def buildValuesBackwards(self, startDate, endDate, location, tracking, storageMWh): #reverse the arrays containing price and solar data. print "Building Values Backwards" tz = SydneyTimezone() lat= location[0] lon = location[1] state= location[2] # locationName = location[3] startDate = startDate.astimezone(tz) endDate = endDate.astimezone(tz) controller = Controller(gui=False) data = controller.getTimeseriesNemDNICos(state=state, lat = lat, lon = lon, startDate = startDate, endDate = endDate) plant = Plant(namePlateMW = 1) storage = Storage(storageLevels = self.numStates, effectiveCapacityMWh = storageMWh, batteryType = self.batteryType) plant = Plant(1) values=np.zeros(shape=(data.shape[0] + 1, storage.getNumStorageLevels()+1)) destinations = np.zeros(shape=( data.shape[0] + 1, storage.getNumStorageLevels()+1)) totalArrayOutput = 0 # if self.shaved: # data = self.shaveNemData(data) if tracking: COS_INDEX = self.TRACKING_COS_INDEX else: COS_INDEX = self.FIXED_COS_INDEX #iterate backwards through time. for timeIndex in np.arange(0, data.shape[0])[::-1]: # Get the timeperiod outputs cos = data[timeIndex][COS_INDEX] price = data[timeIndex][self.PRICE_INDEX] if self.capped: price = min(price, 300) dni = data[timeIndex][self.DNI_INDEX] ghi_factor = data[timeIndex][self.GHI_FACTOR_INDEX] ghi = np.multiply(ghi_factor, dni) arrayOutput = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=cos) totalArrayOutput = totalArrayOutput + arrayOutput date = datetime.datetime(year=int(data[timeIndex][self.YEAR_INDEX]), month = int(data[timeIndex][self.MONTH_INDEX]), day = int(data[timeIndex][self.DAY_INDEX]), hour=int(data[timeIndex][self.HOUR_INDEX]), minute=int(data[timeIndex][self.MINUTE_INDEX]), tzinfo=SydneyTimezone()) # iterate through the storagelevels for storageLevel in np.arange(storage.getNumStorageLevels()+1): # Find the optimal destination and its corresponding value valueAndDestination = self.getValueAndDestination(storage = storage, price=price, solarOutputMWh=arrayOutput, storageLevel=storageLevel, nextTimePeriodValues = values[timeIndex + 1], date = date) values[ timeIndex, storageLevel] = valueAndDestination[0] destinations[timeIndex, storageLevel] = valueAndDestination[1] prices = np.transpose(data)[self.PRICE_INDEX] sun = np.transpose(data)[self.DNI_INDEX] return (values, destinations,prices, sun, totalArrayOutput)
def getLCOEHighLow(tracking, location): print "Beginning LCOE Simulation" controller = Controller(gui=False) tz = SydneyTimezone() lat= location[0] lon = location[1] state= location[2] locationName = location[3] totalPSHIncident = 0 timePeriodCouner = 0 yearCounter = 0 for year in np.arange(2005, 2012): yearCounter = yearCounter + 1 print year startDate = datetime.datetime(year=year, month=1, day=1, hour=1, tzinfo = tz) endDate= datetime.datetime(year=year, month=12, day=31, hour=23, minute=30, tzinfo = tz) print "retrieving data" data = controller.getTimeseriesNemDNICos(state=state, lat = lat, lon = lon, startDate = startDate, endDate = endDate) print "data retrieved" plant = SolarPlant(namePlateMW = 1) # Iterate through the time series and perform calculations. for i in np.arange(data.shape[0]): timePeriodCouner = timePeriodCouner + 1 dni = data[i][7] # date = datetime.datetime(year=int(data[i][0]), month = int(data[i][1]), day = int(data[i][2]), hour=int(data[i][3]), minute=int(data[i][4]), tzinfo=SydneyTimezone()) if dni > 0: if tracking: cos = data[i][10] else: cos = data[i][9] ghi_factor = data[i][8] ghi = np.multiply(ghi_factor, dni) output = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=cos) totalPSHIncident = totalPSHIncident + ((dni * cos)/1000.0) # Write to the averages data file. numDays = timePeriodCouner / 48.0 averagePSHPerDay = totalPSHIncident / (numDays) if tracking: lcoeLow = getLCOELowTracking(averagePSHPerDay) lcoeHigh = getLCOEHighTracking(averagePSHPerDay) else: lcoeLow = getLCOELowFixed(averagePSHPerDay) lcoeHigh = getLCOEHighFixed(averagePSHPerDay) return (lcoeHigh, lcoeLow)
def followPathBackThroughDataset(self, destinations, startDate, endDate, location, storageMWh, tracking): #reverse the arrays containing price and solar data. print "Following Path back through dataset." tz = SydneyTimezone() lat= location[0] lon = location[1] state= location[2] # locationName = location[3] startDate = startDate.astimezone(tz) endDate = endDate.astimezone(tz) controller = Controller(gui=False) data = controller.getTimeseriesNemDNICos(state=state, lat = lat, lon = lon, startDate = startDate, endDate = endDate) plant = Plant(namePlateMW = 1) storage = Storage(storageLevels = self.numStates, effectiveCapacityMWh = storageMWh, batteryType = self.batteryType) plant = Plant(1) #iterate backwards through time. totalRevenue = 0 currentLevel = 0 if tracking: COS_INDEX = self.TRACKING_COS_INDEX else: COS_INDEX = self.FIXED_COS_INDEX for timeIndex in np.arange(0, data.shape[0]): # Get the timeperiod outputs cos = COS_INDEX price = data[timeIndex][self.PRICE_INDEX] if self.capped and price > 300: price = 300 dni = data[timeIndex][self.DNI_INDEX] ghi_factor = data[timeIndex][self.GHI_FACTOR_INDEX] ghi = np.multiply(ghi_factor, dni) arrayOutput = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=cos) date = datetime.datetime(year=int(data[timeIndex][self.YEAR_INDEX]), month = int(data[timeIndex][self.MONTH_INDEX]), day = int(data[timeIndex][self.DAY_INDEX]), hour=int(data[timeIndex][self.HOUR_INDEX]), minute=int(data[timeIndex][self.MINUTE_INDEX]), tzinfo=SydneyTimezone()) nextLevel = destinations[timeIndex][currentLevel] storageEnergy = storage.getEnergyOut(currentLevel, nextLevel) costOfMovement = storage.getCostOfMovement(currentLevel, nextLevel) outputMWh = arrayOutput + storageEnergy value = self.valueFunction(outputMWh, price, date) - costOfMovement totalRevenue = totalRevenue + value currentLevel = nextLevel return totalRevenue
