feat: single objective optimization started
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@ -10,9 +10,8 @@ from energy_system_modelling_package.energy_system_modelling_factories.system_si
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class GeneticAlgorithm:
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def __init__(self, city, population_size=100, generations=50, crossover_rate=0.8, mutation_rate=0.1,
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def __init__(self, population_size=100, generations=50, crossover_rate=0.8, mutation_rate=0.1,
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optimization_scenario='cost', output_path=None):
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self.city = city
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self.population_size = population_size
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self.generations = generations
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self.crossover_rate = crossover_rate
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@ -24,8 +23,11 @@ class GeneticAlgorithm:
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def energy_system_archetype_optimal_sizing(self):
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pass
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# Initialize Population
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def initialize_population(self, energy_system):
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# Initialize Population
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def initialize_population(self, building, energy_system, heating_design_load=None, cooling_design_load=None,
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available_space=None, dt=1800, fuel_price_index=0.05, electricity_tariff_type='fixed',
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consumer_price_index=0.04, interest_rate=0.04, discount_rate=0.03, percentage_credit=0,
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credit_years=15):
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"""
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The population to optimize the sizes of generation and storage components of any energy system are optimized.
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The initial population will be a list of individuals where each of them represent a possible solution to the
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@ -35,21 +37,28 @@ class GeneticAlgorithm:
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"""
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population = []
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for i in range(self.population_size):
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population.append(Individual(energy_system, self.optimization_scenario))
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population.append(Individual(building, energy_system, self.optimization_scenario,
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heating_design_load=heating_design_load, cooling_design_load=cooling_design_load,
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available_space=available_space, dt=dt, fuel_price_index=fuel_price_index,
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electricity_tariff_type=electricity_tariff_type,
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consumer_price_index=consumer_price_index, interest_rate=interest_rate,
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discount_rate=discount_rate, percentage_credit=percentage_credit,
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credit_years=credit_years))
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return population
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def solve_ga(self, energy_system):
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def solve_ga(self, building, energy_system):
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"""
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Solving GA for a single energy system. Here are the steps:
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1- Population is initialized using the "initialize_population" method in this class.
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:param building:
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:param energy_system:
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:return:
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"""
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energy_system = energy_system
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best_individuals = []
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best_solution = None
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population = self.initialize_population(energy_system)
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population = self.initialize_population(building, energy_system)
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for individual in population:
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individual.score_evaluation()
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print(individual.individual['feasible'])
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print(individual.individual['fitness_score'])
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@ -12,9 +12,9 @@ import numpy_financial as npf
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class Individual:
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def __init__(self, building, energy_system, optimization_scenario, heating_design_load=None,
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cooling_design_load=None, available_space=None, dt=1800, fuel_price_index=0.05,
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electricity_tariff_type='fixed', consumer_price_index=0.04, interest_rate=0.04,
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discount_rate=0.03, percentage_credit=0, credit_years=15):
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cooling_design_load=None, available_space=None, dt=None, fuel_price_index=None,
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electricity_tariff_type='fixed', consumer_price_index=None, interest_rate=None,
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discount_rate=None, percentage_credit=None, credit_years=None):
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"""
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:param building: building object
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:param energy_system: energy system to be optimized
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@ -95,10 +95,12 @@ class Individual:
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f'{energy_storage_system.type_energy_stored}_storage'))
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if energy_storage_system.type_energy_stored == cte.THERMAL:
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heating_coil_capacity = energy_storage_system.heating_coil_capacity
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heating_coil_fuel_cost = self.fuel_cost_per_kwh(f'{cte.ELECTRICITY}', 'fixed')
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volume = 0
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capacity = None
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else:
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heating_coil_capacity = None
