derConsumer: Added adhoc TOU rate function, dispatch schedule, and to…

…tal solar cost functions

omf/models/derConsumer.py

@@ -216,6 +216,20 @@ def create_REopt_jl_jsonFile(modelDir, inputDict):
 
 
 def get_tou_rates(modelDir, lat, lon): ## TODO: change lat,lon to inputDict once model is working
+	'''
+	Function that pulls rate information from the OEDI utility rate database via API functionality.
+
+	** Inputs
+	modelDir: (str) Currently model directory
+	lat: (float) Latitude of the utility
+	lon: (float) Longitude of the utility
+	##inputDict: (dict) Input dictionary containing latitude and longitude information (NOTE: this will replace lat and lon
+
+	** Outputs
+	data: (JSON file) Ouput JSON file containing the API response fields \
+		(see https://openei.org/services/doc/rest/util_rates/?version=3#json-output-format for more info)
+	'''
+
 	api_url = 'https://api.openei.org/utility_rates?parameters'
 	api_key = '5dFShfSVRt2XJpPYCbzBeLM6nHrOXc0VFPTWxfJJ' ## API key generated by following this website: 'https://openei.org/services/'
 
@@ -224,15 +238,17 @@ def get_tou_rates(modelDir, lat, lon): ## TODO: change lat,lon to inputDict once
 		'format': 'json',
 		'lat': lat,
 		'lon': lon,
-		'api_key': api_key
+		'api_key': api_key,
+		'getpage': '5b311c595457a3496d8367be',
 		}
+
 	try:
 		response = requests.get(api_url, params=params)
 		response.raise_for_status()  ## Raise an exception for HTTP errors
 
 		data = response.json()
 
-		# Save the retrieved data as a JSON file
+		## Save the retrieved data as a JSON file
 		with open(pJoin(modelDir, 'OEDIrateData.json'), 'w') as json_file:
 			json.dump(data, json_file)
 
@@ -241,7 +257,137 @@ def get_tou_rates(modelDir, lat, lon): ## TODO: change lat,lon to inputDict once
 	except requests.exceptions.RequestException as e:
 		print('Error:', e)
 		return None
+
+
+def get_tou_rates_adhoc():
+	'''
+	Ad-hoc function that arbitrarily assigns TOU rates for summer/winter on- and 0ff-peaks.
+	NOTE: This function is temporarily used in place of get_tou_rates() due to issues with API \
+	not returning rate data at this time.
+	'''
 
+	tou_rates = {
+		## Example TOU rates data for ad-hoc use
+		'summer_on_peak': 0.25,
+		'summer_off_peak': 0.15,
+		'winter_on_peak': 0.20,
+		'winter_off_peak': 0.12
+	}
+	return tou_rates
+
+def generate_day_hours(monthHours):
+	'''
+	Create an array of start and stop hours for each day if given an array of start/stop hours for each month.
+
+	** Inputs
+	monthHours: (list) List of (start, stop) hours for each month in a year's worth of hourly data (length 8760)
+	
+	** Outputs
+	dayHours: (list) List of (start, stop) hours for each day
+	'''
+
+	dayHours = []
+	for start_hour, end_hour in monthHours:
+		for day_start in range(start_hour, end_hour, 24):
+			day_end = min(day_start + 24, end_hour)
+			dayHours.append((day_start, day_end))
+	return dayHours
+
+def create_dispatch_schedule(temperature_array, dayHours):
+	'''
+	Create a dispatch schedule for solar, battery, and grid technologies for the span of a year. \
+	The max temperature is determined every day during hotter months (March-August) and solar is \
+	dispatched for two hours starting on the start hour. For the rest of the colder months, the \
+	same is done but for the min temperature for that day. NOTE: normally, there would be 2 minimum \
+	peak temperatures in the winter months where DERs would be dispatched, but only 1 is modeled \
+	here for simplicity. The function returns a list of strings of either 'Grid', 'Battery', or 'Solar' \
+	to signify what technology is dispatched at that hour.
+
+	** Inputs
+	temperature_array: (arr; length 8760) Hourly temperature data for a year
+	dayHours: (list) List of (start, stop) times for each hour in a year
+
+	** Outputs
+	dispatch_schedule (list): List of strings that say either 'Grid', 'Battery', or 'Solar' to \
+		signify which technology is dispatched at that start hour. The duration of dispatch is \
+		at least 2 hours.
+
+	'''
+	## Initialize a dispatch schedule with "Grid" for all hours
+	dispatch_schedule = ["Grid"] * len(temperature_array)  
+
+	## Assign hourly dispatch according to the max temp in hotter months and min temp in colder months
+	for start_hour, end_hour in dayHours:
+		day_temperatures = temperature_array[start_hour:end_hour]
+		max_temp = max(day_temperatures)
+		min_temp = min(day_temperatures)
+
+		## March to August (hotter months)
+		if start_hour in range(1416, 5832):  
+			for i in range(start_hour, end_hour):
+				if temperature_array[i] == max_temp: ## max temperature peak for dispatch
+					for j in range(i, min(i + 2, end_hour)): ## Duration of dispatch >2hrs
+						dispatch_schedule[j] = "Solar"
+
+		## Other months (colder months)
+		else:  
+			for i in range(start_hour, end_hour):
+				if temperature_array[i] == min_temp: ## min temperature peak for dispatch
+					for j in range(i, min(i + 2, end_hour)): ## Duration of dispatch >2hrs
+						dispatch_schedule[j] = "Battery"
+
+	return dispatch_schedule
+
+def solar_demand(PV, hourDispatch, on_peak_hours, off_peak_hours):
+	'''
+	Extract solar demand while keeping track of on-peak or off-peak hours.
+
+	** Inputs
+	PV: (list) List of PV dispatch values (from REopt).
+	hour_types: (list) List indicating the type of each hour, where 'Solar' signifies solar demand.
+	on_peak_hours: (range) Range representing on-peak hours.
+	off_peak_hours: (list) List representing off-peak hours.
+
+	** Output
+	solar_demand: (list) List of tuples containing (PV value, hour_of_year, peak_type).
+	'''
+
+	solar_demand = []
+
+	for i, dispatchType in enumerate(hourDispatch):
+		if dispatchType == 'Solar':
+			## Determine the peak type (on-peak or off-peak)
+			peak_type = 'On-Peak' if i % 24 in on_peak_hours else 'Off-Peak'
+
+			## Append to the solar_demand list as a tuple
+			solar_demand.append((PV[i], i, peak_type))
+			#print(PV[i])
+
+	return solar_demand
+
+def total_solar_cost(solar_demand, tou_rates):
+	'''
+	Calculate the total cost for solar demand based on the TOU rates.
+
+	** Inputs
+	solar_demand: (list) List of tuples containing (demand_value, hour_of_year, peak_type).
+	tou_rates: (dict) Dictionary containing the TOU rates for summer_on_peak and summer_off_peak hours.
+
+	** Output
+	total_cost: (float) Total cost for solar demand based on TOU rates.
+	'''
+
+	## Initialize total cost
+	total_cost = 0.0
+
+	## Loop through each tuple in solar_demand
+	for demand_value, hour_of_year, peak_type in solar_demand:
+		## Determine the rate based on the peak type
+		rate = tou_rates['summer_on_peak'] if peak_type == 'On-Peak' else tou_rates['summer_off_peak']
+		## Add the cost for this hour
+		total_cost += demand_value * rate
+
+	return total_cost
 
 def work(modelDir, inputDict):
 	''' Run the model in its directory. '''
@@ -588,7 +734,7 @@ def makeGridLine(x,y,color,name):
 	outData['exportedPowerData'] = json.dumps(fig.data, cls=plotly.utils.PlotlyJSONEncoder)
 	outData['exportedPowerLayout'] = json.dumps(fig.layout, cls=plotly.utils.PlotlyJSONEncoder)
 
