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Scenario-Based Approach to Solve Optimal Reactive Power Dispatch Problem with Integration of Solar Energy Using Modified Ant Line Optimizer

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Abstract

This paper considers a scenario-based approach, a stochastic ORPD formulation and solution that accommodates uncertain load demand, and solar power. The optimization tasks are based on the Modified Ant Line Optimizer (MALO) algorithm. PV system was used in place of the conventional thermal generator at bus 8, then the IEEE 30-bus system is modified. In addition, calculate the available solar power, and use the lognormal probability density function. In this paper, minimization of active power losses and voltage deviation are considered as objectives. This is delineated as an optimization problem by considering solar energy uncertainties and load uncertainties. Introducing solar energy sources to the power system along with the existing conventional sources to improve the performance of the system. An analysis was carried out using MALO to examine the proposed approach for IEEE 30-bus test system. The proposed method has been compared to other approaches and found to be effective.

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References

  1. Ebeed M, Ali A, Mosaad MI, Kamel S. An improved lightning attachment procedure optimizer for optimal reactive power dispatch with uncertainty in renewable energy resources. IEEE Access. 2020;8:168721–31. https://doi.org/10.1109/ACCESS.2020.3022846.

    Article  Google Scholar 

  2. Biswas P. Genetic algorithm based multi objective bilevel programming for optimal real and reactive power dispatch under uncertainty. In: Azar A, Vaidyanathan S, editors. Computational intelligence applications in modeling and control. Studies in computational intelligence, vol. 575. Springer, Cham; 2015. https://doi.org/10.1007/978-3-319-11017-2_8.

  3. Chaitanya SNVSK, Rao BV, Bakkiyaraj RA. Solution of an optimal reactive power dispatch problem: an application of modified Ant Lion Optimizer. In: 2021 31st Australasian universities power engineering conference (AUPEC), 2021. pp. 1–6. https://doi.org/10.1109/AUPEC52110.2021.9597756.

  4. Abou El-Ela A, Abido M, Spea S. Differential evolution algorithm for optimal reactive power dispatch. Electr Power Syst Res. 2011;81:458–64. https://doi.org/10.1016/j.epsr.2010.10.005

  5. Ben Oualid Medani K, Sayah S, Bekrar A. Whale optimization algorithm based optimal reactive power dispatch: a case study of the Algerian power system. Electr Power Syst Res. 2018;163(Part B):696–705 (ISSN 0378-7796).

  6. Duman S, Sonmez Y, Guvenc U, Yörükeren N. Application of gravitational search algorithm for optimal reactive power dispatch problem. In: INISTA 2011—2011 international symposium on INnovations in intelligent SysTems and applications. 2011. https://doi.org/10.1109/INISTA.2011.5946133.

  7. Dutta S, Roy PK, Nandi D. Optimal location of STATCOM using chemical reaction optimization for reactive power dispatch problem. Ain Shams Eng J. 2016;7(1):233–47 (ISSN 2090-4479).

    Article  Google Scholar 

  8. Mouassa S, Bouktir T. Artificial Bee Colony Algorithm for discrete optimal reactive power dispatch. In: 2015 international conference on industrial engineering and systems management (IESM), 2015. pp. 654–62.https://doi.org/10.1109/IESM.2015.7380228.

  9. Valipour K, Ghasemi A. Using a new modified harmony search algorithm to solve multi-objective reactive power dispatch in deterministic and stochastic models. J AI Data Min. 2017;5(1):89–100. https://doi.org/10.22044/jadm.2016.655.

    Article  Google Scholar 

  10. Abbasy, Hosseini SH. Ant Colony Optimization-based approach to optimal reactive power dispatch: a comparison of various ant systems. In: 2007 IEEE power engineering society conference and exposition in Africa—PowerAfrica, 2007, pp. 1–8. https://doi.org/10.1109/PESAFR.2007.4498067.

  11. Mugemanyi S, Qu Z, Rugema FX, Dong Y, Bananeza C, Wang L. Optimal reactive power dispatch using Chaotic Bat Algorithm. IEEE Access. 2020;8:65830–67. https://doi.org/10.1109/ACCESS.2020.2982988.

