To attain the highest economic and energy-saving characteristics of gas turbine cogeneration plants, it is necessary to rationally determine capacities and numbers of gas turbines and auxiliary equipment in consideration of their operational strategies corresponding to seasonal and hourly variations in energy demands. Some optimization approaches based on the mixed-integer linear programing have been proposed to this design problem. However, equipment capacities have been treated as continuous variables, and correspondingly, performance characteristics and capital costs of equipment have been assumed to be continuous functions with respect to their capacities. This is because if equipment capacities are treated discretely, the number of integer variables increases drastically and the problem becomes too difficult to solve. As a result, the treatment of equipment capacities as continuous variables causes discrepancies between existing and optimized values of capacities and expresses the dependence of performance characteristics and capital costs on capacities with worse approximations. In this paper, an optimal design method is proposed in consideration of discreteness of equipment capacities. A formulation for keeping the number of integer variables as small as possible is presented to solve the optimal design problem easily. This method is applied to the design of a gas turbine cogeneration plant, and its validity and effectiveness are clarified.

1.
Papoulias
,
S. A.
, and
Grossmann
,
I. E.
, 1983, “
A Structural Optimization Approach in Process Synthesis—I: Utility Systems
,”
Comput. Chem. Eng.
0098-1354,
7
(
6
), pp.
695
706
.
2.
Horii
,
S.
,
Ito
,
K.
,
Pak
,
P. S.
, and
Suzuki
,
Y.
, 1987, “
Optimal Planning of Gas Turbine Co-Generation Plants Based on Mixed-Integer Linear Programming
,”
Int. J. Energy Res.
0363-907X,
11
(
4
), pp.
507
518
.
3.
Iyer
,
R. R.
, and
Grossmann
,
I. E.
, 1998, “
Synthesis and Operational Planning of Utility Systems for Multiperiod Operation
,”
Comput. Chem. Eng.
0098-1354,
22
(
7-8
), pp.
979
993
.
4.
Yokoyama
,
R.
,
Hasegawa
,
Y.
, and
Ito
,
K.
, 2000, “
Structural Optimization of Energy Supply Systems by a Decomposition Method for Mixed-Integer Linear Programming
,”
Trans. JSME, Ser. C
,
66
(
652
), pp.
4016
4023
(in Japanese).
5.
Yokoyama
,
R.
,
Hasegawa
,
Y.
, and
Ito
,
K.
, 2002, “
A MILP Decomposition Approach to Large Scale Optimization in Structural Design of Energy Supply Systems
,”
Energy Convers. Manage.
0196-8904,
43
(
6
), pp.
771
790
.
6.
Adjiman
,
C. S.
,
Schweiger
,
C. A.
, and
Floudas
,
C. A.
, 1998, “Mixed-Integer Nonlinear Optimization in Process Synthesis,”
Handbook of Combinatorial Optimization
,
Kluwer
, Dordrecht, Vol. 1, pp.
1
76
.
7.
Painton
,
L. A.
, and
Diwekar
,
U. M.
, 1994, “
Synthesizing Optimal Design Configurations for a Brayton Cycle Power Plant
,”
Comput. Chem. Eng.
0098-1354,
18
(
5
), pp.
369
381
.
8.
Fujita
,
K.
,
Akagi
,
S.
,
Hirokawa
,
N.
, and
Yoshida
,
K.
, 1998, “
Optimal Planning Method of Energy Plant Configurations Based on a Genetic Algorithm
,”
Trans. JSME, Ser. C
,
64
(
617
), pp.
354
361
(in Japanese).
9.
Brooke
,
A.
,
Kendrick
,
D.
,
Meeraus
,
A.
, and
Raman
,
R.
, 1998,
GAMS a User’s Guide
,
GAMS Development Corp.
, Washington, DC.
10.
Yokoyama
,
R.
, and
Ito
,
K.
, 1995, “
Multi-Objective Optimization in Unit Sizing of a Gas Turbine Cogeneration Plant
,”
ASME J. Eng. Gas Turbines Power
0742-4795,
117
(
1
), pp.
53
59
.
You do not currently have access to this content.