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Merge pull request 'Costing initiated' (#5) from hp_cop into main

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Reviewed-on: https://nextgenerations-cities.encs.concordia.ca/gitea/s_ranjbar/energy_system_modelling_workflow/pulls/5
This commit is contained in:
Saeed Ranjbar 2024-05-10 16:17:40 -04:00
commit 2664974d48
38 changed files with 201 additions and 53850 deletions
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14
hub/catalog_factories/data_models/energy_systems/non_pv_generation_system.py
View File

@ -289,6 +289,18 @@ class NonPvGenerationSystem(GenerationSystem):
self.distribution_systems] if self.distribution_systems is not None else None
_energy_storage_systems = [_energy_storage_system.to_dictionary() for _energy_storage_system in
self.energy_storage_systems] if self.energy_storage_systems is not None else None
_heat_output_curve = self.heat_output_curve.to_dictionary() if (
self.heat_output_curve is not None) else None
_heat_fuel_consumption_curve = self.heat_fuel_consumption_curve.to_dictionary() if (
self.heat_fuel_consumption_curve is not None) else None
_heat_efficiency_curve = self.heat_efficiency_curve.to_dictionary() if (
self.heat_efficiency_curve is not None) else None
_cooling_output_curve = self.cooling_output_curve.to_dictionary() if (
self.cooling_output_curve is not None) else None
_cooling_fuel_consumption_curve = self.cooling_fuel_consumption_curve.to_dictionary() if (
self.cooling_fuel_consumption_curve is not None) else None
_cooling_efficiency_curve = self.cooling_efficiency_curve.to_dictionary() if (
self.cooling_efficiency_curve is not None) else None
content = {
'Energy Generation component':
@ -318,7 +330,7 @@ class NonPvGenerationSystem(GenerationSystem):
'minimum cooling supply temperature [Celsius]': self.minimum_cooling_supply_temperature,
'heat output curve': self.heat_output_curve,
'heat fuel consumption curve': self.heat_fuel_consumption_curve,
'heat efficiency curve': self.heat_efficiency_curve,
'heat efficiency curve': _heat_efficiency_curve,
'cooling output curve': self.cooling_output_curve,
'cooling fuel consumption curve': self.cooling_fuel_consumption_curve,
'cooling efficiency curve': self.cooling_efficiency_curve,

8
hub/catalog_factories/data_models/energy_systems/performance_curves.py
View File

@ -24,13 +24,13 @@ class PerformanceCurves:
def curve_type(self):
"""
The type of the fit function from the following
Linear =>>> y = a*x + b
Linear =>>> y = a + b*x
Exponential =>>> y = a*(b**x)
Polynomial =>>> y = a*(x**2) + b*x + c
Second degree polynomial =>>> y = a + b*x + c*(x**2)
Power =>>> y = a*(x**b)
Second degree multivariable =>>> y = a*(x**2) + b*x + c*x*z + d*z + e*(z**2) + f
Bi-Quadratic =>>> y = a + b*x + c*(x**2) + d*z + e*(z**2) + f*x*z
Get the type of function from ['linear', 'exponential', 'polynomial', 'power', 'second degree multivariable']
Get the type of function from ['linear', 'exponential', 'second degree polynomial', 'power', 'bi-quadratic']
:return: string
"""
return self._curve_type

2
hub/data/costs/montreal_costs_completed.xml
View File

@ -132,7 +132,7 @@
</fuel>
<fuel fuel_type="Gas">
<fixed_monthly cost_unit="currency/month"> 17.71 </fixed_monthly>
<variable cost_unit="currency/kWh"> 0.0640 </variable>
<variable cost_unit="currency/m3"> 0.4 </variable>
<density density_unit="kg/m3"> 0.777 </density>
<lower_heating_value lhv_unit="MJ/kg"> 47.1 </lower_heating_value>
</fuel>

