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7ff94a56c1
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26c744ded0
@ -6,7 +6,7 @@ from hub.helpers.monthly_values import MonthlyValues
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class DomesticHotWaterHeatPumpTes:
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def __init__(self, hp, tes, hourly_dhw_demand_joules, upper_limit_tes,
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outdoor_temperature, dt=None):
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outdoor_temperature, dt=900):
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self.hp = hp
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self.tes = tes
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self.dhw_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in hourly_dhw_demand_joules]
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@ -30,8 +30,8 @@ class DomesticHotWaterHeatPumpTes:
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hp_delta_t = 8
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number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
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source_temperature_hourly = self.hp_characteristics.hp_source_temperature()
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source_temperature = [0] + [x for x in source_temperature_hourly for _ in range(number_of_ts)]
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demand = [0] + [x for x in self.dhw_demand for _ in range(number_of_ts)]
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source_temperature = [x for x in source_temperature_hourly for _ in range(number_of_ts)]
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demand = [x for x in self.dhw_demand for _ in range(number_of_ts)]
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variable_names = ["t_sup_hp", "t_tank", "m_ch", "m_dis", "q_hp", "q_coil", "hp_cop",
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"hp_electricity", "available hot water (m3)", "refill flow rate (kg/s)", "total_consumption"]
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num_hours = len(demand)
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@ -8,15 +8,12 @@ from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_
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class HeatPumpBoilerTesHeating:
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def __init__(self, hp, boiler, tes, hourly_heating_demand_joules, heating_peak_load_watts, upper_limit_tes,
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outdoor_temperature, dt=None):
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outdoor_temperature, dt=900):
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self.hp = hp
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self.boiler = boiler
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self.tes = tes
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self.heating_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in hourly_heating_demand_joules]
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if heating_peak_load_watts is not None:
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self.heating_peak_load = heating_peak_load_watts
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else:
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self.heating_peak_load = max(hourly_heating_demand_joules) / cte.HOUR_TO_SECONDS
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self.heating_peak_load = heating_peak_load_watts
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self.upper_limit_tes = upper_limit_tes
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self.hp_characteristics = HeatPump(self.hp, outdoor_temperature)
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self.t_out = outdoor_temperature
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@ -133,25 +130,23 @@ class HeatPumpBoilerTesHeating:
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coil_sum = 0
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hp.energy_consumption[cte.HEATING] = {}
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hp.energy_consumption[cte.HEATING][cte.HOUR] = hp_hourly
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hp.energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
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hp.energy_consumption[cte.HEATING][cte.HOUR])
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hp.energy_consumption[cte.HEATING][cte.YEAR] = [
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sum(hp.energy_consumption[cte.HEATING][cte.MONTH])]
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boiler.energy_consumption[cte.HEATING] = {}
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boiler.energy_consumption[cte.HEATING][cte.HOUR] = boiler_hourly
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if len(self.heating_demand) == 8760:
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hp.energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
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hp.energy_consumption[cte.HEATING][cte.HOUR])
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hp.energy_consumption[cte.HEATING][cte.YEAR] = [
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sum(hp.energy_consumption[cte.HEATING][cte.MONTH])]
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boiler.energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
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boiler.energy_consumption[cte.HEATING][cte.HOUR])
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boiler.energy_consumption[cte.HEATING][cte.YEAR] = [
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sum(boiler.energy_consumption[cte.HEATING][cte.MONTH])]
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boiler.energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
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boiler.energy_consumption[cte.HEATING][cte.HOUR])
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boiler.energy_consumption[cte.HEATING][cte.YEAR] = [
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sum(boiler.energy_consumption[cte.HEATING][cte.MONTH])]
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if tes.heating_coil_capacity is not None:
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tes.heating_coil_energy_consumption[cte.HEATING] = {}
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if len(self.heating_demand) == 8760:
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tes.heating_coil_energy_consumption[cte.HEATING][cte.HOUR] = coil_hourly
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tes.heating_coil_energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
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tes.heating_coil_energy_consumption[cte.HEATING][cte.HOUR])
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tes.heating_coil_energy_consumption[cte.HEATING][cte.YEAR] = [
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sum(tes.heating_coil_energy_consumption[cte.HEATING][cte.MONTH])]
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tes.heating_coil_energy_consumption[cte.HEATING][cte.HOUR] = coil_hourly
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tes.heating_coil_energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
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tes.heating_coil_energy_consumption[cte.HEATING][cte.HOUR])
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tes.heating_coil_energy_consumption[cte.HEATING][cte.YEAR] = [
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sum(tes.heating_coil_energy_consumption[cte.HEATING][cte.MONTH])]
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self.results['Heating Demand (W)'] = demand
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self.results['HP Heat Output (W)'] = q_hp
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self.results['HP Source Temperature'] = source_temperature
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@ -10,178 +10,46 @@ from energy_system_modelling_package.energy_system_modelling_factories.system_si
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class GeneticAlgorithm:
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def __init__(self, population_size=50, generations=50, crossover_rate=0.8, mutation_rate=0.1,
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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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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.population = []
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self.generations = generations
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self.crossover_rate = crossover_rate
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self.mutation_rate = mutation_rate
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self.optimization_scenario = optimization_scenario
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self.list_of_solutions = []
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self.best_solution = None
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self.best_solution_generation = None
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self.output_path = output_path
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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, building, energy_system):
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# Initialize Population
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def initialize_population(self, energy_system):
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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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problem.
