def test_tower_with_dispatch_model(site):
"""Testing pySSC tower model using HOPP built-in dispatch model"""
expected_energy = 3842225.688
interconnection_size_kw = 50000
technologies = {
'tower': {
'cycle_capacity_kw': 50 * 1000,
'solar_multiple': 2.0,
'tes_hours': 6.0,
'optimize_field_before_sim': False
}
}
system = HybridSimulation(technologies,
site,
interconnect_kw=interconnection_size_kw,
dispatch_options={
'is_test_start_year': True,
'is_test_end_year': True
})
system.tower.value('helio_width', 10.0)
system.tower.value('helio_height', 10.0)
system.tower.value('h_tower', 117.7)
system.tower.value('rec_height', 11.3)
system.tower.value('D_rec', 11.12)
system.ppa_price = (0.12, )
system.simulate()
assert system.tower.annual_energy_kwh == pytest.approx(
expected_energy, 1e-2)
# Check dispatch targets
disp_outputs = system.tower.outputs.dispatch
ssc_outputs = system.tower.outputs.ssc_time_series
for i in range(len(ssc_outputs['gen'])):
# cycle start-up allowed
target = 1 if (disp_outputs['is_cycle_generating'][i] +
disp_outputs['is_cycle_starting'][i]) > 0.01 else 0
assert target == pytest.approx(ssc_outputs['is_pc_su_allowed'][i],
1e-5)
# receiver start-up allowed
target = 1 if (disp_outputs['is_field_generating'][i] +
disp_outputs['is_field_starting'][i]) > 0.01 else 0
assert target == pytest.approx(ssc_outputs['is_rec_su_allowed'][i],
1e-5)
# cycle thermal power
start_power = system.tower.dispatch.allowable_cycle_startup_power if disp_outputs[
'is_cycle_starting'][i] else 0
target = disp_outputs['cycle_thermal_power'][i] + start_power
assert target == pytest.approx(ssc_outputs['q_dot_pc_target_on'][i],
1e-3)
# thermal energy storage state-of-charge
if i % system.dispatch_builder.options.n_roll_periods == 0:
tes_estimate = disp_outputs['thermal_energy_storage'][i]
tes_actual = ssc_outputs['e_ch_tes'][i]
assert tes_estimate == pytest.approx(tes_actual, 0.01)
def test_trough_with_dispatch_model(site):
"""Testing pySSC tower model using HOPP built-in dispatch model"""
expected_energy = 1873589.560
interconnection_size_kw = 50000
technologies = {
'trough': {
'cycle_capacity_kw': 50 * 1000,
'solar_multiple': 2.0,
'tes_hours': 6.0
}
}
system = HybridSimulation(technologies,
site,
interconnect_kw=interconnection_size_kw,
dispatch_options={
'is_test_start_year': True,
'is_test_end_year': True
})
system.ppa_price = (0.12, )
system.simulate()
assert system.trough.annual_energy_kwh == pytest.approx(
expected_energy, 1e-2)
def init_simulation_pv():
"""
Create the simulation object needed to calculate the objective of the problem
:return: The HOPP simulation as defined for this problem
"""
# Create the site for the design evaluation
site = 'irregular'
location = locations[3]
if site == 'circular':
site_data = make_circular_site(lat=location[0], lon=location[1], elev=location[2])
elif site == 'irregular':
site_data = make_irregular_site(lat=location[0], lon=location[1], elev=location[2])
else:
raise Exception("Unknown site '" + site + "'")
# Load in weather and price data files
solar_file = Path(
__file__).parent.parent / "resource_files" / "solar" / WEATHER_FILE #"Beni_Miha" / "659265_32.69_10.90_2019.csv"
grid_file = Path(__file__).parent.parent / "resource_files" / "grid" / PRICE_FILE #"tunisia_est_grid_prices.csv"
# Combine the data into a site definition
site_info = SiteInfo(site_data, solar_resource_file=solar_file, grid_resource_file=grid_file)
# set up hybrid simulation with all the required parameters
solar_size_mw = 200
battery_capacity_mwh = 15
battery_capacity_mw = 100
interconnection_size_mw = 100
technologies = {'pv': {'system_capacity_kw': solar_size_mw * 1000,
'array_type': 2,
'dc_ac_ratio': 1.1},
'battery': {'system_capacity_kwh': battery_capacity_mwh * 1000,
'system_capacity_kw': battery_capacity_mw * 1000},
'grid': interconnection_size_mw * 1000}
# Create the hybrid plant simulation
# TODO: turn these off to run full year simulation
dispatch_options = {'is_test_start_year': False,
'is_test_end_year': False,
'solver': 'gurobi_ampl',
'grid_charging': False,
'pv_charging_only': True}
# TODO: turn-on receiver and field optimization before... initial simulation
hybrid_plant = HybridSimulation(technologies,
site_info,
interconnect_kw=interconnection_size_mw * 1000,
dispatch_options=dispatch_options)
# Customize the hybrid plant assumptions here...
hybrid_plant.pv.value('inv_eff', 95.0)
hybrid_plant.pv.value('array_type', 0)
hybrid_plant.pv.dc_degradation = [0] * 25
return hybrid_plant
def test_hybrid_dispatch_financials(site):
wind_solar_battery = {key: technologies[key] for key in ('pv', 'wind', 'battery')}
hybrid_plant = HybridSimulation(wind_solar_battery, site, interconnect_mw * 1000,
dispatch_options={'grid_charging': True})
hybrid_plant.ppa_price = (0.06,)
hybrid_plant.simulate(1)
assert sum(hybrid_plant.battery.Outputs.P) < 0.0
def test_hybrid_dispatch_one_cycle_heuristic(site):
dispatch_options = {'battery_dispatch': 'one_cycle_heuristic',
'grid_charging': False}
wind_solar_battery = {key: technologies[key] for key in ('pv', 'wind', 'battery')}
hybrid_plant = HybridSimulation(wind_solar_battery, site, interconnect_mw * 1000,
dispatch_options=dispatch_options)
hybrid_plant.simulate(1)
assert sum(hybrid_plant.battery.Outputs.P) < 0.0
def test_hybrid_solar_battery_dispatch(site):
expected_objective = 20819.456
solar_battery_technologies = {k: technologies[k] for k in ('pv', 'battery')}
hybrid_plant = HybridSimulation(solar_battery_technologies, site, interconnect_mw * 1000,
dispatch_options={'grid_charging': False})
hybrid_plant.grid.value("federal_tax_rate", (0., ))
hybrid_plant.grid.value("state_tax_rate", (0., ))
hybrid_plant.pv.dc_degradation = [0.5] * 1
hybrid_plant.pv.simulate(1)
hybrid_plant.dispatch_builder.dispatch.initialize_parameters()
hybrid_plant.dispatch_builder.dispatch.update_time_series_parameters(0)
hybrid_plant.battery.dispatch.initial_SOC = hybrid_plant.battery.dispatch.minimum_soc # Set to min SOC
n_look_ahead_periods = hybrid_plant.dispatch_builder.options.n_look_ahead_periods
