A New Method of Artificial to Solve the Optimization Problems without the Violated Constraints
Main Article Content
Abstract
There are some problems in optimization that cannot be derived mathematically. Various methods have been developed to solve the optimization problem with various functional forms, whether differentiated or not, to overcome the problem, which are known as artificial methods such as artificial neural networks, particle swarm optimization, and genetic algorithms. In the literature, it is said that there is an artificial method that frequently falls to the minimum local solution. The local minimum results are proof that the artificial method is not accurate. This paper proposes the Large to Small Area Technique for power system optimization, which works based on reducing feasible areas. This method can work accurately, which that never violates all constraints in reaching the optimal point. However, to conclude that this method is superior to others, logical arguments and tests with mathematical simulations are needed. This proposed method has been tested with 24 target points using ten functions consisting of a quadratic function and a first-order function. The results showed that this method has an average accuracy of 99.97% and an average computation time of 62 seconds. The proposed technique can be an alternative in solving the economic dispatch problem in the power system.
Downloads
Download data is not yet available.
Article Details
How to Cite
[1]
J. Raharjo, H. Zein, A. Soeprijanto, and K. B. Adam, “A New Method of Artificial to Solve the Optimization Problems without the Violated Constraints”, INFOTEL, vol. 13, no. 2, pp. 31-37, May 2021.
Section
Informatics
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
References
[1] S. Sabach and M. Teboulle, Lagrangian methods for composite optimization, 1st ed., vol. 20. Elsevier B.V., 2019.
[2] D. M. Devia Narvaez, G. C. Velez, and D. F. Devia Narvaez, “Application of the gradient method in the economic dispatch,” Contemp. Eng. Sci., vol. 11, no. 96, pp. 4761–4768, 2018, doi: 10.12988/ces.2018.89513.
[3] S. Pan, J. Jian, H. Chen, and L. Yang, “A full mixed-integer linear programming formulation for economic dispatch with valve-point effects, transmission loss and prohibited operating zones,” Electr. Power Syst. Res., vol. 180, no. December 2019, p. 106061, 2020, doi: 10.1016/j.epsr.2019.106061.
[4] Z. Feng, W. Niu, S. Wang, C. Cheng, and Z. Song, “Mixed integer linear programming model for peak operation of gas-fired generating units with disjoint-prohibited operating zones,” Energies, vol. 12, no. 11, 2019, doi: 10.3390/en12112179.
[5] N. Awadah, R. A. S. Ali, S. AlBaijan, G. Al-Qallaf, M. Al-Hassawi, and S. Kasap, “Dynamic Programming approach to unit commitment problem for Kuwait power generations,” Proc. Int. Conf. Ind. Eng. Oper. Manag., vol. 2018, no. JUL, pp. 1939–1947, 2018.
[6] H. Zein, Y. Sabri, and A. Mashar, “Implementation of electricity competition framework with economic dispatch direct method,” Telkomnika, vol. 10, no. 4, pp. 667–674, 2012, doi: 10.12928/telkomnika.v10i4.854.
[7] B. M. S. M. Ramadan, T. Logenthiran, R. T. Naayagi, and C. Su, “Accelerated Lambda Iteration Method for solving economic dispatch with transmission line losses management,” IEEE PES Innov. Smart Grid Technol. Conf. Eur., no. Mli, pp. 138–143, 2016, doi: 10.1109/ISGT-Asia.2016.7796375.
[8] M. Abu Siam, O. Mohamed, and H. Al-Nazer, “Comparative Study between Genetic Algorithms and Iterative Optimization for Economic Dispatch of Practical Power System,” Int. Rev. Electr. Eng., vol. 13, no. 2, p. 128, Apr. 2018, doi: 10.15866/iree.v13i2.13870.
[9] L. Daniel and K. T. Chaturvedi, “A Crazy Particle Swarm Optimization with Time Varying Acceleration Coefficients for Economic Load Dispatch,” Int. J. Eng. Adv. Technol., vol. 9, no. 2, pp. 1205–1213, 2019, doi: 10.35940/ijeat.b3614.129219.
[10] P. Wongchai and S. Phichaisawat, “Load feasible region determination by using adaptive particle swarm optimization,” Eng. J., vol. 23, no. 6, pp. 239–263, 2019, doi: 10.4186/ej.2019.23.6.239.
[11] K. Chayakulkheeree, V. Hengsritawat, and P. Nantivatana, “Particle swarm optimization based equivalent circuit estimation for on-service three-phase induction motor efficiency assessment,” Eng. J., vol. 21, no. 6 Special Issue, pp. 101–110, 2017, doi: 10.4186/ej.2017.21.6.101.
[12] B. M. Hussein and A. S. Jaber, “Unit commitment based on modified firefly algorithm,” Meas. Control (United Kingdom), vol. 53, no. 3–4, pp. 320–327, 2020, doi: 10.1177/0020294019890630.
