"""
## GAMSSOURCE: https://www.gams.com/latest/psoptlib_ml/libhtml/psoptlib_EnergyHub.html
## LICENSETYPE: Demo
## MODELTYPE: LP
Optimal operation of energy hub
For more details please refer to Chapter 10 (Gcode10.3), of the following book:
Soroudi, Alireza. Power System Optimization Modeling in GAMS. Springer, 2017.
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Model type: MIP
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Contributed by
Dr. Alireza Soroudi
IEEE Senior Member
email: alireza.soroudi@gmail.com
We do request that publications derived from the use of the developed GAMS code
explicitly acknowledge that fact by citing
Soroudi, Alireza. Power System Optimization Modeling in GAMS. Springer, 2017.
DOI: doi.org/10.1007/978-3-319-62350-
"""
from __future__ import annotations
import os
import pandas as pd
from gamspy import Container, Equation, Model, Parameter, Set, Sum, Variable
def reformat_df(dataframe):
return dataframe.reset_index().melt(
id_vars="index", var_name="Category", value_name="Value"
)
def data_records():
# data records table
cols = ["Dh", "De", "Dc", "lamda"]
inds = [f"t{i}" for i in range(1, 25)]
data = [
[21.4, 52.1, 11.5, 36.7],
[23.2, 66.7, 13.7, 40.4],
[26.1, 72.2, 16.0, 38.5],
[26.7, 78.4, 21.4, 38.0],
[25.6, 120.2, 22.0, 40.2],
[26.4, 83.5, 30.8, 38.6],
[39.5, 110.4, 38.9, 52.3],
[47.3, 124.3, 46.8, 67.3],
[52.1, 143.6, 51.0, 70.5],
[49.1, 149.3, 48.9, 66.2],
[69.3, 154.2, 34.8, 73.3],
[62.0, 147.3, 32.7, 60.8],
[68.0, 200.7, 27.8, 63.2],
[68.6, 174.4, 32.0, 70.8],
[56.4, 176.5, 33.2, 63.1],
[41.3, 136.1, 34.1, 52.5],
[37.4, 108.7, 40.8, 57.0],
[25.4, 96.9, 43.6, 49.2],
[25.7, 89.1, 51.5, 47.5],
[21.9, 82.5, 43.1, 49.5],
[22.4, 76.9, 36.5, 53.1],
[24.6, 66.8, 27.7, 51.6],
[22.7, 47.2, 19.1, 50.5],
[22.6, 64.7, 11.0, 36.4],
]
data_recs = reformat_df(pd.DataFrame(data, columns=cols, index=inds))
return data_recs
def main():
m = Container(
system_directory=os.getenv("SYSTEM_DIRECTORY", None),
)
# SET #
t = Set(
m,
name="t",
records=[f"t{i}" for i in range(1, 25)],
description="hours",
)
# DATA PARAMETER #
data = Parameter(m, name="data", domain=[t, "*"], records=data_records())
# SCALARS #
CBmax = Parameter(m, name="CBmax", records=500)
eta_ee = Parameter(m, name="eta_ee", records=0.98)
eta_ghf = Parameter(m, name="eta_ghf", records=0.9)
eta_hc = Parameter(m, name="eta_hc", records=0.95)
# VARIABLES #
E = Variable(m, name="E", type="positive", domain=t)
G = Variable(m, name="G", type="positive", domain=t)
H1 = Variable(m, name="H1", type="positive", domain=t)
H2 = Variable(m, name="H2", type="positive", domain=t)
H2.up[t] = CBmax
# EQUATIONS #
eq1 = Sum(t, data[t, "lamda"] * E[t] + 12 * G[t])
eq2 = Equation(m, name="eq2", type="regular", domain=t)
eq3 = Equation(m, name="eq3", type="regular", domain=t)
eq4 = Equation(m, name="eq4", type="regular", domain=t)
eq5 = Equation(m, name="eq5", type="regular", domain=t)
eq2[t] = eta_ee * E[t] == data[t, "De"]
eq3[t] = H1[t] == data[t, "Dh"]
eq4[t] = eta_ghf * G[t] == H1[t] + H2[t]
eq5[t] = eta_hc * H2[t] == data[t, "Dc"]
hub = Model(
m,
name="hub",
equations=m.getEquations(),
problem="lp",
sense="min",
objective=eq1,
)
hub.solve()
import math
assert math.isclose(hub.objective_value, 173570.3851, rel_tol=0.001)
# Reporting Parameter
report = Parameter(m, name="report", domain=[t, "*"])
report[t, "E"] = E.l[t]
report[t, "G"] = G.l[t]
report[t, "h1"] = H1.l[t]
report[t, "h2"] = H2.l[t]
print("report: \n", report.pivot().round(4))
if __name__ == "__main__":
main()