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heating_coil_fuel_cost = None
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volume = None
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capacity = 0
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self.individual['Energy Storage Components'].append(
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@ -108,6 +110,7 @@ class Individual:
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'heating_coil_capacity': heating_coil_capacity,
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'unit_investment_cost': investment_cost,
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'unit_reposition_cost': reposition_cost,
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'heating_coil_fuel_cost': heating_coil_fuel_cost,
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'unit_maintenance_cost': 0,
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'lifetime': lifetime
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})
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@ -122,12 +125,18 @@ class Individual:
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generation_components = self.individual['Generation Components']
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storage_components = self.individual['Energy Storage Components']
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for generation_component in generation_components:
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if generation_component['nominal_heating_efficiency'] is not None and self.heating_design_load is not None:
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generation_component['heating_capacity'] = random.uniform(0, self.heating_design_load)
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if generation_component['nominal_heating_efficiency'] is not None:
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if self.heating_design_load is not None:
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generation_component['heating_capacity'] = random.uniform(0, self.heating_design_load)
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else:
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generation_component['heating_capacity'] = random.uniform(0, self.building.heating_peak_load[cte.YEAR][0])
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else:
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generation_component['heating_capacity'] = None
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if generation_component['nominal_cooling_efficiency'] is not None and self.cooling_design_load is not None:
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generation_component['cooling_capacity'] = random.uniform(0, self.cooling_design_load)
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if generation_component['nominal_cooling_efficiency'] is not None:
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if self.cooling_design_load is not None:
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generation_component['cooling_capacity'] = random.uniform(0, self.cooling_design_load)
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else:
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generation_component['cooling_capacity'] = random.uniform(0, self.building.cooling_peak_load[cte.YEAR][0])
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else:
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generation_component['cooling_capacity'] = None
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if generation_component['nominal_electricity_efficiency'] is None:
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@ -135,12 +144,15 @@ class Individual:
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for storage_component in storage_components:
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if storage_component['type'] == f'{cte.THERMAL}_storage':
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storage_component['volume'] = random.uniform(0, 0.01 * self.available_space)
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if storage_component['heating_coil_capacity'] is not None:
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storage_component['heating_coil_capacity'] = random.uniform(0, self.heating_design_load)
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def score_evaluation(self):
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self.initialization()
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self.system_simulation()
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self.individual['feasible'] = self.feasibility
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if self.feasibility:
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if self.optimization_scenario == 'cost':
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if 'cost' in self.optimization_scenario:
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investment_cost = 0
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operation_cost_year_0 = 0
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maintenance_cost_year_0 = 0
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@ -157,17 +169,27 @@ class Individual:
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for storage_system in self.individual['Energy Storage Components']:
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if cte.THERMAL in storage_system['type']:
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investment_cost += storage_system['volume'] * storage_system['unit_investment_cost']
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if storage_system['heating_coil_capacity'] is not None:
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operation_cost_year_0 += (storage_system['yearly_energy_consumption(kWh)'] *
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storage_system['heating_coil_fuel_cost'])
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lcc = self.life_cycle_cost_calculation(investment_cost=investment_cost,
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operation_cost_year_0=operation_cost_year_0,
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maintenance_cost_year_0=maintenance_cost_year_0)
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self.individual['fitness score'] = lcc
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elif self.optimization_scenario == 'energy_consumption':
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pass
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self.individual['lcc'] = lcc
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self.individual['fitness_score'] = lcc
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elif 'energy_consumption' in self.optimization_scenario:
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total_energy_consumption = 0
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for generation_system in self.individual['Generation Components']:
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total_energy_consumption += generation_system['yearly_energy_consumption(kWh)']
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for storage_system in self.individual['Energy Storage Components']:
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if cte.THERMAL in storage_system['type'] and storage_system['heating_coil_capacity'] is not None:
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total_energy_consumption += storage_system['yearly_energy_consumption(kWh)']
