Vol. 1 No. 1 (2022): International Journal of Automation and Digital Transformation
Articles

Smart Load Flow Analysis using Conventional method and modern method

Published 2022-03-23

Keywords

  • Optimal Power Flow,
  • Microgrid,
  • Newton-Raphson,
  • Neural Network

How to Cite

Smart Load Flow Analysis using Conventional method and modern method. (2022). International Journal of Automation and Digital Transformation, 1(1), 60-86. https://doi.org/10.54878/kxtqe195

Abstract

The optimal power flow for networked microgrids with different renewable energy sources (PV panels and wind turbines), storage systems, generators, and load is investigated in this study. A conventional method and an Artificial Intelligence method are applied to solve the OPF problem. The performance of MGs system with renewable energy integration was investigated in this study, with a focus on power flow studies. The power flow is calculated using the well-known Newton-Raphson method and the Neural Network method. The power flow calculation is used to assess grid performance parameters like voltage bus magnitude, angle, and real and reactive power flow in system transmission lines. under given load conditions. The standard test system used was a benchmark test system for Networked MGs with four MGs and 40 buses. The data for the entire system has been chosen as per the IEEE Standard 1547-2018. The results showed minimumlosses and higher efficiency when performing OPF using NN than the Newton-Raphson method. The efficiency of the power system for the networked MG is 99.3% using Neural Network and 97% using the Newton- Raphson method. The Neural Network method, which mimics how the human brain works based on AI technologies, gave the best results and better efficiency in both cases (Battery as Load/Battery as Source) than the conventional method. 