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:param building:
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:param energy_system:
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:return: List
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"""
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population = []
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for i in range(self.population_size):
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self.population.append(Individual(building, energy_system, self.optimization_scenario))
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population.append(Individual(energy_system, self.optimization_scenario))
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return population
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def order_population(self):
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"""
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ordering the population based on the fitness score in ascending order
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:return:
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"""
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self.population = sorted(self.population, key=lambda x: x.individual['fitness_score'])
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def tournament_selection(self):
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selected = []
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for _ in range(len(self.population)):
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i, j = random.sample(range(self.population_size), 2)
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if self.population[i].individual['fitness_score'] < self.population[j].individual['fitness_score']:
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selected.append(copy.deepcopy(self.population[i]))
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else:
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selected.append(copy.deepcopy(self.population[j]))
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return selected
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def crossover(self, parent1, parent2):
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"""
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Crossover between two parents to produce two children.
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Swaps generation components and storage components between the two parents with a 50% chance.
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:param parent1: First parent individual.
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:param parent2: Second parent individual.
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:return: Two child individuals (child1 and child2).
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"""
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if random.random() < self.crossover_rate:
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# Deep copy of the parents to create children
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child1, child2 = copy.deepcopy(parent1), copy.deepcopy(parent2)
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# Crossover for Generation Components
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for i in range(len(parent1.individual['Generation Components'])):
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if random.random() < 0.5:
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# Swap the entire generation component
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child1.individual['Generation Components'][i], child2.individual['Generation Components'][i] = (
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child2.individual['Generation Components'][i],
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child1.individual['Generation Components'][i]
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)
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# Crossover for Energy Storage Components
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for i in range(len(parent1.individual['Energy Storage Components'])):
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if random.random() < 0.5:
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# Swap the entire storage component
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child1.individual['Energy Storage Components'][i], child2.individual['Energy Storage Components'][i] = (
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child2.individual['Energy Storage Components'][i],
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child1.individual['Energy Storage Components'][i]
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)
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return child1, child2
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else:
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# If crossover does not happen, return copies of the original parents
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return copy.deepcopy(parent1), copy.deepcopy(parent2)
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def mutate(self, individual, building):
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"""
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Mutates the individual's generation and storage components.
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- `individual`: The individual to mutate (contains generation and storage components).
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- `building`: Building data that contains constraints such as peak heating load and available space.
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Returns the mutated individual.
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"""
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# Mutate Generation Components
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for generation_component in individual['Generation Components']:
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if random.random() < self.mutation_rate:
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if generation_component['nominal_heating_efficiency'] is not None:
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# Mutate heating capacity
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generation_component['heating_capacity'] = random.uniform(
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0, building.heating_peak_load[cte.YEAR][0])
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if generation_component['nominal_cooling_efficiency'] is not None:
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# Mutate cooling capacity
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generation_component['cooling_capacity'] = random.uniform(
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0, building.cooling_peak_load[cte.YEAR][0])
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# Mutate Storage Components
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for storage_component in individual['Energy Storage Components']:
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if random.random() < self.mutation_rate:
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if storage_component['type'] == f'{cte.THERMAL}_storage':
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# Mutate the volume of thermal storage
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max_available_space = 0.01 * building.volume / building.storeys_above_ground
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storage_component['volume'] = random.uniform(0, max_available_space)
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if storage_component['heating_coil_capacity'] is not None:
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storage_component['volume'] = random.uniform(0, building.heating_peak_load[cte.YEAR][0])
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return individual
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def solve_ga(self, building, energy_system):
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def solve_ga(self, 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. Initialize population using the "initialize_population" method in this class.
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2. Evaluate the initial population using the "score_evaluation" method in the Individual class.
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3. Sort population based on fitness score.
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4. Repeat selection, crossover, and mutation for a fixed number of generations.
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5. Track the best solution found during the optimization process.
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:param building: Building object for the energy system.
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:param energy_system: Energy system to optimize.
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:return: Best solution after running the GA.
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1- Population is initialized using the "initialize_population" method in this class.