-	## DER Sharing Program options
+	########## DER Sharing Program options ##########
 	if (inputDict['utilityProgram']):
 		print('Considering utility DER sharing program \n')
 
@@ -605,16 +751,21 @@ def makeGridLine(x,y,color,name):
 		for name in filtered_names: ## Print the filtered names
 			print(name)	
 
-		TOUname = 'Residential Time of Use'
 		TOUdata = []
+		TOUname = 'Residential Time of Use'
+		TOUlabel = '5b311c595457a3496d8367be'
 
 		for item in rate_info['items']:
 			if item['name'] == TOUname:
 				TOUdata.append(item)
 
 		print(TOUdata)
 
+		## NOTE: until the above section is working, use ad-hoc TOU rates
+		tou_rates = get_tou_rates_adhoc()
+
 		'''
+		## NOTE: The following dict is not being used because the the API is not returning these variables yet
 		rate_info = {
 			'rate_name': data['items'][0]['name'], ## Rate name
 			'fixed_monthly_charge': data['items'][0]['fixedmonthlycharge'], ## Fixed monthly charge ($)
@@ -628,13 +779,45 @@ def makeGridLine(x,y,color,name):
 		}
 		'''
 
-		## Generate peak shave schedule
-
-		## Determine area under the curve
-
-		## Apply rate compensations to DERs deployed
+		########## Generate peak shave schedule ##########
+
+		## List of (start, stop) hours for each month in a year
+		monthHours = [(0, 744), (744, 1416), (1416, 2160), (2160, 2880), 
+		(2880, 3624), (3624, 4344), (4344, 5088), (5088, 5832), 
+		(5832, 6552), (6552, 7296), (7296, 8016), (8016, 8760)]
+		dayHours = generate_day_hours(monthHours) ## Generate (start, stop) hours for each day
+		dispatch_schedule = create_dispatch_schedule(temperatures, dayHours)
+
+		## Plot dispatch schedule
+		import matplotlib.pyplot as plt
+		def plot_dispatch_schedule(dispatch_schedule):
+			plt.figure(figsize=(12, 4))
+			plt.plot(dispatch_schedule)
+			plt.xlabel("Hour of the Year")
+			plt.ylabel("Dispatch")
+			plt.title("Dispatch Schedule")
+			plt.grid(True)
+			plt.show()
+		plot_dispatch_schedule(dispatch_schedule)
+
+		########## Determine area under the curve ##########
+		## Typical on-peak and off-peak hours during hotter months (March to August)
+		on_peak_hours = range(14, 20)  ## On-peak hours typically from 2:00 PM to 8:00 PM
+		off_peak_hours = list(range(0, 6)) + list(range(22, 24))  ## Off-peak hours typically from 10:00 PM to 6:00 AM
+
+		#print("Typical On-Peak Hours (24-hour format):", list(on_peak_hours))
+		#print("Typical Off-Peak Hours (24-hour format):", off_peak_hours)
+
+		## Get solar demand 
+		solarDemand = solar_demand(PV, dispatch_schedule, on_peak_hours, off_peak_hours)
+		#print(solarDemand)
+
+		########## Apply rate compensations to DERs deployed ##########
+		## NOTE: the rateCompensation, fixedCompensation, and fixedCompensationFrequency variables are not used yet
+		total_cost = total_solar_cost(solarDemand, tou_rates)  ## Calculate the total solar cost
+		print("Total cost for solar demand based on TOU rates: ${:.2f}".format(total_cost))
 
-		## Plot the peak shave schedule?
+		########## Plot the peak shave schedule? ##########
 
 	# Stdout/stderr.
 	outData['stdout'] = 'Success'
@@ -665,7 +848,7 @@ def new(modelDir):
 		'tempFileName': 'residential_extended_temperature_data.csv',
 		'demandCurve': demand_curve,
 		'tempCurve': temp_curve,
-		'PV': 'No',
+		'PV': 'Yes',
 		'BESS': 'Yes',
 		'generator': 'No',
 		'outage': True,