Skip to main content
SpringerLink
Log in

Challenges of Smart Grids Implementation

  • Chapter
  • First Online:
Demand-side Flexibility in Smart Grid

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

Abstract

Increasing share of renewable energy resources, implementation of new technologies and data management methods in power system, development of communication systems from one side, and higher demand of electricity and concerns for increasing existing transmission lines while maintaining grid stability and reliability have been the main motivations for moving toward Smart Grid.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. G. Dileep, A survey on smart grid technologies and applications. Renew. Energy 146, 2589–2625 (2020)

    Article  Google Scholar 

  2. Office of the National Coordinator for Smart Grid Interoperability Engineering Laboratory in collaboration with Physical Measurement Laboratory and Information Technology Laboratory. NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 2.0 (NIST Special Publication, 1108R2, 2012)

    Google Scholar 

  3. https://www.nist.gov/programs-projects/smart-grid-communications-0. NIST, 7 May 2018. [Online]. Accessed 6 Sept 2019

  4. https://smartgrid.ieee.org/domains. [Online]. Accessed 10 June 2019

  5. R. AhmadiAhangar, A. Rosin, A.N. Niaki, I. Palu, T. Korõtko, A review on real-time simulation and analysis methods of microgrids. Int. Trans. Electr. Energy Syst. 29(11), e12106 (2019)

    Article  Google Scholar 

  6. Summary Report: 2012 DOE Microgrid. Available: http://energy.gov/sites/prod/files/2012%20Microgrid%20workshop%20Report%2009102012.pdf. Accessed 13 Nov 2014. (2012)

  7. S. Parhizi, H. Lotfi, A. Khodaei, S. Bahramirad, State of the art in research on microgrids: a review. IEEE Access 3, 890–925 (2015)

    Article  Google Scholar 

  8. D. Lebedev, A. Rosin, L. Kütt, Simulation of real time electricity price based energy management system, in IECON 2016—42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy (2016)

    Google Scholar 

  9. D. Lebedev, A. Rosin, Practical use of the energy management system with day-ahead electricity prices, in IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG), Riga, Latvia (2015)

    Google Scholar 

  10. M. Mohammadi, F. Talebpour, E. Safaee, N. Ghadimi, O. Abedinia, Small-scale building load forecast based on hybrid forecast engine. Neural Process. Lett. 48(1), 329–351 (2017)

    Article  Google Scholar 

  11. O. Abedinia, M. Bekravi, N. Ghadimi, Intelligent controller based wide-area control in power system. Int. J. Uncertainty, Fuzziness Knowl. Based Syst. 25(1), 1–30 (2017)

    Article  Google Scholar 

  12. R. Ahmadi, A. Sheykholeslami, A.N. Niaki, H. Ghaffari, Power flow control and solutions with dynamic flow controller, in Electric Power Conference, EPEC 2008. IEEE Canada, Vancouver (2008)

    Google Scholar 

  13. T. Logenthiran, R.T. Naayagi, W.L. Woo, V.T. Phan, K. Abidi, Intelligent control system for microgrids using multiagent system. IEEE J. Emerg. Sel. Top. Power Electron. 3(4), 1036–1045 (2015)

    Article  Google Scholar 

  14. E. Imaie, A. Sheikholeslami, R. Ahmadi Ahangar, Improving short-term wind power prediction with neural network and ICA algorithm and input feature selection. J. Adv. Comput. Res. 5(3), 13–34 (2014)

    Google Scholar 

  15. J. Xiao, P. Wang, L. Setyawan, X. Qianwen, Multi-level energy management system for real-time scheduling of dc microgrids with multiple slack terminals. IEEE Trans. Energy Convers. 31(1), 392–401 (2016)

    Article  Google Scholar 

  16. M.H. Cintuglu, T. Youssef, Development and application of a real-time testbed for multiagent system interoperability: a case study on hierarchical microgrid control. IEEE Trans. Smart Grid 9(3), 1759–1765 (2018)

    Article  Google Scholar 

  17. M.C. Magro, M. Giannettoni, P. Pinceti, Real time simulator for microgrids. Electric Power Syst. Res. 160, 381–396 (2018)

