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 = '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.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_schedule_power(cls):
print('Running test_schedule_power()')
pf = 'pass'
# Create a test Market object.
test_mkt = Market()
# Create and store a couple TimeInterval objects at a known date and
# time.
dt = datetime(2017, 11, 1, 12, 0, 0) # Wednesday Nov. 1, 2017 at noon
at = dt
dur = timedelta(hours=1)
mkt = test_mkt
mct = dt
st = dt
test_intervals = [TimeInterval(at, dur, mkt, mct, st)]
st = st + dur # 1p on the same day
test_intervals.append(TimeInterval(at, dur, mkt, mct, st))
test_mkt.timeIntervals = test_intervals
# Create a test TemperatureForecastModel object and give it some
# temperature values in the test TimeIntervals.
# test_forecast = TemperatureForecastModel
# # The information type should be specified so the test object will
# # correctly identivy it.
# test_forecast.informationType = 'temperature'
# # test_forecast.update_information(test_mkt)
# test_forecast.predictedValues(1) = IntervalValue(test_forecast, test_intervals(1), test_mkt, 'Temperature', 20) # Heating regime
# test_forecast.predictedValues(2) = IntervalValue(test_forecast, test_intervals(2), test_mkt, 'Temperature', 100) # Cooling regime
# test_obj.informationServiceModels = {test_forecast}
# Create a OpenLoopRichlandLoadPredictor test object.
test_forecast = TemperatureForecastModel()
test_forecast.informationType = 'temperature'
test_forecast.predictedValues = [
IntervalValue(test_forecast, test_intervals[0], test_mkt,
MeasurementType.Temperature, 20),
# Heating regime
IntervalValue(test_forecast, test_intervals[1], test_mkt,
MeasurementType.Temperature, 100)
# Cooling regime
]
test_obj = OpenLoopRichlandLoadPredictor(test_forecast)
# Manually evaluate from the lookup table and the above categorical inputs
# DOW = Wed. ==>
Intercept1 = 146119
Intercept2 = 18836
Intercept3 = -124095
Factor1 = -1375
Factor2 = 1048
Temperature1 = 20
Temperature2 = 100
LOAD = [
-(Intercept1 + Intercept2 + Factor1 * Temperature1),
-(Intercept1 + Intercept3 + Factor2 * Temperature2)
]
try:
test_obj.schedule_power(test_mkt)
print('- the method ran without errors')
except:
pf = 'fail'
_log.warning('- the method had errors when called')
#if any(abs([test_obj.scheduledPowers(1: 2).value] - [LOAD])) > 5
if any([
abs(test_obj.scheduledPowers[i].value - LOAD[i]) > 5
for i in range(len(test_obj.scheduledPowers))
]):
pf = 'fail'
_log.warning('- the calculated powers were not as expected')
else:
print('- the calculated powers were as expected')
# Success
print('- the test ran to completion')
print('Result: #s\n\n', pf)