def gspDemand(supplyPoint, m, d):
electricalData = process_data_normal(
(electricalGSP.objects.filter(GSP=supplyPoint)), electricalGSP, 3)
electricalData = electricalData[0:, 2] / 2
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData = pd.DataFrame({'Time': dS[0:8784], 'Data': electricalData})
data = proData.set_index('Time')
d1, d2, S = month(m, data)
elec = S['Data']
elec = elec.iloc[0:(24 * d)]
return elec
def plot_charge_demand(request):
supplyPoint = request.GET['supplyPoint']
m = int(request.GET['month'])
d = request.GET['days']
gspName = GSP.objects.filter(idx=supplyPoint)
na0 = ([p.name for p in gspName])
gspAuthority = gspLocalAuthority.objects.filter(gsp=na0[0])
na1 = ([p.localAuthority for p in gspAuthority])
mediumEV = request.GET['mediumEV']
smallEV = request.GET['smallEV']
geographicArea = int(request.GET['geographicArea'])
if geographicArea == 1:
gA = 'Urban'
else:
gA = 'Rural'
if d == '':
d = int(0)
else:
d = int(d)
if m == 13:
d = 366
if mediumEV == '':
mediumEV = int(0)
else:
mediumEV = int(mediumEV)
if smallEV == '':
smallEV = int(0)
else:
smallEV = int(smallEV)
electricalData = process_data(
(electricalGSP.objects.filter(GSP=supplyPoint)), electricalGSP, 3)
chargeDataSmall = process_data((Journey.objects.filter(
localAuthority=na1[0], Area=gA, typeEV='Economy')), Journey, 7)
chargeDataMedium = process_data((Journey.objects.filter(
localAuthority=na1[0], Area=gA, typeEV='Midsize')), Journey, 7)
# good to here
electricalData = electricalData[0:, 2] / 2
chargeDataSmall = chargeDataSmall[0:, 0:4]
chargeDataMedium = chargeDataMedium[0:, 0:4]
profileSmall = formatChargeDemand(smallEV, chargeDataSmall, 'Economy', d)
profileMedium = formatChargeDemand(mediumEV, chargeDataMedium, 'Midsize',
d)
profileTotal = np.zeros([len(profileSmall), 1])
profileTotal[0:, 0] = (profileSmall['Charge'].as_matrix() +
profileMedium['Charge'].as_matrix()) / 1000
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData = pd.DataFrame({'Time': dS[0:8784], 'Data': electricalData})
data = proData.set_index('Time')
d1, d2, S = month(m, data)
elec = S['Data']
baseData = np.zeros([(24 * d), 1])
baseData[0:, 0] = elec.iloc[0:(24 * d)]
newData = baseData + profileTotal
fmax = max(baseData)[0]
newDemandMax = max(newData)[0]
fMax = round(fmax, 2)
newDemandMax = round(newDemandMax, 2)
statHP = process_data_normal((gspStats.objects.filter(index=supplyPoint)),
gspStats, 16)
rating = statHP[0, 1]
if d == 1:
formatter = DateFormatter('%H-%M')
rule = rrulewrapper(HOURLY, interval=6)
elif d < 4:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=1)
elif d < 10:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=2)
elif d > 9 and d < 16:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=3)
elif d > 15 and d < 32:
formatter = DateFormatter('%d-%m')
rule = rrulewrapper(DAILY, interval=5)
if m == 13:
formatter = DateFormatter('%b-%Y')
rule = rrulewrapper(MONTHLY, interval=3)
loc = RRuleLocator(rule)
delta = datetime.timedelta(hours=1)
dates = drange(d1, d2, delta)
finalDates = dates[0:(24 * d)]
fig, ax = plt.subplots()
ax.plot_date(finalDates, baseData, linestyle='-', marker='None')
ax.plot_date(finalDates, newData, linestyle='-', marker='None')
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(loc)
plt.ylabel('Electrical Demand (MWh)')
plt.grid(True)
buffer = BytesIO()
canvas = pylab.get_current_fig_manager().canvas
canvas.draw()
pilImage = PIL.Image.frombytes("RGB", canvas.get_width_height(),
canvas.tostring_rgb())
pilImage.save(buffer, "PNG")
pylab.close()
image_png = buffer.getvalue()
graphic = base64.b64encode(image_png)
graphic = graphic.decode('utf-8')
##
return render(
request, 'transport/show_plot.html', {
'graphic': graphic,
'fMax': fMax,
'newDemandMax': newDemandMax,
'rating': rating
})
def plot_electrical(request):
supplyPoint = request.GET['supplyPoint']
m = int(request.GET['month'])
d = request.GET['days']
gspName = GSP.objects.get(idx=supplyPoint)
if d == '':
d = int(0)
else:
d = int(d)
if m == 13:
d = 366
electricalData = electricalGSP.objects.filter(GSP=supplyPoint)
vlqs = electricalData.values_list()
r = np.core.records.fromrecords(
vlqs, names=[f.name for f in electricalGSP._meta.fields])
l = np.array(r)
process = np.zeros([len(l), 3])
for i in range(
0, len(process)): # extract the information we need for inputs.
