Loadability Maximisation in Bilateral Network for Real-Time Forecasting System Using Cuckoo Search Algorithm

  • Venkatasivanagaraju S. Jawaharlal Nehru Technological University Anantapur, India
  • M. Venkateswara Rao Jawaharlal Nehru Technological University Anantapur, India


This manuscript proposes an optimal power flow (OPF) solution in a coordinated bilateral power network. The primary goal of this project is to maximise the benefits of the power market using Newton–Raphson (NR) and cuckoo search algorithm CSA methodologies. The global solution is found using a CSA-based optimisation approach. The study is conducted on real-time bus system. To avoid this, creative techniques have lately been used to handle the OPF problem, such as loadability maximisation for real-time prediction systems employing the CSA. In this work, cuckoo search (CS) is used to optimise the obtained parameters that help to minimise parameters in the predecessor and consequent units of each sub-model. The proposed approach is used to estimate the power load in the local area. The constructed models show excellent predicting performance based on derived performance. The results confirm the method’s validity. The outcomes are compared with those obtained by using the NR method. CSA outperformed the other methods in this investigation and gave more accurate predictions. The OPF problem is solved via CSA in this study. Implementing a real-time data case bus system is recommended to test the performance of the established method in the MATLAB programme.


optimal power flow, NR method, short-term and long-term load forecasting, cuckoo search algorithm, optimisation and loss minimisation,


1. E.O. Kontis, T.A. Papadopoulos, A.I. Chrysochos, G.K. Papagiannis, Measurement-based dynamic load modeling using the vector fitting technique, IEEE Transactions on Power Systems, 33(1): 338–351, 2017, doi: 10.1109/TPWRS.2017.2697004.
2. Y. Adianto, C. Baguley, U. Madawala, N. Hariyanto, S. Suwarno, T. Kurniawan, The coordinated operation of vertically structured power systems for electric vehicle charge scheduling, Energies, 15(1): 27, 2021, doi: 10.3390/en15010027.
3. L. Chávarro-Barrera, S. Pérez-Londoño, J. Mora-Flórez, An adaptive approach for dynamic load modeling in microgrids, IEEE Transactions on Smart Grid, 12(4): 2834–2843, 2021, doi: 10.1109/TSG.2021.3064046.
4. X.-S. Yang, Suash Deb, Cuckoo search via Lévy flights, [in:] 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 210–214, 2009, doi: 10.1109/NABIC.2009.5393690.
5. Y. Wang et al., Online realization of an ambient signal based load modeling algorithm and its application in field measurement data, IEEE Transactions on Industrial Electronics, 69(7): 7451–7460, 2021, doi: 10.1109/TIE.2021.3102428.
6. H. Gao, C. Koch, Y.Wu, Building information modelling based building energy modelling: A review, Applied Energy, 238: 320–343, 2019, doi: 10.1016/j.apenergy.2019.01.032.
7. P. Regulski, D.S. Vilchis-Rodriguez, S. Djurovic, V. Terzija, Estimation of composite load model parameters using an improved particle swarm optimization method, IEEE Transactions on Power Delivery, 30(2): 553–560, 2014, doi: 10.1109/TPWRD.2014.2301219.
8. P. Aree, Aggregating method of induction motor group using energy conservation law, [in:] 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp. 1–5, Krabi, Thailand, May 15–17, 2013, doi: 10.1109/ECTICon.2013.6559506.
9. J.K. Muriuki, C.M. Muriithi, Comparison of aggregation of small and large induction motors for power system stability study, Global Engineers and Technologists Review, 3(2): 9–13, 2013.
10. D. Lew et al., The power of small: the effects of distributed energy resources on system reliability, IEEE Power and Energy Magazine, 15(6): 50–60, 2017, doi: 10.1109/MPE.2017.2729104.
11. S. Eftekharnejad, V. Vittal, G.T. Heydt, B. Keel, J. Loehr, Impact of increased penetration of photovoltaic generation on power systems, IEEE Transactions on Power Systems, 28(2): 893–901, 2012, doi: 10.1109/TPWRS.2012.2216294.
12. K. D., H. B. E., S. M, Mathematical modeling and analysis of demand response using distributed algorithm in distribution power system, [in:] 2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), pp. 237–241, 2021, doi: 10.1109/DISCOVER52564.2021.9663598.
13. A. Arif, Z. Wang, J. Wang, B. Mather, H. Bashualdo, D. Zhao, Load modeling – A review, IEEE Transactions on Smart Grid, 9(6): 5986–5999, 2017, doi: 10.1109/TSG.2017.2700436.
14. D. Krishna, M. Sasikala, V. Ganesh, Adaptive FLC-based UPQC in distribution power systems for power quality problems, International Journal of Ambient Energy, 43(1): 1719–1729, 2022, doi: 10.1080/01430750.2020.1722232.
15. S.H. Lee, S.E. Son, S.M. Lee, J.M. Cho, K.B. Song, J.W. Park, Kalman-filter based static load modeling of real power system using K-EMS data, Journal of Electrical Engineering and Technology, 7(3): 304–311, 2012, doi: 10.5370/JEET.2012.7.3.304.
16. F. Aminifar, F. Rahmatian, M. Shahidehpour, State-of-the-art in synchrophasor measurement technology applications in distribution networks and microgrids, IEEE Access, 9: 153875–153892, 2021, doi: 10.1109/ACCESS.2021.3127915.
17. D. Krishna, M. Sasikala, R. Kiranmayi, FOPI and FOFL controller based UPQC for mitigation of power quality problems in distribution power system, Journal of Electrical Engineering and Technology, 17: 1543–1554, 2022, doi: 10.1007/s42835-022-00996-6.
18. B. Singh, R. Mahanty, S.P. Singh, Optimal power flow with benefit maximisation in coordinated bilateral power market, International Journal of Power and Energy Conversion, 4(3): 268–277, 2013, doi: 10.1504/IJPEC.2013.054845.
Sep 23, 2022
How to Cite
S., Venkatasivanagaraju; RAO, M. Venkateswara. Loadability Maximisation in Bilateral Network for Real-Time Forecasting System Using Cuckoo Search Algorithm. Computer Assisted Methods in Engineering and Science, [S.l.], sep. 2022. ISSN 2299-3649. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/475>. Date accessed: 08 dec. 2022. doi: http://dx.doi.org/10.24423/cames.475.
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