"""
## LICENSETYPE: Demo
## MODELTYPE: NLP
Optimal management of a river system which includes water resources,
reservoirs, consumers and an estuary.
October 6, 2005
Adapted from:
McKinney, D.C. and Savitsky, A.G., "Basic optimization models
for water and energy management", Revision 6, February 2003.
"""
from __future__ import annotations
import os
import pandas as pd
import gamspy.math as gams_math
from gamspy import Alias
from gamspy import Container
from gamspy import Equation
from gamspy import Model
from gamspy import Ord
from gamspy import Parameter
from gamspy import Set
from gamspy import Sum
from gamspy import Variable
def reformat_df(dataframe):
return dataframe.reset_index().melt(
id_vars="index", var_name="Category", value_name="Value"
)
def data_records():
# Source records table
cols = [
"Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec",
]
inds = ["Source_1", "Source_2"]
data = [
[98, 115, 244, 390, 641, 754, 807, 512, 367, 210, 181, 128],
[29, 49, 78, 121, 198, 144, 105, 98, 79, 72, 45, 29],
]
Source_recs = reformat_df(pd.DataFrame(data, columns=cols, index=inds))
# Demand records table
cols = [
"Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec",
]
inds = ["User_1", "User_2", "Outlet"]
data = [
[0.0, 0.0, 10, 64.5, 189.8, 184.4, 243.7, 200.9, 99.5, 0.0, 0.0, 0.0],
[0.0, 0.0, 10, 13.5, 15.0, 22.1, 26.0, 24.9, 13.0, 0.0, 0.0, 0.0],
[
500,
500,
500,
100.0,
100.0,
100.0,
100.0,
500.0,
500.0,
500,
500,
500,
],
]
Demand_recs = reformat_df(pd.DataFrame(data, columns=cols, index=inds))
return Source_recs, Demand_recs
def main():
m = Container(
system_directory=os.getenv("SYSTEM_DIRECTORY", None),
delayed_execution=int(os.getenv("DELAYED_EXECUTION", False)),
)
# SETS #
n = Set(
m,
name="n",
records=[
"Source_1",
"Source_2",
*[f"Node_{n}" for n in range(1, 6)],
"User_1",
"User_2",
"Res_1",
"Res_2",
"Outlet",
],
description="nodes",
)
nn = Set(
m,
name="nn",
domain=n,
records=[f"Node_{n}" for n in range(1, 6)],
description="Nodes",
)
ns = Set(
m,
name="ns",
domain=n,
records=["Source_1", "Source_2"],
description="Sources nodes",
)
nr = Set(
m,
name="nr",
domain=n,
records=["User_1", "User_2", "Outlet"],
description="Users nodes",
)
nl = Set(
m,
name="nl",
domain=n,
records=["Res_1", "Res_2"],
description="Reservoir nodes",
)
n_from_n = Set(
m,
name="n_from_n",
domain=[n, n],
records=[
("Res_1", "Source_1"),
("Res_2", "Source_2"),
("Node_1", "Res_1"),
("Node_1", "Res_2"),
("Node_2", "Node_1"),
("User_1", "Node_2"),
("Node_3", "Node_2"),
("Node_3", "User_1"),
("Node_4", "Node_3"),
("User_2", "Node_4"),
("Node_5", "Node_4"),
("Node_5", "User_2"),
("outlet", "Node_5"),
],
description="Topology of the network",
)
n_to_nr = Set(
m,
name="n_to_nr",
domain=[n, n],
records=[
("Node_2", "User_1"),
("Node_4", "User_2"),
("Node_5", "Outlet"),
],
description="Topology of the network",
)
t = Set(
m,
name="t",
records=[
"Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec",
],
description="months",
)
# ALIAS #
n1 = Alias(m, name="n1", alias_with=n)
# PARAMETERS #
Ini_S = Parameter(
m,
name="Ini_S",
domain=n,
records=[("Res_1", 1000), ("Res_2", 300)],
description="Initial storage in reservoirs. (m3)",
)
ret = Parameter(
m,
name="ret",
domain=n,
records=[("User_1", 0.5), ("User_2", 0.5), ("Outlet", 0.0)],
description="Return flow coefficients",
)
Source = Parameter(
m,
name="Source",
domain=[n, t],
records=data_records()[0],
description="Flow of water (m3 per sec)",
)
Demand = Parameter(
m,
name="Demand",
domain=[n, t],
records=data_records()[1],
description="Water demands (m3 per sec)",
)
# VARIABLES #
U = Variable(
m,
name="U",
type="positive",
domain=[n, t],
description="Diversions of water from node n in period t (m3)",
)
q = Variable(
m,
name="q",
type="positive",
domain=[n, t],
description="Inflows in nodde n in period t (m3)",
)
r = Variable(
m,
name="r",
type="positive",
domain=[n, t],
description="Releases from node n in period t (m3)",
)
s = Variable(
m,
name="s",
type="positive",
domain=[n, t],
description="Storages of water in node n in period t (m3)",
)
obj = Variable(m, name="obj")
# Upper bounds on users.
U.up[n, t] = Demand[n, t]
# Upper bounds on reservoirs.
s.up["Res_1", t] = 1000
s.up["Res_2", t] = 300
# Lower storage in reservoirs (December).
s.lo["Res_1", "Dec"] = 1000
s.lo["Res_2", "Dec"] = 300
# EQUATIONS #
R_no = Equation(
m, name="R_no", type="regular", domain=[n, t], description="Node"
)
R_ns = Equation(
m, name="R_ns", type="regular", domain=[n, t], description="Source"
)
R_nr = Equation(
m,
name="R_nr",
type="regular",
domain=[n, t],
description="Irrigation node",
)
R_nl = Equation(
m,
name="R_nl",
type="regular",
domain=[n, t],
description="Reservoirs node",
)
R_nn = Equation(
m, name="R_nn", type="regular", domain=[n, t], description="Node"
)
Objective = Equation(
m, name="Objective", type="regular", description="Objective function"
)
R_no[n, t].where[nn[n]] = r[n, t] == q[n, t]
R_ns[n, t].where[ns[n]] = r[n, t] == Source[n, t]
R_nr[n, t].where[nr[n]] = r[n, t] == ret[n] * U[n, t]
R_nl[n, t].where[nl[n]] = (
s[n, t]
== Ini_S[n].where[Ord(t) == 1]
+ s[n, t.lag(1)].where[Ord(t) >= 1]
+ q[n, t]
- r[n, t]
)
R_nn[n, t] = q[n, t] == Sum(n1.where[n_from_n[n, n1]], r[n1, t]) - Sum(
n1.where[n_to_nr[n, n1]], U[n1, t]
)
Objective[...] = obj == Sum(
t, Sum(n.where[nr[n]], gams_math.power((U[n, t] - Demand[n, t]), 2))
)
riversys = Model(
m,
name="riversys",
equations=m.getEquations(),
problem="nlp",
sense="min",
objective=obj,
)
riversys.solve()
print("Objective Function Value: ", round(obj.toValue(), 4))
# Show the solution
rep = Parameter(m, name="rep", domain=[t, "*"])
rep[t, "User_1"] = U.l["User_1", t]
rep[t, "Demand_1"] = Demand["User_1", t]
rep[t, "User_2"] = U.l["User_2", t]
rep[t, "Demand_2"] = Demand["User_2", t]
rep[t, "Outlet"] = U.l["Outlet", t]
rep[t, "Demand"] = Demand["Outlet", t]
print("Solution:\n", rep.pivot().round(3))
# End riversys
if __name__ == "__main__":
main()
