skip to main content

Optimal power flow solutions to power systems with wind energy using a highly effective meta-heuristic algorithm

1Department of Electric Power Systems, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam

2Faculty of Mechanical and Electrical, Naval Academy, Nhatrang, Viet Nam

3Faculty of Electrical Engineering, The University of Danang - University of Science and Technology, Danang, Viet Nam

Received: 1 Jan 2023; Revised: 4 Mar 2023; Accepted: 14 Mar 2023; Available online: 30 Mar 2023; Published: 16 May 2023.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2023 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract

This paper implements two novel meta-heuristic algorithms, including the Coati optimization algorithm (COA) and War strategy optimization (WSO) for determining the optimal solutions to the optimal power flow problem incorporating the use of wind turbines (WTs). Two objective functions are considered in this study, including minimizing the entire electricity generation expenditure (EEGE) with the value point effect and minimizing the voltage fluctuation index (VFI). IEEE 30-bus system is chosen to conduct the whole study and validate the efficiency of the two applied methods. Furthermore, DFIG WTs are used in grids with varying power output and power factor ranges. The comparison of the results obtained from the two methods in all case studies reveals that WSO is vastly superior to COA in almost all aspects. In addition, the positive contributions of WTs to the EEGE and VFI while they are properly placed in the grid are also clarified by using WSO. As a result, WSO is acknowledged as a highly effective search method for dealing with such optimal power flow (OPF) problems considering the presence of renewable energy sources.

Fulltext View|Download
Keywords: Optimal power flow; renewable energy sources; wind turbines; Coati optimization algorithms; War strategy optimization

Article Metrics:

  1. Abdollahi, A., Ghadimi, A. A., Miveh, M. R., Mohammadi, F., & Jurado, F. (2020). Optimal power flow incorporating FACTS devices and stochastic wind power generation using krill herd algorithm. Electronics, 9(6), 1043; https://doi.org/10.3390/electronics9061043
  2. Abdullah, M., Javaid, N., Khan, I. U., Khan, Z. A., Chand, A., & Ahmad, N. (2019, March). Optimal power flow with uncertain renewable energy sources using flower pollination algorithm. In International Conference on Advanced Information Networking and Applications (pp. 95-107). Springer, Cham; https://doi.org/10.1007/978-3-030-15032-7_8
  3. Ahgajan, V. H., Rashid, Y. G., & Tuaimah, F. M. (2021). Artificial bee colony algorithm applied to optimal power flow solution incorporating stochastic wind power. International Journal of Power Electronics and Drive Systems (IJPEDS), 12(3), 1890-1899; https://doi.org/10.11591/ijpeds.v12.i3.pp1890-1899
  4. Alasali, F., Nusair, K., Obeidat, A. M., Foudeh, H., & Holderbaum, W. (2021). An analysis of optimal power flow strategies for a power network incorporating stochastic renewable energy resources. International Transactions on Electrical Energy Systems, 31(11), e13060; htpps://doi.org/10.1002/2050-7038.13060
  5. Alghamdi, A. S. (2022). A Hybrid Firefly–JAYA Algorithm for the Optimal Power Flow Problem Considering Wind and Solar Power Generations. Applied Sciences, 12(14), 7193. htpps://doi.org/10.3390/app12147193
  6. Ali, M. A., Kamel, S., Hassan, M. H., Ahmed, E. M., & Alanazi, M. (2022). Optimal Power Flow Solution of Power Systems with Renewable Energy Sources Using White Sharks Algorithm. Sustainability, 14(10), 6049; htpps://doi.org/10.3390/su14106049
  7. Ali, Z. M., Aleem, S. H. A., Omar, A. I., & Mahmoud, B. S. (2022). Economical-environmental-technical operation of power networks with high penetration of renewable energy systems using multi-objective coronavirus herd immunity algorithm. Mathematics, 10(7), 1201; htpps://doi.org/10.3390/math10071201
  8. Ayyarao, T. S., Rama Krishna, N. S. S., Elavarasan, R. M., Polumahanthi, N., Rambabu, M., Saini, G., ... & Alatas, B. (2022). War strategy optimization algorithm: a new effective metaheuristic algorithm for global optimization. IEEE Access, 10, 25073-25105; htpps://doi.org/10.1109/ACCESS.2022.3153493
  9. Bamane, P. D. (2019, March). Application of Crow Search Algorithm to solve Real Time Optimal Power Flow Problem. In 2019 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC) (pp. 123-129). IEEE; htpps://doi.org/10.1109/ICCPEIC45300.2019.9082372
  10. Birogul, S. (2019). Hybrid harris hawk optimization based on differential evolution (HHODE) algorithm for optimal power flow problem. IEEE Access, 7, 184468-184488; htpps://doi.org/ 10.1109/ACCESS.2019.2958279
  11. Dehghani, M., Montazeri, Z., Trojovská, E., & Trojovský, P. (2022). Coati Optimization Algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems. Knowledge-Based Systems, 110011; htpps://doi.org/10.1016/j.knosys.2022.110011
  12. Duman, S., Rivera, S., Li, J., & Wu, L. (2020). Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization. International Transactions on Electrical Energy Systems, 30(4), e12270; htpps://doi.org/10.1002/2050-7038.12270
  13. Duong, M. Q., Nguyen, T. T., & Nguyen, T. T. (2021). Optimal placement of wind power plants in transmission power networks by applying an effectively proposed metaheuristic algorithm. Mathematical Problems in Engineering, 2021; htpps://doi.org/10.1155/2021/1015367
  14. Elattar, E. E., & ElSayed, S. K. (2019). Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement. Energy, 178, 598-609; htpps://doi.org/10.1016/j.energy.2019.04.159
  15. Farhat, M., Kamel, S., Atallah, A. M., & Khan, B. (2022). Developing a Marine Predator Algorithm for Optimal Power Flow Analysis considering Uncertainty of Renewable Energy Sources. International Transactions on Electrical Energy Systems, 2022; htpps://doi.org/10.1155/2022/3714475
  16. Frank, S., & Rebennack, S. (2016). An introduction to optimal power flow: Theory, formulation, and examples. IIE transactions, 48, 1172-1197; htpps://doi.org/10.1080/0740817X.2016.1189626
  17. Frank, S., Steponavice, I., & Rebennack, S. (2012). Optimal power flow: A bibliographic survey I: Formulations and deterministic methods. Energy systems, 3, 221-258; htpps://doi.org/10.1007/s12667-012-0056-y
  18. Ginidi, A., Elattar, E., Shaheen, A., Elsayed, A., El-Sehiemy, R., & Dorrah, H. (2022). Optimal Power Flow Incorporating Thyristor-Controlled Series Capacitors Using the Gorilla Troops Algorithm. International Transactions on Electrical Energy Systems, 2022; htpps://doi.org/10.1155/2022/9448199
  19. Hassan, M. H., Kamel, S., Selim, A., Khurshaid, T., & Domínguez-García, J. L. (2021). A modified Rao-2 algorithm for optimal power flow incorporating renewable energy sources. Mathematics, 9(13), 1532; htpps://doi.org/10.3390/math9131532
  20. Khunkitti, S., Siritaratiwat, A., & Premrudeepreechacharn, S. (2021). Multi-objective optimal power flow problems based on slime mould algorithm. Sustainability, 13(13), 7448; htpps://doi.org/10.3390/su13137448
  21. Ladumor, D. P., Trivedi, I. N., Bhesdadiya, R. H., & Jangir, P. (2017, February). Optimal Power Flow problems solution with SVC using meta-heuristic algorithm. In 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB) (pp. 283-288). IEEE; htpps://doi.org/ 10.1109/AEEICB.2017.7972430
  22. Li, Z., Cao, Y., Dai, L. V., Yang, X., & Nguyen, T. T. (2019). Optimal power flow for transmission power networks using a novel metaheuristic algorithm. Energies, 12(22), 4310; htpps://doi.org/10.3390/en12224310
  23. Messaoudi, A., & Belkacemi, M. (2020). Optimal Power Flow Solution using Efficient Sine Cosine Optimization Algorithm. International Journal of Intelligent Systems and Applications, 10(2), 34; https://www.mecs-press.org/ijisa/ijisa-v12-n2/IJISA-V12-N2-4.pdf
  24. Nguyen, K. P., & Fujita, G. (2018). Self-Learning Cuckoo search algorithm for optimal power flow considering tie-line constraints in large-scale systems. GMSARN International Journal, 12(2), 118-126. http://gmsarnjournal.com/home/wp-content/uploads/2018/07/vol12no2-6.pdf
  25. Nguyen, T. T. (2019). A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization. Energy, 171, 218-240; htpps://doi.org/10.1016/j.energy.2019.01.021
  26. Nusair, K., & Alhmoud, L. (2020). Application of equilibrium optimizer algorithm for optimal power flow with high penetration of renewable energy. Energies, 13(22), 6066; htpps://doi.org/10.3390/en13226066
  27. Pandya, S. B., & Jariwala, H. R. (2020). Renewable energy resources integrated multi-objective optimal power flow using non-dominated sort grey wolf optimizer. Journal of Green Engineering, 10(1), 180-205; https://journals.iau.ir/article_696430_635045319b797c9ad055404855cecf7d.pdf
  28. Pham, L. H., Dinh, B. H., & Nguyen, T. T. (2022). Optimal power flow for an integrated wind-solar-hydro-thermal power system considering uncertainty of wind speed and solar radiation. Neural Computing and Applications, 1-35; htpps://doi.org/10.1007/s00521-022-07000-2
  29. Reddy, S.S. (2016) Optimal power flow with renewable energy resources including storage, Electrical Engineering, 99(2), 685–695; htpps://doi.org/10.1007/s00202-016-0402-5
  30. Rojanaworahiran, K., & Chayakulkheeree, K. (2021). Probabilistic optimal power flow considering load and solar power uncertainties using particle swarm optimization. GMSARN International Journal, 15, 37-43; http://gmsarnjournal.com/home/wp-content/uploads/2020/04/vol15no1-5.pdf
  31. Sulaiman, M. H., & Mustaffa, Z. (2021). Optimal power flow incorporating stochastic wind and solar generation by metaheuristic optimizers. Microsystem Technologies, 27(9), 3263-3277; htpps://doi.org/10.1007/s00542-020-05046-7
  32. Tiwari, S., Vaddi, N., Metta, S. B., & Kumar, M. (2020, April). Optimal power flow solution with nature inspired Antlion meta-heuristic algorithm. In Journal of Physics: Conference Series, 1478(1), 012035. IOP Publishing; htpps://doi.org/10.1088/1742-6596/1478/1/012035
  33. Tran, T. T., & Vo, N. D. (2016). Transient Stability Constrained Optimal Power Flow Using Improved Particle Swarm Optimization. GMSARN International Journal, 10, 87 – 94, 2016; http://gmsarnjournal.com/home/wp-content/uploads/2016/10/vol10no3-1.pdf
  34. Warid, W. (2020). Optimal power flow using the AMTPG-Jaya algorithm. Applied Soft Computing, 91, 106252; http://gmsarnjournal.com/home/wp-content/uploads/2018/07/vol12no2-6.pdf
  35. Warid, W., Hizam, H., Mariun, N., Abdul-Wahab, N.I. (2016). Optimal power flow using the Jaya algorithm. Energies, 9(9), 678; htpps://doi.org/10.3390/en9090678
  36. Zimmerman, R. D., & Murillo-Sánchez, C. E. (2016). Matpower 6.0 user’s manual. Power Systems Engineering Research Center, 9. https://matpower.org/docs/MATPOWER-manual-6.0.pdf

Last update:

  1. Powerformer: A temporal-based transformer model for wind power forecasting

    Site Mo, Haoxin Wang, Bixiong Li, Zhe Xue, Songhai Fan, Xianggen Liu. Energy Reports, 11 , 2024. doi: 10.1016/j.egyr.2023.12.030
  2. Review of several key processes in wind power forecasting: Mathematical formulations, scientific problems, and logical relations

    Mao Yang, Yutong Huang, Chuanyu Xu, Chenyu Liu, Bozhi Dai. Applied Energy, 377 , 2025. doi: 10.1016/j.apenergy.2024.124631
  3. Enhancing Medium Term Wind Power Forecasting Accuracy With Dual Stage Attention Based TCN-GRU Model and White Shark Optimization

    C. Bharathi Priya, N. Arulanand. Electric Power Components and Systems, 2024. doi: 10.1080/15325008.2024.2348039
  4. VRE Integrating in PIAT grid with aFRR using PSS, MPPT, and PSO-based Techniques: A Case Study Kabertene

    Ali Abderrazak Tadjeddine, Mohammed Sofiane Bendelhoum, Ridha Ilyas Bendjillali, Hichem Hamiani, Soumia Djelaila. EAI Endorsed Transactions on Energy Web, 10 , 2023. doi: 10.4108/ew.3378

Last update: 2024-10-14 16:56:32

No citation recorded.