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:param energy_system:
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:return:
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"""
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# Step 1: Initialize the population
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self.initialize_population(building, energy_system)
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# Step 2: Evaluate the initial population
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for individual in self.population:
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individual.score_evaluation()
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# Step 3: Order population based on fitness scores
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self.order_population()
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# Track the best solution
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self.best_solution = self.population[0]
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self.list_of_solutions.append(copy.deepcopy(self.best_solution.individual))
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# Step 4: Run GA for a fixed number of generations
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for generation in range(self.generations):
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print(f"Generation {generation}")
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# Selection (using tournament selection)
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selected_population = self.tournament_selection()
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# Create the next generation through crossover and mutation
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next_population = []
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for i in range(0, self.population_size, 2):
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parent1 = selected_population[i]
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parent2 = selected_population[i + 1] if (i + 1) < len(selected_population) else selected_population[0]
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# Step 5: Apply crossover
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child1, child2 = self.crossover(parent1, parent2)
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# Step 6: Apply mutation
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self.mutate(child1.individual, building)
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self.mutate(child2.individual, building)
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# Step 7: Evaluate the children
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child1.score_evaluation()
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child2.score_evaluation()
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next_population.extend([child1, child2])
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# Replace old population with the new one
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self.population = next_population
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# Step 8: Sort the new population based on fitness
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self.order_population()
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# Track the best solution found in this generation
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if self.population[0].individual['fitness_score'] < self.best_solution.individual['fitness_score']:
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self.best_solution = self.population[0]
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self.best_solution_generation = generation
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# Store the best solution in the list of solutions
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self.list_of_solutions.append(copy.deepcopy(self.population[0]))
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print(f"Best solution found in generation {self.best_solution_generation}")
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print(f"Best solution: {self.best_solution.individual}")
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# Return the best solution after running GA
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return self.best_solution
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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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@ -1,50 +1,43 @@
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import math
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import random
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from hub.helpers.dictionaries import Dictionaries
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from hub.catalog_factories.costs_catalog_factory import CostsCatalogFactory
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import hub.helpers.constants as cte
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from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.domestic_hot_water_heat_pump_with_tes import \
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DomesticHotWaterHeatPumpTes
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from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_boiler_tes_heating import \
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HeatPumpBoilerTesHeating
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import numpy_financial as npf
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class Individual:
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def __init__(self, building, energy_system, design_period_energy_demands, design_period_outdoor_temperature,
