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changed calculation of capex from investment -> project development c…
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…osts were lower than needed be
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@smartie2076
smartie2076 committed Jun 12, 2019
1 parent bb75e1f commit df688ca
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79 changes: 42 additions & 37 deletions standalone_scripts/compare_time_of_grid_arrival.py
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Original file line number Diff line number Diff line change
Expand Up @@ -117,13 +117,16 @@ def evaluate_nesp(save_subfolder):
capacities_stage['RES percent'][nesp_id] = data['res_share'][item] * 100
capacities_stage['LCOE USD per kWh'][nesp_id] =\
data['lcoe'][item] + (20000*crf20 - 3200)/data['total_demand_annual_kWh'][item]

capacities_stage['NPV USD'][nesp_id] = data['npv'][item] + (20000-3200/crf20)
capacities_stage['NPV per kW USD per kW'][nesp_id] = capacities_stage['NPV USD'][nesp_id] / \
data['demand_peak_kW'][item]

if case == 'sole_maingrid':
capacities_stage['LCOE extension USD per kWh'][nesp_id] \
= data['lcoe'][item]
= data['lcoe'][item] + (20000 * crf20 - 3200) / \
(data['supply_reliability_kWh'][item]* data['total_demand_annual_kWh'][item])

capacities_stage['Cost extension USD'][nesp_id] \
= data['costs_maingrid_extension'][item]
capacities_stage['Cost transformers USD'][nesp_id] \
Expand All @@ -132,7 +135,7 @@ def evaluate_nesp(save_subfolder):
capacities_stage['Supply reliability extension percent'][nesp_id] \
= data['supply_reliability_kWh'][item]
capacities_stage['NPV extension USD'][nesp_id] \
= data['npv'][item]
= data['npv'][item] + (20000 - 3200 / crf20)
capacities_stage['NPV per kW extension USD per kW'][nesp_id] \
= capacities_stage['NPV extension USD'][nesp_id] / data['demand_peak_kW'][item]

Expand Down Expand Up @@ -293,10 +296,10 @@ def evaluate_nesp(save_subfolder):
'Additional branch distance km',
'Customers ']

number = plot_x_y_stages(folder, save_subfolder, prefix, comparison_list_x, comparison_list_y, stage1, stage2, stage3, no_mgs, number)
number = plot_x_y_stages(folder, grid_arrival, save_subfolder, prefix, comparison_list_x, comparison_list_y, stage1, stage2, stage3, no_mgs, number)
return number

def plot_comparison_intercon(folder, save_subfolder, prefix, comparison_list_x, comparison_list_y, costs, number, perspective):
def plot_comparison_intercon(folder, grid_arrival, save_subfolder, prefix, comparison_list_x, comparison_list_y, costs, number, perspective):
color_dict = {'LCOE USD per kWh spp': (0.5803921568627451, 0.5803921568627451, 0.5803921568627451),
'LCOE USD per kWh offgrid_mg': (0.00392156862745098, 0.45098039215686275, 0.6980392156862745),
'LCOE USD per kWh ongrid_mg_cons_prod': (0.792156862745098, 0.5686274509803921, 0.3803921568627451),
Expand All @@ -317,19 +320,20 @@ def plot_comparison_intercon(folder, save_subfolder, prefix, comparison_list_x,
plots_file = plots.copy()
plots_file['NESP ID']=[str(name) for name in costs['nesp_id'].values]
plots_file.to_csv(folder + save_subfolder + "interconnected_lcoe_" + perspective + '_' + prefix[:-1] + ".csv")
for x in comparison_list_x:
plotting = 0
number += 1
for y in comparison_list_y:
if plotting == 0:
fig = plots.plot.scatter(x=x, y=y, label=y[15:], color=color_dict[y]) # s=df['c'] * 200 plot THREE VALUES
plotting = 1
else:
plots.plot.scatter(x=x, y=y, ax=fig, label=y[15:], color=color_dict[y])
fig.set(ylabel='LCOE USD per kWh', title='Cost comparison, '+ perspective)
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
plt.savefig(folder + save_subfolder + 'graph_' + prefix + str(number) + ' '+ x +' ' + y +'.png', bbox_inches="tight")
plt.close()
if grid_arrival == 5:
for x in comparison_list_x:
plotting = 0
number += 1
for y in comparison_list_y:
if plotting == 0:
fig = plots.plot.scatter(x=x, y=y, label=y[15:], color=color_dict[y]) # s=df['c'] * 200 plot THREE VALUES
plotting = 1
else:
plots.plot.scatter(x=x, y=y, ax=fig, label=y[15:], color=color_dict[y])
fig.set(ylabel='LCOE USD per kWh', title='Cost comparison, '+ perspective)
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
plt.savefig(folder + save_subfolder + 'graph_' + prefix + str(number) + ' '+ x +' ' + y +'.png', bbox_inches="tight")
plt.close()
return number

