def test_add_levelized_cost_of_energy_carriers_two_sectors():
dict_values[KPI][KPI_SCALARS_DICT].update({TOTAL_DEMAND + h2: 100})
dict_values[KPI][KPI_SCALARS_DICT].update({
TOTAL_DEMAND + h2 + SUFFIX_ELECTRICITY_EQUIVALENT:
dict_values[KPI][KPI_SCALARS_DICT][TOTAL_DEMAND + h2] *
DEFAULT_WEIGHTS_ENERGY_CARRIERS[h2][VALUE]
})
dict_values[KPI][KPI_SCALARS_DICT].update({
TOTAL_DEMAND + SUFFIX_ELECTRICITY_EQUIVALENT:
dict_values[KPI][KPI_SCALARS_DICT][TOTAL_DEMAND + electricity +
SUFFIX_ELECTRICITY_EQUIVALENT] +
dict_values[KPI][KPI_SCALARS_DICT][TOTAL_DEMAND + h2 +
SUFFIX_ELECTRICITY_EQUIVALENT]
})
dict_values[PROJECT_DATA][SECTORS].update({h2: h2})
E3.add_levelized_cost_of_energy_carriers(dict_values)
expected_value = {
ATTRIBUTED_COSTS + electricity:
1000 *
dict_values[KPI][KPI_SCALARS_DICT][TOTAL_DEMAND + electricity +
SUFFIX_ELECTRICITY_EQUIVALENT] /
dict_values[KPI][KPI_SCALARS_DICT][TOTAL_DEMAND +
SUFFIX_ELECTRICITY_EQUIVALENT],
ATTRIBUTED_COSTS + h2:
1000 *
dict_values[KPI][KPI_SCALARS_DICT][TOTAL_DEMAND + h2 +
SUFFIX_ELECTRICITY_EQUIVALENT] /
dict_values[KPI][KPI_SCALARS_DICT][TOTAL_DEMAND +
SUFFIX_ELECTRICITY_EQUIVALENT],
}
expected_value.update({
LCOeleq + electricity:
dict_values[KPI][KPI_SCALARS_DICT][ATTRIBUTED_COSTS + electricity] *
dict_values[ECONOMIC_DATA][CRF][VALUE] /
dict_values[KPI][KPI_SCALARS_DICT][TOTAL_DEMAND + electricity],
LCOeleq + electricity:
dict_values[KPI][KPI_SCALARS_DICT][ATTRIBUTED_COSTS + h2] *
dict_values[ECONOMIC_DATA][CRF][VALUE] /
dict_values[KPI][KPI_SCALARS_DICT][TOTAL_DEMAND + h2],
LCOeleq:
1000 * dict_values[ECONOMIC_DATA][CRF][VALUE] /
dict_values[KPI][KPI_SCALARS_DICT][TOTAL_DEMAND +
SUFFIX_ELECTRICITY_EQUIVALENT],
})
for kpi in [ATTRIBUTED_COSTS, LCOeleq]:
assert kpi + electricity in dict_values[KPI][KPI_SCALARS_DICT]
assert (dict_values[KPI][KPI_SCALARS_DICT][kpi + electricity] ==
expected_value[kpi + electricity])
assert LCOeleq in dict_values[KPI][KPI_SCALARS_DICT]
assert dict_values[KPI][KPI_SCALARS_DICT][LCOeleq] == expected_value[
LCOeleq]
def test_add_levelized_cost_of_energy_carriers_one_sector():
E3.add_levelized_cost_of_energy_carriers(dict_values)
expected_value = {
ATTRIBUTED_COSTS + electricity: 1000,
LCOeleq + electricity:
1000 * dict_values[ECONOMIC_DATA][CRF][VALUE] / total_demand,
LCOeleq: 1000 * dict_values[ECONOMIC_DATA][CRF][VALUE] / total_demand,
}
for kpi in [ATTRIBUTED_COSTS, LCOeleq]:
assert kpi + electricity in dict_values[KPI][KPI_SCALARS_DICT]
assert (dict_values[KPI][KPI_SCALARS_DICT][kpi + electricity] ==
expected_value[kpi + electricity])
assert LCOeleq in dict_values[KPI][KPI_SCALARS_DICT]
assert dict_values[KPI][KPI_SCALARS_DICT][LCOeleq] == expected_value[
LCOeleq]
def test_add_levelized_cost_of_energy_carriers_two_sectors():
dict_values[KPI][KPI_SCALARS_DICT].update({TOTAL_DEMAND + h2: total_demand})
dict_values[KPI][KPI_SCALARS_DICT].update(
{
TOTAL_DEMAND
+ h2
+ SUFFIX_ELECTRICITY_EQUIVALENT: dict_values[KPI][KPI_SCALARS_DICT][
TOTAL_DEMAND + h2
]