    Article  Google Scholar 

  18. F. Huerta, R.L. Tello, M. Prodanovic, Real-time power-hardware-in-the-loop implementation of variable-speed wind turbines. IEEE Trans. Industr. Electron. 64(3), 1893–1904 (2017)

    Article  Google Scholar 

  19. A. Rahmoun, A. Armstorfer, H. Biechi, A. Rosin, Mathematical modeling of a battery energy storage system in grid forming mode, in IEEE 58th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON) (2017)

    Google Scholar 

  20. I. Roasto, T. Lehtla, T. Moller, A. Rosin, Control of ultracapacitors energy exchange, in 12th International Power Electronics and Motion Control Conference, EPE-PEMC 2006

    Google Scholar 

  21. S. Choudhury, T.P. Dash, P. Bhowmik, P.K. Rout, A novel control approach based on hybrid fuzzy logic and seeker optimization for optimal energy management between micro-sources and supercapacitor in an islanded Microgrid. J. King Saud Univ. Eng. Sci. https://doi.org/10.1016/j.jksues.2018.03.006 (in Press, 2019)

  22. Q. Jiang, M. Xue, G. Geng, Energy management of microgrid in grid-connected and stand-alone modes. IEEE Trans. Power Syst. 28(3), 3380–3389 (2013)

    Article  Google Scholar 

  23. R. Ahmadi, F. Ghardashi, D. Kabiri, A. Sheykholeslami, H. Haeri, Voltage and frequency control in smart distribution systems in presence of DER using flywheel energy storage system, in IET Digital Library, pp. 1307–1307 (2013)

    Google Scholar 

  24. A. Selakov, D. Bekut, A.T. Sarić, A novel agent-based microgrid optimal control for grid-connected, planned island and emergency island operations. Int. Trans. Electr. Energy Syst. 26, 1999–2022 (2016)

    Article  Google Scholar 

  25. F. Zhang, H. Zhao, M. Hong, Operation of networked microgrids in a distribution system. CSEE J. Power Energy Syst. 1(4), 12–21 (2015)

    Article  Google Scholar 

  26. S. Chandak, P. Bhowmik, M. Mishra, P.K. Rout, Autonomous microgrid operation subsequent to an anti-islanding scheme. Sustain. Cities Soc. 39, 430–448 (2018)

    Article  Google Scholar 

  27. K. Peterson, R. Ahmadiahangar, N. Shabbir, T. Vinnal, Analysis of microgrid configuration effects on energy efficiency, in 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), Riga (2019)

    Google Scholar 

  28. J. Li, Y. Liu, W. Lei, Optimal operation for community-based multi-party microgrid in grid-connected and islanded modes. IEEE Trans. Smart Grid 9(2), 756–765 (2018)

    Google Scholar 

  29. P.M. de Quevedo, J. Contreras, A. Mazza, G. Chicco, R. Porumb, Reliability assessment of microgrids with local and mobile generation, time-dependent profiles, and intraday reconfiguration. IEEE Trans. Ind. Appl. 54(1), 61–72 (2017)

    Article  Google Scholar 

  30. R. Ahmadi, A. Sheikholeslami, A. Nabavi Niaki, A. Ranjbar, Dynamic participation of doubly fed induction generators in multi-control area load frequency control. Int. Trans. Electr. Energy Syst. 25(7), 1130–1147 (2015)

    Article  Google Scholar 

  31. R. Ahmadiahangar, A. Sheykholeslami, Bulk virtual power plant, a novel concept for improving frequency control and stability in presence of large scale RES. Int. J. Mechatron. Electr. Comput. Technol. 4(10), 1017–1044 (2014)

    Google Scholar 

  32. L. Guo, J. Su, J. Lai, Y. Wang, Research on power scheduling strategy for microgrid in islanding mode. Int. Trans. Electr. Energy Syst. 28(2), e2493 (2018). https://doi.org/10.1002/etep.2493

    Article  Google Scholar 

  33. A. Rahmoun, N. Beg, A. Rosin, H. Biechl, Stability and eigenvalue sensitivity analysis of a BESS model in a microgrid, in 2018 IEEE International Energy Conference (ENERGYCON) (2018)

    Google Scholar 

  34. S. Choudhury, P. Bhowmik, P.K. Rout, Robust dynamic fuzzy-based enhanced VPD/FQB controller for load sharing in microgrid with distributed generators. Electr. Eng. 100(4), 2457–2472 (2018)