# This was done because the default peak prices coincide with solar production...
available_resource = hybrid_plant.pv.generation_profile[0:n_look_ahead_periods]
prices = [0.] * len(available_resource)
for t in hybrid_plant.dispatch_builder.pyomo_model.forecast_horizon:
if available_resource[t] > 0.0:
prices[t] = 30.0
else:
prices[t] = 110.0
hybrid_plant.grid.dispatch.electricity_sell_price = prices
hybrid_plant.grid.dispatch.electricity_purchase_price = prices
results = HybridDispatchBuilderSolver.glpk_solve_call(hybrid_plant.dispatch_builder.pyomo_model)
assert results.solver.termination_condition == TerminationCondition.optimal
gross_profit_objective = pyomo.value(hybrid_plant.dispatch_builder.dispatch.objective_value)
assert gross_profit_objective == pytest.approx(expected_objective, 1e-3)
available_resource = hybrid_plant.pv.generation_profile[0:n_look_ahead_periods]
dispatch_generation = hybrid_plant.pv.dispatch.generation
for t in hybrid_plant.dispatch_builder.pyomo_model.forecast_horizon:
assert dispatch_generation[t] * 1e3 == pytest.approx(available_resource[t], 1e-3)
assert sum(hybrid_plant.battery.dispatch.charge_power) > 0.0
assert sum(hybrid_plant.battery.dispatch.discharge_power) > 0.0
assert (sum(hybrid_plant.battery.dispatch.charge_power)
* hybrid_plant.battery.dispatch.round_trip_efficiency / 100.0
== pytest.approx(sum(hybrid_plant.battery.dispatch.discharge_power)))
transmission_limit = hybrid_plant.grid.value('grid_interconnection_limit_kwac')
system_generation = hybrid_plant.grid.dispatch.system_generation
for t in hybrid_plant.dispatch_builder.pyomo_model.forecast_horizon:
assert system_generation[t] * 1e3 <= transmission_limit
assert system_generation[t] * 1e3 >= 0.0
def test_hybrid_tax_incentives(site):
wind_pv_battery = {
key: technologies[key]
for key in ('pv', 'wind', 'battery')
}
hybrid_plant = HybridSimulation(
wind_pv_battery,
site,
interconnect_kw=interconnection_size_kw,
dispatch_options={'battery_dispatch': 'one_cycle_heuristic'})
hybrid_plant.ppa_price = (0.03, )
hybrid_plant.pv.dc_degradation = [0] * 25
hybrid_plant.pv._financial_model.TaxCreditIncentives.itc_fed_percent = 0.0
hybrid_plant.wind._financial_model.TaxCreditIncentives.ptc_fed_amount = (
1, )
hybrid_plant.pv._financial_model.TaxCreditIncentives.ptc_fed_amount = (2, )
hybrid_plant.battery._financial_model.TaxCreditIncentives.ptc_fed_amount = (
3, )
hybrid_plant.wind._financial_model.TaxCreditIncentives.ptc_fed_escal = 0
hybrid_plant.pv._financial_model.TaxCreditIncentives.ptc_fed_escal = 0
hybrid_plant.battery._financial_model.TaxCreditIncentives.ptc_fed_escal = 0
hybrid_plant.simulate()
ptc_wind = hybrid_plant.wind._financial_model.value("cf_ptc_fed")[1]
assert ptc_wind == approx(
hybrid_plant.wind._financial_model.value("ptc_fed_amount")[0] *
hybrid_plant.wind.annual_energy_kwh,
rel=1e-3)
ptc_pv = hybrid_plant.pv._financial_model.value("cf_ptc_fed")[1]
assert ptc_pv == approx(
hybrid_plant.pv._financial_model.value("ptc_fed_amount")[0] *
hybrid_plant.pv.annual_energy_kwh,
rel=1e-3)
ptc_batt = hybrid_plant.battery._financial_model.value("cf_ptc_fed")[1]
assert ptc_batt == approx(
hybrid_plant.battery._financial_model.value("ptc_fed_amount")[0] *
hybrid_plant.battery._financial_model.LCOS.
batt_annual_discharge_energy[1],
rel=1e-3)
ptc_hybrid = hybrid_plant.grid._financial_model.value("cf_ptc_fed")[1]
ptc_fed_amount = hybrid_plant.grid._financial_model.value(
"ptc_fed_amount")[0]
assert ptc_fed_amount == approx(1.229, rel=1e-2)
assert ptc_hybrid == approx(
ptc_fed_amount *
hybrid_plant.grid._financial_model.Outputs.cf_energy_net[1],
rel=1e-3)
def init_simulation_csp():
"""
Create the simulation object needed to calculate the objective of the problem
:return: The HOPP simulation as defined for this problem
"""
# Create the site for the design evaluation
site = 'irregular'
location = locations[3]
if site == 'circular':
site_data = make_circular_site(lat=location[0], lon=location[1], elev=location[2])
elif site == 'irregular':
site_data = make_irregular_site(lat=location[0], lon=location[1], elev=location[2])
else:
raise Exception("Unknown site '" + site + "'")
# Load in weather and price data files
solar_file = Path(
__file__).parent.parent / "resource_files" / "solar" / WEATHER_FILE #"Beni_Miha" / "659265_32.69_10.90_2019.csv"
grid_file = Path(__file__).parent.parent / "resource_files" / "grid" / PRICE_FILE #"tunisia_est_grid_prices.csv"
# Combine the data into a site definition
site_info = SiteInfo(site_data, solar_resource_file=solar_file, grid_resource_file=grid_file)
# set up hybrid simulation with all the required parameters
tower_cycle_mw = 100
interconnection_size_mw = 100
technologies = {'tower': {'cycle_capacity_kw': tower_cycle_mw * 1000,
'solar_multiple': 2.0,
'tes_hours': 12.0,
'optimize_field_before_sim': True}, # TODO: turn on
'grid': interconnection_size_mw * 1000}
# Create the hybrid plant simulation
# TODO: turn these off to run full year simulation
dispatch_options = {'is_test_start_year': False,
'is_test_end_year': False,
'solver': 'gurobi_ampl'}
# TODO: turn-on receiver and field optimization before... initial simulation
hybrid_plant = HybridSimulation(technologies,
site_info,
interconnect_kw=interconnection_size_mw * 1000,
dispatch_options=dispatch_options)
return hybrid_plant
def test_hybrid_wind_only(site):
wind_only = {'wind': technologies['wind']}
hybrid_plant = HybridSimulation(wind_only,
site,
interconnect_kw=interconnection_size_kw)
hybrid_plant.layout.plot()
hybrid_plant.ppa_price = (0.01, )
hybrid_plant.simulate(25)
aeps = hybrid_plant.annual_energies
npvs = hybrid_plant.net_present_values
assert aeps.wind == approx(33615479, 1e3)
assert aeps.hybrid == approx(33615479, 1e3)
assert npvs.wind == approx(-13692784, 1e3)
assert npvs.hybrid == approx(-13692784, 1e3)
def test_pv_wind_battery_hybrid_dispatch(site):
expected_objective = 39460.698
wind_solar_battery = {key: technologies[key] for key in ('pv', 'wind', 'battery')}
hybrid_plant = HybridSimulation(wind_solar_battery, site, interconnect_mw * 1000,
dispatch_options={'grid_charging': False,
'include_lifecycle_count': False})
hybrid_plant.grid.value("federal_tax_rate", (0., ))
hybrid_plant.grid.value("state_tax_rate", (0., ))
hybrid_plant.ppa_price = (0.06, )
hybrid_plant.pv.dc_degradation = [0.5] * 1
hybrid_plant.pv.simulate(1)
hybrid_plant.wind.simulate(1)
hybrid_plant.dispatch_builder.dispatch.initialize_parameters()
hybrid_plant.dispatch_builder.dispatch.update_time_series_parameters(0)
hybrid_plant.battery.dispatch.initial_SOC = hybrid_plant.battery.dispatch.minimum_soc # Set to min SOC
results = HybridDispatchBuilderSolver.glpk_solve_call(hybrid_plant.dispatch_builder.pyomo_model)
