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tabulate_static.py
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"""
Script loads a Equilibrium Spotmarkets simulation group and:
-- tabulates main results in each individual simulation
-- plots generation and welfare on storage price and supply variance
Script is run on single core via IPython interface
"""
import numpy as np
import matplotlib.pyplot as plt
from helpfuncs.results import runres
import matplotlib.pyplot as plt
import dill as pickle
from tabulate import tabulate
from numpy import genfromtxt
from sklearn.utils.extmath import cartesian
def latex_float(f):
float_str = "{0:.3g}".format(np.float64(f))
if "e" in float_str:
base, exponent = float_str.split("e")
return r"{0} $\times 10^{{{1}}}$".format(base, int(exponent))
else:
return float_str
def tabulate_storage(V_index,\
K_index,\
S_index,\
filename,\
model_name,\
sim_name,\
tab = False):
#results = {}
table = []
table_rs = np.empty((6,48))
table_ds = np.empty((6,48))
table_u = np.empty((6,48))
table_k = np.empty((6,48))
table_s = np.empty((6,48))
table_v = np.empty((6,48))
table_pi_s = np.empty((6,48))
table_pi_k = np.empty((6,48))
VS_range = np.linspace(.01, .18, 4)
# Load no storage results in table
#results = pickle.load(open("/scratch/pv33/{}/static/{}/model_nostor_static_res.pkl"\
# .format(model_name,sim_name),\
# "rb"))
#results['r_s_star'] = results['r_s_star']*1e-9
#results['r_k_star'] = results['r_k_star']*1e-9
# if tab == True:
# results_row = [
# "%.2f (%.2f)" % (results['mean_generation'],
# results['var_generation']),
# "%.2f" %
# results['K'],
# "%.2f" %
# results["S_bar_star"],
# latex_float(results['mean_price']*
# 1e-3 ) +
# "(" + latex_float(results['var_price']*
# 1e-3 ) + ")",
# "%.2f (%.2f)" %
# (results['mean_demand'],
# results['var_demand']),
# "%.2f (%.2f)" %
# (results['mean_stor'],
# results['var_stor']),
# latex_float(results['r_s_star']),
# latex_float(results['r_k_star']),
# ]
# table.append(results_row)
# First plot for different vals of K on S
for i_v in V_index:
for i_k in K_index:
for i_s in S_index:
# Unpack results, make table
print("Calculating results from {}".format(i_k))
results = pickle.load(
open("/scratch/pv33/{}/static/{}/model_{}_{}_{}_static_res.pkl"\
.format(model_name,sim_name,\
i_s,\
i_k,\
i_v),\
"rb"))
results['r_s_star'] = results['r_s_star']*1e-9
results['r_k_star'] = results['r_k_star']*1e-9
if tab == True:
print(results['var_generation'])
results_row = [
"%.2f" %
results['K'],
"%.2f" %
results["S_bar_star"],
"%.2f (%.2f)" % (results['mean_generation'],
results['var_generation']),
latex_float(results['mean_price']*1e-3 ) +
"(" + latex_float(results['var_price']*1e-3) + ")",
"%.2f (%.2f)" %
(results['mean_demand'],
results['var_demand']),
"%.2f (%.2f)" %
(results['mean_stor'],
results['var_stor']),
latex_float(results['r_s_star']),
latex_float(results['r_k_star']),
]
table.append(results_row)
table_ds[np.where(K_index == i_k),i_s] = results['var_demand']
table_rs[np.where(K_index == i_k),i_s] = results['var_price']
table_u[np.where(K_index == i_k),i_s] = results['WF']
table_k[np.where(K_index == i_k),i_s] = results['K']
table_s[np.where(K_index == i_k),i_s] = results["S_bar_star"]
table_v[np.where(K_index == i_k),i_s] = VS_range[i_v]
table_pi_s[np.where(K_index == i_k),i_s] = results['r_s_star']
table_pi_k[np.where(K_index == i_k),i_s] = results['r_k_star']
header = [
"Av gen.",
"Gen. cap.",
"Str. cap.",
"Pr.",
"Dem.",
"Av str."
