SpringerLink
Log in

Solving the cost minimization problem of optimal reactive power dispatch in a renewable energy integrated distribution system using rock hyraxes swarm optimization

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

The optimal reactive power dispatch problem optimizes the shunt capacitor bank installation in distribution systems, reducing power loss and also reducing the financial loss for the electricity market associated with power loss. Moreover, the sharing of both active and reactive power from different renewable energy sources like PV and wind in the form of distributed generation also contributes toward reducing power loss and improving the voltage profile of the system. But the installation and maintenance costs associated with these additional set-ups are rarely taken into consideration any optimization problem. This paper aims to reduce the power loss and improve the voltage profile of a radial distribution network with the integration of capacitor banks, PV, and wind energy sources, while taking into account the overall associated cost of each parameter during optimization. The problem is formulated as a novel cost minimization problem aiming to achieve the optimal settings for a life-long capacitor bank-PV-wind integrated distribution network with the least possible installation, operational, and maintenance costs while reducing its power loss significantly for a span of 20 years. The uncertain nature of PV and wind power output has been modeled using the beta probability distribution function and the Weibull probability distribution function, respectively. This unique proposed problem statement of the capacitor bank-PV-wind power integrated distribution network has been tested on the IEEE 33 and IEEE 141 bus systems and solved using the rock hyraxes swarm optimization (RHSO) algorithm. The results were compared with those from other nine well-established techniques, from which it was concluded that the RHSO algorithm has obtained optimal conditions for both systems to operate efficiently. The problem has also been tested on a practical 13-bus 33 kV distribution network in Maharashtra, India, to validate its performance on a practical system. The RHSO has successfully reduced the power loss to almost 17.48% w.r.t. the base case for the practical network while maintaining a minimum overall cost of $51,073,687.7582 for an entire life-span of 20 years.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Data availability

No data were used for the research described in the article.

References

  1. Singh H, Sawle Y, Dixit S, Malik H, Márquez FPG (2023) Optimization of reactive power using dragonfly algorithm in DG integrated distribution system. Electr Power Syst Res 220:109351

    Article  Google Scholar 

  2. Ianțoc A, Bulac C, Sidea D (2022) Optimal reactive power dispatch in active distribution power systems using grey wolf optimizer. UPB sci Bull Ser C Electr Eng Comput Sci 84:235–246

    Google Scholar 

  3. Li X, Zhao W, Lu Z (2023) Hierarchical optimal reactive power dispatch for active distribution network with multi-microgrids. J Electr Eng Technol 18(3):1705–1718

    Article  Google Scholar 

  4. Singh H, Srivastava L (2017) September. Multi-objective optimal reactive power dispatch for distribution system. In 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI) (pp. 558–563). IEEE

  5. Babu MR, Kumar CV, Anitha S (2021) Simultaneous reconfiguration and optimal capacitor placement using adaptive whale optimization algorithm for radial distribution system. J Electr Eng Technol 16:181–190

    Article  Google Scholar 

  6. Hosseini-Hemati S, Sheisi GH, Karimi S (2021) Allocation-based optimal reactive power dispatch considering polynomial load model using improved grey wolf optimizer. Iran J Sci Technol Trans Electr Eng 45:921–944

    Article  Google Scholar 

  7. Zhang L, Tang W, Liang J, Cong P, Cai Y (2015) Coordinated day-ahead reactive power dispatch in distribution network based on real power forecast errors. IEEE Trans Power Syst 31(3):2472–2480

    Article  Google Scholar 

  8. Liu Y, Ćetenović D, Li H, Gryazina E, Terzija V (2022) An optimized multi-objective reactive power dispatch strategy based on improved genetic algorithm for wind power integrated systems. Int J Electr Power Energy Syst 136:107764

    Article  Google Scholar 

  9. Hosseini-Hemati S, Karimi S, Sheisi GH (2021) Multi-objective ORPD considering different load models for active distribution networks. Int J Ind Electr Control Optim 4(2):191–210

    Google Scholar 

  10. Wagle R, Sharma P, Sharma C, Amin M (2024) Optimal power flow based coordinated reactive and active power control to mitigate voltage violations in smart inverter enriched distribution network. Int J Green Energy 21(2):359–375

