"""
The wntr.metrics.economic module contains economic metrics.
"""
from wntr.network import Tank, Pipe, Pump, Valve
import numpy as np
import pandas as pd
import logging
import scipy
logger = logging.getLogger(__name__)
[docs]
def annual_network_cost(wn, tank_cost=None, pipe_cost=None, prv_cost=None,
pump_cost=None):
"""
Compute annual network cost :cite:p:`sokz12`.
Use the closest value from the lookup tables to compute annual cost for each
component in the network.
Parameters
----------
wn : wntr WaterNetworkModel
Water network model. The water network model is needed to
define tank volume, pipe and valve diameter, and pump power conditions.
tank_cost : pandas Series, optional
Annual tank cost indexed by volume
(default values below, from :cite:p:`sokz12`).
============= ================================
Volume (m3) Annual Cost ($/yr)
============= ================================
500 14020
1000 30640
2000 61210
3750 87460
5000 122420
10000 174930
============= ================================
pipe_cost : pandas Series, optional
Annual pipe cost per pipe length indexed by diameter
(default values below, from :cite:p:`sokz12`).
============= ============= ================================
Diameter (in) Diameter (m) Annual Cost ($/m/yr)
============= ============= ================================
4 0.102 8.31
6 0.152 10.10
8 0.203 12.10
10 0.254 12.96
12 0.305 15.22
14 0.356 16.62
16 0.406 19.41
18 0.457 22.20
20 0.508 24.66
24 0.610 35.69
28 0.711 40.08
30 0.762 42.60
============= ============= ================================
prv_cost : pandas Series, optional
Annual PRV valve cost indexed by diameter
(default values below, from :cite:p:`sokz12`).
============= ============= ================================
Diameter (in) Diameter (m) Annual Cost ($/m/yr)
============= ============= ================================
4 0.102 323
6 0.152 529
8 0.203 779
10 0.254 1113
12 0.305 1892
14 0.356 2282
16 0.406 4063
18 0.457 4452
20 0.508 4564
24 0.610 5287
28 0.711 6122
30 0.762 6790
============= ============= ================================
pump_cost : pd.Series, optional
Annual pump cost indexed by maximum power input to pump
(default values below, from :cite:p:`sokz12`).
Maximum Power for a HeadPump is computed from the pump curve
as follows:
.. math:: Pmp = g*rho/eff*exp(ln(A/(B*(C+1)))/C)*(A - B*(exp(ln(A/(B*(C+1)))/C))^C)
where
:math:`Pmp` is the maximum power (W),
:math:`g` is acceleration due to gravity (9.81 m/s^2),
:math:`rho` is the density of water (1000 kg/m^3),
:math:`eff` is the global efficiency (0.75 default),
:math:`A`, :math:`B`, and :math:`C` are the pump curve coefficients.
================== ================================
Maximum power (W) Annual Cost ($/yr)
================== ================================
11310 2850
22620 3225
24880 3307
31670 3563
38000 3820
45240 4133
49760 4339
54280 4554
59710 4823
================== ================================
Returns
----------
Annual network cost in dollars (float)
"""
# Initialize network construction cost
network_cost = 0
# Set defaults
if tank_cost is None:
volume = [500, 1000, 2000, 3750, 5000, 10000]
cost = [14020, 30640, 61210, 87460, 122420, 174930]
tank_cost = pd.Series(cost, volume)
if pipe_cost is None:
diameter = [4, 6, 8, 10, 12, 14, 16, 18, 20, 24, 28, 30] # inch
diameter = np.array(diameter)*0.0254 # m
cost = [8.31, 10.1, 12.1, 12.96, 15.22, 16.62, 19.41, 22.2, 24.66, 35.69, 40.08, 42.6]
pipe_cost = pd.Series(cost, diameter)
if prv_cost is None:
diameter = [4, 6, 8, 10, 12, 14, 16, 18, 20, 24, 28, 30] # inch
diameter = np.array(diameter)*0.0254 # m
cost = [323, 529, 779, 1113, 1892, 2282, 4063, 4452, 4564, 5287, 6122, 6790]
prv_cost = pd.Series(cost, diameter)
if pump_cost is None:
Pmp = [11310, 22620, 24880, 31670, 38000, 45240, 49760, 54280, 59710]
cost = [2850, 3225, 3307, 3563, 3820, 4133, 4339, 4554, 4823]
pump_cost = pd.Series(cost, Pmp)