    Article  Google Scholar 

  12. Ramkee P, Chaitanya SNVSK, Venkateswara Rao B, Ashok Bakkiyaraj R (2022) Optimal reactive power dispatch under load uncertainty incorporating solar power using firefly algorithm. In: Bansal RC, Agarwal A, Jadoun VK, editors. Advances in energy technology. Lecture notes in electrical engineering, vol. 766. Springer, Singapore

  13. Niknam T, Narimani MR, Jabbari M, Malekpour AR. A modified shuffle frog leaping algorithm for multi-objective optimal power flow. Energy. 2011;36(11):6420–32 (ISSN 0360-5442).

  14. Lenin K, Reddy BR, Suryakalavathi M. Hybrid Tabu search-simulated annealing method to solve optimal reactive power problem. Int J Electr Power Energy Syst. 2016;82:87–91 (ISSN 0142-0615).

    Article  Google Scholar 

  15. Pandya S, Roy R. Particle Swarm Optimization based optimal reactive power dispatch. In: 2015 IEEE international conference on electrical, computer and communication technologies (ICECCT), 2015. pp. 1–5. https://doi.org/10.1109/ICECCT.2015.7225981.

  16. Raha SB, Som T, Mandal KK, Chakraborty N. Cuckoo search algorithm based optimal reactive power dispatch. In: Proceedings of the 2014 international conference on control, instrumentation, energy and communication (CIEC), 2014. pp. 412–16. https://doi.org/10.1109/CIEC.2014.6959121.

  17. Yan W, Lu S, Yu DC. A novel optimal reactive power dispatch method based on an improved hybrid evolutionary programming technique. IEEE Trans Power Syst. 2004;19(2):913–8. https://doi.org/10.1109/TPWRS.2004.826716.

    Article  Google Scholar 

  18. Sahli Z, Abdellatif H, Sayah S, Trentesaux D, Bekrar A. Efficient hybrid algorithm solution for optimal reactive power flow using the sensitive bus approach. Eng Technol Appl Sci Res. 2022;12:8210–6. https://doi.org/10.48084/etasr.4680.

    Article  Google Scholar 

  19. Shaheen A, El-Sehiemy R, Farrag S. Optimal reactive power dispatch using backtracking search algorithm. Aust J Electr Electron Eng. 2017. https://doi.org/10.1080/1448837X.2017.1325134.

    Article  Google Scholar 

  20. Ben Oualid Medani K, Sayah S. Optimal reactive power dispatch using particle swarm optimization with time varying acceleration coefficients. In: 2016 8th international conference on modelling, identification and control (ICMIC), 2016. pp. 780–85. https://doi.org/10.1109/ICMIC.2016.7804219.

  21. Mohamed AMS, Hasanien HM, Alkuhayli A. A novel hybrid GWO-PSO optimization technique for optimal reactive power dispatch problem solution. Ain Shams Eng J. 2021;12(1):621–30 (ISSN 2090-4479).

    Article  Google Scholar 

  22. Vishnu M, TK, SK. An improved solution for reactive power dispatch problem using diversity-Enhanced Particle Swarm Optimization. Energies. 2020;13:2862. https://doi.org/10.3390/en13112862.

  23. Abdel-Fatah S, Ebeed M, Kamel S. Optimal reactive power dispatch using modified sine cosine algorithm. 2019. https://doi.org/10.1109/ITCE.2019.8646460.

  24. Tudose AM, Picioroaga II, Sidea DO, Bulac C. Solving single- and multi-objective optimal reactive power dispatch problems using an Improved Salp Swarm Algorithm. Energies. 2021;14:1222.

    Article  Google Scholar 

  25. Niknam T, Narimani MR, Azizipanah-Abarghooee R, Bahmani-Firouzi B. Multiobjective optimal reactive power dispatch and voltage control: a new opposition-based self-adaptive modified gravitational search algorithm. IEEE Syst J. 2013;7(4):742–53. https://doi.org/10.1109/JSYST.2012.2227217.

    Article  Google Scholar 

  26. Das T, Roy R. Optimal reactive power dispatch using JAYA algorithm. In: 2018 emerging trends in electronic devices and computational techniques (EDCT). IEEE, 2018.

  27. Mouassa S, Bouktir T, Salhi A. Ant lion optimizer for solving optimal reactive power dispatch problem in power systems. Eng Sci Technol. 2017;20(3):885–95 (ISSN 2215-0986).