def getStrikePrice(startDate, endDate, location): tz = SydneyTimezone() lat= location[0] lon = location[1] state= location[2] startDate = startDate.astimezone(tz) endDate = endDate.astimezone(tz) controller = Controller(gui=False) data = controller.getTimeseriesNemDNICos(state=state, lat = lat, lon = lon, startDate = startDate, endDate = endDate) counter = 0 average = 0 for i in np.arange(data.shape[0]): counter = counter + 1 price = data[i][6] average = average + price average = np.divide(float(average), float(counter)) return average
def getStrikePrice(startDate, endDate, location): tz = SydneyTimezone() lat= location[0] lon = location[1] state= location[2] startDate = startDate.astimezone(tz) endDate = endDate.astimezone(tz) controller = Controller(gui=False) data = controller.getTimeseriesNemDNICos(state=state, lat = lat, lon = lon, startDate = startDate, endDate = endDate) counter = 0 average = 0 for i in np.arange(data.shape[0]): date = datetime.datetime(year=int(data[i][0]), month = int(data[i][1]), day = int(data[i][2]), hour=int(data[i][3]), minute=int(data[i][4]), tzinfo=SydneyTimezone()) if date.hour >= 7 and date.hour < 22 and date.weekday() < 5: counter = counter + 1 price = data[i][6] average = average + price average = np.divide(float(average), float(counter)) return average
def printTimeseries():
print "Price and Solar Daily Trends"
controller = Controller(gui=False)
tz = SydneyTimezone()
inflation = 1.027
contractStartHour = 7
contractEndHour = 16
strikePrices = [100,150,200,250]
for strikePrice in strikePrices:
averagesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/shapedCapRevenue/"+str(strikePrice)+".csv"
averagesFile = open(averagesPath, 'w')
averagesFile.write("Location, Revenue Fixed, Revenue Tracking\n")
for location in locations.getLocations():
lat= location[0]
lon = location[1]
state= location[2]
locationName = location[3]
if state == "nsw":
capFactor = 0.280152271502
capFactorTracking = 0.337531327017
if state == "vic":
capFactor = 0.259125863986
capFactorTracking = 0.319551495158
if state == "qld":
capFactor = 0.28828551756
capFactorTracking = 0.342014584626
if state == "sa":
capFactor = 0.317160237904
capFactorTracking = 0.391816336203
dayCounter = 0
lastDays = 0
counter = 0
contractStartHour = 7
contractEndHour = 16
countAboveStrike = 0
countFixedDemandMet = 0
countTrackingDemandMet = 0
countContractTimePeriods = averagePayoutFixed = averagePayoutTracking = 0
spotRevenueFixed = spotRevenueTracking = 0
totalFixedMWh = totalTrackingMWh = 0
for year in np.arange(2005, 2012):
print year
startDate = datetime.datetime(year=year, month=1, day=1, hour=1, tzinfo = tz)
date = datetime.datetime(year=year, month=1, day=1, hour=1, tzinfo = tz)
endDate= datetime.datetime(year=year, month=12, day=31, hour=23, minute=30, tzinfo = tz)
data = controller.getTimeseriesNemDNICos(state=state, lat = lat, lon = lon, startDate = startDate, endDate = endDate)
plant = SolarPlant(namePlateMW = 1)
timeDiff = endDate - startDate
if timeDiff != lastDays:
dayCounter = dayCounter + timeDiff.days
lastDays = timeDiff.days
for i in np.arange(data.shape[0]):
counter = counter + 1
price = data[i][6]
dni = data[i][7]
date = datetime.datetime(year=int(data[i][0]), month = int(data[i][1]), day = int(data[i][2]), hour=int(data[i][3]), minute=int(data[i][4]), tzinfo=SydneyTimezone())
if dni > 0:
trackingCos = data[i][10]
cos = data[i][9]
ghi_factor = data[i][8]
ghi = np.multiply(ghi_factor, dni)
fixedOutputMWh = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=cos)
trackingOutputMWh = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=trackingCos)
else:
fixedOutputMWh = 0
trackingOutputMWh = 0
price = int(price)
totalFixedMWh = totalFixedMWh + fixedOutputMWh
totalTrackingMWh = totalTrackingMWh + trackingOutputMWh
if price < 0:
price = 0
hour = date.hour + (float(date.minute)/60.0)
timePeriodHrs = 0.5
mwNameplate = 1
if hour >= contractStartHour and hour <= contractEndHour:
spotRevenueFixed = spotRevenueFixed + fixedOutputMWh * min(price, strikePrice)
spotRevenueTracking = spotRevenueTracking + trackingOutputMWh * min(price, strikePrice)
countContractTimePeriods = countContractTimePeriods + 1
if price > strikePrice:
averagePayoutFixed = averagePayoutFixed + (capFactor * (price - strikePrice) * timePeriodHrs)
averagePayoutTracking = averagePayoutTracking + (capFactorTracking * (price - strikePrice) * timePeriodHrs)
countAboveStrike = countAboveStrike + 1
if fixedOutputMWh > (capFactor * timePeriodHrs * mwNameplate):
countFixedDemandMet = countFixedDemandMet + 1
if trackingOutputMWh > (capFactorTracking * timePeriodHrs * mwNameplate):
countTrackingDemandMet = countTrackingDemandMet + 1
else:
spotRevenueFixed = spotRevenueFixed + fixedOutputMWh * min(price, 300)
spotRevenueTracking = spotRevenueTracking + trackingOutputMWh * min(price, 300)
averagePayoutFixed = float(averagePayoutFixed) / float(countAboveStrike)
averagePayoutTracking = float(averagePayoutTracking) / float(countAboveStrike)
probabilityFixedMet = float(countFixedDemandMet) / float(countAboveStrike)
probabilityTrackingMet = float(countTrackingDemandMet) / float(countAboveStrike)
probabilityAboveStrike = float(countAboveStrike) / float(countContractTimePeriods)
trackingPrice = (averagePayoutTracking * probabilityAboveStrike / probabilityTrackingMet) * countContractTimePeriods
fixedPrice = (averagePayoutFixed * probabilityAboveStrike / probabilityFixedMet) * countContractTimePeriods
totalRevenueTracking = trackingPrice + spotRevenueTracking
totalRevenueFixed = fixedPrice + spotRevenueFixed
revenueTrackingPerMWh = float(totalRevenueTracking) / float(totalTrackingMWh)
revenueFixedPerMWh = float(totalRevenueFixed) / float(totalFixedMWh)
averageString = state+", "+str(revenueFixedPerMWh)+","+str(revenueTrackingPerMWh)+"\n"
averagesFile.write(averageString)
print "Averages written to file."
