def test_custom_weights_and_divisions():
clusterer = clustering.Clustering(
power_sources=['tower'],
solar_resource_file=
"resource_files/solar/35.2018863_-101.945027_psmv3_60_2012.csv")
clusterer.use_default_weights = True
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
sim_start_end_times_first = clusterer.get_sim_start_end_times(0)
sim_start_end_times_last = clusterer.get_sim_start_end_times(n_clusters -
1)
exemplars = list(clusterer.clusters['exemplars'])
weights, divisions, bounds = clusterer.get_default_weights()
# Reinstantiate clusterer object
clusterer = clustering.Clustering(
power_sources=['tower'],
solar_resource_file=
"resource_files/solar/35.2018863_-101.945027_psmv3_60_2012.csv")
clusterer.use_default_weights = False
clusterer.weights = weights
clusterer.divisions = divisions
clusterer.bounds = bounds
clusterer.run_clustering()
assert len(clusterer.clusters['count']) == n_clusters
assert clusterer.get_sim_start_end_times(0) == sim_start_end_times_first
assert clusterer.get_sim_start_end_times(n_clusters -
1) == sim_start_end_times_last
assert list(clusterer.clusters['exemplars']) == exemplars
def test_initial_state_heuristics():
# Battery heuristics
clusterer = clustering.Clustering(
power_sources=['tower', 'pv', 'battery'],
solar_resource_file=
"resource_files/solar/35.2018863_-101.945027_psmv3_60_2012.csv")
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
cluster_id = 0
assert clusterer.battery_soc_heuristic(clusterid=cluster_id) == 20
assert clusterer.battery_soc_heuristic(clusterid=n_clusters - 1) == 20
initial_battery_states = {
'day': [
clusterer.sim_start_days[cluster_id] - 4,
clusterer.sim_start_days[cluster_id] - 3
],
'soc': [0, 100], # state-of-charge [%]
}
# Note: sim_start_days has the day of the year (Jan. 1 = 0) of the first 'production' day in each exemplar group, not the 'previous' day.
# The initial state given by the heuristic is for the beginning of the 'previous' day.
# Therefore, if you want to get the initial state for the cluster ID corresponding to days 2-5 (production days 3 and 4),
# the closest states you can provide are for (the beginning of) days 0 and 1, as the calculated initial state would be for
# the beginning of day 2, the 'previous' day.
assert clusterer.battery_soc_heuristic(
clusterid=cluster_id,
initial_states=initial_battery_states) == approx(98.18, rel=1e-3)
# CSP heuristics
clusterer = clustering.Clustering(
power_sources=['tower', 'pv', 'battery'],
solar_resource_file=
"resource_files/solar/35.2018863_-101.945027_psmv3_60_2012.csv")
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
cluster_id = 0
assert clusterer.csp_initial_state_heuristic(clusterid=cluster_id) == (
10, False, 0.) # == (initial_soc, is_cycle_on, initial_cycle_load)
assert clusterer.csp_initial_state_heuristic(clusterid=n_clusters -
1) == (10, False, 0.)