def add_sum_save(folder, save_subfolder, prefix, number_of_favoured, name, perspective):
Expand Down Expand Up @@ -368,26 +372,27 @@ def add_statistics_matrix(matrix):
matrix = matrix.append(add.transpose(), sort=False)
return matrix

def plot_x_y_stages(folder, save_subfolder, prefix, comparison_list_x, comparison_list_y, stage1, stage2, stage3, no_mgs, number):
def plot_x_y_stages(folder, grid_arrival, save_subfolder, prefix, comparison_list_x, comparison_list_y, stage1, stage2, stage3, no_mgs, number):

comparison_list = comparison_list_y + comparison_list_x
plots_1 = pd.DataFrame([stage1[name].values for name in comparison_list], index=comparison_list).transpose()
plots_2 = pd.DataFrame([stage2[name].values for name in comparison_list], index=comparison_list).transpose()
plots_3 = pd.DataFrame([stage3[name].values for name in comparison_list], index=comparison_list).transpose()
plots_no = pd.DataFrame([no_mgs[name].values for name in comparison_list], index=comparison_list).transpose()

for x_value in comparison_list_x:
for y_value in comparison_list_y:
if x_value != y_value:
number += 1
fig = plots_no.plot.scatter(x=x_value, y=y_value, color='#cc0000', label='No MGs')
plots_2.plot.scatter(x=x_value, y=y_value, ax=fig, color='#0033cc', label='Stage 2')
plots_3.plot.scatter(x=x_value, y=y_value, ax=fig, color='#990099', label='Stage 3')
plots_1.plot.scatter(x=x_value, y=y_value, ax=fig, color='#66cc33', label='Stage 1')
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
fig.set(title='Comparison of trends in stages')
plt.savefig(folder + save_subfolder + 'graph_' + prefix + str(number) + ' '+ x_value +' ' + y_value +'.png', bbox_inches="tight")
plt.close()

if grid_arrival == 5:
for x_value in comparison_list_x:
for y_value in comparison_list_y:
if x_value != y_value:
number += 1
fig = plots_no.plot.scatter(x=x_value, y=y_value, color='#cc0000', label='No MGs')
plots_2.plot.scatter(x=x_value, y=y_value, ax=fig, color='#0033cc', label='Stage 2')
plots_3.plot.scatter(x=x_value, y=y_value, ax=fig, color='#990099', label='Stage 3')
plots_1.plot.scatter(x=x_value, y=y_value, ax=fig, color='#66cc33', label='Stage 1')
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
fig.set(title='Comparison of trends in stages')
plt.savefig(folder + save_subfolder + 'graph_' + prefix + str(number) + ' '+ x_value +' ' + y_value +'.png', bbox_inches="tight")
plt.close()

return number

Expand Down Expand Up @@ -735,7 +740,7 @@ def evaluate_grid_arrival_time(grid_arrival, save_subfolder):
'ongrid_mg_cons_prod']:
if item == 'sole_maingrid':
costs_stage['NPV USD ' + item + ' mg perspective'] = \
(costs_stage['NPV USD ' + item] / d_t - costs_stage['Grid extension incl pcc USD'])
(costs_stage['NPV USD ' + item] / d_t) - costs_stage['Grid extension incl pcc USD'] * crf_20_t/crf_20

costs_stage['NPV USD ' + item + ' global perspective'] = costs_stage['NPV USD ' + item] / d_t