assert results.solver.termination_condition == TerminationCondition.optimal
gross_profit_objective = pyomo.value(hybrid_plant.dispatch_builder.dispatch.objective_value)
assert gross_profit_objective == pytest.approx(expected_objective, 1e-3)
n_look_ahead_periods = hybrid_plant.dispatch_builder.options.n_look_ahead_periods
available_resource = hybrid_plant.pv.generation_profile[0:n_look_ahead_periods]
dispatch_generation = hybrid_plant.pv.dispatch.generation
for t in hybrid_plant.dispatch_builder.pyomo_model.forecast_horizon:
assert dispatch_generation[t] * 1e3 == pytest.approx(available_resource[t], 1e-3)
available_resource = hybrid_plant.wind.generation_profile[0:n_look_ahead_periods]
dispatch_generation = hybrid_plant.wind.dispatch.generation
for t in hybrid_plant.dispatch_builder.pyomo_model.forecast_horizon:
assert dispatch_generation[t] * 1e3 == pytest.approx(available_resource[t], 1e-3)
assert sum(hybrid_plant.battery.dispatch.charge_power) > 0.0
assert sum(hybrid_plant.battery.dispatch.discharge_power) > 0.0
assert (sum(hybrid_plant.battery.dispatch.charge_power)
* hybrid_plant.battery.dispatch.round_trip_efficiency / 100.0
== pytest.approx(sum(hybrid_plant.battery.dispatch.discharge_power)))
transmission_limit = hybrid_plant.grid.value('grid_interconnection_limit_kwac')
system_generation = hybrid_plant.grid.dispatch.system_generation
for t in hybrid_plant.dispatch_builder.pyomo_model.forecast_horizon:
assert system_generation[t] * 1e3 <= transmission_limit + 1e-3
assert system_generation[t] * 1e3 >= 0.0
def test_hybrid_om_costs_error(site):
wind_pv_battery = {
key: technologies[key]
for key in ('pv', 'wind', 'battery')
}
hybrid_plant = HybridSimulation(
wind_pv_battery,
site,
interconnect_kw=interconnection_size_kw,
dispatch_options={'battery_dispatch': 'one_cycle_heuristic'})
hybrid_plant.ppa_price = (0.03, )
hybrid_plant.pv.dc_degradation = [0] * 25
hybrid_plant.battery._financial_model.SystemCosts.om_production = (1, )
try:
hybrid_plant.simulate()
except ValueError as e:
assert e
def test_hybrid_pv_only(site):
solar_only = {'pv': technologies['pv']}
hybrid_plant = HybridSimulation(solar_only,
site,
interconnect_kw=interconnection_size_kw)
hybrid_plant.layout.plot()
hybrid_plant.ppa_price = (0.01, )
hybrid_plant.pv.dc_degradation = [0] * 25
hybrid_plant.simulate()
aeps = hybrid_plant.annual_energies
npvs = hybrid_plant.net_present_values
assert aeps.pv == approx(9884106.55, 1e-3)
assert aeps.hybrid == approx(9884106.55, 1e-3)
assert npvs.pv == approx(-5121293, 1e3)
assert npvs.hybrid == approx(-5121293, 1e3)
def test_tower_pv_battery_hybrid(site):
interconnection_size_kw_test = 50000
technologies_test = {
'tower': {
'cycle_capacity_kw': 50 * 1000,
'solar_multiple': 2.0,
'tes_hours': 12.0
},
'pv': {
'system_capacity_kw': 50 * 1000
},
'battery': {
'system_capacity_kwh': 40 * 1000,
'system_capacity_kw': 20 * 1000
}
}
solar_hybrid = {
key: technologies_test[key]
for key in ('tower', 'pv', 'battery')
}
hybrid_plant = HybridSimulation(
solar_hybrid,
site,
interconnect_kw=interconnection_size_kw_test,
dispatch_options={
'is_test_start_year': True,
'is_test_end_year': True
})
hybrid_plant.ppa_price = (0.12, ) # $/kWh
hybrid_plant.pv.dc_degradation = [0] * 25
hybrid_plant.tower.value('helio_width', 10.0)
hybrid_plant.tower.value('helio_height', 10.0)
hybrid_plant.simulate()
aeps = hybrid_plant.annual_energies
npvs = hybrid_plant.net_present_values
assert aeps.pv == approx(104053614.17, 1e-3)
assert aeps.tower == approx(3769716.50, 5e-2)
assert aeps.battery == approx(-9449.70, 2e-1)
assert aeps.hybrid == approx(107882747.80, 1e-2)
assert npvs.pv == approx(45233832.23, 1e3)
def test_hybrid_dispatch_heuristic(site):
dispatch_options = {'battery_dispatch': 'heuristic',
'grid_charging': False}
wind_solar_battery = {key: technologies[key] for key in ('pv', 'wind', 'battery')}
hybrid_plant = HybridSimulation(wind_solar_battery, site, interconnect_mw * 1000,
dispatch_options=dispatch_options)
fixed_dispatch = [0.0]*6
fixed_dispatch.extend([-1.0]*6)
fixed_dispatch.extend([1.0]*6)
fixed_dispatch.extend([0.0]*6)
hybrid_plant.battery.dispatch.user_fixed_dispatch = fixed_dispatch
hybrid_plant.simulate(1)
assert sum(hybrid_plant.battery.dispatch.charge_power) > 0.0
assert sum(hybrid_plant.battery.dispatch.discharge_power) > 0.0
def test_tower_field_optimize_before_sim(site):
"""Testing pySSC tower model using HOPP built-in dispatch model"""
interconnection_size_kw = 50000
technologies = {
'tower': {
'cycle_capacity_kw': 50 * 1000,
'solar_multiple': 2.0,
'tes_hours': 6.0,
'optimize_field_before_sim': True
},
'grid': 50000
}
system = {key: technologies[key] for key in ('tower', 'grid')}
system = HybridSimulation(system,
site,
interconnect_kw=interconnection_size_kw,
dispatch_options={'is_test_start_year': True})
system.ppa_price = (0.12, )
system.tower.value('helio_width', 10.0)
system.tower.value('helio_height', 10.0)
system.tower.generate_field()
# Get old field:
field_parameters = [
'N_hel', 'D_rec', 'rec_height', 'h_tower', 'land_area_base', 'A_sf_in'
]
old_values = {}
for k in field_parameters:
old_values[k] = system.tower.value(k)
system.simulate()
new_values = {}
for k in field_parameters:
new_values[k] = system.tower.value(k)
assert system.tower.optimize_field_before_sim
for k in field_parameters:
assert old_values[k] != new_values[k]
def test_hybrid(site):
"""
Performance from Wind is slightly different from wind-only case because the solar presence modified the wind layout
"""
solar_wind_hybrid = {key: technologies[key] for key in ('pv', 'wind')}
hybrid_plant = HybridSimulation(solar_wind_hybrid,
site,
interconnect_kw=interconnection_size_kw)
hybrid_plant.layout.plot()
hybrid_plant.ppa_price = (0.01, )
hybrid_plant.pv.dc_degradation = [0] * 25
hybrid_plant.simulate()
# plt.show()
aeps = hybrid_plant.annual_energies
npvs = hybrid_plant.net_present_values
assert aeps.pv == approx(8703525.94, 13)
assert aeps.wind == approx(33615479.57, 1e3)
assert aeps.hybrid == approx(41681662.63, 1e3)
assert npvs.pv == approx(-5121293, 1e3)
assert npvs.wind == approx(-13909363, 1e3)
assert npvs.hybrid == approx(-19216589, 1e3)
def test_trough_pv_hybrid(site):
interconnection_size_kw_test = 50000
technologies_test = {
'trough': {
'cycle_capacity_kw': 50 * 1000,
'solar_multiple': 2.0,
'tes_hours': 12.0
},
'pv': {
'system_capacity_kw': 50 * 1000
}
}
solar_hybrid = {key: technologies_test[key] for key in ('trough', 'pv')}
hybrid_plant = HybridSimulation(
solar_hybrid,
site,
interconnect_kw=interconnection_size_kw_test,
dispatch_options={
'is_test_start_year': True,
'is_test_end_year': True
})
hybrid_plant.ppa_price = (0.12, ) # $/kWh
