Enhanced BBO technique used to solving EED problems in electrical power systems

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Abstract

This paper proposes an improved biogeography-based optimization (BBO) algorithm to effectively solve the economic and environmental dispatch (EED) problem in power systems. The EED problem is a crucial optimization challenge in power system operations, which aims to balance the minimization of operating costs and environmental impacts. Various metaheuristic algorithms have been explored in the literature to address this problem, including the original BBO algorithm. However, the complex constraints and non-linearities associated with the EED problem, such as ramp-rate limits (RRLs), prohibited operating Zones (POZs), and valve point loading effects (VPLEs), pose significant challenges for the original BBO approach. The EED problem is subject to a range of practical constraints that significantly impact the optimal dispatch solution. Addressing these constraints accurately and efficiently is essential for realistic power system optimization. In this work, we present an enhanced BBO algorithm that incorporates several innovative features to improve its performance and overcome the limitations of the original approach. The key enhancement is the incorporation of the Cauchy distribution as the mutation operator, which helps the algorithm to better explore the search space and escape local optima. Comprehensive experiments were conducted on standard 10-bus and 40-bus test systems to evaluate the effectiveness of the proposed algorithm. The results demonstrate that the improved BBO algorithm outperforms other state-of-the-art optimization techniques in terms of convergence speed, solution quality, and robustness. Specifically, the enhanced BBO algorithm achieved a 12% reduction in operating costs and a 15% decrease in emissions compared to the original BBO method. The proposed improved BBO algorithm provides a promising solution for effectively addressing the EED problem in power systems, considering the practical constraints and non-linearities that are commonly encountered in real-world scenarios.

Keywords

economic and environmental dispatch (EED) problem / cost function / emission function / biogeography-based optimization (BBO) / enhanced BBO algorithm / cauchy operator

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References

[1] Roy PK, Bhui S (2016) A multi-objective hybrid evolutionary algorithm for dynamic economic emission load dispatch. Int Trans Electr Energ Syst 26: 49-78. https://doi.org/10.1002/etep.2066

[2] Muthuswamy R, Krishnan M, Subramanian K, et al. (2014) Environmental and economic power dispatch of thermal generators using modified NSGA-II algorithm. Int Trans Electr Energ Syst 25: 1552-1569. https://doi.org/10.1002/etep.1918

[3] Basu M (2011) Economic environmental dispatch using multi-objective differential evolution. Appl Soft Comput 11: 2845-2853. https://doi.org/10.1016/j.asoc.2010.11.014

[4] Sharma MK, Phonrattanasak P, Leeprechanon N (2015) Improved bees algorithm for dynamic economic dispatch considering prohibited operating zones. IEEE-Innovative Smart Grid Technologies - Asia (ISGT ASIA), 3-6 November. https://doi.org/10.1109/ISGT-Asia.2015.7386972

[5] Tlijani K, Guesmi T, Hadj Abdallah H (2016) Extended dynamic economic environmental dispatch using multiObjective particle swarm optimization. Int J Electr Eng Inf 8: 117-131. https://doi.org/10.15676/ijeei.2016.8.1.9

[6] Behnam MI, Abbas R, Alireza S (2013) Nonconvex dynamic economic power dispatch problems solution using hybrid immune-genetic algorithm. IEEE Syst J 7: 777-785. https://doi.org/10.1109/JSYST.2013.2258747

[7] Ganjefar S, Tofighi M (2011) Dynamic economic dispatch solution using an improved genetic algorithm with non-stationary penalty functions. Eur Trans Electr Power 21: 1480-1492. https://doi.org/10.1002/etep.520

[8] Sen T, Mathur HD (2016) A new approach to solve economic dispatch problem using a hybrid ACO-ABC-HS optimization algorithm. Electr Power Energy Syst 78: 735-744. https://doi.org/10.1016/j.ijepes.2015.11.121

[9] Panigrahi CK, Chattopadhyay PK, Chakrabarti RN, et al. (2006) Simulated annealing technique for dynamic economic dispatch. Electr Power Compon Syst 34: 577-586. https://doi.org/10.1080/15325000500360843

[10] Ziane I, Benhamida F, Graa A (2016) Simulated annealing algorithm for combined economic and emission power dispatch using max/max price penalty factor. Neural Comput Applic 1-9. DOI 10.1007/s00521-016-2335-3. https://doi.org/10.1007/s00521-016-2335-3

