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article_figs.py
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article_figs.py
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import figutils as fig
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import interp1d
import aurespf.solvers as au
from nhgrid import nh_Nodes
from europe_plusgrid import europe_plus_Nodes
from FCResult import FCResult
from FCResult import myhist
from FlowCalculation import FlowCalculation
import costtools as ct
savepath = './results/figures/Articlefigs/'
green1 = '#7bed08'
green2 = '#5eb406'
green3 = '#407b04'
green4 = '#234202'
def make_mismatchfig(interactive=True):
plt.close()
plt.rcParams['axes.color_cycle'] = fig.color_cycle
if interactive:
plt.ion()
layouts = ['EU_RU_NA_ME', 'US_EU_RU_NA_ME', 'eurasia', 'US_eurasia_closed']
labels = ['EU-RU-NA-ME', 'US-EU-RU-NA-ME', 'Eurasia', 'US-Eurasia']
bins = np.linspace(-1.2, 2.3,500)
N = nh_Nodes(admat='./settings/EU_RU_NA_MEadmat.txt')
EUmismatch = N[0].mismatch/N[0].mean
EUvalue, EUcount = myhist(EUmismatch, bins=bins, normed=True)
ax1 = plt.subplot(2,1,1)
ax1.plot(EUvalue, EUcount, label='EU', lw=2)
for layout in layouts:
admat = './settings/' + layout + 'admat.txt'
N = nh_Nodes(admat=admat)
total_mean_load = sum([n.mean for n in N])
mismatch = sum([n.mismatch for n in N])/total_mean_load
value, count = myhist(mismatch, bins=bins, normed=True)
ax1.plot(value, count, label=labels[layouts.index(layout)], lw=2)
ax1.legend()
ax1.set_xlim((-1.2,2.3))
ax1.set_xlabel('Aggregated mismatch [normalized]')
ax1.set_ylabel('Probability density')
ax1.text(-1.12,2.1,'(a)', fontdict={'size':20})
ax2 = plt.subplot(2,1,2)
ax2.plot(EUvalue, EUcount, label='EU', lw=2)
for layout in layouts:
load_filename = layout+'_aHE_copper_lin.npz'
N = nh_Nodes(load_filename=load_filename)
normed_res_mismatches = []
for n in [N[0]]:
normed_res_mismatches.extend((n.curtailment - n.balancing)/n.mean)
nonzero_res_mismatch = [d for d in normed_res_mismatches if (d>0.01 or d<-0.01)]
value, count = myhist(normed_res_mismatches, bins=bins, normed=True)
nonzero_values = [v for v in value if (v<-0.005 or v>0.01)]
nonzero_indices = [np.where(value==v)[0][0] for v in value if (v<-0.005 or v>0.01)]
nonzero_count = count[nonzero_indices]
ax2.plot(nonzero_values, nonzero_count,label=labels[layouts.index(layout)], lw=2)
legend2 = ax2.legend(title='EU embedded in:')
legend2 = ax2.legend(title='EU embedded in:')
plt.setp(legend2.get_title(),fontsize=14)
ax2.set_xlim((-1.2,2.3))
ax2.set_ylim((0,1.2))
ax2.set_xlabel('Non-zero residual mismatch [normalized]')
ax2.set_ylabel('Probability density')
ax2.text(-1.12,1,'(b)', fontdict={'fontsize':20})
plt.tight_layout()
figfilename = 'mismatch_hists.pdf'
if not interactive:
plt.savefig(savepath + figfilename)
def make_vs_layout_barplot(interactive=True):
barwidth=0.5
datapath='./results/BalvsTrans/'
plt.close()
plt.rcParams['axes.color_cycle'] = fig.color_cycle
if interactive:
plt.ion()
BE = []
BC = []
BC_sqr = []
TC = []
TC_sqr = []
layouts = ['EU_RU_NA_ME', 'US_EU_RU_NA_ME', 'eurasia', 'US_eurasia_closed']
flowcalcs = [FlowCalculation(layout, 'aHE', '1.5q99', 'lin')\
for layout in layouts]
print [str(fc) for fc in flowcalcs]
N = nh_Nodes()
EU = N[0]
EU_bal = -au.get_negative(EU.mismatch)
EU_BE = np.sum(EU_bal)/(EU.mean*len(EU.mismatch))
BE.append(EU_BE)
EU_BC = au.get_q(EU_bal, 0.99)/EU.mean
BC.append(EU_BC)
TC.append(1e-8)
TC_sqr.append(1e-8)
for fc in flowcalcs:
fc_sqr = FlowCalculation(fc.layout, 'aHE', '1.5q99', 'sqr')
print fc.layout
admat = "./settings/" + fc.layout + "admat.txt"
N = nh_Nodes(admat=admat)
total_mean_load = np.sum([n.mean for n in N])
print total_mean_load
filename = str(fc) + '.pkl'
filename_sqr = str(fc_sqr) + '.pkl'
flowfilename = './results/' + fc.layout + '_aHE_copper_lin_flows.npy'
flowfilename_sqr = './results/' + fc.layout + '_aHE_copper_sqr_flows.npy'
unnormalized_BE = [fig.get_data(filename, 'BE', \
path=datapath)[n.id] for n in N]
unnormalized_BC = [fig.get_data(filename, 'BC', \
path=datapath)[n.id] for n in N]
unnormalized_BC_sqr = [fig.get_data(filename_sqr, 'BC', \
path=datapath)[n.id] for n in N]
BE.append(np.sum(unnormalized_BE)/total_mean_load)
BC.append(np.sum(unnormalized_BC)/total_mean_load)
BC_sqr.append(np.sum(unnormalized_BC_sqr)/total_mean_load)
LI = au.linfo(admat)
energywiseTC = au.biggestpair(au.get_quant_caps(filename=flowfilename))
energywiseTC_sqr = au.biggestpair(au.get_quant_caps(filename=flowfilename_sqr))
print np.sum(energywiseTC)
TC.append(np.sum([energywiseTC[i]*float(LI[i][2]) \
for i in range(len(LI))])/(1e3*total_mean_load))
TC_sqr.append(np.sum([energywiseTC_sqr[i]*float(LI[i][2]) \
for i in range(len(LI))])/(1e3*total_mean_load))
print TC
index1 = np.arange(len(BE))
left = index1 + 0.5*barwidth
left2 = left + 0.5*barwidth
plt.ion()
ax1 = plt.subplot(3,1,1)
if not interactive:
plt.gcf().set_size_inches([7.5,12])
plt.gcf().set_dpi(400)
ax1.bar(left, BE, width=barwidth, color=fig.orange,\
label='Localized/synchronized flow')
layoutlist1 = ['EU', 'EU-RU-NA-ME', 'US-EU-RU-NA-ME', 'Eurasia', 'US-Eurasia']
ax1.set_xticks(left + 0.5*barwidth)
ax1.set_xticklabels(layoutlist1)
ax1.set_ylabel('Backup energy [normalized]')#r'$E_\mathrm{total}^B$' + ' [normalized]')
ax1.set_ylim((0,0.2))
ax1.text(0.1*barwidth, 0.182, '(a)',fontdict={'fontsize':20})
ax1.legend()
ax2 = plt.subplot(3,1,2)
