def main(solver=config.user_config.solver):
logging.basicConfig(level=logging.CRITICAL, format='%(levelname)s: %(message)s')
from minpower import solve
dirNm=splitFilename(__file__)[0]
if dirNm=='': dirNm='.'
excludeL=[]
for fileNm in os.listdir(dirNm):
if fileNm in excludeL: continue
testDir = joindir(dirNm, fileNm)
if not os.path.isdir(testDir): continue
print 'testing: ',fileNm
wipeTestSlate(testDir)
fResults=open(joindir(testDir,'results.txt'),'w+')
fError=open(joindir(testDir,'error.txt'),'w+')
sys.stdout=fResults #switch output to results file
if hasPyscript(testDir):
sys.stdout = sys.__stdout__ #switch back to standard outputting
os.system('python {s}'.format(s=hasPyscript(testDir)[0]))
else:
try:
user_config.scenarios = 2
solve.solve_problem(testDir)
sys.stdout = sys.__stdout__ #switch back to standard outputting
fError.close()
os.remove(joindir(testDir,'error.txt'))
except: #write the error to file
exc_type, exc_value, exc_traceback = sys.exc_info()
traceback.print_tb(exc_traceback, file=fError )
traceback.print_exception(exc_type, exc_value, exc_traceback, file=fError)
sys.stdout = sys.__stdout__ #switch back to standard outputting
print '\t had error' #note that this dir produced error
else:
sys.stdout = sys.__stdout__ #switch back to standard outputting
def main(args):
generators, loads, _, times, _, data = parsedir()
generators = [gen for gen in generators if gen.is_controllable]
gen_data = data["generators"]
if args["min"] == 0:
args["min"] = 1.1 * gen_data.pmin.sum()
if args["max"] == 0:
args["max"] = 0.99 * gen_data.pmax.sum()
load_values = np.arange(args["min"], args["max"], args["interval"])
results = DataFrame(columns=["prices", "committed", "last_committed"],
index=load_values)
committed_gen_names = Index([])
for load_val in load_values:
print(load_val)
loads_times = make_loads_times(Pd=load_val)
power_system, times = solve_problem(generators,
do_reset_config=False,
**loads_times)
t = times[0]
results.loc[load_val, "prices"] = power_system.buses[0].price(t)
statuses = Series(
dict([(gen.name, gen.status(t).value)
for gen in power_system.generators()]))
results.loc[load_val, "committed"] = statuses.sum()
results.loc[load_val, "last_committed"] = statuses[
statuses == 1].index.difference(committed_gen_names)
committed_gen_names = statuses[statuses == 1].index
if (load_values[-1] == 0.99 *
gen_data.pmax.sum()) and (statuses.sum() != len(generators)):
print("warning: uncommitted generation:")
print((gen_data.set_index("name").loc[statuses[statuses == 0].index]))
results.to_csv(joindir(user_config.directory, "ed_sweep.csv"))
if args["hide_units_committed"]:
ax = results.prices.plot(drawstyle="steps")
else:
ax = results[["prices", "committed"]].plot(drawstyle="steps",
secondary_y=["committed"])
ax.right_ax.set_ylabel("Units committed")
ax.set_xlabel("System Load (MW)")
ax.set_ylabel("Estimated System Price ($/MWh)")
plt.savefig(joindir(user_config.directory, "ed_sweep.png"))
def main(args):
generators, loads, _, times, _, data = parsedir()
generators = filter(lambda gen: gen.is_controllable, generators)
gen_data = data['generators']
if args['min'] == 0:
args['min'] = 1.1 * gen_data.pmin.sum()
if args['max'] == 0:
args['max'] = 0.99 * gen_data.pmax.sum()
load_values = np.arange(args['min'], args['max'], args['interval'])
results = DataFrame(columns=['prices', 'committed', 'last_committed'],
index=load_values)
committed_gen_names = Index([])
for load_val in load_values:
print load_val
loads_times = make_loads_times(Pd=load_val)
power_system, times = solve_problem(generators,
do_reset_config=False,
**loads_times)
t = times[0]
results.ix[load_val, 'prices'] = power_system.buses[0].price(t)
statuses = Series(
dict([(gen.name, gen.status(t).value)
for gen in power_system.generators()]))
results.ix[load_val, 'committed'] = statuses.sum()
results.ix[load_val, 'last_committed'] = \