32
hub/data/energy_systems/montreal_future_systems.xml
View File

@ -493,7 +493,7 @@
<heat_output_curve/>
<heat_fuel_consumption_curve/>
<heat_efficiency_curve>
<curve_type>second degree multivariable function</curve_type>
<curve_type>bi-quadratic</curve_type>
<dependant_variable>COP</dependant_variable>
<parameters>source_temperature</parameters>
<parameters>supply_temperature</parameters>
@ -538,7 +538,7 @@
<heat_output_curve/>
<heat_fuel_consumption_curve/>
<heat_efficiency_curve>
<curve_type>second degree multivariable function</curve_type>
<curve_type>bi-quadratic</curve_type>
<dependant_variable>COP</dependant_variable>
<parameters>source_temperature</parameters>
<parameters>supply_temperature</parameters>
@ -583,7 +583,7 @@
<heat_output_curve/>
<heat_fuel_consumption_curve/>
<heat_efficiency_curve>
<curve_type>second degree multivariable function</curve_type>
<curve_type>bi-quadratic</curve_type>
<dependant_variable>COP</dependant_variable>
<parameters>source_temperature</parameters>
<parameters>supply_temperature</parameters>
@ -690,7 +690,7 @@
<nominal_heat_output/>
<minimum_heat_output/>
<maximum_heat_output/>
<heat_efficiency>3</heat_efficiency>
<heat_efficiency>2</heat_efficiency>
<reversible>True</reversible>
<fuel_type>electricity</fuel_type>
<source_medium>Air</source_medium>
@ -709,10 +709,22 @@
<minimum_cooling_supply_temperature/>
<heat_output_curve/>
<heat_fuel_consumption_curve/>
<heat_efficiency_curve/>
<heat_efficiency_curve>
<curve_type>bi-quadratic</curve_type>
<dependant_variable>COP</dependant_variable>
<parameters>source_temperature</parameters>
<parameters>supply_temperature</parameters>
<coefficients a="0.132733" b="0.012322" c="0.000032" d="-0.011109" e="-0.000125" f="-0.000123"/>
</heat_efficiency_curve>
<cooling_output_curve/>
<cooling_fuel_consumption_curve/>
<cooling_efficiency_curve/>
<cooling_efficiency_curve>
<curve_type>bi-quadratic</curve_type>
<dependant_variable>COP</dependant_variable>
<parameters>source_temperature</parameters>
<parameters>supply_temperature</parameters>
<coefficients a="0.951894" b="-0.010518" c="0.000126" d="-0.003399" e="0.000183" f="-0.000206"/>
</cooling_efficiency_curve>
<distribution_systems/>
<energy_storage_systems>
<storage_id>6</storage_id>
@ -910,7 +922,13 @@
<minimum_cooling_supply_temperature/>
<heat_output_curve/>
<heat_fuel_consumption_curve/>
<heat_efficiency_curve/>
<heat_efficiency_curve>
<curve_type>bi-quadratic</curve_type>
<dependant_variable>COP</dependant_variable>
<parameters>source_temperature</parameters>
<parameters>supply_temperature</parameters>
<coefficients a="-0.000277" b="0.019639" c="0.000004" d="0.012190" e="-0.00010" f="-0.000277"/>
</heat_efficiency_curve>
<cooling_output_curve/>
<cooling_fuel_consumption_curve/>
<cooling_efficiency_curve/>

2
hub/exports/formats/simplified_radiosity_algorithm.py
View File

@ -66,7 +66,7 @@ class SimplifiedRadiosityAlgorithm:
else:
i = (total_days + day - 1) * 24 + hour - 1
representative_building = self._city.buildings[0]
_global = representative_building.global_horizontal[cte.HOUR][i] * cte.WATTS_HOUR_TO_JULES
_global = representative_building.diffuse[cte.HOUR][i] * cte.WATTS_HOUR_TO_JULES
_beam = representative_building.beam[cte.HOUR][i] * cte.WATTS_HOUR_TO_JULES
content += f'{day} {month} {hour} {_global} {_beam}\n'
with open(file, 'w', encoding='utf-8') as file:

2
hub/imports/results/simplified_radiosity_algorithm.py
View File

@ -34,7 +34,7 @@ class SimplifiedRadiosityAlgorithm:
for key in self._results:
_irradiance = {}
header_name = key.split(':')
result = [x / cte.WATTS_HOUR_TO_JULES for x in self._results[key]]
result = [x for x in self._results[key]]
city_object_name = header_name[1]
building = self._city.city_object(city_object_name)
surface_id = header_name[2]

7
input_files/142alone.geojson
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@ -1,7 +0,0 @@
{
"type": "FeatureCollection",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
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]
}

