One of the main tasks solved in planning short-term and managing operational modes
of electric power systems (EPS) is the optimization of their network modes on the adjustable parameters.
For modern complex EPS, this task is often characterized by the multi-extremality of the objective
function, the appearance of discontinuous functions, the presence of initial information of a
probabilistic and partially uncertain nature. In such conditions, solving the problem by traditional algorithms using mainly linear and nonlinear programming methods, Lagrange, gradient, etc., is
associated with a number of difficulties in simplifying them and bringing them to a convenient form for
calculations. Such simplifications lead to a decrease in the expected effect of optimization. In this
regard, research is relevant on the development and implementation of algorithms for solving this
problem based on the use of artificial intelligence methods, in which a number of the listed difficulties
are easily overcome. The current existence of some algorithms for solving the problems of calculating
EPS modes using artificial intelligence methods cannot be considered sufficiently perfect. A
characteristic drawback is their lack of universality and the impossibility of using them for all cases.
This paper proposes a new method for optimizing the electrical grid modes by reactive power and node
voltage based on a genetic algorithm that has fast and reliable convergence of the iterative calculation
process. It effectively takes into account all types of simple and functional limitations, and overcomes
many shortcomings typical for traditional algorithms for solving this problem. The results of a study of
the effectiveness of the proposed method are presented using the example of optimizing the electrical
grid mode using the 14-node IEEE test scheme.
One of the main tasks solved in planning short-term and managing operational modes
of electric power systems (EPS) is the optimization of their network modes on the adjustable parameters.
For modern complex EPS, this task is often characterized by the multi-extremality of the objective
function, the appearance of discontinuous functions, the presence of initial information of a
probabilistic and partially uncertain nature. In such conditions, solving the problem by traditional algorithms using mainly linear and nonlinear programming methods, Lagrange, gradient, etc., is
associated with a number of difficulties in simplifying them and bringing them to a convenient form for
calculations. Such simplifications lead to a decrease in the expected effect of optimization. In this
regard, research is relevant on the development and implementation of algorithms for solving this
problem based on the use of artificial intelligence methods, in which a number of the listed difficulties
are easily overcome. The current existence of some algorithms for solving the problems of calculating
EPS modes using artificial intelligence methods cannot be considered sufficiently perfect. A
characteristic drawback is their lack of universality and the impossibility of using them for all cases.
This paper proposes a new method for optimizing the electrical grid modes by reactive power and node
voltage based on a genetic algorithm that has fast and reliable convergence of the iterative calculation
process. It effectively takes into account all types of simple and functional limitations, and overcomes
many shortcomings typical for traditional algorithms for solving this problem. The results of a study of
the effectiveness of the proposed method are presented using the example of optimizing the electrical
grid mode using the 14-node IEEE test scheme.
| № | Muallifning F.I.Sh. | Lavozimi | Tashkilot nomi |
|---|---|---|---|
| 1 | Gayibov .S. | DSc, Professor | Tashkent State Technical University, Tashkent city, Republic of Uzbekistan)* |
| 2 | Turmanova G.M. | PhD student, | Tashkent State Technical University, Tashkent city, Republic of Uzbekistan)* |
| № | Havola nomi |
|---|---|
| 1 | 1. Fazylov, Kh.F., & Nasyrov, T.Kh. (1999). SteadyState Modes of Electric Power Systems and Their Optimization. Tashkent: Moliya. 370 p. (in Rus) 2. Rudenko, Yu.N., & Semenova, V.A. (2000). Automation of Dispatch Control in Electric Power Industry. Edited by Yu.N. Rudenko. Moscow: MEI Publishing House. 648 p. (in Rus) 3. Nasyrov, T.Kh., & Gayibov, T.Sh. (2014). Theoretical Foundations of Power System Modes Optimization. Tashkent: Fan va texnologiya. 184 p. (in Uzb) 4. Gayibov, T.Sh. (2014). Methods and Algorithms for Optimization of Electric Power System Modes. Tashkent: TashSTU Publishing. 178 p. (in Uzb) 5. Bazaraa, M.S., Sherali, H.D., & Shetty, C.M. (2006). Nonlinear Programming: Theory and Algorithms. Wiley. 6. Farhat, I.A., & El-Hawary, M.E. (2009). Optimization methods applied for solving the short-term hydrothermal coordination problem. Electric Power Systems Research, 79, 1308-1320. https://doi.org/10.1016/j.epsr.2009.04.001 |
| 2 | 7. Carpentier, C. (1985). Optimal Power Flows: Uses, Methods and Developments. IFAC Proceedings Volumes, 18(7), 11-21. https://doi.org/10.1016/S1474- 6670(17)60410-5 8. Braspenning, P.J., Thuijsman, F., & Weijter, A.J.M.M. (1995). Artificial Neural Networks: An Introduction to ANN Theory and Practice. Springer, pp. 1- 100. 9. Abaci, K., & Yamacli, V. (2019). Hybrid Artificial Neural Network by Using Differential Search Algorithm for Solving Power Flow Problem. Advances in Electrical and Computer Engineering, 19(4), 57-64. https://doi.org/10.4316/AECE.2019.04007 10. Reimann, M. (2002). Ant Based Optimization in Goods Transportation. PhD Thesis. University of Vienna, Austria. 149 p. 11. Chakraborty, A., & Kar, A. (2017). Swarm intelligence: A review of algorithms. In Nature-Inspired Computing and Optimization (pp. 475-494). Springer, Cham. |
| 3 | 12. Tsai, H. (2020). Artificial bee colony directive for continuous optimization. Applied Soft Computing, 87. 13. Wang, X., Zhang, Y., Sun, X., Wang, Y., & Du, C. (2020). Multi-objective feature selection based on artificial bee colony: An acceleration approach with variable sample size. Applied Soft Computing, 88. 14. Nagnevitsky, M., & Kelareva, G. (2006). Genetic algorithms for maintenance scheduling in power systems. School of Engineering, University of Tasmania, Australia, pp. 217-222. 15. Gayibov, T.Sh., & Pulatov, B.M. (2021). Taking into account the constraints in power system mode optimization by genetic algorithms. E3S Web of Conferences, 264, 04045. https://doi.org/10.1051/e3sconf/202126404045 |
| 4 | 16. Gayibov, T., Pulatov, B., Latipov, S., & Turmanova, G. (2019). Power System Optimization in Terms of Uncertainty of Initial Information. E3S Web of Conferences, 139, 01031. https://doi.org/10.1051/e3sconf/201913901031 17. Gayibov, T., & Pulatov, B. (2020). Optimization of Short-term Modes of Hydrothermal Power System. E3S Web of Conferences, 209, 07014. https://doi.org/10.1051/e3sconf/202020907014 |