Description
This example illustrates the use of nonlinear programming in the design of water distribution systems. The model captures the main features of an actual application for a city in Indonesia. This is a variant of the model WATER from this library. In this version the domain of two of the variables is limited in the model statement.
Small Model of Type : DNLP
Category : GAMS Model library
Main file : waterld.gms
$title Design of a Water Distribution Network with Limited Domain of Variables (WATERLD,SEQ=426)
$onText
This example illustrates the use of nonlinear programming in the design of
water distribution systems. The model captures the main features of an
actual application for a city in Indonesia.
This is a variant of the model WATER from this library. In this version
the domain of two of the variables is limited in the model statement.
Brooke, A, Drud, A S, and Meeraus, A, Modeling Systems and Nonlinear
Programming in a Research Environment. In Ragavan, R, and Rohde, S M,
Eds, Computers in Engineering, Vol. III. ACME, 1985.
Drud, A S, and Rosenborg, A, Dimensioning Water Distribution Networks.
Masters thesis, Institute of Mathematical Statistics and Operations
Research, Technical University of Denmark, 1973. (in Danish)
Keywords: nonlinear programming, discontinuous derivatives, water distribution,
network optimization, engineering
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Set
n 'nodes' / nw 'north west reservoir', e 'east reservoir'
cc 'central city', w 'west'
sw 'south west', s 'south'
se 'south east', n 'north' /
a(n,n) 'arcs (arbitrarily directed)' / nw.(w,cc,n) , e.(n,cc,s,se)
cc.(w,sw,s,n), s.se, s.sw, sw.w /
rn(n) 'reservoirs' / nw, e /
dn(n) 'demand nodes';
dn(n) = yes;
dn(rn) = no;
display dn;
Alias (n,np);
Table node(n,*) 'node data'
demand height x y supply wcost pcost
* m**3/sec m over base m m m**3/sec rp/m**3 rp/m**4
nw 6.50 1200 3600 2.500 0.20 1.02
e 3.25 4000 2200 6.000 0.17 1.02
cc 1.212 3.02 2000 2300
w 0.452 5.16 750 2400
sw 0.245 4.20 900 1200
s 0.652 1.50 2000 1000
se 0.252 0.00 4000 900
n 0.456 6.30 3700 3500 ;
Parameter dist(n,n) 'distance between nodes (m)';
dist(a(n,np)) = sqrt(sqr(node(n,"x") - node(np,"x")) + sqr(node(n,"y") - node(np,"y")));
display dist;
Scalar
dpow 'power on diameter in pressure loss equation' / 5.33 /
qpow 'power on flow in pressure loss equation' / 2.00 /
dmin 'minimum diameter of pipe' / 0.15 /
dmax 'maximum diameter of pipe' / 2.00 /
hloss 'constant in the pressure loss equation' / 1.03e-3 /
dprc 'scale factor in the investment cost equation' / 6.90e-2 /
cpow 'power on diameter in the cost equation' / 1.29 /
r 'interest rate' / 0.10 /
davg 'average diameter (geometric mean)'
rr 'ratio of demand to supply';
davg = sqrt(dmin*dmax);
rr = sum(dn, node(dn,"demand"))/sum(rn, node(rn,"supply"));
Variable
q(n,n) 'flow on each arc - signed (m**3 per sec)'
d(n,n) 'pipe diameter for each arc (m)'
h(n) 'pressure at each node (m)'
s(n) 'supply at reservoir nodes (m**3 per sec)'
pcost 'annual recurrent pump costs (mill rp)'
dcost 'investment costs for pipes (mill rp)'
wcost 'annual recurrent water costs (mill rp)'
cost 'total discounted costs (mill rp)';
Equation
cont(n) 'flow conservation equation at each node'
loss(n,n) 'pressure loss on each arc'
peq 'pump cost equation'
deq 'investment cost equation'
weq 'water cost equation'
obj 'objective function';
cont(n).. sum(np, q(np,n)) - sum(np, q(n,np)) + s(n)$rn(n) =e= node(n,"demand");
loss(a(n,np)).. h(n) - h(np) =e= (hloss*dist(n,np)*abs(q(n,np))**(qpow-1)*q(n,np)/d(n,np)**dpow)$(qpow <> 2)
+ (hloss*dist(n,np)*abs(q(n,np)) *q(n,np)/d(n,np)**dpow)$(qpow = 2);
peq.. pcost =e= sum(rn, s(rn)*node(rn,"pcost")*(h(rn) - node(rn,"height")));
deq.. dcost =e= dprc*sum((n,np), dist(n,np)*d(n,np)**cpow);
weq.. wcost =e= sum(rn, s(rn)*node(rn,"wcost"));
obj.. cost =e= (pcost + wcost)/r + dcost;
d.lo(a) = dmin;
d.up(a) = dmax;
h.lo(rn) = node(rn,"height");
h.lo(dn) = node(dn,"height") + 7.5 + 5.0*node(dn,"demand");
s.lo(rn) = 0;
s.up(rn) = node(rn,"supply");
d.l(a) = davg;
h.l(n) = h.lo(n) + 1.0;
s.l(rn) = node(rn,"supply")*rr;
Model network / all, q(a), d(a) /;
solve network using dnlp minimizing cost;
display q.l;