derConsumer: Added utility rate API function and fixed utilityProgram…

… option in html. \ derUtilityCost: Changed lat/lon to Brighton, CO; minor edits

omf/models/derConsumer.html

@@ -224,10 +224,10 @@
 			</div>
 			<hr style="border-style: solid; border-color: #196b12; margin-top: 10px;">
 			<div class="shortInput">
-				<label>Consider Utility DER program?</label>
+				<label>Consider utility DER sharing program?</label>
 				<select id="utilityProgram" name="utilityProgram" value="{{allInputDataDict.utilityProgram}}">
-					<option value="True" {{ 'selected' if allInputDataDict.outage == 'Yes' }}>Yes</option>
-					<option value="False" {{ 'selected' if allInputDataDict.outage == 'No' }}>No</option>
+					<option value="True" {{ 'selected' if allInputDataDict.utilityProgram == 'Yes' }}>Yes</option>
+					<option value="False" {{ 'selected' if allInputDataDict.utilityProgram == 'No' }}>No</option>
 				</select>
 			</div>
 			<div class="shortInput">

omf/models/derConsumer.py

@@ -12,7 +12,7 @@
 import pandas as pd
 import plotly.graph_objs as go
 import plotly.utils
-from numpy_financial import npv
+import requests
 
 # OMF imports
 from omf.models import __neoMetaModel__
@@ -214,6 +214,35 @@ def create_REopt_jl_jsonFile(modelDir, inputDict):
 		json.dump(scenario, jsonFile)
 	return scenario
 
+
+def get_tou_rates(modelDir, lat, lon): ## TODO: change lat,lon to inputDict once model is working
+	api_url = 'https://api.openei.org/utility_rates?parameters'
+	api_key = '5dFShfSVRt2XJpPYCbzBeLM6nHrOXc0VFPTWxfJJ' ## API key generated by following this website: 'https://openei.org/services/'
+
+	params = {
+		'version': '3',
+		'format': 'json',
+		'lat': lat,
+		'lon': lon,
+		'api_key': api_key
+		}
+	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
+		with open(pJoin(modelDir, 'OEDIrateData.json'), 'w') as json_file:
+			json.dump(data, json_file)
+
+		return data
+
+	except requests.exceptions.RequestException as e:
+		print('Error:', e)
+		return None
+
+
 def work(modelDir, inputDict):
 	''' Run the model in its directory. '''
 
@@ -233,7 +262,7 @@ def work(modelDir, inputDict):
 	#	print('Successfully loaded REopt test file. \n')
 
 	## Run REopt.jl 
-	reopt_jl.run_reopt_jl(modelDir, "reopt_input_scenario.json", outages=inputDict['outage'])
+	reopt_jl.run_reopt_jl(modelDir, 'reopt_input_scenario.json', outages=inputDict['outage'])
 	with open(pJoin(modelDir, 'results.json')) as jsonFile:
 		reoptResults = json.load(jsonFile)
 	outData.update(reoptResults) ## Update output file outData with REopt results data
@@ -386,7 +415,7 @@ def work(modelDir, inputDict):
 	#				 showlegend=showlegend))
 	#fig.update_traces(legendgroup='Demand', visible='legendonly', selector=dict(name='Original Load (kW)')) ## Make demand hidden on plot by default
 
-	## PV piece, if enabled
+	## PV plot, if enabled
 	if (inputDict['PV'] == 'Yes'):
 		fig.add_trace(go.Scatter(x=timestamps,
 						y=PV,
@@ -402,16 +431,16 @@ def work(modelDir, inputDict):
 	fig.update_layout(
     	#title='Residential Data',
     	xaxis=dict(title='Timestamp'),
-    	yaxis=dict(title="Power (kW)"),
+    	yaxis=dict(title='Power (kW)'),
     	yaxis2=dict(title='degrees Celsius',
                 overlaying='y',
                 side='right'
                 ),
     	legend=dict(
 			orientation='h',
-			yanchor="bottom",
+			yanchor='bottom',
 			y=1.02,
-			xanchor="right",
+			xanchor='right',
 			x=1
 			)
 	)
@@ -472,20 +501,20 @@ def makeGridLine(x,y,color,name):
 		plotData.append(resilience)
 		plotlyLayout['yaxis'].update(title='Longest Outage survived (Hours)')
 		plotlyLayout['xaxis'].update(title='Start Hour')
-		outData["resilienceData"] = json.dumps(plotData, cls=plotly.utils.PlotlyJSONEncoder)
-		outData["resilienceLayout"] = json.dumps(plotlyLayout, cls=plotly.utils.PlotlyJSONEncoder)
+		outData['resilienceData'] = json.dumps(plotData, cls=plotly.utils.PlotlyJSONEncoder)
+		outData['resilienceLayout'] = json.dumps(plotlyLayout, cls=plotly.utils.PlotlyJSONEncoder)
 