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self.individual['total_energy_consumption'] = total_energy_consumption
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self.individual['fitness_score'] = total_energy_consumption
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elif self.optimization_scenario == 'cost_energy_consumption':
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pass
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else:
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self.individual['fitness score'] = float('inf')
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print(self.individual)
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self.individual['fitness_score'] = float('inf')
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def system_simulation(self):
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"""
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@ -183,9 +205,12 @@ class Individual:
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hp.nominal_heat_output = self.individual['Generation Components'][1]['heating_capacity']
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tes = self.energy_system.generation_systems[0].energy_storage_systems[0]
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tes.volume = self.individual['Energy Storage Components'][0]['volume']
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tes.height = self.building.average_storey_height - 1.5
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tes.height = self.building.average_storey_height - 1
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tes.heating_coil_capacity = self.individual['Energy Storage Components'][0]['heating_coil_capacity'] \
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if self.individual['Energy Storage Components'][0]['heating_coil_capacity'] is not None else 0
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heating_demand_joules = self.building.heating_demand[cte.HOUR]
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heating_peak_load_watts = self.heating_design_load
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heating_peak_load_watts = self.heating_design_load if (self.heating_design_load is not
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None) else self.building.heating_peak_load[cte.YEAR][0]
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upper_limit_tes_heating = 55
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outdoor_temperature = self.building.external_temperature[cte.HOUR]
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results = HeatPumpBoilerTesHeating(hp=hp,
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@ -200,7 +225,12 @@ class Individual:
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self.feasibility = False
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elif cte.DOMESTIC_HOT_WATER in self.demand_types:
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hp = self.energy_system.generation_systems[0]
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hp.nominal_heat_output = self.individual['Generation Components'][0]['heating_capacity']
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tes = self.energy_system.generation_systems[0].energy_storage_systems[0]
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tes.volume = self.individual['Energy Storage Components'][0]['volume']
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tes.height = self.building.average_storey_height - 1
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tes.heating_coil_capacity = self.individual['Energy Storage Components'][0]['heating_coil_capacity'] \
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if self.individual['Energy Storage Components'][0]['heating_coil_capacity'] is not None else 0
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dhw_demand_joules = self.building.domestic_hot_water_heat_demand[cte.HOUR]
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upper_limit_tes = 65
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outdoor_temperature = self.building.external_temperature[cte.HOUR]
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@ -218,8 +248,80 @@ class Individual:
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for generation_component in self.individual['Generation Components']:
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if generation_component['type'] in generation_system_types:
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index = generation_system_types.index(generation_component['type'])
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generation_component['yearly_energy_consumption(kWh)'] = (
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self.energy_system.generation_systems[index].energy_consumption[cte.HEATING][cte.YEAR][0] / 3.6e6)
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for demand_type in self.demand_types:
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if demand_type in self.energy_system.generation_systems[index].energy_consumption:
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generation_component['yearly_energy_consumption(kWh)'] = (
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self.energy_system.generation_systems[index].energy_consumption[demand_type][cte.YEAR][0] / 3.6e6)
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for storage_component in self.individual['Energy Storage Components']:
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if storage_component['type'] == f'{cte.THERMAL}_storage' and storage_component[
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'heating_coil_capacity'] is not None:
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for generation_system in self.energy_system.generation_systems:
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if generation_system.energy_storage_systems is not None:
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for storage_system in generation_system.energy_storage_systems:
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if storage_system.type_energy_stored == cte.THERMAL:
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for demand_type in self.demand_types:
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if demand_type in storage_system.heating_coil_energy_consumption:
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storage_component['yearly_energy_consumption(kWh)'] = (
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storage_system.heating_coil_energy_consumption[demand_type][cte.YEAR][0] / 3.6e6)
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def life_cycle_cost_calculation(self, investment_cost, operation_cost_year_0, maintenance_cost_year_0,
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life_cycle_duration=41):
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"""
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Calculating the life cycle cost of the energy system. The original cost workflow in hub is not used to reduce
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computation time.Here are the steps:
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1- Costs catalog and different components are imported.
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2- Capital costs (investment and reposition) are calculated and appended to a list to have the capital cash
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flow.
