Description

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.

This model is also used as an example in the
GAMS/DECIS user's guide.

Small Model of Type : SP

Category : GAMS EMP library

Main file : apl1pcasp.gms

$title Stochastic Electric Power Expansion Planning Problem (APL1PCASP,SEQ=71)

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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.

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.

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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 hm1(dl) / h 300, m 400, l 200 /;
parameter hm2(dl) / h 100, m 150, l 300 /;
parameter df1 random demand multiplier /1/
          df2 random demand multiplier /1/;
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= df1*hm1(dl) + df2*hm2(dl);

model apl1p /all/;

file emp / '%emp.info%' /; put emp '* problem %gams.i%' /;
$onPut
randvar df1 discrete 0.5 2.1   0.5 1.0
randvar df2 discrete 0.2 2.0   0.8 0.2
stage 2 df1 df2
stage 2 omax demand
stage 2 y s
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putclose;

Set scen scenarios / s1*s4 /;
parameter  s_df1(scen), s_df2(scen);

Set dict / scen .scenario. ''
           df1  .randvar . s_df1
           df2  .randvar . s_df2 /;

solve apl1p using emp min tcost scenario dict;