    Article  Google Scholar 

  11. Yesuratnam G, Sriker A, Jena MK, Prabhat P (2024) A practical approach to real-time network topology processing in a radial distribution system. Electr Power Syst Res 226:109951

    Article  Google Scholar 

  12. Li Y, Sun Y, Wang Q, Sun K, Li KJ, Zhang Y (2023) Probabilistic harmonic forecasting of the distribution system considering time-varying uncertainties of the distributed energy resources and electrical loads. Appl Energy 329:120298

    Article  Google Scholar 

  13. Püvi V, Lehtonen M (2023) Evaluating distribution network optimal structure with respect to solar hosting capacity. Electr Power Syst Res 216:109019

    Article  Google Scholar 

  14. Zishan F, Mansouri S, Abdollahpour F, Grisales-Noreña LF, Montoya OD (2023) Allocation of renewable energy resources in distribution systems while considering the uncertainty of wind and solar resources via the multi-objective salp swarm algorithm. Energies 16(1):474

    Article  Google Scholar 

  15. Dashtaki AA, Hakimi SM, Hasankhani A, Derakhshani G, Abdi B (2023) Optimal management algorithm of microgrid connected to the distribution network considering renewable energy system uncertainties. Int J Electr Power Energy Syst 145:108633

    Article  Google Scholar 

  16. Yang Z, Yang F, Min H, Tian H, Hu W, Liu J, Eghbalian N (2023) Energy management programming to reduce distribution network operating costs in the presence of electric vehicles and renewable energy sources. Energy 263:125695

    Article  Google Scholar 

  17. Li LL, Fan XD, Wu KJ, Sethanan K, Tseng ML (2024) Multi-objective distributed generation hierarchical optimal planning in distribution network: improved beluga whale optimization algorithm. Expert Syst Appl 237:121406

    Article  Google Scholar 

  18. Garrido-Arévalo, V.M., Gil-González, W., Montoya, O.D., Grisales-Noreña, L.F. and Hernández, J.C., 2024. Optimal Dispatch of DERs and Battery-Based ESS in Distribution Grids While Considering Reactive Power Capabilities and Uncertainties: A Second-Order Cone Programming Formulation. IEEE Access.

  19. Xu Y, Han J, Yin Z, Liu Q, Dai C, Ji Z (2024) Voltage and reactive power-optimization model for active distribution networks based on second-order cone algorithm. Computers 13(4):95

    Article  Google Scholar 

  20. Borousan F, Hamidan MA (2023) Distributed power generation planning for distribution network using chimp optimization algorithm in order to reliability improvement. Electr Power Syst Res 217:109109

    Article  Google Scholar 

  21. Jahed YG, Mousavi SYM, Golestan S (2024) Optimal sizing and siting of distributed generation systems incorporating reactive power tariffs via water flow optimization. Electr Power Syst Res 231:110278

    Article  Google Scholar 

  22. Kyeremeh F, Fang Z, Liu F, Peprah F (2024) Techno-economic analysis of reactive power management in a solar PV microgrid: a case study of Sunyani to Becheam MV feeder, Ghana. Energy Rep 11:83–96

    Article  Google Scholar 

  23. Meng L, Yang X, Zhu J, Wang X, Meng X (2024) Network partition and distributed voltage coordination control strategy of active distribution network system considering photovoltaic uncertainty. Appl Energy 362:122846

    Article  Google Scholar 

  24. Ebadi, R., Aboshady, F.M., Ceylan, O., Pisica, I. and Ozdemir, A., 2024. Multi-Criteria Decision Making in Optimal Operation Problem of Unbalanced Distribution Networks Integrated with Photovoltaic Units. IEEE Access.

  25. Khasanov M, Kamel S, Halim Houssein E, Rahmann C, Hashim FA (2023) Optimal allocation strategy of photovoltaic-and wind turbine-based distributed generation units in radial distribution networks considering uncertainty. Neural Comput Appl 35(3):2883–2908

    Article  Google Scholar 

  26. Khasanov, M., Kamel, S., Hassan, M.H. and Domínguez-García, J.L., 2024. Maximizing renewable energy integration with battery storage in distribution systems using a modified Bald Eagle Search Optimization Algorithm. Neural Computing and Applications, pp.1–29.