* DEFAULT_WEIGHTS_ENERGY_CARRIERS[h2][VALUE]
}
)
dict_values[KPI][KPI_SCALARS_DICT].update(
{
TOTAL_DEMAND
+ SUFFIX_ELECTRICITY_EQUIVALENT: dict_values[KPI][KPI_SCALARS_DICT][
TOTAL_DEMAND + electricity + SUFFIX_ELECTRICITY_EQUIVALENT
]
+ dict_values[KPI][KPI_SCALARS_DICT][
TOTAL_DEMAND + h2 + SUFFIX_ELECTRICITY_EQUIVALENT
]
}
)
dict_values[PROJECT_DATA][LES_ENERGY_VECTOR_S].update({h2: h2})
E3.add_levelized_cost_of_energy_carriers(dict_values)
expected_value = {
ATTRIBUTED_COSTS
+ electricity: npc
* dict_values[KPI][KPI_SCALARS_DICT][
TOTAL_DEMAND + electricity + SUFFIX_ELECTRICITY_EQUIVALENT
]
/ dict_values[KPI][KPI_SCALARS_DICT][
TOTAL_DEMAND + SUFFIX_ELECTRICITY_EQUIVALENT
],
ATTRIBUTED_COSTS
+ h2: npc
* dict_values[KPI][KPI_SCALARS_DICT][
TOTAL_DEMAND + h2 + SUFFIX_ELECTRICITY_EQUIVALENT
]
/ dict_values[KPI][KPI_SCALARS_DICT][
TOTAL_DEMAND + SUFFIX_ELECTRICITY_EQUIVALENT
],
}
expected_value.update(
{
LCOeleq
+ electricity: dict_values[KPI][KPI_SCALARS_DICT][
ATTRIBUTED_COSTS + electricity
]
* dict_values[ECONOMIC_DATA][CRF][VALUE]
/ dict_values[KPI][KPI_SCALARS_DICT][TOTAL_DEMAND + electricity],
LCOeleq
+ h2: dict_values[KPI][KPI_SCALARS_DICT][ATTRIBUTED_COSTS + h2]
* dict_values[ECONOMIC_DATA][CRF][VALUE]
/ dict_values[KPI][KPI_SCALARS_DICT][TOTAL_DEMAND + h2],
LCOeleq: npc
* dict_values[ECONOMIC_DATA][CRF][VALUE]
/ dict_values[KPI][KPI_SCALARS_DICT][
TOTAL_DEMAND + SUFFIX_ELECTRICITY_EQUIVALENT
],
}
)
for sector in [electricity, h2]:
for kpi in [ATTRIBUTED_COSTS, LCOeleq]:
assert (
kpi + sector in dict_values[KPI][KPI_SCALARS_DICT]
), f"The KPI {kpi+sector} is not in dict_values[KPI][KPI_SCALARS_DICT]."
assert (
dict_values[KPI][KPI_SCALARS_DICT][kpi + sector]
== expected_value[kpi + sector]
), f"The expected {kpi + sector} of {expected_value[kpi + sector]} is not equal to the calculated result {dict_values[KPI][KPI_SCALARS_DICT][kpi + sector]}."
assert (
LCOeleq in dict_values[KPI][KPI_SCALARS_DICT]
), f"The KPI {LCOeleq} is not in dict_values[KPI][KPI_SCALARS_DICT]"
assert (
dict_values[KPI][KPI_SCALARS_DICT][LCOeleq] == expected_value[LCOeleq]
), f"The expected {LCOeleq} of {expected_value[LCOeleq]} is not equal to the calculated result {dict_values[KPI][KPI_SCALARS_DICT][LCOeleq]}."
def evaluate_dict(dict_values, results_main, results_meta):
"""
Parameters
----------
dict_values: dict
simulation parameters
results_main: DataFrame
oemof simulation results as output by processing.results()
results_meta: DataFrame
oemof simulation meta information as output by processing.meta_results()
Returns
-------
"""
dict_values.update(
{
KPI: {
KPI_COST_MATRIX: pd.DataFrame(columns=KPI_COST_MATRIX_ENTRIES),
KPI_SCALAR_MATRIX: pd.DataFrame(columns=KPI_SCALAR_MATRIX_ENTRIES),
KPI_SCALARS_DICT: {},
}
}
)
bus_data = {}
# Store all information related to busses in bus_data
for bus in dict_values[ENERGY_BUSSES]:
# Read all energy flows from busses
bus_data.update({bus: solph.views.node(results_main, bus)})
logging.info("Evaluating optimized capacities and dispatch.")