7
input_files/78alone.geojson
View File

@ -1,7 +0,0 @@
{
"type": "FeatureCollection",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": { "OBJECTID_12": 78, "gml_id": "1341084", "gml_parent": "fme-gen-57964590-baa9-48a7-99b0-fa11ca012d7c", "citygml_ta": "http://www.opengis.net/citygml/building/2.0", "citygml_fe": "cityObjectMember", "citygml__1": " ", "citygml__2": " ", "gml_descri": " ", "gml_name": " ", "citygml_cr": " ", "citygml_te": " ", "externalRe": " ", "external_1": " ", "external_2": " ", "citygml_ge": " ", "citygml_re": " ", "citygml__3": " ", "citygml_ap": " ", "citygml_cl": " ", "citygml__4": " ", "citygml_fu": " ", "citygml__5": " ", "citygml_us": " ", "citygml__6": " ", "citygml_ye": " ", "citygml__7": " ", "citygml_ro": " ", "citygml__8": " ", "citygml_me": 28.164, "citygml__9": "#m", "citygml_st": " ", "citygml_10": " ", "citygml_11": " ", "citygml_12": " ", "citygml_13": " ", "citygml_14": " ", "citygml_ou": " ", "citygml_in": " ", "citygml_bo": " ", "citygml_le": " ", "citygml_15": " ", "citygml_co": " ", "citygml_ad": " ", "Volume": "20172.450", "parcelle": " ", "OBJECTID": 7115, "gml_id_1": "25a2efa0-4d04-4eeb-b04c-7ceb338b062c", "gml_pare_1": "1341084", "citygml_16": "http://www.opengis.net/citygml/building/2.0", "citygml_17": "boundedBy", "citygml_18": " ", "citygml_19": " ", "gml_desc_1": " ", "gml_name_1": " ", "citygml_20": " ", "citygml_21": " ", "external_3": " ", "external_4": " ", "external_5": " ", "citygml_22": " ", "citygml_23": " ", "citygml_24": " ", "citygml_25": " ", "citygml_26": " ", "citygml_op": " ", "Area": "716.259", "FID_": 0, "Join_Count": 3, "TARGET_FID": 7120, "gml_id_12": "1341084", "gml_pare_2": "fme-gen-57964590-baa9-48a7-99b0-fa11ca012d7c", "citygml_27": "http://www.opengis.net/citygml/building/2.0", "citygml_28": "cityObjectMember", "citygml_29": " ", "citygml_30": " ", "gml_desc_2": " ", "gml_name_2": " ", "citygml_31": " ", "citygml_32": " ", "external_6": " ", "external_7": " ", "external_8": " ", "citygml_33": " ", "citygml_34": " ", "citygml_35": " ", "citygml_36": " ", "citygml_37": " ", "citygml_38": " ", "citygml_39": " ", "citygml_40": " ", "citygml_41": " ", "citygml_42": " ", "citygml_43": " ", "citygml_44": " ", "citygml_45": " ", "citygml_46": " ", "citygml_47": 28.164, "citygml_48": "#m", "citygml_49": " ", "citygml_50": " ", "citygml_51": " ", "citygml_52": " ", "citygml_53": " ", "citygml_54": " ", "citygml_55": " ", "citygml_56": " ", "citygml_57": " ", "citygml_58": " ", "citygml_59": " ", "citygml_60": " ", "citygml_61": " ", "Volume_1": "20172.450", "Field": 0, "Field1": 0, "OBJECTID_1": 7115, "gml_id_12_": "25a2efa0-4d04-4eeb-b04c-7ceb338b062c", "gml_pare_3": "1341084", "citygml_62": "http://www.opengis.net/citygml/building/2.0", "citygml_63": "boundedBy", "citygml_64": " ", "citygml_65": " ", "gml_desc_3": " ", "gml_name_3": " ", "citygml_66": " ", "citygml_67": " ", "external_9": " ", "externa_10": " ", "externa_11": " ", "citygml_68": " ", "citygml_69": " ", "citygml_70": " ", "citygml_71": " ", "citygml_72": " ", "citygml_73": " ", "Area_1": "716.259", "cityGML_hi": 0, "Z_Min": 39.2444, "Z_Max": 67.408, "Shape_Leng": 140.36783883300001, "ID_UEV": "01002600", "CIVIQUE_DE": " 1420", "CIVIQUE_FI": " 1420", "NOM_RUE": "rue Sainte-Catherine Ouest (MTL+WMT)", "MUNICIPALI": "50", "ETAGE_HORS": 4, "NOMBRE_LOG": 4, "ANNEE_CONS": 1914, "CODE_UTILI": "6000", "LIBELLE_UT": "Immeuble à bureaux", "CATEGORIE_": "Régulier", "MATRICULE8": "9839-85-4994-3-000-0000", "SUPERFICIE": 803, "SUPERFIC_1": 2953, "NO_ARROND_": "REM19", "Shape_Le_1": 0.0013155280635399999, "Shape_Ar_1": 9.24688109061e-08, "Z_Min_1": null, "Z_Max_1": null, "Shape_Length": 140.3678388326679, "Shape_Area": 716.25939654725767 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -73.576326707327183, 45.496116905446513 ], [ -73.576250734313831, 45.496195431606957 ], [ -73.57635494025277, 45.49624529435664 ], [ -73.57640388728268, 45.496268720567464 ], [ -73.576411543664449, 45.496272386810624 ], [ -73.576562645953913, 45.496116207640782 ], [ -73.576415293070468, 45.496045695530078 ], [ -73.576420653070528, 45.496040157901724 ], [ -73.576426013069508, 45.496034620273157 ], [ -73.576390874544217, 45.496017807691075 ], [ -73.576355736039815, 45.496000995098171 ], [ -73.576350382434597, 45.496006532719178 ], [ -73.576345028828328, 45.496012070339987 ], [ -73.576230158246304, 45.495957103581226 ], [ -73.576199840227886, 45.495942591324486 ], [ -73.57616952222503, 45.495928079059674 ], [ -73.57601853434798, 45.496084140658738 ], [ -73.576148538438346, 45.496146359284836 ], [ -73.576177140896519, 45.496160035843573 ], [ -73.576252998979967, 45.496081635786481 ], [ -73.576326707327183, 45.496116905446513 ] ] ] } }
]
}

10
input_files/Citylayers_neighbours.geojson
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@ -1,10 +0,0 @@
{
"type": "FeatureCollection",
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"comment": "Detection of Lidar points classified as ground in the building. D\u00c3\u00a9tection de points Lidar classifi\u00c3\u00a9s sol dans le b\u00c3\u00a2timent.",
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113
input_files/selected_building_839.geojson
View File

@ -1,113 +0,0 @@
{
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{
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311
input_files/selected_building_9526.geojson
View File

@ -1,311 +0,0 @@
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21
main.py
View File