[11] Lin WM, Cheng FS, Tsay MT (2002) An improved Tabu search for economic dispatch with multiple minima. IEEE Trans Power Syst 17: 108-112. https://doi.org/10.1109/59.982200

[12] Amjady N, Nasiri-Rad H (2010) Solution of nonconvex and nonsmooth economic dispatch by a new adaptive real coded genetic algorithm. Expert Syst Appl 37: 5239-5245. https://doi.org/10.1016/j.eswa.2009.12.084

[13] Z. Xin-gang, L.Ji, M.Jin, Z.Ying (2020) An improved quantum particle swarm optimization algorithm for environmental economic dispatch. Expert Syst Appl 152: 113370. ttps://doi.org/10.1016/j.eswa.2020.113370

[14] S.Habib, M.A.Kamarposhti, H.Shokouhandeh, et al. (2023) Economic dispatch optimization considering operation cost and environmental constraints using the HBMO method. Energy Reports. 10: 1718-1725. https://doi.org/10.1016/j.egyr.2023.08.032

[15] Bai Y, Wu X, Xia A (2021) An enhanced multi-objective differential evolution algorithm for dynamic environmental economic dispatch of power system with wind power. Energy Sci Eng 9: 316-329. https://doi.org/10.1002/ese3.827

[16] Simon D (2009) A probabilistic analysis of a simplified biogeographybased optimization algorithm. http://academic.csuohio.edu/simond/bbo/simplified/bbosimplified.pdf

[17] Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12: 702-713. https://doi.org/10.1109/TEVC.2008.919004

[18] Du D, Simon D, Ergezer M (2009) Biogeography-based optimization combined with evolutionary strategy and immigration refusal. IEEE International Conference on Systems, Man, and Cybernetics. San Antonio, TX, 1023-1028. https://doi.org/10.1109/ICSMC.2009.5346055

[19] Ergezer M, Simon D, Du DW (2009) Oppositional biogeographybased optimization. IEEE Conference on Systems, Man, and Cybernetics, San Antonio, TX, 1035-1040. https://doi.org/10.1109/ICSMC.2009.5346043

[20] Ma H, Chen X (2009) Equilibrium species counts and migration model tradeoffs for biogeography-based optimization. 48th IEEE Conference on Decision and Control

[21] Zheng JG, Zhang CQ, Zhou YQ (2015) Artificial bee colony algorithm combined with grenade explosion method and Cauchy operator for global optimization. Math Probl Eng 1-14. https://doi.org/10.1155/2015/739437

[22] Sharma MK, Phonrattanasak P, Leeprechanon N (2015) Improved bees algorithm for dynamic economic dispatch considering prohibited operating zones. IEEE-Innovative Smart Grid Technologies - Asia (ISGT ASIA), 3-6 November. https://doi.org/10.1109/ISGT-Asia.2015.7386972

[23] Yang XS, Hosseini SSS, Gandomi AH (2012) Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput J 12: 1180-1186. https://doi.org/10.1016/j.asoc.2011.09.017

[24] Labbi Y, Ben Attous D, Mahdad B (2014) Artificial bee colony optimization for economic dispatch with valve point effect. Front Energy 8: 449-458. https://doi.org/10.1007/s11708-014-0316-8

[25] Sinha N, Chakrabarti R, Chattopadhyay PK (2003) Evolutionary programming techniques for economic load dispatch. IEEE Trans Evol Comput 7: 83-94. https://doi.org/10.1109/TEVC.2002.806788

[26] Sharma R, Samantaray P, Mohanty DP, et al. (2011) Environmental economic load dispatch using multi-objective differential evolution algorithm. International Conference on Energy, Automation, and Signal (ICEAS), India, 28-30 December. https://doi.org/10.1109/ICEAS.2011.6147132

[27] Pothiya S, Ngamroo I, Kongprawechnon W (2010) Ant colony optimization for economic dispatch problem with non-smooth cost functions. Electr Power Energy Syst 32: 478-487. https://doi.org/10.1016/j.ijepes.2009.09.016

[28] Yang Q, Wang J (2015) Multi-Level Wavelet Shannon Entropy-Based Method for Single-Sensor Fault Location, Entropy 17: 7101-7117. https://doi.org/10.3390/e17107101