file_level=logging.DEBUG)
# Rechne NEP 1-Knoten-Szenario
#main.model_scenario('/home/dbeier/reegis/scenarios/deflex/2014/deflex_2014_de02_NEP.xls', name="NEP2030", rmap="de02", year=2014)
main.model_scenario('/home/dbeier/reegis/scenarios/deflex/2014/deflex_2014_de02.xls')
#main.model_scenario('/home/dbeier/reegis/scenarios/deflex/2014/deflex_2014_de21_no-heat_no-reg-merit.xls')
#scenario_tools.DeflexScenario()
path= '/home/dbeier/reegis/scenarios/deflex/2014/results_cbc/NEP2030.esys'
de02_NEP = results.load_es(path)
results_obj = de02_NEP.results['main']
# Auswertung
cost_em = upstream_analysis.get_emissions_and_costs(de02_NEP,with_chp=True)
vlh = results.fullloadhours(de02_NEP)
mrbb = results.get_multiregion_bus_balance(de02_NEP)
# Extrahiere relevante Größen
demand = mrbb.DE01['out']['demand']['electricity']['all']
transformer = mrbb.DE01['in']['trsf']
ee_single = mrbb.DE01['in']['source']['ee']
ee_single["bioenergy"] = transformer["pp"]["bioenergy"] + transformer["chp"]["bioenergy"]
ee_single["offshore"] = mrbb.DE02['in']['source']['ee']['wind']
residual_load = demand - ee_single.sum(axis=1)
excess = mrbb.DE01['out']['excess']+mrbb.DE02['out']['excess']
shortage = mrbb.DE01['in']['shortage']
fossil = pd.DataFrame()
fossil_em=pd.DataFrame()
scens_dict = {}
for i in range(0, len(de21_scens)):
tmp = results.load_es(de21_scens[i])
key = de21_scens[i][-8:-5]
scens_dict[key] = tmp
scens = [x for x in scens_dict.keys()]
# Jahresdauerlinie der Strompreise / Emissionen im Vergleich
erg_em, erg_mcp, em_total, new_df, em_mean = pd.DataFrame(), pd.DataFrame(
), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
for n in scens:
cost_em = upstream_analysis.get_emissions_and_costs(scens_dict.get(n),
with_chp=True)
#tmp_em, tmp_mcp= cost_em.emission.sort_values(ascending=False), cost_em.mcp.sort_values(ascending=False)
erg_em[n] = cost_em.emission
erg_mcp[n] = cost_em.mcp
em_total[n] = cost_em.emission_absolute
mrbb = results.get_multiregion_bus_balance(scens_dict.get(n)).groupby(
level=[0, 3], axis=1).sum()
EE = mrbb.DE01['ee'] + mrbb.DE02['ee']
share_ee = EE / mrbb.DE01['electricity']
tmp = (1 - share_ee) * cost_em.emission
em_mean[n] = tmp
#plt.plot(np.arange(8760),tmp_em)
#plt.plot(np.arange(8760),tmp_mcp)
# Plot 0: Mittlere Emissionen
plt_df = multiple_jdl(em_mean)
# Schritt 4: Analyse der Ergebnisse
# Laden des dumps
# Festlegen von EE-Anteil, Jahr und Geometrie zum Laden der Ergebnisse
ee, year, geom = 200, 2014, 'de21'
inc_ee = '_' + str(ee)
name = '{0}_{1}_{2}'.format('deflex', year, geom)
path = os.path.join('/home/dbeier/reegis/scenarios/deflex', str(year))
res_path = os.path.join(path, 'results_cbc', 'DB', name + inc_ee + '.esys')
# Lade gelöstes Energiesystem der Wahl in den Workspace
sc_calc = results.load_es(res_path)
#sc_calc = results.load_es('/home/dbeier/reegis/scenarios/deflex/2014/results_cbc/deflex_2014_de02.esys')
# Market Clearing Prices und Emissionen des Systems
cost_em_param = results.fetch_cost_emission(sc_calc, with_chp=True)
cost_em = upstream_analysis.get_emissions_and_costs(sc_calc, with_chp=True)
# Plotte MCP und Emissionen
cost_em.mcp[1000:2000].plot()
