def create_um(input_data, timesteps):
"""
Creates an urbs model for given input, time steps
Args:
input_data: input data
timesteps: simulation timesteps
Returns:
model: a model instance
"""
# create model
print('CREATING urbs MODEL')
start = time.perf_counter()
model = urbs.create_model(input_data, 1, timesteps)
end = time.perf_counter()
# solve model and read results
optim = SolverFactory('glpk')
result = optim.solve(model, logfile='urbs_log.txt', tee=False)
# write LP file
filename = os.path.join(os.path.dirname(__file__), 'mimo_urbs.lp')
model.write(filename, io_options={'symbolic_solver_labels': True})
return model, end - start
def run_scenario(input_file,
timesteps,
scenario,
result_dir,
plot_tuples=None,
plot_periods=None,
report_tuples=None):
""" run an urbs model for given input, time steps and scenario
Args:
input_file: filename to an Excel spreadsheet for urbs.read_excel
timesteps: a list of timesteps, e.g. range(0,8761)
scenario: a scenario function that modifies the input data dict
result_dir: directory name for result spreadsheet and plots
plot_tuples: (optional) list of plot tuples (c.f. urbs.result_figures)
plot_periods: (optional) dict of plot periods (c.f. urbs.result_figures)
report_tuples: (optional) list of (sit, com) tuples (c.f. urbs.report)
Returns:
the urbs model instance
"""
# scenario name, read and modify data for scenario
sce = scenario.__name__
data = urbs.read_excel(input_file)
data = scenario(data)
# create model
prob = urbs.create_model(data, timesteps)
# refresh time stamp string and create filename for logfile
now = prob.created
log_filename = os.path.join(result_dir, '{}.log').format(sce)
# solve model and read results
optim = SolverFactory('glpk') # cplex, glpk, gurobi, ...
optim = setup_solver(optim, logfile=log_filename)
result = optim.solve(prob, tee=True)
# copy input file to result directory
shutil.copyfile(input_file, os.path.join(result_dir, input_file))
# save problem solution (and input data) to HDF5 file
urbs.save(prob, os.path.join(result_dir, '{}.h5'.format(sce)))
# write report to spreadsheet
urbs.report(prob,
os.path.join(result_dir, '{}.xlsx').format(sce),
report_tuples=report_tuples)
# result plots
urbs.result_figures(prob,
os.path.join(result_dir, '{}'.format(sce)),
plot_title_prefix=sce.replace('_', ' '),
plot_tuples=plot_tuples,
periods=plot_periods,
figure_size=(24, 9))
return prob
def run_scenario(input_file, timesteps, scenario, result_dir, plot_periods={}):
""" run an urbs model for given input, time steps and scenario
Args:
input_file: filename to an Excel spreadsheet for urbs.read_excel
timesteps: a list of timesteps, e.g. range(0,8761)
scenario: a scenario function that modifies the input data dict
result_dir: directory name for result spreadsheet and plots
Returns:
the urbs model instance
"""
# scenario name, read and modify data for scenario
sce = scenario.__name__
data = urbs.read_excel(input_file)
data = scenario(data)
# create model
prob = urbs.create_model(data, timesteps)
if PYOMO3:
prob = prob.create()
# refresh time stamp string and create filename for logfile
now = prob.created
log_filename = os.path.join(result_dir, '{}.log').format(sce)
# solve model and read results
optim = SolverFactory('glpk') # cplex, glpk, gurobi, ...
optim = setup_solver(optim, logfile=log_filename)
result = optim.solve(prob, tee=True)
if PYOMO3:
prob.load(result)
else:
prob.solutions.load_from(result)
# copy input file in result directory
cdir = os.getcwd()
respath = os.path.join(cdir, result_dir)
shutil.copy(input_file,respath)
# write report to spreadsheet
urbs.report(
prob,
os.path.join(result_dir, '{}.xlsx').format(sce),
prob.com_demand, prob.sit)
# store optimisation problem for later re-analysis
urbs.save(
prob,
os.path.join(result_dir, '{}.pgz').format(sce))
urbs.result_figures(
prob,
os.path.join(result_dir, '{}'.format(sce)),
plot_title_prefix=sce.replace('_', ' ').title(),
periods=plot_periods)
return prob
def run_scenario(input_file, timesteps, scenario, result_dir,
plot_tuples=None, plot_periods=None, report_tuples=None):
""" run an urbs model for given input, time steps and scenario
Args:
input_file: filename to an Excel spreadsheet for urbs.read_excel
timesteps: a list of timesteps, e.g. range(0,8761)
scenario: a scenario function that modifies the input data dict
result_dir: directory name for result spreadsheet and plots
plot_tuples: (optional) list of plot tuples (c.f. urbs.result_figures)
plot_periods: (optional) dict of plot periods (c.f. urbs.result_figures)
report_tuples: (optional) list of (sit, com) tuples (c.f. urbs.report)
Returns:
the urbs model instance
"""
# scenario name, read and modify data for scenario
sce = scenario.__name__
data = urbs.read_excel(input_file)
data = scenario(data)
# create model
prob = urbs.create_model(data, timesteps)
# refresh time stamp string and create filename for logfile
now = prob.created
log_filename = os.path.join(result_dir, '{}.log').format(sce)
# solve model and read results
optim = SolverFactory('glpk') # cplex, glpk, gurobi, ...
