def __init__(self, inputs_directory):
# List of "periods" for allocation of large pumped storage across year and for each dispatch optimization
self.periods = list()
with open(os.path.join(inputs_directory, "periods.csv"), "Ur") as periods_file:
periods_reader = csv.reader(periods_file)
periods_reader.next()
for row in periods_reader:
self.periods.append(int(row[0]))
# List of regions
self.regions = list()
with open(os.path.join(inputs_directory, "regions.csv"), "Ur") as regions_file:
regions_reader = csv.reader(regions_file)
regions_reader.next()
for row in regions_reader:
self.regions.append(str(row[0]))
# ### Inputs for year to period allocation ### #
# Average load across all periods
# Data structure: dictionary with regions as keys
self.average_net_load = util.recursivedict()
with open(os.path.join(inputs_directory, "alloc_average_net_load.csv"), "Ur") as avg_net_load_file:
average_net_load_reader = csv.reader(avg_net_load_file)
average_net_load_reader.next()
for row in average_net_load_reader:
self.average_net_load[str(row[0])] = float(row[1])
# Total load in each period
# Dictionary with two indices: period and region
self.period_net_load = dict()
with open(os.path.join(inputs_directory, "alloc_period_net_load.csv"), "Ur") as period_net_load_file:
period_total_net_load_reader = csv.reader(period_net_load_file)
period_total_net_load_reader.next()
for row in period_total_net_load_reader:
self.period_net_load[(str(row[0]), int(row[1]))] = float(row[2])
# List of resources
self.alloc_resources = list()
# Resource params: region, technology, power capacity, energy capacity
# Data structrue: dictionaries with the resource names as keys
self.alloc_region = dict()
self.alloc_technology = dict()
self.alloc_power_capacity = dict()
self.alloc_energy_capacity = dict()
with open(os.path.join(inputs_directory, "alloc_resources.csv"), "Ur") as alloc_resources_file:
alloc_resources_reader = csv.reader(alloc_resources_file)
alloc_resources_reader.next()
for row in alloc_resources_reader:
self.alloc_resources.append(str(row[0]))
self.alloc_region[str(row[0])] = str(row[1])
self.alloc_technology[str(row[0])] = str(row[2])
self.alloc_power_capacity[str(row[0])] = float(row[3])
self.alloc_energy_capacity[str(row[0])] = float(row[4])
# Penalties; these are all single values
# TODO: define outside of class
self.upward_imbalance_penalty = 1000.0
self.downward_imbalance_penalty = 100.0
def __init__(self, inputs_directory):
# List of "periods" for allocation of large pumped storage across year and for each dispatch optimization
self.periods = list()
with open(os.path.join(inputs_directory, "periods.csv"),
"Ur") as periods_file:
periods_reader = csv.reader(periods_file)
periods_reader.next()
for row in periods_reader:
self.periods.append(int(row[0]))
# List of regions
self.regions = list()
with open(os.path.join(inputs_directory, "regions.csv"),
"Ur") as regions_file:
regions_reader = csv.reader(regions_file)
regions_reader.next()
for row in regions_reader:
self.regions.append(str(row[0]))
# ### Inputs for year to period allocation ### #
# Average load across all periods
# Data structure: dictionary with regions as keys
self.average_net_load = util.recursivedict()
with open(os.path.join(inputs_directory, "alloc_average_net_load.csv"),
"Ur") as avg_net_load_file:
average_net_load_reader = csv.reader(avg_net_load_file)
average_net_load_reader.next()
for row in average_net_load_reader:
self.average_net_load[str(row[0])] = float(row[1])
# Total load in each period
# Dictionary with two indices: period and region
self.period_net_load = dict()
with open(os.path.join(inputs_directory, "alloc_period_net_load.csv"),
"Ur") as period_net_load_file:
period_total_net_load_reader = csv.reader(period_net_load_file)
period_total_net_load_reader.next()
for row in period_total_net_load_reader:
self.period_net_load[(str(row[0]),
int(row[1]))] = float(row[2])
# List of resources
self.alloc_resources = list()