References

  1. R. Morello, C. De Capua, G. Fulco and S. C. Mukhopadhyay, "A Smart Power Meter to Monitor Energy Flow in Smart Grids: The Role of Advanced Sensing and IoT in the Electric Grid of the Future," in IEEE Sensors Journal, vol. 17, no. 23, pp. 7828-7837, 1 Dec.1, 2017, doi: 10.1109/JSEN.2017.2760014.
  2. B. Hanna and A. El-Shahat, "Optimal power flow for microgrids," 2017 IEEE Global Humanitarian Technology Conference (GHTC), 2017, pp. 1-3, doi: 10.1109/GHTC.2017.8239313
  3. R. Rigo-Mariani, B. Sareni, X. Roboam, and C. Turpin. “Optimal power dispatching strategies in smart-microgrids with storage.” Renewable and Sustainable Energy Reviews, Elsevier, 2014, vol. 40, pp. 649-658. 10.1016/j.rser.2014.07.138. hal-01064368
  4. H. Abdia, S. D. Beigvanda, and M. L. Scala, “A review of optimal power flow studies applied to smart grids and microgrids, Renewable and Sustainable Energy Reviews”, Volume 71, 2017, Pages 742-766, ISSN 1364-0321, doi.org/10.1016/j.rser.2016.12.102.
  5. S. Lin, J. Chen, “Distributed optimal power flow for smart grid transmission system with renewable energy sources”, Volume 56, 2013, Pages 184-192, ISSN 0360-5442, https://doi.org/10.1016/j.energy.2013.04.011.
  6. O. Amanifar and M. E. Hamedani Golshan, “Optimal Distributed Generation Placement and Sizing for Loss and THD Reduction and Voltage Profile Improvement,” Technical and Physical Problems of Engineering (IJTPE), vol. 3, no. 2, 2011.
  7. F. R. Pazheri, M. F. Othman, N. H. Malik, and S. O. K, “Economic and Environmental Dispatch at Highly Potential Renewable Area with Renewable Storage,” International Journal of Environmental Science and Development, pp. 177–182, 2012.
  8. Y. M. Atwa, E. F. El-Saadany, M. M. A. Salama, and R. Seethapathy, “Optimal renewable resources mix for distribution system energy loss minimization,” IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 360–370, 2010.
  9. S. Sichilalu, T. Mathaba, and X. Xia, “Optimal control of a wind–PV-hybrid powered heat pump water heater,” Applied Energy, vol. 185, pp. 1173–1184, 2017.
  10. E. R. Sanseverino, M. L. Di Silvestre, M. G. Ippolito, A. De Paola, and G. Lo Re, “An execution, monitoring and replanning approach for optimal energy management in microgrids,” Energy, vol. 36, no. 5, pp. 3429–3436, 2011
  11. C. Chen, S. Duan,T. Cai, B. Liu, andG. Hu, “Smart energy management system for optimal microgrid economic operation,” IET Renewable Power Generation, vol. 5, no. 3, pp. 258–267, 2011.
  12. Y. Levron, J. M. Guerrero, and Y. Beck, “Optimal power flow in microgrids with energy storage,” IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 3226–3234, 2013.
  13. A. Bracale, P.Caramia, G.Carpinelli, E.Mancini, and F.Mottola, “Optimal control strategy of a DC micro grid,” International Journal of Electrical Power & Energy Systems, vol. 67, pp. 25–38, 2015.
  14. E. Riva Sanseverino, N. Nguyen Quang, M. L. Di Silvestre, J. M. Guerrero, and C. Li, “Optimal power flow in three-phase islanded microgrids with inverter interfaced units,” Electric Power Systems Research, vol. 123, pp. 48–56, 2015.
  15. J. Shen, C. Jiang, Y. Liu, and X. Wang, “A Microgrid Energy Management System and Risk Management under an Electricity Market Environment,” IEEE Access, vol. 4, pp. 2349–2356, 2016.
  16. H. Hassanzadehfard, S. M. Moghaddas-Tafreshi, and S. M. Hakimi, “Optimization of grid-connected microgrid consisting of PV/FC/UC with considered frequency control,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 23, no. 1, pp. 1–16, 2015.
  17. R. Yu, W. Zhong, S. Xie, C. Yuen, S. Gjessing, and Y. Zhang, “Balancing Power Demand Through EV Mobility in Vehicle-to-Grid Mobile Energy Networks,” IEEE Transactions on Industrial Informatics, vol. 12, no. 1, pp. 79–90, 2016.
  18. F. Laureri, L. Puliga, M. Robba, F. Delfino, and G. Odena Bult`o, “An optimization model for the integration of electric vehicles and smart grids Problem definition and experimental validation,” in Proceedings of the 2nd IEEE International Smart Cities Conference, ISC2 2016, September 2016.
  19. N. G. Paterakis, O. Erdinc, I.N. Pappi, A. G. Bakirtzis, and J. P. S. Catalao, “Coordinated Operation of a Neighborhood of Smart Households Comprising Electric Vehicles, Energy Storage and Distributed Generation,” IEEE Transactions on Smart Grid, vol. 7, no. 6, pp. 2736–2747, 2016.