@ -8,7 +8,6 @@ from hub.imports.construction_factory import ConstructionFactory
from hub.imports.usage_factory import UsageFactory
from hub.imports.weather_factory import WeatherFactory
from hub.imports.results_factory import ResultFactory
from hub.exports.exports_factory import ExportsFactory
from scripts.energy_system_analysis_report import EnergySystemAnalysisReport
from scripts import random_assignation
from hub.imports.energy_systems_factory import EnergySystemsFactory
@ -17,7 +16,7 @@ from scripts.energy_system_retrofit_results import system_results, new_system_re
from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
from scripts.costs.cost import Cost
from scripts.costs.constants import SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV
from hub.exports.exports_factory import ExportsFactory
# Specify the GeoJSON file path
geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.0001)
file_path = (Path(__file__).parent.parent / 'input_files' / f'{geojson_file}')
@ -32,6 +31,7 @@ city = GeometryFactory('geojson',
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
# Enrich city data
ConstructionFactory('nrcan', city).enrich()
UsageFactory('nrcan', city).enrich()
WeatherFactory('epw', city).enrich()
ExportsFactory('sra', city, output_path).export()
@ -39,19 +39,24 @@ sra_path = (output_path / f'{city.name}_sra.xml').resolve()
subprocess.run(['sra', str(sra_path)])
ResultFactory('sra', city, output_path).enrich()
energy_plus_workflow(city)
ExportsFactory('obj', city, output_path).export()
random_assignation.call_random(city.buildings, random_assignation.residential_systems_percentage)
EnergySystemsFactory('montreal_custom', city).enrich()
SystemSizing(city.buildings).montreal_custom()
current_system = system_results(city.buildings)
current_system = new_system_results(city.buildings)
random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
EnergySystemsFactory('montreal_future', city).enrich()
for building in city.buildings:
EnergySystemsSimulationFactory('archetype1', building=building, output_path=output_path).enrich()
new_system = new_system_results(city.buildings)
# EnergySystemAnalysisReport(city, output_path).create_report(current_system, new_system)
# for building in city.buildings:
# costs = Cost(building=building, retrofit_scenario=SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV).life_cycle
# costs.loc['global_operational_costs', f'Scenario {SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV}'].to_csv(output_path / f'{building.name}_op.csv')
new_system = system_results(city.buildings)
EnergySystemAnalysisReport(city, output_path).create_report(current_system, new_system)
for building in city.buildings:
costs = Cost(building=building, retrofit_scenario=SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV).life_cycle
costs.to_csv(output_path / f'{building.name}_lcc.csv')
(costs.loc['global_operational_costs', f'Scenario {SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV}'].
to_csv(output_path / f'{building.name}_op.csv'))
costs.loc['global_capital_costs', f'Scenario {SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV}'].to_csv(
output_path / f'{building.name}_cc.csv')

2
scripts/costs/capital_costs.py
View File

@ -273,7 +273,7 @@ class CapitalCosts(CostBase):
if generation_system.system_type != cte.PHOTOVOLTAIC:
heating_capacity = generation_system.nominal_heat_output or 0
cooling_capacity = generation_system.nominal_cooling_output or 0
installed_capacity = max(heating_capacity, cooling_capacity)
installed_capacity = max(heating_capacity, cooling_capacity) / 1000
if cte.DOMESTIC_HOT_WATER in demand_types and cte.HEATING not in demand_types:
component_categories.append('dhw')
sizes.append(installed_capacity)

2
scripts/costs/cost.py
View File

@ -127,7 +127,7 @@ class Cost:
life_cycle_costs_capital_skin = self._npv_from_list(df_capital_costs_skin.values.tolist())
life_cycle_costs_capital_systems = self._npv_from_list(df_capital_costs_systems.values.tolist())
life_cycle_costs_end_of_life_costs = self._npv_from_list(df_end_of_life_costs.values.tolist())
life_cycle_operational_costs = self._npv_from_list([df_operational_costs])
life_cycle_operational_costs = self._npv_from_list(df_operational_costs)
life_cycle_maintenance_costs = self._npv_from_list(df_maintenance_costs.values.tolist())
life_cycle_operational_incomes = self._npv_from_list(df_operational_incomes.values.tolist())
life_cycle_capital_incomes = self._npv_from_list(df_capital_incomes.values.tolist())

2
scripts/costs/peak_load.py
View File

@ -45,7 +45,7 @@ class PeakLoad:
conditioning_peak[i] = self._building.heating_peak_load[cte.MONTH][i] * heating
else:
conditioning_peak[i] = self._building.cooling_peak_load[cte.MONTH][i] * cooling
monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
monthly_electricity_peak[i] += 0.8 * conditioning_peak[i] / 3600
electricity_peak_load_results = pd.DataFrame(
monthly_electricity_peak,

2
scripts/costs/total_operational_costs.py
View File

@ -51,7 +51,7 @@ class TotalOperationalCosts(CostBase):
if fuel.type in fuel_consumption_breakdown.keys():
if fuel.type == cte.ELECTRICITY:
variable_electricity_cost_year_0 = (
total_electricity_consumption / cte.WATTS_HOUR_TO_JULES * fuel.variable[0]
total_electricity_consumption * fuel.variable[0] / 1000
)
peak_electricity_cost_year_0 = peak_electricity_demand * fuel.fixed_power * 12
monthly_electricity_cost_year_0 = fuel.fixed_monthly * 12 * factor

