Multiobjective fuzzy dominance based bacterial foraging algorithm to solve economic emission dispatch problem

被引:112
作者
Panigrahi, B. K. [1 ]
Pandi, V. Ravikumar [1 ]
Das, Sanjoy [2 ]
Das, Swagatam [3 ]
机构
[1] Indian Inst Technol, Dept Elect Engn, New Delhi 110016, India
[2] Kansas State Univ, Manhattan, KS 66506 USA
[3] Jadavpur Univ, Dept Elect & Telecomm Engn, Kolkata, India
关键词
Multiobjective optimization; Environmental/economic dispatch; Pareto front; Bacterial foraging; Non-dominated sorting; Fuzzy dominance sorting; DISTRIBUTED OPTIMIZATION; LOAD DISPATCH; BIOMIMICRY; MODELS;
D O I
10.1016/j.energy.2010.09.014
中图分类号
O414.1 [热力学];
学科分类号
摘要
This paper proposes the bacterial foraging meta-heuristic algorithm for multiobjective optimization. In this multiobjective bacterial foraging optimization technique, the most recent bacterial locations are obtained by chemotaxis process. Next, Fuzzy dominance based sorting procedure is used here to select the Pareto optimal front (POF). To test the suitability of our proposed algorithm we have considered a highly constrained optimization problem namely economic/emission dispatch. Now-a-days environmental concern that arises due to the operation of fossil fuel fired electric generators and global warming, transforms the classical economic load dispatch problem into multiobjective environmental/economic dispatch (EED) problem. In the proposed work, we have considered the standard IEEE 30-bus six-generator test system and the results obtained by proposed algorithm are compared with the other recently reported results. Simulation results demonstrate that the proposed algorithm is a capable candidate in solving the multiobjective economic emission load dispatch problem. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码:4761 / 4770
页数:10
相关论文
共 33 条
[1]   Environmental/economic power dispatch using multiobjective evolutionary algorithms [J].
Abido, MA .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2003, 18 (04) :1529-1537
[2]   A novel multiobjective evolutionary algorithm or environmental/economic power dispatch [J].
Abido, MA .
ELECTRIC POWER SYSTEMS RESEARCH, 2003, 65 (01) :71-81
[3]   A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch [J].
Abido, MA .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2003, 25 (02) :97-105
[4]   Differential evolution algorithm for emission constrained economic power dispatch problem [J].
Abou El Ela, A. A. ;
Abido, M. A. ;
Spea, S. R. .
ELECTRIC POWER SYSTEMS RESEARCH, 2010, 80 (10) :1286-1292
[5]   Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch [J].
Agrawal, Shubham ;
Panigrahi, B. K. ;
Tiwari, Manoj Kumar .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2008, 12 (05) :529-541
[6]   ASSESSING THE INFLUENCE OF POWER POOLS ON EMISSION CONSTRAINED ECONOMIC-DISPATCH [J].
BRODSKY, SFJ ;
HAHN, RW .
IEEE TRANSACTIONS ON POWER SYSTEMS, 1986, 1 (01) :57-62
[7]   SECURITY-CONSTRAINED MULTIOBJECTIVE GENERATION DISPATCH USING BICRITERION GLOBAL OPTIMIZATION [J].
CHANG, CS ;
WONG, KP ;
FAN, B .
IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION, 1995, 142 (04) :406-414
[8]   Handling multiple objectives with particle swarm optimization [J].
Coello, CAC ;
Pulido, GT ;
Lechuga, MS .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2004, 8 (03) :256-279
[9]   STOCHASTIC ECONOMIC EMISSION LOAD DISPATCH [J].
DHILLON, JS ;
PARTI, SC ;
KOTHARI, DP .
ELECTRIC POWER SYSTEMS RESEARCH, 1993, 26 (03) :179-186
[10]   Multi-objective congestion management incorporating voltage and transient stabilities [J].
Esmaili, Masoud ;
Shayanfar, Heidar Ali ;
Amjady, Nima .
ENERGY, 2009, 34 (09) :1401-1412