, "r_s", "r_k"] # , "lowstor %"]
restab = open(
"Results/{}/static_results_tab.tex".format(model_name + '/' + sim_name + '/'), 'w')
restab.write(
tabulate(
table,
headers=header,
tablefmt="latex_booktabs",
floatfmt=".2f"))
restab.close()
return table_ds,table_rs, table_u, table_k, table_s, table_v, table_pi_s,table_pi_k, table
if __name__ == '__main__':
model_name = 'ERCOT_main_v_1'
import seaborn as sns
# First get table
K_index = np.array([0,10])
S_index = np.array([0,5, 10])
V_index = np.array([2])
#all_tabs = []
#all_tabs.append('$\eta$ = 0.3')
table_ds, table_rs, table_u, table_k, table_s,\
table_v, table_pi_s,table_pi_k, table\
= tabulate_storage(V_index,\
K_index,\
S_index,\
'baselines',\
model_name,\
'baseline_3',\
tab= True)
#all_tabs.append(table)
#all_tabs.append('$\eta = 0.1')
table_ds, table_rs, table_u, table_k, table_s,\
table_v, table_pi_s,table_pi_k, table_1\
= tabulate_storage(V_index,\
K_index,\
S_index,\
'baselines',\
model_name,\
'baseline_1',\
tab= True)
table = table+ table_1
#all_tabs.append('$\eta = 0.05')
table_ds, table_rs, table_u, table_k, table_s,\
table_v, table_pi_s,table_pi_k, table_05\
= tabulate_storage(V_index,\
K_index,\
S_index,\
'baselines',\
model_name,\
'baseline_05',\
tab= True)
table = table + table_05
header = [
"Gen. cap.",
"Str. cap.",
"Generation",
"Price (USD/Mw)",
"Elec. consumed",
"Elec. stored"
, "r_s", "r_k"] # , "lowstor %"]
restab = open(
"Results/{}/all_static.tex".format(model_name), 'w')
restab.write(
tabulate(
table,
headers=header,
tablefmt="latex_raw"))
restab.close()
# Plot storage returns on S_bar for different values of K
# Loop over different elasicity of demands
for e in('3'):
cmap_here = sns.diverging_palette(220, 20, as_cmap=True)
K_index = np.array([5,10,20, 35,47])
S_index = np.arange(48)
V_index = np.array([2])
K_range = np.linspace(30, 100, 48)
S_range = np.linspace(30, 100, 48)
VS_range = np.linspace(.01, .18, 4)
sim_name ='baseline_' + e
#all_tabs = []
#all_tabs.append('$\eta$ = 0.3')
table_ds,table_rs, table_u, table_k, table_s,\
table_v, table_pi_s,table_pi_k, table\
= tabulate_storage(V_index,\
K_index,\
S_index,\
'baselines',\
model_name,\
'baseline_{}'.format(e),\
tab = False)
plt.close()
figure, axes = plt.subplots(nrows=1, ncols=2)
colors = cmap_here(np.linspace(0,1,6))
for i_k in range(len(K_index)):
axes[0].plot(table_s[i_k], table_pi_s[i_k], label='K = {:.2f}'\
.format(K_range[K_index[i_k]],e), color=colors[i_k])
axes[0].set_xlabel('Storage')
#axes[0].legend()
axes[0].spines['top'].set_visible(False)
axes[0].spines['right'].set_visible(False)
axes[0].set_ylabel('Storage returns')
#plt.savefig("Results/{}/{}/static/returns_r_s_S.png"\
# .format(model_name, sim_name))
plt.close()
colors = cmap_here(np.linspace(0,1,6))
for i_k in range(len(K_index)):
axes[1].plot(table_s[i_k], table_pi_k[i_k], label='K = {:.2f}'\
.format(K_range[K_index[i_k]],e), color=colors[i_k])
axes[1].set_xlabel('Storage')
axes[1].legend()
axes[1].spines['top'].set_visible(False)
axes[1].spines['right'].set_visible(False)
axes[1].set_ylabel('Generator returns')
figure.savefig("Results/{}/{}/static/returns_r_s.png"\
.format(model_name, sim_name))
plt.close()
figure, axes = plt.subplots(nrows=1, ncols=2)
colors = cmap_here(np.linspace(0,1,6))
for i_k in range(len(K_index)):