initial_csp_states = {
'day': [
clusterer.sim_start_days[cluster_id] - 4,
clusterer.sim_start_days[cluster_id] - 3
],
'soc': [20, 89], # state-of-charge [%]
'load': [0, 0], # power cycle load [%]
}
assert clusterer.csp_initial_state_heuristic(clusterid=cluster_id, solar_multiple=3, initial_states=initial_csp_states) == \
(approx(89., 1e-3), False, 0.) # == (initial_soc, is_cycle_on, initial_cycle_load)
def test_too_high_clusters():
clusterer = clustering.Clustering(
power_sources=['tower'],
solar_resource_file=
"resource_files/solar/35.2018863_-101.945027_psmv3_60_2012.csv")
clusterer.n_cluster = 1000 # make unachievably high
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 181
assert clusterer.get_sim_start_end_times(0) == (0, 96)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8640, 8736)
def test_alternate_solar_file():
clusterer = clustering.Clustering(
power_sources=['tower'],
solar_resource_file=
"resource_files/solar/34.865371_-116.783023_psmv3_60_tmy.csv")
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (48, 144)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8016, 8112)
assert list(clusterer.clusters['exemplars']) == \
[1, 5, 6, 15, 19, 23, 39, 78, 89, 95, 102, 124, 131, 136, 139, 143, 158, 162, 164, 167]
def test_minimum_specification():
clusterer = clustering.Clustering(
power_sources=['tower'],
solar_resource_file=
"resource_files/solar/35.2018863_-101.945027_psmv3_60_2012.csv")
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (864, 960)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8496, 8592)
assert list(clusterer.clusters['exemplars']) == \
[18, 21, 23, 29, 34, 42, 64, 69, 96, 107, 111, 118, 131, 134, 139, 147, 164, 172, 175, 177]
def test_wind_defaults():
clusterer = clustering.Clustering(
power_sources=['wind'],
solar_resource_file=
"resource_files/solar/35.2018863_-101.945027_psmv3_60_2012.csv")
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (240, 336)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (7920, 8016)
assert list(clusterer.clusters['exemplars']) == \
[5, 6, 7, 10, 28, 32, 41, 52, 57, 72, 86, 90, 108, 111, 121, 126, 131, 134, 154, 165]
def test_price_parameter():
clusterer = clustering.Clustering(
power_sources=['tower'],
solar_resource_file=
"resource_files/solar/35.2018863_-101.945027_psmv3_60_2012.csv",
price_data=parse_price_data(
"resource_files/grid/pricing-data-2015-IronMtn-002_factors.csv"))
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (960, 1056)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8496, 8592)
assert list(clusterer.clusters['exemplars']) == \
[20, 21, 22, 29, 34, 42, 62, 69, 95, 96, 110, 111, 120, 131, 138, 139, 151, 164, 166, 177]
def test_wind_resource_parameter():
clusterer = clustering.Clustering(
power_sources=['wind'],
solar_resource_file=
"resource_files/solar/35.2018863_-101.945027_psmv3_60_2012.csv",
wind_resource_data=parse_wind_file(
"resource_files/wind/35.2018863_-101.945027_windtoolkit_2012_60min_100m.srw"
))
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (336, 432)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8640, 8736)
assert list(clusterer.clusters['exemplars']) == \
[7, 38, 41, 54, 55, 58, 61, 86, 95, 116, 125, 138, 146, 154, 158, 159, 162, 174, 175, 180]
def test_annual_array_from_cluster_exemplars():
# Run clustering on weather file
clusterer = clustering.Clustering(
power_sources=['tower'],
solar_resource_file=
"resource_files/solar/34.865371_-116.783023_psmv3_60_tmy.csv")
clusterer.ndays = 2
clusterer.run_clustering()
# Format result to get all days to simulate
simulation_days = []
for day in clusterer.sim_start_days:
simulation_days.append(
day + 1) # change from base 0 to 1 (Jan. 1 is now = 1)
simulation_days.append(day + 2)
# Read separately simulated powers (for all days) and zero-out non-exemplar days
filename = 'tests/hybrid/tower_model_annual_powers.csv'
df = pd.read_csv(filename, sep=',', header=0, parse_dates=[0])
df['day_of_year'] = pd.DatetimeIndex(
df['timestamp']).dayofyear # add day of year column
