def test_benchmark_ABE_grid_pv_bat(self, margs):
r"""
Benchmark test for using a grid connected PV system with storage. In this case, the excess production should be used to charge the battery.
"""
# define the two cases needed for comparison (grid + PV) and (grid + PV + battery)
use_case = ["AB_grid_PV", "ABE_grid_PV_battery"]
# define an empty dictionary for excess electricity
excess = {}
for case in use_case:
main(
overwrite=True,
display_output="warning",
path_input_folder=os.path.join(TEST_INPUT_PATH, case),
input_type=CSV_EXT,
path_output_folder=os.path.join(TEST_OUTPUT_PATH, case),
)
busses_flow = pd.read_excel(
os.path.join(TEST_OUTPUT_PATH, case,
"timeseries_all_busses.xlsx"),
sheet_name=bus_suffix("Electricity"),
)
# compute the sum of the excess electricity for all timesteps
excess[case] = sum(busses_flow[bus_suffix("Electricity") + EXCESS +
AUTO_SINK])
# compare the total excess electricity between the two cases
assert excess["AB_grid_PV"] < excess["ABE_grid_PV_battery"]
def test_benchmark_AB_grid_pv(self, margs):
r"""
Benchmark test for simple case grid connected PV. Since the PV is already installed, this tests makes sure that the PV generation is totally used to supply the load and the rest in take from the grid.
"""
use_case = "AB_grid_PV"
main(
overwrite=True,
display_output="warning",
path_input_folder=os.path.join(TEST_INPUT_PATH, use_case),
input_type=CSV_EXT,
path_output_folder=os.path.join(TEST_OUTPUT_PATH, use_case),
)
df_busses_flow = pd.read_excel(
os.path.join(TEST_OUTPUT_PATH, use_case,
"timeseries_all_busses.xlsx"),
sheet_name=bus_suffix("Electricity"),
)
# make the time the index
df_busses_flow = df_busses_flow.set_index("Unnamed: 0")
# compute the sum of the in and out of the electricity bus
df_busses_flow["net_sum"] = df_busses_flow.sum(axis=1)
# make sure the sum of the bus flow is always zero (there are rounding errors)
assert df_busses_flow.net_sum.map(lambda x: 0
if x < 1e-4 else 1).sum() == 0
def test_benchmark_AFG_grid_heatpump_heat(self, margs):
r"""
Benchmark test for a sector coupled energy system, including electricity and heat demand. A heat pump is used as a sector coupling asset. Both an electricity and heat DSO are present. The electricity tariff is defined as a time series. The heat pump is only used when its cost (energy_price/efficiency) is less than the heat DSO price.
"""
use_case = "AFG_grid_heatpump_heat"
main(
overwrite=True,
display_output="warning",
path_input_folder=os.path.join(TEST_INPUT_PATH, use_case),
input_type=CSV_EXT,
path_output_folder=os.path.join(TEST_OUTPUT_PATH, use_case),
)
# read json with results file
with open(
os.path.join(TEST_OUTPUT_PATH, use_case,
"json_with_results.json"), "r") as results:
data = json.load(results)
# read excel sheet with time series
busses_flow = pd.read_excel(
os.path.join(TEST_OUTPUT_PATH, use_case,
"timeseries_all_busses.xlsx"),
sheet_name=bus_suffix("Heat"),
)
# create dict with electricity prices
electricity_price = data[ENERGY_PROVIDERS]["Grid_DSO"][ENERGY_PRICE][
VALUE][VALUE]
# compare cost of using heat pump with electricity price to heat price
cost_of_using_heatpump = "electricity_price[i] / data[ENERGY_CONVERSION]['heat_pump'][EFFICIENCY][VALUE] comp.data[ENERGY_PROVIDERS]['Heat_DSO'][ENERGY_PRICE][VALUE]"
cost_of_using_heat_dso = (
"data[ENERGY_PROVIDERS]['Heat_DSO'][ENERGY_PRICE][VALUE]")
for i in range(0, len(electricity_price)):
if (electricity_price[i] /
data[ENERGY_CONVERSION]["heat_pump"][EFFICIENCY][VALUE] >
data[ENERGY_PROVIDERS]["Heat_DSO"][ENERGY_PRICE][VALUE]):
assert busses_flow["Heat_DSO_consumption_period"][i] == approx(
abs(busses_flow["demand_heat"][i])
), f"Even though the marginal costs to use the heat pump are higher than the heat DSO price with {cost_of_using_heatpump} comp. {cost_of_using_heat_dso}, the heat DSO is not solely used for energy supply."
else:
assert busses_flow["heat_pump"][i] == approx(
abs(busses_flow["demand_heat"][i]))
def test_benchmark_AE_grid_battery(self, margs):
r"""
Benchmark test for simple case grid and battery scenario. The grid should solely be used to feed the load.