axes[0].plot(table_s[i_k], table_rs[i_k]*1e-4, label = 'K = {:.2f}'\
.format(K_range[K_index[i_k]],e), color = colors[i_k])
axes[0].set_xlabel('Storage')
axes[0].spines['top'].set_visible(False)
axes[0].spines['right'].set_visible(False)
axes[0].legend()
axes[0].set_ylabel('Price variance')
for i_k in range(len(K_index)):
axes[1].plot(table_s[i_k], table_ds[i_k], label = 'K = {:.2f}'\
.format(K_range[K_index[i_k]],e), color = colors[i_k])
axes[1].set_xlabel('Storage')
axes[1].spines['top'].set_visible(False)
axes[1].spines['right'].set_visible(False)
#axes[1].legend()
axes[1].set_ylabel('Demand variance')
figure.savefig("Results/{}/{}/static/returns_r_s_pvar.png"\
.format(model_name, sim_name))
"""
table2 = [mod3, mod4, mod5]
print(tabulate(table2, headers = header, tablefmt="latex_booktabs", floatfmt=".2f"))
restab = open("results234_tab.tex", 'w')
restab.write(tabulate(table2, headers = header, tablefmt="latex_booktabs", floatfmt=".2f"))
restab.close()
#table3 = [cmod0, cmod1, cmod2, cmod3, cmod4]
print(tabulate(table3, headers = header, tablefmt="latex_booktabs", floatfmt=".2f"))
restab = open("resultscov_tab.tex", 'w')
restab.write(tabulate(table3, headers = header, tablefmt="latex_booktabs", floatfmt=".2f"))
restab.close()
swe_price = np.genfromtxt('price_swe.csv', delimiter=',')
f, axarr = plt.subplots(1,3, sharey='row')
axarr[0].boxplot(np.log(swe_price[1:,]))
axarr[0].set_xlabel('Swedish prices today', fontsize = 10)
axarr[0].set_ylabel('Log of price')
axarr[1].boxplot(np.log(resmod0['price']))
axarr[1].set_xlabel('Current batt.price/ low var.', fontsize = 10)
axarr[2].boxplot(np.log(resmod3['price']))
axarr[2].set_xlabel('Low batt. price/ low var', fontsize = 10)
f.tight_layout()
plt.savefig("/home/akshay_shanker/model_box_price.png")
f, axarr = plt.subplots(1,2, sharey='row')
axarr[0].boxplot(np.log(resmod0['stored']))
axarr[0].set_xlabel('Current batt. price/ low variance', fontsize = 10)
axarr[0].set_ylabel('Log of stored power')
axarr[1].boxplot(np.log(resmod3['stored']))
axarr[1].set_xlabel('Low batt. price/ low variance', fontsize = 10)
f.tight_layout()
plt.savefig("/home/akshay_shanker/model_box_stor.png")
swe_dem = np.genfromtxt('dem_swe.csv', delimiter=',')
f, axarr = plt.subplots(1,3, sharey='row')
axarr[0].boxplot(np.log(swe_dem[1:,]))
axarr[0].set_xlabel('Swedish demand today', fontsize = 10)
axarr[0].set_ylabel('Log of eqm. demand')
axarr[1].boxplot(np.log(resmod0['demand']))
axarr[1].set_xlabel('Current batt. price/ low var', fontsize = 10)
axarr[2].boxplot(np.log(resmod3['demand']))
axarr[2].set_xlabel('Low batt. price/ low var', fontsize = 10)
f.tight_layout()
plt.savefig("/home/akshay_shanker/model_box_dem.png")
f, axarr = plt.subplots(1,3, sharey='row')
axarr[0].boxplot(np.log(swe_dem[1:,]))
axarr[0].set_xlabel('Swedish demand today', fontsize = 10)
axarr[0].set_ylabel('Log of eqm. demand')
axarr[1].boxplot(np.log(resmod0['demand']))
axarr[1].set_xlabel('Current batt. price/ low var', fontsize = 10)
axarr[2].boxplot(np.log(resmod3['demand']))
axarr[2].set_xlabel('Low batt. price/ low var', fontsize = 10)
f.tight_layout()
plt.clf()
plt.plot(og.grid, og.p_inv(-og.shock_X[2,1],og.rho_star[2])+ og.K*og.shock_X[2,0])
plt.plot(og.grid, og.p_inv(-og.shock_X[3,1],og.rho_star[3])+ og.K*og.shock_X[3,0])
"""