apl1pca.gms : Stochastic Programming Example for DECIS
Stochastic Electric Power Expansion Planning Problem.
This is a two-stage stochastic linear program.
Facing uncertain demand, decisions about generation
capacity need to be made.
Compared to APL1P this model introduces dependent
stochastic parameters.
This model is also used as an example in the
GAMS/DECIS user's guide.
Reference:
- Infanger, G, Planning Under Uncertainty - Solving Large-Scale Stochastic Linear Programs.
Small Model of Type: DECIS
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$title APL1PCA Stochastic Programming Example for GAMS/DECIS (APL1PCA,SEQ=198)
$ontext
Stochastic Electric Power Expansion Planning Problem.
This is a two-stage stochastic linear program.
Facing uncertain demand, decisions about generation
capacity need to be made.
Compared to APL1P this model introduces dependent
stochastic parameters.
This model is also used as an example in the
GAMS/DECIS user's guide.
Infanger, G, Planning Under Uncertainty - Solving Large-Scale
Stochastic Linear Programs, 1988.
$offtext
$if not set decisalg $set decisalg decism
set g generators / g1, g2/;
set dl demand levels /h, m, l/;
parameter alpha(g) availability / g1 0.68, g2 0.64 /;
parameter ccmin(g) min capacity / g1 1000, g2 1000 /;
parameter ccmax(g) max capacity / g1 10000, g2 10000 /;
parameter c(g) investment / g1 4.0, g2 2.5 /;
table f(g,dl) operating cost
h m l
g1 4.3 2.0 0.5
g2 8.7 4.0 1.0;
parameter d(dl) demand / h 1040, m 1040, l 1040 /;
parameter us(dl) cost of unserved demand / h 10, m 10, l 10 /;
* -----------------------------------------------
* define the core model
* -----------------------------------------------
free variable tcost total cost;
positive variable x(g) capacity of generators;
positive variable y(g, dl) operation level;
positive variable s(dl) unserved demand;
equations
cost total cost
cmin(g) minimum capacity
cmax(g) maximum capacity
omax(g) maximum operating level
demand(dl) satisfy demand;
cost .. tcost =e= sum(g, c(g)*x(g))
+ sum(g, sum(dl, f(g,dl)*y(g,dl)))
+ sum(dl,us(dl)*s(dl));
cmin(g) .. x(g) =g= ccmin(g);
cmax(g) .. x(g) =l= ccmax(g);
omax(g) .. sum(dl, y(g,dl)) =l= alpha(g)*x(g);
demand(dl) .. sum(g, y(g,dl)) + s(dl) =g= d(dl);
model apl1p /all/;
* -----------------------------------------------
* setting decision stages
* -----------------------------------------------
x.stage(g) = 1;
y.stage(g, dl) = 2;
s.stage(dl) = 2;
cmin.stage(g) = 1;
cmax.stage(g) = 1;
omax.stage(g) = 2;
demand.stage(dl) = 2;
* -----------------------------------------------
* defining independent stochastic parameters
* -----------------------------------------------
set stoch /out, pro/;
set omega1 / o11, o12 /;
table v1(stoch,omega1)
o11 o12
out 2.1 1.0
pro 0.5 0.5 ;
set omega2 / o21, o22 /;
table v2(stoch, omega2)
o21 o22
out 2.0 1.0
pro 0.2 0.8 ;
parameter hm1(dl) / h 300, m 400, l 200 /;
parameter hm2(dl) / h 100, m 150, l 300 /;
* -----------------------------------------------
* outputting stochastic file
* -----------------------------------------------
file stg / MODEL.STG /;
put stg;
put "BLOCKS DISCRETE" /;
scalar h1;
loop(omega1,
put "BL v1 period2 ", v1("pro", omega1)/;
loop(dl,
h1 = hm1(dl) * v1("out", omega1);
put "RHS demand ",dl.tl:1, " ", h1/;
);
);
loop(omega2,
put "BL v2 period2 ", v2("pro", omega2)/;
loop(dl,
h1 = hm2(dl) * v2("out", omega2);
put "RHS demand ",dl.tl:1, " ", h1/;
);
);
putclose;
* -----------------------------------------------
* output a MINOS option file
* -----------------------------------------------
file mopt / MINOS.SPC /;
put mopt;
put "begin"/;
put "rows 250"/;
put "columns 250"/;
put "elements 10000"/;
put "end"/;
putclose;
* -----------------------------------------------
* solve the model
* -----------------------------------------------
option lp=%decisalg%;
solve apl1p using lp minimizing tcost;
scalar ccost capital cost;
scalar ocost operating cost;
ccost = sum(g, c(g) * x.l(g));
ocost = tcost.l - ccost;
display x.l, tcost.l, ccost, ocost, y.l, s.l;
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