    Article  Google Scholar 

  35. S. Choudhury, P. Bhowmik, P.K. Rout, Seeker optimization approach to dynamic PI based virtual impedance drooping for economic load sharing between PV and SOFC in an islanded microgrid. Sustain. Cities Soc. 37, 550–562 (2018)

    Article  Google Scholar 

  36. M. Cucuzzella, G.P. Incremona, A. Ferrara, Decentralized sliding mode control of islanded ac microgrids with arbitrary topology. IEEE Trans. Industr. Electron. 64(8), 6706–6713 (2017)

    Article  Google Scholar 

  37. A. Bidram, A. Davoudi, F.L. Lewis, J.M. Guerrero, Distributed cooperative secondary control of microgrids using feedback linearization. IEEE Trans. Power Syst. 28(3), 3462–3470 (2013)

    Article  Google Scholar 

  38. W. Liu, W. Giu, W. Sheng, X. Meng, S. Xue, M. Chen, Pinning based distributed cooperative control for autonomous microgrids under uncertain communication topologies. IEEE Trans. Power Syst. 31(2), 1320–1329 (2016)

    Article  Google Scholar 

  39. S. Manaffam, M. Talebi, A.K. Jain, A. Behal, Intelligent pinning based cooperative secondary control of distributed generators for microgrid in islanding operation mode. IEEE Trans. Power Syst. 33(2), 1364–1373 (2018)

    Article  Google Scholar 

  40. Economic load sharing in a D-STATCOM integrated islanded microgrid based on fuzzy logic and seeker optimization approach. Sustain. Cities Soc. 37, 57–69 (2018)

    Google Scholar 

  41. G.G. Talapur, H.M. Suryawanshi, A reliable microgrid with seamless transition between grid connected and islanded mode for residential community with enhanced power quality. IEEE Trans. Ind. Appl. 54(5), 5246–5255 (2018)

    Article  Google Scholar 

  42. L.G. Meegahapola, D. Robinson, A.P. Agalgaonkar, S. Perera, P. Ciufo, Microgrids of commercial buildings: strategies to manage mode transfer from grid connected to islanded mode. IEEE Trans. Sustain. Energy. 5(4), 1337–1347 (2014)

    Article  Google Scholar 

  43. Hybrid islanding detection with optimum feature selection and minimum NDZ. Int. Trans. Electr. Energy Syst. 28(10), e2602 (2018)

    Google Scholar 

  44. A. Micallef, M. Apap, C. Spiteri-Staines, J.M. Guerrero, Single-phase microgrid with seamless transition capabilities between modes of operation. IEEE Trans. Smart Grid 6(6), 2736–2745 (2015)

    Article  Google Scholar 

  45. Y.A. Mohamed, A.A. Radwan, Hierarchical control system for robust microgrid operation and seamless mode transfer in active distribution systems. IEEE Trans. Smart Grid 2(2), 352–362 (2016)

    Article  Google Scholar 

  46. P. Bhowmik, S. Chandak, P. Kumar, State of charge and state of power management among the energy storage systems by the fuzzy tuned dynamic exponent and the dynamic PI controller. J. Energy Storage 19, 348–363 (2018)

    Article  Google Scholar 

  47. P. Bhowmik, S. Chandak, P.K. Rout, State of charge and state of power management in a hybrid energy storage system by the self-tuned dynamic exponent and the fuzzy-based dynamic PI controller. Int. Trans. Electr. Energy Syst. 29(5), e2848 (2019). https://doi.org/10.1002/2050-7038.2848

    Article  Google Scholar 

  48. M. Moretti, S.N. Djomo, H. Azadi, K. May, K. De Vos, S. Van Passel, N. Witters, A systematic review of environmental and economic impacts of smart grids. Renew. Sustain. Energy Rev 68, 888–889 (2017)

    Article  Google Scholar 

  49. M. Mahmudizad, R. Ahmadiahangar, Improving load frequency control of multi-area power system by considering uncertainty by using optimized type 2 fuzzy pid controller with the harmony search algorithm. World Acad. Sci. Eng. Technol. Int. J. Electr. Comput. Energ. Electron. Commun Eng. 10(8), 1051–1061 (2016)

