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main.py
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import pandapower.networks as pn
import networkx as nx
import numpy as np
import pandas as pd
import time
from count_elements import count_elements
from diversity import calculate_shannon_evenness_and_variety
from disparity import calculate_disparity_space, calculate_line_disparity, calculate_transformer_disparity, \
calculate_load_disparity
from GenerationFactors import calculate_generation_factors
from Redundancy_new import Redundancy
from Redundancy import n_3_redundancy_check
from visualize import plot_spider_chart
from initialize import add_indicator
from initialize import add_disparity
from indi_gt import GraphenTheorieIndicator
from adjustments_new import set_missing_limits
from adjustments_new import determine_minimum_ext_grid_power
from self_sufficiency import selfsuff
from self_sufficiency import selfsufficiency_neu
from flexibility import calculate_flexibility
from buffer import calculate_buffer
from fxor import flexibility_fxor
from stressors import stress_scenarios
from Evaluate_scenario import run_scenario
import os
import pandapower.converter as pc
import simbench as sb
import pandapower as pp
# Dictionary to including all grid names to functions, including special cases for test grids, whose opp converges
grids = {
# "GBreducednetwork": pn.GBreducednetwork,
# "case118": pn.case118,
# "case14": pn.case14,
# "case24_ieee_rts": pn.case24_ieee_rts,
# "case30": pn.case30,
# "case33bw": pn.case33bw,
# "case39": pn.case39,
# "case5": pn.case5,
# "case6ww": pn.case6ww,
# "case9": pn.case9,
# "create_cigre_network_lv": pn.create_cigre_network_lv,
# #"create_cigre_network_mv": pn.create_cigre_network_mv,
# "create_cigre_network_mv_all": lambda: pn.create_cigre_network_mv(with_der="all"),
# # #"create_cigre_network_mv_pv_wind": lambda: pn.create_cigre_network_mv(with_der="pv_wind"),
# "ieee_european_lv_asymmetric": pn.ieee_european_lv_asymmetric,
# #
# # # Special Cases with Adjustments
# # # "mv_all_high10": lambda: increase_generation(pn.create_cigre_network_mv(with_der="all"), factor=10),
# # # "mv_all_high5": lambda: increase_generation(pn.create_cigre_network_mv(with_der="all"), factor=5),
# #
# # "example_multivoltage": lambda: increase_line_limits(pn.example_multivoltage(), 1.5),
# # "example_simple": lambda: increase_line_limits(pn.example_simple(), 1.5),
# # "mv_oberrhein": lambda: increase_line_limits(pn.mv_oberrhein(), 1.5),
# #
# # # # High-voltage grids
"1-HV-mixed--0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HV-mixed--0-sw"), 1.5),
# # "1-HV-mixed--1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HV-mixed--1-sw"), 1.5),
# # "1-HV-urban--0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HV-urban--0-sw"), 1.5),
# # "1-HV-urban--1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HV-urban--1-sw"), 1.5),
# # #
# # # # Low-voltage grids
# # "1-LV-rural1--0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-rural1--0-sw"), 1.5),
# # "1-LV-rural2--0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-rural2--0-sw"), 1.5),
# # "1-LV-rural2--1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-rural2--1-sw"), 1.5),
# # "1-LV-rural2--2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-rural2--2-sw"), 1.5),
# # "1-LV-rural3--0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-rural3--0-sw"), 1.5),
# # "1-LV-rural3--1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-rural3--1-sw"), 1.5),
# # "1-LV-rural3--2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-rural3--2-sw"), 1.5),
# # "1-LV-semiurb4--0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-semiurb4--0-sw"), 1.5),
# # "1-LV-semiurb4--1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-semiurb4--1-sw"), 1.5),
# # "1-LV-semiurb4--2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-semiurb4--2-sw"), 1.5),
# # "1-LV-semiurb5--0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-semiurb5--0-sw"), 1.5),
# # "1-LV-semiurb5--1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-semiurb5--1-sw"), 1.5),
# # "1-LV-semiurb5--2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-semiurb5--2-sw"), 1.5),
# # "1-LV-urban6--0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-urban6--0-sw"), 1.5),
# # "1-LV-urban6--1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-urban6--1-sw"), 1.5),
# # "1-LV-urban6--2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-LV-urban6--2-sw"), 1.5),
# #
# # # Medium-voltage grids (not already added)
"1-MV-comm--0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MV-comm--0-sw"), 1.5),
# # "1-MV-comm--1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MV-comm--1-sw"), 1.5),
# # "1-MV-comm--2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MV-comm--2-sw"), 1.5),
# # "1-MV-rural--0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MV-rural--0-sw"), 1.5),
# # "1-MV-rural--1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MV-rural--1-sw"), 1.5),
# # "1-MV-rural--2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MV-rural--2-sw"), 1.5),
# # "1-MV-semiurb--0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MV-semiurb--0-sw"), 1.5),
# # "1-MV-semiurb--1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MV-semiurb--1-sw"), 1.5),
# # "1-MV-semiurb--2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MV-semiurb--2-sw"), 1.5),
# # "1-MV-urban--0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MV-urban--0-sw"), 1.5),
# # "1-MV-urban--1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MV-urban--1-sw"), 1.5),
# # "1-MV-urban--2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MV-urban--2-sw"), 1.5),
# #
# # # -- HVMV-mixed grids --
"1-HVMV-mixed-1.105-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-mixed-1.105-0-no_sw"), 1.5),
# # "1-HVMV-mixed-1.105-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-mixed-1.105-0-sw"), 1.5),
# # "1-HVMV-mixed-1.105-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-mixed-1.105-1-no_sw"), 1.5),
# # "1-HVMV-mixed-1.105-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-mixed-1.105-1-sw"), 1.5),
# # "1-HVMV-mixed-2.102-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-mixed-2.102-1-no_sw"), 1.5),
# # "1-HVMV-mixed-2.102-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-mixed-2.102-1-sw"), 1.5),
# # "1-HVMV-mixed-4.101-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-mixed-4.101-1-no_sw"), 1.5),
# # "1-HVMV-mixed-4.101-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-mixed-4.101-1-sw"), 1.5),
# # # "1-HVMV-mixed-all-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-mixed-all-1-no_sw"), 1.5),
# # # "1-HVMV-mixed-all-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-mixed-all-1-sw"), 1.5),
#
# # # -- HVMV-urban grids --
# # "1-HVMV-urban-2.203-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-2.203-0-no_sw"), 1.5),
# # "1-HVMV-urban-2.203-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-2.203-0-sw"), 1.5),
# # "1-HVMV-urban-2.203-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-2.203-1-no_sw"), 1.5),
# # "1-HVMV-urban-2.203-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-2.203-1-sw"), 1.5),
# # "1-HVMV-urban-3.201-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-3.201-0-no_sw"), 1.5),
# # "1-HVMV-urban-3.201-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-3.201-0-sw"), 1.5),
# # "1-HVMV-urban-3.201-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-3.201-1-no_sw"), 1.5),
# # "1-HVMV-urban-3.201-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-3.201-1-sw"), 1.5),
# # "1-HVMV-urban-4.201-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-4.201-0-no_sw"), 1.5),
# # "1-HVMV-urban-4.201-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-4.201-0-sw"), 1.5),
# # "1-HVMV-urban-4.201-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-4.201-1-no_sw"), 1.5),
# # "1-HVMV-urban-4.201-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-4.201-1-sw"), 1.5),
# # # "1-HVMV-urban-all-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-all-0-no_sw"), 1.5),
# # # "1-HVMV-urban-all-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-all-0-sw"), 1.5),
# # # "1-HVMV-urban-all-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-all-1-no_sw"), 1.5),
# # # "1-HVMV-urban-all-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-HVMV-urban-all-1-sw"), 1.5),
# # #
# # # # MVLV grids – Combined medium and low voltage
# # "1-MVLV-comm-3.403-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-3.403-0-no_sw"), 1.5),
# # # #"1-MVLV-comm-3.403-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-3.403-0-sw"), 1.5),
# # "1-MVLV-comm-3.403-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-3.403-1-no_sw"), 1.5),
# # # #"1-MVLV-comm-3.403-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-3.403-1-sw"), 1.5),
# # "1-MVLV-comm-3.403-2-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-3.403-2-no_sw"), 1.5),
# # # #"1-MVLV-comm-3.403-2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-3.403-2-sw"), 1.5),
# # "1-MVLV-comm-4.416-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-4.416-0-no_sw"), 1.5),
# # # #"1-MVLV-comm-4.416-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-4.416-0-sw"), 1.5),
# # "1-MVLV-comm-4.416-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-4.416-1-no_sw"), 1.5),
# # # #"1-MVLV-comm-4.416-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-4.416-1-sw"), 1.5),
# # "1-MVLV-comm-4.416-2-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-4.416-2-no_sw"), 1.5),
# # # #"1-MVLV-comm-4.416-2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-4.416-2-sw"), 1.5),
# # "1-MVLV-comm-5.401-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-5.401-0-no_sw"), 1.5),
# # # #"1-MVLV-comm-5.401-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-5.401-0-sw"), 1.5),
# # "1-MVLV-comm-5.401-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-5.401-1-no_sw"), 1.5),
# # # #"1-MVLV-comm-5.401-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-5.401-1-sw"), 1.5),
# # "1-MVLV-comm-5.401-2-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-5.401-2-no_sw"), 1.5),
# # # #"1-MVLV-comm-5.401-2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-5.401-2-sw"), 1.5),
# # #"1-MVLV-comm-all-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-all-0-no_sw"), 1.5),
# # # #"1-MVLV-comm-all-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-comm-all-0-sw"), 1.5),
# # #
# # "1-MVLV-rural-1.108-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-1.108-0-no_sw"), 1.5),
# # "1-MVLV-rural-1.108-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-1.108-0-sw"), 1.5),
# #
"1-MVLV-rural-2.107-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-2.107-0-no_sw"), 1.5),