print "Finished"
averagesFile.close()
def printTimeseries():
print "Price and Solar Daily Trends"
controller = Controller(gui=False)
tz = SydneyTimezone()
inflation = 1.027
contractStartHour = 7
contractEndHour = 16
strikePrices = [100,150,200,250]
for strikePrice in strikePrices:
averagesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/maximumShapedCapValue/"+str(strikePrice)+".csv"
averagesFile = open(averagesPath, 'w')
averagesFile.write("Location, Maximum Value\n")
for location in locations.getLocations():
lat= location[0]
lon = location[1]
state= location[2]
locationName = location[3]
maxPrice = 300
maxBucket = int(maxPrice / 10.0)
priceFrequency = np.zeros(shape=(maxBucket,1))
dayCounter = 0
lastDays = 0
counter = 0
contractStartHour = 7
contractEndHour = 16
maxValue = 0
for year in np.arange(2005, 2012):
print year
startDate = datetime.datetime(year=year, month=1, day=1, hour=1, tzinfo = tz)
date = datetime.datetime(year=year, month=1, day=1, hour=1, tzinfo = tz)
endDate= datetime.datetime(year=year, month=12, day=31, hour=23, minute=30, tzinfo = tz)
data = controller.getTimeseriesNemDNICos(state=state, lat = lat, lon = lon, startDate = startDate, endDate = endDate)
plant = SolarPlant(namePlateMW = 1)
timeDiff = endDate - startDate
if timeDiff != lastDays:
dayCounter = dayCounter + timeDiff.days
lastDays = timeDiff.days
for i in np.arange(data.shape[0]):
counter = counter + 1
price = data[i][6]
dni = data[i][7]
demand = data[i][5]
date = datetime.datetime(year=int(data[i][0]), month = int(data[i][1]), day = int(data[i][2]), hour=int(data[i][3]), minute=int(data[i][4]), tzinfo=SydneyTimezone())
if dni > 0:
trackingCos = data[i][10]
cos = data[i][9]
ghi_factor = data[i][8]
ghi = np.multiply(ghi_factor, dni)
output = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=cos)
trackingOutput = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=trackingCos)
else:
output = 0
trackingOutput = 0
price = int(price)
if price < 0:
price = 0
hour = date.hour + (float(date.minute)/60.0)
timePeriodHrs = 0.5
if hour >= contractStartHour and hour <= contractEndHour:
if price > strikePrice:
# print "WOOOOO"
maxValue = maxValue + (price - strikePrice) * timePeriodHrs
maxValueMonthly = float(maxValue) / float(7 * 12)
averageString = state+", "+str(float(maxValueMonthly))+"\n"
averagesFile.write(averageString)
print "Averages written to file."
print "Finished"
averagesFile.close()
def printTimeseries():
print "Price and Solar Daily Trends"
controller = Controller(gui=False)
tz = SydneyTimezone()
inflation = 1.027
for location in locations.getLocations():
lat= location[0]
lon = location[1]
state= location[2]
locationName = location[3]
averagesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/cumulativeShapedPriceFrequency/"+state+".csv"
averagesFile = open(averagesPath, 'w')
averagesFile.write("Price, Count\n")
print "==============="+locationName+"==============="
maxPrice = 300
maxBucket = int(maxPrice / 10.0)
priceFrequency = np.zeros(shape=(maxBucket,1))
dayCounter = 0
lastDays = 0
counter = 0
contractStartHour = 7
contractEndHour = 16
for year in np.arange(2005, 2012):
print year
startDate = datetime.datetime(year=year, month=1, day=1, hour=1, tzinfo = tz)
date = datetime.datetime(year=year, month=1, day=1, hour=1, tzinfo = tz)
endDate= datetime.datetime(year=year, month=12, day=31, hour=23, minute=30, tzinfo = tz)
data = controller.getTimeseriesNemDNICos(state=state, lat = lat, lon = lon, startDate = startDate, endDate = endDate)
plant = SolarPlant(namePlateMW = 1)
timeDiff = endDate - startDate
if timeDiff != lastDays:
dayCounter = dayCounter + timeDiff.days
lastDays = timeDiff.days
for i in np.arange(data.shape[0]):
counter = counter + 1
price = data[i][6]
dni = data[i][7]
date = datetime.datetime(year=int(data[i][0]), month = int(data[i][1]), day = int(data[i][2]), hour=int(data[i][3]), minute=int(data[i][4]), tzinfo=SydneyTimezone())
if dni > 0:
trackingCos = data[i][10]
cos = data[i][9]
ghi_factor = data[i][8]
ghi = np.multiply(ghi_factor, dni)
output = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=cos)
trackingOutput = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=trackingCos)
else:
output = 0
trackingOutput = 0
price = int(price)
if price < 0:
price = 0
hour = date.hour + (float(date.minute)/60.0)
if date.hour >= contractStartHour and date.hour <= contractEndHour:
index = int(price / 10.0)
index = min(index, priceFrequency.shape[0] - 1)
priceFrequency[index] = priceFrequency[index] + 1
# Make cumulative
for i in np.arange(priceFrequency.shape[0])[::-1]:
if i < priceFrequency.shape[0] - 1:
priceFrequency[i] = priceFrequency[i] + priceFrequency[i+1]
for i in np.arange(priceFrequency.shape[0]):
averageString = "> "+str(i*10)+", "+str(float(priceFrequency[i]))+"\n"
averagesFile.write(averageString)
print "Averages written to file."