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optimization_scenario,heating_design_load=None, cooling_design_load=None, dt=900, 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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def __init__(self, building, energy_system, energy_demands, optimization_scenario, heating_design_load=None,
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cooling_design_load=None, available_space=None, dt=3600, electricity_tariff_type='fixed'):
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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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:param design_period_energy_demands: A dictionary demand types as its keys and hourly values of the day with the
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maximum daily demand and the days before and after it.
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:param energy_demands: energy demands that the system should supply. A dictionary in the format of
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{'demand_type1':[hourly_values], 'demand_type2':[hourly_values]} must be provided. all values must be in Joules.
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:param optimization_scenario: a string indicating the objective function from minimization of cost,
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energy consumption, and both together
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:param heating_design_load: heating design load in W
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:param cooling_design_load: cooling design load in W
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:param available_space: available space for the system. currently it is only used to have a constraint on the
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maximum size of the storage tank
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:param dt the time step size used for simulations
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:param fuel_price_index the price increase index of all fuels. A single value is used for all fuels.
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:param electricity_tariff_type the electricity tariff type between 'fixed' and 'variable' for economic optimization
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:param consumer_price_index
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"""
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self.building = building
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self.energy_system = energy_system
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self.design_period_energy_demands = design_period_energy_demands
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self.demand_types = energy_system.demand_types
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self.energy_demands = energy_demands
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self.optimization_scenario = optimization_scenario
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self.heating_design_load = heating_design_load
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self.cooling_design_load = cooling_design_load
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self.available_space = building.volume / building.storeys_above_ground
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self.available_space = available_space
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self.dt = dt
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self.fuel_price_index = fuel_price_index
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self.electricity_tariff_type = electricity_tariff_type
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self.consumer_price_index = consumer_price_index
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self.interest_rate = interest_rate
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self.discount_rate = discount_rate
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self.credit_years = credit_years
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self.percentage_credit = percentage_credit
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self.costs = self.costs_archetype()
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self.feasibility = True
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self.fitness_score = 0
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@ -73,7 +66,14 @@ class Individual:
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cooling_capacity = 0
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heat_efficiency = generation_component.heat_efficiency
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cooling_efficiency = generation_component.cooling_efficiency
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unit_fuel_cost = self.fuel_cost_per_kwh(generation_component.fuel_type, 'fixed')
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unit_fuel_cost = 0
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if generation_component.fuel_type == cte.GAS:
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gas_cost = self.fuel_cost(generation_component.fuel_type, 'fixed')
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unit_fuel_cost = gas_cost.variable.values[0]
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elif generation_component.fuel_type == cte.ELECTRICITY:
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electricity_cost = self.fuel_cost(generation_component.fuel_type, self.electricity_tariff_type)