hybrid_plant.pv.dc_degradation = [0] * 25
hybrid_plant.simulate()
aeps = hybrid_plant.annual_energies
npvs = hybrid_plant.net_present_values
assert aeps.pv == approx(104053614.17, 1e-3)
assert aeps.trough == approx(1871471.58, 2e-2)
assert aeps.hybrid == approx(105926003.55, 1e-3)
assert npvs.pv == approx(45233832.23, 1e3)
def test_hybrid_om_costs(site):
wind_pv_battery = {
key: technologies[key]
for key in ('pv', 'wind', 'battery')
}
hybrid_plant = HybridSimulation(
wind_pv_battery,
site,
interconnect_kw=interconnection_size_kw,
dispatch_options={'battery_dispatch': 'one_cycle_heuristic'})
hybrid_plant.ppa_price = (0.03, )
hybrid_plant.pv.dc_degradation = [0] * 25
# set all O&M costs to 0 to start
hybrid_plant.wind.om_fixed = 0
hybrid_plant.wind.om_capacity = 0
hybrid_plant.wind.om_variable = 0
hybrid_plant.pv.om_fixed = 0
hybrid_plant.pv.om_capacity = 0
hybrid_plant.pv.om_variable = 0
hybrid_plant.battery.om_fixed = 0
hybrid_plant.battery.om_capacity = 0
hybrid_plant.battery.om_variable = 0
# test variable costs
hybrid_plant.wind.om_variable = 5
hybrid_plant.pv.om_variable = 2
hybrid_plant.battery.om_variable = 3
hybrid_plant.simulate()
var_om_costs = hybrid_plant.om_variable_expenses
total_om_costs = hybrid_plant.om_total_expenses
for i in range(len(var_om_costs.hybrid)):
assert var_om_costs.pv[i] + var_om_costs.wind[
i] + var_om_costs.battery[i] == approx(var_om_costs.hybrid[i],
rel=1e-1)
assert total_om_costs.pv[i] == approx(var_om_costs.pv[i])
assert total_om_costs.wind[i] == approx(var_om_costs.wind[i])
assert total_om_costs.battery[i] == approx(var_om_costs.battery[i])
assert total_om_costs.hybrid[i] == approx(var_om_costs.hybrid[i])
hybrid_plant.wind.om_variable = 0
hybrid_plant.pv.om_variable = 0
hybrid_plant.battery.om_variable = 0
# test fixed costs
hybrid_plant.wind.om_fixed = 5
hybrid_plant.pv.om_fixed = 2
hybrid_plant.battery.om_fixed = 3
hybrid_plant.simulate()
fixed_om_costs = hybrid_plant.om_fixed_expenses
total_om_costs = hybrid_plant.om_total_expenses
for i in range(len(fixed_om_costs.hybrid)):
assert fixed_om_costs.pv[i] + fixed_om_costs.wind[i] + fixed_om_costs.battery[i] \
== approx(fixed_om_costs.hybrid[i])
assert total_om_costs.pv[i] == approx(fixed_om_costs.pv[i])
assert total_om_costs.wind[i] == approx(fixed_om_costs.wind[i])
assert total_om_costs.battery[i] == approx(fixed_om_costs.battery[i])
assert total_om_costs.hybrid[i] == approx(fixed_om_costs.hybrid[i])
hybrid_plant.wind.om_fixed = 0
hybrid_plant.pv.om_fixed = 0
hybrid_plant.battery.om_fixed = 0
# test capacity costs
hybrid_plant.wind.om_capacity = 5
hybrid_plant.pv.om_capacity = 2
hybrid_plant.battery.om_capacity = 3
hybrid_plant.simulate()
cap_om_costs = hybrid_plant.om_capacity_expenses
total_om_costs = hybrid_plant.om_total_expenses
for i in range(len(cap_om_costs.hybrid)):
assert cap_om_costs.pv[i] + cap_om_costs.wind[i] + cap_om_costs.battery[i] \
== approx(cap_om_costs.hybrid[i])
assert total_om_costs.pv[i] == approx(cap_om_costs.pv[i])
assert total_om_costs.wind[i] == approx(cap_om_costs.wind[i])
assert total_om_costs.battery[i] == approx(cap_om_costs.battery[i])
assert total_om_costs.hybrid[i] == approx(cap_om_costs.hybrid[i])
hybrid_plant.wind.om_capacity = 0
hybrid_plant.pv.om_capacity = 0
hybrid_plant.battery.om_capacity = 0
def test_capacity_credit(site):
site = SiteInfo(data=flatirons_site,
solar_resource_file=solar_resource_file,
wind_resource_file=wind_resource_file,
capacity_hours=capacity_credit_hours)
wind_pv_battery = {
key: technologies[key]
for key in ('pv', 'wind', 'battery')
}
hybrid_plant = HybridSimulation(wind_pv_battery,
site,
interconnect_kw=interconnection_size_kw)
hybrid_plant.battery.dispatch.lifecycle_cost_per_kWh_cycle = 0.01
hybrid_plant.ppa_price = (0.03, )
hybrid_plant.pv.dc_degradation = [0] * 25
# Backup values for resetting before tests
gen_max_feasible_orig = hybrid_plant.battery.gen_max_feasible
capacity_hours_orig = hybrid_plant.site.capacity_hours
interconnect_kw_orig = hybrid_plant.interconnect_kw
def reinstate_orig_values():
hybrid_plant.battery.gen_max_feasible = gen_max_feasible_orig
hybrid_plant.site.capacity_hours = capacity_hours_orig
hybrid_plant.interconnect_kw = interconnect_kw_orig
# Test when 0 gen_max_feasible
reinstate_orig_values()
hybrid_plant.battery.gen_max_feasible = [0] * 8760
capacity_credit_battery = hybrid_plant.battery.calc_capacity_credit_percent(
hybrid_plant.interconnect_kw)
assert capacity_credit_battery == approx(0, rel=0.05)
# Test when representative gen_max_feasible
reinstate_orig_values()
hybrid_plant.battery.gen_max_feasible = [2500] * 8760
capacity_credit_battery = hybrid_plant.battery.calc_capacity_credit_percent(
hybrid_plant.interconnect_kw)
assert capacity_credit_battery == approx(50, rel=0.05)
# Test when no capacity hours
reinstate_orig_values()
hybrid_plant.battery.gen_max_feasible = [2500] * 8760
hybrid_plant.site.capacity_hours = [False] * 8760
capacity_credit_battery = hybrid_plant.battery.calc_capacity_credit_percent(
hybrid_plant.interconnect_kw)
assert capacity_credit_battery == approx(0, rel=0.05)
# Test when no interconnect capacity
reinstate_orig_values()
hybrid_plant.battery.gen_max_feasible = [2500] * 8760
hybrid_plant.interconnect_kw = 0
capacity_credit_battery = hybrid_plant.battery.calc_capacity_credit_percent(
hybrid_plant.interconnect_kw)
assert capacity_credit_battery == approx(0, rel=0.05)
# Test integration with system simulation
reinstate_orig_values()
cap_payment_mw = 100000
hybrid_plant.assign({"cp_capacity_payment_amount": [cap_payment_mw]})
hybrid_plant.simulate()
total_gen_max_feasible = np.array(hybrid_plant.pv.gen_max_feasible) \
+ np.array(hybrid_plant.wind.gen_max_feasible) \
+ np.array(hybrid_plant.battery.gen_max_feasible)
assert sum(hybrid_plant.grid.gen_max_feasible) == approx(sum(np.minimum(hybrid_plant.grid.interconnect_kw * hybrid_plant.site.interval / 60, \
total_gen_max_feasible)), rel=0.01)
total_nominal_capacity = hybrid_plant.pv.calc_nominal_capacity(hybrid_plant.interconnect_kw) \
+ hybrid_plant.wind.calc_nominal_capacity(hybrid_plant.interconnect_kw) \
+ hybrid_plant.battery.calc_nominal_capacity(hybrid_plant.interconnect_kw)
assert total_nominal_capacity == approx(18845.8, rel=0.01)
assert total_nominal_capacity == approx(
hybrid_plant.grid.hybrid_nominal_capacity, rel=0.01)
capcred = hybrid_plant.capacity_credit_percent
assert capcred['pv'] == approx(8.03, rel=0.05)
assert capcred['wind'] == approx(33.25, rel=0.10)
assert capcred['battery'] == approx(58.95, rel=0.05)
assert capcred['hybrid'] == approx(43.88, rel=0.05)
cp_pay = hybrid_plant.capacity_payments
np_cap = hybrid_plant.system_nameplate_mw # This is not the same as nominal capacity...
