Description

This is a linear programming formulation for optimal petroleum
stockpile policy based on a stochastic dynamic programming approach.
Each state of the Markov process is a pair (s,i) where s is the size
of the inventory and i is the state of the world (normal or disrupted).
However, we assume the probability of entering state (s',j) from
state (s,i) is independent of the stockpile levels.

Small Model of Type : LP

Category : GAMS Model library

Main file : markov.gms

$title Strategic Petroleum Reserve (MARKOV,SEQ=82)

$onText
This is a linear programming formulation for optimal petroleum
stockpile policy based on a stochastic dynamic programming approach.
Each state of the Markov process is a pair (s,i) where s is the size
of the inventory and i is the state of the world (normal or disrupted).
However, we assume the probability of entering state (s',j) from
state (s,i) is independent of the stockpile levels.


Teisberg, T J, A Dynamic Programming Model of the U.S. Strategic
Petroleum Reserve. Bell Journal of Economics (1981).

Keywords: linear programming, stochastic dynamic programming, Markov process,
          energy economics
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Set
   s 'level of the reserve'    / empty, 3, 6, 9, 12, 15, 18, 21 /
   i 'state of the oil market' / normal, disrupted              /;

Alias (s,sp,spp), (i,j);

* remember that supply is fixed at q = 110 million barrel per year
* and the shape of the demand curve is : d(p) = d + k*p**-e

Scalar
   b    'discount factor'             /     .95   /
   beta                               /     .0625 /
   g    'u.s. demand'                 /     .25   /
   e                                  /     .1    /
   q    'supply'                      /  110      /
   d                                  / -130      /
   k                                  /  342      /
   pn   'normal price (us$ per bbl)'  /   34.526  /
   h    'storage cost'                /     .32   /;

Table pr(i,j) 'transition probability of the word oil market'
               normal  disrupted
   normal          .8         .2
   disrupted       .5         .5;

Parameter
   lev(s)           'level of reserve'
   dis(i)           'disruption'                    / disrupted 11 /
   p(s,sp,i)        'price affected by action a'
   c(s,sp,i)        'cost of taking action a'
   pi(s,i,sp,j,spp) 'probability matrix for problem';

lev(s)    = 3*(ord(s)-1);
p(s,sp,i) = (k / (q - dis(i) - d - (lev(sp)-lev(s))))**(1/e);
c(s,sp,i) = g*(d*(p(s,sp,i) - pn) + k*(p(s,sp,i)**(1 - e) - pn**(1 - e))/(1 - e))
          + p(s,sp,i)*(lev(sp) - lev(s)) + h*lev(sp);

pi(s,i,sp,j,sp) = pr(i,j);

display lev, dis, p, c, pi;

Variable
   z(s,i,sp) 'multiple of joint probability'
   pvcost    'present value of expected cost';

Positive Variable z;

Equation
   constr(s,i)
   equil(s,sp)
   cost        'cost definition';

constr(sp,j).. sum(spp, z(sp,j,spp)) - b*sum((s,i,spp), pi(s,i,sp,j,spp)*z(s,i,spp)) =e= beta;

equil(s,spp).. z(s,"disrupted",spp)*(ord(spp) - ord(s)) =l= 0;

cost..         pvcost =e= sum((s,i,spp), c(s,spp,i)*z(s,i,spp));

Model strategic / all /;

solve strategic using lp minimizing pvcost;