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unit_fuel_cost = electricity_cost.variable.values
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self.individual['Generation Components'].append({
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'type': generation_component.system_type,
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'heating_capacity': heating_capacity,
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@ -85,7 +85,7 @@ class Individual:
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'fuel_type': generation_component.fuel_type,
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'unit_investment_cost': investment_cost,
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'unit_reposition_cost': reposition_cost,
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'unit_fuel_cost(CAD/kWh)': unit_fuel_cost,
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'unit_fuel_cost': unit_fuel_cost,
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'unit_maintenance_cost': maintenance_cost,
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'lifetime': lifetime
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})
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@ -96,12 +96,10 @@ 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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@ -111,10 +109,10 @@ class Individual:
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'heating_coil_capacity': heating_coil_capacity,
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||||
'unit_investment_cost': investment_cost,
|
||||
'unit_reposition_cost': reposition_cost,
|
||||
'heating_coil_fuel_cost': heating_coil_fuel_cost,
|
||||
'unit_maintenance_cost': 0,
|
||||
'lifetime': lifetime
|
||||
})
|
||||
print(self.individual)
|
||||
|
||||
def initialization(self):
|
||||
"""
|
||||
@ -126,74 +124,29 @@ class Individual:
|
||||
generation_components = self.individual['Generation Components']
|
||||
storage_components = self.individual['Energy Storage Components']
|
||||
for generation_component in generation_components:
|
||||
if generation_component['nominal_heating_efficiency'] is not None:
|
||||
if self.heating_design_load is not None:
|
||||
generation_component['heating_capacity'] = random.uniform(0, self.heating_design_load)
|
||||
else:
|
||||
generation_component['heating_capacity'] = random.uniform(0, max(self.design_period_energy_demands[cte.HEATING]))
|
||||
if generation_component['nominal_heating_efficiency'] is not None and self.heating_design_load is not None:
|
||||
generation_component['heating_capacity'] = random.uniform(0, self.heating_design_load)
|
||||
else:
|
||||
generation_component['heating_capacity'] = None
|
||||
if generation_component['nominal_cooling_efficiency'] is not None:
|
||||
if self.cooling_design_load is not None:
|
||||
generation_component['cooling_capacity'] = random.uniform(0, self.cooling_design_load)
|
||||
else:
|
||||
generation_component['cooling_capacity'] = random.uniform(0, self.design_period_energy_demands[cte.COOLING])
|
||||
if generation_component['nominal_cooling_efficiency'] is not None and self.cooling_design_load is not None:
|
||||
generation_component['cooling_capacity'] = random.uniform(0, self.cooling_design_load)
|
||||
else:
|
||||
generation_component['cooling_capacity'] = None
|
||||
if generation_component['nominal_electricity_efficiency'] is None:
|
||||
generation_component['electricity_capacity'] = None
|
||||
for storage_component in storage_components:
|
||||
if storage_component['type'] == f'{cte.THERMAL}_storage':
|
||||
storage_component['volume'] = random.uniform(0, 0.01 * self.available_space)
|
||||
if storage_component['heating_coil_capacity'] is not None:
|
||||
if self.heating_design_load is not None:
|
||||
storage_component['heating_coil_capacity'] = random.uniform(0, self.heating_design_load)
|
||||
else:
|
||||
storage_component['heating_coil_capacity'] = random.uniform(0, self.design_period_energy_demands[cte.HEATING])
|
||||
if storage_component['type'] == cte.THERMAL:
|
||||
storage_component['volume'] = 0.05 * self.available_space
|
||||
|
||||
def score_evaluation(self):
|
||||
self.initialization()
|
||||
self.system_simulation()
|
||||
self.individual['feasible'] = self.feasibility
|
||||
results = self.system_simulation()
|
||||
if self.feasibility:
|
||||
if 'cost' in self.optimization_scenario:
|
||||
investment_cost = 0
|
||||
operation_cost_year_0 = 0
|
||||
maintenance_cost_year_0 = 0
|
||||
for generation_system in self.individual['Generation Components']:
|
||||
heating_capacity = 0 if generation_system['heating_capacity'] is None else generation_system[
|
||||
'heating_capacity']
|
||||
cooling_capacity = 0 if generation_system['cooling_capacity'] is None else generation_system[
|
||||
'cooling_capacity']
|
||||
capacity = max(heating_capacity, cooling_capacity)
|
||||
investment_cost += capacity * generation_system['unit_investment_cost'] / 1000
|
||||
maintenance_cost_year_0 += capacity * generation_system['unit_maintenance_cost'] / 1000
|
||||
operation_cost_year_0 += (generation_system['yearly_energy_consumption(kWh)'] *
|
||||
generation_system['unit_fuel_cost(CAD/kWh)'])
|
||||
for storage_system in self.individual['Energy Storage Components']:
|
||||
if cte.THERMAL in storage_system['type']:
|
||||
investment_cost += storage_system['volume'] * storage_system['unit_investment_cost']
|
||||
if storage_system['heating_coil_capacity'] is not None:
|
||||
operation_cost_year_0 += (storage_system['yearly_energy_consumption(kWh)'] *
|
||||
storage_system['heating_coil_fuel_cost'])
|
||||
lcc = self.life_cycle_cost_calculation(investment_cost=investment_cost,
|
||||
operation_cost_year_0=operation_cost_year_0,