 		plotData = []
 		survivalProb = go.Scatter(
 			x=outData['survivalProbX'],
 			y=outData['survivalProbY'],
 			line=dict( color=('red') ),
-			name="Probability of Surviving Outage of a Given Duration")
+			name='Probability of Surviving Outage of a Given Duration')
 		plotData.append(survivalProb)
 		plotlyLayout['yaxis'].update(title='Probability of meeting critical Load')
 		plotlyLayout['xaxis'].update(title='Outage Length (Hours)')
-		outData["resilienceProbData" ] = json.dumps(plotData, cls=plotly.utils.PlotlyJSONEncoder)
-		outData["resilienceProbLayout"] = json.dumps(plotlyLayout, cls=plotly.utils.PlotlyJSONEncoder)
+		outData['resilienceProbData' ] = json.dumps(plotData, cls=plotly.utils.PlotlyJSONEncoder)
+		outData['resilienceProbLayout'] = json.dumps(plotlyLayout, cls=plotly.utils.PlotlyJSONEncoder)
 
 
 	## Create Exported Power plot object
@@ -545,12 +574,12 @@ def makeGridLine(x,y,color,name):
 
 	fig.update_layout(
     	xaxis=dict(title='Timestamp'),
-    	yaxis=dict(title="Power (kW)"),
+    	yaxis=dict(title='Power (kW)'),
     	legend=dict(
 			orientation='h',
-			yanchor="bottom",
+			yanchor='bottom',
 			y=1.02,
-			xanchor="right",
+			xanchor='right',
 			x=1
 			)
 	)
@@ -559,18 +588,65 @@ 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)
 
-	# Model operations typically ends here.
+	## DER Sharing Program options
+	if (inputDict['utilityProgram']):
+		print('Considering utility DER sharing program \n')
+
+		## Gather TOU rates
+		#latitude = float(inputDict['latitude'])
+		#longitude = float(inputDict['longitude'])
+		## NOTE: Temporarily use the lat/lon for a utility that has TOU rates specifically
+		latitude = 39.986771 
+		longitude = -104.812599 ## Brighton, CO
+		rate_info = get_tou_rates(modelDir, latitude, longitude) ## NOTE: lan/lon will be replaced by inputDict
+
+		## Extract "name" keys containing "TOU" or "time of use"
+		filtered_names = [item['name'] for item in rate_info['items'] if 'TOU' in item['name'] or 'time-of-use' in item['name'] or 'Time of Use' in item['name']]
+		for name in filtered_names: ## Print the filtered names
+			print(name)	
+
+		TOUname = 'Residential Time of Use'
+		TOUdata = []
+
+		for item in rate_info['items']:
+			if item['name'] == TOUname:
+				TOUdata.append(item)
+
+		print(TOUdata)
+
+		'''
+		rate_info = {
+			'rate_name': data['items'][0]['name'], ## Rate name
+			'fixed_monthly_charge': data['items'][0]['fixedmonthlycharge'], ## Fixed monthly charge ($)
+			'demand_rate_unit': data['items'][0]['demandrateunit'], ## Time of Use Rate Units
+			'demand_rate_structure': data['items'][0]['demandratestructure'], ## Time of Use Demand Charge Structure
+			'demand_weekday_schedule': data['items'][0]['demandweekdayschedule'],
+			'demand_weekend_schedule': data['items'][0]['demandweekendschedule'],
+			'flat_demand_structure': data['items'][0]['flatdemandstructure'],
+			'flat_demand_months': data['items'][0]['flatdemandmonths'],
+			'flat_demand_unit': data['items'][0]['flatdemandunit']
+		}
+		'''
+
+		## Generate peak shave schedule
+
+		## Determine area under the curve
+
+		## Apply rate compensations to DERs deployed
+
+		## Plot the peak shave schedule?
+
 	# Stdout/stderr.
-	outData["stdout"] = "Success"
-	outData["stderr"] = ""
+	outData['stdout'] = 'Success'
+	outData['stderr'] = ''
 
 	return outData
 
 def new(modelDir):
 	''' Create a new instance of this model. Returns true on success, false on failure. '''
-	with open(pJoin(__neoMetaModel__._omfDir,"static","testFiles","residential_PV_load.csv")) as f:
+	with open(pJoin(__neoMetaModel__._omfDir,'static','testFiles','residential_PV_load.csv')) as f:
 		demand_curve = f.read()
-	with open(pJoin(__neoMetaModel__._omfDir,"static","testFiles","residential_extended_temperature_data.csv")) as f:
+	with open(pJoin(__neoMetaModel__._omfDir,'static','testFiles','residential_extended_temperature_data.csv')) as f:
 		temp_curve = f.read()
 
 	defaultInputs = {

omf/models/derUtilityCost.py

@@ -277,8 +277,8 @@ def new(modelDir):
 		'created': str(datetime.datetime.now()),
 
 		## REopt inputs:
-		'latitude' : '39.7392358', 
-		'longitude' : '-104.990251', ## Brighton, CO
+		'latitude' : '39.986771 ', 
+		'longitude' : '-104.812599', ## Brighton, CO
 		'year' : '2018',
 		'analysis_years' : '25', 
 		'urdbLabel' : '612ff9c15457a3ec18a5f7d3', ## Brighton, CO