optim = setup_solver(optim, logfile=log_filename)
result = optim.solve(prob, tee=True)
# copy input file to result directory
shutil.copyfile(input_file, os.path.join(result_dir, input_file))
# save problem solution (and input data) to HDF5 file
urbs.save(prob, os.path.join(result_dir, '{}.h5'.format(sce)))
# write report to spreadsheet
urbs.report(
prob,
os.path.join(result_dir, '{}.xlsx').format(sce),
report_tuples=report_tuples)
# result plots
urbs.result_figures(
prob,
os.path.join(result_dir, '{}'.format(sce)),
plot_title_prefix=sce.replace('_', ' '),
plot_tuples=plot_tuples,
periods=plot_periods,
figure_size=(24, 9))
return prob
def run_scenario(input_file, timesteps, scenario, result_dir, plot_periods={}):
""" run an urbs model for given input, time steps and scenario
Args:
input_file: filename to an Excel spreadsheet for urbs.read_excel
timesteps: a list of timesteps, e.g. range(0,8761)
scenario: a scenario function that modifies the input data dict
result_dir: directory name for result spreadsheet and plots
Returns:
the urbs model instance
"""
# scenario name, read and modify data for scenario
sce = scenario.__name__
data = urbs.read_excel(input_file)
data = scenario(data)
# create model
model = urbs.create_model(data, timesteps)
prob = model.create()
# refresh time stamp string and create filename for logfile
now = prob.created
log_filename = os.path.join(result_dir, '{}.log').format(sce)
# solve model and read results
optim = SolverFactory('glpk') # cplex, glpk, gurobi, ...
optim = setup_solver(optim, logfile=log_filename)
result = optim.solve(prob, tee=True)
prob.load(result)
# copy input file in result directory
cdir = os.getcwd()
respath = os.path.join(cdir, result_dir)
shutil.copy(input_file, respath)
# write report to spreadsheet
urbs.report(prob,
os.path.join(result_dir, '{}.xlsx').format(sce),
prob.com_demand, prob.sit)
# store optimisation problem for later re-analysis
urbs.save(prob, os.path.join(result_dir, '{}.pgz').format(sce))
urbs.result_figures(prob,
os.path.join(result_dir, '{}'.format(sce)),
plot_title_prefix=sce.replace('_', ' ').title(),
periods=plot_periods)
return prob
def run_scenario(input_file, timesteps, scenario, result_dir, plot_periods={}):
""" run an urbs model for given input, time steps and scenario
Args:
input_file: filename to an Excel spreadsheet for urbs.read_excel
timesteps: a list of timesteps, e.g. range(0,8761)
scenario: a scenario function that modifies the input data dict
result_dir: directory name for result spreadsheet and plots
Returns:
the urbs model instance
"""
# scenario name, read and modify data for scenario
sce = scenario.__name__
data = urbs.read_excel(input_file)
data = scenario(data)
# create model
prob = urbs.create_model(data, timesteps)
# refresh time stamp string and create filename for logfile
now = prob.created
log_filename = os.path.join(result_dir, "{}.log").format(sce)
# solve model and read results
optim = SolverFactory("glpk") # cplex, glpk, gurobi, ...
optim = setup_solver(optim, logfile=log_filename)
result = optim.solve(prob, tee=True)
# copy input file to result directory
shutil.copyfile(input_file, os.path.join(result_dir, input_file))
# write report to spreadsheet
urbs.report(prob, os.path.join(result_dir, "{}.xlsx").format(sce), prob.com_demand, prob.sit)
urbs.result_figures(
prob,
os.path.join(result_dir, "{}".format(sce)),
plot_title_prefix=sce.replace("_", " ").title(),
periods=plot_periods,
)
return prob
import pyomo.environ
import urbs
from pyomo.core import Constraint
from pyomo.opt.base import SolverFactory
data = urbs.read_excel('mimo-example.xlsx')
prob = urbs.create_model(data, timesteps=range(1, 8), dual=True)
optim = SolverFactory('glpk')
result = optim.solve(prob, tee=True)
res_vertex_duals = urbs.get_entity(prob, 'res_vertex')
marg_costs = res_vertex_duals.xs(('Elec', 'Demand'), level=('com', 'com_type'))
print(marg_costs)
def run_scenario(input_file, timesteps, scenario, result_dir):
""" run an urbs model for given input, time steps and scenario
Args:
input_file: filename to an Excel spreadsheet for urbs.read_excel
timesteps: a list of timesteps, e.g. range(0,8761)
scenario: a scenario function that modifies the input data dict
result_dir: directory name for result spreadsheet and plots
Returns:
the urbs model instance
"""
# scenario name, read and modify data for scenario
sce = scenario.__name__
data = urbs.read_excel(input_file)
data = scenario(data)
# create model, solve it, read results
model = urbs.create_model(data, timesteps)
prob = model.create()
optim = SolverFactory('glpk') # cplex, glpk, gurobi, ...
