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Cleaning up comments
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@ostenst
ostenst committed Jun 26, 2023
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Showing 1 changed file with 19 additions and 31 deletions.
50 changes: 19 additions & 31 deletions view.py
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"""ROBUST DECISION MAKING (RDM) MODEL FOR BECCS
This program was developed to perform a Robust Decision Making (RDM) analysis of the investment
decision of deploying a bioenergy carbon capture and storage deployment (BECCS) facility. The
case studied is the planned BECCS facility of Stockholm Exergi (more info at www.beccs.se).
The program utilizes Rhodium, an open-source Python library for RDM and exploratory modeling:
decision of deploying a bioenergy carbon capture and storage deployment (BECCS) facility. The
program utilizes Rhodium, an open-source Python library for RDM and exploratory modeling:
https://github.com/Project-Platypus/Rhodium
To run the program, first follow the installation guide provided in the repository. Make sure
this file "BECCS_investment_paper_version.py" is in the root "Rhodium" folder.
the file "controller.py" is in the root "Rhodium" folder before running it in the terminal.
"""
__author__ = "Oscar Stenström"
__date__ = "2023-06-05"
__date__ = "2023-06-26"

from scipy.optimize import brentq as root
from rhodium import scatter2d, Cart, pairs, DataSet, Model, joint
Expand All @@ -22,7 +21,6 @@


def plot_results(model: Model, model_results: DataSet):
## BELOW IS A MIX OF RESULTS ANALYSIS AND PLOTTING
print("-------------BEGIN RESPONSE PLOTTING NOW-------------")
fig = scatter2d(model, model_results, x="NPV_wait", y="NPV_invest", c="Regret")
fig.savefig("2_NPV_Regret.png", dpi=600)
Expand Down Expand Up @@ -52,9 +50,11 @@ def plot_results(model: Model, model_results: DataSet):
plt.savefig("2_Responses_Pair.png", dpi=600)
plt.clf()

# These rows illustrate how to find the density of zero-Regret futures. The price of
# 120 EUR/tCO2 was arbitrarily chosen.
n_successful = len(model_results.find("Regret==0 and pNE_supported>120"))
n_unsuccessful = len(model_results.find("Regret >0 and pNE_supported>120"))
print( n_successful/(n_successful+n_unsuccessful)*100 , "% of scenarios have Regret = 0 when the NE price is above 120 EUR/t") #94%
print( n_successful/(n_successful+n_unsuccessful)*100 , "% of scenarios have Regret = 0 when the NE price is above 120 EUR/t")

def robustness_analysis(model_results: DataSet):
"""Prints robustness analytics to the terminal"""
Expand All @@ -77,10 +77,10 @@ def robustness_analysis(model_results: DataSet):
for SOW in model_results:
invest_regret = (
max(SOW["NPV_invest"], SOW["NPV_wait"]) - SOW["NPV_invest"]
) # Finding Regret again from model results.
)
wait_regret = (
max(SOW["NPV_invest"], SOW["NPV_wait"]) - SOW["NPV_wait"]
) # Finding Regret again from model results.
)
invest_regret_vec.append(invest_regret)
wait_regret_vec.append(wait_regret)
print("Investing has maximum regret ", max(invest_regret_vec), " EUR")
Expand All @@ -91,18 +91,13 @@ def robustness_analysis(model_results: DataSet):


def scenario_discovery(model: Model, model_results: DataSet) -> list:
#NOW I WANT TO FIND THE INTERESTING SCENARIOS. WITH EXACT VALUES!
#1) Find Regret=0. See what pop outs.


# The scenario discovery produces ranges of uncertainties (i.e. scenarios) where Invest performs well (i.e. have Regret = 0).
print("-------------BEGIN SCENARIO DISCOVERY NOW-------------")
classification = model_results.apply("'Reliable' if (Regret == 0 and NPV_invest >= 0) else 'Unreliable'")
# Alternative classifications that can be applied depending on what analysis is made:
# Regret == 0 and NPV_invest >= 0 # Drivers are then: yCLAM and pelectricity
# Below are some alternative classifications that can be applied, depending on what analysis is of interest:
# Regret == 0
# pNE_supported-Cost_specific > 0
# Regret != 0
# Maybe find scenarios based on Exergi adaptation: pelectricity-pheat < 40 MWh. In such scenarios, what are the key drivers?

cart_results = Cart(
model_results,
Expand All @@ -114,8 +109,7 @@ def scenario_discovery(model: Model, model_results: DataSet) -> list:
cart_results.save("4_CART_tree.png")
node_list = cart_results.print_tree(
coi="Reliable"
) # NOTE: in the classification.py file of rhodium, the print_tree() function was edited.
# These edits basically just save the scenario nodes of the CART tree into a list, and return this list.
)
return node_list


Expand Down Expand Up @@ -175,7 +169,7 @@ def plot_scenario_of_interest(model: Model, model_results: DataSet):
# represents a discovered scenario. The drawing is hard coded and can be changed as desired, depending on the scenario of interest.

#-----------------The 1st scenario is plotted below----------
# 2 Reliable 91.54516608426718 39.385508648844805 pelectricity_mean <= 109.776455 yCLAIM <= 2034.040527 (REGRET = 0)
# The classifier Regret = 0 was used.
fig = scatter2d(model, model_results, x="yCLAIM", y="pelectricity_mean", c="Regret")
scenario_area = mpatches.Rectangle(
(2024, 20),
Expand All @@ -191,7 +185,7 @@ def plot_scenario_of_interest(model: Model, model_results: DataSet):
plt.clf()

#-----------------The 2nd scenario is plotted below----------
# 1170 Reliable 75.04733607154301 61.26316790716477 pelectricity_mean > 81.914059 yCLAIM > 2034.032410 (REGRET != 0)
# The classifier Regret != 0 was used.
fig = scatter2d(model, model_results, x="yCLAIM", y="pelectricity_mean", c="Regret")
scenario_area = mpatches.Rectangle(
(2034, 82),
Expand All @@ -207,7 +201,7 @@ def plot_scenario_of_interest(model: Model, model_results: DataSet):
plt.clf()

#-----------------The 3rd scenario is plotted below----------
# 206 Reliable 96.2998361551065 29.347147671951067 pNE_mean > 150.728317 yCLAIM <= 2030.209717 (REGRET = 0 AND Pelec>82)
# The classifier Regret = 0 was used.
fig = scatter2d(model, model_results.find("pelectricity_mean>82"), x="yCLAIM", y="pNE_mean", c="Regret")
scenario_area = mpatches.Rectangle(
(2024, 151),
Expand All @@ -224,8 +218,7 @@ def plot_scenario_of_interest(model: Model, model_results: DataSet):
plt.clf()

#-----------------The 4th scenario is plotted below----------
# 95 Reliable 70.06802721088435 39.56005586592179 pETS_2050 > 232.763702 yBIOban <= 2034.975525 (REGRET = 0, AND Pelec>82 AND yCLAIM>2034)
# 169 Reliable 88.32214765100672 22.97486033519553 pETS_2050 > 232.763702 AUCTION > 0.532030 yBIOban <= 2034.975525
# The classifier Regret = 0 was used.
fig = scatter2d(model, model_results.find("pelectricity_mean>82 and yCLAIM>2034"), x="pETS_2050", y="yBIOban", c="Regret")
scenario_area = mpatches.Rectangle(
(233, 2030),
Expand All @@ -242,7 +235,6 @@ def plot_scenario_of_interest(model: Model, model_results: DataSet):
plt.clf()

#-----------------These rows can be used to combine plots into subplots----------
# crop the right side of fig2 and fig3 by 5%
img1 = Image.open("4_Scenario_1.png")
img1_width, img1_height = img1.size
img1_cropped = img1.crop((0, img1_width * 0.05, img1_width * 0.95, img1_height))
Expand All @@ -263,7 +255,7 @@ def plot_scenario_of_interest(model: Model, model_results: DataSet):
img4_cropped = img4.crop((0, img4_width * 0.05, img4_width * 0.95, img4_height))
img4_cropped.save("4_Scenario_4_cropped.png")

# create a new figure and combine fig2 and fig3
# Creates a new figure and combine fig2 and fig3
fig, axs = plt.subplots(2, 2, figsize=(12,9))
axs[0,0].imshow(img1_cropped)
axs[0,0].set_xlabel("(a)", fontsize=12)
Expand All @@ -278,18 +270,14 @@ def plot_scenario_of_interest(model: Model, model_results: DataSet):
axs[1,1].set(xlabel="(d)")
axs[1,1].axis("off")

# adjust the layout by reducing the height spacing between subplots
# plt.subplots_adjust(hspace=0)

# adjust the layout and save the combined figure
plt.tight_layout()
plt.savefig("4_Scenarios_ALL.png", dpi=600)
plt.clf()

def save_sensitivity_analysis(
model: Model, sobol_result, RDM_results_excel: openpyxl.Workbook
):
# Save Sobol results to a CART excel sheet:
# Save Sobol results to a separate excel sheet:
sheet = RDM_results_excel.create_sheet("Sobol_results")
RDM_results_excel.active = RDM_results_excel["Sobol_results"]
sheet["A1"] = "Uncertainty"
Expand All @@ -311,7 +299,7 @@ def save_sensitivity_analysis(


def plot_sensitivity_analysis_results(sobol_result):
#NOTE: can comment out the print, if desired.
#NOTE: you can comment out the print, if desired.
print(sobol_result)
plt.clf()
fig = sobol_result.plot_sobol(
Expand Down

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