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updates: - [github.com/PyCQA/isort: 5.13.0 → 5.13.2](https://github.com/PyCQA/isort/compare/5.13.0...5.13.2) - [github.com/psf/black: 23.11.0 → 23.12.0](https://github.com/psf/black/compare/23.11.0...23.12.0)
1 parent 1d3d4c9
multi-decade-example.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 17 15:25:05 2021.
example script to illustrate new features of the multi-decade investment
@author: Lisa
"""
import numpy as np
import pandas as pd
import pypsa
n = pypsa.Network()
# snapshot weightings are now separated between weightings for
# (i) the objective function (objective)
# (ii) elapsed time in the filling of Store and StorageUnit components (stores)
# (ii) for the contribution of generators to GlobalConstraint components for e.g. CO2 budgets (generators)
print(n.snapshot_weightings)
# there is a new component investment period analog to n.snapshots
n.investment_periods
# along with corresponding weightings split by weighting
# for the objective function (objective)
# and weightings for the elapsed time between the investment periods (years)
# to calculate CO2 emissions or assets lifetime
# default is an empty pd.DataFrame
print(n.investment_period_weightings)
# default snapshots are still single Index
print(n.snapshots)
# when a pypsa network from an older version is imported, the old snapshot weightings
# are set row-wise and a warning is given
# n = pypsa.examples.storage_hvdc() -> "HTTPError: Not Found"
n = pypsa.Network("opf-storage-hvdc/opf-storage-data")
print(n.snapshot_weightings)
# snapshots can be set as a multiindex
# a convenience functionality is provided: when setting the investment periods
# directly, the all time-series are repeated for each period.
single_snapshots = n.snapshots
n.investment_periods = [2020, 2030]
print(n.snapshots)
print(n.investment_period_weightings)
assert all(n.snapshots == pd.MultiIndex.from_product([[2020, 2030], single_snapshots]))
# new attributes for generators/store/storage/links/lines/transformers used to
# calculate when an asset is active:
# "build_year": indicating when an asset is built (integer, default: 0)
# "lifetime": lifetime of asset (float, default: inf)
print(n.generators[["build_year", "lifetime"]])
# store/storage unit behaviour
# cyclic ('e_cyclic_per_period'/'cyclic_state_of_charge_per_period') means now:
# cyclic within each investment period -> default: True
print(n.storage_units["cyclic_state_of_charge_per_period"])
print(n.stores.e_initial_per_period)
# there is a new attribute (bool) called for stores "e_initial_per_period" and
# for storage units 'state_of_charge_initial_per_period'
# default is true, which sets the state of charge at the beginning of each
# investment period to the same value
print(n.storage_units.state_of_charge_initial_per_period)
print(n.stores.e_initial_per_period)
# there is a new attribute "investment_period" for global constraints where the investment period can
# be specified, for which the constraint is valid
# if this attribute is not set it applies for all investment periods
print(n.global_constraints.columns)
# when calling the lopf new attribute "multi_investment_periods" (Bool, default:False)
# only implemented for pyomo = False
# PYOMO = True
# single investment as before if snapshots single Index
# if snapshots are pd.MultiIndex or multi_investment_periods=True returns a not implemented error
try:
n.lopf(pyomo=True, multi_investment_periods=False)
except Exception as e:
print(e)
# this returns a not implemented error
try:
n.lopf(pyomo=True, multi_investment_periods=True)
except Exception as e:
print(e)
# this returns multi investment optimisation if the snapshots are pd.MultiIndex
# otherwise returns a typeError
n.lopf(pyomo=False, multi_investment_periods=True)
# %% small test network
def get_social_discount(t, r=0.01):
"""
Calaculate social discount rate.
"""
return 1 / (1 + r) ** t
def get_investment_weighting(energy_weighting, r=0.01):
"""
Return cost weightings.
Weightings depend on the the energy_weighting (pd.Series) and the
social discountrate r.
"""
end = energy_weighting.cumsum()
start = energy_weighting.cumsum().shift().fillna(0)
return pd.concat([start, end], axis=1).apply(
lambda x: sum(get_social_discount(t, r) for t in range(int(x[0]), int(x[1]))),
axis=1,
)
# create pypsa network
n = pypsa.Network()
# ## How to set snapshots and investment periods
# First set some parameters as years and temporal resolution
years = [2020, 2030, 2040, 2050]
freq = "24"
# init snapshots (format -> DatetimeIndex)
snapshots = pd.DatetimeIndex([])
for year in years:
period = pd.date_range(
start=f"{year}-01-01 00:00",
freq=f"{freq}H",
periods=8760 / float(freq),
)
snapshots = snapshots.append(period)
# convert to multiindex and assign to network
n.snapshots = pd.MultiIndex.from_arrays([snapshots.year, snapshots])
n.investment_periods = years
print(n.snapshots)
print(n.investment_periods)
r = 0.01 # social discountrate
# set energy weighting -> last year is weighted by 10
n.investment_period_weightings.loc[:, "years"] = (
n.investment_periods.to_series().diff().shift(-1).fillna(10)
)
# set investment_weighting
n.investment_period_weightings.loc[:, "objective"] = get_investment_weighting(
n.investment_period_weightings["years"], r
)
print(n.investment_period_weightings)
# add three buses
for i in range(3):
n.add("Bus", f"bus {i}")
# There are 2 new attribute for the components ("Line", "Link", "Generator", Storage", ...) <br>
# (1) "**build_year**" : time when the asset is build (=first year of operation) <br>
# (2) "**lifetime**": time of operation (not used to annualise the capital costs) <br>
# - If build_year and lifetime is not specified, it is assumed that the asset can operate in all investment_periods. - If only the build_year and no lifetime is specified, it is assumed the the asset can operate from build_year until the end of the optimisation time range
# - If the lifetime and no build_year is specified, it is assumed that the assets operates from the first timestep until end of lifetime
# - If the investment periods are a pd.DatetimeIndex a build year before the considered time frame is considered. E.g. n.investment_periods = [2020, 2030, 2040] and lifetime of an asset is 15 year, build year is 2010, than the asset can only operate in 2020.
# add three lines in a ring
n.add(
"Line",
"line 0->1",
bus0="bus 0",
bus1="bus 1",
x=0.0001,
s_nom=0,
# build_year=2030,
s_nom_extendable=True,
)
n.add(
"Line",
"line 1->2",
bus0="bus 1",
bus1="bus 2",
x=0.0001,
capital_cost=10,
build_year=2030,
s_nom=0,
s_nom_extendable=True,
)
n.add(
"Line",
"line 2->0",
bus0="bus 2",
bus1="bus 0",
x=0.0001,
s_nom=0,
s_nom_extendable=True,
build_year=2030,
)
# the function **n.determine_network_topology()** takes now as an optional
# argument the investment_period, be aware that the column values of sub_network are only valid for a certain investment_period. In this example, in the first investment period 2020 "bus 2" would be not connected to "bus 0" and "bus 1"
# works as before -> not considering if the assets are active
n.determine_network_topology()
print(n.buses.sub_network)
# determines network topolgy in first investment period (bus 2 isolated)
n.determine_network_topology(n.investment_periods[0])
print(n.buses.sub_network)
# determines network topology in third investment period (all lines are build)
n.determine_network_topology(n.investment_periods[2])