print "Finished"
averagesFile.close()
def printTimeseries():
print "Price and Solar Daily Trends"
controller = Controller(gui=False)
tz = SydneyTimezone()
inflation = 1.027
for location in locations.getLocations():
lat= location[0]
lon = location[1]
state= location[2]
locationName = location[3]
averagesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/dailyTrends/highPrices/"+state+".csv"
averagesFile = open(averagesPath, 'w')
averagesFile.write("Hour, Count, Price, Fixed Output, Tracking Output, DNI, Demand, Overall Probability, Average Fixed Output, Average Tracking Output\n")
print "==============="+locationName+"==============="
priceAndSolarTrends = np.zeros(shape=(48,9))
eventCounter = 0
lastDays = 0
for year in np.arange(2005, 2012):
print year
startDate = datetime.datetime(year=year, month=1, day=1, hour=1, tzinfo = tz)
date = datetime.datetime(year=year, month=1, day=1, hour=1, tzinfo = tz)
endDate= datetime.datetime(year=year, month=12, day=31, hour=23, minute=30, tzinfo = tz)
print "retrieving data"
data = controller.getTimeseriesNemDNICos(state=state, lat = lat, lon = lon, startDate = startDate, endDate = endDate)
print "data retrieved"
plant = SolarPlant(namePlateMW = 1)
timeDiff = endDate - startDate
print str(data.shape[0])
if timeDiff != lastDays:
eventCounter = eventCounter + timeDiff.days
lastDays = timeDiff.days
print "=====================+++++++++++++++++"+str(data.shape[0])
for i in np.arange(data.shape[0]):
halfHourIndex = int(round(((date.hour * 60)+ date.minute) / 30))
price = data[i][6]
demand = data[i][5]
dni = data[i][7]
# date = datetime.datetime(year=int(data[i][0]), month = int(data[i][1]), day = int(data[i][2]), hour=int(data[i][3]), minute=int(data[i][4]), tzinfo=SydneyTimezone())
if dni > 0:
trackingCos = data[i][10]
cos = data[i][9]
ghi_factor = data[i][8]
ghi = np.multiply(ghi_factor, dni)
output = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=cos)
trackingOutput = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=trackingCos)
else:
cos = 0
ghi_factor = 0
ghi = 0
output = 0
trackingOutput = 0
if price > 300:
priceAndSolarTrends[halfHourIndex][0] = price + priceAndSolarTrends[halfHourIndex][0]
priceAndSolarTrends[halfHourIndex][1] = (2 *output) + priceAndSolarTrends[halfHourIndex][1]
priceAndSolarTrends[halfHourIndex][2] = (2 *trackingOutput) + priceAndSolarTrends[halfHourIndex][2]
priceAndSolarTrends[halfHourIndex][3] = dni + priceAndSolarTrends[halfHourIndex][3]
priceAndSolarTrends[halfHourIndex][4] = demand + priceAndSolarTrends[halfHourIndex][4]
priceAndSolarTrends[halfHourIndex][5] = priceAndSolarTrends[halfHourIndex][5] + 1
priceAndSolarTrends[halfHourIndex][7] = priceAndSolarTrends[halfHourIndex][7] + (float(output) / 0.5)
priceAndSolarTrends[halfHourIndex][8] = priceAndSolarTrends[halfHourIndex][8] + (float(trackingOutput) / 0.5)
priceAndSolarTrends[halfHourIndex][6] = priceAndSolarTrends[halfHourIndex][6] + 1
date = date + datetime.timedelta(seconds = (30 * 60))
for i in np.arange(48):
eventCounter = priceAndSolarTrends[i][5]
if eventCounter > 0:
priceAndSolarTrends[i][0] = float(priceAndSolarTrends[i][0]) / float(eventCounter)
priceAndSolarTrends[i][1] = float(priceAndSolarTrends[i][1]) / float(eventCounter)
priceAndSolarTrends[i][2] = float(priceAndSolarTrends[i][2]) / float(eventCounter)
priceAndSolarTrends[i][3] = float(priceAndSolarTrends[i][3]) / float(eventCounter)
priceAndSolarTrends[i][4] = float(priceAndSolarTrends[i][4]) / float(eventCounter)
priceAndSolarTrends[i][7] = float(priceAndSolarTrends[i][7]) / float(eventCounter)
priceAndSolarTrends[i][8] = float(priceAndSolarTrends[i][8]) / float(eventCounter)
print str(priceAndSolarTrends[i][6])
priceAndSolarTrends[i][6] = eventCounter / float(priceAndSolarTrends[i][6])
else:
priceAndSolarTrends[i][6] = 0
for i in np.arange(48):
averageString = str(round((i/2.0), 1))+", "+str(priceAndSolarTrends[i][5])+", "+str(priceAndSolarTrends[i][0])+", "+str(priceAndSolarTrends[i][1])+", "+str(priceAndSolarTrends[i][2])+", "+str(priceAndSolarTrends[i][3])+", "+str(priceAndSolarTrends[i][4])+", "+str(priceAndSolarTrends[i][6])+", "+str(priceAndSolarTrends[i][7])+", "+str(priceAndSolarTrends[i][8])+"\n"
averagesFile.write(averageString)
print "Averages written to file."