|
||||
maintenance_cost_year_0=maintenance_cost_year_0)
|
||||
self.individual['lcc'] = lcc
|
||||
self.individual['fitness_score'] = lcc
|
||||
elif 'energy_consumption' in self.optimization_scenario:
|
||||
total_energy_consumption = 0
|
||||
for generation_system in self.individual['Generation Components']:
|
||||
total_energy_consumption += generation_system['yearly_energy_consumption(kWh)']
|
||||
for storage_system in self.individual['Energy Storage Components']:
|
||||
if cte.THERMAL in storage_system['type'] and storage_system['heating_coil_capacity'] is not None:
|
||||
total_energy_consumption += storage_system['yearly_energy_consumption(kWh)']
|
||||
self.individual['total_energy_consumption'] = total_energy_consumption
|
||||
self.individual['fitness_score'] = total_energy_consumption
|
||||
if self.optimization_scenario == 'cost':
|
||||
pass
|
||||
elif self.optimization_scenario == 'energy_consumption':
|
||||
pass
|
||||
elif self.optimization_scenario == 'cost_energy_consumption':
|
||||
pass
|
||||
else:
|
||||
self.individual['fitness_score'] = float('inf')
|
||||
|
||||
def system_simulation(self):
|
||||
"""
|
||||
@ -201,20 +154,14 @@ class Individual:
|
||||
Based on cluster id and demands, model is imported and run.
|
||||
:return: dictionary of results
|
||||
"""
|
||||
if self.building.energy_systems_archetype_cluster_id == '1':
|
||||
results = None
|
||||
if self.building.energy_systems_archetype_cluster_id == 1:
|
||||
if cte.HEATING in self.demand_types:
|
||||
boiler = self.energy_system.generation_systems[0]
|
||||
boiler.nominal_heat_output = self.individual['Generation Components'][0]['heating_capacity']
|
||||
hp = self.energy_system.generation_systems[1]
|
||||
hp.nominal_heat_output = self.individual['Generation Components'][1]['heating_capacity']
|
||||
tes = self.energy_system.generation_systems[0].energy_storage_systems[0]
|
||||
tes.volume = self.individual['Energy Storage Components'][0]['volume']
|
||||
tes.height = self.building.average_storey_height - 1
|
||||
tes.heating_coil_capacity = self.individual['Energy Storage Components'][0]['heating_coil_capacity'] \
|
||||
if self.individual['Energy Storage Components'][0]['heating_coil_capacity'] is not None else None
|
||||
heating_demand_joules = self.design_period_energy_demands[cte.HEATING]
|
||||
heating_peak_load_watts = max(self.design_period_energy_demands[cte.HEATING]) if (self.heating_design_load is not
|
||||
None) else self.building.heating_peak_load[cte.YEAR][0]
|
||||
heating_demand_joules = self.energy_demands[f'{cte.HEATING}']
|
||||
heating_peak_load_watts = self.heating_design_load
|
||||
upper_limit_tes_heating = 55
|
||||
outdoor_temperature = self.building.external_temperature[cte.HOUR]
|
||||
results = HeatPumpBoilerTesHeating(hp=hp,
|
||||
@ -229,13 +176,8 @@ class Individual:
|
||||
self.feasibility = False
|
||||
elif cte.DOMESTIC_HOT_WATER in self.demand_types:
|
||||
hp = self.energy_system.generation_systems[0]
|
||||
hp.nominal_heat_output = self.individual['Generation Components'][0]['heating_capacity']
|
||||
tes = self.energy_system.generation_systems[0].energy_storage_systems[0]
|
||||
tes.volume = self.individual['Energy Storage Components'][0]['volume']
|
||||
tes.height = self.building.average_storey_height - 1
|
||||
tes.heating_coil_capacity = self.individual['Energy Storage Components'][0]['heating_coil_capacity'] \
|
||||
if self.individual['Energy Storage Components'][0]['heating_coil_capacity'] is not None else None
|
||||
dhw_demand_joules = self.design_period_energy_demands[cte.DOMESTIC_HOT_WATER]
|
||||
dhw_demand_joules = self.energy_demands[f'{cte.DOMESTIC_HOT_WATER}']
|
||||
upper_limit_tes = 65
|
||||
outdoor_temperature = self.building.external_temperature[cte.HOUR]
|
||||
results = DomesticHotWaterHeatPumpTes(hp=hp,
|
||||
@ -246,30 +188,9 @@ class Individual:
|
||||
dt=self.dt).simulation()
|
||||
if min(results['DHW TES Temperature']) < 55:
|
||||
self.feasibility = False
|
||||
if self.feasibility:
|
||||
generation_system_types = [generation_system.system_type for generation_system in
|
||||
self.energy_system.generation_systems]
|
||||
for generation_component in self.individual['Generation Components']:
|
||||
if generation_component['type'] in generation_system_types:
|
||||
index = generation_system_types.index(generation_component['type'])
|
||||
for demand_type in self.demand_types:
|
||||
if demand_type in self.energy_system.generation_systems[index].energy_consumption:
|
||||
generation_component['yearly_energy_consumption(kWh)'] = (
|
||||
self.energy_system.generation_systems[index].energy_consumption[demand_type][cte.YEAR][0] / 3.6e6)
|
||||
for storage_component in self.individual['Energy Storage Components']:
|
||||
if storage_component['type'] == f'{cte.THERMAL}_storage' and storage_component[
|
||||
'heating_coil_capacity'] is not None:
|
||||
for generation_system in self.energy_system.generation_systems:
|
||||
if generation_system.energy_storage_systems is not None:
|
||||
for storage_system in generation_system.energy_storage_systems:
|
||||
if storage_system.type_energy_stored == cte.THERMAL:
|
||||
for demand_type in self.demand_types:
|
||||
if demand_type in storage_system.heating_coil_energy_consumption:
|
||||
storage_component['yearly_energy_consumption(kWh)'] = (
|
||||
storage_system.heating_coil_energy_consumption[demand_type][cte.YEAR][0] / 3.6e6)
|
||||
return results
|
||||
|
||||
def life_cycle_cost_calculation(self, investment_cost, operation_cost_year_0, maintenance_cost_year_0,
|
||||
life_cycle_duration=41):
|
||||
def life_cycle_cost_calculation(self, life_cycle_duration=41):
|
||||
"""
|
||||
Calculating the life cycle cost of the energy system. The original cost workflow in hub is not used to reduce
|
||||
computation time.Here are the steps:
|
||||
@ -277,55 +198,10 @@ class Individual:
|
||||
2- Capital costs (investment and reposition) are calculated and appended to a list to have the capital cash
|
||||
flow.
|
||||
3-
|
||||
:param maintenance_cost_year_0:
|
||||
:param operation_cost_year_0:
|
||||
:param investment_cost:
|
||||
:param life_cycle_duration:
|
||||
:return:
|
||||
"""
|
||||
capital_costs_cash_flow = [investment_cost]
|
||||
operational_costs_cash_flow = [0]
|
||||
maintenance_costs_cash_flow = [0]
|
||||
end_of_life_costs = [0] * (life_cycle_duration + 1)
|
||||
for i in range(1, life_cycle_duration + 1):
|
||||
yearly_operational_cost = math.pow(1 + self.fuel_price_index, i) * operation_cost_year_0
|
||||
yearly_maintenance_cost = math.pow(1 + self.consumer_price_index, i) * maintenance_cost_year_0
|
||||
yearly_capital_cost = 0
|
||||
for generation_system in self.individual['Generation Components']:
|
||||
if (i % generation_system['lifetime']) == 0 and i != (life_cycle_duration - 1):
|
||||
cost_increase = math.pow(1 + self.consumer_price_index, i)
|
||||
heating_capacity = 0 if generation_system['heating_capacity'] is None else generation_system[
|
||||
'heating_capacity']
|
||||
cooling_capacity = 0 if generation_system['cooling_capacity'] is None else generation_system[
|
||||
'cooling_capacity']
|
||||
capacity = max(heating_capacity, cooling_capacity)
|
||||
yearly_capital_cost += generation_system['unit_reposition_cost'] * capacity * cost_increase / 1000
|
||||
yearly_capital_cost += -npf.pmt(self.interest_rate, self.credit_years,
|
||||
investment_cost * self.percentage_credit)
|
||||
for storage_system in self.individual['Energy Storage Components']:
|
||||
if (i % storage_system['lifetime']) == 0 and i != (life_cycle_duration - 1):
|
||||
cost_increase = math.pow(1 + self.consumer_price_index, i)
|
||||
capacity = storage_system['volume'] if cte.THERMAL in storage_system['type'] else storage_system['capacity']
|
||||
yearly_capital_cost += storage_system['unit_reposition_cost'] * capacity * cost_increase
|
||||
yearly_capital_cost += -npf.pmt(self.interest_rate, self.credit_years,
|
||||
investment_cost * self.percentage_credit)
|
||||
capital_costs_cash_flow.append(yearly_capital_cost)
|
||||
operational_costs_cash_flow.append(yearly_operational_cost)
|
||||
maintenance_costs_cash_flow.append(yearly_maintenance_cost)
|
||||
for year in range(1, life_cycle_duration + 1):
|
||||
price_increase = math.pow(1 + self.consumer_price_index, year)
|
||||
if year == life_cycle_duration:
|
||||
end_of_life_costs[year] = (
|
||||
self.building.thermal_zones_from_internal_zones[0].total_floor_area *
|
||||
self.individual['End of Life Cost'] * price_increase
|
||||
)
|
||||
|
||||
life_cycle_capital_cost = npf.npv(self.discount_rate, capital_costs_cash_flow)
|
||||
life_cycle_operational_cost = npf.npv(self.discount_rate, operational_costs_cash_flow)
|
||||
life_cycle_maintenance_cost = npf.npv(self.discount_rate, maintenance_costs_cash_flow)
|
||||
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
|
||||
return 0
|
||||
|
||||
def costs_archetype(self):
|
||||
costs_catalogue = CostsCatalogFactory('montreal_new').catalog.entries().archetypes
|
||||
@ -379,7 +255,7 @@ class Individual:
|
||||
if generation_system.energy_storage_systems is not None:
|
||||
for energy_storage_system in generation_system.energy_storage_systems:
|
||||
if energy_storage_system.type_energy_stored == cte.THERMAL:
|
||||
if energy_storage_system.heating_coil_capacity is not None:
|
||||
if energy_storage_system.heating_coil_capacity is None:
|
||||
investment_cost += float(capital_costs_chapter.item('D306010_storage_tank').initial_investment[0])
|
||||
reposition_cost += float(capital_costs_chapter.item('D306010_storage_tank').reposition[0])
|
||||
lifetime += float(capital_costs_chapter.item('D306010_storage_tank').lifetime)
|
||||
@ -418,19 +294,10 @@ class Individual:
|
||||
maintenance_cost += item.maintenance[0]
|
||||
return maintenance_cost
|
||||
|
||||
def fuel_cost_per_kwh(self, fuel_type, fuel_cost_tariff_type):
|
||||
fuel_cost = 0
|
||||
def fuel_cost(self, fuel_type, fuel_cost_tariff_type):
|
||||
catalog_fuels = self.costs.operational_cost.fuels
|
||||
for fuel in catalog_fuels:
|
||||
if fuel_type == fuel.type and fuel_cost_tariff_type == fuel.variable.rate_type:
|
||||
if fuel.type == cte.ELECTRICITY and fuel_cost_tariff_type == 'fixed':
|
||||
fuel_cost = fuel.variable.values[0]
|
||||
elif fuel.type == cte.ELECTRICITY and fuel_cost_tariff_type == 'variable':
|
||||
fuel_cost = fuel.variable.values[0]
|
||||
else:
|
||||
if fuel.type == cte.BIOMASS:
|
||||
conversion_factor = 1
|
||||
else:
|
||||
conversion_factor = fuel.density[0]
|
||||
fuel_cost = fuel.variable.values[0] / (conversion_factor * fuel.lower_heating_value[0] * 0.277)
|
||||
return fuel_cost
|
||||
return fuel
|
||||
|
||||
|
||||
|
@ -1274,7 +1274,7 @@
|
||||
<storage_type>sensible</storage_type>
|
||||
<nominal_capacity/>
|
||||
<losses_ratio/>
|
||||
<heating_coil_capacity>0</heating_coil_capacity>
|
||||
<heating_coil_capacity>5000</heating_coil_capacity>
|
||||
</templateStorages>
|
||||
</energy_storage_components>
|
||||
<materials>
|
||||
|
7
main.py
7
main.py
@ -3,7 +3,8 @@ 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.genetic_algorithm import GeneticAlgorithm
|
||||
from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.individual import \
|
||||
Individual
|
||||
from hub.imports.geometry_factory import GeometryFactory
|
||||
from hub.helpers.dictionaries import Dictionaries
|
||||
