import numpy as np
import pandas as pd
import logging
logger = logging.getLogger(__name__)
import gc
import os
import pypsa
from pypsa.linopt import get_var, linexpr, define_constraints
from pypsa.descriptors import free_output_series_dataframes
# Suppress logging of the slack bus choices
pypsa.pf.logger.setLevel(logging.WARNING)
from vresutils.benchmark import memory_logger
#First tell PyPSA that links can have multiple outputs by
#overriding the component_attrs. This can be done for
#as many buses as you need with format busi for i = 2,3,4,5,....
#See https://pypsa.org/doc/components.html#link-with-multiple-outputs-or-inputs
override_component_attrs = pypsa.descriptors.Dict({k : v.copy() for k,v in pypsa.components.component_attrs.items()})
override_component_attrs["Link"].loc["bus2"] = ["string",np.nan,np.nan,"2nd bus","Input (optional)"]
override_component_attrs["Link"].loc["bus3"] = ["string",np.nan,np.nan,"3rd bus","Input (optional)"]
override_component_attrs["Link"].loc["efficiency2"] = ["static or series","per unit",1.,"2nd bus efficiency","Input (optional)"]
override_component_attrs["Link"].loc["efficiency3"] = ["static or series","per unit",1.,"3rd bus efficiency","Input (optional)"]
override_component_attrs["Link"].loc["p2"] = ["series","MW",0.,"2nd bus output","Output"]
override_component_attrs["Link"].loc["p3"] = ["series","MW",0.,"3rd bus output","Output"]
def patch_pyomo_tmpdir(tmpdir):
# PYOMO should write its lp files into tmp here
import os
if not os.path.isdir(tmpdir):
os.mkdir(tmpdir)
from pyutilib.services import TempfileManager
TempfileManager.tempdir = tmpdir
def prepare_network(n, solve_opts=None):
if solve_opts is None:
solve_opts = snakemake.config['solving']['options']
if 'clip_p_max_pu' in solve_opts:
for df in (n.generators_t.p_max_pu, n.generators_t.p_min_pu, n.storage_units_t.inflow):
df.where(df>solve_opts['clip_p_max_pu'], other=0., inplace=True)
if solve_opts.get('load_shedding'):
n.add("Carrier", "Load")
n.madd("Generator", n.buses.index, " load",
bus=n.buses.index,
carrier='load',
sign=1e-3, # Adjust sign to measure p and p_nom in kW instead of MW
marginal_cost=1e2, # Eur/kWh
# intersect between macroeconomic and surveybased
# willingness to pay
# http://journal.frontiersin.org/article/10.3389/fenrg.2015.00055/full
p_nom=1e9 # kW
)
if solve_opts.get('noisy_costs'):
for t in n.iterate_components():
#if 'capital_cost' in t.df:
# t.df['capital_cost'] += 1e1 + 2.*(np.random.random(len(t.df)) - 0.5)
if 'marginal_cost' in t.df:
np.random.seed(174)
t.df['marginal_cost'] += 1e-2 + 2e-3*(np.random.random(len(t.df)) - 0.5)
for t in n.iterate_components(['Line', 'Link']):
np.random.seed(123)
t.df['capital_cost'] += (1e-1 + 2e-2*(np.random.random(len(t.df)) - 0.5)) * t.df['length']
if solve_opts.get('nhours'):
nhours = solve_opts['nhours']
n.set_snapshots(n.snapshots[:nhours])
n.snapshot_weightings[:] = 8760./nhours
return n
def add_opts_constraints(n, opts=None):
if opts is None:
opts = snakemake.wildcards.opts.split('-')
if 'BAU' in opts:
mincaps = snakemake.config['electricity']['BAU_mincapacities']
def bau_mincapacities_rule(model, carrier):
gens = n.generators.index[n.generators.p_nom_extendable & (n.generators.carrier == carrier)]
return sum(model.generator_p_nom[gen] for gen in gens) >= mincaps[carrier]
n.model.bau_mincapacities = pypsa.opt.Constraint(list(mincaps), rule=bau_mincapacities_rule)
if 'SAFE' in opts:
peakdemand = (1. + snakemake.config['electricity']['SAFE_reservemargin']) * n.loads_t.p_set.sum(axis=1).max()
conv_techs = snakemake.config['plotting']['conv_techs']
exist_conv_caps = n.generators.loc[n.generators.carrier.isin(conv_techs) & ~n.generators.p_nom_extendable, 'p_nom'].sum()
ext_gens_i = n.generators.index[n.generators.carrier.isin(conv_techs) & n.generators.p_nom_extendable]
n.model.safe_peakdemand = pypsa.opt.Constraint(expr=sum(n.model.generator_p_nom[gen] for gen in ext_gens_i) >= peakdemand - exist_conv_caps)
def add_eps_storage_constraint(n):
if not hasattr(n, 'epsilon'):
n.epsilon = 1e-5
fix_sus_i = n.storage_units.index[~ n.storage_units.p_nom_extendable]
n.model.objective.expr += sum(n.epsilon * n.model.state_of_charge[su, n.snapshots[0]] for su in fix_sus_i)
def add_battery_constraints(n):
nodes = n.buses.index[n.buses.carrier == "battery"]
link_p_nom = get_var(n, "Link", "p_nom")
lhs = linexpr((1,link_p_nom[nodes + " charger"]),