print "Finished"
averagesFile.close()
def printTimeseries(tracking): if tracking: print "Tracking" lcoePath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/lcoe/trackingAll.csv" stateAveragesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/lcoe/tracking.csv" else: print "Non-Tracking" lcoePath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/lcoe/fixedAll.csv" stateAveragesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/lcoe/fixed.csv" lcoeFile = open(lcoePath, 'w') headingString = "Location, State, PSH, LCOE Low, LCOE High\n" lcoeFile.write(headingString) stateAveragesFile = open(stateAveragesPath, 'w') headingString = "Location, PSH, LCOE Low, LCOE High\n" stateAveragesFile.write(headingString) print "Beginning LCOE Simulation" controller = Controller(gui=False) tz = SydneyTimezone() stateAverages = [["nsw",0,0,0,0], ["qld",0,0,0,0], ["vic",0,0,0,0], ["sa",0,0,0,0]] for location in locations.getLocations(): lat= location[0] lon = location[1] state= location[2] locationName = location[3] print "==============="+locationName+"===============" totalPSHIncident = 0 timePeriodCouner = 0 yearCounter = 0 for year in np.arange(2005, 2012): yearCounter = yearCounter + 1 print year startDate = datetime.datetime(year=year, month=1, day=1, hour=1, tzinfo = tz) endDate= datetime.datetime(year=year, month=12, day=31, hour=23, minute=30, tzinfo = tz) print "retrieving data" data = controller.getTimeseriesNemDNICos(state=state, lat = lat, lon = lon, startDate = startDate, endDate = endDate) print "data retrieved" plant = SolarPlant(namePlateMW = 1) # Iterate through the time series and perform calculations. for i in np.arange(data.shape[0]): timePeriodCouner = timePeriodCouner + 1 price = data[i][6] dni = data[i][7] # date = datetime.datetime(year=int(data[i][0]), month = int(data[i][1]), day = int(data[i][2]), hour=int(data[i][3]), minute=int(data[i][4]), tzinfo=SydneyTimezone()) if dni > 0: if tracking: cos = data[i][10] else: cos = data[i][9] ghi_factor = data[i][8] ghi = np.multiply(ghi_factor, dni) output = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=cos) totalPSHIncident = totalPSHIncident + ((dni * cos)/1000.0) # Write to the averages data file. numDays = timePeriodCouner / 48.0 averagePSHPerDay = totalPSHIncident / (numDays) if tracking: lcoeLow = getLCOELowTracking(averagePSHPerDay) lcoeHigh = getLCOEHighTracking(averagePSHPerDay) else: lcoeLow = getLCOELowFixed(averagePSHPerDay) lcoeHigh = getLCOEHighFixed(averagePSHPerDay) averageString = locationName +", "+state+", "+str(round(averagePSHPerDay, 2)) +", "+str(round(lcoeLow, 2))+", "+str(round(lcoeHigh, 2))+"\n" lcoeFile.write(averageString) print "Averages written to file." for stateAverage in stateAverages: if stateAverage[0] == state: stateAverage[1] = lcoeLow + stateAverage[1] stateAverage[2] = lcoeHigh + stateAverage[2] stateAverage[3] = stateAverage[3] + averagePSHPerDay stateAverage[4] = stateAverage[4] + 1 for stateAverage in stateAverages: state = stateAverage[0] lcoeLow = float(stateAverage[1]) / float(stateAverage[4]) lcoeHigh = float(stateAverage[2]) / float(stateAverage[4]) averagePSHPerDay = float(stateAverage[3]) / float(stateAverage[4]) averageString = state+", "+str(round(averagePSHPerDay, 2)) +", "+str(round(lcoeLow, 2))+", "+str(round(lcoeHigh, 2))+"\n" stateAveragesFile.write(averageString) print "Finished" lcoeFile.close() stateAveragesFile.close()
def printTimeseries(tracking):
if tracking:
print "Tracking"
stateAveragesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/capacityFactor/tracking.csv"
else:
print "Non-Tracking"
stateAveragesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/capacityFactor/fixed.csv"
nameplateCapacityMW = 1
stateAveragesFile = open(stateAveragesPath, 'w')
stateAveragesFile.write("Location, Capacity Factor\n")
print "Beginning Capacity Factor Simulation"
controller = Controller(gui=False)
tz = SydneyTimezone()
inflation = 1.027
stateAverages = [["nsw",0,0], ["qld",0,0], ["vic",0,0], ["sa",0,0]]
for location in locations.getLocations():
lat= location[0]
lon = location[1]
state= location[2]
totalMWh = 0
totalHours = 0
for year in np.arange(2005, 2012):
print year
startDate = datetime.datetime(year=year, month=1, day=1, hour=1, tzinfo = tz)
endDate= datetime.datetime(year=year, month=12, day=31, hour=23, minute=30, tzinfo = tz)
data = controller.getTimeseriesNemDNICos(state=state, lat = lat, lon = lon, startDate = startDate, endDate = endDate)
plant = SolarPlant(namePlateMW = 1)
# Iterate through the time series and perform calculations.
for i in np.arange(data.shape[0]):
totalHours = totalHours + 0.5
date = datetime.datetime(year=int(data[i][0]), month = int(data[i][1]), day = int(data[i][2]), hour=int(data[i][3]), minute=int(data[i][4]), tzinfo=SydneyTimezone())
dni = data[i][7]
if dni > 0:
if tracking:
cos = data[i][10]
else:
cos = data[i][9]
ghi_factor = data[i][8]
ghi = np.multiply(ghi_factor, dni)
output = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=cos)
else:
cos = 0
ghi_factor = 0
ghi = 0
output = 0
totalMWh = totalMWh + output
for stateAverage in stateAverages:
if state == stateAverage[0]:
stateAverage[1] = stateAverage[1] + totalMWh
stateAverage[2] = stateAverage[2] + totalHours
for stateAverage in stateAverages:
state = stateAverage[0]
capacityFactor = float(stateAverage[1]) / (float(stateAverage[2]) * float(nameplateCapacityMW))
stateAveragesFile.write(str(state)+", "+str(capacityFactor)+"\n")
print "Finished"
stateAveragesFile.close()
def printTimeseries(tracking, fractionContracted):
if tracking:
print "Tracking"
stateAveragesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/shapedCap/tracking/average/"+str(fractionContracted)+"Contracted.csv"
averagesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/shapedCap/tracking/averagesAll/"+str(fractionContracted)+"Contracted.csv"
yearlyPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/shapedCap/tracking/yearly/"+str(fractionContracted)+"Contracted.csv"
else:
print "Non-Tracking"
stateAveragesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/shapedCap/fixed/average/"+str(fractionContracted)+"Contracted.csv"
averagesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/shapedCap/fixed/averagesAll/"+str(fractionContracted)+"Contracted.csv"
yearlyPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/shapedCap/fixed/yearly/"+str(fractionContracted)+"Contracted.csv"
yearlyFile = open(yearlyPath, 'w')
averagesFile = open(averagesPath, 'w')
stateAveragesFile = open(stateAveragesPath, 'w')
yearlyFile.write("Location, State, Year, Average Market Price, Average Revenue, Total Generation MWh, Average PSH/Day, Value of Call, Count Over 300 And Generation, Count Over 300 No Generation\n" )
averagesFile.write("Location, State, Average Market Price, Average Revenue, Average Call Value, Average Count Over 300 And Generation, Average Count Over 300 No Generation\n")
stateAveragesFile.write("Location, Average Market Price, Average Revenue, Average Call Value, Average Count Over 300 And Generation, Average Count Over 300 No Generation\n")
stateAverages = [["nsw",0,0,0,0,0,0], ["qld",0,0,0,0,0,0], ["vic",0,0,0,0,0,0], ["sa",0,0,0,0,0,0]]
print "Beginning Shaped $300 Cap Simulation"
controller = Controller(gui=False)
tz = SydneyTimezone()
inflation = 1.027
startHour = 7
finishHour = 20
for location in locations.getLocations():
lat= location[0]
lon = location[1]
state= location[2]
locationName = location[3]
print "==============="+locationName+"==============="
averageMarketPrice = averageRevenue = averageCallValue = 0
averageCountOver300Gen = averageCountOver300NoGen = 0
startYear = 2005
endYear = 2011
for year in np.arange(startYear, endYear + 1):
print year
startDate = datetime.datetime(year=year, month=1, day=1, hour=1, tzinfo = tz)
endDate= datetime.datetime(year=year, month=12, day=31, hour=23, minute=30, tzinfo = tz)
print "retrieving data"
data = controller.getTimeseriesNemDNICos(state=state, lat = lat, lon = lon, startDate = startDate, endDate = endDate)
print "data retrieved"
plant = SolarPlant(namePlateMW = 1)
totalYearlyRevenue = totalYearlyMWh = totalYearlySpotPrice = totalYearlyPSH = callValue = yearlyCountOver300Gen = yearlyCountOver300NoGen = 0
# Iterate through the time series and perform calculations.
for i in np.arange(data.shape[0]):
price = data[i][6]
dni = data[i][7]
date = datetime.datetime(year=int(data[i][0]), month = int(data[i][1]), day = int(data[i][2]), hour=int(data[i][3]), minute=int(data[i][4]), tzinfo=SydneyTimezone())
if dni > 0:
if tracking:
cos = data[i][10]
else:
cos = data[i][9]
ghi_factor = data[i][8]
ghi = np.multiply(ghi_factor, dni)
output = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=cos)
totalYearlyPSH = totalYearlyPSH + (ghi/1000.0)
else:
cos = 0
ghi_factor = 0
ghi = 0
output = 0
# ######################################################################
# Put options pricing stuff here.
timePeriodHrs = 0.5
if price > 300 and date.hour >= startHour and date.hour <= finishHour:
payout = (timePeriodHrs * (price - 300) ) * fractionContracted
callValue = callValue + payout
totalYearlyRevenue= totalYearlyRevenue + (output * price) - payout
if output > 0:
yearlyCountOver300Gen = yearlyCountOver300Gen + 1
else:
yearlyCountOver300NoGen = yearlyCountOver300NoGen + 1
else:
totalYearlyRevenue = totalYearlyRevenue + (output * price)
totalYearlyMWh = totalYearlyMWh + output
totalYearlySpotPrice = totalYearlySpotPrice + price
# ######################################################################
# Calculate Yearly Averages
averageYearlyRevenue = np.divide(totalYearlyRevenue, totalYearlyMWh)
averageYearlyMarketPrice = np.divide(totalYearlySpotPrice, data.shape[0])
averagePshPerDay = (totalYearlyPSH / 365.0)
# Write to the yearly data file.
yearlyString = locationName +", "+state+", "+ str(endDate.year) +", "+ str(np.round(averageYearlyMarketPrice, decimals = 2)) +", "+ str(np.round(averageYearlyRevenue, decimals = 2))
yearlyString = yearlyString + ", "+ str(totalYearlyMWh) + ", "+ str(averagePshPerDay)+ ", "+str(callValue)+ ", "+str(yearlyCountOver300Gen)+ ", "+str(yearlyCountOver300NoGen)+"\n"
yearlyFile.write(yearlyString)
print "Yearly data written to file."
# Add to cumulative totals for averaging.
averageCallValue = averageCallValue + callValue
averageCountOver300Gen = averageCountOver300Gen + yearlyCountOver300Gen
averageCountOver300NoGen = averageCountOver300NoGen + yearlyCountOver300NoGen
averageRevenue = averageRevenue +(averageYearlyRevenue * np.power(inflation, (2014 - year) ) )
averageMarketPrice = averageMarketPrice + (averageYearlyMarketPrice * np.power(inflation, 2014 - year))
# Write to the averages data file.
numYears = float(endYear - startYear + 1)
averageCountOver300Gen = averageCountOver300Gen / numYears
averageCountOver300NoGen = averageCountOver300NoGen / numYears
averageCallValue = averageCallValue / numYears
averageRevenue = averageRevenue / numYears
averageMarketPrice = averageMarketPrice / numYears
# Write averages to data file.
averageString = locationName +", "+state+", "+ str(np.round(averageMarketPrice, decimals = 2))+ ", "+ str(np.round(averageRevenue, decimals = 2))
averageString = averageString +", "+ str(averageCallValue)+", "+str(averageCountOver300Gen)+", "+str(averageCountOver300NoGen)+"\n"
averagesFile.write(averageString)
print "Averages written to file."