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3-
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:param maintenance_cost_year_0:
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:param operation_cost_year_0:
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:param investment_cost:
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:param life_cycle_duration:
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:return:
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"""
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capital_costs_cash_flow = [investment_cost]
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operational_costs_cash_flow = [0]
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maintenance_costs_cash_flow = [0]
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end_of_life_costs = [0] * (life_cycle_duration + 1)
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for i in range(1, life_cycle_duration + 1):
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yearly_operational_cost = math.pow(1 + self.fuel_price_index, i) * operation_cost_year_0
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yearly_maintenance_cost = math.pow(1 + self.consumer_price_index, i) * maintenance_cost_year_0
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yearly_capital_cost = 0
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for generation_system in self.individual['Generation Components']:
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if (i % generation_system['lifetime']) == 0 and i != (life_cycle_duration - 1):
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cost_increase = math.pow(1 + self.consumer_price_index, i)
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heating_capacity = 0 if generation_system['heating_capacity'] is None else generation_system[
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'heating_capacity']
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cooling_capacity = 0 if generation_system['cooling_capacity'] is None else generation_system[
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'cooling_capacity']
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capacity = max(heating_capacity, cooling_capacity)
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yearly_capital_cost += generation_system['unit_reposition_cost'] * capacity * cost_increase / 1000
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yearly_capital_cost += -npf.pmt(self.interest_rate, self.credit_years,
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investment_cost * self.percentage_credit)
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for storage_system in self.individual['Energy Storage Components']:
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if (i % storage_system['lifetime']) == 0 and i != (life_cycle_duration - 1):
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cost_increase = math.pow(1 + self.consumer_price_index, i)
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capacity = storage_system['volume'] if cte.THERMAL in storage_system['type'] else storage_system['capacity']
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yearly_capital_cost += storage_system['unit_reposition_cost'] * capacity * cost_increase
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yearly_capital_cost += -npf.pmt(self.interest_rate, self.credit_years,
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investment_cost * self.percentage_credit)
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capital_costs_cash_flow.append(yearly_capital_cost)
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operational_costs_cash_flow.append(yearly_operational_cost)
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maintenance_costs_cash_flow.append(yearly_maintenance_cost)
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for year in range(1, life_cycle_duration + 1):
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price_increase = math.pow(1 + self.consumer_price_index, year)
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if year == life_cycle_duration:
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end_of_life_costs[year] = (
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self.building.thermal_zones_from_internal_zones[0].total_floor_area *
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self.individual['End of Life Cost'] * price_increase
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)
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life_cycle_capital_cost = npf.npv(self.discount_rate, capital_costs_cash_flow)
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life_cycle_operational_cost = npf.npv(self.discount_rate, operational_costs_cash_flow)
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life_cycle_maintenance_cost = npf.npv(self.discount_rate, maintenance_costs_cash_flow)
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life_cycle_end_of_life_cost = npf.npv(self.discount_rate, end_of_life_costs)
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total_life_cycle_cost = life_cycle_capital_cost + life_cycle_operational_cost + life_cycle_maintenance_cost + life_cycle_end_of_life_cost
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return total_life_cycle_cost
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def costs_archetype(self):
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costs_catalogue = CostsCatalogFactory('montreal_new').catalog.entries().archetypes
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@ -328,62 +430,3 @@ class Individual:
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conversion_factor = fuel.density[0]
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fuel_cost = fuel.variable.values[0] / (conversion_factor * fuel.lower_heating_value[0] * 0.277)
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return fuel_cost
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def life_cycle_cost_calculation(self, investment_cost, operation_cost_year_0, maintenance_cost_year_0,
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life_cycle_duration=41):
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"""
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Calculating the life cycle cost of the energy system. The original cost workflow in hub is not used to reduce
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computation time.Here are the steps:
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1- Costs catalog and different components are imported.
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2- Capital costs (investment and reposition) are calculated and appended to a list to have the capital cash
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flow.
