def fetchSection1_11_windPLF(appDbConnStr: str, startDt: dt.datetime,
endDt: dt.datetime) -> ISection_1_11_PLFCUF:
constituentsInfos = getREConstituentsMappings()
mRepo = MetricsDataRepo(appDbConnStr)
numOfDays = (endDt - startDt).days + 1
soFarHighestAllEntityGenVals = mRepo.getSoFarHighestAllEntityData(
'soFarHighestWindGen', addMonths(startDt, -1))
soFarHighestGenLookUp = {}
for v in soFarHighestAllEntityGenVals:
soFarHighestGenLookUp[v['constituent']] = {
'value': v['data_value'],
'ts': v['data_time']
}
dispRows: List[IPLFCUFDataRow] = []
for cIter in range(len(constituentsInfos)):
constInfo = constituentsInfos[cIter]
if (math.isnan(constInfo['windCapacity'])):
continue
maxGenData = mRepo.getEntityMetricHourlyData(constInfo["entity_tag"],
"Wind(MW)", startDt,
endDt)
if constInfo['entity_tag'] == 'central':
windEnerConsumption = mRepo.getEntityMetricDailyData(
'wr', 'CGS Wind(Mus)', startDt, endDt)
elif constInfo['entity_tag'] == 'wr':
windEnerConsumption = mRepo.getEntityMetricDailyData(
'wr', "Wind(MU)", startDt, endDt)
cgsWindEnerConsumption = mRepo.getEntityMetricDailyData(
'wr', 'CGS Wind(Mus)', startDt, endDt)
for w, c in zip(windEnerConsumption, cgsWindEnerConsumption):
w["data_value"] += c["data_value"]
else:
windEnerConsumption = mRepo.getEntityMetricDailyData(
constInfo['entity_tag'], 'Wind(MU)', startDt, endDt)
energyConsumption = mRepo.getEntityMetricDailyData(
constInfo['entity_tag'], 'Consumption(MU)', startDt, endDt)
maxGenDf = pd.DataFrame(maxGenData)
maxGenDf = maxGenDf.pivot(index='time_stamp',
columns='metric_name',
values='data_value')
windEnerConsumptionSum = pd.DataFrame(windEnerConsumption).groupby(
'entity_tag').sum().iloc[0]['data_value']
if (len(energyConsumption) == 0):
# This is for central sector as we dont have consumption data
# calculate mu from mw
# df = pd.DataFrame(maxGenData)
# average = df.groupby('entity_tag').mean()
# windEnerConsumptionSumDf = average * 0.024 * numOfDays #To Calculate Avg MU from MW
# windEnerConsumptionSum = windEnerConsumptionSumDf.iloc[0]['data_value']
EnerConsumptionSum = 0
penetrationLevel = 0
else:
EnerConsumptionSum = pd.DataFrame(energyConsumption).groupby(
'entity_tag').sum().iloc[0]['data_value']
penetrationLevel = round(
(windEnerConsumptionSum / EnerConsumptionSum) * 100, 2)
maxWind = maxGenDf["Wind(MW)"].max()
maxWindDt = maxGenDf["Wind(MW)"].idxmax()
plf = (windEnerConsumptionSum *
1000) / (int(constInfo['windCapacity']) * 24 * numOfDays)
cuf = maxWind * 100 / (int(constInfo['windCapacity']))
prevHighestWindObj = soFarHighestGenLookUp[constInfo["entity_tag"]]
newHighestWind = maxWind
newHighestWindTime = maxWindDt.to_pydatetime()
if newHighestWind < prevHighestWindObj["value"]:
newHighestWind = prevHighestWindObj["value"]
newHighestWindTime = prevHighestWindObj["ts"]
mRepo.insertSoFarHighest(constInfo['entity_tag'],
"soFarHighestWindGen", startDt,
newHighestWind, newHighestWindTime)
# soFarHighestAllEntityGenVals = mRepo.getSoFarHighestAllEntityData(
# 'soFarHighestWindGen', startDt)
# soFarHighestGenLookUp = {}
# for v in soFarHighestAllEntityGenVals:
# soFarHighestGenLookUp[v['constituent']] = {
# 'value': v['data_value'], 'ts': v['data_time']}
so_far_high_gen_str = str(
round(newHighestWind)) + ' on ' + dt.datetime.strftime(
newHighestWindTime,
'%d-%b-%Y') + ' at ' + dt.datetime.strftime(
newHighestWindTime, '%H:%S')
const_display_row: IPLFCUFDataRow = {
'entity': constInfo['display_name'],
'capacityMW': round(constInfo['windCapacity']),
'maxgenerationMW': round(maxWind),
'soFarHighestGenMW': so_far_high_gen_str,
'energyGeneration': round(windEnerConsumptionSum),
'energyConsumption': round(EnerConsumptionSum),