# Resource params: region, technology, power capacity, energy capacity
# Data structrue: dictionaries with the resource names as keys
self.alloc_region = dict()
self.alloc_technology = dict()
self.alloc_power_capacity = dict()
self.alloc_energy_capacity = dict()
with open(os.path.join(inputs_directory, "alloc_resources.csv"),
"Ur") as alloc_resources_file:
alloc_resources_reader = csv.reader(alloc_resources_file)
alloc_resources_reader.next()
for row in alloc_resources_reader:
self.alloc_resources.append(str(row[0]))
self.alloc_region[str(row[0])] = str(row[1])
self.alloc_technology[str(row[0])] = str(row[2])
self.alloc_power_capacity[str(row[0])] = float(row[3])
self.alloc_energy_capacity[str(row[0])] = float(row[4])
# Penalties; these are all single values
# TODO: define outside of class
self.upward_imbalance_penalty = 1000.0
self.downward_imbalance_penalty = 100.0
def __init__(self, inputs_directory):
# List of "periods" to optimize (can be optimized in parallel)
self.periods = list()
with open(os.path.join(inputs_directory, "periods.csv"), "Ur") as periods_file:
periods_reader = csv.reader(periods_file)
periods_reader.next()
for row in periods_reader:
self.periods.append(int(row[0]))
# List of regions
# Also importing the penalty thresholds for the distribution and bulk systems
self.regions = list()
self.dist_net_load_threshold = dict()
self.bulk_net_load_threshold = dict()
with open(os.path.join(inputs_directory, "regions_dist.csv"), "Ur") as regions_file:
regions_reader = csv.reader(regions_file)
regions_reader.next()
for row in regions_reader:
self.dist_net_load_threshold[(str(row[0]),int(row[1]))] = float(row[2])
with open(os.path.join(inputs_directory, "regions_bulk.csv"), "Ur") as regions_file:
regions_reader = csv.reader(regions_file)
regions_reader.next()
for row in regions_reader:
self.regions.append(str(row[0]))
self.bulk_net_load_threshold[str(row[0])] = float(row[1])
# Dictionary with period as key and the list of timepoints for each period as values
self.period_timepoints = dict()
# List of timepoints in each period (e.g. 1-24 for days, 1-168 for weeks, etc.)
self.timepoints = list()
# Dictionary with the order in which the cumulative flexible load constraint is enforced,
# e.g. if starting it on timepoint 7, flex_load_timepoint 1 is assigned to timepoint 7
# {7: 1, 8: 2, 9: 3 ...}
self.flex_load_timepoints = dict()
# self.ev_load_timepoints = dict()
self.period_flex_load_timepoints = dict()
# self.period_ev_load_timepoints = dict()
with open(os.path.join(inputs_directory, "timepoints.csv"), "Ur") as timepoints_file:
timepoints_reader = csv.reader(timepoints_file)
timepoints_reader.next()
for row in timepoints_reader:
self.timepoints.append(int(row[1]))
self.flex_load_timepoints[int(row[1])] = int(row[2])
# self.ev_load_timepoints[int(row[1])] = int(row[3])
if int(row[1]) == 168:
self.period_timepoints[int(row[0])] = self.timepoints
self.timepoints = list()
self.period_flex_load_timepoints[int(row[0])] = self.flex_load_timepoints
self.flex_load_timepoints = dict()
# self.period_ev_load_timepoints[int(row[0])] = self.ev_load_timepoints
# self.ev_load_timepoints = dict()
# List of storage technologies
self.storage_technologies = list()
# Dictionaries with technology params (storage technologies as keys): large-storage boolean flag,
# charging efficiency, discharging efficiency
self.large_storage = dict()
self.charging_efficiency = dict()
self.discharging_efficiency = dict()
with open(os.path.join(inputs_directory, "storage_technologies.csv"), "Ur") as storage_technologies_file:
storage_technologies_reader = csv.reader(storage_technologies_file)
storage_technologies_reader.next()
for row in storage_technologies_reader:
self.storage_technologies.append(str(row[0]))
self.large_storage[str(row[0])] = int(row[1])
self.charging_efficiency[str(row[0])] = float(row[2])
self.discharging_efficiency[str(row[0])] = float(row[3])