assert cp_pay['pv'][1] / (np_cap['pv']) / (capcred['pv'] / 100) == approx(
cap_payment_mw, 0.05)
assert cp_pay['wind'][1] / (np_cap['wind']) / (capcred['wind'] /
100) == approx(
cap_payment_mw, 0.05)
assert cp_pay['battery'][1] / (np_cap['battery']) / (capcred['battery'] /
100) == approx(
cap_payment_mw,
0.05)
assert cp_pay['hybrid'][1] / (np_cap['hybrid']) / (capcred['hybrid'] /
100) == approx(
cap_payment_mw,
0.05)
aeps = hybrid_plant.annual_energies
assert aeps.pv == approx(9882421, rel=0.05)
assert aeps.wind == approx(33637983, rel=0.05)
assert aeps.battery == approx(-31287, rel=0.05)
assert aeps.hybrid == approx(43489117, rel=0.05)
npvs = hybrid_plant.net_present_values
assert npvs.pv == approx(-565098, rel=5e-2)
assert npvs.wind == approx(-1992106, rel=5e-2)
assert npvs.battery == approx(-4773045, rel=5e-2)
assert npvs.hybrid == approx(-5849767, rel=5e-2)
taxes = hybrid_plant.federal_taxes
assert taxes.pv[1] == approx(86826, rel=5e-2)
assert taxes.wind[1] == approx(348124, rel=5e-2)
assert taxes.battery[1] == approx(239607, rel=5e-2)
assert taxes.hybrid[1] == approx(633523, rel=5e-2)
apv = hybrid_plant.energy_purchases_values
assert apv.pv[1] == approx(0, rel=5e-2)
assert apv.wind[1] == approx(0, rel=5e-2)
assert apv.battery[1] == approx(40158, rel=5e-2)
assert apv.hybrid[1] == approx(2980, rel=5e-2)
debt = hybrid_plant.debt_payment
assert debt.pv[1] == approx(0, rel=5e-2)
assert debt.wind[1] == approx(0, rel=5e-2)
assert debt.battery[1] == approx(0, rel=5e-2)
assert debt.hybrid[1] == approx(0, rel=5e-2)
esv = hybrid_plant.energy_sales_values
assert esv.pv[1] == approx(353105, rel=5e-2)
assert esv.wind[1] == approx(956067, rel=5e-2)
assert esv.battery[1] == approx(80449, rel=5e-2)
assert esv.hybrid[1] == approx(1352445, rel=5e-2)
depr = hybrid_plant.federal_depreciation_totals
assert depr.pv[1] == approx(762811, rel=5e-2)
assert depr.wind[1] == approx(2651114, rel=5e-2)
assert depr.battery[1] == approx(1486921, rel=5e-2)
assert depr.hybrid[1] == approx(4900847, rel=5e-2)
insr = hybrid_plant.insurance_expenses
assert insr.pv[0] == approx(0, rel=5e-2)
assert insr.wind[0] == approx(0, rel=5e-2)
assert insr.battery[0] == approx(0, rel=5e-2)
assert insr.hybrid[0] == approx(0, rel=5e-2)
om = hybrid_plant.om_total_expenses
assert om.pv[1] == approx(74993, rel=5e-2)
assert om.wind[1] == approx(420000, rel=5e-2)
assert om.battery[1] == approx(75000, rel=5e-2)
assert om.hybrid[1] == approx(569993, rel=5e-2)
rev = hybrid_plant.total_revenues
assert rev.pv[1] == approx(393226, rel=5e-2)
assert rev.wind[1] == approx(1288603, rel=5e-2)
assert rev.battery[1] == approx(375215, rel=5e-2)
assert rev.hybrid[1] == approx(2229976, rel=5e-2)
tc = hybrid_plant.tax_incentives
assert tc.pv[1] == approx(1123104, rel=5e-2)
assert tc.wind[1] == approx(504569, rel=5e-2)
assert tc.battery[1] == approx(0, rel=5e-2)
assert tc.hybrid[1] == approx(1646170, rel=5e-2)
def init_hybrid_plant():
"""
Initialize hybrid simulation object using specific project inputs
:return: HybridSimulation as defined for this problem
"""
is_test = False # Turns off full year dispatch and optimize tower and receiver
techs_in_sim = ['tower',
'pv',
'battery',
'grid']
site_data = {
"lat": 34.85,
"lon": -116.9,
"elev": 641,
"year": 2012,
"tz": -8,
"no_wind": True
}
root = "C:/Users/WHamilt2/Documents/Projects/HOPP/CSP_PV_battery_dispatch_plots/"
solar_file = root + "34.865371_-116.783023_psmv3_60_tmy.csv"
prices_file = root + "constant_nom_prices.csv"
schedule_scale = 100 # MWe
desired_schedule_file = root + "sample_load_profile_normalized.csv"
# Reading in desired schedule
with open(desired_schedule_file) as f:
csvreader = csv.reader(f)
desired_schedule = []
for row in csvreader:
desired_schedule.append(float(row[0])*schedule_scale)
# If normalized pricing is used, then PPA price must be adjusted after HybridSimulation is initialized
site = SiteInfo(site_data,
solar_resource_file=solar_file,
grid_resource_file=prices_file,
desired_schedule=desired_schedule
)
technologies = {'tower': {
'cycle_capacity_kw': 100 * 1000, #100 * 1000,
'solar_multiple': 3.0, #2.0,
'tes_hours': 16.0, #16.0,
'optimize_field_before_sim': not is_test,
'scale_input_params': True,
},
'trough': {
'cycle_capacity_kw': 100 * 1000,
'solar_multiple': 4.0,
'tes_hours': 20.0
},
'pv': {
'system_capacity_kw': 50 * 1000
},
'battery': {
'system_capacity_kwh': 300 * 1000,
'system_capacity_kw': 100 * 1000
},
'grid': 150 * 1000}
# Create model
hybrid_plant = HybridSimulation({key: technologies[key] for key in techs_in_sim},
site,
interconnect_kw=technologies['grid'],
dispatch_options={
'is_test_start_year': is_test,
'is_test_end_year': is_test,
'solver': 'xpress',
'grid_charging': False,
'pv_charging_only': True,
},
simulation_options={
'storage_capacity_credit': False,
}
)
# Defaults:
# {'cost_per_field_generation': 0.5,
# 'cost_per_field_start_rel': 1.5,
# 'cost_per_cycle_generation': 2.0,
# 'cost_per_cycle_start_rel': 40.0,
# 'cost_per_change_thermal_input': 0.5}
csp_dispatch_obj_costs = dict()
csp_dispatch_obj_costs = {
'cost_per_field_generation': 0.0, #0.5,
# 'cost_per_field_start_rel': 0.0,
# 'cost_per_cycle_generation': 2.0,
'cost_per_cycle_start_rel': 0.0,
'cost_per_change_thermal_input': 0.5}
# Set CSP costs
if hybrid_plant.tower:
hybrid_plant.tower.dispatch.objective_cost_terms.update(csp_dispatch_obj_costs)
hybrid_plant.tower.value('cycle_max_frac', 1.0)
if hybrid_plant.trough:
hybrid_plant.trough.dispatch.objective_cost_terms.update(csp_dispatch_obj_costs)
hybrid_plant.trough.value('cycle_max_frac', 1.0)
# if hybrid_plant.battery:
# hybrid_plant.battery.dispatch.lifecycle_cost_per_kWh_cycle = 0.0265 / 100
# # hybrid_plant.battery.dispatch.lifecycle_cost_per_kWh_cycle = 1e-6
if hybrid_plant.pv:
hybrid_plant.pv.dc_degradation = [0.5] * 25
hybrid_plant.pv.value('array_type', 2) # 1-axis tracking
hybrid_plant.pv.value('tilt', 0) # Tilt for 1-axis
# This is required if normalized prices are provided
hybrid_plant.ppa_price = (0.12,) # $/kWh
return hybrid_plant
'layout_mode':
'boundarygrid',
'layout_params':
WindBoundaryGridParameters(border_spacing=2,
border_offset=0.5,
grid_angle=0.5,
grid_aspect_power=0.5,
row_phase_offset=0.5)
}
}
# Get resource
# Create model
hybrid_plant = HybridSimulation(technologies,
site_info,
interconnect_kw=interconnection_size_mw * 1000)
# hybrid_plant.plot_layout()
# plt.show()
hybrid_plant.simulate(1)
# Setup Optimization Candidate
class HybridLayoutProblem(OptimizationProblem):
"""
Optimize the layout of the wind and solar plant
"""
def __init__(self, simulation: HybridSimulation) -> None:
"""
Optimize layout with fixed number of turbines and solar capacity
def run_hopp_calc(Site, scenario_description, bos_details,
total_hybrid_plant_capacity_mw, solar_size_mw, wind_size_mw,
nameplate_mw, interconnection_size_mw,
load_resource_from_file, ppa_price, results_dir):
""" run_hopp_calc Establishes sizing models, creates a wind or solar farm based on the desired sizes,
and runs SAM model calculations for the specified inputs.
save_outputs contains a dictionary of all results for the hopp calculation.
