Combined heat and power economic dispatch problem using firefly algorithm

Abstract

Cogeneration units, which produce both heat and electric power, are found in many process industries. These industries also consume heat directly in addition to electricity. The cogeneration units operate only within a feasible zone. Each point within the feasible zone consists of a specific value of heat and electric power. These units are used along with other units, which produce either heat or power exclusively. Hence, the economic dispatch problem for these plants to optimize the fuel cost is quite complex and several classical and meta-heuristic algorithms have been proposed earlier. This paper applies the firefly algorithm, which is inspired by the behavior of fireflies which attract each other based on their luminosity. The results obtained have been compared with those obtained by other methods earlier and showed a marked improvement over the earlier methods.

Keywords

combined heat and power (CHP) economic dispatch / meta-heuristic algorithm / firefly algorithm / cogeneration

References

[1] Rooijers F J, van Amerongen R A M. Static economic dispatch for co-generation systems. IEEE Transactions on Power Systems, 1994, 9(3): 1392–1398

[2] Guo T, Henwood M I, van Ooijen M. An algorithm for combined heat and power economic dispatch. IEEE Transactions on Power Systems, 1996, 11(4): 1778–1784

[3] Vasebi A, Fesanghary M, Bathaee S M T. Combined heat and power economic dispatch by harmony search algorithm. International Journal of Electrical Power & Energy Systems, 2007, 29(10): 713–719

[4] Khorram E, Jaberipour M. Harmony search algorithm for solving combined heat and power economic dispatch problems. Energy Conversion and Management, 2011, 52(2): 1550–1554

[5] Wong K P, Algie C. Evolutionary programming approach for combined heat and power dispatch. Electric Power Systems Research, 2002, 61(3): 227–232

[6] Wang L F, Singh C. Stochastic combined heat and power dispatch based on multi-objective particle swarm optimization. International Journal of Electrical Power & Energy Systems, 2008, 30(3): 226–234

[7] Subbaraj P, Rengaraj R, Salivahanan S. Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm. Applied Energy, 2009, 86(6): 915–921

[8] Sinha N, Bhattacharya T. Genetic Algorithms for non-convex combined heat and power dispatch problems. In: Proceedings of TENCON 2008—2008 IEEE Region 10 Conference, Hyderabad, India, 2008, 1–5

[9] Sinha N, Saikia L C, Malakar T. Optimal solution for non-convex combined heat and power dispatch problems using differential evolution. In: Proceedings of 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India, 2010,1-5

[10] Sudhakaran M, Slochanal S M R. Integrating genetic algorithms and tabu search for combined heat and power economic dispatch. In: Proceedings of TENCON 2003-Conference on Convergent Technologies for Asia-Pacific Region, Bangalore, India, 2003, 67–71

[11] Song Y H, Chou C S, Stonham T J. Combined heat and power economic dispatch by improved ant colony search algorithm. Electric Power Systems Research, 1999, 52(2): 115–121

[12] Basu M. Bee colony optimization for combined heat and power economic dispatch. Expert Systems with Applications, 2011, 38(11): 13527–13531

[13] Rong A, Lahdelma R. Efficient algorithms for combined heat and power production planning under the deregulated electricity market. European Journal of Operational Research, 2007, 176(2): 1219–1245

[14] Hosseini S S S, Jafarnejad A, Behrooz A H, Gandomi A H. Combined heat and power economic dispatch by mesh adaptive direct search algorithm. Expert Systems with Applications, 2011, 38(6): 6556–6564

[15] Su C T, Chiang C L. An incorporated algorithm for combined heat and power economic dispatch. Electric Power Systems Research, 2004, 69(2,3): 187–195

[16] Yang X S. Firefly algorithms for multimodal optimization in stochastic algorithms: foundations and applications. SAGA 2009. Lecture Notes in Computer Science, 2009, 5792: 169–178

[17] Yang X S, Sadat Hosseini S S, Gandomi A H. Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect. Applied Soft Computing, 2012, 12(3): 1180–1186

[18] Apostolopoulos T, Vlachos A. Application of the firefly algorithm for solving the economic emissions load dispatch problem. International Journal of Combinatorics, 2011, 2011: 1–23

[19] Rampriya B, Mahadevan K, Kannan S. Unit commitment in deregulated power system using Lagrangian firefly algorithm. In: Proceedings of 2010 IEEE International Conference on Communication Control and Computing Technologies (ICCCCT), Ramanathapuram, India, 2010, 389–393

[20] Horng M H, Liou R J. Multilevel minimum cross entropy threshold selection based on the firefly algorithm. Expert Systems with Applications, 2011, 38(12): 14805–14811

[21] Dos Santos Coelho L, de Andrade Bernert D L, Mariani V C. A chaotic firefly algorithm applied to reliability-redundancy optimization. In: Proceedings of 2011 IEEE Congress on Evolutionary Computation (CEC), New Orleans, USA, 2011, 517–521

[22] Gandomi A H, Yang X-S, Alavi A H. Mixed variable structural optimization using firefly algorithm. Computers and Structures, 2011, 89(23,24): 2325–2336

[23] Horng M H. Vector quantization using the firefly algorithm for image compression. Expert Systems with Applications, 2012, 39(1): 1078–1091

[24] Senthilnath J, Omkar S N, Mani V. Clustering using firefly algorithm: Performance study. Swarm and Evolutionary Computation, 2011, 1(3): 164–171

Acknowledgements

Gratitude goes to the Chancellor, Vice-Chancellor and Vice-Presidents of VIT University, Vellore, for providing the excellent infrastructure facilities and encouragement which have made this research work possible.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg