Abstract
In power system process Economic Dispatch (ED) foremost typical task. Minimize the total fuel cost of the generating units is the key objective of this problem subjected to satisfying inequality and equality constraints. Also, it converted into nonconvex and nonlinear complicated optimization problem, due to some practical constraints like prohibited operating zones, transmission losses and ramp rate limits. Recently, particle swarm optimization (PSO) is one of the well-known algorithm for solving nonconvex optimization problems. But, particularly for multimodal problem it may suffer to trap at local minima. To overcome the issues of PSO, this paper offering an adapted particle swarm optimization (viz. aPSO) for solving ED problems. In aPSO, new factors for inertia, cognitive and social components (i.e. acceleration coefficients and inertia weight) are familiarized. As per the concerts these factors are dynamically changed and properly maintained the diversity of the PSO. The projected aPSO is implemented to solve three altered unit test structures (3, 6 and 15) of ED problem. The experimental results of aPSO compared with related algorithms. It shows that the performance of aPSO are enhanced than the other algorithms in terms of searching ability and rate of convergence.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wood AJ, Wollenberg BF (1996) Power generation. In: Operation and control, 2nd ed. Wiley, New York
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Opt 11:341–359
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, vol 4, pp 1942–1948
Das KN, Parouha RP (2015) An ideal tri-population approach for unconstrained optimization and applications. App Math Comput 256:666–701
Parouha RP, Das KN (2016) A novel hybrid optimizer for solving Economic Load Dispatch problem. Int J Electr Power Energ Syst 78:108–126
Chen Y, Li L, Peng H, Xiao J, Wu Q (2018) Dynamic multi-swarm differential learning particle swarm optimizer. Swarm Evol Comput 39:209–221
Espitia HE, Sofrony JI (2018) Statistical analysis for vortex particle swarm optimization. App Soft Comput 67:370–386
Yu H, Tan Y, Zeng J, Sun C, Jin Y (2018) Surrogate-assisted hierarchical particle swarm optimization. Inf Sci 454–455:59–72
Chen Y, Li L, Xiao J, Yang Y, Liang J, Li T (2018) Particle swarm optimizer with crossover operation. Eng App Art Intel 70:59–169
Parouha RP (2019) Nonconvex/nonsmooth economic load dispatch using modified time-varying particle swarm optimization. Comput Intel 1–28. https://doi.org/10.1111/coin.12210
Hosseini SA, Hajipour A, Tavakoli H (2019) Design and optimization of a CMOS power amplifier using innovative fractional-order particle swarm optimization. App Soft Comput 85:1–10
Kohler M, Vellasco MMBR, Tanscheit R (2019) PSO+: a new particle swarm optimization algorithm for constrained problems. App Soft Comput 85:1–26
Khajeh A, Ghasemi MR, Arab HG (2019) Modified particle swarm optimization with novel population initialization. J Inf Opt Sci 40(6):1167–1179
Ang KM, Lim WH, Isa NAM, Tiang SS, Wong CH (2020) A constrained multi-swarm particle swarm optimization without velocity for constrained optimization problems. Exp Syst App 140:1–23
Parouha RP, Verma P (2020) An innovative hybrid algorithm to solve nonconvex economic load dispatch problem with or without valve point effects. Int Trans Elect Energ Syst 34(1):1–67
Huynh DC, Dunnigan MW (2010) Parameter estimation of an induction machine using advanced particle swarm optimization algorithms. IET J Elec Power App 4:748–760
Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of IEEE international conference on evolutionary computation, Piscataway, New Jersey, pp 69–73
Alatas B, Akin E, Ozer AB (2009) Chaos embedded particle swarm optimization algorithms. Chaos Solitons Fractals 40:1715–1734
Lee T-Y, Chen C-L (2007) Unit commitment with probabilistic reserve: an IPSO approach. Energ Convers Manag 48(6):486–493
Selvakumar AI, Thanushkodi K (2007) A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Trans Power Syst 22:42–51
Chaturvedi KT, Pandit M, Srivastava L (2009) Particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch. Int J Electr Power Energ Syst 31:249–257
Khokhar B, Parmar KPS, Dahiya S (2012) An efficient particle swarm optimization with time varying acceleration coefficients to solve economic dispatch problem with valve point loading. Energ Power 2(4):74–80
Abdullah MN, Bakar AH, Rahim NA, Mokhlis H, Illias HA, Jamian JJ (2014) Modified particle swarm optimization with time varying acceleration coefficients for economic load dispatch with generator constraints. J Electr Eng Technol 9(1):15–26
Sinha N, Chakrabarti R, Chattopadhyay PK (2003) Evolutionary programming techniques for economic load dispatch. IEEE Trans Evol Comput 7:83–94
Zwe-Lee G (2003) Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans Power Syst 18:1187–1195
Acknowledgements
This research supported by DST-SERB Govt. of India; grant number CRG/2020/000817.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Parouha, R.P. (2023). Economic Dispatch Using Adapted Particle Swarm Optimization. In: Thakur, M., Agnihotri, S., Rajpurohit, B.S., Pant, M., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Lecture Notes in Networks and Systems, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-19-6525-8_39
Download citation
DOI: https://doi.org/10.1007/978-981-19-6525-8_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-6524-1
Online ISBN: 978-981-19-6525-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)