Open Access Open Access  Restricted Access Subscription or Fee Access

Neural Network Based on Artificial Intelligence Solution for a Fast Economic Load Dispatch Using the Hybrid Lagrangian Method

Majed Jabri(1*), Aloui Helmi(2)

(1) Saudi Electricity Company, Saudi Arabia
(2) Gabes Engineering School (GES), Tunisia
(*) Corresponding author


DOI: https://doi.org/10.15866/irecon.v7i5.17794

Abstract


This paper comes up with a new approach in order to solve the Economic Load Dispatch problem by using a hybrid technique. This technique combines the artificial neural network with the Lagrangian multiplier method. The adaptive behavior of the controller is obtained through the action of the neural network on the Lagrangian multipliers’ parameters. The suggested technique has been implemented on 10 test-system units with different constraints like the spinning reserve, the generator capacity limits, the start-up cost, the prohibited zones, the minimum up and the minimum down time constraints and power balance. The results obtained from the proposed approach are compared with the ones of the conventional Lagrangian multiplier method. They prove the efficiency, the fast convergence with less computational time and, accordingly, the proficiency of the suggested approach.
Copyright © 2019 Praise Worthy Prize - All rights reserved.

Keywords


Economic Dispatch; Lagrangian Multiplier; Optimization; Neural Network

Full Text:

PDF


References


M. Mutlaq, S. Kumar, environmental economic dispatch of thermal power plants in Saudi Arabia: a case study, IEEE International Conference on Industrial & Systems Enginering, 187–192, 2019.
https://doi.org/10.1109/iasec.2019.8686538

Y. Tingfang, S. Sheng, Methodological priority list for unit commitment problem, IEEE International Conference on computation science software engineering, 176–179, 2008.
https://doi.org/10.1109/csse.2008.714

Z. X. Liang, J. D. Glover, A zoom feature for a dynamic programming solution to economic dispatch including transmission losses. IEEE Transaction Power System, vol. 7, pp. 544–550, 1992.
https://doi.org/10.1109/59.141757

M. Javadi, T. Amraee, Mixed integer linear formulation for under voltage load shedding to provide voltage stability, IET Generation Transmission & Distribution, 2018.
https://doi.org/10.1049/iet-gtd.2017.1118

J. Masoud, T. Amraee, Economic Dispatch: A Mixed-Integer Linear Model for Thermal Generating Units, IEEE International Conference on Environment and Electrical Engineering, pp. 105–112, 2018.
https://doi.org/10.1109/eeeic.2018.8493794

F. Moustafa, A. El-Rafei, M. Badra, Y. Abdelaziz, Application and performance comparison of variants of the firefly algorithm to the economic load dispatch problem, in Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics, pp. 147–15, 2017.
https://doi.org/10.1109/aeeicb.2017.7972401

T. Thang, Solving economic dispatch problem with piecewise quadratic cost functions using Lagrange multiplier theory, International Conference on Computer Technology and Development, pp. 359–363, 2011.

C. C. Columbus, K. Chandrasekaran, S. P. Simon, Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs, Applied Soft Computing, 12(1): 145–160, 2012.
https://doi.org/10.1016/j.asoc.2011.08.057

Q. Qin, S. Cheng, X. Chu, X. Lei, and Y. Shi, Solving non-convex/non-smooth economic load dispatch problems via an enhanced particle swarm optimization, Applied Soft Computing, vol. 59, pp. 229–242, 2017.
https://doi.org/10.1016/j.asoc.2017.05.034

S. Duman, N. Yorukeren, I. Altas, A novel modified hybrid psogsa based on fuzzy logic for non-convex economic dispatch problem with valve-point effect. International. Journal on Electrical and Power Energy System, Vol 64, 121–135, 2015.
https://doi.org/10.1016/j.ijepes.2014.07.031

A. Amudha, C. C. A. Rajan, Integrating gradient search logistic regression and artificial neural network for profitbased unit commitment, International Journal of Computational Intelligence Systems, 7(1): 90–104, 2014.
https://doi.org/10.1080/18756891.2013.862355

S. S. Kumar & V. Palanisamy, A dynamic programming based fast computation Hopfield neural network for unit commitment and economic dispatch, Electric Power System, 77(8): 917–925, 2007.
https://doi.org/10.1016/j.epsr.2006.08.005

Jabri, M., Aloui, H., Genetic Lagrangian Relaxation Selection Method for the Solution of Unit Commitment Problem, (2019) International Journal on Engineering Applications (IREA), 7 (2), pp. 59-64.
https://doi.org/10.15866/irea.v7i2.17022

D. Abdellah, L. Djamel, Power System Economic Dispatch Using Traditional and Neural Networks Programs, International Journal of Scientific & Engineering Research, Vol 3, Issue 4, 2012.

E. Castillo, B.G. Berdi, O. Romero & A. Betanzos, A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis, Journal of Machine Learning Research, 2006 .

