Limit grid component capacity as function of maximum demand
demand.calculate_time_serie()
peak_demand = -demand.state.power.min()
grid.installed = 1/3 * peak_demand
import pandas as pd
from LESO import System
from LESO import PhotoVoltaic, Wind
import seaborn as sns
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import tsam.timeseriesaggregation as tsam
import numpy as np
from scipy.stats import kde
from durationcurves import kotzur_normalize
from scipy.signal import savgol_filter
import LESO.plotly_extension as pe
modelname = "ETM Noord-Veluwe trial 1"
lat, lon = 52.4, 5.8
system = System(lat, lon, model_name=modelname)
wind = Wind("Onshore turbine", lat=lat, lon=lon, installed=1, use_dowa=False)
pv = PhotoVoltaic("PV Full south", installed=1)
system.add_components([wind, pv])
system.fetch_input_data()
system.calculate_time_series()
source = "PVGIS-SARAH ERA-Interim 2005-2016 TMY"
#%% aggregation
raw = pd.DataFrame([wind.state.power, pv.state.power], index=["Wind", "PV"]).transpose()
aggregation = tsam.TimeSeriesAggregation(
raw,
noTypicalPeriods=8,
hoursPerPeriod=24,
price_filepath = os.path.join(os.path.dirname(__file__), price_filename)
retail_prices = pd.read_pickle(price_filepath)
if correct_SDE:
profile_factor = 0.65 # CE Delft, pag 22- Scenario’s zon op grote daken
basis_price = 55 # Arbitrary
correction_price = lambda retail_prices, profile_factor: retail_prices.mean(
) * profile_factor
SDE_price = basis_price - correction_price(retail_prices, profile_factor)
retail_prices[retail_prices < SDE_price] = SDE_price
retail_prices = list(
(retail_prices / 1e6).values) # convert from eu/MWh to M eu/MWh
# initiate System component
system = System(lat=lat,
lon=lon,
model_name=modelname,
equity_share=equity_share)
#%%
grid = Grid("Grid connection",
installed=10,
variable_cost=999e-6,
variable_income=retail_prices)
wind = Wind(
"Nordex N100 2500",
dof=False,
installed=10,
turbine_type="Nordex N100 2500", # actually Lagerwey L100 2.5 MW, best match
hub_height=100,
use_ninja=True,
)
# example_EVhub_optimization.py
#%% Import relevant packages
from LESO import System
from LESO import PhotoVoltaic, Wind, Lithium, FastCharger, Consumer, Grid, FinalBalance
import os
#%% Define system and components
modelname = "VREEVHUB_Utrecht_plain"
lat, lon = 52.09, 5.15 # Utrecht
target_dir = "G:\\My Drive\\0 Thesis\\LESO results\\examples"
# initiate System component
system = System(latitude=lat, longitude=lon, model_name=modelname)
# initiate and define components
pv_s = PhotoVoltaic("PV Full south", dof=True)
pv_w = PhotoVoltaic("PV West", azimuth=90, dof=True, capex=0.55) # slightly cheaper (lower land use, straight-forward construction)
pv_e = PhotoVoltaic("PV East", azimuth=-90, dof=True, capex=0.55) # slightly cheaper (lower land use, straight-forward construction)
wind = Wind("Onshore turbine", dof=True)
bat = Lithium("Li-ion EES", dof=True)
charger = FastCharger("DC quickcharger", dof=False)
petrolstation = Consumer("Petrolstation E. demand")
grid = Grid("Grid connection", installed=150e3)
final = FinalBalance(name="curtailment_underload")
#%% add the components to the system
component_list = [pv_s, pv_w, pv_e, wind, bat, charger, petrolstation, final, grid]
system.add_components(component_list)
from LESO import System
from LESO import PhotoVoltaic, Wind, Lithium, Grid, FinalBalance
import os
from experiments_overview import MODEL_FOLDER
import pandas as pd
import matplotlib.pyplot as plt
#%% Define system and components
modelname = "Cablepooling"
lat, lon = 51.81, 5.84 # Nijmegen
equity_share = 0.5 # to bump the roi up to about 7.5%
# initiate System component
system = System(lat=lat,
lon=lon,
model_name=modelname,
equity_share=equity_share)
# initiate and define components
pv_s = PhotoVoltaic("PV South", azimuth=180, use_ninja=True, dof=True)
pv_e = PhotoVoltaic("PV East", azimuth=90, use_ninja=True, dof=True)
pv_w = PhotoVoltaic("PV West", azimuth=270, use_ninja=True, dof=True)
#%% add the components to the system
# note that we do not add wind now!
component_list = [
pv_s,
pv_w,
pv_e,
]
system.add_components(component_list)
#%%
from LESO import System
from LESO import Grid, Wind
import pandas as pd
import os
#%% read system definition from regular cable pool
modelname = "cablepool_greenfield"
system = System.read_pickle(
os.path.join(os.path.dirname(__file__), "cablepool.pkl"))
system.name = modelname
# extract grid component
for component in system.components:
if isinstance(component, Grid):
grid = component
# extract wind component
for component in system.components:
if isinstance(component, Wind):
wind = component
#%% dynamic part
# read .pkl from dynamic_price_analysis
price_filename = "etm_dynamic_savgol_filtered_etmprice_31ch4_85co2.pkl"
mwh_prices = pd.read_pickle(
os.path.join(os.path.dirname(__file__), price_filename))
energy_market_price = mwh_prices.values / 1e6 # convert from [eu / MWh] to [Meu / MWh]
# set the dynamic pricing to the grid component
grid.variable_income = list(energy_market_price)
# set the wind component to a DOF
wind.dof = True