# # #"1-MVLV-rural-2.107-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-2.107-0-sw"), 1.5),
# "1-MVLV-rural-2.107-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-2.107-1-no_sw"), 1.5),
# # #"1-MVLV-rural-2.107-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-2.107-1-sw"), 1.5),
# "1-MVLV-rural-2.107-2-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-2.107-2-no_sw"), 1.5),
# # #"1-MVLV-rural-2.107-2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-2.107-2-sw"), 1.5),
# "1-MVLV-rural-4.101-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-4.101-0-no_sw"), 1.5),
# # #"1-MVLV-rural-4.101-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-4.101-0-sw"), 1.5),
# "1-MVLV-rural-4.101-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-4.101-1-no_sw"), 1.5),
# # #"1-MVLV-rural-4.101-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-4.101-1-sw"), 1.5),
# "1-MVLV-rural-4.101-2-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-4.101-2-no_sw"), 1.5),
# #"1-MVLV-rural-4.101-2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-4.101-2-sw"), 1.5),
# "1-MVLV-rural-all-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-all-0-no_sw"), 1.5),
# #"1-MVLV-rural-all-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-rural-all-0-sw"), 1.5),
#
"1-MVLV-semiurb-3.202-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-3.202-0-no_sw"),1.5),
# # #"1-MVLV-semiurb-3.202-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-3.202-0-sw"), 1.5),
# "1-MVLV-semiurb-3.202-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-3.202-1-no_sw"), 1.5),
# #"1-MVLV-semiurb-3.202-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-3.202-1-sw"), 1.5),
# "1-MVLV-semiurb-3.202-2-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-3.202-2-no_sw"),
# 1.5),
# #"1-MVLV-semiurb-3.202-2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-3.202-2-sw"), 1.5),
# "1-MVLV-semiurb-4.201-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-4.201-0-no_sw"),
# 1.5),
# #"1-MVLV-semiurb-4.201-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-4.201-0-sw"), 1.5),
# "1-MVLV-semiurb-4.201-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-4.201-1-no_sw"),
# 1.5),
# #"1-MVLV-semiurb-4.201-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-4.201-1-sw"), 1.5),
# "1-MVLV-semiurb-4.201-2-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-4.201-2-no_sw"),
# 1.5),
# #"1-MVLV-semiurb-4.201-2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-4.201-2-sw"), 1.5),
# "1-MVLV-semiurb-5.220-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-5.220-0-no_sw"),
# 1.5),
# #"1-MVLV-semiurb-5.220-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-5.220-0-sw"), 1.5),
# "1-MVLV-semiurb-5.220-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-5.220-1-no_sw"),
# 1.5),
# #"1-MVLV-semiurb-5.220-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-5.220-1-sw"), 1.5),
# "1-MVLV-semiurb-5.220-2-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-5.220-2-no_sw"),
# 1.5),
# #"1-MVLV-semiurb-5.220-2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-5.220-2-sw"), 1.5),
# #"1-MVLV-semiurb-all-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-all-0-no_sw"), 1.5),
# #"1-MVLV-semiurb-all-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-semiurb-all-0-sw"), 1.5),
# #
# # # Urban MVLV grids
"1-MVLV-urban-5.303-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-5.303-0-no_sw"), 1.5),
# #"1-MVLV-urban-5.303-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-5.303-0-sw"), 1.5),
# "1-MVLV-urban-5.303-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-5.303-1-no_sw"), 1.5),
# #"1-MVLV-urban-5.303-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-5.303-1-sw"), 1.5),
# "1-MVLV-urban-5.303-2-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-5.303-2-no_sw"), 1.5),
# #"1-MVLV-urban-5.303-2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-5.303-2-sw"), 1.5),
# "1-MVLV-urban-6.305-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-6.305-0-no_sw"), 1.5),
# #"1-MVLV-urban-6.305-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-6.305-0-sw"), 1.5),
# "1-MVLV-urban-6.305-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-6.305-1-no_sw"), 1.5),
# #"1-MVLV-urban-6.305-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-6.305-1-sw"), 1.5),
# "1-MVLV-urban-6.305-2-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-6.305-2-no_sw"), 1.5),
# #"1-MVLV-urban-6.305-2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-6.305-2-sw"), 1.5),
# "1-MVLV-urban-6.309-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-6.309-0-no_sw"), 1.5),
# #"1-MVLV-urban-6.309-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-6.309-0-sw"), 1.5),
# "1-MVLV-urban-6.309-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-6.309-1-no_sw"), 1.5),
# #"1-MVLV-urban-6.309-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-6.309-1-sw"), 1.5),
# "1-MVLV-urban-6.309-2-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-6.309-2-no_sw"), 1.5),
# #"1-MVLV-urban-6.309-2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-6.309-2-sw"), 1.5),
# #"1-MVLV-urban-all-0-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-all-0-no_sw"), 1.5),
# #"1-MVLV-urban-all-0-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-all-0-sw"), 1.5),
# #"1-MVLV-urban-all-1-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-all-1-no_sw"), 1.5),
# #"1-MVLV-urban-all-1-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-all-1-sw"), 1.5),
# #"1-MVLV-urban-all-2-no_sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-all-2-no_sw"), 1.5),
# #"1-MVLV-urban-all-2-sw": lambda: increase_line_limits(sb.get_simbench_net("1-MVLV-urban-all-2-sw"), 1.5),
# local saved grids
#"caseIEEE37_DG": lambda: use_local_grid("caseIEEE37_DG.m")
}
# Function to increase generation and storage capacities
def increase_generation(net, factor):
print(f"Verteilte Erzeugung und Speicher um den Faktor {factor} erhöht.")