# Tank construction cost - not only looking at direct volume capacity but also
# but a rough estimate of the support structure size
# below the tank.
for node_name, node in wn.nodes(Tank):
if node.vol_curve is None:
tank_construction_volume = np.pi*(node.diameter/2)**2 *node.max_level
else:
vcurve = np.array(node.vol_curve.points)
tank_volume = np.interp(node.max_level,vcurve[:,0],vcurve[:,1])
avg_area = tank_volume / (node.max_level - node.min_level)
tank_base_volume = node.min_level * avg_area
tank_construction_volume = tank_volume + tank_base_volume
# choose the entry that is closest (keep the cost structure discreet)
idx = np.argmin([np.abs(tank_cost.index - tank_construction_volume)])
#print(node_name, tank_cost.iloc[idx])
network_cost = network_cost + tank_cost.iloc[idx]
# Pipe construction cost
for link_name, link in wn.links(Pipe):
idx = np.argmin([np.abs(pipe_cost.index - link.diameter)])
#print(link_name, pipe_cost.iloc[idx], link.length)
network_cost = network_cost + pipe_cost.iloc[idx]*link.length
# Pump construction cost
for link_name, link in wn.head_pumps():
coeff = link.get_head_curve_coefficients()
A = coeff[0]
B = coeff[1]
C = coeff[2]
Pmax = 9.81*1000*np.exp(np.log(A/(B*(C+1)))/C)*(A - B*(np.exp(np.log(A/(B*(C+1)))/C))**C)
Pmax = Pmax / wn.options.energy.global_efficiency
idx = np.argmin([np.abs(pump_cost.index - Pmax)])
#print(link_name, Pmax, pump_cost.iloc[idx])
network_cost = network_cost + pump_cost.iloc[idx]
for link_name, link in wn.power_pumps():
Pmax = link.power
Pmax = Pmax / wn.options.energy.global_efficiency
idx = np.argmin([np.abs(pump_cost.index - Pmax)])
#print(link_name, Pmax, pump_cost.iloc[idx])
network_cost = network_cost + pump_cost.iloc[idx]
# PRV valve construction cost
for link_name, link in wn.links(Valve):
if link.valve_type == 'PRV':
idx = np.argmin([np.abs(prv_cost.index - link.diameter)])
#print(link_name, link.diameter, prv_cost.iloc[idx])
network_cost = network_cost + prv_cost.iloc[idx]
return network_cost
[docs]
def annual_ghg_emissions(wn, pipe_ghg=None):
"""
Compute annual greenhouse gas emissions :cite:p:`sokz12`.
Use the closest value in the lookup table to compute annual GHG emissions
for each pipe in the network.
Parameters
----------
wn : wntr WaterNetworkModel
Water network model. The water network model is needed to
define pipe diameter.
pipe_ghg : pandas Series, optional
Annual GHG emissions indexed by pipe diameter
(default values below, from :cite:p:`sokz12`).
============= ================================
Diameter (mm) Annualized EE (kg-CO2-e/m/yr)
============= ================================
102 5.90
152 9.71
203 13.94
254 18.43
305 23.16
356 28.09
406 33.09
457 38.35
508 43.76
610 54.99
711 66.57
762 72.58
============= ================================
Returns
----------
Annual greenhouse gas emissions (float)
"""
# Initialize network GHG emissions
network_ghg = 0
# Set defaults
if pipe_ghg is None:
diameter = [4, 6, 8, 10, 12, 14, 16, 18, 20, 24, 28, 30] # inches
diameter = np.array(diameter)*0.0254 # m
cost = [5.9, 9.71, 13.94, 18.43, 23.16, 28.09, 33.09, 38.35, 43.76, 54.99, 66.57, 72.58]
pipe_ghg = pd.Series(cost, diameter)
# GHG emissions from pipes
for link_name, link in wn.links(Pipe):
idx = np.argmin([np.abs(pipe_ghg.index - link.diameter)])
#print(link_name, link.diameter, pipe_ghg.iloc[idx],link.length)
network_ghg = network_ghg + pipe_ghg.iloc[idx]*link.length
return network_ghg
[docs]
def pump_power(flowrate, head, wn):
"""
Compute pump power.