    Google Scholar 

  50. K.M. Tan, V.K. Ramachandaramurthy, J.Y. Yong, Integration of electric vehicles in smart grid: a review on vehicle to grid technologies and optimization techniques. Renew. Sustain. Energy Rev. 53, 720–732 (2016)

    Article  Google Scholar 

  51. S.V. Oprea, A. Bâra, G. Ifrim, Flattening the electricity consumption peak and reducing the electricity payment for residential consumers in the context of smart grid by means of shifting optimization algorithm. Comput. Industr. Eng. 122, 125–139 (2018)

    Article  Google Scholar 

  52. R. Ahmadiahangar, T. Häring, A. Rosin, T. Korõtko, J. Martins, Residential load forecasting for flexibility prediction using machine learning-based regression model, in 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Genoa, Italy, pp. 2–19

    Google Scholar 

  53. N. Shabbir, R. Ahmadiahangar, L. Kütt, A. Rosin, Comparison of machine learning based methods for residential load forecasting, in 2019 Electric Power Quality and Supply Reliability Conference (PQ) & 2019 Symposium on Electrical Engineering and Mechatronics (SEEM) (2019)

    Google Scholar 

  54. T. Häring, R. Ahmadiahangar, A. Rosin, H. Biechl, Impact of load matching algorithms on the battery capacity with different household occupancies, in IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society, Lisbon (2019)

    Google Scholar 

  55. VassaETT, Smart grid 2013 global impact report. SMARTGRID.GOV, DOE, U.S., (October 2013)

    Google Scholar 

  56. R. Kappagantu, S.A. Daniel, Journal of Electrical Systems and Information Technology 5, 453–467 (2018)

    Article  Google Scholar 

  57. C. Tu, X. He, Z. Shuai, F. Jiang, Big data issues in smart grid–A review. Renew. Sustain. Energy Rev. 79, 1099–1107 (2017)

    Article  Google Scholar 

  58. H. Daki, A. El Hannani, A. Aqqal, A. Haidine, A. Dahbi, Big Data management in smart grid: concepts, requirements and implementation. Big Data 4(13), 1–19 (2017)

    Google Scholar 

  59. A.B. Dayani, H. Fazlollahtabar, R. Ahmadiahangar, A. Rosin, M.S. Naderi, M. Bagheri, Applying reinforcement learning method for real-time energy management, in 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) (2019)

    Google Scholar 

  60. H. Fallahzadeh-Abarghouei, S. Hasanvand, A. Nikoobakht, Decentralized and hierarchical voltage management of renewable energy resources in distribution smart grid. Int. J. Electr. Power Energy Syst. 100, 117–128 (2018)

    Article  Google Scholar 

  61. K.G. Firouzjah, R. Ahmadiahangar, A. Rosin, T. Häring, A fast current harmonic detection and mitigation strategy for shunt active filter, in 2019 Electric Power Quality and Supply Reliability Conference (PQ) & 2019 Symposium on Electrical Engineering and Mechatronics (SEEM) (2019)

    Google Scholar 

  62. A. Fernández-Guillamón, E. Gómez-Lázaro, E. Muljadi, Power systems with high renewable energy sources: a review of inertia and frequency control strategies over time. Renew. Sustain. Energy Rev. 115, 109369 (2019)

    Article  Google Scholar 

Download references

Acknowledgements

This work has been supported by the European Commission through the H2020 project Finest Twins (grant No. 856602).

Author information

Authors and Affiliations

  1. Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, Tallinn, Estonia

    Roya Ahmadiahangar, Argo Rosin & Ivo Palu

  2. School of Engineering, Computing and Mathematics, Oxford Brookes University, Oxford, UK

    Aydin Azizi

Authors
  1. Roya Ahmadiahangar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Argo Rosin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ivo Palu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aydin Azizi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aydin Azizi .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ahmadiahangar, R., Rosin, A., Palu, I., Azizi, A. (2020). Challenges of Smart Grids Implementation. In: Demand-side Flexibility in Smart Grid. SpringerBriefs in Applied Sciences and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-15-4627-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-4627-3_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4626-6

  • Online ISBN: 978-981-15-4627-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation