def make_supplier(self):
# Add supplier
supplier = Neighbor()
supplier.name = 'BPA'
supplier.description = 'The Bonneville Power Administration as electricity supplier to the City of Richland, WA'
supplier.lossFactor = self.supplier_loss_factor
supplier.maximumPower = 200800 # [avg.kW, twice the average COR load]
supplier.minimumPower = 0.0 # [avg.kW, will not export]
# Add supplier model
supplierModel = BulkSupplier_dc()
supplierModel.name = 'BPAModel'
#supplierModel.demandThreshold = 0.75 * supplier.maximumPower
supplierModel.converged = False # Dynamically assigned
supplierModel.convergenceThreshold = 0 # Not yet implemented
supplierModel.effectiveImpedance = 0.0 # Not yet implemented
supplierModel.friend = False # Separate business entity from COR
supplierModel.transactive = False # Not a transactive neighbor
# Add vertices
# The first default vertex is, for now, based on the flat COR rate to
# PNNL. The second vertex includes 2# losses at a maximum power that
# is twice the average electric load for COR. This is helpful to
# ensure that a unique price, power point will be found. In this
# model the recipient pays the cost of energy losses.
# The first vertex is based on BPA Jan HLH rate at zero power
# importation.
d1 = Vertex(0, 0, 0) # create first default vertex
d1.marginalPrice = 0.04196 # HLH BPA rate Jan 2018 [$/kWh]
d1.cost = 2000.0 # Const. price shift to COR customer rate [$/h]
d1.power = 0.0 # [avg.kW]
# The second default vertex represents imported and lost power at a power
# value presumed to be the maximum deliverable power from BPA to COR.
d2 = Vertex(0, 0, 0) # create second default vertex
# COR pays for all sent power but receives an amount reduced by
# losses. This creates a quadratic term in the production cost and
# a slope to the marginal price curve.
d2.marginalPrice = d1.marginalPrice / (1 - supplier.lossFactor
) # [$/kWh]
# From the perspective of COR, it receives the power sent by BPA,
# less losses.
d2.power = (1 -
supplier.lossFactor) * supplier.maximumPower # [avg.kW]
# The production costs can be estimated by integrating the
# marginal-price curve.
d2.cost = d1.cost + d2.power * (d1.marginalPrice + 0.5 *
(d2.marginalPrice - d1.marginalPrice)
) # [$/h]
supplierModel.defaultVertices = [d1, d2]
# COST PARAMTERS
# A constant cost parameter is being used here to account for the
# difference between wholesale BPA rates to COR and COR distribution
# rates to customers like PNNL. A constant of $2,000/h steps the rates
# from about 0.04 $/kWh to about 0.06 $/kWh. This may be refined later.
# IMPORTANT: This shift has no affect on marginal pricing.
supplierModel.costParameters[0] = 2000.0 # [$/h]
# Cross-reference object & model
supplierModel.object = supplier
supplier.model = supplierModel
# Meter
bpaElectricityMeter = MeterPoint() # Instantiate an electricity meter
bpaElectricityMeter.name = 'BpaElectricityMeter'
bpaElectricityMeter.description = 'BPA electricity to COR'
bpaElectricityMeter.measurementType = MeasurementType.PowerReal
bpaElectricityMeter.measurementUnit = MeasurementUnit.kWh
supplierModel.meterPoints = [bpaElectricityMeter]
return supplier
def init_objects(self):
# Add meter
meter = MeterPoint()
meter.measurementType = MeasurementType.PowerReal
meter.name = 'CoRElectricMeter'
meter.measurementUnit = MeasurementUnit.kWh
self.meterPoints.append(meter)
# Add weather forecast service
weather_service = TemperatureForecastModel(self.config_path, self)
self.informationServiceModels.append(weather_service)
# Add inelastive asset
# Source: https://www.ci.richland.wa.us/home/showdocument?id=1890
# Residential customers: 23,768
# Electricity sales in 2015:
# Total: 100.0# 879,700,000 kWh 100,400 avg. kW)
# Resident: 46.7 327,200,000 37,360
# Gen. Serv.: 38.1 392,300,000 44,790
# Industrial: 15.2 133,700,000 15,260
# Irrigation: 2.4 21,110,000 2,410
# Other: 0.6 5,278,000 603
# 2015 Res. rate: $0.0616/kWh
# Avg. annual residential cust. use: 14,054 kWh
# Winter peak, 160,100 kW (1.6 x average)
# Summer peak, 180,400 kW (1.8 x average)
# Annual power supply expenses: $35.5M
# *************************************************************************
inelastive_load = LocalAsset()
inelastive_load.name = 'InelasticLoad'
inelastive_load.maximumPower = -50000 # Remember that a load is a negative power [kW]
inelastive_load.minimumPower = -200000 # Assume twice the averag PNNL load [kW]
# Add inelastive asset model
inelastive_load_model = OpenLoopRichlandLoadPredictor(weather_service)
inelastive_load_model.name = 'InelasticLoadModel'
inelastive_load_model.defaultPower = -100420 # [kW]
inelastive_load_model.defaultVertices = [
Vertex(float("inf"), 0.0, -100420.0)
]
# Cross-reference asset & asset model
inelastive_load_model.object = inelastive_load
inelastive_load.model = inelastive_load_model
# Add inelastic as city's asset
self.localAssets.extend([inelastive_load])
# Add Market
market = Market()
market.name = 'dayAhead'
market.commitment = False
market.converged = False
market.defaultPrice = 0.0428 # [$/kWh]
market.dualityGapThreshold = self.duality_gap_threshold # [0.02 = 2#]
market.initialMarketState = MarketState.Inactive
market.marketOrder = 1 # This is first and only market
market.intervalsToClear = 1 # Only one interval at a time
market.futureHorizon = timedelta(
hours=24) # Projects 24 hourly future intervals
market.intervalDuration = timedelta(
hours=1) # [h] Intervals are 1 h long
market.marketClearingInterval = timedelta(hours=1) # [h]
market.marketClearingTime = Timer.get_cur_time().replace(
hour=0, minute=0, second=0,
microsecond=0) # Aligns with top of hour
market.nextMarketClearingTime = market.marketClearingTime + timedelta(
hours=1)
self.markets.append(market)
self.campus = self.make_campus()
supplier = self.make_supplier()
# Add campus as city's neighbor
self.neighbors.extend([self.campus, supplier])
def init_objects(self):
# Add meter
# meter = MeterPoint()
# meter.name = 'BuildingElectricMeter'
# meter.measurementType = MeasurementType.PowerReal
# meter.measurementUnit = MeasurementUnit.kWh
# self.meterPoints.append(meter)
# Add weather forecast service
weather_service = TemperatureForecastModel(self.config_path, self)
self.informationServiceModels.append(weather_service)
# # Add inelastive asset
# inelastive_load = LocalAsset()
# inelastive_load.name = 'InelasticBldgLoad'
# inelastive_load.maximumPower = 0 # Remember that a load is a negative power [kW]
# inelastive_load.minimumPower = -200
#
# # Add inelastive asset model
# inelastive_load_model = LocalAssetModel()
# inelastive_load_model.name = 'InelasticBuildingModel'
# inelastive_load_model.defaultPower = -100 # [kW]
# inelastive_load_model.defaultVertices = [Vertex(float("inf"), 0, -100, True)]
#
# # Cross-reference asset & asset model
# inelastive_load_model.object = inelastive_load
# inelastive_load.model = inelastive_load_model
# Add elastive asset
elastive_load = LocalAsset()
elastive_load.name = 'TccLoad'
elastive_load.maximumPower = 0 # Remember that a load is a negative power [kW]
elastive_load.minimumPower = -self.max_deliver_capacity
# Add inelastive asset model
# self.elastive_load_model = LocalAssetModel()
self.elastive_load_model = TccModel()