exemplar_days_indices = df['day_of_year'].isin(
simulation_days) # exemplar days filter
df = df.assign(system_power_kW_exemplars_only=(
df.system_power_kW).where(exemplar_days_indices, 0))
# Generate powers for all days from only exemplar days
df['system_power_kW_from_exemplars'] = clusterer.compute_annual_array_from_cluster_exemplar_data(
list(df['system_power_kW_exemplars_only']))
# Ensure exemplar days haven't changed
assert all(df['system_power_kW'][exemplar_days_indices] ==
df['system_power_kW_from_exemplars'][exemplar_days_indices])
# Basic tests
assert len(df['system_power_kW_from_exemplars']) == 8760
assert min(df['system_power_kW_from_exemplars']) == approx(-4388.5, 1e-4)
assert max(df['system_power_kW_from_exemplars']) == approx(117783, 1e-4)
assert sum(df['system_power_kW_from_exemplars']) == approx(566354062, 1e-4)
# Compare annual datas
assert (sum(df['system_power_kW_from_exemplars']) / \
sum(df['system_power_kW']) - 1) * 100 == approx(-0.448, 1e-3) # percent diff of sums [%]
assert (((df['system_power_kW_from_exemplars'] - df['system_power_kW'])**2).sum() / \
len(df['system_power_kW']))**(1/2) == approx(27134, 1e-3) # root-mean-square deviation
def test_cluster_avgs_from_timeseries():
# Run clustering on weather file
clusterer = clustering.Clustering(
power_sources=['tower'],
solar_resource_file=
"resource_files/solar/34.865371_-116.783023_psmv3_60_tmy.csv")
clusterer.ndays = 2
clusterer.run_clustering()
# Format result to get all days to simulate
simulation_days = []
for day in clusterer.sim_start_days:
simulation_days.append(
day + 1) # change from base 0 to 1 (Jan. 1 is now = 1)
simulation_days.append(day + 2)
# Read separately simulated powers (for all days) and zero-out non-exemplar days
filename = 'tests/hybrid/tower_model_annual_powers.csv'
df = pd.read_csv(filename, sep=',', header=0, parse_dates=[0])
df['day_of_year'] = pd.DatetimeIndex(
df['timestamp']).dayofyear # add day of year column
exemplar_days_indices = df['day_of_year'].isin(
simulation_days) # exemplar days filter
df = df.assign(system_power_kW_exemplars_only=(
df.system_power_kW).where(exemplar_days_indices, 0))
# Generate powers for all days from only exemplar days
df['system_power_kW_from_exemplars'] = clusterer.compute_annual_array_from_cluster_exemplar_data(
list(df['system_power_kW_exemplars_only']))
# Compute cluster averages
cluster_averages = clusterer.compute_cluster_avg_from_timeseries(
list(df['system_power_kW_from_exemplars']))
assert len(cluster_averages) == len(clusterer.clusters['count'])
list_lengths = [len(list_) for list_ in cluster_averages]
assert min(list_lengths) == (1 + clusterer.ndays + 1) * 24 * int(
len(df['system_power_kW_from_exemplars']) / 8760)
assert max(list_lengths) == min(list_lengths)
assert sum(cluster_averages[0]) == approx(495893, 1e-3)
assert sum(cluster_averages[-1]) == approx(2734562, 1e-3)
def test_various_simulation_days():
clusterer = clustering.Clustering(
power_sources=['tower'],
solar_resource_file=
"resource_files/solar/35.2018863_-101.945027_psmv3_60_2012.csv")
clusterer.ndays = 1
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (72, 144)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8688, 8760)
assert list(clusterer.clusters['exemplars']) == \
[3, 7, 8, 23, 42, 65, 84, 117, 126, 190, 237, 254, 259, 281, 282, 294, 306, 310, 333, 362]
clusterer.ndays = 3
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (0, 120)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8424, 8544)
assert list(clusterer.clusters['exemplars']) == \
[0, 7, 15, 26, 27, 42, 43, 63, 73, 74, 75, 77, 78, 80, 84, 89, 103, 107, 112, 117]
def test_default_weights_and_divisions():
clusterer = clustering.Clustering(
power_sources=['trough'],
solar_resource_file=
"resource_files/solar/34.865371_-116.783023_psmv3_60_tmy.csv")
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (48, 144)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8016, 8112)
assert list(clusterer.clusters['exemplars']) == \
