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Research and Solution Proposals to Optimize Distribution Power Grids in Smart Grid Condition

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Smart Grid is a concept for transforming the electric power grid by using advanced automatic control and communications techniques and other forms of information technology. It integrates innovative tools and technologies from: generation, transmission and distribution. This also includes consumer appliances and equipment. This concept integrates energy infrastructure, processes, devices, information and markets into a coordinated and collaborative process. All allowing energy to be generated, distributed and consumed flexibly and efficiently. However, the Smart Grid with the integration of distributed generation itself also creates a several disadvantages. There can be problems with: stability and reliability, relay protection, isolation and operational isolation in which the problem is to create a burden on the distribution grid when transmitting electrical energy sources. Optimizing power flow and bringing high operating efficiency on Smart Grid conditions is an urgent issue. This paper focuses on researching and proposing solutions for optimal calculation of power flow on Smart Grid. The paper has researched, and analyzed calculation solutions to optimize power flow and proposed to use the Lagrange multiplier method. The study performed calculations for a typical Smart Grid model with three distributed generations. Calculation results have shown that the role of the method is to fully perform the optimal calculation of the power flow on the grid. This is in order to reduce power loss and energy loss as well as increasing operational efficiency while improving power quality in Smart Grid conditions.
Rocznik
Tom
Strony
479--486
Opis fizyczny
Bibliogr. 29 poz., rys., tab.
Twórcy
  • Hanoi University of Mining and Geology, 18 Vien street, Hanoi, Vietnam
  • Hanoi University of Industry, Hanoi, Vietnam
  • Thuyloi University, Hanoi, Vietnam
  • Vinh University of Technology Education, Nghe An, Vietnam
Bibliografia
  • 1. Hamdi Abdia, Soheil Derafshi Beigvanda, Massimo La Scalab, 2017. A review of optimal power flow studies applied to smart grids and microgrids. Renewable and Sustainable Energy Reviews. 71: 742-766.
  • 2. Siano, P., 2014. Demand response and smart grids - A survey. Renew Sustain Energy Rev, 30: 461–78.
  • 3. O׳Connell, N., Pinson, P., Madsen, H., O׳Malley, M., 2014. Benefits and challenges of electrical demand response: A critical review. Renew Sustain Energy Rev, 39: 686–99.
  • 4. Mehrtash, A., Wang, P., Goel, L., 2013. Reliability evaluation of restructured power systems using a novel optimal power-flow-based approach. IET Gener Transm Distrib, 7: 192–9.
  • 5. Lin, S.Y., Chen, J.F. Distributed optimal power flow for smart grid transmission system with renewable energy sources. Energy 2013; 56: 184–92.
  • 6. Lidula, N.W.A, Rajapakse, A.D. Microgrids research: a review of experimental microgrids and test systems. Renew Sustain Energy Rev 2011; 15: 186–202.
  • 7. Mohn, T. and Piasecki, R. A smarter grid enables communal microgrids. 2011 IEEE Green Technologies Conference (IEEE-Green), 1–6, 2011
  • 8. Vasquez, J.C., Guerrero, J.M., Miret, J., Castilla, M., Vicuna, L.G.D., 2010. Hierarchical control of intelligent microgrids. IEEE Ind Electron Mag 4: 23–9.
  • 9. Farhangi, H. The path of the smart grid. IEEE Power Energy Mag 2010; 8: p 18–28.
  • 10. Mehrizi-Sani, A., Iravani, R. Potential-function based control of a microgrid in islanded and gridconnected modes. IEEE Trans Power Syst 2010; 25: 1883–91.
  • 11. British Research. Smart grid (IEC61850) 2016. 〈http://www.britishresearch.com/ products/smartgrid-iec61850〉 [accessed 06.06.16].
  • 12. Bouzid, A.M., Guerrero, J.M., Cheriti, A., Bouhamida, M., Sicard, P., Benghanem, M. A survey on control of electric power distributed generation systems for microgrid applications. Renew Sustain Energy Rev 2015; 44: 51–66.
  • 13. Fadaeenejad, M., Saberian, A.M., Fadaee, M., Radzi, M.A.M., Hizam, H., AbKadir, M.Z.A. The present and future of smart power grid in developing countries. Renew Sustain Energy Rev 2014; 29: 828–34.