:param scenario_description: Project scenario - 'greenfield' or 'solar addition'.
:param bos_details: contains bos details including type of analysis to conduct (cost/mw, json lookup, HybridBOSSE).
:param total_hybrid_plant_capacity_mw: capacity in MW of hybrid plant.
:param solar_size_mw: capacity in MW of solar component of plant.
:param wind_size_mw: capacity in MW of wind component of plant.
:param nameplate_mw: nameplate capacity of total plant.
:param interconnection_size_mw: interconnection size in MW.
:param load_resource_from_file: flag determining whether resource is loaded directly from file or through
interpolation routine.
:param ppa_price: PPA price in USD($)
:return: collection of outputs from SAM and hybrid-specific calculations (includes e.g. AEP, IRR, LCOE),
plus wind and solar filenames used
(save_outputs)
"""
# Get resource data
if load_resource_from_file:
pass
else:
Site[
'resource_filename_solar'] = "" # Unsetting resource filename to force API download of wind resource
Site[
'resource_filename_wind'] = "" # Unsetting resource filename to force API download of solar resource
site = SiteInfo(Site,
solar_resource_file=sample_site['resource_filename_solar'],
wind_resource_file=sample_site['resource_filename_wind'])
#TODO: Incorporate this in SiteInfo
# if 'roll_tz' in Site.keys():
# site.solar_resource.roll_timezone(Site['roll_tz'], Site['roll_tz'])
# Set up technology and cost model info
technologies = {
'solar': solar_size_mw, # mw system capacity
'wind': wind_size_mw, # mw system capacity
'grid': interconnection_size_mw
} # mw interconnect
# Create model
hybrid_plant = HybridSimulation(technologies,
site,
interconnect_kw=interconnection_size_mw *
1000)
hybrid_plant.setup_cost_calculator(
create_cost_calculator(interconnection_size_mw,
bos_details['BOSSource'], scenario_description))
hybrid_plant.ppa_price = ppa_price
hybrid_plant.discount_rate = 6.4
hybrid_plant.solar.system_capacity_kw = solar_size_mw * 1000
hybrid_plant.wind.system_capacity_by_num_turbines(wind_size_mw * 1000)
actual_solar_pct = hybrid_plant.solar.system_capacity_kw / \
(hybrid_plant.solar.system_capacity_kw + hybrid_plant.wind.system_capacity_kw)
logger.info("Run with solar percent {}".format(actual_solar_pct))
hybrid_plant.simulate()
outputs = hybrid_plant.hybrid_outputs()
for k, v in outputs.items():
outputs[k] = [v]
return outputs, site.wind_resource.filename, site.solar_resource.filename
def setup_power_calcs(scenario, solar_size_mw, storage_size_mwh,
storage_size_mw, interconnection_size_mw):
"""
A function to facilitate plant setup for POWER calculations, assuming one wind turbine.
INPUT VARIABLES
scenario: dict, the H2 scenario of interest
solar_size_mw: float, the amount of solar capacity in MW
storage_size_mwh: float, the amount of battery storate capacity in MWh
storage_size_mw: float, the amount of battery storate capacity in MW
interconnection_size_mw: float, the interconnection size in MW
OUTPUTS
hybrid_plant: the hybrid plant object from HOPP for power calculations
"""
# Set API key
load_dotenv()
NREL_API_KEY = os.getenv("NREL_API_KEY")
set_developer_nrel_gov_key(
NREL_API_KEY
) # Set this key manually here if you are not setting it using the .env
# Step 1: Establish output structure and special inputs
year = 2013
sample_site['year'] = year
useful_life = 30
custom_powercurve = True
electrolyzer_size = 50000
sample_site['lat'] = scenario['Lat']
sample_site['lon'] = scenario['Long']
tower_height = scenario['Tower Height']
scenario['Useful Life'] = useful_life
site = SiteInfo(sample_site, hub_height=tower_height)
technologies = {
'pv': {
'system_capacity_kw': solar_size_mw * 1000
},
'wind': {
'num_turbines': 1,
'turbine_rating_kw': scenario['Turbine Rating'] * 1000,
'hub_height': scenario['Tower Height'],
'rotor_diameter': scenario['Rotor Diameter']
},
'grid': electrolyzer_size,
'battery': {
'system_capacity_kwh': storage_size_mwh * 1000,
'system_capacity_kw': storage_size_mw * 1000
}
}
dispatch_options = {'battery_dispatch': 'heuristic'}
hybrid_plant = HybridSimulation(technologies,
site,
interconnect_kw=electrolyzer_size,
dispatch_options=dispatch_options)
hybrid_plant.wind._system_model.Turbine.wind_resource_shear = 0.33
if custom_powercurve:
powercurve_file = open(scenario['Powercurve File'])
powercurve_data = json.load(powercurve_file)
powercurve_file.close()
hybrid_plant.wind._system_model.Turbine.wind_turbine_powercurve_windspeeds = \
powercurve_data['turbine_powercurve_specification']['wind_speed_ms']
hybrid_plant.wind._system_model.Turbine.wind_turbine_powercurve_powerout = \
powercurve_data['turbine_powercurve_specification']['turbine_power_output']
hybrid_plant.pv.system_capacity_kw = solar_size_mw * 1000
return hybrid_plant
def run_hopp_calc(Site, scenario_description, bos_details, total_hybrid_plant_capacity_mw, solar_size_mw, wind_size_mw,
nameplate_mw, interconnection_size_mw, load_resource_from_file,
ppa_price, results_dir):
""" run_hopp_calc Establishes sizing models, creates a wind or solar farm based on the desired sizes,
and runs SAM model calculations for the specified inputs.
save_outputs contains a dictionary of all results for the hopp calculation.
:param scenario_description: Project scenario - 'greenfield' or 'solar addition'.
:param bos_details: contains bos details including type of analysis to conduct (cost/mw, json lookup, HybridBOSSE).
:param total_hybrid_plant_capacity_mw: capacity in MW of hybrid plant.
:param solar_size_mw: capacity in MW of solar component of plant.
:param wind_size_mw: capacity in MW of wind component of plant.
:param nameplate_mw: nameplate capacity of total plant.
:param interconnection_size_mw: interconnection size in MW.
:param load_resource_from_file: flag determining whether resource is loaded directly from file or through
interpolation routine.
