Version:

dispatch.gms : Economic Load Dispatch Including Transmission Losses

**Description**

The economic dispatch problem can be defined as determining the least-cost power generation schedule from a set of on-line generating units to meet the total power demand at a given point of time. The generating unit cost and the network transmission losses are modeled as quadratic function of the power generation. In the present problem, both the cost-minimizing and loss-minimizing solutions have been obtained.

**Reference**

- Wood, A J, and Wollenberg, B F, Example Problem 4e. In Power Generation, Operation and Control. John Wiley and Sons, 1984, pp. 85-88.

**Small Model of Type :** NLP

**Category :** GAMS Model library

**Main file :** dispatch.gms

```
$title Economic Load Dispatch Including Transmission Losses (DISPATCH,SEQ=166)
$onText
The economic dispatch problem can be defined as determining the
least-cost power generation schedule from a set of on-line generating
units to meet the total power demand at a given point of time. The
generating unit cost and the network transmission losses are modeled
as quadratic function of the power generation.
In the present problem, both the cost-minimizing and loss-minimizing
solutions have been obtained.
Wood, A J, and Wollenberg, B F, Example Problem 4e. In Power Generation,
Operation and Control. John Wiley and Sons, 1984, pp. 85-88.
Keywords: nonlinear programming, engineering, power generation, economic dispatch
$offText
Set
i 'generators' / p1*p3 /
genchar 'generator characteristics' / a, b, c, upplim, lowlim /
cg(genchar) 'cost categories' / a, b, c /;
Alias (i,j);
Table gendata(i,genchar) 'generator cost characteristics and limits'
a b c upplim lowlim
p1 213.1 11.669 0.00533 200 50
p2 200.0 10.333 0.00889 150 37.5
p3 240.0 10.833 0.00741 180 45;
Parameter pexp(cg) 'exponent for cost function' / a 0, b 1, c 2 /;
Table b(i,j) 'the b-matrix loss coefficients - squared components'
p1 p2 p3
p1 0.0676 0.00953 -0.00507
p2 0.00953 0.05210 0.00901
p3 -0.00507 0.00901 0.02940;
Parameter b0(i) 'linear loss coefficients' / p1 -0.0766
p2 -0.00342
p3 0.0189 /;
Scalar
b00 'loss equation constant' / 0.040357 /
demand 'total power demand in MW' / 210 /;
Variable
p(i) 'power generation level in MW'
loss 'total transmission loss in MW'
cost 'total generation cost - the objective function';
Positive Variable p;
p.up(i) = gendata(i,"upplim");
p.lo(i) = gendata(i,"lowlim");
Equation
costfn 'total cost calculation'
lossfn 'total loss calculation'
demcons 'total generation must equal demand and loss';
costfn.. cost =e= sum((i,cg), gendata(i,cg)*power(p(i),pexp(cg)));
lossfn.. loss =e= b00 + sum(i, b0(i)*p(i))/100
+ sum((i,j), p(i)*b(i,j)*p(j))/100;
demcons.. sum(i, p(i)) =g= demand + loss;
Model edc / all /;
solve edc minimizing cost using nlp;
* trace a trade-off frontier
Set
s 'trade-off points' / min-loss, s1*s4, min-cost /
st(s) 'in between points' / s1*s4 /;
Parameter trace 'trace report';
trace('cost','min-cost') = cost.l;
trace('loss','min-cost') = loss.l;
option limRow = 0, limCol = 0;
solve edc minimizing loss using nlp;
trace('cost','min-loss') = cost.l;
trace('loss','min-loss') = loss.l;
edc.solPrint = %solPrint.quiet%;
loop(st,
loss.fx = trace('loss','min-loss')
+ ord(st)/(card(st) + 1)*(trace('loss','min-cost')
-trace('loss','min-loss'));
solve edc minimizing cost using nlp;
trace('cost',st) = cost.l;
trace('loss',st) = loss.l;
);
display trace;
```