  20. C.-H. Lin, W.-L. Hsieh, C.-S. Chen, C.-T. Hsu, and T.-T. Ku, “Optimization of photovoltaic penetration in distribution systems considering annual duration curve of solar irradiation,” IEEE Transactions on Power Systems, vol. 27, no. 2, pp. 1090–1097, 2012.
  21. M. Bianchi, L. Branchini, C. Ferrari, and F. Melino, “Optimal sizing of grid-independent hybrid photovoltaic–battery power systems for household sector,” Applied Energy, vol. 136, pp. 805–816, 2014.
  22. R. Nazir, K. Kanada, Syafii, and P. Coveria, “Optimization active and reactive power flow for PV connected to grid system using Newton Raphson method,” in Proceedings of the 2nd International Conference on Sustainable Energy Engineering and Application, ICSEEA 2014, pp. 77–86, idn, October 2014.
  23. M. GEORGIEV, R. Stanev and A. Krusteva, "Optimized power flow control of smart grids with electric vehicles and DER," 2019 16th Conference on Electrical Machines, Drives and Power Systems (ELMA), 2019, pp. 1-6, doi: 10.1109/ELMA.2019.8771575.
  24. B. Kim, O. Lavrova, "Optimal power flow and energy-sharing among multi-agent smart buildings in the smart grid," 2013 IEEE Energytech, 2013, pp. 1-5, doi: 10.1109/EnergyTech.2013.6645336.
  25. D. Ke, C. Y. Chung, and Y. Sun, “A novel probabilistic optimal power flow model with uncertain wind power generation described by customized Gaussian mixture model,” IEEE Transactions on Sustainable Energy, vol. 7, no. 1, pp. 200–212, 2016.
  26. G. J. Sebasti´an, C. J. Alexander, and G.Mauricio, “Stochastic AC Optimal Power Flow Considering the Probabilistic Behavior of theWind, Loads and Line Parameters,” Ingenier´ıa, Investigaci´ony Tecnolog´ıa, vol. 15, no. 4, pp. 529–538, 2014.
  27. H. T. Jadhav and R. Roy, “Stochastic optimal power flow incorporating offshore wind farmand electric vehicles,” International Journal of Electrical Power&Energy Systems, vol. 69, pp. 173–187, 2015.
  28. S.-Y. Lin and A.-C. Lin, “RLOPF (risk-limiting optimal power flow) for systems with high penetration of wind power,” Energy, vol. 71, pp. 49–61, 2014.
  29. D. Owerko, F. Gama and A. Ribeiro, "Optimal Power Flow Using Graph Neural Networks," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 5930-5934, doi: 10.1109/ICASSP40776.2020.9053140.
  30. H. kaur, Y. S. Brar and J. S. Randhawa, "Optimal power flow using power world simulator," 2010 IEEE Electrical Power & Energy Conference, 2010, pp. 1 -6, doi: 10.1109/EPEC.2010.5697188.
  31. S.A. Soliman and A.H. Mantawy, Optimal Power Flow: Modern Optimization Techniques with Applications in Electric Power Systems, Energy Systems, DOI 10.1007/978-1-4614-1752-1_5#Springer Science+Business Media, LLC 2012
  32. M. Ebeed, S. Kamel, and, F. Jurado, “Optimal Power Flow Using Recent Optimization Techniques” in Classical and Recent Aspects of Power System Optimization, 2018, pp. 157 -183
  33. A. Ismail. Class Lecture. Topic:” Lecture 5 - OPTIMAL POWER FLOW.”. College of Electrical Engineering, Rochester Institute of technology, Dubai, 2020.
  34. M. N. Alam, S. Chakrabarti and X. Liang, "A Benchmark Test System for Networked Microgrids," in IEEE Transactions on Industrial Informatics, vol. 16, no. 10, pp. 6217 -6230, Oct. 2020, doi: 10.1109/TII.2020.2976893.
  35. H. Saadat, Power System Analysis. McGraw -Hill Book Company, New York, 1999.
  36. IBM Education, 2020. What are Neural Networks?. [online] Ibm.com. Available at: <https://www.ibm.com/cloud/learn/neural -networks> [Accessed 22 October 2021].
  37. P. Kim, MATLAB Deep Learning with Machine Learning, Neural Networks and Artificial Intelligence. 2017.
  38. D. Johnson, 2021. Back Propagation Neural Network: What is Backpropagation Algorithm in Machine Learning?. [online] Guru99. Available at: <https://www.guru99.com/backpropogation-neural-network.html> [Accessed 24 October 2021].
  39. Analytics Vidhya. How Does the Gradient Descent Algorithm Work in Machine Learning?. 2020. [online] Available at: <https://www.analyticsvidhya.com/blog/2020/10/how-does-the-gradient-descent-algorithm-work-inmachine-learning/> [Accessed 27 October 2021].
  40. Wikiwand. n.d. Levenberg –Marquardt algorithm | Wikiwand. [online] Available at: <https://www.wikiwand.com/en/Levenberg%E2%80 %93Marquardt_algorithm> [Accessed 28 October 2021].