The computation uses pump flow rate, node head (used to compute headloss at
each pump), and pump efficiency. Pump efficiency is defined in
``wn.options.energy.global_efficiency``. Pump efficiency curves are currently
not supported.
wn.options.energy.global_efficiency = 75 # This means 75% or 0.75
Parameters
----------
flowrate : pandas DataFrame
A pandas DataFrame containing pump flowrates
(index = times, columns = pump names).
head : pandas DataFrame
A pandas DataFrame containing node head
(index = times, columns = node names).
wn: wntr WaterNetworkModel
Water network model. The water network model is needed to
define energy efficiency.
Returns
-------
A DataFrame that contains pump power in W (index = times, columns = pump names).
"""
pumps = wn.pump_name_list
time = flowrate.index
headloss = pd.DataFrame(data=None, index=time, columns=pumps)
for pump_name, pump in wn.pumps():
start_node = pump.start_node_name
end_node = pump.end_node_name
start_head = head.loc[:,start_node]
end_head = head.loc[:,end_node]
headloss.loc[:,pump_name] = end_head - start_head
efficiency_dict = {}
for pump_name, pump in wn.pumps():
if pump.efficiency is None:
efficiency_dict[pump_name] = [wn.options.energy.global_efficiency/100.0 for i in time]
else:
raise NotImplementedError('WNTR does not support pump efficiency curves yet.')
# TODO: WNTR does not support pump efficiency curves yet
# curve = wn.get_curve(pump.efficiency)
# x = [point[0] for point in curve.points]
# y = [point[1]/100.0 for point in curve.points]
# interp = scipy.interpolate.interp1d(x, y, kind='linear')
# efficiency_dict[pump_name] = interp(np.array(flowrate.loc[:, pump_name]))
efficiency = pd.DataFrame(data=efficiency_dict, index=time, columns=pumps)
power = 1000.0 * 9.81 * headloss * flowrate / efficiency # Watts = J/s
return power
[docs]
def pump_energy(flowrate, head, wn):
"""
Compute the pump energy over time.
The computation uses pump flow rate, node head (used to compute headloss at
each pump), and pump efficiency. Pump efficiency is defined in
``wn.options.energy.global_efficiency``. Pump efficiency curves are currently
not supported.
wn.options.energy.global_efficiency = 75 # This means 75% or 0.75
Parameters
----------
flowrate : pandas DataFrame
A pandas DataFrame containing pump flowrates
(index = times, columns = pump names).
head : pandas DataFrame
A pandas DataFrame containing node head
(index = times, columns = node names).
wn: wntr WaterNetworkModel
Water network model. The water network model is needed to
define energy efficiency.
Returns
-------
A DataFrame that contains pump energy in J (index = times, columns = pump names).
"""
power = pump_power(flowrate, head, wn) # Watts = J/s
energy = power * wn.options.time.report_timestep # J = Ws
return energy
[docs]
def pump_cost(energy, wn):
"""
Compute the pump cost over time.
Energy cost is defined in ``wn.options.energy.global_price``. Pump energy
price and price patterns are currently not supported.
wn.options.energy.global_price = 3.61e-8 # $/J; equal to $0.13/kW-h
Parameters
----------
energy : pandas DataFrame
A pandas DataFrame containing pump energy (J), computed from ``wntr.metrics.pump_energy``
(index = times, columns = pump names).
wn: wntr WaterNetworkModel
Water network model. The water network model is needed to
define pump enery prices and patterns.
Returns
-----------
A DataFrame that contains pump cost in $ (index = times, columns = pump names).
"""
time = energy.index
pumps = wn.pump_name_list
# TODO: Need to get this unit tested and not just functionally tested
if wn.options.energy.demand_charge is not None and wn.options.energy.demand_charge != 0:
# Additional energy charge per maximum kilowatt usage
raise ValueError('WNTR does not support demand charge yet.')
price_dict = {}
for pump_name, pump in wn.pumps():
if pump.energy_price is None and pump.energy_pattern is None:
if wn.options.energy.global_pattern is None:
price_dict[pump_name] = [wn.options.energy.global_price for i in time]
else:
raise NotImplementedError('WNTR does not support price patterns yet.')
elif pump.energy_pattern is None:
if wn.options.energy.global_pattern is None:
price_dict[pump_name] = [pump.energy_price for i in time]
else:
raise NotImplementedError('WNTR does not support price patterns yet.')
else:
raise NotImplementedError('WNTR does not support price patterns yet.')
price = pd.DataFrame(data=price_dict, index=time, columns=pumps)
pump_cost = energy * price
return pump_cost