(-n.links.loc[nodes + " discharger", "efficiency"].values,
link_p_nom[nodes + " discharger"].values))
define_constraints(n, lhs, "=", 0, 'Link', 'charger_ratio')
def add_chp_constraints(n):
electric = n.links.index[n.links.index.str.contains("urban central") & n.links.index.str.contains("CHP") & n.links.index.str.contains("electric")]
heat = n.links.index[n.links.index.str.contains("urban central") & n.links.index.str.contains("CHP") & n.links.index.str.contains("heat")]
if not electric.empty:
link_p_nom = get_var(n, "Link", "p_nom")
#ratio of output heat to electricity set by p_nom_ratio
lhs = linexpr((n.links.loc[electric,"efficiency"]
*n.links.loc[electric,'p_nom_ratio'],
link_p_nom[electric]),
(-n.links.loc[heat,"efficiency"].values,
link_p_nom[heat].values))
define_constraints(n, lhs, "=", 0, 'chplink', 'fix_p_nom_ratio')
link_p = get_var(n, "Link", "p")
#backpressure
lhs = linexpr((n.links.loc[electric,'c_b'].values
*n.links.loc[heat,"efficiency"],
link_p[heat]),
(-n.links.loc[electric,"efficiency"].values,
link_p[electric].values))
define_constraints(n, lhs, "<=", 0, 'chplink', 'backpressure')
#top_iso_fuel_line
lhs = linexpr((1,link_p[heat]),
(1,link_p[electric].values),
(-1,link_p_nom[electric].values))
define_constraints(n, lhs, "<=", 0, 'chplink', 'top_iso_fuel_line')
def extra_functionality(n, snapshots):
#add_opts_constraints(n, opts)
#add_eps_storage_constraint(n)
add_chp_constraints(n)
add_battery_constraints(n)
def fix_branches(n, lines_s_nom=None, links_p_nom=None):
if lines_s_nom is not None and len(lines_s_nom) > 0:
n.lines.loc[lines_s_nom.index,"s_nom"] = lines_s_nom.values
n.lines.loc[lines_s_nom.index,"s_nom_extendable"] = False
if links_p_nom is not None and len(links_p_nom) > 0:
n.links.loc[links_p_nom.index,"p_nom"] = links_p_nom.values
n.links.loc[links_p_nom.index,"p_nom_extendable"] = False
def solve_network(n, config=None, solver_log=None, opts=None):
if config is None:
config = snakemake.config['solving']
solve_opts = config['options']
solver_options = config['solver'].copy()
if solver_log is None:
solver_log = snakemake.log.solver
solver_name = solver_options.pop('name')
def run_lopf(n, allow_warning_status=False, fix_zero_lines=False, fix_ext_lines=False):
free_output_series_dataframes(n)
if fix_zero_lines:
fix_lines_b = (n.lines.s_nom_opt == 0.) & n.lines.s_nom_extendable
fix_links_b = (n.links.carrier=='DC') & (n.links.p_nom_opt == 0.) & n.links.p_nom_extendable
fix_branches(n,
lines_s_nom=pd.Series(0., n.lines.index[fix_lines_b]),
links_p_nom=pd.Series(0., n.links.index[fix_links_b]))
if fix_ext_lines:
fix_branches(n,
lines_s_nom=n.lines.loc[n.lines.s_nom_extendable, 's_nom_opt'],
links_p_nom=n.links.loc[(n.links.carrier=='DC') & n.links.p_nom_extendable, 'p_nom_opt'])
if "line_volume_constraint" in n.global_constraints.index:
n.global_constraints.drop("line_volume_constraint",inplace=True)
else:
if "line_volume_constraint" not in n.global_constraints.index:
line_volume = getattr(n, 'line_volume_limit', None)
if line_volume is not None and not np.isinf(line_volume):
n.add("GlobalConstraint",
"line_volume_constraint",
type="transmission_volume_expansion_limit",
carrier_attribute="AC,DC",
sense="<=",
constant=line_volume)
# Firing up solve will increase memory consumption tremendously, so
# make sure we freed everything we can
gc.collect()
#from pyomo.opt import ProblemFormat
#print("Saving model to MPS")
#n.model.write('/home/ka/ka_iai/ka_kc5996/projects/pypsa-eur/128-B-I.mps', format=ProblemFormat.mps)
#print("Model is saved to MPS")
#sys.exit()
status, termination_condition = n.lopf(pyomo=False,
solver_name=solver_name,
solver_logfile=solver_log,
solver_options=solver_options,
extra_functionality=extra_functionality,
formulation=solve_opts['formulation'])
#extra_postprocessing=extra_postprocessing
#keep_files=True
#free_memory={'pypsa'}
assert status == "ok" or allow_warning_status and status == 'warning', \
("network_lopf did abort with status={} "
"and termination_condition={}"
.format(status, termination_condition))
if not fix_ext_lines and "line_volume_constraint" in n.global_constraints.index:
n.line_volume_limit_dual = n.global_constraints.at["line_volume_constraint","mu"]
print("line volume limit dual:",n.line_volume_limit_dual)
return status, termination_condition
lines_ext_b = n.lines.s_nom_extendable
if lines_ext_b.any():
