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Loadability maximisation in bilateral network for real-time forecasting system using cuckoo search algorithm

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This manuscript proposes an optimal power flow (OPF) solution in a coordinated bilateralpower network. The primary goal of this project is to maximise the benefits of the powermarket using Newton–Raphson (NR) and cuckoo search algorithm CSA methodologies.The global solution is found using a CSA-based optimisation approach. The study isconducted on real-time bus system. To avoid this, creative techniques have lately beenused to handle the OPF problem, such as loadability maximisation for real-time predictionsystems employing the CSA. In this work, cuckoo search (CS) is used to optimise theobtained parameters that help to minimise parameters in the predecessor and consequentunits of each sub-model. The proposed approach is used to estimate the power load in thelocal area. The constructed models show excellent predicting performance based on derivedperformance. The results confirm the method’s validity. The outcomes are compared withthose obtained by using the NR method. CSA outperformed the other methods in thisinvestigation and gave more accurate predictions. The OPF problem is solved via CSAin this study. Implementing a real-time data case bus system is recommended to test theperformance of the established method in the MATLAB programme.
Rocznik
Strony
73--88
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
  • Department of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, India
  • Department of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, India
Bibliografia
  • 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/en15010027.87.
  • 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. Djurović, 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, Telecom- munications 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. D. Krishna, E. Hima Bindu, M. Sasikala, 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 measure- ment 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.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1b2261f7-93cf-403c-b910-cc54f1db87f4
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