change = l[i]
for j in range(1, 4):
process[i, (j - 1)] = change[j]
g = np.array(process)
dS = pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData = pd.DataFrame({'Time': dS[0:8784], 'Data': g[0:, 2]})
data = proData.set_index('Time')
d1, d2, S = month(m, data)
elec = S['Data']
finalData = elec.iloc[0:(24 * d)] / 2
if max(finalData) > abs(min(finalData)):
maxGSP_proxy = max(finalData)
else:
maxGSP_proxy = min(finalData)
maxGSP = round(maxGSP_proxy, 2)
statHP = process_data_normal((gspStats.objects.filter(index=supplyPoint)),
gspStats, 16)
rating = statHP[0, 1]
if d == 1:
formatter = DateFormatter('%H-%M')
rule = rrulewrapper(HOURLY, interval=6)
elif d < 4:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=1)
elif d < 10:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=2)
elif d > 9 and d < 16:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=3)
elif d > 15 and d < 32:
formatter = DateFormatter('%d-%m')
rule = rrulewrapper(DAILY, interval=5)
if m == 13:
formatter = DateFormatter('%b-%Y')
rule = rrulewrapper(MONTHLY, interval=3)
loc = RRuleLocator(rule)
delta = datetime.timedelta(hours=1)
dates = drange(d1, d2, delta)
finalDates = dates[0:(24 * d)]
fig, ax = plt.subplots()
ax.plot_date(finalDates,
finalData,
linestyle='-',
marker='None',
label='Demand')
## if (finalData<0).any() == True and (finalData>0).any() == False:
## ax.plot_date(finalDates,ratingNeg, linestyle='-', marker='None', color='r', label='GSP Rating')
## elif (finalData<0).any() == False and (finalData>0).any() == True:
## ax.plot_date(finalDates,rating, linestyle='-', marker='None', color='r', label='GSP Rating')
## elif (finalData<0).any() == True and (finalData>0).any() == True:
## ax.plot_date(finalDates,ratingNeg, linestyle='-', marker='None', color='r', label='GSP Rating')
## ax.plot_date(finalDates,rating, linestyle='-', marker='None', color='r')
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(loc)
plt.ylabel('Electrical Demand (MWh)')
plt.title('Electrical Demand Plot for ' + gspName.name + ' GSP')
plt.grid(True)
buffer = BytesIO()
canvas = pylab.get_current_fig_manager().canvas
canvas.draw()
pilImage = PIL.Image.frombytes("RGB", canvas.get_width_height(),
canvas.tostring_rgb())
pilImage.save(buffer, "PNG")
pylab.close()
image_png = buffer.getvalue()
graphic = base64.b64encode(image_png)
graphic = graphic.decode('utf-8')
##
return render(request, 'electrical/show_plot.html', {
'graphic': graphic,
'maxGSP': maxGSP,
'rating': rating
})
def plot_gsp_storage(request):
supplyPoint=request.GET['supplyPoint']
m=int(request.GET['month'])
d=request.GET['days']
gspName=GSP.objects.get(idx=supplyPoint)
esCap=int(request.GET['esCap'])
if d =='':
d=int(0)
else:
d = int(d)
if m == 13:
d=366
electricalData = electricalGSP.objects.filter(GSP = supplyPoint)
vlqs = electricalData.values_list()
r = np.core.records.fromrecords(vlqs, names=[f.name for f in electricalGSP._meta.fields])
l=np.array(r)
process = np.zeros([len(l),3])
for i in range(0,len(process)): # extract the information we need for inputs.
