Optimization of Optimal Power Flow considering Location of FACTS Devices using Partial Reinforcement Optimizer

Abstract

Optimal power flow (OPF) is the most addressed modern power system planning and operating optimization problem. The complexity of the OPF problem is quite high due to constraints. It becomes a very difficult and high complexity optimization problem with the inclusion of the optimal location and rating of flexible AC transmission system (FACTS) devices. Therefore, in order to obtain the optimal solution for the problem, it is necessary to use the most suitable meta-heuristic search (MHS) algorithm for the structure of OPF problem. In this paper, an up-to-date and strong MHS algorithm known as partial reinforcement optimizer (PRO) were used to solve the OPF problem considering optimal location and rating of the multi-types FACTS devices. The objectives considered in the study were minimization of total cost, minimization of total cost with valve-point loading effect, and minimization of the real power loss. In the simulation studies, four case studies were solved by PRO algorithm and its three rivals such as dingo optimization algorithm, evolutionary mating algorithm, and snow geese algorithm. According to the results of the case studies, PRO algorithm obtained the best solution among them. The performance of PRO algorithm were evaluated using Friedman and Wilcoxon tests. The Friedman test results show that PRO algorithm achieved the best rank first with 1.2333 score value among them. In summary, PRO algorithm achieved a superior performance in solving these case studies.

Keywords

• Optimization
• Partial reinforcement optimizer
• Optimal power flow
• FACTS devices

References

1. [1] S. Duman, “Solution of the optimal power flow problem considering FACTS devices by using lightning search algorithm”, Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 43, pp. 969-997, Dec. 2019.
2. [2] M. H. Sulaiman and Z. Mustaffa, “Optimal placement and sizing of FACTS devices for optimal power flow using metaheuristic optimizers”, Results in Control and Optimization, vol. 8, 100145, Sep. 2022.
3. [3] A. Mukherjee and V. Mukherjee, “Solution of optimal power flow with FACTS devices using a novel oppositional krill herd algorithm”, International Journal of Electrical Power & Energy Systems, vol. 78, pp. 700-714, June 2016.
4. [4] E. Naderi, M. Pourakbari-Kasmaei, and H. Abdi, “An efficient particle swarm optimization algorithm to solve optimal power flow problem integrated with FACTS devices”, Applied Soft Computing, vol. 80, pp. 243-262, July 2019.
5. [5] J. B. Edward, N. Rajasekar, K. Sathiyasekar, N. Senthilnathan, and R. Sarjila, “An enhanced bacterial foraging algorithm approach for optimal power flow problem including FACTS devices considering system loadability”, ISA Transactions, vol. 52, no. 5, pp. 622-628, Sep. 2013.
6. [6] D. Prasad and V. Mukherjee, “A novel symbiotic organisms search algorithm for optimal power flow of power system with FACTS devices”, Engineering Science and Technology, an International Journal, vol. 19, no. 1, pp. 79-89, March 2016.
7. [7] B. Mahdad, “Improvement optimal power flow solution considering SVC and TCSC controllers using new partitioned ant lion algorithm”, Electrical Engineering, vol. 102, no. 4, pp. 2655-2672, Dec. 2020.
8. [8] M. Ebeed, A. Mostafa, M. M. Aly, F. Jurado, and S. Kamel, “Stochastic optimal power flow analysis of power systems with wind/PV/TCSC using a developed Runge Kutta optimizer”, International Journal of Electrical Power & Energy Systems, vol. 152, 109250, Oct. 2023.

