"""
## GAMSSOURCE: https://www.gams.com/latest/gamslib_ml/libhtml/gamslib_fuel.html
## LICENSETYPE: Demo
## MODELTYPE: MINLP
## KEYWORDS: mixed integer nonlinear programming, scheduling, engineering, power generation, unit commitment problem
Fuel Scheduling and Unit Commitment Problem (FUEL)
Fuel scheduling and unit commitment addresses the problem of
fuel supply to plants and determining on/off status of units
simultaneously to minimize total operating cost.
The present problem: there are two generating units to
meet a total load over a 6-hour period. One of the unit is oil-based
and has to simultaneously meet the storage requirements, flow rates
etc. There are limits on the generation levels for both the units.
Wood, A J, and Wollenberg, B F, Example Problem 4e. In Power Generation,
Operation and Control. John Wiley and Sons, 1984, pp. 85-88.
"""
from __future__ import annotations
import os
import pandas as pd
from gamspy import (
Card,
Container,
Equation,
Model,
Ord,
Parameter,
Sense,
Set,
Sum,
Variable,
)
def main():
m = Container(
system_directory=os.getenv("SYSTEM_DIRECTORY", None),
)
# Set
t = Set(
m,
name="t",
records=["period-1", "period-2", "period-3"],
description="scheduling periods (2hrs)",
)
# Data
load = Parameter(
m,
name="load",
domain=t,
records=pd.DataFrame(
[["period-1", 400], ["period-2", 900], ["period-3", 700]]
),
description="system load",
)
initlev = Parameter(
m,
name="initlev",
domain=t,
records=pd.DataFrame([["period-1", 3000]]),
description="initial level of the oil storage tank",
)
# Variable
status = Variable(
m,
name="status",
domain=t,
type="Binary",
description="on or off status of the oil based generating unit",
)
poil = Variable(
m,
name="poil",
domain=t,
description="generation level of oil based unit",
)
others = Variable(
m, name="others", domain=t, description="other generation"
)
oil = Variable(
m, name="oil", domain=t, type="Positive", description="oil consumption"
)
volume = Variable(
m,
name="volume",
domain=t,
type="Positive",
description="the volume of oil in the storage tank",
)
volume.up[t] = 4000
volume.lo[t].where[Ord(t) == Card(t)] = 2000
others.lo[t] = 50
others.up[t] = 700
# Equation
lowoil = Equation(
m,
name="lowoil",
domain=t,
description="lower limit on oil generating unit",
)
maxoil = Equation(
m,
name="maxoil",
domain=t,
description="upper limit on oil generating unit",
)
floweq = Equation(
m,
name="floweq",
domain=t,
description="the oil flow balance in the storage tank",
)
demcons = Equation(
m,
name="demcons",
domain=t,
description="total generation must meet the load",
)
oileq = Equation(
m, name="oileq", domain=t, description="calculation of oil consumption"
)
cost = Sum(t, 300 + 6 * others[t] + 0.0025 * (others[t] ** 2))
lowoil[t] = poil[t] >= 100 * status[t]
maxoil[t] = poil[t] <= 500 * status[t]
floweq[t] = volume[t] == volume[t.lag(1)] + 500 - oil[t] + initlev[t]
oileq[t] = oil[t] == 50 * status[t] + poil[t] + 0.005 * (poil[t] ** 2)
demcons[t] = poil[t] + others[t] >= load[t]
model = Model(
m,
name="ucom",
equations=m.getEquations(),
problem="MINLP",
sense=Sense.MIN,
objective=cost,
)
poil.l[t] = 100
model.solve()
import math
assert math.isclose(model.objective_value, 8566.1190, rel_tol=0.001)
if __name__ == "__main__":
main()