Maintenance_Rate('Shaft', 0.002, 0.007, .001, 1014. * 24. * 1,
1014 * 24. * 4, 3500 * 24. * 5, 1., 3 * 40.),
Maintenance_Rate('Brake', 0.0153, 0.0325, 0.0025, 1014. * 24. * 1,
1014 * 24. * 4, 3500 * 24. * 5, 1., 3 * 40.),
Maintenance_Rate('Cable', 0.225, 0.09247, 0.000002, 1014. * 24. * 1,
1014 * 24. * 4, 3500 * 24. * 5, 1., 3 * 40.),
Maintenance_Rate('Control system', 0.1, 0.1, 0.0001, 1014. * 24. * 1,
1014 * 24. * 4, 3500 * 24. * 5, 1., 3 * 40.)
]
emergency_events = [
maintenance.EmergencyMaintenance(e.minimal_rate,
e.midlevel_rate,
e.severe_rate,
e.minimal_cost,
e.midlevel_cost,
e.severe_cost,
number=e.number,
labor=e.labor,
partname=e.partname)
for e in emergency_maintenance
]
runs = 200
costs = np.zeros(runs)
tracking_average = np.zeros(runs)
tracking_error = np.zeros(runs)
upper_quartile = np.zeros(runs)
lower_quartile = np.zeros(runs)
for i in range(runs):
def LevelizedCostofElectricity(station_id, grid_location, cap_ex, lifetime, K,
Q, B, M, g, h_0, gravity,
emergency_maintentance, installation):
'''
This function will calculated the levelized cost of electricity given the parameters for maintenance, power generation, installation
and lifetime
station_id will determine the location due to the necessity to use harmonic constituents for the calculations
grid_location is where the connections will be made
cap_ex are the capital expenditures for the cost of the turbine and fixtures
this function was written with a sensitivity analysis in mind
'''
MCT = Turbine(K, Q, B, M, g)
tidal_station = TidalStation(station_id)
emergency_events = [
maintenance.EmergencyMaintenance(
e['minimal_rate'], e['midlevel_rate'], e['severe_rate'],
e['minimal_cost'], e['midlevel_cost'], e['severe_cost'],
e['minimal_downtime'], e['midlevel_downtime'],
e['severe_downtime'], e['number'], e['labor'], e['partname'])
for e in emergency_maintentance
]
#installation_cost = installation.calculateInstallation()
###
# The following code is used to run the monte carlo simulation with feedback to the power generation functions
# where the downtime requires the turbine to no longer generate an output
###
time = []
results = []
end_loop = False
time_tracker = 0.
maintenance_costs = []
maintenance_times = []
#time to run the simulation
while not end_loop:
maintenance_event, uptime = monteCarlo(emergency_events)
if time_tracker + uptime > lifetime:
end_loop = True
uptime = lifetime - time_tracker
end_time = time_tracker + uptime
results_array, time_array = calculate_power(tidal_station, MCT,
results[-1][-1],
time_tracker, end_time,
gravity, h_0)
maintenance_costs.append(maintenance_event.event_cost)
maintenance_times.append(time_tracker + uptime)
time_tracker += uptime + maintenance_event.downtime
results.append(results_array)
time.append(time_array)
powerGen = np.concatenate(results)
times = np.concatenate(time)
# Process the final costs and return the levelized cost
total_cost = maintenance_costs[-1] + installation_cost
total_power = powerGen[-1]
return total_cost / total_power