"""
use_case = "AE_grid_battery"
main(
overwrite=True,
display_output="warning",
path_input_folder=os.path.join(TEST_INPUT_PATH, use_case),
input_type=CSV_EXT,
path_output_folder=os.path.join(TEST_OUTPUT_PATH, use_case),
)
df_busses_flow = pd.read_excel(
os.path.join(TEST_OUTPUT_PATH, use_case,
"timeseries_all_busses.xlsx"),
sheet_name=bus_suffix("Electricity"),
)
# make the time the index
df_busses_flow = df_busses_flow.set_index("Unnamed: 0")
# make sure battery is not used
assert sum(df_busses_flow["battery"]) == 0
def test_benchmark_AE_grid_battery_peak_pricing(self, margs):
r"""
Benchmark test for electricity grid peak demand pricing. To evaluate this, a battery is used. The battery should be charged at instances before the grid supplies peak demand. The battery is discharged when demand is higher than peak demand and charged when demand is smaller than peak demand.
"""
use_case = "AE_grid_battery_peak_pricing"
main(
overwrite=True,
display_output="warning",
path_input_folder=os.path.join(TEST_INPUT_PATH, use_case),
input_type=CSV_EXT,
path_output_folder=os.path.join(TEST_OUTPUT_PATH, use_case),
)
# read json with results file
with open(os.path.join(TEST_OUTPUT_PATH, use_case, JSON_WITH_RESULTS),
"r") as results:
data = json.load(results)
peak_demand = [
data[ENERGY_CONVERSION]
["Electricity grid DSO_consumption_period_1"][OPTIMIZED_ADD_CAP]
[VALUE],
data[ENERGY_CONVERSION]
["Electricity grid DSO_consumption_period_2"][OPTIMIZED_ADD_CAP]
[VALUE],
data[ENERGY_CONVERSION]
["Electricity grid DSO_consumption_period_2"][OPTIMIZED_ADD_CAP]
[VALUE],
]
# read timeseries_all_busses excel file
busses_flow = pd.read_excel(
os.path.join(TEST_OUTPUT_PATH, use_case,
"timeseries_all_busses.xlsx"),
sheet_name=bus_suffix("Electricity"),
)
# make the time the index
busses_flow = busses_flow.set_index("Unnamed: 0")
# read the columns with the values to be used
DSO_periods = [
busses_flow["Electricity grid DSO_consumption_period_1"],
busses_flow["Electricity grid DSO_consumption_period_2"],
busses_flow["Electricity grid DSO_consumption_period_3"],
]
demand = busses_flow["demand_01"]
battery_charge = busses_flow["battery"]
# todo storage_discharge calculation should be replaced by timeseries as soon as that is stored to excel or if json is accessed
battery_discharge = (
abs(busses_flow["demand_01"]) -
busses_flow["Electricity grid DSO_consumption_period_1"] -
busses_flow["Electricity grid DSO_consumption_period_2"] -
busses_flow["Electricity grid DSO_consumption_period_3"]
) # todo: replace this by discharge column when implemented
# look for peak demand in period
for j in range(0, 3):
for i in range(0, len(DSO_periods[1])):
# When the DSO is supplying peak demand while demand is smaller than supplied electricity.
# Then, the battery is charged.
if (DSO_periods[j][i] == peak_demand[j]
and abs(demand[i]) < DSO_periods[j][i]):
assert abs(battery_charge[i]) > 0
# When DSO supplies peak demand and demand is larger then the peak demand,
# Then, the battery has to be discharged
if (DSO_periods[j][i] == peak_demand[j]
and abs(demand[i]) > DSO_periods[j][i]):
assert abs(battery_discharge[i]) > 0
# If DSO supplies peak demand and the demand is larger then the supply,
# then, in the previous timestep the battery must be charged,
# as long as in the previous timestep the demand was smaller then the supply.
if (DSO_periods[j][i] == peak_demand[j]
and abs(demand[i]) > DSO_periods[j][i]
and DSO_periods[j][i - 1] > abs(demand[i - 1])):
assert abs(battery_charge[i - 1]) > 0