# puh: ok, we need to iterate, since there is a relation
# between s/p_nom and r, x for branches.
msq_threshold = 0.01
lines = pd.DataFrame(n.lines[['r', 'x', 'type', 'num_parallel']])
lines['s_nom'] = (
np.sqrt(3) * n.lines['type'].map(n.line_types.i_nom) *
n.lines.bus0.map(n.buses.v_nom)
).where(n.lines.type != '', n.lines['s_nom'])
lines_ext_typed_b = (n.lines.type != '') & lines_ext_b
lines_ext_untyped_b = (n.lines.type == '') & lines_ext_b
def update_line_parameters(n, zero_lines_below=10, fix_zero_lines=False):
if zero_lines_below > 0:
n.lines.loc[n.lines.s_nom_opt < zero_lines_below, 's_nom_opt'] = 0.
n.links.loc[(n.links.carrier=='DC') & (n.links.p_nom_opt < zero_lines_below), 'p_nom_opt'] = 0.
if lines_ext_untyped_b.any():
for attr in ('r', 'x'):
n.lines.loc[lines_ext_untyped_b, attr] = (
lines[attr].multiply(lines['s_nom']/n.lines['s_nom_opt'])
)
if lines_ext_typed_b.any():
n.lines.loc[lines_ext_typed_b, 'num_parallel'] = (
n.lines['s_nom_opt']/lines['s_nom']
)
logger.debug("lines.num_parallel={}".format(n.lines.loc[lines_ext_typed_b, 'num_parallel']))
iteration = 1
lines['s_nom_opt'] = lines['s_nom'] * n.lines['num_parallel'].where(n.lines.type != '', 1.)
status, termination_condition = run_lopf(n, allow_warning_status=True)
def msq_diff(n):
lines_err = np.sqrt(((n.lines['s_nom_opt'] - lines['s_nom_opt'])**2).mean())/lines['s_nom_opt'].mean()
logger.info("Mean square difference after iteration {} is {}".format(iteration, lines_err))
return lines_err
min_iterations = solve_opts.get('min_iterations', 2)
max_iterations = solve_opts.get('max_iterations', 999)
while msq_diff(n) > msq_threshold or iteration < min_iterations:
if iteration >= max_iterations:
logger.info("Iteration {} beyond max_iterations {}. Stopping ...".format(iteration, max_iterations))
break
update_line_parameters(n)
lines['s_nom_opt'] = n.lines['s_nom_opt']
iteration += 1
status, termination_condition = run_lopf(n, allow_warning_status=True)
update_line_parameters(n, zero_lines_below=100)
logger.info("Starting last run with fixed extendable lines")
# Not really needed, could also be taken out
# if 'snakemake' in globals():
# fn = os.path.basename(snakemake.output[0])
# n.export_to_netcdf('/home/vres/data/jonas/playground/pypsa-eur/' + fn)
status, termination_condition = run_lopf(n, fix_ext_lines=True)
# Drop zero lines from network
# zero_lines_i = n.lines.index[(n.lines.s_nom_opt == 0.) & n.lines.s_nom_extendable]
# if len(zero_lines_i):
# n.mremove("Line", zero_lines_i)
# zero_links_i = n.links.index[(n.links.p_nom_opt == 0.) & n.links.p_nom_extendable]
# if len(zero_links_i):
# n.mremove("Link", zero_links_i)
return n
if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing
if 'snakemake' not in globals():
from vresutils.snakemake import MockSnakemake, Dict
snakemake = MockSnakemake(
wildcards=dict(network='elec', simpl='', clusters='45', lv='1.25', opts='Co2L-3H-T-H'),
input=dict(network="networks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}.nc"),
output=["results/networks/s{simpl}_{clusters}_lv{lv}_{opts}-test.nc"],
log=dict(gurobi="logs/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_gurobi-test.log",
python="logs/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_python-test.log")
)
tmpdir = snakemake.config['solving'].get('tmpdir')
if tmpdir is not None:
patch_pyomo_tmpdir(tmpdir)
logging.basicConfig(filename=snakemake.log.python,
level=snakemake.config['logging_level'])
with memory_logger(filename=getattr(snakemake.log, 'memory', None), interval=30.) as mem:
n = pypsa.Network(snakemake.input.network,
override_component_attrs=override_component_attrs)
n = prepare_network(n)
n = solve_network(n)
n.export_to_netcdf(snakemake.output[0])
logger.info("Maximum memory usage: {}".format(mem.mem_usage))