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diagonalization.py
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diagonalization.py
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"""
Created by: Nick Harder ([email protected])
Created on August, 21th, 2023
"""
# %%
import os
import pandas as pd
from model_1 import find_optimal_k_method_1 as method_1
from model_2 import find_optimal_k_method_2 as method_2
from utils import calculate_profits
# %% load data and define parameters
def run_diagonalization(
case,
start,
end,
method,
k_max=2,
K=3,
time_limit=180,
big_w_values=10,
print_results=False,
):
if method == "method_1":
find_optimal_k = method_1
elif method == "method_2":
find_optimal_k = method_2
# gens
gens_df = pd.read_csv(f"inputs/{case}/gens.csv", index_col=0)
# 24 hours of demand first increasing and then decreasing
demand_df = pd.read_csv(f"inputs/{case}/demand.csv", index_col=0)
demand_df.index = pd.to_datetime(demand_df.index)
demand_df = demand_df.loc[start:end]
# reset index to start at 0
demand_df = demand_df.reset_index(drop=True)
k_values_df = pd.DataFrame(columns=gens_df.index, index=demand_df.index, data=1.0)
profit_values = pd.DataFrame(columns=gens_df.index, index=demand_df.index, data=0.0)
print(f"Starting diagonalization using {method}")
i = 1
lowest_diff_in_profits = profit_values.copy()
lowest_diff_in_profits = lowest_diff_in_profits.sum(axis=0) + 10e6
while True:
print()
print(f"Iteration {i}")
last_k_values = k_values_df.copy()
last_profit_values = profit_values.copy()
# iterate over units in reverse order
for opt_gen in gens_df.index:
if opt_gen == 3:
continue
print(f"Optimizing for Unit {opt_gen+1}")
try:
main_df, supp_df, k = find_optimal_k(
gens_df=gens_df,
k_values_df=k_values_df,
demand_df=demand_df,
k_max=k_max,
opt_gen=opt_gen,
big_w=big_w_values[opt_gen][method],
time_limit=time_limit,
print_results=print_results,
K=K,
)
except Exception as e:
print(f"Error: {e}")
print(f"Optimization for Unit {opt_gen+1} failed. Continuing...")
continue
k_values_df[opt_gen] = k
profit_values[opt_gen] = calculate_profits(main_df, supp_df, gens_df)[
opt_gen
]
diff_in_k = k_values_df - last_k_values
diff_in_profit = profit_values.sum(axis=0) - last_profit_values.sum(axis=0)
print("Difference in profits:")
print(diff_in_profit)
if (abs(diff_in_k).max() < 0.01).all():
print(f"Actions did not change. Convergence reached at iteration {i}")
break
# if (abs(diff_in_profit) < 3000).all():
# print(f"Profits did not change. Convergence reached at iteration {i}")
# break
if (abs(diff_in_profit) <= 0.03 * profit_values.sum(axis=0)).all():
print(
f"Profits change is below threshold. Convergence reached at iteration {i}"
)
break
if (diff_in_profit <= lowest_diff_in_profits).all():
lowest_diff_in_profits = diff_in_profit
save_results_path = f"outputs/{case}/{method}/temp"
save_results(save_results_path, main_df, supp_df, k_values_df)
i += 1
print("Final results:")
print(main_df)
print()
print("Final bidding decisions:")
print(k_values_df)
save_results_path = f"outputs/{case}/{method}"
save_results(save_results_path, main_df, supp_df, k_values_df)
def save_results(save_results_path, main_df, supp_df, k_values_df):
# make sure output folder exists
if not os.path.exists(save_results_path):
os.makedirs(save_results_path)
main_df.to_csv(f"{save_results_path}/main_df.csv")
supp_df.to_csv(f"{save_results_path}/supp_df.csv")
k_values_df.to_csv(f"{save_results_path}/k_values_df.csv")
# %% run diagonalization
if __name__ == "__main__":
case = "Case_1"
big_w_values = {
0: {"method_1": 1000, "method_2": 100},
1: {"method_1": 10, "method_2": 1},
2: {"method_1": 1000, "method_2": 100},
}
k_max = 2 # maximum multiplier for strategic bidding
time_limit = 1000 # time limit in seconds for each optimization
K = 5
start = pd.to_datetime("2019-03-02 06:00")
end = pd.to_datetime("2019-03-02 14:00")
print_results = False
solve_diag = True
method = "method_2"
if solve_diag:
run_diagonalization(
case=case,
start=start,
end=end,
method=method,
k_max=k_max,
time_limit=time_limit,
big_w_values=big_w_values,
print_results=print_results,
K=K,
)
# %%