change = l[i]
for j in range(1,4):
process[i,(j-1)] = change[j]
g=np.array(process)/2
dS=pd.date_range(start='2015-04-01', end='2016-04-01', freq='H')
proData=pd.DataFrame({'Time':dS[0:8784], 'Data':g[0:,2]})
data=proData.set_index('Time')
d1, d2, S = month(m, data)
elec=S['Data']
fProxy=np.zeros([24, d])
cProxy=np.zeros([24, d])
disProxy=np.zeros([24, d])
newDemandProxy=np.zeros([24, d])
for i in range(1,(d+1)):
model, sol, flo, cha, dis, nD = runSolutionBalance(esCap, (elec.iloc[(24*i)-24:(24*i)]))
fProxy[0:,(i-1)]=flo
cProxy[0:,(i-1)]=cha
disProxy[0:,(i-1)]=dis
newDemandProxy[0:,(i-1)]=nD
f=fProxy.reshape((d*24), order='F')
c=cProxy.reshape((d*24),order='F')
dis=disProxy.reshape((d*24),order='F')
newDemand=newDemandProxy.reshape((d*24),order='F')
check=all(i > 0 for i in f)
if np.absolute(np.amax(f)) > np.absolute(np.amin(f)):
fMax = np.amax(f)
newDemandMax = np.amax(newDemand)
peakReduction = 100 - round(((newDemandMax/fMax)*100),2)
headroom = round((fMax - newDemandMax), 2)
else:
fMax = np.amin(f)
newDemandMax = np.amin(newDemand)
peakReduction = 100 - round(((newDemandMax/fMax)*100),2)
headroom = round((newDemandMax- fMax), 2)
statHP = process_data_normal((gspStats.objects.filter(index=supplyPoint)), gspStats, 16)
rating=statHP[0,1]
####Plotting####
if d == 1:
formatter = DateFormatter('%H-%M')
rule = rrulewrapper(HOURLY, interval=6)
elif d < 4:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=1)
elif d < 10:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=2)
elif d > 9 and d < 16:
formatter = DateFormatter('%d-%m-%y')
rule = rrulewrapper(DAILY, interval=3)
elif d > 15:
formatter = DateFormatter('%d-%m')
rule = rrulewrapper(DAILY, interval=5)
if m==13:
formatter = DateFormatter('%b-%Y')
rule = rrulewrapper(MONTHLY, interval=3)
loc = RRuleLocator(rule)
delta = datetime.timedelta(hours=1)
dates = drange(d1, d2, delta)
finalDates=dates[0:(24*d)]
fig, ax =plt.subplots()
ax.plot_date(finalDates, f, linestyle='-', marker='None', label='Base Case (no BES)')
ax.plot(finalDates, newDemand, linestyle='-', marker='None', label='With BES')
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(loc)
ax.legend()
plt.ylabel('Electrical Demand (MWh)')
plt.title('Electrical Demand Plot for '+ gspName.name + ' GSP' + ' with ' + str(esCap)+ ' MWh BES')
plt.grid(True)
buffer = BytesIO()
canvas = pylab.get_current_fig_manager().canvas
canvas.draw()
pilImage = PIL.Image.frombytes("RGB", canvas.get_width_height(), canvas.tostring_rgb())
pilImage.save(buffer, "PNG")
pylab.close()
image_png = buffer.getvalue()
graphic = base64.b64encode(image_png)
graphic = graphic.decode('utf-8')
return render(request, 'energyStorage/show_plot.html', {'graphic':graphic, 'fMax':fMax, 'newDemandMax': newDemandMax, 'peakReduction':peakReduction,'esCap':esCap, 'headroom':headroom, 'rating':rating})