import numpy as np
import pandas as pd
[docs]def adjust_pmin(grid):
"""Adjust plant Pmin values inplace
:param powersimdata.input.grid.Grid grid: a grid object
"""
mi = grid.model_immutables
plant = grid.plant
pmin_factor = mi.plants["pmin_as_share_of_pmax"]
def _scale(x):
factor = pmin_factor.get(x.type)
return x.Pmin if factor is None else factor * x.Pmax
plant.Pmin = plant.apply(_scale, axis=1)
# set Pmin to 0 for generators that are off or profile based
profile_resource = list(mi.plants["profile_resources"])
plant.loc[plant.type.isin(profile_resource), "Pmin"] = 0
plant.loc[plant.status == 0, "Pmin"] = 0
[docs]def adjust_ramp30(plant):
"""Adjust plant ramp_30 values inplace
:param pandas.DataDrame plant: a plant dataframe
"""
plant.ramp_30 = np.inf
ramp30_points = {
"coal": {"xs": (200, 1400), "ys": (0.4, 0.15)},
"dfo": {"xs": (200, 1200), "ys": (0.5, 0.2)},
"ng": {"xs": (200, 600), "ys": (0.5, 0.2)},
}
for fuel, points in ramp30_points.items():
fuel_idx = plant.loc[plant.type == fuel, "Pmax"]
slope = (points["ys"][1] - points["ys"][0]) / (
points["xs"][1] - points["xs"][0]
)
intercept = points["ys"][0] - slope * points["xs"][0]
for idx in fuel_idx.index:
pmax = fuel_idx.at[idx]
norm_ramp = pmax * slope + intercept
if pmax < points["xs"][0]:
norm_ramp = points["ys"][0]
if pmax > points["xs"][1]:
norm_ramp = points["ys"][1]
plant.loc[idx, "ramp_30"] = norm_ramp * pmax
[docs]def linearize_gencost(gencost_before, plant, num_segments=1):
"""Updates the generator cost information to include piecewise linear cost curve
information. Allows the user to specify the number of piecewise segments into which
the cost curve should be split.
:param pandas.DataFrame gencost_before: the original gencost
:param pandas.DataFrame plant: the generator information containing Pmin/Pmax
:param int num_segments: The number of segments into which the piecewise linear
cost curve will be split.
:return: (*pandas.DataFrame*) -- An updated DataFrame containing the piecewise
linear cost curve parameters.
:raises ValueError: if the generator cost curve is not of an acceptable form.
"""
# Raise errors if the provided cost curves are not in a form that can be handled
if len(gencost_before[gencost_before.type != 2]):
raise ValueError("gencost currently limited to polynomial")
if len(gencost_before[gencost_before.n != 3]):
raise ValueError("gencost currently limited to quadratic")
gencost_before = gencost_before.copy()
# Access the quadratic cost curve information
quad_term = gencost_before.c2
lin_term = gencost_before.c1
const_term = gencost_before.c0
# Convert dispatchable generators to piecewise segments
dispatchable_gens = plant.Pmin != plant.Pmax
if sum(dispatchable_gens) > 0:
gencost_after = pd.DataFrame(
index=gencost_before.index,
columns=["type", "startup", "shutdown", "n", "c2", "c1", "c0"],
)
gencost_after.loc[dispatchable_gens, "type"] = 1
gencost_after[["startup", "shutdown", "c2", "c1", "c0"]] = gencost_before[
["startup", "shutdown", "c2", "c1", "c0"]
]
gencost_after.loc[dispatchable_gens, "n"] = num_segments + 1
power_step = (plant.Pmax - plant.Pmin) / num_segments
for i in range(num_segments + 1):
capacity_label = "p" + str(i + 1)
price_label = "f" + str(i + 1)
capacity_data = plant.Pmin + power_step * i
price_data = (
quad_term * capacity_data**2 + lin_term * capacity_data + const_term
)
gencost_after.loc[dispatchable_gens, capacity_label] = capacity_data[
dispatchable_gens
]
gencost_after.loc[dispatchable_gens, price_label] = price_data[
dispatchable_gens
]
else:
gencost_after = gencost_before.copy()
# Convert non-dispatchable gens to fixed values
nondispatchable_gens = ~dispatchable_gens
if sum(nondispatchable_gens) > 0:
gencost_after.loc[nondispatchable_gens, "type"] = gencost_before.loc[
nondispatchable_gens, "type"
]
gencost_after.loc[nondispatchable_gens, "n"] = gencost_before.loc[
nondispatchable_gens, "n"
]
power = plant.Pmax
price_data = quad_term * power**2 + lin_term * power + const_term
gencost_after.loc[nondispatchable_gens, ["c2", "c1"]] = 0
gencost_after.loc[nondispatchable_gens, "c0"] = price_data[nondispatchable_gens]
if "interconnect" in gencost_before.columns:
gencost_after["interconnect"] = gencost_before["interconnect"]
return gencost_after.fillna(0)