ax2.bar(left[0], BC[0], width=barwidth, color=fig.red)
ax2.bar(left[1:], BC[1:], width=0.5*barwidth, color=fig.red,\
label='Localized flow')
ax2.bar(left2[1:], BC_sqr, width=0.5*barwidth, color=fig.darkred,\
label='Synchronized flow')
ax2.set_xticks(left + 0.5*barwidth)
ax2.set_xticklabels(layoutlist1)
ax2.set_ylabel('Backup capacity [normalized]')#r'$C_\mathrm{total}^B$'+ ' [normalized]')
ax2.set_ylim((0.0, 0.82))
ax2.text(0.1*barwidth, 0.748, '(b)',fontdict={'fontsize':20})
ax2.legend(ncol=2)
layoutlist2 = ['', 'EU-RU-NA-ME', 'US-EU-RU-NA-ME', 'Eurasia', 'US-Eurasia']
ax3 = plt.subplot(3,1,3)
ax3.bar(left, TC, width=0.5*barwidth, color=fig.green, \
label = 'Localized flow')
ax3.bar(left2, TC_sqr, width=0.5*barwidth, color=green4,
label = 'Synchronized flow')
ax3.set_xticks(left + 0.5*barwidth)
ax3.set_xticklabels(layoutlist2)
ax3.set_ylabel('Transmission capacity\n [normalized'\
+ r'$\times$' + '1000 km]')#r'$C_\mathrm{total}^T$' + ' [km]')
ax3.text(0.1*barwidth, 5.46, '(c)',fontdict={'fontsize':20})
ax3.legend(ncol=2)
plt.tight_layout()
figfilename = 'BEBCTC_vs_layout.pdf'
if not interactive:
plt.savefig(savepath+figfilename)
def make_LCOE_barplot(interactive=True, solvermode='lin', ax=None):
if interactive:
plt.ion()
CFw = 0.35
CFs = 0.15
datapath = './results/AlphaSweepsCopper/'
layouts = ['EU_RU_NA_ME', 'US_EU_RU_NA_ME', 'eurasia', 'US_eurasia_closed']
total_LCOE = []
LCOE2 = []
LCOE3 = []
LCOE4 = []
LCOE5 = []
LCOE050 = []
LCOE025 = []
LCOE015 = []
# find LCOE for EU isolated
zerotrans_fc = FlowCalculation('eurasia', 'aHE', 'zerotrans', 'raw')
zerotrans_datapath = './results/AlphaSweepsZerotrans/'
total_EU_energy = ct.total_annual_energy_consumption(zerotrans_fc, onlyEU=True)
EU_BE_LCOE = au.cbe(ct.total_annual_BE(zerotrans_fc, datapath=zerotrans_datapath, onlyEU=True))/total_EU_energy
EU_BC_LCOE = au.cbc(ct.get_total_BC(zerotrans_fc, datapath=zerotrans_datapath, onlyEU=True))/total_EU_energy
EU_wind_LCOE = au.cwc(ct.get_total_wind_capacity(zerotrans_fc, CFw, zerotrans_datapath, onlyEU=True))\
/total_EU_energy
EU_solar_LCOE = au.csc(ct.get_total_solar_capacity(zerotrans_fc, CFs, datapath, onlyEU=True))\
/total_EU_energy
total_LCOE.append(0)
LCOE2.append(sum([EU_BE_LCOE, EU_BC_LCOE, EU_wind_LCOE, EU_solar_LCOE]))
LCOE3.append(sum([EU_BC_LCOE, EU_wind_LCOE, EU_solar_LCOE]))
LCOE4.append(sum([EU_wind_LCOE, EU_solar_LCOE]))
LCOE5.append(sum([EU_wind_LCOE]))
LCOE050.append(0)
LCOE025.append(0)
LCOE015.append(0)
fclist = [FlowCalculation(l, 'aHE', 'copper', solvermode) for l in layouts]
for fc in fclist:
admat = './settings/' + fc.layout + 'admat.txt'
total_energy = ct.total_annual_energy_consumption(fc)
print fc.layout, total_energy