:param ppa_price: PPA price in USD($)
:return: collection of outputs from SAM and hybrid-specific calculations (includes e.g. AEP, IRR, LCOE),
plus wind and solar filenames used
(save_outputs)
"""
# Get resource data
# sample_site['lat'] = Site['Lat']
# sample_site['lon'] = Site['Lon']
# # site = SiteInfo(sample_site, solar_resource_file=Site['resource_filename_solar'],
# # wind_resource_file=Site['resource_filename_wind'])
if load_resource_from_file:
pass
else:
Site['resource_filename_solar'] = "" # Unsetting resource filename to force API download of wind resource
Site['resource_filename_wind'] = "" # Unsetting resource filename to force API download of solar resource
site = SiteInfo(Site)
if 'roll_tz' in Site.keys():
site.solar_resource.roll_timezone(Site['roll_tz'], Site['roll_tz'])
# Set up technology and cost model info
technologies = {'solar': solar_size_mw, # mw system capacity
'wind': wind_size_mw # mw system capacity
}
# Create model
hybrid_plant = HybridSimulation(technologies, site, interconnect_kw=interconnection_size_mw * 1000)
# hybrid_plant.setup_cost_calculator(create_cost_calculator(bos_cost_source='boslookup', interconnection_mw=interconnection_size_mw))
hybrid_plant.setup_cost_calculator(create_cost_calculator(bos_cost_source=bos_details['BOSSource'],
interconnection_mw=interconnection_size_mw,
modify_costs=bos_details['Modify Costs'],
cost_reductions=bos_details,
wind_installed_cost_mw=1696000,
solar_installed_cost_mw=1088600,
storage_installed_cost_mw=0,
storage_installed_cost_mwh=0,
))
hybrid_plant.ppa_price = ppa_price
hybrid_plant.discount_rate = 6.4
hybrid_plant.solar.system_capacity_kw = solar_size_mw * 1000
hybrid_plant.wind.rotor_diameter = 100
# Modify Wind Turbine Coordinates
Nx = 8
Ny = 5
spacing = hybrid_plant.wind.row_spacing
turbine_x = np.linspace(0, (Nx * spacing) - spacing, Nx)
turbine_y = np.linspace(0, (Ny * spacing) - spacing, Ny)
turbX = np.zeros(Nx * Ny)
turbY = np.zeros(Nx * Ny)
count = 0
for i in range(Nx):
for j in range(Ny):
turbX[count] = turbine_x[i]
turbY[count] = turbine_y[j]
count = count + 1
hybrid_plant.wind.modify_coordinates(list(turbX), list(turbY))
hybrid_plant.wind.system_capacity_by_num_turbines(wind_size_mw * 1000)
actual_solar_pct = hybrid_plant.solar.system_capacity_kw / \
(hybrid_plant.solar.system_capacity_kw + hybrid_plant.wind.system_capacity_kw)
logger.info("Run with solar percent {}".format(actual_solar_pct))
hybrid_plant.simulate()
outputs = hybrid_plant.hybrid_outputs()
for k, v in outputs.items():
outputs[k] = [v]
return outputs, site.wind_resource.filename, site.solar_resource.filename
def test_wind_pv_with_storage_dispatch(site):
wind_pv_battery = {
key: technologies[key]
for key in ('pv', 'wind', 'battery')
}
hybrid_plant = HybridSimulation(wind_pv_battery,
site,
interconnect_kw=interconnection_size_kw)
hybrid_plant.battery.dispatch.lifecycle_cost_per_kWh_cycle = 0.01
hybrid_plant.ppa_price = (0.03, )
hybrid_plant.pv.dc_degradation = [0] * 25
hybrid_plant.simulate()
aeps = hybrid_plant.annual_energies
npvs = hybrid_plant.net_present_values
taxes = hybrid_plant.federal_taxes
apv = hybrid_plant.energy_purchases_values
debt = hybrid_plant.debt_payment
esv = hybrid_plant.energy_sales_values
depr = hybrid_plant.federal_depreciation_totals
insr = hybrid_plant.insurance_expenses
om = hybrid_plant.om_total_expenses
rev = hybrid_plant.total_revenues
tc = hybrid_plant.tax_incentives
assert aeps.pv == approx(9882421, rel=0.05)
assert aeps.wind == approx(33637983, rel=0.05)
assert aeps.battery == approx(-31287, rel=0.05)
assert aeps.hybrid == approx(43489117, rel=0.05)
assert npvs.pv == approx(-853226, rel=5e-2)
assert npvs.wind == approx(-4380277, rel=5e-2)
assert npvs.battery == approx(-6889961, rel=5e-2)
assert npvs.hybrid == approx(-11861790, rel=5e-2)
assert taxes.pv[1] == approx(94661, rel=5e-2)
assert taxes.wind[1] == approx(413068, rel=5e-2)
assert taxes.battery[1] == approx(297174, rel=5e-2)
assert taxes.hybrid[1] == approx(804904, rel=5e-2)
assert apv.pv[1] == approx(0, rel=5e-2)
assert apv.wind[1] == approx(0, rel=5e-2)
assert apv.battery[1] == approx(40158, rel=5e-2)
assert apv.hybrid[1] == approx(3050, rel=5e-2)
assert debt.pv[1] == approx(0, rel=5e-2)
assert debt.wind[1] == approx(0, rel=5e-2)
assert debt.battery[1] == approx(0, rel=5e-2)
assert debt.hybrid[1] == approx(0, rel=5e-2)
assert esv.pv[1] == approx(353105, rel=5e-2)
assert esv.wind[1] == approx(956067, rel=5e-2)
assert esv.battery[1] == approx(80449, rel=5e-2)
assert esv.hybrid[1] == approx(1352445, rel=5e-2)
assert depr.pv[1] == approx(762811, rel=5e-2)
assert depr.wind[1] == approx(2651114, rel=5e-2)
assert depr.battery[1] == approx(1486921, rel=5e-2)
assert depr.hybrid[1] == approx(4900847, rel=5e-2)
assert insr.pv[0] == approx(0, rel=5e-2)
assert insr.wind[0] == approx(0, rel=5e-2)
assert insr.battery[0] == approx(0, rel=5e-2)
assert insr.hybrid[0] == approx(0, rel=5e-2)
assert om.pv[1] == approx(74993, rel=5e-2)
assert om.wind[1] == approx(420000, rel=5e-2)
assert om.battery[1] == approx(75000, rel=5e-2)
assert om.hybrid[1] == approx(569993, rel=5e-2)
assert rev.pv[1] == approx(353105, rel=5e-2)
assert rev.wind[1] == approx(956067, rel=5e-2)
assert rev.battery[1] == approx(80449, rel=5e-2)
assert rev.hybrid[1] == approx(1352445, rel=5e-2)
assert tc.pv[1] == approx(1123104, rel=5e-2)
assert tc.wind[1] == approx(504569, rel=5e-2)
assert tc.battery[1] == approx(0, rel=5e-2)
assert tc.hybrid[1] == approx(1646170, rel=5e-2)
def init_hybrid_plant(techs_in_sim: list,
is_test: bool = False,
ud_techs: dict = {}):
"""
Initialize hybrid simulation object using specific project inputs
:param techs_in_sim: List of technologies to include in the simulation
:param is_test: if True, runs dispatch for the first and last 5 days of the year
and turns off tower and receiver optimization
:param ud_techs: Dictionary containing technology initialization parameters required by HybridSimulation
:return: HybridSimulation as defined for this problem
"""
schedule_scale = 100 # MWe
grid_interconnect_mw = 100 # MWe
example_root = get_example_path_root()
# Set plant location
site_data = {
"lat": 34.8653,
"lon": -116.7830,
"elev": 561,
"tz": 1,
"no_wind": True
}
solar_file = example_root + "02_weather_data/daggett_ca_34.865371_-116.783023_psmv3_60_tmy.csv"
prices_file = example_root + "03_cost_load_price_data/constant_norm_prices.csv"
desired_schedule_file = example_root + "03_cost_load_price_data/desired_schedule_normalized.csv"
# Reading in desired schedule
with open(desired_schedule_file) as f:
csvreader = csv.reader(f)
desired_schedule = []
for row in csvreader:
desired_schedule.append(float(row[0]) * schedule_scale)
# If normalized pricing is used, then PPA price must be adjusted after HybridSimulation is initialized
site = SiteInfo(site_data,
solar_resource_file=solar_file,
grid_resource_file=prices_file,
desired_schedule=desired_schedule)
# Load in system costs
with open(example_root +