self.elastive_load_model.name = 'TccModel'
self.elastive_load_model.defaultPower = -0.5*self.max_deliver_capacity # [kW]
self.elastive_load_model.defaultVertices = [Vertex(0.055, 0, -self.elastive_load_model.defaultPower, True),
Vertex(0.06, 0, -self.elastive_load_model.defaultPower/2, True)]
# Cross-reference asset & asset model
self.elastive_load_model.object = elastive_load
elastive_load.model = self.elastive_load_model
# Add inelastive and elastive loads as building' assets
self.localAssets.extend([elastive_load])
# Add Market
market = Market()
market.name = 'dayAhead'
market.commitment = False
market.converged = False
market.defaultPrice = 0.0428 # [$/kWh]
market.dualityGapThreshold = self.duality_gap_threshold # [0.02 = 2#]
market.initialMarketState = MarketState.Inactive
market.marketOrder = 1 # This is first and only market
market.intervalsToClear = 1 # Only one interval at a time
market.futureHorizon = timedelta(hours=24) # Projects 24 hourly future intervals
market.intervalDuration = timedelta(hours=1) # [h] Intervals are 1 h long
market.marketClearingInterval = timedelta(hours=1) # [h]
market.marketClearingTime = Timer.get_cur_time().replace(hour=0,
minute=0,
second=0,
microsecond=0) # Aligns with top of hour
market.nextMarketClearingTime = market.marketClearingTime + timedelta(hours=1)
self.markets.append(market)
# Campus object
campus = Neighbor()
campus.name = 'PNNL_Campus'
campus.description = 'PNNL_Campus'
campus.maximumPower = self.max_deliver_capacity
campus.minimumPower = 0. # [avg.kW]
campus.lossFactor = self.campus_loss_factor
# Campus model
campus_model = NeighborModel()
campus_model.name = 'PNNL_Campus_Model'
campus_model.location = self.name
campus_model.defaultVertices = [Vertex(0.045, 25, 0, True), Vertex(0.048, 0, self.max_deliver_capacity, True)]
campus_model.demand_threshold_coef = self.demand_threshold_coef
# campus_model.demandThreshold = self.demand_threshold_coef * self.monthly_peak_power
campus_model.demandThreshold = self.monthly_peak_power
campus_model.transactive = True
campus_model.inject(self, system_loss_topic=self.system_loss_topic)
# Avg building meter
building_meter = MeterPoint()
building_meter.name = self.name + ' ElectricMeter'
building_meter.measurementType = MeasurementType.AverageDemandkW
building_meter.measurementUnit = MeasurementUnit.kWh
campus_model.meterPoints.append(building_meter)
# Cross-reference object & model
campus_model.object = campus
campus.model = campus_model
self.campus = campus
# Add campus as building's neighbor
self.neighbors.append(campus)
def init_objects(self):
# Add meter
meter = MeterPoint()
meter.measurementType = MeasurementType.PowerReal
meter.name = 'CampusElectricityMeter'
meter.measurementUnit = MeasurementUnit.kWh
self.meterPoints.append(meter)
# Add weather forecast service
weather_service = TemperatureForecastModel(self.config_path, self)
self.informationServiceModels.append(weather_service)
# Add inelastive asset
inelastive_load = LocalAsset()
inelastive_load.name = 'InelasticBuildings' # Campus buildings that are not responsive
inelastive_load.maximumPower = 0 # Remember that a load is a negative power [kW]
inelastive_load.minimumPower = -2 * 8200 # Assume twice the average PNNL load [kW]
# Add inelastive asset model
inelastive_load_model = OpenLoopPnnlLoadPredictor(weather_service)
inelastive_load_model.name = 'InelasticBuildingsModel'
inelastive_load_model.engagementCost = [
0, 0, 0
] # Transition costs irrelevant
inelastive_load_model.defaultPower = -6000 # [kW]
inelastive_load_model.defaultVertices = [Vertex(0, 0, -6000.0, 1)]
# Cross-reference asset & asset model
inelastive_load_model.object = inelastive_load
inelastive_load.model = inelastive_load_model
# Add solar PV asset
solar_pv = SolarPvResource()
solar_pv.maximumPower = self.PV_max_kW # [avg.kW]
solar_pv.minimumPower = 0.0 # [avg.kW]
solar_pv.name = 'SolarPv'
solar_pv.description = '120 kW solar PV site on the campus'
# Add solar PV asset model
solar_pv_model = SolarPvResourceModel()
solar_pv_model.cloudFactor = 1.0 # dimensionless
solar_pv_model.engagementCost = [0, 0, 0]
solar_pv_model.name = 'SolarPvModel'
solar_pv_model.defaultPower = 0.0 # [avg.kW]
solar_pv_model.defaultVertices = [Vertex(0, 0, 30.0, True)]
solar_pv_model.costParameters = [0, 0, 0]
solar_pv_model.inject(self, power_topic=self.solar_topic)
# Cross-reference asset & asset model
solar_pv.model = solar_pv_model
solar_pv_model.object = solar_pv
# Add inelastive and solar_pv as campus' assets
self.localAssets.extend([inelastive_load, solar_pv])
# Add Market
market = Market()
market.name = 'dayAhead'
market.commitment = False
market.converged = False
market.defaultPrice = 0.04 # [$/kWh]
market.dualityGapThreshold = self.duality_gap_threshold # [0.02 = 2#]
market.initialMarketState = MarketState.Inactive
market.marketOrder = 1 # This is first and only market
market.intervalsToClear = 1 # Only one interval at a time
market.futureHorizon = timedelta(
hours=24) # Projects 24 hourly future intervals
market.intervalDuration = timedelta(
hours=1) # [h] Intervals are 1 h long
market.marketClearingInterval = timedelta(hours=1) # [h]
market.marketClearingTime = Timer.get_cur_time().replace(
hour=0, minute=0, second=0,
microsecond=0) # Aligns with top of hour
market.nextMarketClearingTime = market.marketClearingTime + timedelta(
hours=1)
self.markets.append(market)
# City object
city = Neighbor()
city.name = 'CoR'
city.description = 'City of Richland (COR) electricity supplier node'
city.maximumPower = 20000 # Remember loads have negative power [avg.kW]
city.minimumPower = 0 # [avg.kW]
city.lossFactor = self.city_loss_factor
# City model
city_model = NeighborModel()
city_model.name = 'CoR_Model'
city_model.location = self.name
city_model.transactive = True
city_model.defaultPower = 10000 # [avg.kW]
city_model.defaultVertices = [
Vertex(0.046, 160, 0, True),
Vertex(0.048,
160 + city.maximumPower * (0.046 + 0.5 * (0.048 - 0.046)),
city.maximumPower, True)
]
city_model.costParameters = [0, 0, 0]
city_model.demand_threshold_coef = self.demand_threshold_coef
city_model.demandThreshold = self.monthly_peak_power
city_model.inject(self,
system_loss_topic=self.system_loss_topic,
dc_threshold_topic=self.dc_threshold_topic)
# Cross-reference object & model
city_model.object = city
city.model = city_model
self.city = city
# Add city as campus' neighbor
self.neighbors.append(city)
# Add buildings
for bldg_name in self.building_names:
bldg_neighbor = self.make_bldg_neighbor(bldg_name)
self.neighbors.append(bldg_neighbor)
def test_update_dc_threshold():
print('Running BulkSupplier_dc.test_update_dc_threshold()')
pf = 'pass'
## Basic configuration for tests:
# Create a test object and initialize demand-realted properties
test_obj = BulkSupplier_dc()
test_obj.demandMonth = datetime.now().month # month(datetime)
test_obj.demandThreshold = 1000
# Create a test market
test_mkt = Market()
# Create and store two time intervals
dt = datetime.now()
at = dt
dur = timedelta(hours=1) # Hours(1)
mkt = test_mkt
mct = dt
st = dt
ti = [TimeInterval(at, dur, mkt, mct, st)]
st = st + dur
ti.append(TimeInterval(at, dur, mkt, mct, st))
test_mkt.timeIntervals = ti
## Test case when there is no MeterPoint object
test_obj.demandThreshold = 1000
test_obj.demandMonth = datetime.now().month # month(datetime)
test_obj.meterPoints = [] # MeterPoint.empty
# Create and store a couple scheduled powers
# iv(1) = IntervalValue(test_obj, ti[0], test_mkt, MeasurementType.ScheduledPower, 900)
# iv(2) = IntervalValue(test_obj, ti[1], test_mkt, MeasurementType.ScheduledPower, 900)
iv = [
IntervalValue(test_obj, ti[0], test_mkt, MeasurementType.ScheduledPower, 900),
IntervalValue(test_obj, ti[1], test_mkt, MeasurementType.ScheduledPower, 900)
]
test_obj.scheduledPowers = iv
try:
test_obj.update_dc_threshold(test_mkt)
print('- the method ran without errors')
except:
pf = 'fail'
print('- the method encountered errors when called')
if test_obj.demandThreshold != 1000:
pf = 'fail'
print('- the method inferred the wrong demand threshold value')
else:
print('- the method properly kept the old demand threshold value with no meter')
iv = [
IntervalValue(test_obj, ti[0], test_mkt, MeasurementType.ScheduledPower, 1100),
IntervalValue(test_obj, ti[1], test_mkt, MeasurementType.ScheduledPower, 900)
]
test_obj.scheduledPowers = iv
try:
test_obj.update_dc_threshold(test_mkt)
print('- the method ran without errors when there is no meter')
except:
pf = 'fail'
print('- the method encountered errors when there is no meter')
if test_obj.demandThreshold != 1100:
pf = 'fail'
print('- the method did not update the inferred demand threshold value')
else:
print('- the method properly updated the demand threshold value with no meter')
## Test with an appropriate MeterPoint meter
# Create and store a MeterPoint test object
test_mtr = MeterPoint()
test_mtr.measurementType = MeasurementType.AverageDemandkW
test_mtr.currentMeasurement = 900
test_obj.meterPoints = [test_mtr]
# Reconfigure the test object for this test:
iv = [
IntervalValue(test_obj, ti[0], test_mkt, MeasurementType.ScheduledPower, 900),
IntervalValue(test_obj, ti[1], test_mkt, MeasurementType.ScheduledPower, 900)
]
test_obj.scheduledPowers = iv
test_obj.demandThreshold = 1000
test_obj.demandMonth = datetime.now().month
# Run the test. Confirm it runs.
try:
test_obj.update_dc_threshold(test_mkt)
print('- the method ran without errors when there is a meter')
except:
pf = 'fail'
print('- the method encountered errors when there is a meter')
# Check that the old threshold is correctly retained.
if test_obj.demandThreshold != 1000:
pf = 'fail'
print('- the method failed to keep the correct demand threshold value when there is a meter')
else:
print('- the method properly kept the old demand threshold value when there is a meter')
# Reconfigure the test object with a lower current threshold
iv = [
IntervalValue(test_obj, ti[0], test_mkt, MeasurementType.ScheduledPower, 900),
IntervalValue(test_obj, ti[1], test_mkt, MeasurementType.ScheduledPower, 900)
]
test_obj.scheduledPowers = iv
test_obj.demandThreshold = 800
# Run the test.
test_obj.update_dc_threshold(test_mkt)
# Check that a new, higher demand threshold was set.
if test_obj.demandThreshold != 900:
pf = 'fail'
print(['- the method failed to update the demand threshold value when there is a meter'])
else:
print('- the method properly updated the demand threshold value when there is a meter')
## Test rollover to new month
# Configure the test object
test_obj.demandMonth = dt + relativedelta.relativedelta(months=-1) # month(datetime - days(31)) # prior month
test_obj.demandThreshold = 1000
test_obj.scheduledPowers[0].value = 900
test_obj.scheduledPowers[1].value = 900
test_obj.meterPoints = [] # MeterPoint.empty
# Run the test
test_obj.update_dc_threshold(test_mkt)
# See if the demand threshold was reset at the new month.
if test_obj.demandThreshold != 0.8 * 1000:
pf = 'fail'
print('- the method did not reduce the threshold properly in a new month')
else:
print('- the method reduced the threshold properly in a new month')
# Success
print('- the test ran to completion')
print('Result: #s\n\n', pf)
def test_update_dc_threshold():
print('Running BulkSupplier_dc.test_update_dc_threshold()')
# Basic configuration for tests:
# Create a test object and initialize demand-related properties
test_obj = BulkSupplier_dc()
test_obj.demandMonth = datetime.now().month # month(datetime)
test_obj.demandThreshold = 1000
# Create a test market
test_mkt = Market()
# Create and store two time intervals
dt = datetime.now()
at = dt
dur = timedelta(hours=1) # Hours(1)
mkt = test_mkt
mct = dt
st = dt
ti = [TimeInterval(at, dur, mkt, mct, st)]
st = st + dur
ti.append(TimeInterval(at, dur, mkt, mct, st))
test_mkt.timeIntervals = ti
# Test case when there is no MeterPoint object
test_obj.demandThreshold = 1000
test_obj.demandMonth = datetime.now().month # month(datetime)
test_obj.meterPoints = [] # MeterPoint.empty
# Create and store a couple scheduled powers
# iv(1) = IntervalValue(test_obj, ti[0], test_mkt, MeasurementType.ScheduledPower, 900)
# iv(2) = IntervalValue(test_obj, ti[1], test_mkt, MeasurementType.ScheduledPower, 900)
iv = [
IntervalValue(test_obj, ti[0], test_mkt, MeasurementType.ScheduledPower, 900),
IntervalValue(test_obj, ti[1], test_mkt, MeasurementType.ScheduledPower, 900)
]
test_obj.scheduledPowers = iv
try:
test_obj.update_dc_threshold(test_mkt)
print('- the method ran without errors')
except RuntimeWarning:
print('- the method encountered errors when called')
assert test_obj.demandThreshold == 1000, '- the method inferred the wrong demand threshold value'
iv = [
IntervalValue(test_obj, ti[0], test_mkt, MeasurementType.ScheduledPower, 1100),
IntervalValue(test_obj, ti[1], test_mkt, MeasurementType.ScheduledPower, 900)
]
test_obj.scheduledPowers = iv
try:
test_obj.update_dc_threshold(test_mkt)
print('- the method ran without errors when there is no meter')
except RuntimeWarning:
print('- the method encountered errors when there is no meter')
assert test_obj.demandThreshold == 1100, '- the method did not update the inferred demand threshold value'
# Test with an appropriate MeterPoint meter
# Create and store a MeterPoint test object
test_mtr = MeterPoint()
test_mtr.measurementType = MeasurementType.AverageDemandkW
test_mtr.currentMeasurement = 900
test_obj.meterPoints = [test_mtr]
# Reconfigure the test object for this test:
iv = [
IntervalValue(test_obj, ti[0], test_mkt, MeasurementType.ScheduledPower, 900),
IntervalValue(test_obj, ti[1], test_mkt, MeasurementType.ScheduledPower, 900)
]
test_obj.scheduledPowers = iv
test_obj.demandThreshold = 1000
test_obj.demandMonth = datetime.now().month
# Run the test. Confirm it runs.
try:
test_obj.update_dc_threshold(test_mkt)
print('- the method ran without errors when there is a meter')
except RuntimeWarning:
print('- the method encountered errors when there is a meter')
# Check that the old threshold is correctly retained.
assert test_obj.demandThreshold == 1000, \
'- the method failed to keep the correct demand threshold value when there is a meter'
# Reconfigure the test object with a lower current threshold
iv = [
IntervalValue(test_obj, ti[0], test_mkt, MeasurementType.ScheduledPower, 900),
IntervalValue(test_obj, ti[1], test_mkt, MeasurementType.ScheduledPower, 900)]
test_obj.scheduledPowers = iv
test_obj.demandThreshold = 800
# Run the test.
test_obj.update_dc_threshold(test_mkt)
# Check that a new, higher demand threshold was set.
assert test_obj.demandThreshold == 900, \
'- the method failed to update the demand threshold value when there is a meter'
# Test rollover to new month
# Configure the test object
last_month = dt.month - 1
if last_month == 0:
last_month = 12
test_obj.demandMonth = last_month # month(datetime - days(31)) # prior month
test_obj.demandThreshold = 1000
test_obj.scheduledPowers[0].value = 900
test_obj.scheduledPowers[1].value = 900
test_obj.meterPoints = [] # MeterPoint.empty
test_obj.demandThresholdCoef = 0.8
# Run the test
try:
test_obj.update_dc_threshold(test_mkt)
print(' - The method ran without errors')
except RuntimeWarning:
print(' - ERRORS ENCOUNTERED')
# See if the demand threshold was reset at the new month.
assert test_obj.demandThreshold == test_obj.demandThresholdCoef * 1000, \
'- the method did not reduce the threshold properly in a new month'
# Success
print('test_update_dc_threshold() ran to completion.\n')