# Evaluate timeseries and store to a large DataFrame for each bus:
E1.get_timeseries_per_bus(dict_values, bus_data)
# Store all information related to storages in bus_data, as storage capacity acts as a bus
for storage in dict_values[ENERGY_STORAGE]:
bus_data.update(
{
dict_values[ENERGY_STORAGE][storage][LABEL]: solph.views.node(
results_main, dict_values[ENERGY_STORAGE][storage][LABEL],
)
}
)
E1.get_storage_results(
dict_values[SIMULATION_SETTINGS],
bus_data[dict_values[ENERGY_STORAGE][storage][LABEL]],
dict_values[ENERGY_STORAGE][storage],
)
for storage_item in [STORAGE_CAPACITY, INPUT_POWER, OUTPUT_POWER]:
E2.get_costs(
dict_values[ENERGY_STORAGE][storage][storage_item],
dict_values[ECONOMIC_DATA],
)
E2.lcoe_assets(dict_values[ENERGY_STORAGE][storage], ENERGY_STORAGE)
for storage_item in [STORAGE_CAPACITY, INPUT_POWER, OUTPUT_POWER]:
store_result_matrix(
dict_values[KPI], dict_values[ENERGY_STORAGE][storage][storage_item]
)
if (
dict_values[ENERGY_STORAGE][storage][INPUT_BUS_NAME]
in dict_values[OPTIMIZED_FLOWS].keys()
) or (
dict_values[ENERGY_STORAGE][storage][OUTPUT_BUS_NAME]
in dict_values[OPTIMIZED_FLOWS].keys()
):
bus_name = dict_values[ENERGY_STORAGE][storage][INPUT_BUS_NAME]
timeseries_name = (
dict_values[ENERGY_STORAGE][storage][LABEL]
+ " ("
+ str(
round(
dict_values[ENERGY_STORAGE][storage][STORAGE_CAPACITY][
OPTIMIZED_ADD_CAP
][VALUE],
1,
)
)
+ dict_values[ENERGY_STORAGE][storage][STORAGE_CAPACITY][
OPTIMIZED_ADD_CAP
][UNIT]
+ ") SOC"
)
dict_values[OPTIMIZED_FLOWS][bus_name][timeseries_name] = dict_values[
ENERGY_STORAGE
][storage]["timeseries_soc"]
for group in [ENERGY_CONVERSION, ENERGY_PRODUCTION, ENERGY_CONSUMPTION]:
for asset in dict_values[group]:
E1.get_results(
settings=dict_values[SIMULATION_SETTINGS],
bus_data=bus_data,
dict_asset=dict_values[group][asset],
asset_group=group,
)
E2.get_costs(dict_values[group][asset], dict_values[ECONOMIC_DATA])
E2.lcoe_assets(dict_values[group][asset], group)
store_result_matrix(dict_values[KPI], dict_values[group][asset])
logging.info("Evaluating key performance indicators of the system")
E3.all_totals(dict_values)
E3.total_demand_and_excess_each_sector(dict_values)
E3.add_total_feedin_electricity_equivaluent(dict_values)
E3.add_levelized_cost_of_energy_carriers(dict_values)
E3.add_total_renewable_and_non_renewable_energy_origin(dict_values)
E3.add_renewable_share_of_local_generation(dict_values)
E3.add_renewable_factor(dict_values)
# E3.add_degree_of_sector_coupling(dict_values) feature not finished
E3.add_onsite_energy_fraction(dict_values)
E3.add_onsite_energy_matching(dict_values)
E3.add_degree_of_autonomy(dict_values)
# Tests and checks
logging.info("Running validity checks.")
E4.minimal_renewable_share_test(dict_values)
E4.detect_excessive_excess_generation_in_bus(dict_values)