9. [9] M. A. Taher, S. Kamel, F. Jurado, and M. Ebeed, “Optimal power flow solution incorporating a simplified UPFC model using lightning attachment procedure optimization”, International Transactions on Electrical Energy Systems, vol. 30, no. 1, e12170, Jan. 2020.
10. [10] P. P. Biswas, P. Arora, R. Mallipeddi, P. N. Suganthan, and B. K. Panigrahi, “Optimal placement and sizing of FACTS devices for optimal power flow in a wind power integrated electrical network”, Neural Computing and Applications, vol. 33, pp. 6753-6774, June 2021.
11. [11] A. A. Ahmad and R. Sirjani, “Optimal placement and sizing of multi-type FACTS devices in power systems using metaheuristic optimisation techniques: An updated review”, Ain Shams Engineering Journal, vol. 11, no. 3, pp. 611-628, Sep. 2020.
12. [12] S. R. Inkollu and V. R. Kota, “Optimal setting of FACTS devices for voltage stability improvement using PSO adaptive GSA hybrid algorithm”, Engineering Science and Technology, An International Journal, vol. 19, no. 3, pp. 1166-1176, Sep. 2016.
13. [13] W. S. Sakr, R. A. El‐Sehiemy, and A. M. Azmy, “Optimal allocation of TCSCs by adaptive DE algorithm”, IET Generation, Transmission & Distribution, vol. 10, no. 15, pp. 3844-3854, Nov. 2016.
14. [14] S. R. Inkollu and V. R. Kota, “Optimal setting of FACTS devices for voltage stability improvement using PSO adaptive GSA hybrid algorithm”, Engineering Science and Technology, An International Journal, vol. 19, no. 3, pp. 1166-1176, Sep. 2016.
15. [15] O. Ziaee and F. F. Choobineh, “Optimal location-allocation of TCSC devices on a transmission network”, IEEE Transactions on Power Systems, vol. 32, no. 1, pp. 94-102, Jan. 2017.
16. [16] S. Raj and B. Bhattacharyya, “Optimal placement of TCSC and SVC for reactive power planning using Whale optimization algorithm”, Swarm and Evolutionary Computation, vol. 40, pp. 131-143, June 2018.
17. [17] R. Agrawal, S. K. Bharadwaj, and D. P. Kothari, “Population based evolutionary optimization techniques for optimal allocation and sizing of Thyristor Controlled Series Capacitor”, Journal of Electrical Systems and Information Technology, vol. 5, no. 3, pp. 484-501, Dec. 2018.
18. [18] N. H. Khan, Y. Wang, D. Tian, R. Jamal, S. Iqbal, M. A. A. Saif, and M. Ebeed, “A novel modified lightning attachment procedure optimization technique for optimal allocation of the FACTS devices in power systems”, IEEE Access, vol. 9, pp. 47976-47997, Feb. 2021.
19. [19] K. Nusair, F. Alasali, A. Hayajneh, and W. Holderbaum, “Optimal placement of FACTS devices and power‐flow solutions for a power network system integrated with stochastic renewable energy resources using new metaheuristic optimization techniques”, International Journal of Energy Research, vol. 45, no. 13, pp. 18786-18809, Oct. 2021.
20. [20] A. A. Mohamed, S. Kamel, M. H. Hassan, M. I. Mosaad, and M. Aljohani, “Optimal power flow analysis based on hybrid gradient-based optimizer with moth–flame optimization algorithm considering optimal placement and sizing of FACTS/wind power”, Mathematics, vol. 10, no. 3, pp. 361, Jan. 2022.
21. [21] M. H. Hassan, F. Daqaq, , S. Kamel, A. G. Hussien, and H. M. Zawbaa, “An enhanced hunter‐prey optimization for optimal power flow with FACTS devices and wind power integration”, IET Generation, Transmission & Distribution, vol. 17, no. 14, pp. 3115-3139, July 2023.
22. [22] A. Awad, S. Kamel, M. H. Hassan, and H. Zeinoddini‐Meymand, “Optimal allocation of flexible AC transmission system (FACTS) for wind turbines integrated power system”, Energy Science & Engineering, vol. 12, no. 1, pp. 181-200, Jan. 2024.
23. [23] A. A. Mohamed, S. Kamel, M. H. Hassan, and H. Zeinoddini‐Meymand, “CAVOA: A chaotic optimization algorithm for optimal power flow with facts devices and stochastic wind power generation”, IET Generation, Transmission & Distribution, vol. 18, no. 1, pp. 121-144, Jan. 2024.
24. [24] S. Mahapatra, N. Malik, S. Raj, and M. K. Srinivasan, “Constrained optimal power flow and optimal TCSC allocation using hybrid cuckoo search and ant lion optimizer”, International Journal of System Assurance Engineering and Management, vol. 13, pp. 721-734, Apr. 2022.
25. [25] A. Taheri, K. RahimiZadeh, A. Beheshti, J. Baumbach, R. V. Rao, S. Mirjalili, and A. H. Gandomi, “Partial reinforcement optimizer: An evolutionary optimization algorithm”, Expert Systems with Applications, vol. 238, 122070, March 2024.
26. [26] H. Peraza-Vázquez, A. F. Peña-Delgado, G. Echavarría-Castillo, A. B. Morales-Cepeda, J. Velasco-Álvarez, and F. Ruiz-Perez, “A bio-inspired method for engineering design optimization inspired by dingoes hunting strategies”, Mathematical Problems in Engineering, vol. 2021, 9107547, 1-19, Sept. 2021.
27. [27] M. H. Sulaiman, Z. Mustaffa, M. M. Saari, H. Daniyal, and S. Mirjalili, “Evolutionary mating algorithm”, Neural Computing and Applications, vol. 35, no. 1, pp. 487-516, Jan. 2023.
28. [28] A. Q. Tian, F. F. Liu, and H. X. Lv, “Snow Geese Algorithm: A novel migration-inspired meta-heuristic algorithm for constrained engineering optimization problems”, Applied Mathematical Modelling, vol. 126, pp. 327-347, Feb. 2024.
29. [29] C. A. C. Coello, “Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art”, Computer Methods in Applied Mechanics and Engineering, vol. 191, no. 11-12, pp. 1245-1287, Jan. 2002.
30. [30] C. B. Ferster and B. F. Skinner, Schedules of reinforcement. Appleton-Century-Crofts, 1957.
31. [31] The data of the IEEE 30-bus test system, http://labs.ece.uw.edu/pstca/pf30/pg_tca30bus.htm
32. [32] P. P. Biswas, P. N. Suganthan, R. Mallipeddi, and G. A. Amaratunga, “Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques”, Engineering Applications of Artificial Intelligence, vol. 68, pp. 81-100, Feb. 2018.