result = optim.solve(prob, tee=True)
prob.load(result)
# refresh time stamp string
now = prob.created
# write report to spreadsheet
urbs.report(
prob,
os.path.join(result_dir, '{}-{}.xlsx').format(sce, now),
prob.com_demand, prob.sit)
# store optimisation problem for later re-analysis
urbs.save(
prob,
os.path.join(result_dir, '{}-{}.pgz').format(sce, now))
# add or change plot colors
my_colors = {
'Vled Haven': (230, 200, 200),
'Stryworf Key': (200, 230, 200),
'Qlyph Archipelago': (200, 200, 230),
'Jepid Island': (215,215,215)}
for country, color in my_colors.iteritems():
urbs.COLORS[country] = color
# create timeseries plot for each demand (site, commodity) timeseries
for sit, com in prob.demand.columns:
# create figure
fig = urbs.plot(prob, com, sit)
# change the figure title
ax0 = fig.get_axes()[0]
nice_sce_name = sce.replace('_', ' ').title()
new_figure_title = ax0.get_title().replace(
'Energy balance of ', '{}: '.format(nice_sce_name))
ax0.set_title(new_figure_title)
# save plot to files
for ext in ['png', 'pdf']:
fig_filename = os.path.join(
result_dir, '{}-{}-{}-{}.{}').format(sce, com, sit, now, ext)
fig.savefig(fig_filename, bbox_inches='tight')
return prob
def run_scenario(input_file, timesteps, scenario, result_dir):
""" run an urbs model for given input, time steps and scenario
Args:
input_file: filename to an Excel spreadsheet for urbs.read_excel
timesteps: a list of timesteps, e.g. range(0,8761)
scenario: a scenario function that modifies the input data dict
result_dir: directory name for result spreadsheet and plots
Returns:
the urbs model instance
"""
# scenario name, read and modify data for scenario
sce = scenario.__name__
data = urbs.read_excel(input_file)
data = scenario(data)
# create model, solve it, read results
model = urbs.create_model(data, timesteps)
prob = model.create()
optim = SolverFactory('glpk') # cplex, glpk, gurobi, ...
result = optim.solve(prob, tee=True)
prob.load(result)
# refresh time stamp string
now = prob.created
# write report to spreadsheet
urbs.report(prob,
os.path.join(result_dir, '{}-{}.xlsx').format(sce, now),
prob.com_demand, prob.sit)
# store optimisation problem for later re-analysis
urbs.save(prob, os.path.join(result_dir, '{}-{}.pgz').format(sce, now))
# add or change plot colors
my_colors = {
'South': (230, 200, 200),
'Mid': (200, 230, 200),
'North': (200, 200, 230)
}
for country, color in my_colors.iteritems():
urbs.COLORS[country] = color
# create timeseries plot for each demand (site, commodity) timeseries
for sit, com in prob.demand.columns:
# create figure
fig = urbs.plot(prob, com, sit)
# change the figure title
ax0 = fig.get_axes()[0]
nice_sce_name = sce.replace('_', ' ').title()
new_figure_title = ax0.get_title().replace(
'Energy balance of ', '{}: '.format(nice_sce_name))
ax0.set_title(new_figure_title)
# save plot to files
for ext in ['png', 'pdf']:
fig_filename = os.path.join(result_dir, '{}-{}-{}-{}.{}').format(
sce, com, sit, now, ext)
fig.savefig(fig_filename, bbox_inches='tight')
return prob
import pyomo.environ
import urbs
from pyomo.core import Constraint
from pyomo.opt.base import SolverFactory
data = urbs.read_excel('mimo-example.xlsx')
prob = urbs.create_model(data, timesteps=range(1,8), dual=True)
optim = SolverFactory('glpk')
result = optim.solve(prob, tee=True)
res_vertex_duals = urbs.get_entity(prob, 'res_vertex')
marg_costs = res_vertex_duals.xs(('Elec', 'Demand'), level=('com', 'com_type'))
print(marg_costs)