BE_LCOE = au.cbe(ct.total_annual_BE(fc, datapath))/total_energy
BC_LCOE = au.cbc(ct.get_total_BC(fc, datapath))/total_energy
wind_LCOE = au.cwc(ct.get_total_wind_capacity(fc, CFw, datapath))\
/total_energy
solar_LCOE = au.csc(ct.get_total_solar_capacity(fc, CFs, datapath))\
/total_energy
TC_LCOE = au.ctc(ct.get_TCs(fc, datapath), pathadmat=admat,\
scale_factor=1)/total_energy
TC_LCOE50 = au.ctc(ct.get_TCs(fc, datapath), pathadmat=admat,\
scale_factor=0.5)/total_energy
TC_LCOE25 = au.ctc(ct.get_TCs(fc, datapath), pathadmat=admat,\
scale_factor=0.25)/total_energy
TC_LCOE15 = au.ctc(ct.get_TCs(fc, datapath), pathadmat=admat,\
scale_factor=0.15)/total_energy
##################### Make TC_LCOE75 etc variablse
total_LCOE.append(sum([TC_LCOE, BE_LCOE, BC_LCOE, wind_LCOE, solar_LCOE]))
LCOE050.append(sum([TC_LCOE50, BE_LCOE, BC_LCOE, wind_LCOE, solar_LCOE]))
LCOE025.append(sum([TC_LCOE25, BE_LCOE, BC_LCOE, wind_LCOE, solar_LCOE]))
LCOE015.append(sum([TC_LCOE15, BE_LCOE, BC_LCOE, wind_LCOE, solar_LCOE]))
LCOE2.append(sum([BE_LCOE, BC_LCOE, wind_LCOE, solar_LCOE]))
LCOE3.append(sum([BC_LCOE, wind_LCOE, solar_LCOE]))
LCOE4.append(sum([wind_LCOE, solar_LCOE]))
LCOE5.append(sum([wind_LCOE]))
#LCOE2.append(sum([BC_LCOE, wind_LCOE, solar_LCOE, TC_LCOE]))
#LCOE3.append(sum([wind_LCOE, solar_LCOE, TC_LCOE]))
#LCOE4.append(sum([wind_LCOE, TC_LCOE]))
#LCOE5.append(sum([TC_LCOE]))
print total_LCOE
barwidth = 0.5
if ax==None:
ax = plt.subplot(1,1,1)
index = np.arange(len(total_LCOE))
left = index + 0.5*barwidth
ax.bar(left, total_LCOE, width=barwidth, color=green1, label='Transmission capacity')
ax.bar(left, LCOE050, width=barwidth, color=green2)
ax.bar(left, LCOE025, width=barwidth, color=green3)
ax.bar(left, LCOE015, width=barwidth, color=green4)
ax.bar(left, LCOE2, width=barwidth, color=fig.orange, label='Backup energy')
ax.bar(left, LCOE3, width=barwidth, color=fig.red, label='Backup capacity')
ax.bar(left, LCOE4, width=barwidth, color=fig.yellow, label='Solar capacity')
ax.bar(left, LCOE5, width=barwidth, color=fig.blue, label='Wind capacity')
ax.set_xticks(left + 0.5*barwidth)
layoutlist = ['EU', 'EU-RU-NA-ME', 'US-EU-RU-NA-ME', 'Eurasia', 'US-Eurasia']
ax.set_xticklabels(layoutlist)
ax.set_ylabel('LCOE [' + u'\u20AC' + '/MWh]')
ax.legend(loc=2, prop={'size':12})
ax.set_ylim(0,100)
if not interactive:
plt.tight_layout()
figfilename = 'LCOEvslayout.pdf'
plt.savefig(savepath+figfilename)
def LCOE_vs_layout_double():
plt.figure(figsize=(8,12))
ax1 = plt.subplot(2,1,1)
make_LCOE_barplot(solvermode='lin', ax=ax1)
ax1.text(1.85,92, 'a) Localized flow', size=14)
ax2 = plt.subplot(2,1,2)
make_LCOE_barplot(solvermode='sqr', ax=ax2)