[1, 5, 6, 15, 19, 23, 39, 78, 89, 95, 102, 124, 131, 136, 139, 143, 158, 162, 164, 167]
clusterer = clustering.Clustering(
power_sources=['pv'],
solar_resource_file=
"resource_files/solar/34.865371_-116.783023_psmv3_60_tmy.csv")
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (288, 384)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8640, 8736)
assert list(clusterer.clusters['exemplars']) == \
[6, 18, 22, 36, 44, 49, 64, 90, 102, 105, 108, 110, 116, 128, 136, 140, 151, 172, 174, 180]
clusterer = clustering.Clustering(
power_sources=['wind'],
solar_resource_file=
"resource_files/solar/34.865371_-116.783023_psmv3_60_tmy.csv")
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (0, 96)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8400, 8496)
assert list(clusterer.clusters['exemplars']) == \
[0, 6, 7, 31, 49, 50, 53, 55, 82, 86, 109, 116, 134, 144, 149, 153, 165, 168, 172, 175]
clusterer = clustering.Clustering(
power_sources=['trough', 'pv', 'battery'],
solar_resource_file=
"resource_files/solar/34.865371_-116.783023_psmv3_60_tmy.csv")
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 19
assert clusterer.get_sim_start_end_times(0) == (48, 144)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8016, 8112)
assert list(clusterer.clusters['exemplars']) == \
[1, 2, 23, 27, 34, 39, 57, 58, 89, 95, 102, 124, 131, 147, 155, 158, 163, 164, 167]
clusterer = clustering.Clustering(
power_sources=['trough', 'pv'],
solar_resource_file=
"resource_files/solar/34.865371_-116.783023_psmv3_60_tmy.csv")
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (48, 144)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8016, 8112)
assert list(clusterer.clusters['exemplars']) == \
[1, 5, 6, 15, 19, 23, 39, 78, 89, 102, 114, 124, 131, 136, 139, 143, 158, 162, 164, 167]
clusterer = clustering.Clustering(
power_sources=['trough', 'wind'],
solar_resource_file=
"resource_files/solar/34.865371_-116.783023_psmv3_60_tmy.csv")
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (192, 288)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8256, 8352)
assert list(clusterer.clusters['exemplars']) == \
[4, 5, 19, 25, 39, 60, 80, 94, 96, 115, 123, 131, 134, 136, 148, 152, 164, 167, 169, 172]
clusterer = clustering.Clustering(
power_sources=['pv', 'battery'],
solar_resource_file=
"resource_files/solar/34.865371_-116.783023_psmv3_60_tmy.csv")
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (384, 480)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8448, 8544)
assert list(clusterer.clusters['exemplars']) == \
[8, 18, 22, 27, 58, 76, 92, 102, 105, 109, 116, 122, 125, 130, 136, 144, 153, 167, 172, 176]
clusterer = clustering.Clustering(
power_sources=['pv', 'wind'],
solar_resource_file=
"resource_files/solar/34.865371_-116.783023_psmv3_60_tmy.csv")
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (240, 336)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8256, 8352)
assert list(clusterer.clusters['exemplars']) == \
[5, 7, 8, 31, 45, 49, 50, 62, 96, 105, 115, 126, 128, 134, 144, 150, 153, 165, 168, 172]
clusterer = clustering.Clustering(
power_sources=['wind', 'battery'],
solar_resource_file=
"resource_files/solar/34.865371_-116.783023_psmv3_60_tmy.csv")
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (288, 384)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8640, 8736)
assert list(clusterer.clusters['exemplars']) == \
[6, 7, 31, 32, 33, 36, 49, 50, 55, 82, 101, 109, 144, 152, 153, 165, 166, 171, 172, 180]
clusterer = clustering.Clustering(
power_sources=['pv', 'wind', 'battery'],
solar_resource_file=
"resource_files/solar/34.865371_-116.783023_psmv3_60_tmy.csv")
clusterer.run_clustering()
n_clusters = len(clusterer.clusters['count'])
assert n_clusters == 20
assert clusterer.get_sim_start_end_times(0) == (192, 288)
assert clusterer.get_sim_start_end_times(n_clusters - 1) == (8256, 8352)
assert list(clusterer.clusters['exemplars']) == \
[4, 5, 7, 8, 15, 31, 47, 49, 50, 88, 96, 105, 115, 126, 130, 134, 148, 150, 168, 172]