# 1. Increase for gen (zentraler Generator)
for idx, gen in net.gen.iterrows():
net.gen.at[idx, 'p_mw'] *= factor
net.gen.at[idx, 'q_mvar'] *= factor
net.gen.at[idx, 'sn_mva'] = np.sqrt(net.gen.at[idx, 'p_mw'] ** 2 + net.gen.at[idx, 'q_mvar'] ** 2)
# 2. Increase for sgen (verteilte Erzeugung)
for idx, sgen in net.sgen.iterrows():
net.sgen.at[idx, 'p_mw'] *= factor
net.sgen.at[idx, 'q_mvar'] *= factor
net.sgen.at[idx, 'sn_mva'] = np.sqrt(net.sgen.at[idx, 'p_mw'] ** 2 + net.sgen.at[idx, 'q_mvar'] ** 2)
# 3. Increase for storage (Speicher)
for idx, storage in net.storage.iterrows():
net.storage.at[idx, 'p_mw'] *= factor
net.storage.at[idx, 'q_mvar'] *= factor
net.storage.at[idx, 'sn_mva'] = np.sqrt(net.storage.at[idx, 'p_mw'] ** 2 + net.storage.at[idx, 'q_mvar'] ** 2)
return net
def increase_line_limits(net, factor):
print(f"Increase line limits by Factor {factor}.")
# 🔹 **Reduce Line & Transformer Losses**
net.line["max_i_ka"] *= 1.5 # increase line limits by 50 %
if "max_loading_percent" in net.line.columns:
# Set a default loading percent for all lines (e.g., 100%)
net.line["max_loading_percent"] *= 1.5
return net
def use_local_grid(grid_name):
TESTGRID_PATH = r"C:\Users\runte\Dropbox\Zwischenablage\Testnetze"
file_path = os.path.join(TESTGRID_PATH, grid_name)
net = pc.from_mpc(file_path, casename_m="caseIEEE37_DG")
return net
# Configuration
basic = {
"Adjustments": True,
"Overview_Grid": True
}
selected_indicators = {
"all": False,
"Self Sufficiency": True,
"show_self_sufficiency_at_bus": False,
"System Self Sufficiency": True,
"Generation Shannon Evenness": True,
"Generation Variety": True,
"Line Shannon Evenness": True,
"Line Variety": True,
"Load Shannon Evenness": True,
"Load Variety": True,
"Disparity Generators": True,
"Disparity Loads": True,
"Disparity Transformers": True,
"Disparity Lines": True,
"N-3 Redundancy": True,
"n_3_redundancy_print": False,
"Redundancy": True,
"GraphenTheorie": True,
"Flexibility": True,
"Flexibility_fxor": True,
"Buffer": True,
"show_spider_plot": False,
"print_results": True,
"output_excel": True
}
selected_scenario = {
"stress_scenario": True,
"all": False,
"Flood": {"active": True, "runs": 100},
"Earthquake": {"active": True, "runs": 100},
"Dunkelflaute": {"active": True, "runs": 10},
"Storm": {"active": True, "runs": 100},
"Fire": {"active": False, "runs": 20},
"Line Overload": {"active": False, "runs": 10},
"IT-Attack": {"active": False, "runs": 20},
"Geopolitical_gas": {"active": True, "runs": 10},
"Geopolitical_h2": {"active": True, "runs": 10},
"high_EE_generation": {"active": True, "runs": 25},
"high_load": {"active": True, "runs": 25},
"sabotage_trafo": {"active": True, "runs": 20},
"print_results": True,
"output_excel": True
}
# Main Function
def run_analysis_for_single_grid(grid_name):
start_timer = time.time()
dfinalresults = pd.DataFrame(columns=['Indicator', 'Value'])
ddisparity = pd.DataFrame(columns=['Name', 'Value', 'max Value', 'Verhaeltnis'])
dfinalresults = add_indicator(dfinalresults, grid_name, 0)
dfresultsscenario = pd.DataFrame()
dfresultsscenario = add_indicator(dfresultsscenario, grid_name, 0)
# Select and create the grid dynamically
if grid_name in grids:
net = grids[grid_name]()
else:
raise ValueError(f"Unknown Grid Type: {basic['Grid']}")
if basic["Overview_Grid"]:
# Count elements and scaled elements
element_counts = count_elements(net)
# Print both counts in one row
print(net)
print(net.bus)
print(net.trafo)
print(net.line)
# print("Generators:")
print(net.gen)
print(net.sgen)
print(net.storage)
if basic["Adjustments"]:
net = set_missing_limits(net)
if selected_scenario["all"]:
# Setze alle anderen Indikatoren auf True
for key, value in selected_scenario.items():
if isinstance(value, dict):
value["active"] = True
if net.bus_geodata.empty:
selected_scenario["Flood"]["active"] = False
dfresultsscenario = add_indicator(dfresultsscenario, "Flood", 2)
if net.trafo.empty:
selected_scenario["sabotage_trafo"]["active"] = False
dfresultsscenario = add_indicator(dfresultsscenario, "sabotage_trafo", 2)
if net.sgen.empty or not net.sgen["type"].str.contains("fuel cell", case=False, na=False).any():
selected_scenario["Geopolitical_h2"]["active"] = False
dfresultsscenario = add_indicator(dfresultsscenario, "Geopolitical_h2", 2)
if net.sgen.empty or not net.sgen["type"].str.contains("CHP|Gasturbine", case=False, na=False).any():
selected_scenario["Geopolitical_gas"]["active"] = False
dfresultsscenario = add_indicator(dfresultsscenario, "Geopolitical_gas", 2)
if selected_indicators["all"]:
# Setze alle anderen Indikatoren auf True
for key in selected_indicators:
if key != "all": # 'all' selbst bleibt unverändert
selected_indicators[key] = True
if selected_indicators["Self Sufficiency"]:
# Calculate generation factors
generation_factors = calculate_generation_factors(net, "Fraunhofer ISE (2024)")
indi_selfsuff = float(selfsuff(net, generation_factors, selected_indicators["show_self_sufficiency_at_bus"]))
dfinalresults = add_indicator(dfinalresults, 'Self Sufficiency At Bus Level', indi_selfsuff)
# Prozentzahl, keine Normierung notwendig
if selected_indicators["System Self Sufficiency"]:
netsa = net.deepcopy()
indi_selfsuff_neu = selfsufficiency_neu(netsa)
dfinalresults = add_indicator(dfinalresults, 'Self Sufficiency System', indi_selfsuff_neu)
# Prozentzahl, keine Normierung notwendig
if selected_indicators["Generation Shannon Evenness"] or selected_indicators["Line Shannon Evenness"] or \
selected_indicators["Load Shannon Evenness"]:
# Define the maximum known types for each component
max_known_types = {
'generation': 10,
# Adjust this based on your actual known types (sgen: solar, wind, biomass, gen: gas, coal, nuclear, generator, static generator; storage: battery, hydro,
'line': 2, # "ol" (overhead line) and "cs" (cable system)
'load': 10
# Example: 10 known types of loads (residential, commercial, industrial, agricultaral, transport, municipal, dynamic, static, critical, non-critical
}
# Initialize lists to store the values
evenness_values = []
variety_values = []
if selected_indicators["Generation Shannon Evenness"] or selected_indicators["Generation Variety"]:
generation_data = pd.concat([net.sgen, net.gen, net.storage], ignore_index=True)
evenness, variety, variety_scaled, max_variety, evenness_entropy = calculate_shannon_evenness_and_variety(generation_data,
max_known_types[
'generation'])
evenness_values.append(evenness)
variety_values.append(variety_scaled)
dfinalresults = add_indicator(dfinalresults, "Generation Shannon Evenness", evenness_entropy)
dfinalresults = add_indicator(dfinalresults, "Generation Shannon Evenness scaled", evenness)
if selected_indicators["Generation Variety"]:
dfinalresults = add_indicator(dfinalresults, "Generation Variety", variety)
dfinalresults = add_indicator(dfinalresults, "Generation Variety scaled", variety_scaled)
if selected_indicators["Line Shannon Evenness"] or selected_indicators["Line Variety"]:
evenness, variety, variety_scaled, max_variety, evenness_entropy = calculate_shannon_evenness_and_variety(net.line,
max_known_types['line'])
evenness_values.append(evenness)
variety_values.append(variety_scaled)
dfinalresults = add_indicator(dfinalresults, "Line Shannon Evenness", evenness_entropy)
dfinalresults = add_indicator(dfinalresults, "Line Shannon Evenness scaled", evenness)
if selected_indicators["Line Variety"]:
dfinalresults = add_indicator(dfinalresults, "Line Variety", variety)
dfinalresults = add_indicator(dfinalresults, "Line Variety scaled", variety_scaled)
if selected_indicators["Load Shannon Evenness"] or selected_indicators["Load Variety"]:
evenness, variety, variety_scaled, max_variety, evenness_entropy = calculate_shannon_evenness_and_variety(net.load,
max_known_types['load'])
evenness_values.append(evenness)
variety_values.append(variety_scaled)
dfinalresults = add_indicator(dfinalresults, "Load Shannon Evenness", evenness_entropy)
dfinalresults = add_indicator(dfinalresults, "Load Shannon Evenness scaled", evenness)
if selected_indicators["Load Variety"]:
dfinalresults = add_indicator(dfinalresults, "Load Variety", variety)
dfinalresults = add_indicator(dfinalresults, "Load Variety scaled", variety_scaled)
if selected_indicators["Generation Shannon Evenness"] or selected_indicators["Generation Variety"] or \
selected_indicators["Load Shannon Evenness"] or selected_indicators["Load Variety"] or selected_indicators[
"Line Shannon Evenness"] or selected_indicators["Line Variety"]:
# Calculate averages if lists are not empty
if evenness_values:
avg_evenness = sum(evenness_values) / len(evenness_values)
dfinalresults = add_indicator(dfinalresults, "Shannon Evenness Average scaled", avg_evenness)
if variety_values:
avg_variety = sum(variety_values) / len(variety_values)
dfinalresults = add_indicator(dfinalresults, "Variety Average scaled", avg_variety)
if selected_indicators["GraphenTheorie"]:
# Create an empty NetworkX graph
G = nx.Graph()
# 1) Busse als Knoten hinzufügen
for bus_id in net.bus.index:
G.add_node(bus_id)
# 2) Leitungen als Kanten hinzufügen (unter Berücksichtigung geschlossener Schalter)
for idx, line in net.line.iterrows():
from_bus = line.from_bus
to_bus = line.to_bus
# Prüfen, ob ein Schalter (switch.et == 'l') zwischen den Bussen liegt
switch_exists = False
switch_closed = True # wird nur dann False, wenn wir tatsächlich einen offenen Switch finden
for _, sw in net.switch.iterrows():
if sw.et == 'l':
# Bus- und Element-Kombination checken
if (sw.bus == from_bus and sw.element == to_bus) or (sw.bus == to_bus and sw.element == from_bus):
switch_exists = True
switch_closed = sw.closed
break
# Nur Kante hinzufügen, wenn kein Switch existiert ODER er geschlossen ist
if not switch_exists or (switch_exists and switch_closed):
# Als Gewicht nehmen wir hier exemplarisch die Leitungslänge
length = line.length_km
G.add_edge(from_bus, to_bus, weight=length)
# 3) Trafos als Kanten hinzufügen (ebenfalls optional mit Schalter-Check)
for idx, trafo in net.trafo.iterrows():
hv_bus = trafo.hv_bus
lv_bus = trafo.lv_bus
# Prüfen, ob ein Schalter (switch.et == 't') zum Trafo existiert
switch_exists = False
switch_closed = True
for _, sw in net.switch.iterrows():
if sw.et == 't':
# Bei Trafos ist meist bus = hv_bus oder lv_bus und element = trafo.id
# Hier einfacher Check: falls bus einer der beiden ist und switch.element == diesem Trafo
if sw.bus in [hv_bus, lv_bus] and sw.element == idx:
switch_exists = True
switch_closed = sw.closed
break
# Nur Kante hinzufügen, wenn kein Trafo-Switch existiert ODER dieser geschlossen ist
if not switch_exists or (switch_exists and switch_closed):
# Beispiel: Als Gewicht kannst du beliebig etwas hinterlegen (z. B. trafo.sn_mva)
G.add_edge(hv_bus, lv_bus, weight=1.0)
# 4) Prüfen, ob der Graph zusammenhängend ist
if not nx.is_connected(G):
# Größte zusammenhängende Komponente extrahieren
largest_component = max(nx.connected_components(G), key=len)
G = G.subgraph(largest_component).copy()
dfinalresults = GraphenTheorieIndicator(G, dfinalresults)
# Create a list to store individual disparity values
disparity_values = []
if selected_indicators["Disparity Generators"]:
if not selected_indicators["Self Sufficiency"]:
generation_factors = calculate_generation_factors(net, "Fraunhofer ISE (2024)")
disparity_df_gen, max_integral_gen = calculate_disparity_space(net, generation_factors)
integral_value_gen = disparity_df_gen.values.sum()
ratio_gen = min(1,integral_value_gen / max_integral_gen)
ddisparity = add_disparity(ddisparity, 'Generators', integral_value_gen, max_integral_gen, ratio_gen)
dfinalresults = add_indicator(dfinalresults, 'Disparity Generators', integral_value_gen)
dfinalresults = add_indicator(dfinalresults, 'Disparity Generators scaled', ratio_gen)
disparity_values.append(ratio_gen)
if selected_indicators["Disparity Loads"]:
disparity_df_load, max_integral_load = calculate_load_disparity(net)
integral_value_load = disparity_df_load.values.sum()
ratio_load = min(1,integral_value_load / max_integral_load)
ddisparity = add_disparity(ddisparity, 'Load', integral_value_load, max_integral_load, ratio_load)
dfinalresults = add_indicator(dfinalresults, 'Disparity Loads', integral_value_load)
dfinalresults = add_indicator(dfinalresults, 'Disparity Loads scaled', ratio_load)
disparity_values.append(ratio_load)
if selected_indicators["Disparity Transformers"]:
disparity_df_trafo, max_int_trafo = calculate_transformer_disparity(net)
integral_value_trafo = disparity_df_trafo.values.sum()
print(disparity_df_trafo)
print(ddisparity)
if integral_value_trafo == 0 or disparity_df_trafo.empty:
print("Disparity Berechnung für Trafos war fehlerhaft und wird mit 0 ersetzt")
ratio_trafo = 0
ddisparity = add_disparity(ddisparity, 'Trafo', 0, max_int_trafo, 0)
else:
ratio_trafo = min(1,integral_value_trafo / max_int_trafo)
ddisparity = add_disparity(ddisparity, 'Trafo', integral_value_trafo, max_int_trafo, ratio_trafo)
dfinalresults = add_indicator(dfinalresults, 'Disparity Transformers', integral_value_trafo)
dfinalresults = add_indicator(dfinalresults, 'Disparity Transformers scaled', ratio_trafo)
