Squirrel Search Optimization for Non-convex Multi-area Economic Dispatch

Document Type : Original Article

Authors

1 Department of Electrical and Electronics Engineering, Government College of Engineering, Dharmapuri, India

2 Department of Electronics and Communication Engineering, FEAT, Annamalai University, Chidambaram, India

Abstract

Multi-area economic load dispatch (MAELD) decides the measure of power that can be fiscally generated in one area and transfered to another area. The goal of MAELD is to determine the most prudent production arrangement that could deliver the nearby power requirement without violating tie-line limits. This study presents a new swarm algorithm called as squirrel search optimization (SSO) to solve the MAELD problems. The impacts of transmission losses, prohibited operating zones, valve point loading and multi-fuel alternatives are additionally contemplated. SSO impersonates the searching conduct of flying squirrels which depends on the dynamic bouncing and skimming procedures. To demonstrate the potency of the suggested approach, it is examined on three different test systems for solving the MAELD problems. Comparative examinations are performed to analyze the adequacy of the suggested SSO approach with exchange market algorithm and different strategies revealed in the literature. The experimental results show that the proposed SSO approach is equipped for acquiring preferred quality solutions over the other existing strategies.

Keywords


Jayabarathi, T., Sadasivam, G., and Ramachandran, V., “Evolutionary programming based multi-area economic dispatch with tie line constraints”, Electric Machines & Power Systems, Vol. 28, No. 12, (2000), 1165-1176. doi: 10.1080/073135600449044.
Manoharan, P.S., Kannan, P.S., Baskar, S., and Iruthayarajan, M., “Evolutionary algorithm solution and KKT based optimality verification to multi-area economic dispatch”, International Journal of Electrical Power & Energy Systems, Vol. 31, No. 7-8, (2009), 365-73. doi: 10.1016/j.ijepes.2009.03.010.
Sharma, M., Manjaree, P., and Laxmi. S., “Reserve constrained multi-area economic dispatch employing differential evolution with timevarying mutation”, International journal of Electrical Power & Energy Systems, Vol. 33, No. 3, (2011), 753-66. doi: 10.1016/j.ijepes.2010.12.033.
Somasundaram P., and Jothi Swaroopan, N.M., “Fuzzified Particle Swarm Optimization Algorithm for Multi-area Security Constrained Economic Dispatch”, Electric Power Components and Systems, Vol. 39, No. 10, (2011), 979-990. doi: 10.1080/15325008.2011.552094.
Basu, M., “Artificial bee colony optimization for multi-area economic dispatch,” International journal of Electrical Power & Energy Systems, Vol. 49, (2013), 181-187. doi:10.1016/j.ijepes.2013.01.004.
Basu, M., “Teaching–learning-based optimization algorithm for multi-area economic dispatch”, Energy, Vol. 68, (2014), 21-28. doi: 10.1016/j.energy.2014.02.064.
Basu, M., “Fast Convergence Evolutionary Programming for Multi-area Economic Dispatch”, Electric Power Components and Systems, Vol. 45, No. 15, (2017), 1629-1637. doi: 10.1080/15325008.2017.1376234.
Nguyen, K.P., Dinh, N.D., and Fujita, G., “Multi-area economic dispatch using hybrid cuckoo search algorithm”, In: 50th International Universities Power Engineering Conference (UPEC), Stoke on Trent, UK; (2015), 1-6. doi:10.1109/UPEC.2015.7339777.
Ghasemi, M., Aghaei, J.,  Akbari, E.,  Ghavidel, S., and Li, L., “A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems,” Energy, Vol. 107, (2016), 182–195. doi: 10.1016/j.energy.2016.04.002.
Zhang, P., Ma, W., and Dong, Y., “Multi-area economic dispatching using improved grasshopper optimization algorithm”, Evolving Systems, (2019). doi: 10.1007/s12530-019-09320-6.
Valinejad, J., Oladi, Z., Barforoushi, T., and Parvania, M., “Stochastic unit commitment in the presence of demand response program under uncertainties”, International Journal of Engineering, Transactions B: Applications, Vol. 30, No. 8, (2017), 1134-1143. doi: 10.5829/ije.2017.30.08b.04.
Razzaghi, T., and Kianfar, F., “The optimal energy carriers’ substitutes in thermal power plants: A fuzzy linear programming model”, International Journal of Engineering, Transactions C: Aspects, Vol. 25, No. 1, (2012), 55-66. doi:10.5829/idosi.ije.2012.25.01c.07.
Bagheri, A., Sadafi, M., and Safikhani, H., “Multi-objective optimization of solar thermal energy storage using hybrid of particle swarm optimization, multiple crossover and mutation operator”, International Journal of Engineering, Transactions B: Applications, Vol. 24, No. 3, (2011), 367-376. doi:10.5829/idosi.ije.2011.24.04b.07.
Fathollahi-Fard, A.M., Hajiaghaei-Keshteli, M., and Tavakkoli-Moghaddam, R., “Red deer algorithm (RDA): a new nature-inspired meta-heuristic,” Soft Computing, Vol. 24, (2020), 14637-14665. doi: 10.1007/s00500-020-04812-z.
Fathollahi-Fard, A.M., Azari, M.N., and Hajiaghaei-Keshteli, M., “An improved red deer algorithm to address a direct current brushless motor design problem,” Scientia Iranica, (2019). doi: 10.24200/SCI.2019.51909.2419.