    Google Scholar 

  28. Partha P, Biswas PN, Suganthan R, Mallipeddi Gehan AJ, Amaratunga A. Optimal reactive power dispatch with uncertainties in load demand and renewable energy sources adopting scenario-based approach. Appl Soft Comput. 2019;75:616–32 (ISSN 1568-4946).

    Article  Google Scholar 

  29. Ben Oualid Medani K, Sayah S, Bekrar A. Whale optimization algorithm based optimal reactive power dispatch: a case study of the Algerian power system. Electr Power Syst Res. 2018;163:696–705. https://doi.org/10.1016/j.epsr.2017.09.001.

    Article  Google Scholar 

  30. Shaw B, Mukherjee V, Ghoshal SP. Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm. Int J Electr Power Energy Syst. 2014;55:29–40. https://doi.org/10.1016/j.ijepes.2013.08.010.

    Article  Google Scholar 

  31. Li Z, Cao Y, Dai LV, Yang X, Nguyen TT. Finding solutions for optimal reactive power dispatch problem by a novel improved antlion optimization algorithm. Energies. 2019;12(15):2968. https://doi.org/10.3390/en12152968.

    Article  Google Scholar 

  32. Aljohani TM, Ebrahim AF, Mohammed O. Single and multiobjective optimal reactive power dispatch based on hybrid artificial physics-particle swarm optimization. Energies. 2019;12(12):2333. https://doi.org/10.3390/en12122333.

    Article  Google Scholar 

  33. Das T et al. Optimal reactive power dispatch incorporating solar power using Jaya algorithm. In Maharatna K, Kanjilal MR, Konar SC, Nandi S, Das K, editors. Computational advancement in communication circuits and systems, vol. 575. Springer, Singapore; 2020. pp. 37–48. https://doi.org/10.1007/978-981-13-8687-9_4.

  34. Muhammad Y, Khan R, Raja MAZ, Ullah F, Chaudhary NI, He Y. Solution of optimal reactive power dispatch with FACTS devices: a survey. Energy Rep. 2020;6:2211–29. https://doi.org/10.1016/j.egyr.2020.07.030.

    Article  Google Scholar 

  35. Mirjalili S. The Ant Lion Optimizer. Adv Eng Softw. 2015;83:80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010.

    Article  Google Scholar 

  36. Mohseni-Bonab SM, Rabiee A, Mohammadi-ivatloo B, Jalilzadeh S, Nojavan S. A two-point estimate method for uncertainty modeling in multi-objective optimal reactive power dispatch problem. Int J Electr Power Energy Syst. 2016;75:194–204. https://doi.org/10.1016/j.ijepes.2015.08.009.

    Article  Google Scholar 

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Funding

No funding was received for conducting this study. The authors have no relevant financial or non-financial interests to disclose.

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Authors and Affiliations

  1. Department of EEE, Annamalai University, Chidambaram, Tamil Nadu, India

    S. N. V. S. K. Chaitanya

  2. Department of EEE, V R Siddhartha Engineering College (Autonomous), Vijayawada, Andhra Pradesh, India

    S. N. V. S. K. Chaitanya & B. Venkateswara Rao

  3. Department of EE, Annamalai University, Chidambaram, Tamil Nadu, India

    R. Ashok Bakkiyaraj

  4. Department of Computer and Information Science, Annamalai University, Chidambaram, Tamil Nadu, India

    K. Jayanthi

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  1. S. N. V. S. K. Chaitanya
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  3. B. Venkateswara Rao
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Correspondence to B. Venkateswara Rao.

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This article is part of the topical collection “Applications of Artificial intelligence, Optimization and Simulation” guest edited by Juan Carlos Figueroa García, German Jairo Hernandez Perez, Carlos Franco, Roman Neruda and José Luis Villa Ramirez.

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Chaitanya, S.N.V.S.K., Bakkiyaraj, R.A., Rao, B.V. et al. Scenario-Based Approach to Solve Optimal Reactive Power Dispatch Problem with Integration of Solar Energy Using Modified Ant Line Optimizer. SN COMPUT. SCI. 5, 27 (2024). https://doi.org/10.1007/s42979-023-02315-w

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