[13] A. Y. Abdelaziz, E. S. Ali, and S. M. Abd Elazim, “Combined economic and emission dispatch solution using Flower Pollination Algorithm,” Int. J. Electr. Power Energy Syst., vol. 80, pp. 264–274, 2016, doi: 10.1016/j.ijepes.2015.11.093.
[14] G. Villarrubia, J. F. De Paz, P. Chamoso, and F. De la Prieta, “Artificial neural networks used in optimization problems,” Neurocomputing, vol. 272, pp. 10–16, Jan. 2018, doi: 10.1016/j.neucom.2017.04.075.
[15] A. N. Afandi et al., “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,” Int. Rev. Electr. Eng., vol. 13, no. 1, p. 59, Feb. 2018, doi: 10.15866/iree.v13i1.14687.
[16] V. P. Sakthivel, M. Suman, and P. D. Sathya, “Nonconvex Economic Environmental Load Dispatch Using Fuzzy Based Squirrel Search Algorithm,” Int. J. Energy Convers., vol. 8, no. 2, p. 61, Mar. 2020, doi: 10.15866/irecon.v8i2.18593.
[17] V. P. Sakthivel, M. Suman, and P. D. Sathya, “Environmental/Economic Dispatch Problem: Coulomb’s and Franklin’s Laws Based Optimization Algorithm,” Int. Rev. Electr. Eng., vol. 15, no. 5, p. 421, Oct. 2020, doi: 10.15866/iree.v15i5.18568.
[18] “Application of Whale Optimization Algorithm for Environmental Constrained Economic Dispatch of Fixed Head Hydro-Wind-Thermal Power System,” Int. J. Eng. Adv. Technol., vol. 9, no. 1, pp. 5608–5616, Oct. 2019, doi: 10.35940/ijeat.A2261.109119.
[19] F. C. K. and H. Vennila, “Economic and Emission Dispatch using Whale Optimization Algorithm (WOA),” Int. J. Electr. Comput. Eng., vol. 8, no. 3, p. 1297, Jun. 2018, doi: 10.11591/ijece.v8i3.pp1297-1304.
[20] H. Hardiansyah, J. Junaidi, and Y. Yandri, “Combined Economic Emission Dispatch Solution using Simulated Annealing Algorithm,” IOSR J. Electr. Electron. Eng., vol. 11, no. 05, pp. 141–148, May 2016, doi: 10.9790/1676-110502141148.
[21] M. Li, W. Du, and F. Nian, “An adaptive particle swarm optimization algorithm based on directed weighted complex network,” Math. Probl. Eng., vol. 2014, 2014, doi: 10.1155/2014/434972.
[22] J. Raharjo, H. Zein, and K. B. Adam, “Optimal economic load dispatch with prohibited operating zones using large to small area technique,” Int. J. Energy Convers., vol. 9, no. 1, pp. 29–34, 2021, doi: 10.15866/irecon.v9i1.19548.
[23] R. Korah and J. R. P. Perinbam, “A Novel Coarse-to-Fine Search Algorithm for Motion Estimation,” in 2006 IEEE International Conference on Industrial Technology, 2006, pp. 1121–1126, doi: 10.1109/ICIT.2006.372325.
[2] D. M. Devia Narvaez, G. C. Velez, and D. F. Devia Narvaez, “Application of the gradient method in the economic dispatch,” Contemp. Eng. Sci., vol. 11, no. 96, pp. 4761–4768, 2018, doi: 10.12988/ces.2018.89513.
[3] S. Pan, J. Jian, H. Chen, and L. Yang, “A full mixed-integer linear programming formulation for economic dispatch with valve-point effects, transmission loss and prohibited operating zones,” Electr. Power Syst. Res., vol. 180, no. December 2019, p. 106061, 2020, doi: 10.1016/j.epsr.2019.106061.
[4] Z. Feng, W. Niu, S. Wang, C. Cheng, and Z. Song, “Mixed integer linear programming model for peak operation of gas-fired generating units with disjoint-prohibited operating zones,” Energies, vol. 12, no. 11, 2019, doi: 10.3390/en12112179.
[5] N. Awadah, R. A. S. Ali, S. AlBaijan, G. Al-Qallaf, M. Al-Hassawi, and S. Kasap, “Dynamic Programming approach to unit commitment problem for Kuwait power generations,” Proc. Int. Conf. Ind. Eng. Oper. Manag., vol. 2018, no. JUL, pp. 1939–1947, 2018.
[6] H. Zein, Y. Sabri, and A. Mashar, “Implementation of electricity competition framework with economic dispatch direct method,” Telkomnika, vol. 10, no. 4, pp. 667–674, 2012, doi: 10.12928/telkomnika.v10i4.854.
