Fuzzy satisfying interactive multiobjective thermal power dispatch: SWT approach

Abstract

In multiobjective optimization, trade-off analysis plays an important role in determining most preferred solution. This paper presents an explicit interactive trade-off analysis based on the surrogate worth trade-off function to determine the best compromised solution. In the multiobjective framework thermal power dispatch problem is undertaken in which four objectives viz. cost, NO_x emission, SO_x emission and CO_x emission are minimized simultaneously. The interactive process is implemented using a weighting method by regulating the relative weights of objectives in systematic manner. Hence the weighting method facilitates to simulate the trade-off relation between the conflicting objectives in non-inferior domain. Exploiting fuzzy decision making theory to access the indifference band, interaction with the decision maker is obtained via surrogate worth trade-off (SWT) functions of the objectives. The surrogate worth trade-off functions are constructed in the functional space and then transformed into the decision space, so the surrogate worth trade-off functions of objectives relate the decision maker’s preferences to non-inferior solutions through optimal weight patterns. The optimal solution of thermal power dispatch problem is obtained by considering real and reactive power losses. Decoupled load flow analysis is performed to find the transmission losses. The validity of the proposed method is demonstrated on 11-bus, 17-lines IEEE system, comprising of three generators.

Keywords

Multiobjective thermal power dispatch / weighting method / fuzzy membership function non-inferior solutions / SWT functions

References

[1] Abido M.A.. Environmental/economic power dispatch using multiobjective evolutionary algorithms. IEEE Transactions on Power Systems, 2003, 18(4): 1529-1537.

[2] Bath S.K., Dhillon J.S., Kothari D.P.. Fuzzy satisfying stochastic multiobjective generation scheduling by weightage pattern search methods. Electric Power Systems Research, 2004, 69: 311-320.

[3] Brar Y.S., Dhillon J.S., Kothari D.P.. Multiobjective load dispatch by fuzzy logic searching weightage pattern. Electric Power Systems Research, 2002, 63: 149-160.

[4] Charalambous C.. A new approach to multicriterion optimization problem and its application to the design of 1-D digital filters. IEEE Transactions on Circuit Systems, 1989, 36(6): 773-784.

[5] Dhillon J.S., Kothari D.P.. The surrogate worth trade-off approach for multiobjective thermal power dispatch problem. Electric Power Systems Research, 2000, 56: 103-110.

[6] El-Keib A.A., Ma H., Hart J.L.. Economic dispatch in view of clean air act of 1990. IEEE Transactions on Power Systems, 1994, 9(3): 972-978.

[7] Galiana F.D., Phelan M.. Allocation of transmission losses to bilateral contracts in a competitive environment. IEEE Transactions on Power Systems, 2000, 15(1): 143-150.

[8] Huang C.M., Huang Y.C.. A novel approach to real-time economic emission power dispatch. IEEE Transactions on Power Systems, 2003, 18(1): 288-294.

[9] Niimura T., Nakahima T.. Multiobjective trade-off analysis of deregulated electricity transactions. Electrical Power & Energy Systems, 2003, 25: 179-185.

[10] Shin W.S., Ravindran A.. Interactive multiple objective optimization: Survey-I, continuous case. Computer Operation Research, 1991, 18(1): 97-114.

[11] Singh L., Dhillon J.S., Chauhan R.C.. Evaluation of best weight pattern for multiple criteria load dispatch. Electric Power Components and Systems, 2006, 34(1): 21-35.

[12] Talaq J.H., El-Hawary F., El-Hawary M.E.. A summary of environmental/economic dispatch algorithms. IEEE Transactions on Power Systems, 1994, 9(3): 1508-1516.

[13] Tsay M.T., Lin W.M., Lee J.L.. Application of evolutionary programming for economic dispatch of cogeneration systems under emission constraints. Electrical Power and Energy Systems, 2001, 23: 805-812.