ax2.text(1.85,92, 'b) Synchronized flow', size=14)
plt.tight_layout()
plt.savefig(savepath + 'LCOE_vs_layout_double.pdf')
def LCOE_vs_alpha(interactive=True):
masterflowcalc = FlowCalculation('US_eurasia_closed', 'aHO1', 'copper', 'lin')
alphas = np.linspace(0,1,21)
datapath = './results/AlphaSweepsCopper/'
CFw = 0.35
CFs = 0.15
plt.close()
if interactive:
plt.ion()
total_energy = ct.total_annual_energy_consumption(masterflowcalc)
admat = './settings/' + masterflowcalc.layout + 'admat.txt'
BE_LCOE = []
BC_LCOE = []
wind_LCOE = []
solar_LCOE = []
TC_LCOE = []
TC050_LCOE = []
TC025_LCOE = []
TC015_LCOE = []
zerotrans_total_LCOE = []
zerotrans_datapath = './results/AlphaSweepsZerotrans/'
EU_BE_LCOE = []
EU_BC_LCOE = []
EU_wind_LCOE = []
EU_solar_LCOE = []
total_EU_energy = ct.total_annual_energy_consumption(\
masterflowcalc, onlyEU=True)
for a in alphas:
alphacode = ''.join(['aHO', str(a)])
fc = FlowCalculation(masterflowcalc.layout, alphacode, \
masterflowcalc.capacities, masterflowcalc.solvermode)
BE_LCOE.append(au.cbe(ct.total_annual_BE(fc, datapath))/total_energy)
BC_LCOE.append(au.cbc(ct.get_total_BC(fc, datapath))/total_energy)
wind_LCOE.append(au.cwc(ct.get_total_wind_capacity(fc, CFw, datapath))\
/total_energy)
solar_LCOE.append(au.csc(\
ct.get_total_solar_capacity(fc, CFs, datapath))\
/total_energy)
TC_LCOE.append(au.ctc(ct.get_TCs(fc, datapath), pathadmat=admat)\
/total_energy)
TC050_LCOE.append(au.ctc(ct.get_TCs(fc, datapath), pathadmat=admat,\
scale_factor=0.5)/total_energy)
TC025_LCOE.append(au.ctc(ct.get_TCs(fc, datapath), pathadmat=admat,\
scale_factor=0.25)/total_energy)
TC015_LCOE.append(au.ctc(ct.get_TCs(fc, datapath), pathadmat=admat,\
scale_factor=0.15)/total_energy)
zerotrans_fc = FlowCalculation(masterflowcalc.layout, alphacode,\
'zerotrans', 'raw')
zerotrans_total_LCOE.append(
(au.cbe(ct.total_annual_BE(zerotrans_fc, zerotrans_datapath))
+ au.cbc(ct.get_total_BC(zerotrans_fc, zerotrans_datapath))
+ au.cwc(\
ct.get_total_wind_capacity(zerotrans_fc, CFw, zerotrans_datapath))
+ au.csc(\
ct.get_total_solar_capacity(zerotrans_fc, CFs, zerotrans_datapath)))\
/total_energy)
EU_BE_LCOE.append(au.cbe(ct.total_annual_BE(\
zerotrans_fc, zerotrans_datapath, onlyEU=True))\
/total_EU_energy)
EU_BC_LCOE.append(au.cbc(ct.get_total_BC(\
zerotrans_fc, zerotrans_datapath, onlyEU=True))/total_EU_energy)
EU_wind_LCOE.append(au.cwc(ct.get_total_wind_capacity(\
zerotrans_fc, CFw, zerotrans_datapath, onlyEU=True))\
/total_EU_energy)
EU_solar_LCOE.append(au.csc(ct.get_total_solar_capacity(\
zerotrans_fc, CFs, zerotrans_datapath, onlyEU=True))\
/total_EU_energy)
print EU_BE_LCOE, EU_BC_LCOE, EU_wind_LCOE, EU_solar_LCOE
plt.ion()
ax1 = plt.subplot(2,1,1)