18
scripts/energy_system_analysis_report.py
View File

@ -197,9 +197,9 @@ class EnergySystemAnalysisReport:
def building_system_retrofit_results(self, building_name, current_system, new_system):
current_system_archetype = current_system[f'{building_name}']['Energy System Archetype']
current_system_heating = current_system[f'{building_name}']['Heating System']
current_system_cooling = current_system[f'{building_name}']['Cooling System']
current_system_dhw = current_system[f'{building_name}']['DHW System']
current_system_heating = current_system[f'{building_name}']['Heating Equipments']
current_system_cooling = current_system[f'{building_name}']['Cooling Equipments']
current_system_dhw = current_system[f'{building_name}']['DHW Equipments']
current_system_pv = current_system[f'{building_name}']['Photovoltaic System Capacity']
current_system_heating_fuel = current_system[f'{building_name}']['Heating Fuel']
current_system_hvac_consumption = current_system[f'{building_name}']['Yearly HVAC Energy Consumption (MWh)']
@ -209,9 +209,9 @@ class EnergySystemAnalysisReport:
current_operational = current_system[f'{building_name}']['Energy System Average Yearly Operational Cost (CAD)']
current_lcc = current_system[f'{building_name}']['Energy System Life Cycle Cost (CAD)']
new_system_archetype = new_system[f'{building_name}']['Energy System Archetype']
new_system_heating = new_system[f'{building_name}']['Heating System']
new_system_cooling = new_system[f'{building_name}']['Cooling System']
new_system_dhw = new_system[f'{building_name}']['DHW System']
new_system_heating = new_system[f'{building_name}']['Heating Equipments']
new_system_cooling = new_system[f'{building_name}']['Cooling Equipments']
new_system_dhw = new_system[f'{building_name}']['DHW Equipments']
new_system_pv = new_system[f'{building_name}']['Photovoltaic System Capacity']
new_system_heating_fuel = new_system[f'{building_name}']['Heating Fuel']
new_system_hvac_consumption = new_system[f'{building_name}']['Yearly HVAC Energy Consumption (MWh)']
@ -224,9 +224,9 @@ class EnergySystemAnalysisReport:
energy_system_table_data = [
["Detail", "Existing System", "Proposed System"],
["Energy System Archetype", current_system_archetype, new_system_archetype],
["Heating System", current_system_heating, new_system_heating],
["Cooling System", current_system_cooling, new_system_cooling],
["DHW System", current_system_dhw, new_system_dhw],
["Heating Equipments", current_system_heating, new_system_heating],
["Cooling Equipments", current_system_cooling, new_system_cooling],
["DHW Equipments", current_system_dhw, new_system_dhw],
["Photovoltaic System Capacity", current_system_pv, new_system_pv],
["Heating Fuel", current_system_heating_fuel, new_system_heating_fuel],
["Yearly HVAC Energy Consumption (MWh)", current_system_hvac_consumption, new_system_hvac_consumption],

43
scripts/energy_system_retrofit_results.py
View File

@ -3,9 +3,9 @@ import hub.helpers.constants as cte
def system_results(buildings):
system_performance_summary = {}
fields = ["Energy System Archetype", "Heating System", "Cooling System", "DHW System",
fields = ["Energy System Archetype", "Heating Equipments", "Cooling Equipments", "DHW Equipments",
"Photovoltaic System Capacity", "Heating Fuel", "Yearly HVAC Energy Consumption (MWh)",
"DHW Energy Consumption (MWH)", "PV Yearly Production (kWh)",
"DHW Energy Consumption (MWH)", "PV Yearly Production (kWh)", "LCC Analysis Duration (Years)",
"Energy System Capital Cost (CAD)", "Energy System Average Yearly Operational Cost (CAD)",
"Energy System Life Cycle Cost (CAD)"]
for building in buildings:
@ -19,19 +19,28 @@ def system_results(buildings):
energy_systems = building.energy_systems
for energy_system in energy_systems:
demand_types = energy_system.demand_types
if cte.HEATING and cte.DOMESTIC_HOT_WATER in demand_types:
system_performance_summary[f'{building.name}']['Heating System'] = energy_system.name
system_performance_summary[f'{building.name}']['DHW System'] = energy_system.name
for generation_system in energy_system.generation_systems:
fuels.append(generation_system.fuel_type)
elif cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
system_performance_summary[f'{building.name}']['DHW System'] = energy_system.name
elif cte.HEATING in energy_system.demand_types:
system_performance_summary[f'{building.name}']['Heating System'] = energy_system.name
for generation_system in energy_system.generation_systems:
fuels.append(generation_system.fuel_type)
elif cte.COOLING in energy_system.demand_types:
system_performance_summary[f'{building.name}']['Cooling System'] = energy_system.name
for demand_type in demand_types:
if demand_type == cte.COOLING:
equipments = []
for generation_system in energy_system.generation_systems:
if generation_system.fuel_type == cte.ELECTRICITY:
equipments.append(generation_system.name or generation_system.system_type)
cooling_equipments = ", ".join(equipments)
system_performance_summary[f'{building.name}']['Cooling Equipments'] = cooling_equipments
elif demand_type == cte.HEATING:
equipments = []
for generation_system in energy_system.generation_systems:
if generation_system.nominal_heat_output is not None:
equipments.append(generation_system.name or generation_system.system_type)
fuels.append(generation_system.fuel_type)
heating_equipments = ", ".join(equipments)
system_performance_summary[f'{building.name}']['Heating Equipments'] = heating_equipments
elif demand_type == cte.DOMESTIC_HOT_WATER:
equipments = []
for generation_system in energy_system.generation_systems:
equipments.append(generation_system.name or generation_system.system_type)
dhw_equipments = ", ".join(equipments)
system_performance_summary[f'{building.name}']['DHW Equipments'] = dhw_equipments
for generation_system in energy_system.generation_systems:
if generation_system.system_type == cte.PHOTOVOLTAIC:
system_performance_summary[f'{building.name}'][
@ -47,9 +56,9 @@ def system_results(buildings):
def new_system_results(buildings):
new_system_performance_summary = {}
fields = ["Energy System Archetype", "Heating System", "Cooling System", "DHW System",
fields = ["Energy System Archetype", "Heating Equipments", "Cooling Equipments", "DHW Equipments",
"Photovoltaic System Capacity", "Heating Fuel", "Yearly HVAC Energy Consumption (MWh)",
"DHW Energy Consumption (MWH)", "PV Yearly Production (kWh)",
"DHW Energy Consumption (MWH)", "PV Yearly Production (kWh)", "LCC Analysis Duration (Years)",
"Energy System Capital Cost (CAD)", "Energy System Average Yearly Operational Cost (CAD)",
"Energy System Life Cycle Cost (CAD)"]
for building in buildings:

8
scripts/random_assignation.py
View File

@ -15,8 +15,8 @@ from hub.city_model_structure.building import Building
energy_systems_format = 'montreal_custom'
# parameters:
residential_systems_percentage = {'system 1 gas': 100,
'system 1 electricity': 0,
residential_systems_percentage = {'system 1 gas': 44,
'system 1 electricity': 6,
'system 2 gas': 0,
'system 2 electricity': 0,
'system 3 and 4 gas': 0,
@ -25,8 +25,8 @@ residential_systems_percentage = {'system 1 gas': 100,
'system 5 electricity': 0,
'system 6 gas': 0,
'system 6 electricity': 0,
'system 8 gas': 0,
'system 8 electricity': 0}
'system 8 gas': 44,
'system 8 electricity': 6}
residential_new_systems_percentage = {'PV+ASHP+GasBoiler+TES': 100,
'PV+4Pipe+DHW': 0,

5
scripts/report_creation.py
View File

@ -1,9 +1,4 @@
import subprocess
import hub.helpers.constants as cte
import matplotlib.pyplot as plt
import random
import matplotlib.colors as mcolors
from matplotlib import cm
import datetime
class LatexReport:

132
scripts/system_simulation_models/archetype1.py
View File

@ -18,7 +18,7 @@ class Archetype1:
self._hourly_cooling_demand = [demand / 3600 for demand in building.cooling_demand[cte.HOUR]]
self._hourly_dhw_demand = building.domestic_hot_water_heat_demand[cte.HOUR]
self._output_path = output_path
self._t_out = building.external_temperature[cte.HOUR]
self._t_out = [0] + building.external_temperature[cte.HOUR]
def hvac_sizing(self):
storage_factor = 3
@ -32,11 +32,11 @@ class Archetype1:
(self._heating_peak_load * storage_factor) / (cte.WATER_HEAT_CAPACITY * cte.WATER_DENSITY * 30))
return heat_pump, boiler, thermal_storage
def hvac_simulation(self):
def heating_system_simulation(self):
hp, boiler, tes = self.hvac_sizing()
cop_curve_coefficients = [float(coefficient) for coefficient in hp.heat_efficiency_curve.coefficients]
demand = self._hourly_heating_demand
if hp.source_medium == cte.AIR:
hp.source_temperature = self._t_out
hp.source_temperature = self._t_out
# Heating System Simulation
variable_names = ["t_sup_hp", "t_tank", "t_ret", "m_ch", "m_dis", "q_hp", "q_boiler", "hp_cop",
"hp_electricity", "boiler_gas", "boiler_consumption", "t_sup_boiler", "heating_consumption"]
@ -45,7 +45,6 @@ class Archetype1:
(t_sup_hp, t_tank, t_ret, m_ch, m_dis, q_hp, q_boiler, hp_cop,
hp_electricity, boiler_gas, boiler_consumption, t_sup_boiler, heating_consumption) = [variables[name] for name in
variable_names]
t_tank[0] = 30
dt = 3600
hp_heating_cap = hp.nominal_heat_output
@ -66,7 +65,7 @@ class Archetype1:
m_dis[i] * (t_tank[i] - t_ret[i])) * (dt / (cte.WATER_DENSITY * v)))
if t_tank[i + 1] < 40:
q_hp[i + 1] = hp_heating_cap
m_ch[i + 1] = q_hp[i + 1] / (cte.WATER_HEAT_CAPACITY * 7)
m_ch[i + 1] = q_hp[i + 1] / (cte.WATER_HEAT_CAPACITY * 5)
t_sup_hp[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY)) + t_tank[i + 1]
elif 40 <= t_tank[i + 1] < 55 and q_hp[i] == 0:
q_hp[i + 1] = 0
@ -78,54 +77,116 @@ class Archetype1:
t_sup_hp[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY)) + t_tank[i + 1]
else:
q_hp[i + 1], m_ch[i + 1], t_sup_hp[i + 1] = 0, 0, t_tank[i + 1]
hp_electricity[i + 1] = q_hp[i + 1] / hp_efficiency
t_sup_hp_fahrenheit = 1.8 * t_sup_hp[i + 1] + 32
t_out_fahrenheit = 1.8 * self._t_out[i + 1] + 32
if q_hp[i + 1] > 0:
hp_cop[i + 1] = (cop_curve_coefficients[0] +
cop_curve_coefficients[1] * t_sup_hp_fahrenheit +
cop_curve_coefficients[2] * t_sup_hp_fahrenheit ** 2 +
cop_curve_coefficients[3] * t_out_fahrenheit +
cop_curve_coefficients[4] * t_out_fahrenheit ** 2 +
cop_curve_coefficients[5] * t_sup_hp_fahrenheit * t_out_fahrenheit)
hp_electricity[i + 1] = q_hp[i + 1] / hp_cop[i + 1]
else:
hp_cop[i + 1] = 0
hp_electricity[i + 1] = 0
if demand[i + 1] == 0:
m_dis[i + 1], t_return, t_ret[i + 1] = 0, t_tank[i + 1], t_tank[i + 1]
else:
if demand[i + 1] > 0.5 * self._heating_peak_load:
if demand[i + 1] > 0.5 * self._heating_peak_load / dt:
factor = 8
else:
factor = 4
m_dis[i + 1] = self._heating_peak_load / (cte.WATER_HEAT_CAPACITY * factor * dt)
t_return = t_tank[i + 1] - demand[i + 1] / (m_dis[i + 1] * cte.WATER_HEAT_CAPACITY * dt)
if m_dis[i + 1] == 0 or (m_dis[i + 1] > 0 and t_return < 25):
t_ret[i + 1] = max(25, t_tank[i + 1])
else:
t_ret[i + 1] = t_tank[i + 1] - demand[i + 1] / (m_dis[i + 1] * cte.WATER_HEAT_CAPACITY * dt)
tes_output = m_dis[i + 1] * cte.WATER_HEAT_CAPACITY * (t_tank[i + 1] - t_ret[i + 1])
if tes_output < (demand[i + 1] / dt):
q_boiler[i + 1] = (demand[i + 1] / dt) - tes_output
boiler_gas[i + 1] = (q_boiler[i + 1] * dt) / cte.NATURAL_GAS_LHV
if q_boiler[i + 1] > 0:
t_sup_boiler[i + 1] = q_boiler[i + 1] / (m_dis[i + 1] * cte.WATER_HEAT_CAPACITY) + t_tank[i + 1]
else:
t_sup_boiler[i + 1] = t_tank[i + 1]
t_return = t_tank[i + 1] - demand[i + 1] / (m_dis[i + 1] * cte.WATER_HEAT_CAPACITY)
if t_return >= 25:
t_ret[i + 1] = t_tank[i + 1] - demand[i + 1] / (m_dis[i + 1] * cte.WATER_HEAT_CAPACITY)
q_boiler[i + 1] = 0
t_sup_boiler[i + 1] = t_tank[i + 1]
else:
t_ret[i + 1] = 25
t_sup_boiler[i + 1] = t_ret[i + 1] + (demand[i + 1] / (m_dis[i + 1] * cte.WATER_HEAT_CAPACITY))
q_boiler[i + 1] = m_dis[i + 1] * cte.WATER_HEAT_CAPACITY * (t_sup_boiler[i + 1] - t_tank[i + 1])
boiler_gas[i + 1] = (q_boiler[i + 1] * dt) / cte.NATURAL_GAS_LHV
boiler_consumption[i + 1] = q_boiler[i + 1] / boiler_efficiency
heating_consumption[i + 1] = boiler_consumption[i + 1] + hp_electricity[i + 1]
data = list(zip(q_hp, hp_electricity, hp_cop, m_ch, t_sup_hp, t_tank, m_dis, q_boiler,
data = list(zip(self._t_out, q_hp, hp_electricity, hp_cop, m_ch, t_sup_hp, t_tank, m_dis, q_boiler,
boiler_gas, t_sup_boiler, t_ret, demand))
file_name = f'simulation_results_{self._name}.csv'
file_name = f'heating_system_simulation_results_{self._name}.csv'
with open(self._output_path / file_name, 'w', newline='') as csvfile:
output_file = csv.writer(csvfile)
# Write header
output_file.writerow(['HP_heat_output(W)', 'HP_electricity_consumption(W)', 'HP_COP',
'TES_charge_flow_rate(kg/s)', 'TES_supply_temperature(C)', 'TES_temperature(C)',
output_file.writerow(['Outside_air_temperature', 'HP_heat_output(W)', 'HP_heating_electricity_consumption(W)',
'HP_COP', 'TES_charge_flow_rate(kg/s)', 'TES_supply_temperature(C)', 'TES_temperature(C)',
'TES_discharge_flow_rate(kg/s)', 'Boiler_heat_output(W)', 'Boiler_gas_consumption(m3)',
'Boiler_supply_temperature(C)', 'Return_temperature(C)', 'heating_demand'])
'Boiler_supply_temperature(C)', 'Heating_loop_return_temperature(C)', 'Heating_demand (W)'])
# Write data
output_file.writerows(data)
return heating_consumption, hp_electricity, boiler_consumption, t_sup_hp, t_sup_boiler
def cooling_system_simulation(self):
hp = self.hvac_sizing()[0]
eer_curve_coefficients = [float(coefficient) for coefficient in hp.cooling_efficiency_curve.coefficients]
demand = self._hourly_heating_demand
hp.source_temperature = self._t_out
variable_names = ["t_sup_hp", "t_ret", "m", "q_hp", "hp_electricity", "hp_eer"]
num_hours = len(demand)
variables = {name: [0] * num_hours for name in variable_names}
(t_sup_hp, t_ret, m, q_hp, hp_electricity, hp_eer) = [variables[name] for name in variable_names]
t_ret[0] = 13
dt = 3600
for i in range(len(demand) - 1):
if demand[i] > 0:
m[i] = self._cooling_peak_load / (cte.WATER_HEAT_CAPACITY * 5 * dt)
if t_ret[i] > 13:
q_hp[i] = hp.nominal_cooling_output
t_sup_hp[i] = t_ret[i] - q_hp[i] / (m[i] * cte.WATER_HEAT_CAPACITY)
else:
q_hp[i] = 0
t_sup_hp[i] = t_ret[i]
t_ret[i + 1] = t_sup_hp[i] + demand[i] / (m[i] * cte.WATER_HEAT_CAPACITY)