print(n.buses.sub_network)
n.lines.loc["line 2->0", "build_year"] = 2020
# add some generators
p_nom_max = pd.Series(
(np.random.uniform() for _ in range(len(n.snapshots))),
index=n.snapshots,
name="generator ext 2020",
)
# renewable (can operate 2020, 2030)
n.add(
"Generator",
"generator ext 0 2020",
bus="bus 0",
p_nom=50,
build_year=2020,
lifetime=20,
marginal_cost=2,
capital_cost=1,
p_max_pu=p_nom_max,
carrier="solar",
p_nom_extendable=True,
)
# add an expensive generator (can operate in all investment periods)
# n.add("Generator",
# "generator fix expensive 2 2020",
# bus="bus 2",
# p_nom=100,
# build_year=2020,
# lifetime=31,
# carrier="lignite",
# marginal_cost=1000,
# capital_cost=10)
# can operate 2040, 2050
n.add(
"Generator",
"generator ext 0 2040",
bus="bus 0",
p_nom=50,
build_year=2040,
lifetime=11,
marginal_cost=25,
capital_cost=10,
carrier="OCGT",
p_nom_extendable=True,
)
# can operate in 2040
n.add(
"Generator",
"generator fix 1 2040",
bus="bus 1",
p_nom=50,
build_year=2040,
lifetime=10,
carrier="CCGT",
marginal_cost=20,
capital_cost=1,
)
# add StorageUnits
# n.add("StorageUnit",
# "storageunit cyclic 2030",
# bus="bus 2",
# p_nom=0,
# # marginal_cost=5,
# capital_cost=0.1,
# build_year=2030,
# lifetime=21,
# # efficiency_dispatch=0.9,
# # efficiency_store=0.99,
# cyclic_state_of_charge=True,
# p_nom_extendable=True,
# max_hours=180
# )
n.add(
"StorageUnit",
"storageunit non-periodic 2030",
bus="bus 2",
p_nom=0,
# marginal_cost=5,
capital_cost=2,
build_year=2030,
lifetime=21,
# efficiency_dispatch=0.9,
# efficiency_store=0.99,
# cyclic_state_of_charge=True,
p_nom_extendable=False,
# max_hours=180
)
n.add(
"StorageUnit",
"storageunit periodic 2020",
bus="bus 2",
p_nom=0,
# marginal_cost=5,
capital_cost=1,
build_year=2020,
lifetime=21,
# efficiency_dispatch=0.9,
# efficiency_store=0.99,
# cyclic_state_of_charge=True,
p_nom_extendable=True,
# max_hours=180
)
# n.add("StorageUnit",
# "storageunit noncyclic 2030",
# bus="bus 2",
# p_nom=0,
# # marginal_cost=5,
# capital_cost=0.1,
# build_year=2030,
# lifetime=21,
# state_of_charge_period = False,
# # efficiency_dispatch=0.9,
# # efficiency_store=0.99,
# # cyclic_state_of_charge=True,
# p_nom_extendable=True,
# max_hours=180
# )
# add battery store
n.add("Bus", "bus 2 battery")
# #
n.add(
"Store",
"store 2 battery 2020",
bus="bus 2 battery",
# e_cyclic=True,
e_nom_extendable=True,
e_initial=20,
build_year=2020,
lifetime=20,
capital_cost=0.1,
)
n.add(
"Link",
"bus2 battery charger",
bus0="bus 2",
bus1="bus 2" + " battery",
# efficiency=0.8,
# capital_cost=2,
p_nom_extendable=True,
)
n.add(
"Link",
"My bus2 battery discharger",
bus0="bus 2 battery",
bus1="bus 2",
efficiency=0.8,
# marginal_cost=1,
p_nom_extendable=True,
)
# add a Load
load_var = pd.Series(
(100 * np.random.uniform() for _ in range(len(n.snapshots))),
index=n.snapshots,
name="load",
)
load_fix = pd.Series(
[250 for _ in range(len(n.snapshots))], index=n.snapshots, name="load"
)
# add a load at bus 2
n.add("Load", "load 2", bus="bus 2", p_set=load_fix)
n.add("Load", "load 1", bus="bus 1", p_set=0.3 * load_fix)
# currently only for pyomo=False
n.lopf(pyomo=False, multi_investment_periods=True)
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