from hub.imports.construction_factory import ConstructionFactory
|
||||
@ -57,7 +58,9 @@ 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
|
||||
GeneticAlgorithm(optimization_scenario='cost').solve_ga(building=building, energy_system=building.energy_systems[1])
|
||||
Individual(building, building.energy_systems[2], energy_demands=None, 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').system_components()
|
||||
# EnergySystemsSizingFactory('pv_sizing', city).enrich()
|
||||
# EnergySystemsSizingFactory('peak_load_sizing', city).enrich()
|
||||
# EnergySystemsSizingFactory('heuristic_sizing', city).enrich()
|
||||
|
84
test.py
84
test.py
@ -1,84 +0,0 @@
|
||||
import csv
|
||||
from pathlib import Path
|
||||
from building_modelling.ep_run_enrich import energy_plus_workflow
|
||||
from energy_system_modelling_package.energy_system_modelling_factories.energy_system_sizing_factory import \
|
||||
EnergySystemsSizingFactory
|
||||
from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_boiler_tes_heating import \
|
||||
HeatPumpBoilerTesHeating
|
||||
from hub.imports.geometry_factory import GeometryFactory
|
||||
from hub.helpers.dictionaries import Dictionaries
|
||||
from hub.imports.construction_factory import ConstructionFactory
|
||||
from hub.imports.usage_factory import UsageFactory
|
||||
from hub.imports.weather_factory import WeatherFactory
|
||||
import hub.helpers.constants as cte
|
||||
from building_modelling.geojson_creator import process_geojson
|
||||
from energy_system_modelling_package import random_assignation
|
||||
from hub.imports.energy_systems_factory import EnergySystemsFactory
|
||||
|
||||
# Specify the GeoJSON file path
|
||||
input_files_path = (Path(__file__).parent / 'input_files')
|
||||
input_files_path.mkdir(parents=True, exist_ok=True)
|
||||
geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.00006)
|
||||
geojson_file_path = input_files_path / 'output_buildings.geojson'
|
||||
output_path = (Path(__file__).parent / 'out_files').resolve()
|
||||
output_path.mkdir(parents=True, exist_ok=True)
|
||||
energy_plus_output_path = output_path / 'energy_plus_outputs'
|
||||
energy_plus_output_path.mkdir(parents=True, exist_ok=True)
|
||||
simulation_results_path = (Path(__file__).parent / 'out_files' / 'simulation_results').resolve()
|
||||
simulation_results_path.mkdir(parents=True, exist_ok=True)
|
||||
sra_output_path = output_path / 'sra_outputs'
|
||||
sra_output_path.mkdir(parents=True, exist_ok=True)
|
||||
cost_analysis_output_path = output_path / 'cost_analysis'
|
||||
cost_analysis_output_path.mkdir(parents=True, exist_ok=True)
|
||||
city = GeometryFactory(file_type='geojson',
|
||||
path=geojson_file_path,
|
||||
height_field='height',
|
||||
year_of_construction_field='year_of_construction',
|
||||
function_field='function',
|
||||
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
|
||||
ConstructionFactory('nrcan', city).enrich()
|
||||
UsageFactory('nrcan', city).enrich()
|
||||
WeatherFactory('epw', city).enrich()
|
||||
energy_plus_workflow(city, energy_plus_output_path)
|
||||
random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
|
||||
EnergySystemsFactory('montreal_future', city).enrich()
|
||||
EnergySystemsSizingFactory('peak_load_sizing', city).enrich()
|
||||
hp = city.buildings[0].energy_systems[1].generation_systems[1]
|
||||
boiler = city.buildings[0].energy_systems[1].generation_systems[0]
|
||||
tes = city.buildings[0].energy_systems[1].generation_systems[0].energy_storage_systems[0]
|
||||
# Step 1: Calculate daily demands
|
||||
daily_demands = [sum(city.buildings[0].heating_demand[cte.HOUR][i:i + 24]) for i in
|
||||
range(0, len(city.buildings[0].heating_demand[cte.HOUR]), 24)]
|
||||
# Step 2: Find the day with maximum demand
|
||||
max_day_index = daily_demands.index(max(daily_demands))
|
||||
# Step 3: Extract the hourly demands for the day before, the day with max demand, and the day after
|
||||
# Ensure you don't go out of bounds if max_day_index is 0 or the last day
|
||||
if max_day_index > 0 and max_day_index < len(daily_demands) - 1:
|
||||
start_index = (max_day_index - 1) * 24
|
||||
end_index = (max_day_index + 2) * 24
|
||||
three_day_demands = city.buildings[0].heating_demand[cte.HOUR][start_index:end_index]
|
||||
elif max_day_index == 0: # If max is the first day, just include day 1 and day 2
|
||||
start_index = max_day_index * 24
|
||||
end_index = (max_day_index + 2) * 24
|
||||
three_day_demands = city.buildings[0].heating_demand[cte.HOUR][start_index:end_index]
|
||||
else: # If max is the last day, just include the last two days
|
||||
start_index = (max_day_index - 1) * 24
|
||||
end_index = len(city.buildings[0].heating_demand[cte.HOUR])
|
||||
three_day_demands = city.buildings[0].heating_demand[cte.HOUR][start_index:end_index]
|
||||
results = HeatPumpBoilerTesHeating(hp=hp,
|
||||
boiler=boiler,
|
||||
tes=tes,
|
||||
hourly_heating_demand_joules=three_day_demands,
|
||||
heating_peak_load_watts=max(three_day_demands),
|
||||
upper_limit_tes=55,
|
||||
outdoor_temperature=city.buildings[0].external_temperature[cte.HOUR],
|
||||
dt=900).simulation()
|
||||
heating_eui = city.buildings[0].heating_demand[cte.YEAR][0] / (
|
||||
city.buildings[0].thermal_zones_from_internal_zones[0].total_floor_area * 3.6e6)
|
||||
file_name = f'energy_system_simulation_results_{city.buildings[0].name}.csv'
|
||||
with open(simulation_results_path / file_name, 'w', newline='') as csvfile:
|
||||
output_file = csv.writer(csvfile)
|
||||
# Write header
|
||||
output_file.writerow(results.keys())
|
||||
# Write data
|
||||
output_file.writerows(zip(*results.values()))
|
Loading…
Reference in New Issue
Block a user