disparity_values.append(ratio_trafo)
if selected_indicators["Disparity Lines"]:
disparity_df_lines, max_int_disp_lines = calculate_line_disparity(net)
integral_value_line = disparity_df_lines.values.sum()
ratio_line = min(1,integral_value_line / max_int_disp_lines)
ddisparity = add_disparity(ddisparity, 'Lines', integral_value_line, max_int_disp_lines, ratio_line)
dfinalresults = add_indicator(dfinalresults, 'Disparity Lines', integral_value_line)
dfinalresults = add_indicator(dfinalresults, 'Disparity Lines scaled', ratio_line)
disparity_values.append(ratio_line)
# Calculate overall disparity average
if disparity_values:
avg_disparity = sum(disparity_values) / len(disparity_values)
dfinalresults = add_indicator(dfinalresults, 'Disparity Average scaled', avg_disparity)
if selected_indicators["N-3 Redundancy"]:
if not basic["Overview_Grid"]:
# Count elements and scaled elements
element_counts = count_elements(net)
# Liste der zu prüfenden Elementtypen
element_types = ["line", "sgen", "gen", "trafo", "bus", "storage", "switch", "load"]
n3_redundancy_results = {}
Success = 0
Failed = 0
timeout = 180
# Über alle relevanten Elementtypen iterieren
for element_type in element_types:
start_time = time.time()
results = n_3_redundancy_check(net, start_time, element_type, timeout, 300)
n3_redundancy_results[element_type] = results[element_type]
# Summiere die Ergebnisse
Success += results[element_type]['Success']
Failed += results[element_type]['Failed']
print(time.time() - start_time)
# Gesamtrate berechnen
total_checks = Success + Failed
rate = Success / total_checks if total_checks != 0 else 0
# Ergebnis in DataFrame speichern
dfinalresults = add_indicator(dfinalresults, 'Redundancy N-3', rate)
# Prozentzahl, keine Normierung notwendig
if selected_indicators["Redundancy"]:
Lastfluss, n2_Redundanz, kombi, component_indicators, red_results = Redundancy(net, 300)
dfinalresults = add_indicator(dfinalresults, "Redundancy Loadflow", Lastfluss)
dfinalresults = add_indicator(dfinalresults, "Redundancy N-2", n2_Redundanz)
dfinalresults = add_indicator(dfinalresults, "Redundancy Average", kombi)
# Prozentzahl, keine Normierung notwendig
# dfinalresults = add_indicator(dfinalresults, "Load Shannon Evenness", evenness)
# Ausgabe der Indikatoren pro Komponente:
print("Komponentenindikatoren (1 = optimal, 0 = schlecht):")
for comp, inds in component_indicators.items():
print(f"{comp.capitalize()}:")
print(f" Lastfluss: {inds['lf']:.3f}")
print(f" Redundanz: {inds['red']:.3f}")
print(f" Kombiniert: {inds['combined']:.3f}")
# Ergebnisse ausgeben
print("\nErgebnisse der N-2-Redundanzprüfung:")
for element, stats in red_results.items():
print(f"{element.capitalize()}: Erfolg: {stats['Success']}, Fehlgeschlagen: {stats['Failed']}")
print("\nGesamtindikatoren:")
print(f" Lastfluss Gesamt: {Lastfluss:.3f}")
print(f" N-2 Redundanz Gesamt: {n2_Redundanz:.3f}")
print(f" Kombinierter Gesamtindikator: {kombi:.3f}")
if selected_indicators["Flexibility"]:
dflexiresults = calculate_flexibility(net)
dfinalresults = add_indicator(dfinalresults, 'Flexibility Reserve Lines', dflexiresults.loc[
dflexiresults['Indicator'] == 'Flex Reserve Leitungen', 'Value'].values[0])
# Prozentzahl, keine Normierung notwendig
dfinalresults = add_indicator(dfinalresults, 'Flexibility Reserve Critical Lines scaled', dflexiresults.loc[
dflexiresults['Indicator'] == 'Flex Reserve krit Leitungen scaled', 'Value'].values[0])
dfinalresults = add_indicator(dfinalresults, 'Flexibility Reserve Critical Lines', dflexiresults.loc[
dflexiresults['Indicator'] == 'Flex Reserve krit Leitungen', 'Value'].values[0])
dfinalresults = add_indicator(dfinalresults, 'Flexibility Average', dflexiresults.loc[
dflexiresults['Indicator'] == 'Flexibilität Gesamt', 'Value'].values[0])
if selected_indicators["Buffer"]:
speicher_scaled, speicher = calculate_buffer(net)
dfinalresults = add_indicator(dfinalresults, 'Buffer Capacity scaled', speicher_scaled)
dfinalresults = add_indicator(dfinalresults, 'Buffer Capacity', speicher)
if selected_indicators["Flexibility_fxor"]:
Flex_fxor_scaled, Flex_fxor = flexibility_fxor(net, False)
dfinalresults = add_indicator(dfinalresults, 'Flexibility Feasible Operating Region scaled', Flex_fxor_scaled)
dfinalresults = add_indicator(dfinalresults, 'Flexibility Feasible Operating Region', Flex_fxor)
if selected_indicators["n_3_redundancy_print"]:
print("Results of N-3 Redundancy")
for element_type, counts in n3_redundancy_results.items():
print(f"{element_type.capitalize()} - Success count: {counts['Success']}, Failed count: {counts['Failed']}")
if selected_indicators["show_spider_plot"]:
plot_spider_chart(dfinalresults)
if not dfinalresults.empty:
runtime_required = (time.time() - start_timer)
dfinalresults = add_indicator(dfinalresults, "Time required", runtime_required)
# Separate the first and last row
first_row = dfinalresults.iloc[[0]]
# Sort everything in between
middle_rows = dfinalresults.iloc[1:].sort_values(by="Indicator").reset_index(drop=True)
# Recombine everything
dfinalresults = pd.concat([first_row, middle_rows], ignore_index=True)
if selected_indicators["print_results"]:
print("Results for Indicators:")
print(dfinalresults)
if selected_scenario["stress_scenario"]:
for scenario, params in selected_scenario.items():
if isinstance(params, dict) and params.get("active", False):
stressor = scenario.lower()
scenario_values = []
print(f"{stressor}")
for n in range(params.get("runs", 10)): # fallback to 10 runs if "runs" not defined
modified_nets = stress_scenarios(net, [stressor])
# `modified_nets` is a list of (scenario_name, modified_net) tuples.
if not modified_nets:
print("No modified net returned. Skipping this scenario.")
continue
# Extract the first (and presumably only) tuple
scenario_name, single_net = modified_nets[0]
# Now you can run the OPF using `single_net`
res_scenario = run_scenario(single_net, scenario_name)
scenario_values.append(res_scenario)
del modified_nets # optional
# Compute the average for this scenario
print(f"{scenario_values}")
avg_value = sum(scenario_values) / len(scenario_values)
dfresultsscenario = add_indicator(dfresultsscenario, scenario, avg_value)
if not dfresultsscenario.empty:
# Separate the first and last row
print(dfresultsscenario)
first_row = dfresultsscenario.iloc[[0]]
# Sort everything in between
middle_rows = dfresultsscenario.iloc[1:].sort_values(by="Indicator").reset_index(drop=True)
# Recombine everything
dfresultsscenario = pd.concat([first_row, middle_rows], ignore_index=True)
# Compute the average of all values excluding the first row
if len(dfresultsscenario) > 1: # Ensure there are enough rows to calculate an average
valid_values = dfresultsscenario["Value"].iloc[1:] # Exclude the first row
filtered_values = valid_values[(valid_values >= 0) & (valid_values <= 1)] # Exclude values above 1 = not calculated scenarios
scenario_average_value = filtered_values.mean()
# Add the average as a new row
dfresultsscenario = add_indicator(dfresultsscenario, "Overall Scenario Resilience Score",
scenario_average_value)
dfresultsscenario["Value"] = dfresultsscenario["Value"].replace(2, np.nan)
if selected_scenario["print_results"]:
print(dfresultsscenario)
if selected_scenario["output_excel"] or selected_indicators.get("output_excel"):
# Output-Dateiname basierend auf grid_name
output_filename = f'Ergebnisse_{grid_name}.xlsx'
output_dir = r"C:\Users\runte\Dropbox\Zwischenablage\Regression_Plots"
output_path = os.path.join(output_dir, output_filename)
# ExcelWriter verwenden, um mehrere Sheets in eine Datei zu schreiben
with pd.ExcelWriter(output_path, engine="openpyxl") as writer:
if selected_indicators.get("output_excel"):
dfresultsscenario.T.to_excel(writer, sheet_name="Results Scenario", index=False)
if selected_indicators["output_excel"]:
dfinalresults.T.to_excel(writer, sheet_name="Results Indicator", index=False)
def run_all_grids(start_time):
"""
Loops over the grids dictionary and runs the above 'run_analysis_for_single_grid' on each.
"""
for grid_name in grids:
timer = time.time() - start_time
print(f"\n--- Running analysis for grid: {grid_name} at {timer}---")
run_analysis_for_single_grid(grid_name)
def main():
"""
The 'entry point' that is invoked when you run this script.
"""
# Optionally, you can decide whether to process all grids or just one,
# e.g. based on some config or command line argument
start_time = time.time()
process_all = True # or read from config/CLI
if process_all:
run_all_grids(start_time)
# do final post-processing, exporting, etc.
else:
# Suppose your config says to just run the 'case30' grid
grid_name = "caseIEEE37_DG"
run_analysis_for_single_grid(grid_name)
if __name__ == '__main__':
main()