statuses[statuses == 1].index.diff(committed_gen_names)
committed_gen_names = statuses[statuses == 1].index
if (load_values[-1] == 0.99 * gen_data.pmax.sum()) and \
(statuses.sum() != len(generators)):
print('warning: uncommitted generation:')
print(gen_data.set_index('name').ix[statuses[statuses == 0].index])
results.to_csv(joindir(user_config.directory, 'ed_sweep.csv'))
if args['hide_units_committed']:
ax = results.prices.plot(drawstyle='steps')
else:
ax = results[['prices', 'committed']].plot(drawstyle='steps',
secondary_y=['committed'])
ax.right_ax.set_ylabel('Units committed')
ax.set_xlabel('System Load (MW)')
ax.set_ylabel('Estimated System Price ($/MWh)')
plt.savefig(joindir(user_config.directory, 'ed_sweep.png'))
def main(args):
generators, loads, _, times, _, data = parsedir()
generators = filter(lambda gen: gen.is_controllable, generators)
gen_data = data['generators']
if args['min'] == 0:
args['min'] = 1.1 * gen_data.pmin.sum()
if args['max'] == 0:
args['max'] = 0.99 * gen_data.pmax.sum()
load_values = np.arange(args['min'], args['max'], args['interval'])
results = DataFrame(columns=['prices', 'committed', 'last_committed'], index=load_values)
committed_gen_names = Index([])
for load_val in load_values:
print load_val
loads_times = make_loads_times(Pd=load_val)
power_system, times = solve_problem(generators,
do_reset_config=False, **loads_times)
t = times[0]
results.ix[load_val, 'prices'] = power_system.buses[0].price(t)
statuses = Series(dict([(gen.name, gen.status(t).value)
for gen in power_system.generators()]))
results.ix[load_val, 'committed'] = statuses.sum()
results.ix[load_val, 'last_committed'] = \
statuses[statuses == 1].index.difference(committed_gen_names)
committed_gen_names = statuses[statuses == 1].index
if (load_values[-1] == 0.99 * gen_data.pmax.sum()) and \
(statuses.sum() != len(generators)):
print('warning: uncommitted generation:')
print(gen_data.set_index('name').ix[statuses[statuses == 0].index])
results.to_csv(joindir(user_config.directory, 'ed_sweep.csv'))
if args['hide_units_committed']:
ax = results.prices.plot(drawstyle='steps')
else:
ax = results[['prices', 'committed']].plot(drawstyle='steps', secondary_y=['committed'])
ax.right_ax.set_ylabel('Units committed')
ax.set_xlabel('System Load (MW)')
ax.set_ylabel('Estimated System Price ($/MWh)')
plt.savefig(joindir(user_config.directory, 'ed_sweep.png'))
def initial_dispatch(directory=".", output_filename="initial.csv"):
user_config.directory = directory
user_config.dispatch_decommit_allowed = True
generators_data, loads_data, lines_data, init_data = _load_raw_data()
init_data = pd.DataFrame()
timeseries, times, generators_data, loads_data = setup_times(
generators_data, loads_data)
# get rid of a bunch of stuff to make this an ED problem
generators_data["power"] = None
loads_data["power"] = None
for i, snm in generators_data.pop("schedulename").dropna().iterkv():
generators_data.loc[i, "power"] = timeseries[snm].values[0]
for i, snm in loads_data.pop("schedulename").dropna().iterkv():
loads_data.loc[i, "power"] = timeseries[snm].values[0]
for i, snm in generators_data.pop("observedname").dropna().iterkv():
generators_data.loc[i, "power"] = timeseries[snm].values[0]
valid_columns = pd.Index([
"name",
"pmin",
"pmax",
"fuelcost",
"heatrateequation",
"costcurveequation",
"sheddingallowed",
"power",
])
generators_data = generators_data[generators_data.columns & valid_columns]
generators, loads, lines, times, scenario_values, data = _parse_raw_data(
generators_data, loads_data, lines_data, init_data)
power_system = PowerSystem(generators, loads, lines)
sln = create_solve_problem(power_system, times)
# save a csv of the dispatch
dispatch = pd.DataFrame(
{
"power": sln.generators_power.loc[0].values,
"status": sln.generators_status.loc[0].values,
},
index=[gen.name for gen in sln.generators],
)
dispatch.loc[dispatch.status == 0, "power"] = 0