'penetration': penetrationLevel,
'plf': round(plf * 100),
'cuf': round(cuf)
}
dispRows.append(const_display_row)
secData: ISection_1_11_PLFCUF = {"so_far_hig_win_gen_plf": dispRows}
return secData
def fetchSection1_3_bContext(appDbConnStr: str, startDt: dt.datetime,
endDt: dt.datetime) -> ISection_1_3_b:
constituentsInfos = getConstituentsMappings()
mRepo = MetricsDataRepo(appDbConnStr)
soFarHighestAllEntityReqVals = mRepo.getSoFarHighestAllEntityData(
'soFarHighestRequirement', addMonths(startDt, -1))
soFarHighestReqLookUp = {}
for v in soFarHighestAllEntityReqVals:
soFarHighestReqLookUp[v['constituent']] = {
'value': v['data_value'],
'ts': v['data_time']
}
soFarHighestAllEntityAvailVals = mRepo.getSoFarHighestAllEntityData(
'soFarHighestAvailability', addMonths(startDt, -1))
soFarHighestAvailLookUp = {}
for v in soFarHighestAllEntityAvailVals:
soFarHighestAvailLookUp[v['constituent']] = {
'value': v['data_value'],
'ts': v['data_time']
}
dispRows: List[ISoFarHighestDataRow] = []
for cIter in range(len(constituentsInfos)):
constInfo = constituentsInfos[cIter]
availData = mRepo.getEntityMetricHourlyData(constInfo["entity_tag"],
"Demand(MW)", startDt,
endDt)
loadSheddingData = mRepo.getEntityMetricHourlyData(
constInfo["entity_tag"], "Load Shedding(MW)", startDt, endDt)
availReqData = availData + loadSheddingData
availReqDataDf = pd.DataFrame(availReqData)
availReqDataDf = availReqDataDf.pivot(index='time_stamp',
columns='metric_name',
values='data_value')
availReqDataDf.reset_index(inplace=True)
availReqDataDf["Requirement"] = availReqDataDf["Demand(MW)"] + \
availReqDataDf["Load Shedding(MW)"]
maxReq = availReqDataDf["Requirement"].max()
maxReqDt = availReqDataDf["time_stamp"].loc[
availReqDataDf["Requirement"].idxmax()]
maxAvail = availReqDataDf["Demand(MW)"].max()
maxAvailDt = availReqDataDf["time_stamp"].loc[
availReqDataDf["Demand(MW)"].idxmax()]
maxShortagePerc = round(100 * (maxReq - maxAvail) / maxAvail, 2)
prevHighestReqObj = soFarHighestReqLookUp[constInfo["entity_tag"]]
newHighestReq = maxReq
newHighestReqTime = maxReqDt.to_pydatetime()
if newHighestReq < prevHighestReqObj["value"]:
newHighestReq = prevHighestReqObj["value"]
newHighestReqTime = prevHighestReqObj["ts"]
prevHighestAvailObj = soFarHighestAvailLookUp[constInfo["entity_tag"]]
newHighestAvail = maxAvail
newHighestAvailTime = maxAvailDt.to_pydatetime()
if newHighestAvail < prevHighestAvailObj["value"]:
newHighestAvail = prevHighestAvailObj["value"]
newHighestAvailTime = prevHighestAvailObj["ts"]
newHighestShortagePerc = round(
100 * (newHighestReq - newHighestAvail) / newHighestAvail, 2)
newHighestAvailTime = newHighestAvailTime
newHighestReqTime = newHighestReqTime
mRepo.insertSoFarHighest(constInfo['entity_tag'],
"soFarHighestAvailability", startDt,
newHighestAvail, newHighestAvailTime)
mRepo.insertSoFarHighest(constInfo['entity_tag'],
"soFarHighestRequirement", startDt,
newHighestReq, newHighestReqTime)
const_display_row: ISoFarHighestDataRow = {
'entity':
constInfo['display_name'],
'peakReqMW':
round(maxReq),
'peakAvailMW':
round(maxAvail),
'shortage_X':
maxShortagePerc,
'highestReqMW':
round(newHighestReq),
'highestAvailMW':
round(newHighestAvail),
'shortage_Y':
newHighestShortagePerc,
'highestReqMWDateStr':
'Max on {0} at {1} hrs'.format(
dt.datetime.strftime(newHighestReqTime, "%d.%m.%y"),
dt.datetime.strftime(newHighestReqTime, "%H:%M")),
'highestAvailMWDateStr':
'Max on {0} at {1} hrs'.format(
dt.datetime.strftime(newHighestAvailTime, "%d.%m.%y"),
dt.datetime.strftime(newHighestAvailTime, "%H:%M"))
}
dispRows.append(const_display_row)
secData: ISection_1_3_b = {"so_far_hig_req_avail": dispRows}
return secData