"03_cost_load_price_data/system_costs_SAM.json") as f:
cost_info = json.load(f)
# Initializing technologies
if ud_techs:
technologies = ud_techs
else:
technologies = {
'tower': {
'cycle_capacity_kw': 200 * 1000, #100
'solar_multiple': 4.0, #2.0
'tes_hours': 20.0, #14
'optimize_field_before_sim': not is_test,
'scale_input_params': True,
},
'trough': {
'cycle_capacity_kw': 200 * 1000,
'solar_multiple': 6.0,
'tes_hours': 28.0
},
'pv': {
'system_capacity_kw': 120 * 1000
},
'battery': {
'system_capacity_kwh': 200 * 1000,
'system_capacity_kw': 100 * 1000
},
'grid': grid_interconnect_mw * 1000
}
# Create hybrid simulation class based on the technologies needed in the simulation
sim_techs = {key: technologies[key] for key in techs_in_sim}
sim_techs['grid'] = technologies['grid']
hybrid_plant = HybridSimulation(sim_techs,
site,
interconnect_kw=technologies['grid'],
dispatch_options={
'is_test_start_year': is_test,
'is_test_end_year': is_test,
'solver': 'cbc',
'grid_charging': False,
'pv_charging_only': True
},
cost_info=cost_info['cost_info'])
csp_dispatch_obj_costs = {
'cost_per_field_generation': 0.5,
'cost_per_field_start_rel': 0.0,
'cost_per_cycle_generation': 2.0,
'cost_per_cycle_start_rel': 0.0,
'cost_per_change_thermal_input': 0.5
}
# Set CSP costs
if hybrid_plant.tower:
hybrid_plant.tower.ssc.set(cost_info['tower_costs'])
hybrid_plant.tower.dispatch.objective_cost_terms = csp_dispatch_obj_costs
if hybrid_plant.trough:
hybrid_plant.trough.ssc.set(cost_info['trough_costs'])
hybrid_plant.trough.dispatch.objective_cost_terms = csp_dispatch_obj_costs
# Set O&M costs for all technologies
for tech in ['tower', 'trough', 'pv', 'battery']:
if not tech in techs_in_sim:
cost_info["SystemCosts"].pop(tech)
hybrid_plant.assign(cost_info["SystemCosts"])
# Set financial parameters for singleowner model
with open(example_root +
'03_cost_load_price_data/financial_parameters_SAM.json') as f:
fin_info = json.load(f)
hybrid_plant.assign(fin_info["FinancialParameters"])
hybrid_plant.assign(fin_info["TaxCreditIncentives"])
hybrid_plant.assign(fin_info["Revenue"])
hybrid_plant.assign(fin_info["Depreciation"])
hybrid_plant.assign(fin_info["PaymentIncentives"])
# Set specific technology assumptions here
if hybrid_plant.pv:
hybrid_plant.pv.dc_degradation = [0.5] * 25
hybrid_plant.pv.value('array_type', 2) # 1-axis tracking
hybrid_plant.pv.value('tilt', 0) # Tilt for 1-axis
# This is required if normalized prices are provided
hybrid_plant.ppa_price = (0.10, ) # $/kWh
return hybrid_plant
def test_desired_schedule_dispatch():
# Creating a contrived schedule
daily_schedule = [interconnect_mw]*10
daily_schedule.extend([20] * 8)
daily_schedule.append(interconnect_mw + 5)
daily_schedule.extend([0] * 5)
desired_schedule = daily_schedule*365
desired_schedule_site = SiteInfo(flatirons_site,
desired_schedule=desired_schedule)
tower_pv_battery = {key: technologies[key] for key in ('pv', 'tower', 'battery')}
# Default case doesn't leave enough head room for battery operations
tower_pv_battery['tower'] = {'cycle_capacity_kw': 35 * 1000,
'solar_multiple': 2.0,
'tes_hours': 10.0}
tower_pv_battery['pv'] = {'system_capacity_kw': 80 * 1000}
hybrid_plant = HybridSimulation(tower_pv_battery, desired_schedule_site, interconnect_mw * 1000,
dispatch_options={'is_test_start_year': True,
'is_test_end_year': False,
'grid_charging': False,
'pv_charging_only': True,
'include_lifecycle_count': False
})
hybrid_plant.ppa_price = (0.06, )
# Constant price
# hybrid_plant.site.elec_prices = [100] * hybrid_plant.site.n_timesteps
hybrid_plant.simulate(1)
system_generation = hybrid_plant.dispatch_builder.dispatch.system_generation
system_load = hybrid_plant.dispatch_builder.dispatch.system_load
electricity_sold = hybrid_plant.grid.dispatch.electricity_sold
electricity_purchased = hybrid_plant.grid.dispatch.electricity_purchased
gen_limit = hybrid_plant.grid.dispatch.generation_transmission_limit
transmission_limit = hybrid_plant.grid.value('grid_interconnection_limit_kwac')
schedule = daily_schedule*2
# System generation does not exceed schedule limits
for t in hybrid_plant.dispatch_builder.pyomo_model.forecast_horizon:
assert gen_limit[t] * 1e3 <= transmission_limit
assert system_generation[t] - system_load[t] <= schedule[t] + 1e-3
if system_generation[t] > system_load[t]:
assert electricity_sold[t] == pytest.approx(system_generation[t] - system_load[t], 1e-3)
assert electricity_purchased[t] == pytest.approx(0.0, 1e-3)
else:
assert electricity_purchased[t] == pytest.approx(system_load[t] - system_generation[t], 1e-3)
assert electricity_sold[t] == pytest.approx(0.0, 1e-3)
# Battery charges and discharges
assert sum(hybrid_plant.battery.dispatch.charge_power) > 0.0
assert sum(hybrid_plant.battery.dispatch.discharge_power) > 0.0
# PV can be curtailed
assert sum(hybrid_plant.pv.dispatch.generation) <= sum(hybrid_plant.pv.dispatch.available_generation)
# CSP can run
assert sum(hybrid_plant.tower.dispatch.cycle_generation) > 0.0
assert sum(hybrid_plant.tower.dispatch.receiver_thermal_power) > 0.0
def init_simulation():
"""
Create the simulation object needed to calculate the objective of the problem
TODO: make this representative of the design variables, is there currently a tradeoff in objectives?
:return: The HOPP simulation as defined for this problem
"""
site = 'irregular'
location = locations[1]
if site == 'circular':
site_data = make_circular_site(lat=location[0],
lon=location[1],
elev=location[2])
elif site == 'irregular':
site_data = make_irregular_site(lat=location[0],
lon=location[1],
elev=location[2])
else:
raise Exception("Unknown site '" + site + "'")
solar_file = examples_dir.parent / "resource_files" / "solar" / "Beni_Miha" / "659265_32.69_10.90_2019.csv"
grid_file = examples_dir.parent / "resource_files" / "grid" / "tunisia_est_grid_prices.csv"
site_info = SiteInfo(site_data,
solar_resource_file=solar_file,
grid_resource_file=grid_file)
# set up hybrid simulation with all the required parameters
solar_size_mw = 200
tower_cycle_mw = 125
# battery_capacity_mwh = 15
interconnection_size_mw = 400
technologies = {
'tower': {
'cycle_capacity_kw': tower_cycle_mw * 1000,
'solar_multiple': 2.0,
'tes_hours': 12.0,
'optimize_field_before_sim': False
}, # TODO: turn on
'pv': {
'system_capacity_kw': solar_size_mw * 1000
},
# 'battery': {'system_capacity_kwh': battery_capacity_mwh * 1000,
# 'system_capacity_kw': battery_capacity_mwh * 1000 / 10},
'grid': interconnection_size_mw * 1000
}
# Create model
# TODO: turn these off to run full year simulation
dispatch_options = {'is_test_start_year': True, 'is_test_end_year': False}
# TODO: turn-on receiver and field optimization before... initial simulation
hybrid_plant = HybridSimulation(technologies,
site_info,
interconnect_kw=interconnection_size_mw *
1000,
dispatch_options=dispatch_options)
# Customize the hybrid plant assumptions here...
hybrid_plant.pv.value('inv_eff', 95.0)
hybrid_plant.pv.value('array_type', 0)
return hybrid_plant