C. C. Rajan, M. R. Mohan, An evolutionary programming-based tabu search method for solving the unit commitment problem, IEEE Transactions on Power Systems, vol. 19 n.1,2004, 577-585.
https://doi.org/10.1109/tpwrs.2003.821472

I. J. Raglend, C. Raghuveer, G. Avinash, N. P. Padhy, D.P. Kothari, Solution to profit based unit commitment problem using particle swarm optimization, Applied Soft Computing, vol. 10 n. 4, 2010, 1247–1256.
https://doi.org/10.1016/j.asoc.2010.05.006

S. A. Kazarlis, A. G. Bakirtzis, and V. Petridis, A genetic algorithm solution to the unit commitment problem, IEEE Trans. Power Syst., Vol. 11, No. 1, pp. 83-92, Feb. 1996.
https://doi.org/10.1109/59.485989

K. A. Juste, H. Kita, E. Tanaka, and J. Hasegawa, An evolutionary programming solution to the unit commitment problem, IEEE Trans. Power Syst., Vol. 14, pp. 1452– 1459, Nov. 1999.
https://doi.org/10.1109/59.801925

C. C. Ping, L. C. Wen, and L. C. Chang, Unit Commitment by Lagrangian Relaxation and Genetic Algorithms, IEEE Trans. Power Syst., Vol. 15, No. 2, pp. 707-714, May 2000.
https://doi.org/10.1109/59.867163

T. Senjyu, H. Yamashiro, K. Uezato, and T. Funabashi, A unit commitment problem by using genetic algorithm based on characteristic classification, in Proc. IEEE/Power Eng. Soc. Winter Meet., Vol. 1, pp. 58-63, 2002.
https://doi.org/10.1109/pesw.2002.984954

T. Manojkumar, N. Albert, Solution of Environmental/Economic (EED) Power Dispatch problem using Particle Swarm Optimization Technique, IEEE International Conference on Control, Power, Communication and Computing Technologies, pp. 347–351, 2018.
https://doi.org/10.1109/iccpcct.2018.8574256

X. Zhan, Y. Lou, All, Hybrid Artificial Bee Colony with Covariance Matrix Adaptation Evolution Strategy for Economic Load Dispatch, IEEE Congress on Evolutionary Computation, pp. 115–124, 2019.
https://doi.org/10.1109/cec.2019.8790221

A. Nasiruzzaman, G. Rabbani, Implementation of Genetic Algorithm and fuzzy logic in economic dispatch problem, IEEE Congress on Evolutionary Computation, pp. 360–365, 2008.
https://doi.org/10.1109/icece.2008.4769232

W. Sugsakarn, P. Damrongkulkamjorn, Economic Dispatch with Nonsmooth Cost function using Hybrid Method. Proceedings of ECTI-CON, pp.889-892, 2008.
https://doi.org/10.1109/ecticon.2008.4600573

Abu Siam, M., Mohamed, O., Al-Nazer, H., Comparative Study between Genetic Algorithms and Iterative Optimization for Economic Dispatch of Practical Power System, (2018) International Review of Electrical Engineering (IREE), 13 (2), pp. 128-136.
https://doi.org/10.15866/iree.v13i2.13870

Krishnamurthy, S., Kriger, C., Deivakkannu, G., Development of a Data Acquisition, Storage and Retrieval System for the Real-Time Solution of the Economic Dispatch Problem, (2016) International Review of Electrical Engineering (IREE), 11 (4), pp. 399-410.
https://doi.org/10.15866/iree.v11i4.8577

Afandi, A., Wibawa, A., Padmantara, S., Fujita, G., Triyana, W., Sulistyorini, Y., Miyauchi, H., Tutkun, N., EL-Shimy Mahmoud, M., Gao, X., Designed Operating Approach of Economic Dispatch for Java Bali Power Grid Areas Considered Wind Energy and Pollutant Emission Optimized Using Thunderstorm Algorithm Based on Forward Cloud Charge Mechanism, (2018) International Review of Electrical Engineering (IREE), 13 (1), pp. 59-68.
https://doi.org/10.15866/iree.v13i1.14687

Alshammari, B., Dynamic Environmental/Economic Power Dispatch with Prohibited Zones Using Improved Multi-Objective PSO Algorithm, (2016) International Review of Electrical Engineering (IREE), 11 (4), pp. 411-419.
https://doi.org/10.15866/iree.v11i4.8976

Mauledoux, M., Valencia, A., Avilés, O., Genetic Algorithm Optimization for DC Micro Grid Design, a Case of Study, (2017) International Review of Electrical Engineering (IREE), 12 (4), pp. 318-323.
https://doi.org/10.15866/iree.v12i4.11544

Rizk-Allah, R., Abdel Mageed, H., El-Sehiemy, R., Abdel Aleem, S., El Shahat, A., A New Sine Cosine Optimization Algorithm for Solving Combined Non-Convex Economic and Emission Power Dispatch Problems, (2017) International Journal on Energy Conversion (IRECON), 5 (6), pp. 180-192.
https://doi.org/10.15866/irecon.v5i6.14291

Magdy, G., Shabib, G., Abdel Elbaset, A., Kerdphol, T., Qudaih, Y., Bevrani, H., Mitani, Y., A Novel Design of Decentralized LFC to Enhance Frequency Stability of Egypt Power System Including Wind Farms, (2018) International Journal on Energy Conversion (IRECON), 6 (1), pp. 17-29.
https://doi.org/10.15866/irecon.v6i1.14516

Boukef, H., Benrejeb, M., Borne, P., Genetic Algorithm and Based Particle Swarm Optimization Comparison for Solving a Flow-Shop Multiobjective Scheduling Problem in Pharmaceutical Industries, (2018) International Journal on Engineering Applications (IREA), 6 (6), pp. 221-226.
https://doi.org/10.15866/irea.v6i6.17000


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2021 Praise Worthy Prize