plt.title('Market Clearing Prices')
plt.ylabel('€/MWh')
plt.show()
cost_em.emission[1000:2000].plot()
plt.title('Mittlere Emissionen')
plt.ylabel('g/kWh_el')
plt.show()
# Energieflussbilanzen um alle Knoten: Händisch
# multi_reg_res = results.get_multiregion_bus_balance(sc_calc).groupby(
# level=[1, 2], axis=1).sum()
#
def prepare_deflex_for_comparison(path_to_results):
# Load energy system from results
de_dispatch = results.load_es(path_to_results)
results_obj = de_dispatch.results['main']
# Auswertung
cost_em = upstream_analysis.get_emissions_and_costs(de_dispatch,
with_chp=True)
vlh = results.fullloadhours(de_dispatch)
mrbb = results.get_multiregion_bus_balance(de_dispatch)
# Extrahiere relevante Größen
demand = mrbb.DE01['out']['demand']['electricity']['all']
transformer = mrbb.DE01['in']['trsf']
generation = pd.DataFrame(index=transformer.index)
ee_single = mrbb.DE01['in']['source']['ee']
generation["Biomass"] = transformer["pp"]["bioenergy"] + transformer[
"chp"]["bioenergy"]
generation[
"Wind"] = ee_single["wind"] + mrbb.DE02['in']['source']['ee']['wind']
generation["Hydro"] = ee_single["hydro"]
generation["PV"] = ee_single["solar"]
generation["Nuclear"] = transformer["pp"]["nuclear"]
#generation["Nuclear"] = mrbb.DE01['in']['source']['ee']['geothermal']
generation["Hard Coal"] = transformer["chp"]["hard_coal"] + transformer[
"pp"]["hard_coal"]
generation["Lignite"] = transformer["chp"]["lignite"] + transformer["pp"][
"lignite"]
generation["Natural Gas"] = transformer["chp"][
"natural_gas"] + transformer["pp"]["natural_gas"]
generation["Others"]=transformer["chp"]["other"]+transformer["pp"]["other"] + transformer["chp"]["oil"] + \
transformer["pp"]["oil"] + mrbb.DE01['in']['source']['ee']['geothermal']
generation["Pump"] = mrbb.DE01["out"]["storage"]
# Emissionszeitreihe
em_per_technology = results.fetch_cost_emission(de_dispatch, with_chp=True)
em_mix = pd.DataFrame()
em_mix[
"hardcoal_chp"] = transformer["chp"]["hard_coal"] * em_per_technology[
"emission"]["hard_coal"]["DE01"]["chp"]
em_mix["hardcoal_pp"] = transformer["pp"]["hard_coal"] * em_per_technology[
"emission"]["hard_coal"]["DE01"]["pp"]
em_mix["lignite_chp"] = transformer["chp"]["lignite"] * em_per_technology[
"emission"]["lignite"]["DE01"]["chp"]
em_mix["lignite_pp"] = transformer["pp"]["lignite"] * em_per_technology[
"emission"]["lignite"]["DE01"]["pp"]
em_mix["natural_gas_chp"] = transformer["chp"]["natural_gas"] * \
em_per_technology["emission"]["natural_gas"]["DE01"]["chp"]
em_mix["natural_gas_pp"] = transformer["pp"][
"natural_gas"] * em_per_technology["emission"]["natural_gas"]["DE01"][
"pp"]
em_mix["oil_chp"] = transformer["chp"]["oil"] * em_per_technology[
"emission"]["oil"]["DE01"]["chp"]
em_mix["oil_pp"] = transformer["pp"]["oil"] * em_per_technology[
"emission"]["oil"]["DE01"]["pp"]
em_mix["other_chp"] = transformer["chp"]["other"] * em_per_technology[
"emission"]["other"]["DE01"]["chp"]
em_mix["other_pp"] = transformer["pp"]["other"] * em_per_technology[
"emission"]["other"]["DE01"]["pp"]
em_factor_deflex = em_mix.sum(axis=1) / demand
p_spot_deflex = cost_em["mcp"]
return generation, p_spot_deflex, em_factor_deflex