[7] B. M. S. M. Ramadan, T. Logenthiran, R. T. Naayagi, and C. Su, “Accelerated Lambda Iteration Method for solving economic dispatch with transmission line losses management,” IEEE PES Innov. Smart Grid Technol. Conf. Eur., no. Mli, pp. 138–143, 2016, doi: 10.1109/ISGT-Asia.2016.7796375.
[8] M. Abu Siam, O. Mohamed, and H. Al-Nazer, “Comparative Study between Genetic Algorithms and Iterative Optimization for Economic Dispatch of Practical Power System,” Int. Rev. Electr. Eng., vol. 13, no. 2, p. 128, Apr. 2018, doi: 10.15866/iree.v13i2.13870.
[9] L. Daniel and K. T. Chaturvedi, “A Crazy Particle Swarm Optimization with Time Varying Acceleration Coefficients for Economic Load Dispatch,” Int. J. Eng. Adv. Technol., vol. 9, no. 2, pp. 1205–1213, 2019, doi: 10.35940/ijeat.b3614.129219.
[10] P. Wongchai and S. Phichaisawat, “Load feasible region determination by using adaptive particle swarm optimization,” Eng. J., vol. 23, no. 6, pp. 239–263, 2019, doi: 10.4186/ej.2019.23.6.239.
[11] K. Chayakulkheeree, V. Hengsritawat, and P. Nantivatana, “Particle swarm optimization based equivalent circuit estimation for on-service three-phase induction motor efficiency assessment,” Eng. J., vol. 21, no. 6 Special Issue, pp. 101–110, 2017, doi: 10.4186/ej.2017.21.6.101.
[12] B. M. Hussein and A. S. Jaber, “Unit commitment based on modified firefly algorithm,” Meas. Control (United Kingdom), vol. 53, no. 3–4, pp. 320–327, 2020, doi: 10.1177/0020294019890630.
[13] A. Y. Abdelaziz, E. S. Ali, and S. M. Abd Elazim, “Combined economic and emission dispatch solution using Flower Pollination Algorithm,” Int. J. Electr. Power Energy Syst., vol. 80, pp. 264–274, 2016, doi: 10.1016/j.ijepes.2015.11.093.
[14] G. Villarrubia, J. F. De Paz, P. Chamoso, and F. De la Prieta, “Artificial neural networks used in optimization problems,” Neurocomputing, vol. 272, pp. 10–16, Jan. 2018, doi: 10.1016/j.neucom.2017.04.075.
[15] A. N. Afandi et al., “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,” Int. Rev. Electr. Eng., vol. 13, no. 1, p. 59, Feb. 2018, doi: 10.15866/iree.v13i1.14687.
[16] V. P. Sakthivel, M. Suman, and P. D. Sathya, “Nonconvex Economic Environmental Load Dispatch Using Fuzzy Based Squirrel Search Algorithm,” Int. J. Energy Convers., vol. 8, no. 2, p. 61, Mar. 2020, doi: 10.15866/irecon.v8i2.18593.
[17] V. P. Sakthivel, M. Suman, and P. D. Sathya, “Environmental/Economic Dispatch Problem: Coulomb’s and Franklin’s Laws Based Optimization Algorithm,” Int. Rev. Electr. Eng., vol. 15, no. 5, p. 421, Oct. 2020, doi: 10.15866/iree.v15i5.18568.
[18] “Application of Whale Optimization Algorithm for Environmental Constrained Economic Dispatch of Fixed Head Hydro-Wind-Thermal Power System,” Int. J. Eng. Adv. Technol., vol. 9, no. 1, pp. 5608–5616, Oct. 2019, doi: 10.35940/ijeat.A2261.109119.
[19] F. C. K. and H. Vennila, “Economic and Emission Dispatch using Whale Optimization Algorithm (WOA),” Int. J. Electr. Comput. Eng., vol. 8, no. 3, p. 1297, Jun. 2018, doi: 10.11591/ijece.v8i3.pp1297-1304.
[20] H. Hardiansyah, J. Junaidi, and Y. Yandri, “Combined Economic Emission Dispatch Solution using Simulated Annealing Algorithm,” IOSR J. Electr. Electron. Eng., vol. 11, no. 05, pp. 141–148, May 2016, doi: 10.9790/1676-110502141148.
[21] M. Li, W. Du, and F. Nian, “An adaptive particle swarm optimization algorithm based on directed weighted complex network,” Math. Probl. Eng., vol. 2014, 2014, doi: 10.1155/2014/434972.
[22] J. Raharjo, H. Zein, and K. B. Adam, “Optimal economic load dispatch with prohibited operating zones using large to small area technique,” Int. J. Energy Convers., vol. 9, no. 1, pp. 29–34, 2021, doi: 10.15866/irecon.v9i1.19548.
[23] R. Korah and J. R. P. Perinbam, “A Novel Coarse-to-Fine Search Algorithm for Motion Estimation,” in 2006 IEEE International Conference on Industrial Technology, 2006, pp. 1121–1126, doi: 10.1109/ICIT.2006.372325.