# List of generation technologies; this doesn't vary by period, so no period dictionary needed
self.generation_technologies = list()
# Dictionary of variable cost by generation technologies (generation technologies as keys)
self.variable_cost = dict()
with open(os.path.join(inputs_directory, "generation_technologies.csv"), "Ur") as generation_technologies_file:
generation_technologies_reader = csv.reader(generation_technologies_file)
generation_technologies_reader.next()
for row in generation_technologies_reader:
self.generation_technologies.append(str(row[0]))
self.variable_cost[str(row[0])] = float(row[1])
# Dictionary with periods as keys, list of resources in each period as values
self.period_resources = dict()
# List of resources to populate values in period_resources dictionary
self.resources = list()
# Dictionaries by period, which in turn contain dictionaries by resource with the following parameters:
# region, technology, a boolean for distributed resources, and capacity
self.period_region = dict()
self.region = dict()
self.period_technology = dict()
self.technology = dict()
self.period_feeder = dict()
self.feeder = dict()
self.period_capacity = dict()
self.capacity = dict()
# Dictionary with duration for storage resources only
self.period_duration = dict()
self.duration = dict()
with open(os.path.join(inputs_directory, "resources.csv"), "Ur") as resources_file:
resources_reader = csv.reader(resources_file)
resources_reader.next()
last_period = 1
for row in resources_reader:
if int(row[0]) == last_period:
self.resources.append(str(row[1]))
self.region[str(row[1])] = str(row[2])
self.technology[str(row[1])] = str(row[3])
self.feeder[str(row[1])] = int(row[4])
if row[5] == "":
pass
else:
self.capacity[str(row[1])] = float(row[5])
if row[6] == "":
pass
else:
self.duration[str(row[1])] = float(row[6])
else:
# Assign to period dictionary
self.period_resources[last_period] = self.resources
self.period_region[last_period] = self.region
self.period_technology[last_period] = self.technology
self.period_feeder[last_period] = self.feeder
self.period_capacity[last_period] = self.capacity
self.period_duration[last_period] = self.duration
# Clean up
self.resources = list()
self.region = dict()
self.technology = dict()
self.feeder= dict()
self.capacity = dict()
self.duration = dict()
# Correct last row
last_period = int(row[0])
# Start over
self.resources.append(str(row[1]))
self.region[str(row[1])] = str(row[2])
self.technology[str(row[1])] = str(row[3])
self.feeder[str(row[1])] = int(row[4])
if row[5] == "":
pass
else:
self.capacity[str(row[1])] = float(row[5])
if row[6] == "":
pass
else:
self.duration[str(row[1])] = float(row[6])
# List of transmission lines
self.transmission_lines = list()
# Dictionaries with transmission lines as keys; values are:
# Region where transmission line starts
self.transmission_from = dict()
# Region where transmission line ends
self.transmission_to = dict()
# Transmission capacity
self.transmission_capacity = dict()
with open(os.path.join(inputs_directory, "transmission.csv"), "Ur") as transmission_file:
transmission_reader = csv.reader(transmission_file)
transmission_reader.next()
for row in transmission_reader:
self.transmission_lines.append(str(row[0]))
self.transmission_from[str(row[0])] = str(row[1])
self.transmission_to[str(row[0])] = str(row[2])
self.transmission_capacity[str(row[0])] = int(row[3])
# Loads
# Dictionary with period as keys, containing dictionary with timepoints as keys and load as values
self.period_net_distributed_load = util.recursivedict()
self.net_distributed_load = util.recursivedict()
# Same as above but for minimum cumulative flexible load,
self.period_min_cum_flex_load = util.recursivedict()
self.min_cumulative_flex_load = util.recursivedict()
# maximum cumulative net load,
self.period_max_cum_flex_load = util.recursivedict()
self.max_cumulative_flex_load = util.recursivedict()
