def fetchSection1_11_solar_cContext(appDbConnStr: str, startDt: dt.datetime, endDt: dt.datetime) -> ISection_1_11_solar_c:
constituentsInfos = getREConstituentsMappings()
mRepo = MetricsDataRepo(appDbConnStr)
soFarHighestAllEntityVals = mRepo.getSoFarHighestAllEntityData(
'soFarHighestSolarGen', startDt)
soFarHighestWindLookUp = {}
for v in soFarHighestAllEntityVals:
soFarHighestWindLookUp[v['constituent']] = {
'value': v['data_value'], 'ts': v['data_time']}
dispRows: List[ISoFarHighestDataRow] = []
for cIter in range(len(constituentsInfos)):
constInfo = constituentsInfos[cIter]
if(pd.isna(constInfo['solarCapacity'])):
continue
highestGenerationDateTime = soFarHighestWindLookUp[constInfo['entity_tag']]['ts']
const_display_row: ISoFarHighestDataRow = {
'entity': constInfo['display_name'],
'capacityMW': constInfo['solarCapacity'],
'generationMW': soFarHighestWindLookUp[constInfo['entity_tag']]['value'],
'highestGenerationMWDateStr': dt.datetime.strftime(highestGenerationDateTime, "%d.%m.%Y"),
'highestGenerationMWTimeStr': dt.datetime.strftime(highestGenerationDateTime, "%H:%M")
}
dispRows.append(const_display_row)
secData: ISection_1_11_solar_c = {"so_far_hig_solar_gen": dispRows}
return secData
def test_getSoFarHighestAllEntityData(self) -> None:
"""tests the function that gets hourly data of entity metric
"""
appDbConnStr = self.jsonConf['appDbConnStr']
mRepo = MetricsDataRepo(appDbConnStr)
startDt = dt.datetime(2020, 12, 1)
samples = mRepo.getSoFarHighestAllEntityData(
"soFarHighestRequirement", startDt)
self.assertFalse(len(samples) == 0)
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
def fetchSection1_11_Solar_B(appDbConnStr: str, startDt: dt.datetime,
endDt: dt.datetime) -> dict:
constituentsInfos = getREConstituentsMappings()
mRepo = MetricsDataRepo(appDbConnStr)
allEntitySoFarHighest = mRepo.getSoFarHighestAllEntityData(
'soFarHighestSolarGen', startDt)
wrSoFarHighest = {}
for itr in range(len(allEntitySoFarHighest)):
if allEntitySoFarHighest[itr]['constituent'] == 'wr':
wrSoFarHighest = allEntitySoFarHighest[itr]
soFarHighestDate = wrSoFarHighest['data_time']
hourlyGenerationObj = []
for cIter in range(len(constituentsInfos)):
constInfo = constituentsInfos[cIter]
if (math.isnan(constInfo['solarCapacity'])):
continue
hourlyGen = mRepo.getEntityMetricHourlyData(constInfo['entity_tag'],
'Solar(MW)',
soFarHighestDate,
soFarHighestDate)
hourlyGenerationObj.append(hourlyGen)
if (len(hourlyGenerationObj) > 0):
pltDataObj: list = []
for temp in range(len(hourlyGenerationObj)):
pltDataObj = pltDataObj + [{
'Hours': x["time_stamp"].hour,
'colName': x['entity_tag'],
'val': x["data_value"]
} for x in hourlyGenerationObj[temp]]
pltDataDf = pd.DataFrame(pltDataObj)
pltDataDf = pltDataDf.pivot(index='Hours',
columns='colName',
values='val')
pltDataDf.reset_index(inplace=True)
pltDataDf.to_excel("assets/plot_1_11_solar_2.xlsx", index=True)
pltTitle = 'Highest Solar Generation on {0} '.format(
soFarHighestDate.strftime('%d-%m-%y'))
fig, ax = plt.subplots(figsize=(7.5, 4.5))
ax.set_title(pltTitle)
ax.set_ylabel('MW')
ax.set_xlabel('Hours')
ax.xaxis.set_major_locator(mdates.DayLocator(interval=2))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d'))
clr = [
'#00ccff', '#ff8533', '#ff0000', '#9900ff', '#00ff88', '#3388ff'
]
for col in range(len(pltDataDf.columns) - 1):
ax.plot(pltDataDf['Hours'],
pltDataDf[pltDataDf.columns[col + 1]],
color=clr[col],
label=pltDataDf.columns[col + 1])
ax.yaxis.grid(True)
ax.legend(bbox_to_anchor=(0.5, -0.3, 0.0, 0.0),
loc='center',
ncol=4,
borderaxespad=0.)
# plt.xticks(rotation=90)
ax.set_xlim(xmin=0, xmax=23)
fig.subplots_adjust(bottom=0.25, top=0.8)
fig.savefig('assets/section_1_11_solar_2.png')
# plt.close()
secData: dict = {
'data': wrSoFarHighest['data_value'],
'date': dt.datetime.strftime(wrSoFarHighest['data_time'], '%d.%m.%Y'),
'time': dt.datetime.strftime(wrSoFarHighest['data_time'], '%H:%M')
}
return secData