Expand Down Expand Up @@ -919,20 +924,20 @@ def evaluate_grid_arrival_time(grid_arrival, save_subfolder):

if grid_arrival == 5:

number = plot_x_y_stages(folder, "evaluation/", prefix, comparison_list_x, comparison_list_y_3,
number = plot_x_y_stages(folder, grid_arrival, "evaluation/", prefix, comparison_list_x, comparison_list_y_3,
costs_stage1, costs_stage2, costs_stage3,
costs_no_mgs, number_5)

number = plot_x_y_stages(folder, save_subfolder, prefix, comparison_list_x, comparison_list_y_1, costs_stage1, costs_stage2, costs_stage3,
number = plot_x_y_stages(folder, grid_arrival, save_subfolder, prefix, comparison_list_x, comparison_list_y_1, costs_stage1, costs_stage2, costs_stage3,
costs_no_mgs, number)

number = plot_x_y_stages(folder, save_subfolder, prefix, comparison_list_x, comparison_list_y_2, costs_stage1, costs_stage2, costs_stage3,
number = plot_x_y_stages(folder, grid_arrival, save_subfolder, prefix, comparison_list_x, comparison_list_y_2, costs_stage1, costs_stage2, costs_stage3,
costs_no_mgs, number)

number = plot_comparison_intercon(folder, save_subfolder, prefix, comparison_list_x, comparison_list_y_1, costs,
number = plot_comparison_intercon(folder, grid_arrival, save_subfolder, prefix, comparison_list_x, comparison_list_y_1, costs,
number, 'mg perspective')

number = plot_comparison_intercon(folder, save_subfolder, prefix, comparison_list_x, comparison_list_y_2, costs,
number = plot_comparison_intercon(folder, grid_arrival, save_subfolder, prefix, comparison_list_x, comparison_list_y_2, costs,
number, 'global perspective')

for y in comparison_list_y_1 + comparison_list_y_2 + comparison_list_y_3:
Expand Down
28 changes: 15 additions & 13 deletions standalone_scripts/restore.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -73,11 +73,11 @@
year = 1
for file in list:
data = pd.read_csv(file+item, index_col=False)
costs_trends[year] = data[[title + perspective for title in header]].iloc[546].tolist()#, index=header, columns=[year]))
costs_trends[year] = data[[title + perspective for title in header]].iloc[544].tolist()#, index=header, columns=[year]))
year += 1
if file == list[4]:
average_5[perspective+item]=data[[title + perspective for title in header]].iloc[546].tolist()

average_5[perspective+item]=data[[title + perspective for title in header]].iloc[544].tolist()
print(data[[title + perspective for title in header]].iloc[544])
costs_trends = costs_trends.transpose()
costs_trends['year'] = [i for i in range(1, 20)]
plotting = 0
Expand Down Expand Up @@ -108,17 +108,19 @@
plt.savefig('../simulation_results/grid_arrival_npv_'+perspective+item[:-4]+'.png', bbox_inches="tight")
plt.close()
costs_trends.to_csv('../simulation_results/trends_'+perspective+item+'.csv')


print(len(costs_trends.drop('average')))
fig = plots.boxplot(column=comparison_list_y)
plt.savefig(folder + save_subfolder + 'graph_boxplot_' + prefix + str(number) + '.png',
bbox_inches="tight")
plt.close()

fig = plots.boxplot(column=comparison_list_y)
plt.savefig('../simulation_results/graph_boxplot_npv_' + prefix + str(number) + '.png',
'''
costs_5 = pd.read_csv(list[4]+item, nrows=544)
list_cases = [head[17:] for head in header1]
to_plot=pd.DataFrame(index=costs_5.index)
for case in range(0,len(list_cases)):
to_plot[list_cases[case]]=costs_5[header1[case]+perspective]
#to_plot = pd.DataFrame(costs_5[[title + perspective for title in header1]], columns=list_cases)
print(to_plot)
fig = to_plot.boxplot(column=list_cases, rot=45)
plt.show()
plt.savefig('../simulation_results/graph_boxplot_npv_' + perspective + item + '.png',
bbox_inches="tight")
plt.close()
'''

average_5.to_csv('../simulation_results/average_intercon.csv')

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