# minimum cumulative EV load,
# self.period_min_cum_ev_load = util.recursivedict()
# self.min_cumulative_ev_load = util.recursivedict()
# # maximum cumulative EV load,
# self.period_max_cum_ev_load = util.recursivedict()
# self.max_cumulative_ev_load = util.recursivedict()
# renewable output.
self.period_bulk_renewables = util.recursivedict()
self.bulk_renewables = util.recursivedict()
with open(os.path.join(inputs_directory, "net_load.csv"), "Ur") as net_load_file:
net_load_reader = csv.reader(net_load_file)
net_load_reader.next()
for row in net_load_reader:
self.net_distributed_load[int(row[0])][(row[1], int(row[2]),int(row[3]))] = float(row[4])
self.min_cumulative_flex_load[int(row[0])][(row[1], int(row[2]),int(row[3]))] = float(row[5])
self.max_cumulative_flex_load[int(row[0])][(row[1], int(row[2]),int(row[3]))] = float(row[6])
# self.min_cum_ev_load[int(row[0])][(row[1], int(row[2]),int(row[3]))] = float(row[7])
# self.max_cum_ev_load[int(row[0])][(row[1], int(row[2]),int(row[3]))] = float(row[8])
self.bulk_renewables[int(row[0])][(row[1], int(row[2]),int(row[3]))] = float(row[9])
# self.period_bulk_renewables[row[1], int(row[3], int(row[2]))] = float(row[4])
# if int(row[2]) == 168: # TODO: remove hardcoding; this is super ugly, but works for testing purposes
# region_num += 1
# if region_num == 3:
# self.net_distributed_load[int(row[0])] = self.period_net_distributed_load
# self.period_net_distributed_load = dict()
# self.min_cumulative_flex_load[int(row[0])] = self.period_min_cum_flex_load
# self.period_min_cum_flex_load = dict()
# self.max_cumulative_flex_load[int(row[0])] = self.period_max_cum_flex_load
# self.period_max_cum_flex_load = dict()
# self.min_cumulative_ev_load[int(row[0])] = self.period_min_cum_ev_load
# self.period_min_cum_ev_load = dict()
# self.max_cumulative_ev_load[int(row[0])] = self.period_max_cum_ev_load
# self.period_max_cum_ev_load = dict()
# self.bulk_renewables[int(row[0])] = self.period_bulk_renewables
# self.period_bulk_renewables = dict()
# region_num = 0
# else:
# pass
# else:
# pass
# Dictionary with period as key containing a dictionary with regions as keys and maximum flexible load as values
self.period_max_flex_load = util.recursivedict()
with open(os.path.join(inputs_directory, "max_flex_load.csv"), "Ur") as max_flex_load_file:
max_flex_load_reader = csv.reader(max_flex_load_file)
max_flex_load_reader.next()
for row in max_flex_load_reader:
self.period_max_flex_load[int(row[0])][(row[1],int(row[2]))] = float(row[3])
# # Dictionary with period as key containing a dictionary with regions as keys and maximum EV load as values
# self.max_ev_load = dict()
# self.period_max_ev_load = dict()
# with open(os.path.join(inputs_directory, "max_ev_load.csv"), "Ur") as max_ev_load_file:
# max_ev_load_reader = csv.reader(max_ev_load_file)
# max_ev_load_reader.next()
# last_period = 1
# for row in max_ev_load_reader:
# if int(row[0]) == last_period:
# self.max_ev_load[str(row[1])] = float(row[2])
# else:
# self.period_max_ev_load[int(last_period)] = self.max_ev_load
# self.max_ev_load = dict()
# last_period = int(row[0])
# self.max_ev_load[str(row[1])] = float(row[2])
# These are all single values and inelegantly instantiated inside the class here because it was easy
# TODO: define outside of this class
self.t_and_d_losses = 0
self.curtailment_cost = 10**2
self.unserved_energy_cost = 10**5
self.dist_net_load_penalty = 1.0
self.bulk_net_load_penalty = 1.0
self.feeders = [0,1,2,3]
def __init__(self, inputs_directory):
# List of "periods" to optimize (can be optimized in parallel)
self.periods = list()
with open(os.path.join(inputs_directory, "periods.csv"),
"Ur") as periods_file:
periods_reader = csv.reader(periods_file)
periods_reader.next()
for row in periods_reader:
self.periods.append(int(row[0]))
# List of regions
# Also importing the penalty thresholds for the distribution and bulk systems
self.regions = list()
self.dist_net_load_threshold = dict()
self.bulk_net_load_threshold = dict()
with open(os.path.join(inputs_directory, "regions_dist.csv"),
"Ur") as regions_file:
regions_reader = csv.reader(regions_file)
regions_reader.next()
for row in regions_reader:
self.dist_net_load_threshold[(str(row[0]),
int(row[1]))] = float(row[2])
with open(os.path.join(inputs_directory, "regions_bulk.csv"),
"Ur") as regions_file:
regions_reader = csv.reader(regions_file)
regions_reader.next()
for row in regions_reader:
self.regions.append(str(row[0]))
self.bulk_net_load_threshold[str(row[0])] = float(row[1])
# Dictionary with period as key and the list of timepoints for each period as values
self.period_timepoints = dict()
# List of timepoints in each period (e.g. 1-24 for days, 1-168 for weeks, etc.)
self.timepoints = list()
# Dictionary with the order in which the cumulative flexible load constraint is enforced,
# e.g. if starting it on timepoint 7, flex_load_timepoint 1 is assigned to timepoint 7
# {7: 1, 8: 2, 9: 3 ...}
self.flex_load_timepoints = dict()
# self.ev_load_timepoints = dict()
self.period_flex_load_timepoints = dict()
# self.period_ev_load_timepoints = dict()
with open(os.path.join(inputs_directory, "timepoints.csv"),
"Ur") as timepoints_file:
timepoints_reader = csv.reader(timepoints_file)
timepoints_reader.next()
for row in timepoints_reader:
self.timepoints.append(int(row[1]))
self.flex_load_timepoints[int(row[1])] = int(row[2])
# self.ev_load_timepoints[int(row[1])] = int(row[3])
if int(row[1]) == 168:
self.period_timepoints[int(row[0])] = self.timepoints
self.timepoints = list()
self.period_flex_load_timepoints[int(
row[0])] = self.flex_load_timepoints
self.flex_load_timepoints = dict()
# self.period_ev_load_timepoints[int(row[0])] = self.ev_load_timepoints
# self.ev_load_timepoints = dict()
# List of storage technologies
self.storage_technologies = list()
# Dictionaries with technology params (storage technologies as keys): large-storage boolean flag,
# charging efficiency, discharging efficiency
self.large_storage = dict()
self.charging_efficiency = dict()
self.discharging_efficiency = dict()
with open(os.path.join(inputs_directory, "storage_technologies.csv"),
"Ur") as storage_technologies_file:
storage_technologies_reader = csv.reader(storage_technologies_file)
storage_technologies_reader.next()
for row in storage_technologies_reader:
self.storage_technologies.append(str(row[0]))
self.large_storage[str(row[0])] = int(row[1])
self.charging_efficiency[str(row[0])] = float(row[2])
self.discharging_efficiency[str(row[0])] = float(row[3])
# List of generation technologies; this doesn't vary by period, so no period dictionary needed
self.generation_technologies = list()
# Dictionary of variable cost by generation technologies (generation technologies as keys)
self.variable_cost = dict()
with open(
os.path.join(inputs_directory, "generation_technologies.csv"),
"Ur") as generation_technologies_file:
generation_technologies_reader = csv.reader(
generation_technologies_file)
generation_technologies_reader.next()
for row in generation_technologies_reader:
self.generation_technologies.append(str(row[0]))
self.variable_cost[str(row[0])] = float(row[1])
# Dictionary with periods as keys, list of resources in each period as values
self.period_resources = dict()
# List of resources to populate values in period_resources dictionary
self.resources = list()
# Dictionaries by period, which in turn contain dictionaries by resource with the following parameters:
# region, technology, a boolean for distributed resources, and capacity
self.period_region = dict()
self.region = dict()
self.period_technology = dict()
self.technology = dict()
self.period_feeder = dict()
self.feeder = dict()
self.period_capacity = dict()
self.capacity = dict()
# Dictionary with duration for storage resources only
self.period_duration = dict()
self.duration = dict()
with open(os.path.join(inputs_directory, "resources.csv"),
"Ur") as resources_file:
resources_reader = csv.reader(resources_file)
resources_reader.next()
last_period = 1
for row in resources_reader:
if int(row[0]) == last_period:
self.resources.append(str(row[1]))
self.region[str(row[1])] = str(row[2])
self.technology[str(row[1])] = str(row[3])
self.feeder[str(row[1])] = int(row[4])
if row[5] == "":
pass
else:
self.capacity[str(row[1])] = float(row[5])
if row[6] == "":
pass
else:
self.duration[str(row[1])] = float(row[6])
else:
# Assign to period dictionary
self.period_resources[last_period] = self.resources
self.period_region[last_period] = self.region
self.period_technology[last_period] = self.technology
self.period_feeder[last_period] = self.feeder
self.period_capacity[last_period] = self.capacity
self.period_duration[last_period] = self.duration
# Clean up
self.resources = list()
self.region = dict()
self.technology = dict()
self.feeder = dict()
self.capacity = dict()
self.duration = dict()
# Correct last row
last_period = int(row[0])
# Start over
self.resources.append(str(row[1]))
self.region[str(row[1])] = str(row[2])
self.technology[str(row[1])] = str(row[3])
self.feeder[str(row[1])] = int(row[4])
if row[5] == "":
pass
else:
self.capacity[str(row[1])] = float(row[5])
if row[6] == "":
pass
else:
self.duration[str(row[1])] = float(row[6])
# List of transmission lines
self.transmission_lines = list()
# Dictionaries with transmission lines as keys; values are:
# Region where transmission line starts
self.transmission_from = dict()
# Region where transmission line ends
self.transmission_to = dict()
# Transmission capacity
self.transmission_capacity = dict()
with open(os.path.join(inputs_directory, "transmission.csv"),
"Ur") as transmission_file:
transmission_reader = csv.reader(transmission_file)
transmission_reader.next()
for row in transmission_reader:
self.transmission_lines.append(str(row[0]))
self.transmission_from[str(row[0])] = str(row[1])
self.transmission_to[str(row[0])] = str(row[2])
self.transmission_capacity[str(row[0])] = int(row[3])
# Loads
# Dictionary with period as keys, containing dictionary with timepoints as keys and load as values
self.period_net_distributed_load = util.recursivedict()
self.net_distributed_load = util.recursivedict()
# Same as above but for minimum cumulative flexible load,
self.period_min_cum_flex_load = util.recursivedict()
self.min_cumulative_flex_load = util.recursivedict()
# maximum cumulative net load,
self.period_max_cum_flex_load = util.recursivedict()
self.max_cumulative_flex_load = util.recursivedict()