if not interactive:
plt.gcf().set_size_inches([7.5,12])
plt.gcf().set_dpi(400)
ax2 = plt.subplot(2,1,2)
ax1.fill_between(alphas,
np.array(EU_BE_LCOE) + np.array(EU_BC_LCOE) + \
np.array(EU_solar_LCOE) + np.array(EU_wind_LCOE),
label='Backup energy', color=fig.orange, edgecolor='k')
ax1.fill_between(alphas,
np.array(EU_BC_LCOE) + \
np.array(EU_solar_LCOE) + np.array(EU_wind_LCOE),
label='Backup capacity', color=fig.red,
edgecolor='k')
ax1.fill_between(alphas,
np.array(EU_solar_LCOE) + np.array(EU_wind_LCOE),
label='Solar capacity', color=fig.yellow,
edgecolor='k')
ax1.fill_between(alphas,
np.array(EU_wind_LCOE),
label='Wind capacity', color=fig.blue,
edgecolor='k')
ax2.fill_between(alphas,
np.array(TC_LCOE) + np.array(BE_LCOE) +\
np.array(BC_LCOE) + \
np.array(solar_LCOE) + np.array(wind_LCOE),\
label='Transmission capacity', color=green1,
edgecolor='k')
ax2.fill_between(alphas,
np.array(TC050_LCOE) + np.array(BE_LCOE) +\
np.array(BC_LCOE) + \
np.array(solar_LCOE) + np.array(wind_LCOE),\
color=green2,
edgecolor='k')
ax2.fill_between(alphas,
np.array(TC025_LCOE) + np.array(BE_LCOE) +\
np.array(BC_LCOE) + \
np.array(solar_LCOE) + np.array(wind_LCOE),\
color=green3,
edgecolor='k')
ax2.fill_between(alphas,
np.array(TC015_LCOE) + np.array(BE_LCOE) +\
np.array(BC_LCOE) + \
np.array(solar_LCOE) + np.array(wind_LCOE),\
color=green4,
edgecolor='k')
ax2.fill_between(alphas,
np.array(BE_LCOE) + np.array(BC_LCOE) + \
np.array(solar_LCOE) + np.array(wind_LCOE),\
label='Backup energy', color=fig.orange,
edgecolor='k')
ax2.fill_between(alphas,
np.array(BC_LCOE) +
np.array(solar_LCOE) + np.array(wind_LCOE),\
label='Backup capacity', color=fig.red,
edgecolor='k')
ax2.fill_between(alphas,
np.array(solar_LCOE) + np.array(wind_LCOE),
label='Solar capacity', color=fig.yellow,
edgecolor='k')
ax2.fill_between(alphas,
np.array(wind_LCOE),
label='Wind capacity', color=fig.blue,
edgecolor='k')
ax2.plot(alphas, zerotrans_total_LCOE, color='w', lw=2, ls='--',\
label="Total LCOE, zero transmission")
colors = [green1, fig.orange, fig.red, fig.yellow, fig.blue]
rectangles = [plt.Rectangle((0,0), 1, 1, fc=c) for c in colors]
ax1.legend(rectangles, ['Transmission capacity', 'Backup energy',\
'Backup capacity', \
'Solar capacity', 'Wind capacity'])
ax1.set_ylim(0,200)
ax1.set_xlabel('Wind/solar mix')
ax1.set_ylabel('LCOE [' + u'\u20AC' + '/MWh]')
ax2.set_ylim(0,200)
ax2.set_xlabel('Wind/solar mix')
ax2.set_ylabel('LCOE [' + u'\u20AC' + '/MWh]')
ax1.text(0.04, 185, '(a) EU isolated', fontdict={'fontsize':20})
ax2.text(0.04, 185, '(b) US-Eurasia', fontdict={'fontsize':20})
plt.tight_layout()
if not interactive:
figfilename = 'LCOEvsAlpha.pdf'
plt.savefig(savepath+figfilename)