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3-
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:param maintenance_cost_year_0:
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:param operation_cost_year_0:
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:param investment_cost:
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:param life_cycle_duration:
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:return:
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"""
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capital_costs_cash_flow = [investment_cost]
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operational_costs_cash_flow = [0]
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maintenance_costs_cash_flow = [0]
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end_of_life_costs = [0] * (life_cycle_duration + 1)
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for i in range(1, life_cycle_duration + 1):
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yearly_operational_cost = math.pow(1 + self.fuel_price_index, i) * operation_cost_year_0
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yearly_maintenance_cost = math.pow(1 + self.consumer_price_index, i) * maintenance_cost_year_0
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yearly_capital_cost = 0
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for generation_system in self.individual['Generation Components']:
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if (i % generation_system['lifetime']) == 0 and i != (life_cycle_duration - 1):
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cost_increase = math.pow(1 + self.consumer_price_index, i)
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heating_capacity = 0 if generation_system['heating_capacity'] is None else generation_system[
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'heating_capacity']
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cooling_capacity = 0 if generation_system['cooling_capacity'] is None else generation_system[
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'cooling_capacity']
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capacity = max(heating_capacity, cooling_capacity)
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yearly_capital_cost += generation_system['unit_reposition_cost'] * capacity * cost_increase / 1000
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yearly_capital_cost += -npf.pmt(self.interest_rate, self.credit_years,
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investment_cost * self.percentage_credit)
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for storage_system in self.individual['Energy Storage Components']:
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if (i % storage_system['lifetime']) == 0 and i != (life_cycle_duration - 1):
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cost_increase = math.pow(1 + self.consumer_price_index, i)
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capacity = storage_system['volume'] if cte.THERMAL in storage_system['type'] else storage_system['capacity']
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yearly_capital_cost += storage_system['unit_reposition_cost'] * capacity * cost_increase
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yearly_capital_cost += -npf.pmt(self.interest_rate, self.credit_years,
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investment_cost * self.percentage_credit)
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capital_costs_cash_flow.append(yearly_capital_cost)
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operational_costs_cash_flow.append(yearly_operational_cost)
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maintenance_costs_cash_flow.append(yearly_maintenance_cost)
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for year in range(1, life_cycle_duration + 1):
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price_increase = math.pow(1 + self.consumer_price_index, year)
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if year == life_cycle_duration:
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end_of_life_costs[year] = (
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self.building.thermal_zones_from_internal_zones[0].total_floor_area *
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self.individual['End of Life Cost'] * price_increase
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)
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life_cycle_capital_cost = npf.npv(self.discount_rate, capital_costs_cash_flow)
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life_cycle_operational_cost = npf.npv(self.discount_rate, operational_costs_cash_flow)
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life_cycle_maintenance_cost = npf.npv(self.discount_rate, maintenance_costs_cash_flow)
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life_cycle_end_of_life_cost = npf.npv(self.discount_rate, end_of_life_costs)
|
||||
total_life_cycle_cost = life_cycle_capital_cost + life_cycle_operational_cost + life_cycle_maintenance_cost + life_cycle_end_of_life_cost
|
||||
return total_life_cycle_cost
|
||||
|
|
|
|||
|
|
@ -1274,7 +1274,7 @@
|
|||
<storage_type>sensible</storage_type>
|
||||
<nominal_capacity/>
|
||||
<losses_ratio/>
|
||||
<heating_coil_capacity>5000</heating_coil_capacity>
|
||||
<heating_coil_capacity>0</heating_coil_capacity>
|
||||
</templateStorages>
|
||||
</energy_storage_components>
|
||||
<materials>
|
||||
|
|
|
|||
7
main.py
7
main.py
|
|
@ -3,8 +3,7 @@ import subprocess
|
|||
from building_modelling.ep_run_enrich import energy_plus_workflow
|
||||
from energy_system_modelling_package.energy_system_modelling_factories.montreal_energy_system_archetype_modelling_factory import \
|
||||
MontrealEnergySystemArchetypesSimulationFactory
|
||||
from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.individual import \
|
||||
Individual
|
||||
from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.genetic_algorithm import GeneticAlgorithm
|
||||
from hub.imports.geometry_factory import GeometryFactory
|
||||
from hub.helpers.dictionaries import Dictionaries
|
||||
from hub.imports.construction_factory import ConstructionFactory
|
||||
|
|
@ -58,9 +57,7 @@ for building in city.buildings:
|
|||
heating_design_load = building.heating_peak_load[cte.YEAR][0]
|
||||
cooling_design_load = building.cooling_peak_load[cte.YEAR][0]
|
||||
available_space = building.volume / building.storeys_above_ground
|
||||
Individual(building, building.energy_systems[1], optimization_scenario='cost',
|
||||
heating_design_load=heating_design_load, cooling_design_load=cooling_design_load,
|
||||
available_space=available_space, dt=3600, electricity_tariff_type='fixed').score_evaluation()
|
||||
GeneticAlgorithm(optimization_scenario='cost').solve_ga(building=building, energy_system=building.energy_systems[1])
|
||||
# EnergySystemsSizingFactory('pv_sizing', city).enrich()
|
||||
# EnergySystemsSizingFactory('peak_load_sizing', city).enrich()
|
||||
# EnergySystemsSizingFactory('heuristic_sizing', city).enrich()
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user