def test_renewable_share_of_local_generation_one_sector():
""" """
dict_res = {
PROJECT_DATA: {LES_ENERGY_VECTOR_S: {electricity: electricity}},
KPI: {
KPI_SCALARS_DICT: {
TOTAL_NON_RENEWABLE_GENERATION_IN_LES: 50,
TOTAL_RENEWABLE_GENERATION_IN_LES: 50,
},
KPI_UNCOUPLED_DICT: {
TOTAL_RENEWABLE_GENERATION_IN_LES: {electricity: 50},
TOTAL_NON_RENEWABLE_GENERATION_IN_LES: {electricity: 50},
},
},
}
E3.add_renewable_share_of_local_generation(dict_res)
assert (
RENEWABLE_SHARE_OF_LOCAL_GENERATION in dict_res[KPI][KPI_UNCOUPLED_DICT]
), f"KPI {RENEWABLE_SHARE_OF_LOCAL_GENERATION} not added to dict_values[KPI][KPI_UNCOUPLED_DICT]"
assert (
electricity
in dict_res[KPI][KPI_UNCOUPLED_DICT][RENEWABLE_SHARE_OF_LOCAL_GENERATION]
), f"{RENEWABLE_SHARE_OF_LOCAL_GENERATION} of sector {electricity} not defined."
assert (
dict_res[KPI][KPI_UNCOUPLED_DICT][RENEWABLE_SHARE_OF_LOCAL_GENERATION][
electricity
]
== 0.5
), f"{RENEWABLE_SHARE_OF_LOCAL_GENERATION} of sector {electricity} not of expected value."
assert (
dict_res[KPI][KPI_SCALARS_DICT][RENEWABLE_SHARE_OF_LOCAL_GENERATION] == 0.5
), f"System-wide {RENEWABLE_SHARE_OF_LOCAL_GENERATION} not of expected value."
def test_renewable_share_of_local_generation_two_sectors():
dict_res = {
PROJECT_DATA: {LES_ENERGY_VECTOR_S: {electricity: electricity, h2: h2}},
KPI: {
KPI_SCALARS_DICT: {
TOTAL_NON_RENEWABLE_GENERATION_IN_LES: 100,
TOTAL_RENEWABLE_GENERATION_IN_LES: 100,
},
KPI_UNCOUPLED_DICT: {
TOTAL_RENEWABLE_GENERATION_IN_LES: {electricity: 50, h2: 50},
TOTAL_NON_RENEWABLE_GENERATION_IN_LES: {electricity: 50, h2: 50},
},
},
}
E3.add_renewable_share_of_local_generation(dict_res)
exp_res_sector = {electricity: 0.5, h2: 0.5}
assert RENEWABLE_SHARE_OF_LOCAL_GENERATION in dict_res[KPI][KPI_UNCOUPLED_DICT]
for k in [electricity, h2]:
assert (
k in dict_res[KPI][KPI_UNCOUPLED_DICT][RENEWABLE_SHARE_OF_LOCAL_GENERATION]
), f"{RENEWABLE_SHARE_OF_LOCAL_GENERATION} of sector {k} not added."
assert (
dict_res[KPI][KPI_UNCOUPLED_DICT][RENEWABLE_SHARE_OF_LOCAL_GENERATION][k]
== exp_res_sector[k]
), f"Expected value of {RENEWABLE_SHARE_OF_LOCAL_GENERATION} for vector {k} not reached."
assert (
dict_res[KPI][KPI_SCALARS_DICT][RENEWABLE_SHARE_OF_LOCAL_GENERATION] == 0.5
), f"Expected value of sector-wide {RENEWABLE_SHARE_OF_LOCAL_GENERATION} not reached."
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)