for stateAverage in stateAverages:
if state == stateAverage[0]:
stateAverage[1] = stateAverage[1] + averageMarketPrice
stateAverage[2] = stateAverage[2] + averageRevenue
stateAverage[3] = stateAverage[3] + averageCallValue
stateAverage[4] = stateAverage[4] + averageCountOver300Gen
stateAverage[5] = stateAverage[5] + averageCountOver300NoGen
stateAverage[6] = stateAverage[6] + 1
for stateAverage in stateAverages:
state = stateAverage[0]
count = float(stateAverage[6])
averageMarketPrice = float(stateAverage[1]) / count
averageRevenue = float(stateAverage[2]) / count
averageCallValue = float(stateAverage[3]) / count
averageCountOver300Gen = float(stateAverage[4]) / count
averageCountOver300NoGen = float(stateAverage[5]) / count
averageString = state+", "+ str(np.round(averageMarketPrice, decimals = 2))+ ", "+ str(np.round(averageRevenue, decimals = 2))
averageString = averageString +", "+ str(averageCallValue)+", "+str(averageCountOver300Gen)+", "+str(averageCountOver300NoGen)+"\n"
stateAveragesFile.write(averageString)
print "Finished"
averagesFile.close()
yearlyFile.close()
def printTimeseries(tracking):
if tracking:
print "Tracking"
stateAveragesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/merchantModelCappedSpot/tracking/average.csv"
averagesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/merchantModelCappedSpot/tracking/averageAll.csv"
yearlyPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/merchantModelCappedSpot/tracking/yearly.csv"
else:
print "Non-Tracking"
stateAveragesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/merchantModelCappedSpot/fixed/average.csv"
averagesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/merchantModelCappedSpot/fixed/averageAll.csv"
yearlyPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/merchantModelCappedSpot/fixed/yearly.csv"
yearlyFile = open(yearlyPath, 'w')
averagesFile = open(averagesPath, 'w')
stateAveragesFile = open(stateAveragesPath, 'w')
yearlyFile.write("Location, State, Year, Average Market Price, Average Revenue, Total Generation MWh, Average PSH/Day\n" )
averagesFile.write("Location, State, Average Market Price, Average Revenue\n")
stateAveragesFile.write("Location, Average Market Price, Average Revenue\n")
print "Beginning Merchant Model Simulation"
controller = Controller(gui=False)
tz = SydneyTimezone()
inflation = 1.027
stateAverages = [["nsw",0,0,0], ["qld",0,0,0], ["vic",0,0,0], ["sa",0,0,0]]
for location in locations.getLocations():
lat= location[0]
lon = location[1]
state= location[2]
locationName = location[3]
print "==============="+locationName+"==============="
averageMarketPrice = averageRevenue = count = 0
for year in np.arange(2005, 2012):
print year
startDate = datetime.datetime(year=year, month=1, day=1, hour=1, tzinfo = tz)
endDate= datetime.datetime(year=year, month=12, day=31, hour=23, minute=30, tzinfo = tz)
print "retrieving data"
data = controller.getTimeseriesNemDNICos(state=state, lat = lat, lon = lon, startDate = startDate, endDate = endDate)
print "data retrieved"
plant = SolarPlant(namePlateMW = 1)
totalYearlyRevenue = totalYearlyMWh = totalYearlySpotPrice = totalYearlyPSH = 0
# Iterate through the time series and perform calculations.
for i in np.arange(data.shape[0]):
price = min(data[i][6], 300)
dni = data[i][7]
# date = datetime.datetime(year=int(data[i][0]), month = int(data[i][1]), day = int(data[i][2]), hour=int(data[i][3]), minute=int(data[i][4]), tzinfo=SydneyTimezone())
if dni > 0:
if tracking:
cos = data[i][10]
else:
cos = data[i][9]
ghi_factor = data[i][8]
ghi = np.multiply(ghi_factor, dni)
output = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=cos)
totalYearlyPSH = totalYearlyPSH + (ghi/1000.0)
else:
cos = 0
ghi_factor = 0
ghi = 0
output = 0
totalYearlyRevenue = totalYearlyRevenue + (output * price)
totalYearlyMWh = totalYearlyMWh + output
totalYearlySpotPrice = totalYearlySpotPrice + price
averageYearlyRevenue = np.divide(totalYearlyRevenue, totalYearlyMWh)
averageYearlyMarketPrice = np.divide(totalYearlySpotPrice, data.shape[0])
averagePshPerDay = (totalYearlyPSH / 365.0)
# Write to the yearly data file.
yearlyString = locationName +", "+state+", "+ str(endDate.year) +", "+ str(np.round(averageYearlyMarketPrice, decimals = 2)) +", "+ str(np.round(averageYearlyRevenue, decimals = 2))
yearlyString = yearlyString + ", "+ str(totalYearlyMWh) + ", "+ str(averagePshPerDay)+"\n"
yearlyFile.write(yearlyString)
print "Yearly data written to file."
averageRevenue = averageRevenue +(averageYearlyRevenue * np.power(inflation, (2014 - year) ) )
averageMarketPrice = averageMarketPrice + (averageYearlyMarketPrice * np.power(inflation, 2014 - year))
count = count + 1
# Write to the averages data file.
averageRevenue = averageRevenue / float(count)
averageMarketPrice = averageMarketPrice / float(count)
averageString = locationName +", "+state+", "+ str(np.round(averageMarketPrice, decimals = 2))+ ", "+ str(np.round(averageRevenue, decimals = 2))+"\n"
averagesFile.write(averageString)
print "Averages written to file."