  • 14. Nikmehr, N., Najafi Ravadanegh, S. Optimal power dispatch of multi-microgrids at future smart sistribution grids. IEEE Trans Smart Grid 2015; 6: 1648–57.
  • 15. Wu, D., Lesieutre, B., Ramanathan, P., Kakunoori, B. Preserving privacy of AC optimal power flow models in multi-party electric grids. IEEE Trans Smart Grid. Volume: 7, Issue: 4, July 2016. Page(s): 2050 – 2060. https:// DOI: 10.1109/TSG.2016.2544179
  • 16. Hafiz Tehzeeb-Ul-Hassan, Muhammad Faizan Tahir, Kashif Mehmood, Khalid Mehmood Cheema, Ahmad H. Milyani, Qasim Rasool. Optimization of power flow by using Hamiltonian technique. Energy Reports. Volume 6, November 2020, 2267-2275.
  • 17. Sinsuphun, N., Leeton, U., Kwannetr, U., Uthitsunthorn, D., Kulworawanich pong, T., 2011. Loss minimization using optimal power flow based on swarm intelligences. ECTI Trans. Electr. Eng. Electron. Commun. 9, 212–222.
  • 18. Tahir, M.F., Saqib, M.A., 2016. Optimal scheduling of electrical power in energy deficient scenarios using artificial neural network and bootstrap aggregating. Int. J. Electr. Power Energy Syst. 83, 49–57
  • 19. Khan, B., Singh, P., 2017. Optimal power flow techniques under characterization of conventional and renewable energy sources: A comprehensive analysis. J. Eng. Volume 2017. https://doi.org/10.1155/2017/9539506
  • 20. Chan, K.Y., Pong, G., Chan, K. Investigation of hybrid particle swarm optimization methods for solving transient-stability constrained optimal power flow problems. Proceedings of the World Congress on Engineering 2007. Vol I. p 497-501, July 2 - 4, 2007, London, U.K.
  • 21. Ebeed, M., Kamel, S., Jurado, F., 2018. Optimal power flow using recent optimization techniques. In: Classical and Recent Aspects of Power System Optimization. Elsevier, p 157–183.
  • 22. Ayan, K., Kiliç, U., 2013. Solution of transient stability-constrained optimal power flow using artificial bee colony algorithm. Turk. J. Electr. Eng. Comput. Sci. 21, 360–372. https://doi:10.3906/elk-1112-14
  • 23. Jumani, T.A., Mustafa, M.W., Md Rasid, M., Hussain Mirjat, N., Hussain Baloch, M., Salisu, S., Optimal power flow controller for grid-connected microgrids using grasshopper optimization algorithm. Electronics 2019, 8, 111; doi:10.3390/electronics8010111
  • 24. Bai, W., Lee, D., Lee, K.Y., 2017. Stochastic dynamic AC optimal power flow based on a multivariate short-term wind power scenario forecasting model. Energies 2017, 10, 2138; doi:10.3390/en10122138.
  • 25. Thanh, L.Xuan 2017. Modified Algorithm for finding the optimal nod of closed-medium voltage grids. Journal of Mining and Earth Sciences. 58, 3 (Jun, 2017). Available from: http://jmes.humg.edu.vn/en/archives?article=1191
  • 26. Dang, K.Quang and Tran, T.Trong 2019. Structural optimization of Ha Tinh city medium voltage grid by loop cutting algoritm (in Vietnamese). Journal of Mining and Earth Sciences. 60, 2 (Apr, 2019). Available from: http://jmes.humg.edu.vn/en/archives?article=1000
  • 27. Michel Soustelle. Phase Modeling Tools: Applications to Gases. John Wiley & Sons. 2015.
  • 28. Phil Lucht. The Method of Lagrange Multipliers. Rimrock Digital Technology, Salt Lake City, Utah 84103 last update: Oct 29, 2016. [online] Available at: http://user.xmission.com/~rimrock/Documents/The%20Method%20of%20Lagrange%20Multipliers.pdf
  • 29. Salih, A., 2013. Method of Lagrange Multipliers. Department of Aerospace Engineering. Indian Institute of Space Science and Technology, Thiruvananthapuram – September 2013. [online] Available at: https://www.iist.ac.in/sites/default/files/people/Lagrange-Multiplier.pdf
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-88901b85-3f69-4be7-8dc3-feb8fa05794e
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