else:
m[i] = 0
q_hp[i] = 0
t_sup_hp[i] = t_ret[i]
t_ret[i + 1] = t_ret[i]
t_sup_hp_fahrenheit = 1.8 * t_sup_hp[i] + 32
t_out_fahrenheit = 1.8 * self._t_out[i] + 32
if q_hp[i] > 0:
hp_eer[i] = 1/((eer_curve_coefficients[0] +
eer_curve_coefficients[1] * t_sup_hp_fahrenheit +
eer_curve_coefficients[2] * t_sup_hp_fahrenheit ** 2 +
eer_curve_coefficients[3] * t_out_fahrenheit +
eer_curve_coefficients[4] * t_out_fahrenheit ** 2 +
eer_curve_coefficients[5] * t_sup_hp_fahrenheit * t_out_fahrenheit))
hp_electricity[i] = q_hp[i] / hp_eer[i]
else:
hp_eer[i] = 0
hp_electricity[i] = 0
data = list(zip(q_hp, hp_electricity, hp_eer, m, t_sup_hp, t_ret, demand, self._t_out))
file_name = f'cooling_system_simulation_results_{self._name}.csv'
with open(self._output_path / file_name, 'w', newline='') as csvfile:
output_file = csv.writer(csvfile)
# Write header
output_file.writerow(['HP_cooling_output (W)', 'HP_cooling_electricity_consumption (W)', 'HP_EER',
'Cooling_loop_flow_rate(kg/s)', 'Cooling_supply_temperature(C)',
'Cooling_loop_return_temperature(C)', 'Cooling_demand (W)', 'Outside_Air_Temperature'])
# Write data
output_file.writerows(data)
return hp_electricity, t_sup_hp
def enrich_buildings(self):
(self._building.heating_consumption[cte.HOUR], hp_electricity,
boiler_consumption, t_sup_hp, t_sup_boiler) = self.hvac_simulation()
(self._building.heating_consumption[cte.HOUR], hp_electricity_heating,
boiler_consumption, t_sup_hp, t_sup_boiler) = self.heating_system_simulation()
hp_electricity_cooling, t_sup_hp_cooling = self.cooling_system_simulation()
self._building.heating_consumption[cte.MONTH] = MonthlyValues.get_total_month(
self._building.heating_consumption[cte.HOUR])
self._building.heating_consumption[cte.YEAR] = [sum(self._building.heating_consumption[cte.MONTH])]
self._hvac_system.generation_systems[0].energy_consumption[cte.HEATING] = {}
self._hvac_system.generation_systems[1].energy_consumption[cte.HEATING] = {}
self._hvac_system.generation_systems[0].energy_consumption[cte.HEATING][cte.HOUR] = hp_electricity
self._hvac_system.generation_systems[0].energy_consumption[cte.HEATING][cte.HOUR] = hp_electricity_heating
self._hvac_system.generation_systems[0].energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
self._hvac_system.generation_systems[0].energy_consumption[cte.HEATING][cte.HOUR])
self._hvac_system.generation_systems[0].energy_consumption[cte.HEATING][cte.YEAR] = \
@ -137,5 +198,12 @@ class Archetype1:
[sum(self._hvac_system.generation_systems[1].energy_consumption[cte.HEATING][cte.MONTH])]
self._hvac_system.generation_systems[0].heat_supply_temperature = t_sup_hp
self._hvac_system.generation_systems[1].heat_supply_temperature = t_sup_boiler
self._hvac_system.generation_systems[0].energy_consumption[cte.COOLING] = self._building.cooling_consumption
self._dhw_system.generation_systems[0].energy_consumption[cte.DOMESTIC_HOT_WATER] = self._building.domestic_hot_water_consumption
self._hvac_system.generation_systems[0].energy_consumption[cte.COOLING] = {}
self._hvac_system.generation_systems[0].energy_consumption[cte.COOLING][cte.HOUR] = hp_electricity_cooling
self._hvac_system.generation_systems[0].energy_consumption[cte.COOLING][cte.MONTH] = MonthlyValues.get_total_month(
self._hvac_system.generation_systems[0].energy_consumption[cte.COOLING][cte.HOUR])
self._hvac_system.generation_systems[0].energy_consumption[cte.COOLING][cte.YEAR] = \
[sum(self._hvac_system.generation_systems[0].energy_consumption[cte.COOLING][cte.MONTH])]
self._hvac_system.generation_systems[0].cooling_supply_temperature = t_sup_hp_cooling
self._dhw_system.generation_systems[0].energy_consumption[cte.DOMESTIC_HOT_WATER] = (
self._building.domestic_hot_water_consumption)