for stateAverage in stateAverages:
if state == stateAverage[0]:
stateAverage[1] = stateAverage[1] + averageMarketPrice
stateAverage[2] = stateAverage[2] + averageRevenue
stateAverage[3] = stateAverage[3] + 1
for stateAverage in stateAverages:
state = stateAverage[0]
averageMarketPrice = float(stateAverage[1]) / float(stateAverage[3])
averageRevenue = float(stateAverage[2]) / float(stateAverage[3])
stateAveragesFile.write(str(state)+", "+str(averageMarketPrice)+", "+str(averageRevenue)+"\n")
print "Finished"
stateAveragesFile.close()
averagesFile.close()
yearlyFile.close()
def printTimeseries():
print "Price and Solar Daily Trends"
controller = Controller(gui=False)
tz = SydneyTimezone()
inflation = 1.027
for location in locations.getLocations():
lat= location[0]
lon = location[1]
state= location[2]
locationName = location[3]
averagesPath = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/priceFrequency/"+state+".csv"
averagesFile = open(averagesPath, 'w')
averagesFile.write("Price, Count\n")
print "==============="+locationName+"==============="
maxPrice = 300
maxBucket = int(maxPrice / 10.0)
priceFrequency = np.zeros(shape=(maxBucket,1))
dayCounter = 0
lastDays = 0
counter = 0
for year in np.arange(2005, 2012):
print year
startDate = datetime.datetime(year=year, month=1, day=1, hour=1, tzinfo = tz)
date = datetime.datetime(year=year, month=1, day=1, hour=1, tzinfo = tz)
endDate= datetime.datetime(year=year, month=12, day=31, hour=23, minute=30, tzinfo = tz)
data = controller.getTimeseriesNemDNICos(state=state, lat = lat, lon = lon, startDate = startDate, endDate = endDate)
plant = SolarPlant(namePlateMW = 1)
timeDiff = endDate - startDate
if timeDiff != lastDays:
dayCounter = dayCounter + timeDiff.days
lastDays = timeDiff.days
for i in np.arange(data.shape[0]):
counter = counter + 1
halfHourIndex = int(round(((date.hour * 60)+ date.minute) / 30))
price = min(data[i][6], 300)
demand = data[i][5]
dni = data[i][7]
# date = datetime.datetime(year=int(data[i][0]), month = int(data[i][1]), day = int(data[i][2]), hour=int(data[i][3]), minute=int(data[i][4]), tzinfo=SydneyTimezone())
if dni > 0:
trackingCos = data[i][10]
cos = data[i][9]
ghi_factor = data[i][8]
ghi = np.multiply(ghi_factor, dni)
output = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=cos)
trackingOutput = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=trackingCos)
else:
cos = 0
ghi_factor = 0
ghi = 0
output = 0
trackingOutput = 0
price = int(price)
if price < 0:
price = 0
index = int(price / 10.0)
index = min(index, priceFrequency.shape[0] - 1)
priceFrequency[index] = priceFrequency[index] + 1
# priceFrequency[halfHourIndex][0] = price + priceFrequency[halfHourIndex][0]
# priceFrequency[halfHourIndex][1] = (2 *output) + priceFrequency[halfHourIndex][1]
# priceFrequency[halfHourIndex][2] = (2 *trackingOutput) + priceFrequency[halfHourIndex][2]
# priceFrequency[halfHourIndex][3] = dni + priceFrequency[halfHourIndex][3]
# priceFrequency[halfHourIndex][4] = demand + priceFrequency[halfHourIndex][4]
# priceFrequency[halfHourIndex][5] = priceFrequency[halfHourIndex][5] + 1
date = date + datetime.timedelta(seconds = (30 * 60))
for i in np.arange(priceFrequency.shape[0]):
averageString = str(i*10)+"-"+str(i*10+10)+", "+str(float(priceFrequency[i]))+"\n"
averagesFile.write(averageString)
print "Averages written to file."
print "Finished"
averagesFile.close()
def printTimeseries(tracking):
controller = Controller(gui=False)
tz = SydneyTimezone()
inflation = 1.027
for location in locations.getLocations():
lat= location[0]
lon = location[1]
state= location[2]
locationName = location[3]
lcoes = lcoe.getLCOEHighLow(tracking, location)
lcoeHigh = lcoes[0]
lcoeLow = lcoes[1]
if tracking:
path = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/solarMarketFactorCapped/tracking/"+locationName+".csv"
else:
path = "/Users/lukemarshall/Documents/Workspace/Thesis/simulationResults/market/solarMarketFactorCapped/fixed/"+locationName+".csv"
outFile = open(path, 'w')
outFile.write("Month, Solar-Market Factor High, Solar-Market Factor Low, Average Revenue, Average Market Price\n" )
for month in np.arange(1,13):
lastDayOfMonth = 31
if month == 9 or month == 4 or month == 6 or month == 11:
lastDayOfMonth = 30
elif month == 2:
lastDayOfMonth = 28
counter = 0
averageMonthlyRevenue = 0
averageMonthlySpotPrice =0
totalMonthlyMWh = 0
totalMonthlyRevenue = 0
totalMonthlyMWh = 0
totalMonthlySpotPrice = 0
totalMonthlyRevenue = totalMonthlyMWh = totalMonthlySpotPrice = totalYearlyPSH = 0
for year in np.arange(2005, 2012):
print year
startDate = datetime.datetime(year=year, month=month, day=1, hour=1, tzinfo = tz)
endDate= datetime.datetime(year=year, month=month, day=lastDayOfMonth, hour=23, minute=30, tzinfo = tz)
print "retrieving data"
data = controller.getTimeseriesNemDNICos(state=state, lat = lat, lon = lon, startDate = startDate, endDate = endDate)
print "data retrieved"
plant = SolarPlant(namePlateMW = 1)
# Iterate through the time series and perform calculations.
for i in np.arange(data.shape[0]):
price = min(data[i][6], 300)
dni = data[i][7]
# date = datetime.datetime(year=int(data[i][0]), month = int(data[i][1]), day = int(data[i][2]), hour=int(data[i][3]), minute=int(data[i][4]), tzinfo=SydneyTimezone())
if dni > 0:
if tracking:
cos = data[i][10]
else:
cos = data[i][9]
ghi_factor = data[i][8]
ghi = np.multiply(ghi_factor, dni)
output = plant.getPlantOutput(dni=dni, ghi=ghi, timePeriodHrs=0.5, cosine=cos)
totalYearlyPSH = totalYearlyPSH + (ghi/1000.0)
else:
cos = 0
ghi_factor = 0
ghi = 0
output = 0
totalMonthlyRevenue = totalMonthlyRevenue + (output * price)
totalMonthlyMWh = totalMonthlyMWh + output
totalMonthlySpotPrice = totalMonthlySpotPrice + price
counter = counter + 1
averageMonthlyRevenue = np.divide(totalMonthlyRevenue, totalMonthlyMWh)
averageMonthlySpotPrice = np.divide(totalMonthlySpotPrice, float(counter))
# averagePshPerDay = (totalYearlyPSH / 365.0)
factorHigh = (averageMonthlyRevenue / max(lcoeHigh, averageMonthlySpotPrice)) - 1
factorLow = (averageMonthlyRevenue / max(lcoeLow, averageMonthlySpotPrice)) - 1
# Write to the yearly data file.
outString = str(int(round(month))) +", "+ str(np.round(factorHigh, decimals = 2)) +", "+ str(np.round(factorLow, decimals = 2))+ ", "+str(np.round(averageMonthlyRevenue, decimals = 2))+", "+ str(np.round(averageMonthlySpotPrice, decimals = 2)) +"\n"
outFile.write(outString)
print "Finished"
outFile.close()
