Review on Economic Load Dispatch and Associated Artificial Intelligence Algorithms
DOI:
https://doi.org/10.24113/ijoscience.v7i3.370Keywords:
GA, ELD, Neural Network, Power SystemAbstract
In a practical power system, power plants are not equidistant from the distribution center and their fuel cost is different. Under normal operating conditions, the production capacity is even greater than the required total load and losses. Therefore, there are many options for planning the build. In an interconnected network, the objective is to determine the planning of the active and reactive power of each system in order to minimize operating costs. This means that the active and reactive power of the generator can vary within certain limits in order to cover a certain load requirement with minimal fuel costs. This is called the optimal power flow problem. This paper provides an overview of AI-based algorithms, genetic algorithms and their applications with cost-effective charge transport.
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References
Chowdhury, B.H. and Rahman, S. A review of recent advances in economic dispatch. IEEE transaction on power system. Nov. 1990. Vol. 5. No. 4. pp. 1248-1259
Deschamps, D .. Op timization in power system planning. I n: El-Abiad, AH. Ed. Power system analysis and planning. London: Hemishpere Publishing Corporation. 1981. Pp.201-208
Bakirtzis, A. etal. Genetic algorithm solution to the economic dispatch problem. IEE proceedings- genera tion, transmission and distributio n. Jul 1994. Volume: 141. Issue: 4. pp.: 377-382
Lee, K.Y. etal. Adaptive hopfield neural network for economic dispatch. IEEE transactions on power systems. Vol. 1 8, NO. 2. Feb. 2003 pp. 519-529.
Simopoulos, D., and Contaxis, G. 2004. Unit commitment with ramp rate constraint u sing stimulated annealin g algorithm. IEEE Melecon. May 12-15. Dubrovnik, Croatia. Pp. 845-849.
Zhi-xinZheng, Jun-qing Li, Hong-yan Sang. A hybrid invasive weed optimization algorithm for the economic load dispatch problem in power systems[J]. Mathematical Biosciences and Engineering, 2019, 16(4): 2775-2794. doi: 10.3934/mbe.2019138.
Di Y, Fe M, Wang L, Wu W. Multi-objective optimization for economic emission dispatch using an improved multi-objective binary differential evolution algorithm. Energy Procedia; 2014, 61:p.2016 – 2021.
Bhattacharjee K, Bhattacharya A, Dey SH. Backtracking search optimization based economic environmental power dispatch problems. Electrical Power and Energy Systems; 2015, 73: 830–842.
Abdelaziz AY, Ali ES, Elazim SM. Combined economic and emission dispatch solution using Flower Pollination Algorithm. Electrical Power and Energy Systems; 2016, 80: 264–274.
R M. K. Bavisetti and T. KranthiKiran, “Optimization Of Combined Economic and Emission Dispatch Problem –A Comparative Study for 30 Bus Systems.” IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) 2012; Vol 2, pp. 37–43.
P.K. Singhal, R. Naresh, V. Sharma and N. Goutham Kumar, “Enhanced lambda iteration algorithm for the solution of large scale economic dispatch problem”, Recent Advances and Innovations in Engineering (ICRAIE), 2014, vol., no., pp. 1, 6, 9-11 May 2014. doi: https://doi.org/10.1109/ICRAIE.2014.6909294.
X.P. Wang, L.P. Cao, Genetic Algorithms—Theory, Application and Software Realization, Xi’an Jiaotong University, Xi’an, China, 1998.
http:// en.wikipedia.org/wiki/Genetic_algorithm
Mitchell M., “An Introduction to Genetic Algorithm”, MIT Press, 1998.
Singh S. P., Bhullar S., “Hybrid Approach to Economic Load Dispatch”, National Conference on Artificial Intelligence and Agents: Theory & Applications, IIT (BHU) Varanasi, Dec. 2011.
Arunpreet Kaur, Harinder Pal Singh, Abhishek Bhardwaj, “Analysis of Economic Load Dispatch Using Genetic Algorithm”,Interational Journal Of Application or Innovation in Engineering & Management, Volume 3,Issue 3, March 2014.
Sulaiman M.H., Mustafa M.W., Zakaria Z.N, Aliman O. and Rahim A. S. R., “Firefly Algorithm Technique for Solving Economic Dispatch Problem”, IEEE International Power Engineering and optimization conference, Malaysia, 6-7, , pp. 90-95, June, 2012.
Amoli N.A., Jadid S., Shayanfar H. A., Barzipour F., “Solving Economic Dispatch Problem With Cubic Fuel Cost Function by Firefly Algorithm”, ICTPE conference on Technical Physical Problems of Power Engineering , vol. 1, pp 1-5, 2012.
YounesMimoun , “Environmental/Economic power Dispatch Problem/ Renewable Energy Using Firefly Algorithm”, International Conference on Environmental, Energy, Ecosystem and Development, pp 170-176, 2013.
Swarnkar K.K., AS.P, “Economic load Dispatch Problem with Reduce Power losses using Firefly Algorithm”, Journal of Advanced computer science and technology, vol. 1, pp 42-56, 2012.
Niknam T., Azizipanah A., Roosta R., “Reserve Constrained Dynamic Economic Dispatch: A New Fast Self-Adaptive Modified Firefly Algorithm”, Systems Journal, vol.6, no.4, pp. 635- 646, December 2012.
Apostolopoulos T. and Valchos A., “Application of Firefly Algorithm for Solving the Economic Emission Load Dispatch Problem”, International Journal of Combitronics, volume 2011. 23 pages, doi:10.1155/2011/523806.
Reddy S. and Reddy M. D., “Economic Load Dispatch Using Firefly Algorithm”, International Journal of Engineering Research and Application, vol. 2, issue 4, pp. 2325 2330, July 2012.
Abedinia O., Amjady N., Naderi M.S., “Multi-objective Environmental/Economic Dispatch using firefly technique,” 11th International Conference on Environment and Electrical Engineering (EEEIC), 18-25 May 2012, pp. 461-466, 2012.
Sulaiman M. H., Daniyal H., Mustafa M. W., “ Modified Firefly Algorithm In Solving Economic Dispatch Problem With Practical Constraints”, IEEE International Conference on Power and Energy, PECon, Kota Kinabalu, Malaysia, pp. 157- 161, 2012.
Dekhici L., Belkadi K., Lamosi B. P., “ Firefly Algorithm for Economic Power Dispatching With Pollutants Emission”, ImformaticaEconomica, vol. 16, issue 2, pp 45 57, 2012.
Rao C.V. G.K. and Yesuratnam G., “Big Bang And Big Crunch And Firefly Algorithm Application And Comparison To Optimal Power Flow With Continuous And Discrete Control Variables”, International Journal On Electrical Engineering And Informatics, vol. 4, issue 4, pp. 575-583, December 2012.
D.E.Goldberg, “Genetic algorithms in search, optimization and machine learning”, (Book ), Addison-Wesley, 1989.
Randy L.Haupt and Sue Ellen Haupt, “Practical genetic algorithms”, (Book ), John Wiley and sons, second edition, 2004
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