  27. Alanazi M, Alanazi A, AboRas KM, Ghadi YY (2024) Multiobjective and coordinated reconfiguration and allocation of photovoltaic energy resources in distribution networks using improved clouded leopard optimization algorithm. Int J Energy Res 2024(1):7792658

    Google Scholar 

  28. Kayal P, Chanda CK (2013) Placement of wind and solar based DGs in distribution system for power loss minimization and voltage stability improvement. Int J Electr Power Energy Syst 53:795–809

    Article  Google Scholar 

  29. Das, T., Roy, R., Mandal, K.K., Mondal, S., Mondal, S., Hait, P. and Das, M.K., 2020. Optimal reactive power dispatch incorporating solar power using Jaya algorithm. In Computational advancement in communication circuits and systems: proceedings of ICCACCS 2018 (pp. 37–48). Springer Singapore.

  30. Roy R, Jadhav HT (2015) Optimal power flow solution of power system incorporating stochastic wind power using Gbest guided artificial bee colony algorithm. Int J Electr Power Energy Syst 64:562–578

    Article  Google Scholar 

  31. Wu QH, Ma JT (1995) Power system optimal reactive power dispatch using evolutionary programming. IEEE Trans Power Syst 10(3):1243–1249

    Article  Google Scholar 

  32. Ghatak SR, Sannigrahi S, Acharjee P (2017) Comparative performance analysis of DG and DSTATCOM using improved PSO based on success rate for deregulated environment. IEEE Syst J 12(3):2791–2802

    Article  Google Scholar 

  33. Genc, M.S., 2010. Economic analysis of large-scale wind energy conversion systems in central anatolian Turkey. Clean energy systems and experiences, pp.131-154.

  34. Al-Khateeb B, Ahmed K, Mahmood M, Le DN (2021) Rock hyraxes swarm optimization: A new nature-inspired metaheuristic optimization algorithm. Comput. Mater. Contin 68(1):643

    Google Scholar 

  35. Kennedy, J. and Eberhart, R., 1995, November. Particle swarm optimization. In Proceedings of ICNN’95-international conference on neural networks (Vol. 4, pp. 1942–1948). IEEE.

  36. Pandya, S. and Roy, R., 2015, March. Particle swarm optimization based optimal reactive power dispatch. In 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1–5). IEEE.

  37. Ghoshal SP (2004) Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control. Electr Power Syst Res 72(3):203–212

    Article  Google Scholar 

  38. Naka S, Genji T, Yura T, Fukuyama Y (2003) A hybrid particle swarm optimization for distribution state estimation. IEEE Trans Power Syst 18(1):60–68

    Article  Google Scholar 

  39. Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl-Based Syst 26:69–74

    Article  Google Scholar 

  40. Pan WT (2013) Using modified fruit fly optimisation algorithm to perform the function test and case studies. Connect Sci 25(2–3):151–160

    Article  Google Scholar 

  41. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  42. Zhiheng W, Jianhua L (2021) Flamingo search algorithm: a new swarm intelligence optimization algorithm. IEEE Access 9:88564–88582

    Article  Google Scholar 

  43. Markad, P. and Thosar, D.A., 2020. Optimum Location and Size of DG Using GA with Sensitivity Analysis for a Real Time Radial Distribution System. In 2nd International Conference on Communication & Information Processing (ICCIP).

Download references

Acknowledgements

Not applicable.

Author information

Authors and Affiliations

  1. Department of Power Engineering, Jadavpur University, Salt Lake Campus, Kolkata, India

    Tanmay Das & Kamal Krishna Mandal

  2. Department of Electrical and Electronics Engineering, SRM University, Sonipat, Haryana, India

    Ranjit Roy

Authors
  1. Tanmay Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ranjit Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kamal Krishna Mandal
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Mr. Tanmay Das carried out basic design and simulation work and prepared a draft paper. Dr. Ranjit Roy and Dr. Kamal Krishna Mandal participated in checking simulation work, results and discussions, and sequence of writing and helped to organize the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ranjit Roy.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Das, T., Roy, R. & Mandal, K.K. Solving the cost minimization problem of optimal reactive power dispatch in a renewable energy integrated distribution system using rock hyraxes swarm optimization. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02548-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00202-024-02548-9

Keywords

Search

Navigation