# minimum cumulative EV load,
# self.period_min_cum_ev_load = util.recursivedict()
# self.min_cumulative_ev_load = util.recursivedict()
# # maximum cumulative EV load,
# self.period_max_cum_ev_load = util.recursivedict()
# self.max_cumulative_ev_load = util.recursivedict()
# renewable output.
self.period_bulk_renewables = util.recursivedict()
self.bulk_renewables = util.recursivedict()
with open(os.path.join(inputs_directory, "net_load.csv"),
"Ur") as net_load_file:
net_load_reader = csv.reader(net_load_file)
net_load_reader.next()
for row in net_load_reader:
self.net_distributed_load[int(row[0])][(row[1], int(
row[2]), int(row[3]))] = float(row[4])
self.min_cumulative_flex_load[int(row[0])][(row[1], int(
row[2]), int(row[3]))] = float(row[5])
self.max_cumulative_flex_load[int(row[0])][(row[1], int(
row[2]), int(row[3]))] = float(row[6])
# self.min_cum_ev_load[int(row[0])][(row[1], int(row[2]),int(row[3]))] = float(row[7])
# self.max_cum_ev_load[int(row[0])][(row[1], int(row[2]),int(row[3]))] = float(row[8])
self.bulk_renewables[int(row[0])][(row[1], int(
row[2]), int(row[3]))] = float(row[9])
# self.period_bulk_renewables[row[1], int(row[3], int(row[2]))] = float(row[4])
# if int(row[2]) == 168: # TODO: remove hardcoding; this is super ugly, but works for testing purposes
# region_num += 1
# if region_num == 3:
# self.net_distributed_load[int(row[0])] = self.period_net_distributed_load
# self.period_net_distributed_load = dict()
# self.min_cumulative_flex_load[int(row[0])] = self.period_min_cum_flex_load
# self.period_min_cum_flex_load = dict()
# self.max_cumulative_flex_load[int(row[0])] = self.period_max_cum_flex_load
# self.period_max_cum_flex_load = dict()
# self.min_cumulative_ev_load[int(row[0])] = self.period_min_cum_ev_load
# self.period_min_cum_ev_load = dict()
# self.max_cumulative_ev_load[int(row[0])] = self.period_max_cum_ev_load
# self.period_max_cum_ev_load = dict()
# self.bulk_renewables[int(row[0])] = self.period_bulk_renewables
# self.period_bulk_renewables = dict()
# region_num = 0
# else:
# pass
# else:
# pass
# Dictionary with period as key containing a dictionary with regions as keys and maximum flexible load as values
self.period_max_flex_load = util.recursivedict()
with open(os.path.join(inputs_directory, "max_flex_load.csv"),
"Ur") as max_flex_load_file:
max_flex_load_reader = csv.reader(max_flex_load_file)
max_flex_load_reader.next()
for row in max_flex_load_reader:
self.period_max_flex_load[int(row[0])][(row[1], int(
row[2]))] = float(row[3])
# # Dictionary with period as key containing a dictionary with regions as keys and maximum EV load as values
# self.max_ev_load = dict()
# self.period_max_ev_load = dict()
# with open(os.path.join(inputs_directory, "max_ev_load.csv"), "Ur") as max_ev_load_file:
# max_ev_load_reader = csv.reader(max_ev_load_file)
# max_ev_load_reader.next()
# last_period = 1
# for row in max_ev_load_reader:
# if int(row[0]) == last_period:
# self.max_ev_load[str(row[1])] = float(row[2])
# else:
# self.period_max_ev_load[int(last_period)] = self.max_ev_load
# self.max_ev_load = dict()
# last_period = int(row[0])
# self.max_ev_load[str(row[1])] = float(row[2])
# These are all single values and inelegantly instantiated inside the class here because it was easy
# TODO: define outside of this class
self.t_and_d_losses = 0
self.curtailment_cost = 10**2
self.unserved_energy_cost = 10**5
self.dist_net_load_penalty = 1.0
self.bulk_net_load_penalty = 1.0
self.feeders = [0, 1, 2, 3]