dispatch.index.name = "name"
dispatch.to_csv(joindir(user_config.directory, output_filename))
print(dispatch)
def initial_dispatch(directory='.', output_filename='initial.csv'):
user_config.directory = directory
user_config.dispatch_decommit_allowed = True
generators_data, loads_data, lines_data, init_data = _load_raw_data()
init_data = pd.DataFrame()
timeseries, times, generators_data, loads_data = \
setup_times(generators_data, loads_data)
# get rid of a bunch of stuff to make this an ED problem
generators_data['power'] = None
loads_data['power'] = None
for i, snm in generators_data.pop('schedulename').dropna().iterkv():
generators_data.ix[i, 'power'] = timeseries[snm].values[0]
for i, snm in loads_data.pop('schedulename').dropna().iterkv():
loads_data.ix[i, 'power'] = timeseries[snm].values[0]
for i, snm in generators_data.pop('observedname').dropna().iterkv():
generators_data.ix[i, 'power'] = timeseries[snm].values[0]
valid_columns = pd.Index([
'name', 'pmin', 'pmax', 'fuelcost', 'heatrateequation',
'costcurveequation', 'sheddingallowed', 'power'
])
generators_data = generators_data[generators_data.columns & valid_columns]
generators, loads, lines, times, scenario_values, data = \
_parse_raw_data(generators_data, loads_data,
lines_data, init_data)
power_system = PowerSystem(generators, loads, lines)
sln = create_solve_problem(power_system, times)
# save a csv of the dispatch
dispatch = pd.DataFrame(
{
'power': sln.generators_power.ix[0].values,
'status': sln.generators_status.ix[0].values
},
index=[gen.name for gen in sln.generators])
dispatch.ix[dispatch.status == 0, 'power'] = 0
dispatch.index.name = 'name'
dispatch.to_csv(joindir(user_config.directory, output_filename))
print(dispatch)
def initial_dispatch(directory='.', output_filename='initial.csv'):
user_config.directory = directory
user_config.dispatch_decommit_allowed = True
generators_data, loads_data, lines_data, init_data = _load_raw_data()
init_data = pd.DataFrame()
timeseries, times, generators_data, loads_data = \
setup_times(generators_data, loads_data)
# get rid of a bunch of stuff to make this an ED problem
generators_data['power'] = None
loads_data['power'] = None
for i, snm in generators_data.pop('schedulename').dropna().iterkv():
generators_data.ix[i, 'power'] = timeseries[snm].values[0]
for i, snm in loads_data.pop('schedulename').dropna().iterkv():
loads_data.ix[i, 'power'] = timeseries[snm].values[0]
for i, snm in generators_data.pop('observedname').dropna().iterkv():
generators_data.ix[i, 'power'] = timeseries[snm].values[0]
valid_columns = pd.Index([
'name', 'pmin', 'pmax',
'fuelcost', 'heatrateequation', 'costcurveequation',
'sheddingallowed',
'power'])
generators_data = generators_data[generators_data.columns & valid_columns]
generators, loads, lines, times, scenario_values, data = \
_parse_raw_data(generators_data, loads_data,
lines_data, init_data)
power_system = PowerSystem(generators, loads, lines)
sln = create_solve_problem(power_system, times)
# save a csv of the dispatch
dispatch = pd.DataFrame({
'power': sln.generators_power.ix[0].values,
'status': sln.generators_status.ix[0].values},
index=[gen.name for gen in sln.generators])
dispatch.ix[dispatch.status==0, 'power'] = 0
dispatch.index.name = 'name'
dispatch.to_csv(joindir(user_config.directory, output_filename))
print(dispatch)
def hasPyscript(dir): return glob.glob(joindir(dir,'*.py')) def main(solver=config.user_config.solver):
def wipeTestSlate(dir):
patterns=['*.png','*.lp','commitment.csv','dispatch.csv','error.txt']
for pat in patterns:
for f in glob.glob(joindir(dir,pat)): os.remove(f)
def hasPyscript(dir):
return glob.glob(joindir(dir, "*.py"))
def wipeTestSlate(dir):
patterns = ["*.png", "*.lp", "commitment.csv", "dispatch.csv", "error.txt"]
for pat in patterns:
for f in glob.glob(joindir(dir, pat)):
os.remove(f)
def hasPyscript(dir):
return glob.glob(joindir(dir, '*.py'))
